Game lag judgment method and system, electronic equipment and storage medium

Abstract

The invention provides a game lagging judgment method and system, electronic equipment and a storage medium, and the game lagging judgment method comprises the steps: obtaining performance data of each scene in a target game operation process; performing time sequence statistical comparison on the performance data to obtain a performance change rate between adjacent scenes; and carrying out numerical interval classification on the performance change rate to obtain the jamming condition of the target game. According to the technical scheme of the embodiment, the performance change rate between the adjacent scenes is obtained by obtaining the performance data of each scene in the operation process of the target game and performing time sequence statistics comparison, the accuracy and objectivity of game lag detection are improved through data time sequence statistics, and the dependence on player feedback is greatly reduced; and the performance change rate is subjected to numerical interval classification, so that the game lagging condition can be timely and accurately determined, and the method can be widely applied to the technical field of computers.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a game freeze determination method, system, electronic device and storage medium. Background Art

[0002] In the process of game development and operation, it is crucial to ensure the smoothness of the game. Traditional game freeze detection methods mainly rely on active feedback from players or users, which has obvious limitations. On the one hand, players may fail to promptly or accurately report freezes for various reasons, making it difficult for the development team to obtain real game performance issues in a timely manner; on the other hand, even if players report freezes, they often lack specific freeze data, making it difficult to accurately locate the problem. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a game freeze judgment method, system, electronic device and storage medium, which can timely and effectively judge the game freeze.

[0004] On the one hand, an embodiment of the present invention provides a method for determining game freeze, comprising:

[0005] Get the performance data of each scene during the target game running process;

[0006] Perform time series statistical comparison on performance data to obtain the performance change rate between adjacent scenes;

[0007] The performance change rate is classified into numerical ranges to obtain the lag situation of the target game.

[0008] According to some embodiments of the present invention, before the step of performing time series statistical comparison on the performance data, the method further includes the following steps:

[0009] The performance data is cleaned and the first target scenario and its corresponding performance data are eliminated.

[0010] According to some embodiments of the present invention, the method further comprises the following steps:

[0011] When the target scene does not have a battle scene, the target scene is determined as the first target scene; the battle scene is determined based on the session control data corresponding to the target scene;

[0012] When the target scenario is running, if the running parameters of the target terminal running the target game meet the preset threshold conditions, the target scenario is determined as the first target scenario.

[0013] According to some embodiments of the present invention, before the step of performing time series statistical comparison on the performance data, the method further includes the following steps:

[0014] The second target scenario and its corresponding performance data are retained.

[0015] According to some embodiments of the present invention, the method further comprises the following steps:

[0016] When the number of battles in the target scene reaches a threshold, the target scene is determined as a second target scene; the number of battles is determined based on the session control data corresponding to the target scene.

[0017] According to some embodiments of the present invention, performing time series statistical comparison on performance data to obtain performance change rates between adjacent scenes includes the following steps:

[0018] Perform statistical analysis on performance data to obtain performance indicator values ​​for each scenario;

[0019] The performance indicator values ​​are compared and analyzed based on the time series order to obtain the performance change rate between adjacent scenes.

[0020] According to some embodiments of the present invention, the performance data includes instantaneous performance values ​​collected by multiple sampling nodes during the operation of the scenario; performing statistical analysis on the performance data to obtain the performance index value of each scenario includes the following steps:

[0021] The instantaneous performance values ​​collected by all sampling nodes in the scene are averaged to obtain the average performance value;

[0022] The average performance value corresponding to each scenario is taken as the performance indicator value.

[0023] According to some embodiments of the present invention, comparing and analyzing the performance indicator values ​​based on the time sequence to obtain the performance change rate between adjacent scenes includes the following steps:

[0024] Arrange the performance indicator values ​​corresponding to all scenarios in chronological order to obtain an indicator set;

[0025] The first performance indicator value in the indicator set is taken as the first indicator value;

[0026] Taking the next performance indicator value of the first indicator value in the indicator set as the second indicator value;

[0027] Obtaining a performance change rate between corresponding adjacent scenes according to a ratio of a difference between the second index value and the first index value to the first index value;

[0028] The second indicator value is used as the first indicator value, and the step of using the next performance indicator value of the first indicator value in the indicator set as the second indicator value is returned to be executed until the performance change rate between all adjacent scenes is obtained.

[0029] According to some embodiments of the present invention, the performance change rate is classified into numerical intervals to obtain the jamming situation of the target game, including the following steps:

[0030] Classify the performance change rates between all adjacent scenarios into pre-divided intervals;

[0031] Determine the lag of the target game based on the interval range mapping where the classification data exists.

[0032] According to some embodiments of the present invention, the performance change rate is marked with the scene time corresponding to the scene; and determining the jamming situation of the target game according to the interval range mapping where the classification data exists includes the following steps:

[0033] The interval range where classified data exists is taken as the target interval;

[0034] Determine the maximum number of performance change rates that are continuous in time sequence in the target interval according to the scenario time, and use the maximum number as the adjacent value of the target interval;

[0035] When the numerical range corresponding to the target interval reaches the first numerical threshold, and the adjacent value corresponding to the target interval reaches the second numerical threshold, it is determined that the jamming condition of the target game is continuously increasing jamming.

