Computer performance real-time evaluation method and device based on user experience perception, storage medium and computer equipment

Abstract

The invention discloses a computer performance real-time evaluation method and device based on user experience perception, a storage medium and computer equipment, and the method comprises the steps: obtaining the current operation data of the computer equipment, carrying out the use scene recognition based on the current operation data, and determining a current use scene; obtaining a plurality of user experience perception performance indexes corresponding to the current use scene and the index weight of each user experience perception performance index in a pre-constructed use scene and user experience perception performance weight mapping table; performing performance index data acquisition on the computer equipment according to each user experience perception performance index, and calculating a comprehensive performance score of the computer equipment according to each acquired user experience perception performance index data and the corresponding index weight; and performing real-time performance feedback of the computer equipment according to the comprehensive performance score.

Description

Real-time computer performance evaluation methods, devices, storage media, and computer equipment based on user experience perception Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, storage medium, and computer device for real-time evaluation of computer performance based on user experience perception. Background Technology

[0002] In today's world of widespread computer use, accurately assessing computer performance is crucial for users to understand device status, optimize user experience, and make hardware upgrade decisions. However, existing computer performance assessment technologies have many shortcomings.

[0003] Hardware benchmarking is a common approach, using high-load computing programs like 3DMark and Cinebench to test the peak performance of hardware components such as the CPU and GPU, deriving theoretical hardware performance scores. However, this method cannot provide real-time evaluation in everyday use, and the test results are greatly affected by the environment, making it difficult to reflect the dynamic changes in performance under real-world scenarios.

[0004] The system experience index, represented by the Windows Experience Index, scores the main hardware subsystems and gives a basic score when the system is idle. However, it mainly reflects the basic hardware configuration, not real-time performance, and cannot give users an intuitive understanding of the actual performance of the current device.

[0005] System health assessment methods evaluate system health status through weighted scoring of hardware and software modules, aiming to identify potential faults and anomalies. However, they do not link system resource consumption to user-perceived performance experience; even with a high health score, users may experience lag due to unmet actual performance needs. Summary of the Invention

[0006] In view of this, embodiments of this application provide a method, apparatus, storage medium, and computer device for real-time evaluation of computer performance based on user experience perception.

[0007] According to one aspect of this application, a method for real-time evaluation of computer performance based on user experience perception is provided. The method includes: acquiring current operating data of a computer device, and identifying a usage scenario based on the current operating data to determine the current usage scenario; acquiring multiple user experience perception performance indicators and their respective weights from a pre-constructed mapping table between usage scenarios and user experience perception performance weights; collecting performance indicator data of the computer device based on each user experience perception performance indicator, and calculating a comprehensive performance score for the computer device based on the collected user experience perception performance indicator data and their corresponding weights; and providing real-time performance feedback for the computer device based on the comprehensive performance score.

[0008] Optionally, the types of the current running data include process data, window title data, input behavior data, graphics and media data, and audio / video device data; input behavior data includes keyboard input behavior data, mouse input behavior data, and touchpad input behavior data; graphics and media data includes frame rate and media playback status; audio / video device data includes microphone usage data, speaker usage data, and camera usage data; and based on the current running data, usage scenario identification is performed to determine the current usage scenario, including: obtaining the currently used application name based on process data, obtaining the current window title keywords based on window title data, and determining the initial score of the candidate scenario based on the current application name and the current window title keywords using a scenario mapping template; wherein, the scenario mapping template includes application names and window title keywords under multiple scenarios, as well as each application name and... The scores of keywords in each window title are calculated. In the heuristic scene template, the scene scores of the input behavior data, graphics and media data, and audio / video device data are queried to determine the heuristic scores of each scene corresponding to the input behavior data, graphics and media data, and audio / video device data. The heuristic scene template includes data ranges for each input behavior data, graphics and media data, and audio / video device data under multiple scenes, as well as heuristic scores for these data ranges. Based on the heuristic scores of each scene corresponding to the input behavior data, graphics and media data, and audio / video device data, the initial scores of the candidate scenes are updated to determine the final scores of the candidate scenes. The candidate scene with the highest final score is determined as the current usage scene.

[0009] Optionally, the method further includes: dynamically determining the final score of the corresponding candidate scenario based on the real-time acquired current operating data; if the new candidate scenario with the highest final score belongs to the same scenario group as the most recently determined current usage scenario, then if the difference between the final scores of the new candidate scenario with the highest final score and the most recently determined current usage scenario is less than a preset difference threshold, the original current usage scenario remains unchanged; otherwise, the candidate scenario with the highest final score is determined as the new current usage scenario; wherein, usage scenarios in the same scenario group are scenarios whose operating data determined based on different scenario usage information of computer devices have a similarity greater than a preset similarity; if the new candidate scenario with the highest final score does not belong to the same scenario group as the most recently determined current usage scenario, then the new candidate scenario with the highest final score is determined as the new current usage scenario.

[0010] Optionally, the comprehensive performance score of the computer device is calculated based on the collected user experience perceived performance index data and the corresponding index weights, including: performing standardized calculations on the collected user experience perceived performance index data to obtain standardized index data corresponding to each user experience perceived performance index data; acquiring user experience perceived performance index data within a preset time window, performing performance index trend calculations based on the user experience perceived performance index data within the preset time window to determine the performance index trend factor corresponding to each user experience perceived performance index data; and calculating the comprehensive performance score of the computer device based on the user experience perceived performance index data, the performance index trend factor, and the index weights.

