Cloud game experience quality analysis method and device, electronic equipment, medium and product

Abstract

The embodiment of the invention discloses a cloud game experience quality analysis method and device, electronic equipment, a storage medium and a product, and the method comprises the steps: obtaining preset feature data related to the cloud game experience quality in a previous cloud game session process; grouping the preset feature data corresponding to each previous cloud game session according to a preset segmentation duration, and determining a plurality of cloud game fragment experience quality analysis results corresponding to each previous cloud game session based on each group of feature data; and based on the cloud game fragment experience quality analysis result, determining a target cloud game experience quality analysis result corresponding to each previous cloud game session.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, device, electronic device, medium, and product for analyzing the quality of cloud gaming experience. Background Art

[0002] Cloud gaming is a type of game that implements the computing and rendering processes in the cloud, with cloud rendering and strong interactive features. Evaluating the user experience quality analysis of cloud gaming can provide valuable reference information for game providers, so as to more effectively configure cloud gaming resources and optimize technology, and improve the user experience of cloud gaming. Summary of the invention

[0003] The present disclosure provides a cloud gaming experience quality analysis method, device, electronic device, medium and product, which can overcome the factors that lead to inaccurate analysis results in the unimproved cloud gaming experience quality analysis method, more accurately analyze the user's actual experience in cloud gaming, and provide accurate cloud gaming experience quality results, so as to more efficiently guide cloud gaming resource configuration and technical optimization solutions.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for analyzing cloud gaming experience quality, the method comprising:

[0005] Obtaining preset feature data associated with the quality of cloud gaming experience during all cloud gaming sessions;

[0006] The preset feature data corresponding to each of the previous cloud game sessions are grouped according to the preset segment duration, and based on each group of feature data, a plurality of cloud game segment experience quality analysis results corresponding to each of the previous cloud game sessions are determined;

[0007] Based on the cloud game segment experience quality analysis results, the target cloud game experience quality analysis results of each of the previous cloud game sessions are determined respectively.

[0008] In a second aspect, an embodiment of the present disclosure further provides a cloud gaming experience quality analysis device, the device comprising:

[0009] The experience quality analysis data acquisition module is used to obtain preset feature data associated with the cloud game experience quality during previous cloud game sessions;

[0010] An experience quality segmentation analysis module, used to group the preset feature data corresponding to each of the previous cloud game sessions according to a preset segmentation duration, and determine a plurality of cloud game segment experience quality analysis results corresponding to each of the previous cloud game sessions based on each group of feature data;

[0011] The comprehensive experience quality analysis module is used to determine the target cloud game experience quality analysis results of each of the previous cloud game sessions based on the experience quality analysis results of the cloud game clips.

[0012] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0013] one or more processors;

[0014] a storage device for storing one or more programs,

[0015] When the one or more programs are executed by the one or more processors, the one or more processors implement the cloud gaming experience quality analysis method as described in any of the embodiments of the present disclosure.

[0016] In a fourth aspect, the embodiments of the present disclosure further provide a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the cloud gaming experience quality analysis method as described in any of the embodiments of the present disclosure.

[0017] In a fifth aspect, the embodiments of the present disclosure further provide a computer program product, including a computer program, which, when executed by a processor, implements a cloud gaming experience quality analysis method as described in any one of the embodiments of the present invention.

[0018] The disclosed embodiment obtains preset feature data associated with the cloud gaming experience quality during all previous cloud gaming sessions; groups the preset feature data corresponding to each of the previous cloud gaming sessions according to the preset segment duration, and determines the multiple cloud gaming segment experience quality analysis results corresponding to each of the previous cloud gaming sessions based on each group of feature data; and determines the target cloud gaming experience quality analysis results of each of the previous cloud gaming sessions based on the cloud gaming segment experience quality analysis results, that is, after segmenting all previous cloud gaming sessions, the experience quality analysis is performed on a cloud gaming session segment basis, so as to reduce the impact of feature data affecting the experience quality in any cloud gaming session segment on the experience quality of other cloud gaming session segments, thereby improving the accuracy of the final target cloud gaming experience quality analysis results of all previous cloud gaming sessions. The disclosed embodiment solves the technical problem of poor application effect of the existing cloud gaming experience quality analysis model, can more accurately analyze the actual experience of users in cloud gaming, and provide accurate cloud gaming experience quality results, so as to more efficiently guide cloud gaming resource allocation and technical optimization solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0020] Figure 1 This is a flow chart of a cloud gaming experience quality analysis method provided by an embodiment of the present disclosure;

[0021] Figure 2 It is a schematic diagram of a process of grouping and processing preset characteristic data provided by an embodiment of the present disclosure;

[0022] Figure 3 This is a flow chart of a cloud gaming experience quality analysis method provided by an embodiment of the present disclosure;

[0023] Figure 4 This is a schematic diagram of a cloud gaming experience quality analysis method flow chart provided by an embodiment of the present disclosure;

[0024] Figure 5 It is a schematic diagram of the relationship between each cloud game session experience quality analysis score and the number of cloud game sessions of different durations provided by an embodiment of the present disclosure;

[0025] Figure 6 It is a schematic diagram of the relationship between each cloud game session experience quality analysis score and the corresponding number of long cloud game sessions provided by an embodiment of the present disclosure;

[0026] Figure 7 It is a schematic diagram of the relationship between each cloud game session experience quality analysis score and the corresponding number of short cloud game sessions provided by an embodiment of the present disclosure;

[0027] Figure 8 It is a structural schematic diagram of a cloud gaming experience quality analysis device provided by an embodiment of the present disclosure;

[0028] Fig. 9 It is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0030] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0031] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0032] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0033] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0034] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0035] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0036] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0037] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0038] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0039] At present, cloud gaming user experience quality analysis mostly uses the MOS (Mean Opinion Score)-QoE (Quality of Experience) model based on international general evaluation standards to obtain MOS-QoE rating results. Among them, the MOS-QoE rating results are closely related to the game's graphics, freezes, latency and other conditions.