[0036] On the other hand, an embodiment of the present invention provides a game freeze determination system, including:

[0037] A collection module, configured to obtain performance data of each scene during the running process of the target game;

[0038] A statistical module is configured to perform time series statistical comparison on the performance data to obtain the performance change rate between adjacent scenes;

[0039] The classification module is configured to classify the performance change rate into numerical intervals to obtain the lag situation of the target game.

[0040] According to some embodiments of the present invention, the system further comprises:

[0041] The cleaning module is configured to clean the performance data and remove the first target scene and its corresponding performance data.

[0042] According to some embodiments of the present invention, the system further comprises:

[0043] The first determination module is configured to determine the target scene as a first target scene when there is no battle scene in the target scene; the battle scene is determined based on the session control data corresponding to the target scene;

[0044] The second determination module is configured to determine the target scene as the first target scene when the operation parameters of the target terminal running the target game meet the preset threshold conditions during the operation of the target scene.

[0045] According to some embodiments of the present invention, the system further comprises:

[0046] The screening module is configured to retain the second target scenario and its corresponding performance data.

[0047] According to some embodiments of the present invention, the system further comprises:

[0048] The third determination module is configured to determine the target scene as the second target scene when the number of battle scenes in the target scene reaches a scene threshold; the number of battle scenes is determined based on the session control data corresponding to the target scene.

[0049] On the other hand, an embodiment of the present invention provides an electronic device, comprising at least one control processor and a memory for communicating with at least one control processor; the memory stores instructions that can be executed by at least one control processor, and the instructions are executed by at least one control processor so that the at least one control processor can execute the game stuttering judgment method as described in the first aspect above.

[0050] On the other hand, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the game freeze determination method as described above.

[0051] One of the above technical solutions has the following advantages or beneficial effects: The present application provides a method, system, electronic device and storage medium for determining game jams, which obtains the performance data of each scene in the target game running process; performs time series statistical comparison on the performance data to obtain the performance change rate between adjacent scenes; and classifies the performance change rate into numerical intervals to obtain the jamming situation of the target game. According to the technical solution of this embodiment, on the one hand, by obtaining the performance data of each scene in the target game running process and performing time series statistical comparison, the performance change rate between adjacent scenes is obtained, and the accuracy and objectivity of game jam detection are improved through data time series statistics, and the dependence on player feedback is greatly reduced; on the other hand, by classifying the performance change rate into numerical intervals, the present invention can clearly present the jamming situation of the target game, and provide game developers with intuitive and quantitative optimization basis. This not only helps developers quickly locate and solve jamming problems, but also can conduct sufficient performance testing and optimization before the game is released to ensure that players can get a smooth gaming experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1is a flow chart of a method for determining game freeze provided by an embodiment of the present invention;

[0053] Figure 2 is an expanded flow chart of the extended step S104 of the game freeze determination method provided by one embodiment of the present invention;

[0054] Figure 3 yes Figure 1 The expanded flow chart of step S102;

[0055] Figure 4 yes Figure 3 The expanded flow chart of step S1021;

[0056] Figure 5 yes Figure 3 The expanded flow chart of step S1022;

[0057] Figure 6 yes Figure 1 The expanded flow chart of step S103;

[0058] Figure 7 is a structural diagram of a game freeze judgment system provided by an embodiment of the present invention;

[0059] Figure 8 It is a structural diagram of an electronic device provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0060] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0061] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0062] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0063] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0064] Before describing the embodiments of the present invention in detail, some nouns and terms involved in the embodiments of the present invention are first described. The nouns and terms involved in the embodiments of the present invention are subject to the following explanations.

[0065] Frame rate (FPS): records the number of frames per second. If the frame rate gradually decreases over time, it may be that the game is becoming more and more laggy.

[0066] CPU Usage: Monitors CPU usage during gameplay. Sustained high usage may indicate a performance bottleneck.

[0067] Memory usage: Track memory usage. Gradually increasing and unreleased memory may cause lag (memory leak).

[0068] GPU Usage: Tracks GPU utilization. Excessive GPU utilization may affect game fluency.

[0069] Frame rate change rate: The frame rate change rate in the specified scene or level.

[0070] Memory change rate: The rate of change of memory in a specified scene or level.

[0071] In the relevant technologies, in the practice of game development and operation, the problem of lag has always been one of the key factors affecting the player experience and game reputation. The traditional lag detection method mainly relies on the active feedback of players or users, which has many shortcomings.

[0072] For example, a large-scale role-playing game received a lot of feedback from players about lag in the early stage of its launch. However, these feedbacks often lack specificity and timeliness. Some players may encounter lag during the game, but because they are immersed in the game world, they do not immediately realize that it is a lag problem, or even if they realize it, they may not provide timely feedback to the development team due to various reasons (such as game progress, emotions, etc.).