[0011] Optionally, performance indicator trend calculation is performed based on the user experience perception performance indicator data within a preset time window to determine the performance indicator trend factor corresponding to each user experience perception performance indicator data. This includes: fitting the user experience perception performance indicator data within the preset time window to determine the performance indicator trend slope corresponding to each user experience perception performance indicator data; calculating the ratio of the performance indicator trend slope corresponding to each user experience perception performance indicator data to the trend sensitivity threshold of the corresponding user experience perception performance indicator, and truncating the ratio to obtain the performance indicator trend parameter, wherein the truncation calculation restricts the ratio to the range of [0,1]; calculating the difference between 1 and each performance indicator trend parameter to obtain the performance indicator trend factor corresponding to each type of user experience perception performance indicator data.

[0012] Optionally, the comprehensive performance score of the computer device is calculated based on the user experience perceived performance index data, the trend factor of each performance index, and the weight of each index. This includes: calculating the product of each user experience perceived performance index data and its corresponding performance index trend factor to obtain the index score of each user experience perceived performance index data; and weighting and summing the index scores of each user experience perceived performance index data according to the index weight corresponding to each user experience perceived performance index data to obtain the comprehensive performance score of the computer device.

[0013] Optionally, real-time performance feedback of the computer device is performed based on the comprehensive performance score, including: determining the current user experience level based on the comprehensive performance score and the score range of the user experience level corresponding to the current usage scenario; if the current user experience level belongs to the level that needs optimization, then obtaining the preset performance optimization information corresponding to the current usage scenario; and performing real-time performance feedback of the computer device based on the current user experience level, the comprehensive performance score, and the preset performance optimization information.

[0014] According to another aspect of this application, a real-time computer performance evaluation device based on user experience perception is provided. The device includes: a scene recognition module, used to acquire current operating data of a computer device and identify the current usage scenario based on the current operating data; an indicator determination module, used to acquire multiple user experience perception performance indicators corresponding to the current usage scenario and the indicator weights of each user experience perception performance indicator from a pre-constructed mapping table between usage scenarios and user experience perception performance weights; a performance scoring module, used to collect performance indicator data of the computer device according to each user experience perception performance indicator, and calculate a comprehensive performance score of the computer device based on the collected user experience perception performance indicator data and the corresponding indicator weights; and a performance feedback module, used to provide real-time performance feedback of the computer device based on the comprehensive performance score.

[0015] Optionally, the types of currently running data include process data, window title data, input behavior data, graphics and media data, and audio / video device data; input behavior data includes keyboard input behavior data, mouse input behavior data, and touchpad input behavior data; graphics and media data includes frame rate and media playback status; audio / video device data includes microphone usage data, speaker usage data, and camera usage data; the scene recognition module is specifically used to: obtain the currently used application name based on process data, obtain the current window title keyword based on window title data, and determine the initial score of candidate scenes based on the scene mapping template, according to the currently used application name and the current window title keyword; wherein, the scene mapping template includes application names and window title keywords under multiple scenes, as well as the application names and window title keywords of each application name and each window title keyword. Scores; In the heuristic scene template, query the scene scores matched by the input behavior data, the graphics and media data, and the audio / video device data to determine the heuristic scores of each scene corresponding to the input behavior data, the graphics and media data, and the audio / video device data; wherein, the heuristic scene template includes data ranges for each input behavior data, each graphics and media data, and each audio / video device data under multiple scenes, as well as heuristic scores for each data range for each input behavior data, each graphics and media data, and each audio / video device data under multiple scenes; based on the heuristic scores of each scene corresponding to the input behavior data, the graphics and media data, and the audio / video device data, the initial scores of the candidate scenes are updated to determine the final scores of the candidate scenes; the candidate scene with the highest final score is determined as the current usage scene.

[0016] Optionally, the scene recognition module is further configured to: dynamically determine the final score of the corresponding candidate scene based on the real-time acquired current running data; if the new candidate scene with the highest final score belongs to the same scene group as the most recently determined current usage scene, then if the difference between the final scores of the new candidate scene with the highest final score and the most recently determined current usage scene is less than a preset difference threshold, the original current usage scene remains unchanged; otherwise, the candidate scene with the highest final score is determined as the new current usage scene; wherein, usage scenes in the same scene group are scenes whose similarity between running data determined based on different scene usage information of computer devices is greater than a preset similarity; if the new candidate scene with the highest final score does not belong to the same scene group as the most recently determined current usage scene, then the new candidate scene with the highest final score is determined as the new current usage scene.

[0017] Optionally, the performance scoring module is specifically used for: standardizing the collected user experience perception performance index data to obtain standardized index data corresponding to each user experience perception performance index data; acquiring user experience perception performance index data within a preset time window, performing performance index trend calculation based on the user experience perception performance index data within the preset time window, and determining the performance index trend factor corresponding to each user experience perception performance index data; and calculating the comprehensive performance score of the computer device based on the user experience perception performance index data, the performance index trend factor, and the weight of each index.

[0018] Optionally, the performance scoring module is specifically used for: fitting the user experience perception performance index data within a preset time window to determine the performance index trend slope corresponding to each user experience perception performance index data; calculating the ratio of the performance index trend slope corresponding to each user experience perception performance index data to the trend sensitivity threshold of the corresponding user experience perception performance index, and truncating the ratio to obtain the performance index trend parameter, wherein the truncation calculation is to limit the ratio to the range of [0,1]; calculating the difference between 1 and each performance index trend parameter respectively to obtain the performance index trend factor corresponding to each type of user experience perception performance index data.

[0019] Optionally, the performance scoring module is specifically used to: calculate the product of each user experience perceived performance index data and the corresponding performance index trend factor to obtain the index score of each user experience perceived performance index data; and perform a weighted summation of the index scores of each user experience perceived performance index data according to the index weight corresponding to each user experience perceived performance index data to obtain the comprehensive performance score of the computer device.

[0020] Optionally, the performance feedback module is specifically used to: determine the current user experience level based on the comprehensive performance score and the score range of the user experience level corresponding to the current usage scenario; if the current user experience level belongs to the level that needs optimization, then obtain the preset performance optimization information corresponding to the current usage scenario; and provide real-time performance feedback for the computer device based on the current user experience level, the comprehensive performance score, and the preset performance optimization information.