[0040] However, the correlation between MOS-QoE score results and users' real experience has not been fully verified. In the practice of cloud game experience quality analysis, it is found that the relationship between MOS-QoE score results and game running time does not conform to the logical relationship that the higher the MOS-QoE score, the longer the game running time. This leads to the low accuracy of the evaluation results of the MOS-QoE model, and the evaluation results are of little significance for guiding cloud game resource allocation and technical optimization in the cloud.

[0041] Figure 1 A flow chart of a cloud gaming experience quality analysis method provided in an embodiment of the present disclosure is applicable to scenarios of cloud gaming user experience quality evaluation. The method can be performed by a cloud gaming experience quality analysis device, which can be implemented in the form of software and / or hardware, and optionally, by an electronic device, which can be a mobile terminal, a PC, a server, etc.

[0042] It should be noted that in the embodiments of the present disclosure, in the absence of an indicator that can more accurately reflect the user experience, assuming that the game duration can effectively represent the user's gaming experience, the game session time will be longer if the experience quality analysis result is better.

[0043] Cloud gaming is an emerging form of gaming that breaks the limitations of traditional gaming models. It transfers the computing and rendering capabilities of games from local to cloud, allowing users to directly obtain game resources and play games through the Internet without having to download and install the game itself, thereby lowering the threshold for users to obtain high-quality games. Evaluating the user experience quality of cloud gaming can provide game providers with more effective resource allocation and technical optimization solutions, and has important guiding significance for the development of cloud gaming business. However, considering the cloud rendering and strong interactive characteristics of cloud gaming, this poses certain difficulties for the user experience quality analysis process.

[0044] At present, in response to this problem, the International Telecommunication Union Telecommunication Standardization Department has developed a user experience quality evaluation standard for cloud gaming services. Specifically, first, a large number of users' picture resolution, frame rate, network delay and other information when playing cloud games, as well as the user's subjective evaluation score of the entire game experience (1-5, where 1 point is the worst experience and 5 points is the best experience) are collected. Then feature extraction is performed to extract feature data related to the quality of cloud gaming user experience from the original data. These features may involve network delay, video encoding quality, number of freezes, etc. Finally, machine learning and data analysis techniques are used to model the collected data and propose a cloud gaming user experience quality evaluation model MOS_QoE. This model can associate feature information during the game with corresponding technical indicators and performance parameters, and obtain an evaluation score for evaluating the quality of experience of cloud gaming users. That is, the MOS_QoE experience quality evaluation model provides a common evaluation framework for cloud gaming experience quality analysis. The scoring results are closely related to the game's picture, freeze, latency and other conditions. It can evaluate and analyze the key performance and user experience of cloud gaming, and promote the quality improvement of cloud gaming services and the improvement of user experience.

[0045] However, in practical applications, the correlation between the MOS_QoE experience quality evaluation model's scoring results and the user's real experience has not been fully verified. In order to explore the relationship between the MOS_QoE experience quality evaluation model's scoring and user experience, the cloud gaming system can be used to obtain the duration of all previous cloud gaming sessions, and the gaming duration can be used as a method to verify the accuracy of the MOS_QoE experience quality evaluation model's scoring. However, the practical results do not conform to the principle that "the higher the MOS_QoE experience quality evaluation model score, the longer the gaming session time", indicating that the MOS_QoE experience quality evaluation model cannot be directly used for the experience quality analysis of cloud gaming, because there is a large uncertainty in this process, and the analysis results are of little significance. Therefore, a cloud gaming experience quality analysis method is proposed to accurately reflect the user's cloud gaming experience quality under the condition that the logical relationship between "game duration" and the cloud gaming experience quality analysis results is satisfied.

[0046] like Figure 1 As shown, the cloud gaming experience quality analysis method includes:

[0047] S110. Obtain preset feature data associated with cloud gaming experience quality during previous cloud gaming sessions.

[0048] Among them, a cloud game session can be understood as a process of any cloud game from game startup to game exit. All previous cloud game sessions can be any process of any cloud game recorded by the game provider's cloud from game startup to game exit.

[0049] The preset feature data associated with the cloud gaming experience quality may be the relevant feature data involved in the above-mentioned MOS_QoE experience quality evaluation model framework, or the feature data may be adjusted according to the actual analysis process, such as reducing or adding one or more features.

[0050] In an optional implementation, the preset feature data can be divided into three categories of feature data as a whole, including audio and video quality feature parameters in cloud game sessions, interaction quality feature parameters in cloud game sessions, and presentation quality parameters in cloud game paintings.

[0051] For example, Figure 2 As shown in the schematic diagram of the process of grouping and processing preset characteristic data, under the cloud game quality analysis framework of this embodiment, the audio and video quality characteristic parameters may include cloud game frame rate, picture bit rate, picture resolution and audio bit rate and other parameters; the interaction quality characteristic parameters may include relevant parameters for reflecting interaction delay; the presentation quality parameters may include cloud game session freeze times, freeze duration and freeze interval and other parameters. These parameters are determined under the general experience quality evaluation framework to reflect the user experience quality of cloud games.

[0052] S120. Group the preset feature data corresponding to each of the previous cloud game sessions according to the preset segment duration, and determine multiple cloud game segment experience quality analysis results corresponding to each of the previous cloud game sessions based on each group of feature data.

[0053] In this step, unlike the MOS_QoE experience quality evaluation model in the prior art, all preset feature data in a cloud game session are analyzed as a whole to obtain the final analysis result. Instead, the preset feature data are grouped, each feature data group corresponds to a cloud game session segment, and the experience quality of all cloud game sessions is analyzed using a segmented feature data analysis method.