[0073] In addition, even if players successfully report the lag problem, they can usually only describe the approximate time and scene of the lag, but cannot provide detailed lag data. This makes it a huge challenge for the development team to locate and solve the lag problem. They often need to spend a lot of time and energy to reproduce the problem, collect data and analyze the cause, which is not only inefficient, but may also miss the best time to fix it.

[0074] Therefore, an embodiment of the present invention provides a game stuttering judgment method, system, electronic device and storage medium, which obtains the performance data of each scene in the target game running process; performs time series statistical comparison on the performance data to obtain the performance change rate between adjacent scenes; and classifies the performance change rate into numerical intervals to obtain the stuttering situation of the target game.

[0075] According to the technical solution of this embodiment, on the one hand, by obtaining the performance data of each scene in the target game running process and performing time series statistical comparison, the performance change rate between adjacent scenes is obtained, and the accuracy and objectivity of game jam detection are improved through data time series statistics, and the dependence on player feedback is greatly reduced; on the other hand, by classifying the performance change rate into numerical intervals, the present invention can clearly present the jamming situation of the target game, providing game developers with an intuitive and quantitative optimization basis. This not only helps developers quickly locate and solve jamming problems, but also allows for sufficient performance testing and optimization before the game is released, ensuring that players can get a smooth gaming experience.

[0076] In the present invention, a game freeze judgment method, system, electronic device and storage medium are provided, which are described in detail one by one in the following embodiments.

[0077] The processing process of a game freeze judgment method provided by an embodiment of the present invention can cover most game freeze judgment scenarios, so that game players or managers can quickly perform game freeze judgment and obtain the game freeze situation.

[0078] Specifically, the present application automatically obtains performance data during the running of the game through technical means, such as key indicators such as frame rate, CPU usage, and memory usage. These data can reflect the performance of the game in real time, providing an objective and accurate evaluation basis for the development team. However, it is not enough to just collect performance data. The present application further performs time series statistical comparison on these data to analyze the performance change rate between adjacent scenes. The performance change rate can intuitively reflect the difference in the fluency of the game in different scenes, thereby helping the development team to quickly locate the problem of stuttering. On this basis, by classifying the performance change rate into numerical intervals, the stuttering situation can be further quantified, providing more specific and targeted guidance for game optimization. For example, the performance change rate can be divided into different levels such as "slight stuttering", "moderate stuttering" and "severe stuttering", so that the development team can take corresponding optimization measures according to the actual situation. The present invention has significant technical advantages and practical application value by obtaining performance data and performing time series statistical comparison, and can detect game stuttering problems in a more efficient and accurate manner, thereby improving game quality and user satisfaction.

[0079] The game freeze judgment method provided in the embodiment of the present invention can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a set-top box, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the image processing method, etc., but is not limited to the above forms.

[0080] The present invention can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0081] Reference Figure 1 , Figure 1 This is a flowchart of a method for determining game freeze provided by an embodiment of the present invention. The method for determining game freeze includes but is not limited to steps S101 to S103:

[0082] Step S101, obtaining performance data of each scene during the running process of the target game;

[0083] Specifically, obtaining the performance data of each scene during the target game is the basis of the technical solution. Specifically, it involves the following operations:

[0084] 1. Determine performance data indicators: First, you need to clarify which performance data indicators you want to obtain, such as frame rate (FPS), CPU usage, memory usage, rendering time, etc., to accurately reflect the performance of the game as a benchmark.

[0085] 2. Select data collection tools: Select appropriate data collection tools based on the type and platform of the target game. These tools can be performance analysis tools that come with the game engine or third-party performance monitoring software. Make sure that the selected tool can accurately and in real time collect the required performance data. You can also directly collect data by calling the game engine interface or the hardware interface of the client running the game.

[0086] 3. Set data collection parameters: Set the corresponding parameters in the data collection tool to ensure that the performance data of each scene can be collected. This may include setting the sampling frequency, selecting the scope of the scene to be monitored, etc.

[0087] 4. Run the game and collect data: Start the target game and use the data collection tool to perform real-time monitoring and data collection during the game. Ensure that the performance data changes can be captured when each scene is switched.

[0088] 5. Store and organize data: Store the collected performance data in files, organize and classify them. This will help with subsequent time series statistical comparison and analysis of the performance data.

[0089] In actual applications, the performance data of each scene in the target game is obtained. The core technology of this step lies in real-time performance monitoring and data collection. By selecting appropriate data collection tools and setting corresponding parameters, comprehensive and accurate monitoring and collection of performance data during the game can be achieved. These performance data can reflect the performance of the game in different scenarios and provide basic data support for subsequent timing statistics comparison and jamming analysis.