[0021] According to another aspect of this application, a storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the above-described real-time computer performance evaluation method based on user experience perception.

[0022] According to another aspect of this application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described real-time computer performance evaluation method based on user experience perception.

[0023] By employing the above technical solutions, this application provides a method, apparatus, storage medium, and computer device for real-time computer performance evaluation based on user experience perception. First, it acquires the computer's current operating data and identifies the usage scenario. Then, it obtains the user experience perception performance indicators and weights corresponding to the current scenario from a pre-built mapping table. Next, it collects performance data according to the indicators and calculates a comprehensive performance score based on the weights. Finally, it provides real-time performance feedback based on the score. This application achieves real-time performance evaluation, allowing users to promptly grasp the dynamics of computer performance; the evaluation is comprehensive and accurate, combining scenarios and multiple indicators and relating them to user experience, overcoming the problems of traditional methods being limited by the environment and disconnected from user experience; at the same time, it has a high degree of automation, requiring no manual operation or data interpretation by the user, automatically providing results, making it convenient to use, and greatly improving the user's understanding and experience of computer performance.

[0024] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and, together with their descriptions, serve to explain this application and do not constitute an undue limitation thereof. In the drawings: Figure 1 shows a flowchart illustrating a real-time computer performance evaluation method based on user experience awareness provided in an embodiment of this application; Figure 2 shows a structural schematic diagram of a real-time computer performance evaluation device based on user experience awareness provided in an embodiment of this application. Detailed Implementation

[0026] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0027] This embodiment provides a real-time computer performance evaluation method based on user experience perception, as shown in Figure 1. The method includes: Step 101: Obtaining the current operating data of the computer device and identifying the current usage scenario based on the current operating data; Step 102: Obtaining multiple user experience perception performance indicators and their respective weights from a pre-constructed mapping table between usage scenarios and user experience perception performance weights; Step 103: Collecting performance indicator data of the computer device based on each user experience perception performance indicator and calculating a comprehensive performance score for the computer device based on the collected data and corresponding weights; Step 104: Providing real-time performance feedback for the computer device based on the comprehensive performance score.

[0028] The real-time computer performance evaluation method based on user experience perception provided in this application aims to comprehensively and accurately evaluate the performance of a computer during actual use and promptly provide feedback to the user. The specific implementation process is as follows: First, acquire the current operating data of the computer device. This data covers various information during computer operation, such as CPU utilization, memory usage, and disk read / write speed. Based on this operating data, identify the usage scenario to determine what activity the user is currently performing on the computer, such as playing games, processing documents, watching videos, or performing graphic design, thus determining the current usage scenario. Next, consult a pre-constructed mapping table of usage scenarios and user experience perception performance weights. This mapping table, derived from extensive research and experimentation, records in detail the various user experience perception performance indicators and their corresponding weights under different usage scenarios. By querying this table, multiple user experience perception performance indicators corresponding to the current usage scenario can be obtained, along with the weight of each indicator in the overall evaluation. Then, based on these user experience perception performance indicators, collect targeted performance indicator data from the computer device. For example, if the current usage scenario is a gaming scenario, then the focus might be on collecting performance indicator data closely related to the gaming experience, such as frame rate and latency. After collecting this data, a comprehensive performance score for the computer device is calculated by combining the weights of each indicator. This score comprehensively reflects the computer's performance from a user experience perspective in the current usage scenario. Finally, based on the calculated comprehensive performance score, real-time performance feedback is provided to the user in an intuitive way. For example, users can be informed of the computer's current performance status through interface displays, sound prompts, etc., so that they can take corresponding actions based on the feedback information, such as closing unnecessary programs or optimizing system settings. From the perspective of real-time evaluation, this application embodiment can acquire computer operating data in real time, perform scenario identification and performance evaluation, and provide timely feedback on performance information, allowing users to keep track of the computer's performance dynamics at any time. This overcomes the problem of delayed evaluation results in existing technologies and helps users adjust their usage strategies in a timely manner to improve the user experience. In terms of the comprehensiveness of the evaluation, this method combines different usage scenarios, considers multiple user experience perceived performance indicators, and assigns different weights to them. This can more comprehensively and accurately reflect the computer's performance in actual use, avoiding the drawbacks of traditional benchmark software being limited by the testing environment and unable to reflect real-world performance changes. Meanwhile, this method directly links system resource consumption with the user-perceived performance experience, solving the problem of the disconnect between existing technology evaluation dimensions and user experience. This allows users to more intuitively understand the impact of computer performance on their actual use. Furthermore, the entire evaluation process is highly automated, eliminating the need for users to manually run test programs or interpret complex data, reducing the user's workload, and automatically providing performance feedback, making it more convenient to use.

[0029] By applying the technical solution of this embodiment, the current operating data of the computer is first obtained and the usage scenario is identified. Then, the user experience perception performance indicators and weights corresponding to the current scenario are obtained from a pre-built mapping table. Next, performance data is collected according to the indicators and a comprehensive performance score is calculated based on the weights. Finally, real-time performance feedback is provided based on the score. This embodiment of the application achieves real-time performance evaluation, allowing users to keep abreast of the dynamics of computer performance. The evaluation is comprehensive and accurate, combining scenarios and multiple indicators and relating them to user experience, overcoming the problems of traditional methods being limited by the environment and disconnected from the user experience. At the same time, it has a high degree of automation, requiring no manual operation or data interpretation by the user, automatically providing feedback on the results, making it convenient to use and greatly improving the user's understanding of computer performance and user experience.