[0054] The reason why the segmented feature data analysis method is used to analyze the experience quality of previous cloud game sessions is to find out in the exploration of the MOS_QoE experience quality evaluation model that some parameter calculation methods in the MOS_QoE experience quality evaluation model, especially in terms of presentation quality and interaction quality. Specifically, in the calculation of presentation quality parameters, the MOS_QoE experience quality evaluation model will score according to the number of freezes, freeze duration, and freeze intervals that occur during the entire cloud game session. This calculation method will exaggerate the impact of a certain freeze event on the entire cloud game session. However, according to the user's subjective feelings and the principle of the forgetting curve, a freeze event will only affect the user experience in a short period of time, and should not be directly applied to the entire cloud game session. The calculation of interaction quality parameters also has similar problems. The impact of a certain delay information should not be expanded to the entire cloud game session, which will lead to inaccurate experience quality analysis results. Based on this finding, the MOS_QoE experience quality evaluation model was improved, and a cloud gaming user experience quality evaluation scheme based on segmented calculation was proposed to effectively avoid the impact of a single event that affects the length of a cloud gaming session on the entire cloud gaming session, which helps to more accurately reflect the user's actual experience in cloud gaming.

[0055] In an optional implementation, each previous cloud game session can be divided into multiple cloud game session segments according to a preset segment duration; then, the preset feature data corresponding to each cloud game session segment of each previous cloud game session is divided into a group of preset feature data. For example, if the preset segment duration is 20s, a cloud game session with a session duration of ten minutes can be divided into 30 cloud game session segments, and the preset feature data in each cloud game session segment is divided into a group, that is, 30 groups of preset feature data can be divided. For each group of preset feature data, the calculation method of the MOS_QoE experience quality evaluation model is used to obtain the scoring result corresponding to the experience quality analysis result of each cloud game segment.

[0056] Furthermore, in order to ensure the linear correlation between the cloud gaming experience quality analysis results and the session duration, the preset segment duration is set to be less than the preset session segment duration threshold, wherein the preset session segment duration threshold may be less than or equal to the duration threshold of 60s. This is the result of exploring the relationship between the cloud gaming experience quality analysis results and the average session duration under different segment durations. When the segment duration is 20s, 30s, etc., the cloud gaming experience quality analysis result score and the average session duration show an obvious positive correlation. When the segment duration exceeds 60s, this positive correlation phenomenon will weaken. This is because short segment durations are more helpful in reducing the impact of freeze events and delay fluctuation events. In actual applications, the final preset segment duration, such as 10s or 20s, can be determined based on relevant factors such as the computing power of the cloud device and the amount of computing data.

[0057] S130. Determine the target cloud game experience quality analysis results of each of the previous cloud game sessions based on the cloud game segment experience quality analysis results.

[0058] In this step, the cloud game segment experience quality analysis results determined in the above steps are integrated to obtain the target cloud game experience quality analysis results of the entire cloud game session. In an optional manner, the average or weighted average of the experience quality analysis results of each cloud game segment can be used as the target cloud game experience quality analysis result, or the functional relationship between the experience quality analysis results of each cloud game segment can be pre-set, and the final target cloud game experience quality analysis result can be determined based on the preset functional relationship.

[0059] In an application example, the game sessions of multiple cloud games of several preset cloud game providers are used as the experience quality analysis objects, and the cloud game experience quality analysis method provided by the embodiment of the present disclosure is used to perform cloud game experience quality analysis. The preset segment duration can be set to 20s, and a coordinate system of the relationship between the target cloud game experience quality analysis result score and the user's cloud game average session duration is established, wherein the horizontal axis is the target cloud game experience quality analysis result score, and the vertical axis uses a bar to represent the average cloud game session duration under the corresponding score. Thus, the functional relationship between the target cloud game experience quality analysis result score and the user's cloud game average session duration can be analyzed. As the target cloud game experience quality analysis result score increases, the user's average cloud game duration has a significant increase, which shows that under the segment duration of 20s, there is a significant positive correlation between the target cloud game experience quality analysis result score and the cloud game session duration, which also shows that the higher the target cloud game experience quality analysis result score, the higher the user's willingness to continue to participate in the game. Therefore, through the technical solution of this embodiment, the cloud game user experience quality is analyzed, and an analysis result that can more accurately reflect the user's real experience can be obtained.

[0060] The technical solution of the disclosed embodiment is to obtain preset feature data associated with the cloud gaming experience quality during previous cloud gaming sessions; group the preset feature data corresponding to each of the previous cloud gaming sessions according to the preset segment duration, and determine the multiple cloud gaming segment experience quality analysis results corresponding to each of the previous cloud gaming sessions based on each group of feature data; and determine the target cloud gaming experience quality analysis results of each of the previous cloud gaming sessions based on the cloud gaming segment experience quality analysis results, that is, after segmenting each of the previous cloud gaming sessions, perform experience quality analysis on a cloud gaming session segment basis, so as to reduce the impact of feature data affecting experience quality in any cloud gaming session segment on the experience quality of other cloud gaming session segments, thereby improving the accuracy of the final target cloud gaming experience quality analysis results of each previous cloud gaming session. The disclosed embodiment solves the technical problem of poor application effect of the existing cloud gaming experience quality analysis model, can more accurately analyze the actual experience of users in cloud gaming, provide accurate cloud gaming experience quality results, and more efficiently guide cloud gaming resource configuration and technical optimization solutions.

[0061] Figure 3 This is a flow chart of another cloud gaming experience quality analysis method provided by the embodiment of the present disclosure. On the basis of the above embodiment, in the process of implementing the cloud gaming experience quality analysis method flow, the process of filtering cloud gaming sessions and determining the weight of the cloud gaming fragment experience quality analysis results is further explained. The method can be executed by a cloud gaming experience quality analysis device, which can be implemented in the form of software and / or hardware, and optionally, by an electronic device, which can be a mobile terminal, a PC, or a server, etc.

[0062] like Figure 3 As shown, the cloud gaming experience quality analysis method includes:

[0063] S210: Perform session filtering on each of the previous cloud game sessions according to the session duration of the previous cloud game sessions.

[0064] In order to avoid the impact of some short-session misoperations, test behaviors or abnormal cloud game sessions on the cloud game experience quality analysis results, in this embodiment, cloud game sessions with short session durations can be filtered out in advance. For example, the time threshold can be set to 60s, and cloud game sessions with a session duration shorter than 60s are filtered out to increase the reliability of the final cloud game session experience quality analysis results.

[0065] S220: Obtain preset feature data associated with cloud gaming experience quality during all cloud gaming sessions determined after session filtering.