[0090] For example, the industry's recognized performance indicators include the number of devices, device ratio, frame rate, memory, loading time, and startup time. The corresponding performance data is collected and reported by the client, usually in the form of SDK, calling various hardware interfaces to obtain various hardware indicators or calling the underlying game framework interface to obtain some indicators. For example, the frame rate needs to be obtained by calling the game engine interface, and the memory needs to be obtained by calling the hardware interface. The specific indicators depend on which part provides them. Some also require game point data, which is obtained by dot data.

[0091] Step S102, performing time series statistical comparison on the performance data to obtain the performance change rate between adjacent scenes;

[0092] Specifically, the performance data is statistically compared in time series to obtain the performance change rate between adjacent scenes. This step may involve the following operations:

[0093] 1. Data collation and alignment: First, the performance data collected for each scenario is sorted in chronological order to ensure that the timestamps of the data are aligned for subsequent comparative analysis.

[0094] 2. Select the comparison indicator: Select the appropriate comparison indicator according to the type of performance data. For example, for frame rate data, you can select the frame rate change or change rate as the comparison indicator; for CPU usage or memory usage data, you can select the increase or decrease of memory usage as the comparison indicator.

[0095] 3. Calculate the performance change rate: For two adjacent scenes, calculate their performance change rate on the selected comparison index. This usually involves subtracting the performance data of the latter scene from the performance data of the former scene, and then dividing it by the performance data of the former scene to obtain the performance change rate in percentage form.

[0096] 4. Record and store the change rate: Record the calculated performance change rate in a corresponding data structure, such as a table or database, to facilitate subsequent numerical range classification and jam analysis.

[0097] In practical applications, time series statistical comparison reveals the laws and trends of data changes over time by comparing and analyzing time series data. In this technical solution, by performing time series statistical comparison on the performance data of each scene during the target game running process, the performance change rate between adjacent scenes can be obtained, thereby reflecting the performance of the game when switching between different scenes. The technical core of this step is to select appropriate comparison indicators and methods for calculating performance change rates to ensure the accuracy and reliability of the analysis results.

[0098] For example, suppose that a performance analysis is performed on a racing game, and the frame rate data of each scene (such as the starting point of the track, the curve, the straight-line acceleration section, etc.) has been collected. Now, it is necessary to perform a time series statistical comparison on these data to obtain the frame rate change rate between adjacent scenes. This can be achieved as follows:

[0099] 1. Data sorting and alignment: Sort the collected frame rate data according to the order of the track to ensure that the data timestamps of each scene are aligned.

[0100] 2. Select a comparison index: Select the frame rate change rate as the comparison index, that is, (frame rate of the next scene - frame rate of the previous scene) / frame rate of the previous scene × 100%.

[0101] 3. Calculate the performance change rate: For two adjacent scenes (such as the starting point to the first curve), calculate their frame rate change rate. For example, if the frame rate of the starting point scene is 60FPS and the frame rate of the first curve scene is 55FPS, the frame rate change rate is (55-60) / 60×100%=-8.33%.

[0102] 4. Record and store the change rate: record the calculated frame rate change rate in a table and mark the corresponding scene pairs. In this way, you can get the frame rate change rate of the racing game when switching between different scenes, and provide basic data for subsequent numerical interval classification and jamming analysis.

[0103] Step S103, classifying the performance change rate into numerical ranges to obtain the lag situation of the target game.

[0104] Specifically, the performance change rate is classified into numerical ranges to obtain the lag situation of the target game. This step may include the following operations:

[0105] 1. Determine the value range: First, a series of value ranges need to be set according to the actual performance change rate. These ranges cover all possible performance change ranges. Specifically, each range can be mapped to a specific physical meaning or business logic meaning.

[0106] 2. Classify performance change rate: Classify the calculated performance change rate between each adjacent scene into a preset numerical interval according to its numerical value. This step usually involves traversing the data set of performance change rate and assigning it to the corresponding interval according to the numerical value.

[0107] 3. Statistics of stuttering: After classification, count the performance change rate in each numerical range to reflect the stuttering of the target game under different performance change levels. In particular, it is necessary to pay attention to those intervals that indicate a significant drop in performance (i.e., may cause stuttering); you can also calculate the proportion or frequency of performance change rates in these intervals.

[0108] 4. Analysis and reporting: Based on the statistical results, analyze the performance of the target game when switching between different scenes, especially the freeze situation. Then, you can also generate reports or charts based on the statistical results so that developers or testers can intuitively understand the performance bottlenecks and optimization directions of the game.

[0109] In practical applications, numerical interval classification divides data into different intervals according to numerical values ​​in order to better understand and explain the distribution characteristics and trends of data. In this technical solution, by classifying the performance change rate into numerical intervals, the performance of the target game when switching between different scenes, especially the jamming situation, can be obtained. The technical core of this step is to determine a reasonable numerical interval and classification method to ensure the accuracy and reliability of the analysis results. At the same time, it is also necessary to interpret the classification results in combination with specific business logic or physical meaning in order to provide valuable reference for subsequent optimization work.