[0030] In this embodiment, optionally, the types of the current running data include process data, window title data, input behavior data, graphics and media data, and audio / video device data; input behavior data includes keyboard input behavior data, mouse input behavior data, and touchpad input behavior data; graphics and media data includes frame rate and media playback status; audio / video device data includes microphone usage data, speaker usage data, and camera usage data; based on the current running data, usage scenario identification is performed to determine the current usage scenario, including: obtaining the currently used application name based on process data, obtaining the current window title keyword based on window title data, and determining the initial score of the candidate scenario based on the current application name and the current window title keyword according to the scenario mapping template; wherein, the scenario mapping template includes application names and window title keywords under various scenarios, as well as each application name and window title keyword. The scores are calculated using the name and keywords of each window title. In the heuristic scene template, the scene scores corresponding to the input behavior data, graphics and media data, and audio / video device data are queried to determine the heuristic scores for each scene corresponding to the input behavior data, graphics and media data, and audio / video device data. The heuristic scene template includes data ranges for each input behavior data, graphics and media data, and audio / video device data under multiple scenes, as well as heuristic scores for these data ranges. Based on the heuristic scores for each scene corresponding to the input behavior data, graphics and media data, and audio / video device data, the initial scores of the candidate scenes are updated to determine the final scores of the candidate scenes. The candidate scene with the highest final score is determined as the current usage scene.

[0031] In this embodiment, the types of currently running data cover process data, window title data, input behavior data, graphics and media data, and audio / video device data. Input behavior data is further subdivided into keyboard, mouse, and touchpad input behavior data; graphics and media data includes frame rate and media playback status; audio / video device data includes microphone, speaker, and camera usage data. In specific application scenarios, processes and windows include: foreground process name, window title, URL / tab, and window status (fullscreen / on top / minimized). Input behavior includes: keyboard / mouse speed, shortcut keys, and scroll wheel / touchpad intensity. Graphics and media includes: DXGI / OpenGL / Vulkan handles, frame rate (FPS), and media playback status (audio / video decoding, exclusive fullscreen). Audio / video devices include: microphone / speaker / camera usage and volume, and virtual audio device switching. Systems and devices include: power plan (high performance / balanced), number / resolution of external displays, CPU / GPU temperature and frequency, and network RTT / packet loss.

[0032] When identifying usage scenarios based on this current operational data, the first step is to obtain the name of the currently used application based on process data, such as the user using Adobe Photoshop for image editing. Simultaneously, the window title keywords are obtained based on window title data, such as "New Project - Photoshop," where "New Project" can be used as a keyword. Next, a scene mapping template is used, which pre-sets application names and window title keywords for various scenarios, along with scores for each application name and window title keyword. Based on the currently obtained application names and window title keywords, an initial score for the candidate scenario is determined. The initial score is based on the number of matched application names and window title keywords; if multiple keywords are matched, the initial score will be relatively high.

[0033] The second step involves querying the heuristic scenario template. This template contains data ranges for various input behavior data, graphics and media data, and audio / video device data across multiple scenarios, along with the corresponding heuristic scores for these data ranges. For example, in a gaming scenario, keyboard input behavior data might manifest as frequent key presses with short intervals between key presses. When the collected keyboard input behavior data falls within this data range, a corresponding heuristic score will be obtained. Similarly, for graphics and media data, game scenarios typically require high frame rates. If the collected frame rate falls within the high frame rate range defined for the game scenario, a corresponding heuristic score will also be obtained. By querying this template, the heuristic scores for each scenario corresponding to the input behavior data, graphics and media data, and audio / video device data can be determined.

[0034] The third step involves updating the initial scores of the previously identified candidate scenarios based on the heuristic scores for each scenario, corresponding to the input behavior data, graphics and media data, and audio / video device data. For example, if a candidate scenario initially has a low score, but the heuristic scores reveal that the input behavior data and graphics and media data score higher in that scenario, then the final score of that candidate scenario will be increased accordingly.

[0035] Finally, the candidate scenario with the highest final score is determined as the current usage scenario. For example, after a series of calculations and updates, if the game scenario candidate scenario has the highest final score, then the current usage scenario of the computer device can be determined as a game scenario. This embodiment first comprehensively utilizes multiple types of operational data to analyze and judge usage scenarios from different dimensions, improving the accuracy and comprehensiveness of scenario recognition. For example, relying solely on the application name may not accurately distinguish whether a user is performing professional image editing or simply viewing images, but by combining window title keywords, input behavior data (such as frequent drawing tool operations), and graphics and media data (such as high resolution, complex layer processing, etc.), it is possible to more accurately determine that the user is in a professional image editing scenario. Secondly, by setting scene mapping templates and heuristic scene templates, scene recognition is automated and standardized, reducing interference from human factors and improving recognition efficiency and stability. Moreover, this data-driven method can adapt to constantly changing application scenarios and user behaviors, possessing strong flexibility and scalability. For example, with the emergence of new applications or changes in user habits, only the corresponding template data needs to be updated to ensure the accuracy of scene recognition. Furthermore, accurately identifying usage scenarios provides a solid foundation for subsequent real-time computer performance evaluation based on user experience perception. It enables more targeted performance evaluation and feedback based on users' differentiated performance needs in different scenarios, thereby improving the overall user experience.

[0036] Optionally, in this embodiment of the application, the method further includes: dynamically determining the final score of the corresponding candidate scenario based on the real-time acquired current running data; if the new candidate scenario with the highest final score belongs to the same scenario group as the most recently determined current usage scenario, then if the difference between the final scores of the new candidate scenario with the highest final score and the most recently determined current usage scenario is less than a preset difference threshold, the original current usage scenario remains unchanged; otherwise, the candidate scenario with the highest final score is determined as the new current usage scenario; wherein, the usage scenarios in the same scenario group are scenarios where the similarity between running data determined based on different scenario usage information of computer devices is greater than a preset similarity; if the new candidate scenario with the highest final score does not belong to the same scenario group as the most recently determined current usage scenario, then the new candidate scenario with the highest final score is determined as the new current usage scenario.