[0066] S230. Group the preset feature data corresponding to each of the previous cloud game sessions that have passed through session filtering according to the preset segment duration, and determine multiple cloud game segment experience quality analysis results corresponding to each of the previous cloud game sessions that have passed through session filtering based on each group of feature data.

[0067] S240. Determine weight parameters of cloud game segment experience quality analysis results corresponding to each cloud game segment according to the number of cloud game segment of each previous cloud game session filtered by the session and / or the position in the session.

[0068] In an optional implementation, each cloud game session segment in each cloud game session can be used as a segment with the same influence on the target cloud game experience quality analysis result. Then, the same weight parameter can be set for the cloud game segment experience quality analysis result of each cloud game session segment according to the number of cloud game session segments in each cloud game session. For example, a cloud game session is divided into 50 cloud game session segments. Then, the weight parameter of the cloud game segment experience quality analysis result of each cloud game session segment can be set to the same value of 0.02 (1 divided by 50).

[0069] In another optional embodiment, each cloud game session segment in each cloud game session may have a different influence on the target cloud game experience quality analysis result. Correspondingly, the weight parameters of the cloud game segment experience quality analysis result of each cloud game session segment are different. Exemplarily, the weight parameters of the cloud game segment experience quality analysis results corresponding to each cloud game session segment can be determined separately according to the positions of the cloud game session segments of each previous cloud game session filtered by the session in the corresponding cloud game session. For example, a linear increasing method can be used so that the weight parameter values ​​corresponding to the earlier cloud game session segments in the time sequence are lower, and the weight parameter values ​​corresponding to the later cloud game session segments in the time sequence are lower. Nonlinear weight settings, such as exponential functions, can also be used to determine different weight parameters corresponding to different cloud game session segments.

[0070] Alternatively, in one embodiment, the weight parameter settings corresponding to each cloud game session segment can be set in a segmented random average manner. For example, the cloud game session segments are grouped, and every 10 consecutive cloud game session segments are randomly assigned the same weight parameter, and the weight parameters corresponding to cloud game session segments in different groups are different, so as to ensure that the sum of the weight parameters corresponding to each cloud game session segment is 1.

[0071] S250. Perform weighted calculation based on the weight parameter and the experience quality analysis results of each of the cloud game clips to obtain a target cloud game experience quality analysis result of the corresponding cloud game session.

[0072] The technical solution of the disclosed embodiment can further improve the reliability of the cloud game experience quality analysis results by filtering all previous cloud game sessions before performing cloud game user experience quality analysis. After filtering the cloud game sessions, the preset feature data associated with the cloud game experience quality in all previous cloud game sessions after session filtering are obtained; and the preset feature data corresponding to each previous cloud game session after session filtering are grouped according to the preset segmentation duration, and based on each group of feature data, multiple cloud game segment experience quality analysis results corresponding to each previous cloud game session after session filtering are determined; based on the cloud game segment experience quality analysis results, the target cloud game experience quality analysis results of each previous cloud game session after session filtering are respectively determined. That is, after segmenting all previous cloud game sessions after session filtering, experience quality analysis is performed in units of cloud game session segments to reduce the impact of feature data affecting experience quality in any cloud game session segment on the experience quality of other cloud game session segments, thereby improving the accuracy of the final target cloud game experience quality analysis results of each previous cloud game session. The disclosed embodiment solves the technical problem of poor application effect of existing cloud game experience quality analysis models, can more accurately analyze the actual experience of users in cloud games, and provide accurate cloud game experience quality results to more efficiently guide cloud game resource configuration and technical optimization solutions.

[0073] Figure 4 This is a flow chart of another cloud gaming experience quality analysis method provided by the embodiment of the present disclosure. On the basis of the above embodiment, it further explains the process of statistically analyzing the target cloud gaming experience quality analysis results and obtaining a cloud gaming experience quality analysis reference standard based on the statistical results. The method can be executed by a cloud gaming experience quality analysis device, which can be implemented in the form of software and / or hardware, and optionally, by an electronic device, which can be a mobile terminal, a PC, or a server.

[0074] like Figure 4 As shown, the cloud gaming experience quality analysis method includes:

[0075] S310. Obtain preset feature data associated with cloud gaming experience quality during all cloud gaming sessions after session filtering.

[0076] S320. Group the preset feature data corresponding to each of the previous cloud game sessions according to the preset segment duration, and determine multiple cloud game segment experience quality analysis results corresponding to each of the previous cloud game sessions based on each group of feature data.

[0077] S330. Determine the target cloud game experience quality analysis results of each of the previous cloud game sessions based on the cloud game segment experience quality analysis results.

[0078] S340. Perform statistical analysis on the target cloud gaming experience quality analysis results to obtain statistical results.

[0079] In this embodiment, the cloud gaming experience quality is further evaluated and analyzed based on the obtained target cloud gaming experience quality analysis results. Cloud gaming session classification analysis can be performed for each target cloud gaming experience quality analysis result score to further determine the impact of cloud gaming experience quality on the gaming time of the user group.

[0080] In an optional embodiment, the distribution characteristics of different cloud game session durations corresponding to the score values ​​of each target cloud game experience quality analysis result can be explored. For example, cloud game sessions can be divided into three categories according to the average cloud game session duration of users: short sessions (60s≤duration<150s), medium sessions (150s≤duration<1950s) and long sessions (1950s≤duration), and then the proportions of different types of cloud game sessions are counted respectively.

[0081] It is understandable that a short session means that the user quits the game within two or three minutes of entering the game. These users are most likely to quickly quit the game due to poor network conditions. A medium session means that the network status is good, with occasional freezes, but the user can still tolerate and continue playing. A long session means that the network status is excellent, and the reason why the user quits the game is more subjective.

[0082] S350: Determine the proportion of long cloud gaming sessions whose session durations meet a preset long session duration standard among the cloud gaming sessions corresponding to different cloud gaming experience quality scores in the statistical results.

[0083] After classifying and analyzing the cloud game sessions, we can get the following: Figure 5 In the distribution results shown, the numbers on the horizontal axis represent the score values ​​of the target cloud gaming experience quality analysis results, and the marked points represent the proportion of people in a certain session duration range under the score value.