[0110] For example, suppose a performance analysis is performed on a racing game, and the frame rate change rate data between adjacent scenes has been calculated. Now, it is necessary to classify these frame rate change rates into numerical intervals to evaluate the game's lag. This can be achieved as follows:

[0111] 1. Determine the numerical range: According to the actual situation of the frame rate change rate, the following numerical ranges are pre-set: [-50%, -10%), [-10%, 0%), [0%, 10%), [10%, 50%) and [50%, +∞%). These ranges are defined to represent a significant decrease in the frame rate, a slight decrease, a basic stability, a slight increase and a significant increase.

[0112] 2. Classify the frame rate change rate: Classify the calculated frame rate change rate between each adjacent scene into the above interval. For example, if the frame rate change rate caused by a scene switch is -20%, it will be classified into the interval [-50%, -10%).

[0113] 3. Statistics of stuttering: After classification, we can count the intervals with data. For example, if there is data in the negative interval, we can determine that there is stuttering, and then we can locate the scene corresponding to the specific change rate to assist in stuttering analysis; further, we can count the number of frame rate changes in each interval and calculate their proportion in the entire data set. The results show that the frame rate change rate in the [-50%, -10%) interval accounts for a high proportion, indicating that the game has obvious stuttering when switching most scenes.

[0114] 4. Analysis and reporting: Based on the statistical results, analyze the performance of the game when switching between different scenes, especially the freeze situation. Then, the data is converted to generate reports and charts, pointing out the specific location of the game performance bottleneck to guide the optimization direction. This information is of great reference value to developers and can help them better optimize game performance and improve user experience.

[0115] In an implementation of the embodiment of the present invention, before the step of performing time series statistical comparison on the performance data, the method may further include the following steps:

[0116] Step S104: clean the performance data to remove the first target scene and its corresponding performance data.

[0117] Specifically, individual scenarios with influencing factors are eliminated through data cleaning. Among them, the performance data may contain invalid or erroneous data caused by equipment failure, sensor error, or other problems in the data collection process. If these data are not cleaned, they may mislead the subsequent analysis results. By cleaning the data, noise and outliers can be removed to make the data set purer, thereby improving the accuracy and reliability of the analysis. In some cases, it may be necessary to clean the data according to specific analysis goals. For example, in this case, it is necessary to eliminate the first target scenario and its corresponding performance data, as well as scenarios with specific influencing factors. Individual scenarios with influencing factors are eliminated through data cleaning. Specifically, the influencing factors may include equipment performance fluctuations, network delays, external interference, etc. These factors may cause the performance data to be abnormal or deviate from normal values. After identifying the affected scenario, its corresponding performance data needs to be eliminated from the data set. This helps to ensure the accuracy and reliability of subsequent analysis.

[0118] It is understandable that eliminating individual scenarios with influencing factors and their corresponding performance data through data cleaning is an important step to ensure the accuracy and reliability of performance analysis.

[0119] Further, refer to Figure 2 In one implementation of the embodiment of the present invention, the method may further include the following steps:

[0120] Step S1041, when the target scene does not have a battle sequence, the target scene is determined as the first target scene; the battle sequence is determined based on the session control data corresponding to the target scene;

[0121] Step S1042: When the target scene is running, the running parameters of the target terminal running the target game meet the preset threshold conditions, and the target scene is determined as the first target scene.

[0122] Specifically, the technical solution of the present invention cleans the performance data, focusing on eliminating some scenes or levels that cannot demonstrate performance, such as login scenes, idle scenes, etc.; and focusing on eliminating data of some additional factors, such as the influence of relevant operating parameters of the terminal device running the game, such as low battery, weak network, etc.

[0123] In practical applications, the number of battles is usually one of the important indicators for measuring game performance, because it involves a lot of complex operations such as graphics rendering, physical calculations, network synchronization, etc. If a scene does not contain any battles, it may not fully reflect the overall performance of the game. Therefore, when cleaning the data, these scenes that do not contain battles can be regarded as the first target scenes and eliminated. In addition to the content of the scene itself, the operating parameters of the target terminal (such as mobile phones, tablets, and computers) will also affect the performance of the game or application. For example, when the device battery is too low or the network signal is weak, the performance of the game or application may be seriously affected. Therefore, when cleaning the data, these additional factors need to be considered, and those scenes whose operating parameters do not meet the preset conditions should be regarded as the first target scenes and eliminated.

[0124] It is understandable that session control data refers to data generated during the operation of a game or application for controlling and managing sessions (i.e., running instances of games or applications). By parsing this data, it is possible to determine whether there are battles in a certain scene. The preset threshold conditions are set in advance based on factors such as the performance requirements of the game or application and the hardware configuration of the target terminal. For example, conditions such as the device battery must be higher than 20% and the network delay must be less than 100 milliseconds can be set.