[0037] In this embodiment, based on the dynamic identification of usage scenarios, an intelligent buffer mechanism for scenario switching is introduced. This mechanism achieves stability and continuity in scenario determination through dynamic analysis of real-time runtime data. Specifically, the system continuously acquires real-time runtime data from the computer and recalculates the final score of each candidate scenario. For example, when a user switches from a document editing scenario to a spreadsheet processing scenario, the system first determines whether the two scenarios belong to the same scenario group. If, based on the similarity of runtime data (such as keyboard input frequency, window focus change patterns, etc.), it determines that they belong to the same office scenario group, then it further compares the difference between the old and new scenario scores. Assuming the original document editing scenario score is 85 and the newly calculated spreadsheet processing scenario score is 90, if the preset score difference threshold is 5, then because 90-85=5 equals the threshold, the system will maintain the current scenario as document editing instead of immediately switching, avoiding accidental scenario jumps due to brief operational changes. If the new scenario score jumps to 95, and the score difference exceeds the threshold by 10, the system determines that the user has substantially shifted to spreadsheet processing needs and automatically updates the current scenario to spreadsheet processing. If the old and new scenarios belong to different scenario groups—for example, if a user suddenly switches from document editing in the office scenario group to a MOBA game in the game scenario group—regardless of the score difference, the system will directly recognize that the scenario has fundamentally changed and immediately update the current usage scenario. This design, through scenario group similarity division and score difference threshold control, ensures the accuracy of scenario recognition while avoiding scenario jitter caused by minor data fluctuations. For example, temporarily adjusting window size or briefly switching applications will not trigger scenario switching, while responding quickly to genuine changes in needs. This achieves a balance between the real-time nature of performance evaluation and the consistency of user experience, improving the overall reliability of the evaluation system and the smoothness of the user experience.

[0038] Optionally, in this embodiment, calculating the comprehensive performance score of the computer device based on the collected user experience perceived performance index data and the corresponding index weights includes: performing standardized calculations on the collected user experience perceived performance index data to obtain standardized index data corresponding to each user experience perceived performance index data; acquiring user experience perceived performance index data within a preset time window, performing performance index trend calculations based on the user experience perceived performance index data within the preset time window to determine the performance index trend factor corresponding to each user experience perceived performance index data; and calculating the comprehensive performance score of the computer device based on the user experience perceived performance index data, the performance index trend factor, and the index weights.

[0039] In this embodiment, taking a game scenario as an example, it is assumed that three metrics are collected: frame rate (FPS), latency (ms), and GPU utilization. The original frame rate may fluctuate from 30 to 200, and the latency may vary from 5 to 100ms; directly adding them together is meaningless. Z-score standardization transforms each metric into a value with a mean of 0 and a standard deviation of 1. For example, if the frame rate is 120 at a certain moment, and the mean frame rate for that scenario is 100 with a standard deviation of 20, then the standardized value is (120-100) / 20=1; if the latency is 30ms, and the mean is 50 with a standard deviation of 20, then the standardized value is (30-50) / 20=-1. Standardized data can be compared across dimensions, avoiding the problem of large numbers distorting smaller ones. Data is continuously collected within a preset time window (e.g., 30 seconds), and the trend of metric changes is fitted using linear regression. For example, in an office scenario, if a user simultaneously opens Word, Excel, and a browser, memory usage gradually increases from an initial 4GB to 7.5GB. If memory usage increases by 0.1GB per second within 30 seconds, the trend factor can be set to +0.2 (a positive value indicates an upward trend); if memory usage subsequently stabilizes at 7.5GB, the trend factor approaches 0. This factor captures the dynamic process of the system gradually slowing down or recovering performance, reflecting the real experience more accurately than data from a single moment. A comprehensive score is generated by combining standardized data, the trend factor, and weights (e.g., frame rate weight 0.5, latency 0.3, GPU utilization 0.2 for game scenarios). This score reflects both the current performance status and incorporates trend changes, making it better at predicting impending stuttering or performance redundancy than static scores, helping users optimize or maintain operation in advance. This design eliminates dimensional differences through data standardization, captures dynamic changes through the trend factor, and ultimately integrates spatiotemporal information into the score, achieving an upgrade from instantaneous snapshots to continuous performance evaluation, better meeting users' actual needs for consistently smooth gameplay.

[0040] Optionally, in this embodiment, performance indicator trend calculation is performed based on the user experience perception performance indicator data within a preset time window to determine the performance indicator trend factor corresponding to each user experience perception performance indicator data. This includes: fitting the user experience perception performance indicator data within the preset time window to determine the performance indicator trend slope corresponding to each user experience perception performance indicator data; calculating the ratio of the performance indicator trend slope corresponding to each user experience perception performance indicator data to the trend sensitivity threshold of the corresponding user experience perception performance indicator, and truncating the ratio to obtain the performance indicator trend parameter, wherein the truncation calculation is to limit the ratio to the range of [0,1]; calculating the difference between 1 and each performance indicator trend parameter respectively to obtain the performance indicator trend factor corresponding to each type of user experience perception performance indicator data.