[0084] from Figure 5It can be seen that the higher the score of the target cloud game experience quality analysis result, the higher the proportion of long sessions. In other words, the improvement of the score of the target cloud game experience quality analysis result increases the proportion of long sessions from 0.99% to 48.76%, which is the reason for the longer average game duration under the target cloud game experience quality analysis result. At the same time, this result also matches the assumption that "game duration can effectively represent the user's game experience". Any user will tend to play for a longer time only if the experience is good, that is, to maintain a longer game session.

[0085] S360. Determine a high quality standard score line for cloud gaming experience quality based on the data change range of the proportion of long cloud gaming sessions at different cloud gaming experience quality scores.

[0086] The high-quality standard score line can be understood as that under the current score, network conditions are no longer the main reason for users to end game sessions and exit the game. When the experience quality is better, most users usually tend to maintain a longer cloud game session duration.

[0087] The threshold of the high-quality standard score line should meet one condition, that is, the number of long-session users in this segment increases the fastest, which is reflected in the graph as the largest slope. Figure 6 The curve shown is the analysis result of different target cloud game experience quality, and the corresponding slope of the change in the proportion of long session users. According to the above conditions, the high quality standard score line of the target cloud game experience quality analysis result should be set to 4.7 points.

[0088] The setting of high-quality standard scores can help cloud game providers adjust the most efficient configuration of cloud games. For example, the relationship between the bit rate, frame rate and target cloud game experience quality analysis results of cloud game sessions can be explored to set a configuration with the highest cost-effectiveness. This can save bandwidth and computing resources without damaging the user experience, thus better implementing cost reduction and efficiency improvement.

[0089] In addition, the proportion of short cloud gaming sessions whose session duration meets the preset short session duration standard in the cloud gaming sessions corresponding to different cloud gaming experience quality scores in the statistical results can be further determined; and the random exit proportion of cloud gaming sessions of the corresponding cloud game can be determined based on the data change range of the short cloud gaming session proportions for different cloud gaming experience quality scores.

[0090] from Figure 7It can be seen that when the target cloud game experience quality analysis result score reaches the high-quality standard score line or above, the proportion of short user sessions (<150s) gradually tends to be stable, which indicates that there are always some users who will quit the game for reasons other than network fluctuations. For example, some novice players, when exploring a new game, quit directly after trying it for a few minutes and find that they don’t like it. The proportion of this group of people can be called the user random exit ratio, and the average proportion of short sessions (<150s) when the target cloud game experience quality analysis result reaches the high-quality standard score line or above is set as the user random exit ratio. According to the above conditions, it can be calculated that the user random exit ratio is 9.51%.

[0091] The target cloud gaming experience quality analysis results can be used to test the cloud capabilities of each cloud gaming provider, saving a lot of manpower for comparative testing. The setting of the user random exit ratio can also help calibrate whether there are differences and problems in the data provided by each cloud gaming provider.

[0092] The technical solution of the disclosed embodiment is to obtain preset feature data associated with the cloud game experience quality during the cloud game sessions after filtering the sessions of shorter duration; group the preset feature data corresponding to each of the cloud game sessions according to the preset segment duration, and determine the experience quality analysis results of multiple cloud game segments corresponding to each of the cloud game sessions based on each group of feature data; and determine the target cloud game experience quality analysis results of each of the cloud game sessions based on the experience quality analysis results of the cloud game segments, that is, after segmenting each of the cloud game sessions, perform experience quality analysis on a cloud game session segment basis, so as to reduce the influence of feature data affecting experience quality in any cloud game session segment on the experience quality of other cloud game session segments, thereby improving the accuracy of the final target cloud game experience quality analysis results of each of the cloud game sessions. In addition, the target cloud game experience quality analysis results are further statistically analyzed to provide a reference for the subsequent update and optimization of cloud game configurations, and provide guiding information for the subsequent cloud game optimization. The disclosed embodiment solves the technical problem of poor application effect of existing cloud game experience quality analysis models, can more accurately analyze the actual experience of users in cloud games, and provide accurate cloud game experience quality results to more efficiently guide cloud game resource configuration and technical optimization solutions.

[0093] Figure 8 A cloud gaming experience quality analysis device is provided in an embodiment of the present disclosure. The device is suitable for scenarios of cloud gaming user experience quality evaluation. The cloud gaming experience quality analysis device can be implemented in the form of software and / or hardware and can be configured on an electronic device, which can be a mobile terminal, a PC, a server, etc.

[0094] like Figure 8 As shown, the cloud gaming experience quality analysis device includes: an experience quality analysis data acquisition module 410, an experience quality segmentation analysis module 420 and an experience quality comprehensive analysis module 430.

[0095] Among them, the experience quality analysis data acquisition module 410 is used to obtain preset feature data associated with the cloud gaming experience quality during previous cloud gaming sessions; the experience quality segmentation analysis module 420 is used to group the preset feature data corresponding to each of the previous cloud gaming sessions according to the preset segmentation duration, and determine multiple cloud gaming fragment experience quality analysis results corresponding to each of the previous cloud gaming sessions based on each group of feature data; the experience quality comprehensive analysis module 430 is used to determine the target cloud gaming experience quality analysis results of each of the previous cloud gaming sessions based on the cloud gaming fragment experience quality analysis results.

[0096] The technical solution of the disclosed embodiment is to obtain preset feature data associated with the cloud gaming experience quality during previous cloud gaming sessions; group the preset feature data corresponding to each of the previous cloud gaming sessions according to the preset segment duration, and determine the multiple cloud gaming segment experience quality analysis results corresponding to each of the previous cloud gaming sessions based on each group of feature data; and determine the target cloud gaming experience quality analysis results of each of the previous cloud gaming sessions based on the cloud gaming segment experience quality analysis results, that is, after segmenting each of the previous cloud gaming sessions, perform experience quality analysis on a cloud gaming session segment basis, so as to reduce the impact of feature data affecting experience quality in any cloud gaming session segment on the experience quality of other cloud gaming session segments, thereby improving the accuracy of the final target cloud gaming experience quality analysis results of each previous cloud gaming session. The disclosed embodiment solves the technical problem of poor application effect of the existing cloud gaming experience quality analysis model, can more accurately analyze the actual experience of users in cloud gaming, provide accurate cloud gaming experience quality results, and more efficiently guide cloud gaming resource configuration and technical optimization solutions.