[0125] For example, suppose there is a role-playing game that contains multiple scenes, such as login scenes, city exploration, wilderness exploration, and battle levels. During data cleaning, it can be found through session control data that the login scene, city exploration scene, and wilderness exploration scene do not contain any battle scenes, so these scenes can be regarded as the first target scenes and eliminated. In addition, when the game is actually running on the device, it is found that when the device power is less than 10%, the frame rate of the game will drop significantly. Therefore, during data cleaning, all scenes with device power less than 10% can also be regarded as the first target scene and eliminated.

[0126] One of the above technical solutions has the following advantages or beneficial effects: Through the above data cleaning method, scenes or levels that cannot fully reflect the performance of the game or application, as well as data that is interfered by additional factors, can be effectively eliminated. This can not only improve the quality and accuracy of the data, but also make subsequent performance analysis more reliable and meaningful. At the same time, this method can also better understand the performance of the game or application under various conditions, providing strong support for optimization and improvement.

[0127] In an implementation of the embodiment of the present invention, before the step of performing time series statistical comparison on the performance data, the method may further include the following steps:

[0128] Step S105: retain the second target scene and its corresponding performance data.

[0129] Specifically, through data screening, high-quality data scenarios and corresponding data are retained. The second target scenario represents those scenarios that can fully reflect the performance of the game or application. These scenarios may include key battles, important tasks, or the use of specific functions in the game. The screening criteria for the second target scenario, setting specific screening conditions, may include scenario type, performance indicator range, data integrity requirements, etc. By screening and retaining the second target scenario and its corresponding performance data, data that does not meet the requirements can be excluded, thereby improving the quality and reliability of the data. It is helpful for subsequent performance analysis, problem location, and optimization and improvement, and provides strong support for the performance improvement of games or applications.

[0130] Furthermore, in an implementation manner of the embodiment of the present invention, the method may further include the following steps:

[0131] Step S1051, when the number of battles in the target scene reaches the number threshold, the target scene is determined as the second target scene; the number of battles is determined based on the session control data corresponding to the target scene.

[0132] Specifically, when the number of battles in a scene reaches a preset threshold, the scene will be automatically determined as the second target scene. The "battles" here are not simply counted, but accurately counted based on the session control data corresponding to the scene. Session control data usually contains information such as user behavior records and interaction events in the scene. By analyzing this data, it is possible to accurately determine whether the user has participated in the battle in the scene and accumulate the battles accordingly.

[0133] In actual applications, session control data: This refers to the data records generated when interactive activities are carried out in the scene, including user operations, system responses, event triggers, etc. These data are usually stored in the form of logs and contain rich contextual information. They are an important basis for analyzing user behavior and scene characteristics. Battle statistics: By parsing session control data, users' battle behaviors in the scene (such as launching attacks, receiving damage, and ending battles, etc.) can be identified. Each complete battle event is considered a battle. When counting battles, it is necessary to ensure that each battle is independent to avoid repeated counting. Game threshold setting: The game threshold is a preset value used to determine whether a scene has reached the standard of becoming the second target scene. The setting of this threshold needs to be comprehensively considered based on the characteristics of the actual scene, the test objectives, and the data distribution. Generally speaking, the game threshold should be high enough to ensure that the selected scenes are sufficiently representative and stable. That is, the data of users performing a scene or level multiple times shall be used as the basis, excluding special data. That is, the more copies of data for the same scene, the more representative it is.

[0134] For example, suppose a role-playing game is performance tested, which includes multiple different battle scenes. In order to determine which scenes are the second target scenes, the threshold of the number of battles is set to 5. During the test, the session control data of each scene is recorded, and the number of battles in each scene is counted. When the number of battles in a scene reaches or exceeds 5, the scene is automatically determined as the second target scene for subsequent performance analysis and optimization.

[0135] One of the above technical solutions has the following advantages or beneficial effects: Improve screening accuracy: By counting the number of battles based on session control data, it can be ensured that the screened second target scene is highly accurate and representative. This helps to avoid performance analysis problems caused by misjudgment or omissions. Optimize test resource allocation: By setting a threshold for the number of battles, the most representative scenes can be automatically screened out as the second target scene, thereby avoiding indiscriminate testing and analysis of all scenes. This helps to optimize the allocation of test resources and improve test efficiency. Improve game performance: By conducting in-depth performance analysis and optimization of the second target scene, the performance bottlenecks and problems in the game can be solved in a targeted manner, thereby improving the overall game performance and user experience.

[0136] Reference Figure 3 In one implementation of the embodiment of the present invention, performing time series statistical comparison on the performance data to obtain the performance change rate between adjacent scenes may include the following steps:

[0137] Step S1021: Statistically analyze the performance data to obtain the performance index value of each scenario;

[0138] Step S1022: Compare and analyze the performance indicator values ​​based on the time sequence to obtain the performance change rate between adjacent scenes.

[0139] Specifically, time series statistical comparison of performance data is a systematic process that aims to reveal the trends and patterns of performance changes between different scenarios. First, the performance index values ​​of each scenario are obtained, and then these index values ​​are further compared and analyzed based on the time series order to obtain the performance change rate between adjacent scenarios.