[0041] In this embodiment, performance index data within a preset time window (e.g., 30 seconds) is used as the basis, and linear regression is employed to fit the data change trend. For example, in a video editing scenario, GPU utilization data is collected: the initial utilization is 30%, rising to 45% in the 10th second, reaching 60% in the 20th second, and stabilizing at 70% in the 30th second. By fitting using the least squares method, the trend slope is obtained as 1.33% / second (i.e., an increase of approximately 1.33 percentage points per second), intuitively reflecting the accelerating upward trend of GPU load. The ratio of the fitted trend slope to a preset trend sensitivity threshold is calculated, and the result is truncated to limit it to the [0,1] interval. Assuming the trend sensitivity threshold for GPU utilization in this scenario is 2% / second (indicating that the system is relatively sensitive to an upward trend exceeding 2% / second), the ratio = 1.33 / 2 ≈ 0.665, which remains 0.665 after truncation. If the slope reaches 3% / second, the ratio = 1.5, and after truncation, it becomes 1; if the slope is only 0.5% / second, the ratio = 0.25, and after truncation, it becomes 0.25. This step maps the trend strength to a standardized value, avoiding extreme values ​​from affecting the stability of the evaluation. The trend factor is calculated by subtracting the truncation ratio from 1, thus establishing a correlation between the trend direction and user experience. For example, when the trend slope is positive (e.g., increased GPU utilization), a larger truncation ratio (closer to 1) and a smaller trend factor (closer to 0) indicate increased performance pressure, and users may perceive a risk of lag; when the trend slope is negative (e.g., decreased memory utilization), a smaller truncation ratio (closer to 0) and a larger trend factor (closer to 1) indicate increased performance redundancy and smoother system operation. Taking an office scenario as an example: if the memory usage trend slope is -0.2GB / s (memory release), and the sensitivity threshold is -0.1GB / s (a negative threshold indicates sensitivity to the downward trend), then the ratio = (-0.2) / (-0.1) = 2, which is truncated to 1, and the trend factor = 1-1 = 0, indicating that memory pressure has eased; if the slope is only -0.05GB / s, the ratio = 0.5, and the trend factor = 0.5, indicating that the memory release trend is weak. This embodiment integrates the instantaneous state and the changing trend into a comprehensive score through the trend factor, so that the performance evaluation can not only reflect the current speed, but also predict whether it will slow down or recover. For example, when the frame rate continues to drop, the trend factor decreases, giving an early warning of potential lag; when memory continues to release, the trend factor increases, indicating that system resources are sufficient. This dynamic trend perception capability upgrades performance evaluation from post-diagnosis to pre-warning, which is more in line with users' actual needs for continuous smooth experience.

[0042] Optionally, in this embodiment, the comprehensive performance score of the computer device is calculated based on each user experience perceived performance index data, each performance index trend factor, and each index weight. This includes: calculating the product of each user experience perceived performance index data and its corresponding performance index trend factor to obtain the index score for each user experience perceived performance index data; and weighting and summing the index scores of each user experience perceived performance index data according to the index weight corresponding to each user experience perceived performance index data to obtain the comprehensive performance score of the computer device.

[0043] In this embodiment, for each user experience perceived performance metric (such as frame rate in a game scenario or memory usage in an office scenario), its current collected value is multiplied by the corresponding performance metric trend factor. For example, if the game frame rate is 120 FPS at a certain moment and the trend factor is 0.8 (indicating a slight downward trend in the frame rate), then the score for this metric is 120 × 0.8 = 96. This product design allows the score to reflect both how fast it is currently and whether it is getting faster / slower, which is closer to the user's perception of the continuity of smoothness than simply using raw data or trend factors. Based on preset metric weights (such as frame rate weight 0.5, latency weight 0.3, and GPU usage weight 0.2 in a game scenario), the scores of each metric are weighted and summed to obtain a comprehensive performance score. For example, in a gaming scenario: Frame rate score 96 (weight 0.5) → Contribution value 96 × 0.5 = 48; Latency score 18 (assuming latency of 30ms × trend factor 0.6, weight 0.3) → Contribution value 18 × 0.3 = 5.4; GPU utilization score 63 (assuming utilization of 70% × trend factor 0.9, weight 0.2) → Contribution value 63 × 0.2 = 12.6; Overall score = 48 + 5.4 + 12.6 = 66. This score directly reflects the combined impact of the current performance status and trend changes. This method, through the product of the current value and the trend factor, more accurately reflects the user's perception of whether smoothness is sustainable. Simultaneously, the weight allocation can be dynamically adjusted according to scenario requirements (e.g., games prioritize frame rate, office work prioritizes memory), making the score more aligned with the user experience priorities in specific scenarios. Ultimately, this achieves a closed loop of performance evaluation, user feedback, and system optimization, improving the overall smoothness and reliability of the user experience.

[0044] In this embodiment of the application, optionally, real-time performance feedback of the computer device is performed based on the comprehensive performance score, including: determining the current user experience level based on the comprehensive performance score and the score range of the user experience level corresponding to the current usage scenario; if the current user experience level belongs to the level that needs optimization, then obtaining preset performance optimization information corresponding to the current usage scenario; and performing real-time performance feedback of the computer device based on the current user experience level, the comprehensive performance score, and the preset performance optimization information.

[0045] In this embodiment, the overall performance score is first compared with the preset user experience level score range for the current usage scenario (such as gaming or office work) to determine the user's actual experience level (e.g., "extremely smooth", "smooth", "slight lag", "severe lag"). If the level falls within the optimization range (e.g., "slight lag" or "severe lag"), the preset optimization strategy for that scenario is automatically retrieved (e.g., reducing resolution in gaming scenarios or closing background processes in office scenarios). Finally, real-time feedback is generated by combining the level identifier, specific score value, and optimization suggestions (e.g., a pop-up message "Current smoothness: 75 points (slight lag), it is recommended to close background applications to improve performance"). Its core advantage lies in transforming abstract performance data into user-perceptible level identifiers and scenario-based optimization solutions. This avoids the cognitive barrier of technical parameters and matches performance pain points in different scenarios, achieving a closed loop from passive monitoring to proactive optimization, thereby improving the smoothness of the user experience and the efficiency of device resource utilization.