[0097] In an optional implementation, the cloud gaming experience quality analysis device further includes a session filtering module, which is used to:

[0098] Before obtaining preset characteristic data associated with the cloud gaming experience quality during the previous cloud gaming sessions, session filtering is performed on each of the previous cloud gaming sessions according to the session duration of each of the previous cloud gaming sessions.

[0099] In an optional implementation manner, the experience quality segment analysis module 420 may be specifically used for:

[0100] Dividing each of the previous cloud game sessions into a plurality of cloud game session segments according to the preset segment duration, wherein the preset segment duration is less than a preset session segment duration threshold;

[0101] The preset feature data corresponding to each cloud game session segment of each of the previous cloud game sessions are divided into a group of preset feature data.

[0102] In an optional implementation manner, the experience quality comprehensive analysis module 430 may be specifically used for:

[0103] Determine, according to the number of cloud game session segments of each of the previous cloud game sessions and / or the position of each cloud game session segment in the session, a weight parameter of a cloud game segment experience quality analysis result corresponding to each of the cloud game session segments;

[0104] A weighted calculation is performed based on the weight parameter and the experience quality analysis results of each of the cloud game clips to obtain a target cloud game experience quality analysis result of the corresponding cloud game session.

[0105] In an optional implementation manner, the experience quality comprehensive analysis module 430 may be specifically used for:

[0106] According to the number of cloud gaming session fragments in each of the previous cloud gaming sessions, the same weight parameter is determined for the cloud gaming fragment experience quality analysis result of each cloud gaming session fragment in each of the previous cloud gaming sessions.

[0107] In an optional implementation manner, the experience quality comprehensive analysis module 430 may be specifically used for:

[0108] According to the position of each cloud game fragment in the corresponding cloud game session, the weight parameters of the cloud game fragment experience quality analysis results corresponding to each cloud game session fragment are determined in a preset linear or nonlinear weight parameter function.

[0109] In an optional implementation manner, the experience quality comprehensive analysis module 430 may also be specifically used for:

[0110] Performing statistical analysis on each of the target cloud gaming experience quality analysis results to obtain statistical results;

[0111] Determine the proportion of long cloud gaming sessions whose session durations meet a preset long session duration standard among the cloud gaming sessions corresponding to different cloud gaming experience quality scores in the statistical results;

[0112] The high quality standard score line of cloud gaming experience quality is determined based on the data change range of the proportion of long cloud gaming sessions for different cloud gaming experience quality scores.

[0113] In an optional implementation manner, the experience quality comprehensive analysis module 430 may also be specifically used for:

[0114] Determine the proportion of short cloud gaming sessions whose session durations meet a preset short session duration standard among the cloud gaming sessions corresponding to different cloud gaming experience quality scores in the statistical results;

[0115] The random exit ratio of cloud game sessions of the corresponding cloud game is determined according to the data change range of the short cloud game session ratio of different cloud game experience quality scores.

[0116] The cloud gaming experience quality analysis device provided in the embodiments of the present disclosure can execute the cloud gaming experience quality analysis method provided in any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0117] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present disclosure.

[0118] Fig. 9 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Fig. 9 , which shows an electronic device (eg, Fig. 9 The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig. 9 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0119] like Fig. 9 As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An edit / output (I / O) interface 505 is also connected to the bus 504.

[0120] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Fig. 9 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0121] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0122] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0123] The electronic device provided in the embodiment of the present disclosure and the cloud gaming experience quality analysis method provided in the above-mentioned embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above-mentioned embodiment, and this embodiment has the same beneficial effects as the above-mentioned embodiment.

[0124] The embodiments of the present disclosure also provide a computer storage medium on which a computer program is stored. When the program is executed by a processor, the cloud gaming experience quality analysis method provided in the above embodiments is implemented.

[0125] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0126] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0127] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0128] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0129] Obtaining preset feature data associated with the quality of cloud gaming experience during all cloud gaming sessions;

[0130] The preset feature data corresponding to each of the previous cloud game sessions are grouped according to the preset segment duration, and based on each group of feature data, a plurality of cloud game segment experience quality analysis results corresponding to each of the previous cloud game sessions are determined;

[0131] Based on the cloud game segment experience quality analysis results, the target cloud game experience quality analysis results of each of the previous cloud game sessions are determined respectively.

[0132] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0133] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0134] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses".

[0135] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0136] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0137] The embodiments of the present disclosure also provide a computer program product, including a computer program, which, when executed by a processor, implements the cloud gaming experience quality analysis method provided in any one of the embodiments of the present disclosure.

[0138] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code for the disclosed operation, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0139] According to one or more embodiments of the present disclosure, [Example 1] provides a method for analyzing cloud gaming experience quality, the method comprising:

[0140] Obtaining preset feature data associated with the quality of cloud gaming experience during all cloud gaming sessions;

[0141] The preset feature data corresponding to each of the previous cloud game sessions are grouped according to the preset segment duration, and based on each group of feature data, a plurality of cloud game segment experience quality analysis results corresponding to each of the previous cloud game sessions are determined;

[0142] Based on the cloud game segment experience quality analysis results, the target cloud game experience quality analysis results of each of the previous cloud game sessions are determined respectively.

[0143] According to one or more embodiments of the present disclosure, [Example 2] provides a cloud gaming experience quality analysis method, further comprising:

[0144] In some optional implementations, before obtaining preset feature data associated with the cloud gaming experience quality during each cloud gaming session, the method further includes:

[0145] Session filtering is performed on each of the previous cloud game sessions according to the session duration of each of the previous cloud game sessions.