[0140] In practical applications, statistical analysis of performance data is the basis of time series statistical comparison, which aims to extract key performance indicator values ​​from the original performance data. These indicator values ​​can usually reflect the performance of the scene in a specific aspect. Taking frame rate as an example, the average value and overall variance of the frame rate can be used as the performance indicator of a single scene. After obtaining the performance indicator value of each scene, it is necessary to perform a comparative analysis according to the time series order of the scene. This usually involves comparing the performance indicator values ​​of adjacent scenes one by one to reveal the changing trends and differences between them. During the comparative analysis, the performance change rate between adjacent scenes can be calculated, that is, the percentage change of the performance indicator value of the latter scene relative to the previous scene. This indicator can intuitively reflect the fluctuation of performance between different scenes.

[0141] For example, suppose that a performance test is performed on multiple scenes of a racing game, each of which contains different tracks and obstacles. In order to analyze the performance changes between these scenes, a time series statistical comparison is performed according to the following steps: First, the performance index value of each scene during the test is recorded. Then, these index values ​​are compared and analyzed according to the time sequence of the scenes. For example, the frame rate changes between the first scene and the second scene, and the frame rate changes between the second scene and the third scene are compared. By calculating the performance change rate between adjacent scenes, it is found that the performance fluctuations between some scenes are large, while other scenes are relatively stable, which can provide an important reference for subsequent performance optimization.

[0142] One of the above technical solutions has the following advantages or beneficial effects: Improve the accuracy of performance analysis: Through time series statistical comparison, the performance change trends and laws between different scenes can be more accurately revealed, thereby providing more accurate guidance for performance optimization. Optimize game performance: By analyzing the performance change rate between adjacent scenes, performance bottlenecks and problems can be discovered and solved in a timely manner, thereby optimizing game performance and improving user experience. Improve test efficiency: Time series statistical comparison can automatically process and analyze a large amount of performance data, thereby greatly improving test efficiency and shortening the game development and optimization cycle.

[0143] Reference Figure 4 In one implementation of the embodiment of the present invention, the performance data includes instantaneous performance values ​​collected by multiple sampling nodes during the running of the scene; performing statistical analysis on the performance data to obtain the performance index value of each scene includes the following steps:

[0144] Step S10211, averaging the instantaneous performance values ​​collected by all sampling nodes in the scene to obtain a performance average value;

[0145] Step S10212: Use the average performance value corresponding to each scenario as the performance indicator value.

[0146] Specifically, the average FPS and average memory of each round of battle scene data can be counted, and the average value can be used as an important reference. Average FPS and average memory: the average FPS or average memory usage during the data reporting interval. In some specific implementations, the numerical average value of the reporting time interval of each level or scene under the same session (session control) in the same scene can also be limited, because the same session can represent the user's gaming experience once.

[0147] In actual applications, a battle or a level will periodically collect instantaneous values ​​of frame rate or memory. When the level ends, the average value of these collected values ​​is calculated as a basis. The reason for using the average value is that a level will be repeatedly entered and exited, and each entry and exit represents a battle, and its performance indicators need to be marked separately. The average value of a series of collected values ​​of a level can best represent the intermediate resource consumption or resource occupation of this battle, that is, the average value is representative of considerable value, and provides a correct and sufficient basis for subsequent calculations.

[0148] For example, taking the average frame rate and average memory as an example, the following steps can be followed to perform a time series statistical comparison on the performance data:

[0149] 1. Collecting instantaneous performance values: In the game scene, multiple (for example, 10, which can be adjusted in actual applications) sampling nodes are set based on fixed periodic intervals. The instantaneous frame rate values ​​collected by each node are 60fps, 58fps, 62fps, etc. (This is just an example, the actual value may be different). At the same time, these nodes also collect instantaneous values ​​of memory usage, such as 200MB, 205MB, 198MB, etc.

[0150] 2. Calculate the performance average: For the frame rate, add the instantaneous frame rate values ​​of all sampling nodes in the game scene, and then divide it by the total number of sampling nodes to get the average frame rate. For example, if the sum of all instantaneous values ​​is 600fps, the average frame rate is 60fps. Similarly, for memory usage, the average processing method is used to get the average memory usage value of the game scene.

[0151] 3. Determine the performance index value: Use the calculated average frame rate and average memory usage values ​​as the performance index values ​​of the game scene.

[0152] 4. Repeat the above steps: Perform the same process on other game scenes to obtain their respective average frame rate and average memory usage values ​​as performance indicator values.

[0153] One of the above technical solutions has the following advantages or beneficial effects: Comprehensively reflect the scene performance: By collecting the instantaneous performance values ​​of multiple sampling nodes and averaging them, more comprehensive and accurate scene performance index values ​​can be obtained. These index values ​​can more truly reflect the performance of the scene in actual operation. Facilitate performance comparison and optimization: With clear performance index values ​​(such as average frame rate and average memory), it is easier to compare and analyze the performance of different scenes. This helps us discover performance bottlenecks and problems, thereby providing a strong basis for subsequent performance optimization.