[0046] Furthermore, as a specific implementation of the method in Figure 1, this application embodiment provides a real-time computer performance evaluation device based on user experience perception, as shown in Figure 2. The device includes: a scene recognition module, used to acquire the current operating data of the computer device and identify the current usage scenario based on the current operating data; an indicator determination module, used to acquire multiple user experience perception performance indicators corresponding to the current usage scenario and the indicator weights of each user experience perception performance indicator from a pre-constructed mapping table between usage scenarios and user experience perception performance weights; a performance scoring module, used to collect performance indicator data of the computer device according to each user experience perception performance indicator, and calculate the comprehensive performance score of the computer device based on the collected user experience perception performance indicator data and the corresponding indicator weights; and a performance feedback module, used to provide real-time performance feedback of the computer device based on the comprehensive performance score.

[0047] Optionally, the types of currently running data include process data, window title data, input behavior data, graphics and media data, and audio / video device data; input behavior data includes keyboard input behavior data, mouse input behavior data, and touchpad input behavior data; graphics and media data includes frame rate and media playback status; audio / video device data includes microphone usage data, speaker usage data, and camera usage data; the scene recognition module is specifically used to: obtain the currently used application name based on process data, obtain the current window title keyword based on window title data, and determine the initial score of candidate scenes based on the scene mapping template, according to the currently used application name and the current window title keyword; wherein, the scene mapping template includes application names and window title keywords under multiple scenes, as well as the application names and window title keywords of each application name and each window title keyword. Scores; In the heuristic scene template, query the scene scores matched by the input behavior data, the graphics and media data, and the audio / video device data to determine the heuristic scores of each scene corresponding to the input behavior data, the graphics and media data, and the audio / video device data; wherein, the heuristic scene template includes data ranges for each input behavior data, each graphics and media data, and each audio / video device data under multiple scenes, as well as heuristic scores for each data range for each input behavior data, each graphics and media data, and each audio / video device data under multiple scenes; based on the heuristic scores of each scene corresponding to the input behavior data, the graphics and media data, and the audio / video device data, the initial scores of the candidate scenes are updated to determine the final scores of the candidate scenes; the candidate scene with the highest final score is determined as the current usage scene.

[0048] Optionally, the scene recognition module is further configured to: dynamically determine the final score of the corresponding candidate scene based on the real-time acquired current running data; if the new candidate scene with the highest final score belongs to the same scene group as the most recently determined current usage scene, then if the difference between the final scores of the new candidate scene with the highest final score and the most recently determined current usage scene is less than a preset difference threshold, the original current usage scene remains unchanged; otherwise, the candidate scene with the highest final score is determined as the new current usage scene; wherein, usage scenes in the same scene group are scenes whose similarity between running data determined based on different scene usage information of computer devices is greater than a preset similarity; if the new candidate scene with the highest final score does not belong to the same scene group as the most recently determined current usage scene, then the new candidate scene with the highest final score is determined as the new current usage scene.

[0049] Optionally, the performance scoring module is specifically used for: standardizing the collected user experience perception performance index data to obtain standardized index data corresponding to each user experience perception performance index data; acquiring user experience perception performance index data within a preset time window, performing performance index trend calculation based on the user experience perception performance index data within the preset time window, and determining the performance index trend factor corresponding to each user experience perception performance index data; and calculating the comprehensive performance score of the computer device based on the user experience perception performance index data, the performance index trend factor, and the weight of each index.

[0050] Optionally, the performance scoring module is specifically used for: fitting the user experience perception performance index data within a preset time window to determine the performance index trend slope corresponding to each user experience perception performance index data; calculating the ratio of the performance index trend slope corresponding to each user experience perception performance index data to the trend sensitivity threshold of the corresponding user experience perception performance index, and truncating the ratio to obtain the performance index trend parameter, wherein the truncation calculation is to limit the ratio to the range of [0,1]; calculating the difference between 1 and each performance index trend parameter respectively to obtain the performance index trend factor corresponding to each type of user experience perception performance index data.

[0051] Optionally, the performance scoring module is specifically used to: calculate the product of each user experience perceived performance index data and the corresponding performance index trend factor to obtain the index score of each user experience perceived performance index data; and perform a weighted summation of the index scores of each user experience perceived performance index data according to the index weight corresponding to each user experience perceived performance index data to obtain the comprehensive performance score of the computer device.

[0052] Optionally, the performance feedback module is specifically used to: determine the current user experience level based on the comprehensive performance score and the score range of the user experience level corresponding to the current usage scenario; if the current user experience level belongs to the level that needs optimization, then obtain the preset performance optimization information corresponding to the current usage scenario; and provide real-time performance feedback for the computer device based on the current user experience level, the comprehensive performance score, and the preset performance optimization information.

[0053] It should be noted that other corresponding descriptions of the functional units involved in the real-time computer performance evaluation device based on user experience perception provided in this application embodiment can be found in the corresponding descriptions in the above method, and will not be repeated here.

[0054] This application also provides a computer device, specifically a personal computer, server, network device, etc. The computer device includes a bus, processor, memory, and communication interface, and may also include input / output interfaces and a display device. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores location information. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the various method embodiments.

[0055] Those skilled in the art will understand that the structure of the computer device described above is only a partial structure 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. A specific computer device may include more or fewer components, or combine certain components, or have different component arrangements.

[0056] In one embodiment, a computer-readable storage medium is provided, which may be non-volatile or volatile, having stored thereon a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0057] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0058] It should be noted that the user personal information involved in the embodiments of this application is all authorized (with the knowledge and consent) by the relevant parties or fully authorized by all parties, and the executing entity can obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with the relevant laws and regulations of the relevant countries and regions, and do not violate public order and good morals. It should be noted that if any software tools or components other than those of this company appear in the embodiments of this application, they are merely illustrative examples and do not represent actual use.