[0146] According to one or more embodiments of the present disclosure, [Example 3] provides a method for analyzing cloud gaming experience quality, including:

[0147] In some optional implementations, the preset feature data corresponding to each of the previous cloud game sessions are grouped according to the preset segment duration, including:

[0148] Dividing each of the previous cloud game sessions into a plurality of cloud game session segments according to the preset segment duration, wherein the preset segment duration is less than a preset session segment duration threshold;

[0149] The preset feature data corresponding to each cloud game session segment of each of the previous cloud game sessions is divided into a group of preset feature data.

[0150] According to one or more embodiments of the present disclosure, [Example 4] provides a cloud gaming experience quality analysis method, further comprising:

[0151] In some optional implementations, determining the target cloud game experience quality analysis results of the corresponding cloud game sessions based on the cloud game segment experience quality analysis results includes:

[0152] Determine, according to the number of cloud game session segments of each of the previous cloud game sessions and / or the position of each cloud game session segment in the session, a weight parameter of a cloud game segment experience quality analysis result corresponding to each of the cloud game session segments;

[0153] A weighted calculation is performed based on the weight parameter and the experience quality analysis results of each of the cloud game clips to obtain a target cloud game experience quality analysis result of the corresponding cloud game session.

[0154] According to one or more embodiments of the present disclosure, [Example 5] provides a cloud gaming experience quality analysis method, further comprising:

[0155] In some optional implementations, according to the number of cloud game session segments of each of the previous cloud game sessions, weight parameters of the cloud game segment experience quality analysis results corresponding to each of the cloud game session segments are determined respectively, including:

[0156] According to the number of cloud gaming session fragments in each of the previous cloud gaming sessions, the same weight parameter is determined for the cloud gaming fragment experience quality analysis result of each cloud gaming session fragment in each of the previous cloud gaming sessions.

[0157] According to one or more embodiments of the present disclosure, [Example 6] provides a cloud gaming experience quality analysis method, further comprising:

[0158] In some optional implementations, according to the position of each cloud game session segment of each of the previous cloud game sessions in the session, a weight parameter of the cloud game segment experience quality analysis result corresponding to each of the cloud game session segments is determined respectively, including

[0159] According to the position of each cloud game fragment in the corresponding cloud game session, the weight parameters of the cloud game fragment experience quality analysis results corresponding to each cloud game session fragment are determined in a preset linear or nonlinear weight parameter function.

[0160] According to one or more embodiments of the present disclosure, [Example 7] provides a cloud gaming experience quality analysis method, further comprising:

[0161] In some optional implementations, the method further includes:

[0162] Performing statistical analysis on each of the target cloud gaming experience quality analysis results to obtain statistical results;

[0163] Determine the proportion of long cloud gaming sessions whose session durations meet a preset long session duration standard among the cloud gaming sessions corresponding to different cloud gaming experience quality scores in the statistical results;

[0164] The high quality standard score line of cloud gaming experience quality is determined based on the data change range of the proportion of long cloud gaming sessions for different cloud gaming experience quality scores.

[0165] According to one or more embodiments of the present disclosure, [Example 8] provides a cloud gaming experience quality analysis method, further comprising:

[0166] In some optional implementations, the method further includes:

[0167] Determine the proportion of short cloud gaming sessions whose session durations meet a preset short session duration standard among the cloud gaming sessions corresponding to different cloud gaming experience quality scores in the statistical results;

[0168] The random exit ratio of cloud game sessions of the corresponding cloud game is determined according to the data change range of the short cloud game session ratio of different cloud game experience quality scores.

[0169] According to one or more embodiments of the present disclosure, [Example 9] provides a cloud gaming experience quality analysis device, including:

[0170] The experience quality analysis data acquisition module is used to obtain preset feature data associated with the cloud game experience quality during previous cloud game sessions;

[0171] An experience quality segmentation analysis module, used to group the preset feature data corresponding to each of the previous cloud game sessions according to a preset segmentation duration, and determine a plurality of cloud game segment experience quality analysis results corresponding to each of the previous cloud game sessions based on each group of feature data;

[0172] The comprehensive experience quality analysis module is used to determine the target cloud game experience quality analysis results of each of the previous cloud game sessions based on the experience quality analysis results of the cloud game clips.

[0173] According to one or more embodiments of the present disclosure, [Example 10] provides a cloud gaming experience quality analysis device, further comprising:

[0174] In an optional implementation, the cloud gaming experience quality analysis device further includes a session filtering module, which is used to:

[0175] Before obtaining preset characteristic data associated with the cloud gaming experience quality during the previous cloud gaming sessions, session filtering is performed on each of the previous cloud gaming sessions according to the session duration of each of the previous cloud gaming sessions.

[0176] According to one or more embodiments of the present disclosure, [Example 11] provides a cloud gaming experience quality analysis device, further comprising:

[0177] In an optional implementation manner, the experience quality segment analysis module may be specifically used to:

[0178] Dividing each of the previous cloud game sessions into a plurality of cloud game session segments according to the preset segment duration, wherein the preset segment duration is less than a preset session segment duration threshold;

[0179] The preset feature data corresponding to each cloud game session segment of each of the previous cloud game sessions are divided into a group of preset feature data.

[0180] According to one or more embodiments of the present disclosure, [Example 12] provides a cloud gaming experience quality analysis device, further comprising:

[0181] In an optional implementation manner, the experience quality comprehensive analysis module may be specifically used for:

[0182] Determine, according to the number of cloud game session segments of each of the previous cloud game sessions and / or the position of each cloud game session segment in the session, a weight parameter of a cloud game segment experience quality analysis result corresponding to each of the cloud game session segments;

[0183] A weighted calculation is performed based on the weight parameter and the experience quality analysis results of each of the cloud game clips to obtain a target cloud game experience quality analysis result of the corresponding cloud game session.

[0184] According to one or more embodiments of the present disclosure, [Example 13] provides a cloud gaming experience quality analysis device, further comprising:

[0185] In an optional implementation manner, the experience quality comprehensive analysis module may be specifically used for:

[0186] According to the number of cloud gaming session fragments in each of the previous cloud gaming sessions, the same weight parameter is determined for the cloud gaming fragment experience quality analysis result of each cloud gaming session fragment in each of the previous cloud gaming sessions.

[0187] According to one or more embodiments of the present disclosure, [Example 14] provides a cloud gaming experience quality analysis device, further comprising:

[0188] In an optional implementation manner, the experience quality comprehensive analysis module 430 may be specifically used for:

[0189] According to the position of each cloud game fragment in the corresponding cloud game session, the weight parameters of the cloud game fragment experience quality analysis results corresponding to each cloud game session fragment are determined in a preset linear or nonlinear weight parameter function.

[0190] According to one or more embodiments of the present disclosure, [Example 15] provides a cloud gaming experience quality analysis device, further comprising:

[0191] In an optional implementation manner, the experience quality comprehensive analysis module may also be specifically used for:

[0192] Performing statistical analysis on each of the target cloud gaming experience quality analysis results to obtain statistical results;

[0193] Determine the proportion of long cloud gaming sessions whose session durations meet a preset long session duration standard among the cloud gaming sessions corresponding to different cloud gaming experience quality scores in the statistical results;

[0194] The high quality standard score line of cloud gaming experience quality is determined based on the data change range of the proportion of long cloud gaming sessions for different cloud gaming experience quality scores.

[0195] According to one or more embodiments of the present disclosure, [Example 16] provides a cloud gaming experience quality analysis device, further comprising:

[0196] In an optional implementation manner, the experience quality comprehensive analysis module may also be specifically used for:

[0197] Determine the proportion of short cloud gaming sessions whose session durations meet a preset short session duration standard among the cloud gaming sessions corresponding to different cloud gaming experience quality scores in the statistical results;

[0198] The random exit ratio of cloud game sessions of the corresponding cloud game is determined according to the data change range of the short cloud game session ratio of different cloud game experience quality scores.

[0199] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0200] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0201] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A cloud gaming experience quality analysis method, characterized in that: include: Obtaining preset feature data associated with the quality of cloud gaming experience during all cloud gaming sessions; The preset feature data corresponding to each of the previous cloud game sessions are grouped according to the preset segment duration, and based on each group of feature data, a plurality of cloud game segment experience quality analysis results corresponding to each of the previous cloud game sessions are determined; Based on the cloud game segment experience quality analysis results, the target cloud game experience quality analysis results of each of the previous cloud game sessions are determined respectively.

2. The method according to claim 1, characterized in that Before obtaining preset characteristic data associated with the cloud gaming experience quality during each cloud gaming session, the method further includes: Session filtering is performed on each of the previous cloud game sessions according to the session duration of each of the previous cloud game sessions.

3. The method according to claim 1, characterized in that The preset feature data corresponding to each of the previous cloud game sessions are grouped according to the preset segment duration, including: Dividing each of the previous cloud game sessions into a plurality of cloud game session segments according to the preset segment duration, wherein the preset segment duration is less than a preset session segment duration threshold; The preset feature data corresponding to each cloud game session segment of each of the previous cloud game sessions is divided into a group of preset feature data.

4. The method according to claim 1, characterized in that: Determining the target cloud game experience quality analysis results of the corresponding cloud game sessions based on the cloud game segment experience quality analysis results respectively includes: Determine, according to the number of cloud game session segments of each of the previous cloud game sessions and / or the position of each cloud game session segment in the session, a weight parameter of the cloud game segment experience quality analysis result corresponding to each of the cloud game session segments; A weighted calculation is performed based on the weight parameter and the experience quality analysis results of each of the cloud game clips to obtain a target cloud game experience quality analysis result of the corresponding cloud game session.

5. The method according to claim 4, characterized in that Determining weight parameters of cloud game segment experience quality analysis results corresponding to each of the cloud game segment sessions according to the number of cloud game segment sessions of each of the previous cloud game sessions, respectively, includes: According to the number of cloud gaming session fragments in each of the previous cloud gaming sessions, the same weight parameter is determined for the cloud gaming fragment experience quality analysis result of each cloud gaming session fragment in each of the previous cloud gaming sessions.

6. The method according to claim 4, characterized in that According to the position of each cloud game session segment of each of the previous cloud game sessions in the session, respectively determine the weight parameters of the cloud game segment experience quality analysis results corresponding to each of the cloud game session segments, including: According to the position of each cloud game fragment in the corresponding cloud game session, the weight parameters of the cloud game fragment experience quality analysis results corresponding to each cloud game session fragment are determined in a preset linear or nonlinear weight parameter function.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: Performing statistical analysis on each of the target cloud gaming experience quality analysis results to obtain statistical results; Determine the proportion of long cloud gaming sessions whose session durations meet a preset long session duration standard among the cloud gaming sessions corresponding to different cloud gaming experience quality scores in the statistical results; The high quality standard score line of the cloud gaming experience quality is determined based on the data change range of the proportion of the long cloud gaming sessions for different cloud gaming experience quality scores.

8. The method according to claim 7, characterized in that The method further comprises: Determine the proportion of short cloud gaming sessions whose session durations meet a preset short session duration standard among the cloud gaming sessions corresponding to different cloud gaming experience quality scores in the statistical results; The random exit ratio of cloud game sessions of the corresponding cloud game is determined according to the data change range of the short cloud game session ratio of different cloud game experience quality scores.

9. A cloud gaming experience quality analysis device, characterized in that: include: The experience quality analysis data acquisition module is used to obtain preset feature data associated with the cloud game experience quality during previous cloud game sessions; An experience quality segmentation analysis module, used to group the preset feature data corresponding to each of the previous cloud game sessions according to a preset segmentation duration, and determine a plurality of cloud game segment experience quality analysis results corresponding to each of the previous cloud game sessions based on each group of feature data; The comprehensive experience quality analysis module is used to determine the target cloud game experience quality analysis results of each of the previous cloud game sessions based on the experience quality analysis results of the cloud game clips.

10. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the cloud gaming experience quality analysis method as described in any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the cloud gaming experience quality analysis method as described in any one of claims 1-8 is implemented.

12. A computer program product, comprising a computer program, characterized in that When executed by a processor, the computer program implements the cloud gaming experience quality analysis method as described in any one of claims 1-8.

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