[0059] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. 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), magnetic 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 take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0060] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this specification.

[0061] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A real-time computer performance evaluation method based on user experience perception, characterized in that, The method includes: acquiring current operating data of a computer device, and identifying the current usage scenario based on the current operating data; acquiring multiple user experience perception performance indicators and their respective weights from a pre-constructed mapping table between usage scenarios and user experience perception performance weights; collecting performance indicator data of the computer device based on each user experience perception performance indicator, and calculating a comprehensive performance score for the computer device based on the collected user experience perception performance indicator data and their corresponding weights; and providing real-time performance feedback for the computer device based on the comprehensive performance score.

2. The method according to claim 1, characterized in that, The types of currently running data include process data, window title data, input behavior data, graphics and media data, and audio / video device data; input behavior data includes keyboard input behavior data, mouse input behavior data, and touchpad input behavior data. Graphics and media data include frame rate and media playback status; Audio and video device data includes microphone usage data, speaker usage data, and camera usage data; Based on the current running data, usage scenario identification is performed to determine the current usage scenario, including: obtaining the currently used application name based on process data, obtaining the current window title keywords based on window title data, and determining the initial score of candidate scenarios based on a scenario mapping template, according to the currently used application name and the current window title keywords; wherein, the scenario mapping template includes application names and window title keywords under multiple scenarios, as well as the scores of each application name and each window title keyword; in the heuristic scenario template, the scenario scores of the input behavior data, the graphics and media data, and the audio and video device data are queried to determine the number of input behaviors. The heuristic scores for each scenario corresponding to the graphics and media data and the audio / video device data are calculated. The heuristic scenario template includes data ranges for each input behavior data, graphics and media data, and audio / video device data under various scenarios, as well as heuristic scores for these data ranges. Based on the heuristic scores for each scenario corresponding to the input behavior data, graphics and media data, and audio / video device data, the initial scores of the candidate scenarios are updated to determine the final scores. The candidate scenario with the highest final score is then determined as the current usage scenario.

3. The method according to claim 2, characterized in that, The method further includes: dynamically determining the final score of the corresponding candidate scenario based on the real-time acquired current operating data; if the new candidate scenario with the highest final score belongs to the same scenario group as the most recently determined current usage scenario, then if the difference between the final scores of the new candidate scenario with the highest final score and the most recently determined current usage scenario is less than a preset difference threshold, the original current usage scenario remains unchanged; otherwise, the candidate scenario with the highest final score is determined as the new current usage scenario; wherein, usage scenarios in the same scenario group are scenarios whose operating data, determined based on different scenario usage information of computer devices, have a similarity greater than a preset similarity; if the new candidate scenario with the highest final score does not belong to the same scenario group as the most recently determined current usage scenario, then the new candidate scenario with the highest final score is determined as the new current usage scenario.

4. The method according to any one of claims 1 to 3, characterized in that, Based on the collected user experience perceived performance index data and their corresponding weights, the comprehensive performance score of the computer device is calculated, including: standardizing the collected user experience perceived performance index data to obtain standardized index data corresponding to each user experience perceived performance index data; acquiring user experience perceived performance index data within a preset time window, calculating performance index trends based on the user experience perceived performance index data within the preset time window, and determining the performance index trend factors corresponding to each user experience perceived performance index data; and calculating the comprehensive performance score of the computer device based on the user experience perceived performance index data, the performance index trend factors, and the weights of each index.

5. The method according to claim 4, characterized in that, The performance indicator trend is calculated based on the user experience perception performance indicator data within a preset time window to determine the performance indicator trend factor corresponding to each user experience perception performance indicator data. This includes: fitting the user experience perception performance indicator data within the preset time window to determine the performance indicator trend slope corresponding to each user experience perception performance indicator data; calculating the ratio of the performance indicator trend slope corresponding to each user experience perception performance indicator data to the trend sensitivity threshold of the corresponding user experience perception performance indicator, and truncating the ratio to obtain the performance indicator trend parameter, wherein the truncation calculation is to limit the ratio to the range [0,1]; calculating the difference between 1 and each performance indicator trend parameter to obtain the performance indicator trend factor corresponding to each type of user experience perception performance indicator data.

6. The method according to claim 5, characterized in that, The comprehensive performance score of the computer device is calculated based on the user experience perceived performance index data, the trend factor of each performance index, and the weight of each index. This includes: calculating the product of each user experience perceived performance index data and its corresponding performance index trend factor to obtain the index score of each user experience perceived performance index data; and weighting and summing the index scores of each user experience perceived performance index data according to the index weight corresponding to each user experience perceived performance index data to obtain the comprehensive performance score of the computer device.

7. The method according to claim 1, characterized in that, Based on the comprehensive performance score, real-time performance feedback of the computer device is provided, including: determining the current user experience level based on the comprehensive performance score and the score range of the user experience level corresponding to the current usage scenario; if the current user experience level belongs to the level that needs optimization, then obtaining the preset performance optimization information corresponding to the current usage scenario; and providing real-time performance feedback of the computer device based on the current user experience level, the comprehensive performance score, and the preset performance optimization information.

8. A real-time computer performance evaluation device based on user experience perception, characterized in that, The device includes: a scene recognition module, used to acquire the current operating data of the computer device, and identify the current usage scenario based on the current operating data; an indicator determination module, used to acquire multiple user experience perception performance indicators corresponding to the current usage scenario and the indicator weights of each user experience perception performance indicator from a pre-constructed mapping table between usage scenarios and user experience perception performance weights; a performance scoring module, used to collect performance indicator data of the computer device according to each user experience perception performance indicator, and calculate the comprehensive performance score of the computer device according to the collected user experience perception performance indicator data and the corresponding indicator weights; and a performance feedback module, used to provide real-time performance feedback of the computer device based on the comprehensive performance score.

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

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

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