[0154] Reference Figure 5 In one implementation of the embodiment of the present invention, comparing and analyzing the performance indicator values ​​based on the time sequence to obtain the performance change rate between adjacent scenes may include the following steps:

[0155] Step S10221: Arrange the performance indicator values ​​corresponding to all scenarios in chronological order to obtain an indicator set;

[0156] Step S10222: taking the first performance indicator value in the indicator set as the first indicator value;

[0157] Step S10223: taking the next performance indicator value of the first indicator value in the indicator set as the second indicator value;

[0158] Step S10224: Obtain the performance change rate between corresponding adjacent scenes according to the ratio of the difference between the second index value and the first index value to the first index value;

[0159] Step S10225: Use the second indicator value as the first indicator value, and return to execute the step of using the next performance indicator value of the first indicator value in the indicator set as the second indicator value, until the performance change rate between all adjacent scenes is obtained.

[0160] Specifically, when performing time series comparison analysis on performance index values, the key is to accurately and systematically calculate the performance change rate between adjacent scenes. This process requires that the embodiment of the present invention first arrange the performance index values ​​of all scenes in time series, then compare the performance index values ​​of adjacent scenes one by one, and finally obtain the performance change rate through mathematical operations. This method not only ensures the accuracy of calculation, but also improves the efficiency of analysis.

[0161] In practical applications, the frame rate change rate and memory change rate of each scene can be calculated. The calculation method is the ratio of the current dot data (the average value calculated in the previous steps is used as a reference for calculation) to the difference between the previous dot data. Taking the frame rate change rate as an example, the formula for the frame rate change rate is as follows: frame rate change rate = (scene average frame rate A-scene average frame rate B) / scene average frame rate A×100%; in the same scene, the ratio of the difference in average frame rates at two adjacent time points is the frame rate change rate. If the change rate is positive, it is considered that the frame rate has increased, that is, the game experience is smoother. On the contrary, if the frame rate is negative and the cut value is getting smaller and smaller, it means that the performance has declined, and the experience is manifested as a freeze.

[0162] For example, assume that the performance data of three consecutive scenes (scene A, scene B, and scene C) of a racing game are analyzed, and the performance index value of each scene has been calculated through the previous steps, namely X (average frame rate of scene A), Y (average frame rate of scene B), and Z (average frame rate of scene C). The following steps can be performed to calculate the performance change rate between adjacent scenes:

[0163] 1. Arrange the performance indicator values ​​in chronological order: X (scenario A), Y (scenario B), Z (scenario C).

[0164] 2. The first performance indicator value X is used as the first indicator value.

[0165] 3. Use the next performance indicator value Y as the second indicator value, and calculate the performance change rate from scenario A to scenario B: (YX) / X (the result is output as a percentage).

[0166] 4. Update the second indicator value Y to the first indicator value, and then continue to step 3, using the next performance indicator value Z as the new second indicator value, and calculate the performance change rate from scene B to scene C: (ZY) / Y (the result is output as a percentage).

[0167] 5. Repeat the above steps until the performance change rates between all adjacent scenarios are calculated.

[0168] One of the above technical solutions has the following advantages or beneficial effects:

[0169] Improve analysis accuracy: By comparing the performance indicator values ​​of adjacent scenes one by one in chronological order and accurately calculating the performance change rate, you can more accurately understand the changing trend of game performance between different scenes.

[0170] Optimize game performance: Performance change rate provides detailed information about how game performance fluctuates between different scenes. This helps developers identify performance bottlenecks and take targeted optimization measures to improve the overall performance of the game.

[0171] Supporting automated analysis: The data processing steps of the embodiments of the present invention can be analyzed automatically through programming, which can greatly improve the efficiency and accuracy of performance analysis and reduce the possibility of human error.

[0172] Reference Figure 6 In one implementation of the embodiment of the present invention, the performance change rate is classified into numerical intervals to obtain the jamming situation of the target game, including the following steps:

[0173] Step S1031: classify the performance change rates between all adjacent scenes into pre-divided interval ranges;

[0174] Step S1032: Determine the lag of the target game according to the interval range mapping where the classification data exists.

[0175] Specifically, when classifying the performance change rate of the target game into numerical intervals, it is first necessary to preset a series of interval ranges, which are usually determined based on the expected threshold of game performance and the distribution of actual test data. Then, the performance change rates between all adjacent scenes are classified into these pre-divided intervals one by one. This process accurately evaluates each performance change rate and ensures that it is correctly assigned to the most appropriate interval. After the classification is completed, the distribution of the classified data can be analyzed and mapped to determine the jamming of the target game; for example, if there is a lot of classified data in a certain interval, and the interval represents a large performance fluctuation, then it can be considered that the target game may have jamming problems when these scenes are switched.

[0176] In actual applications, pre-divided interval range: This is the basis for classification. The determination of the interval range needs to comprehensively consider the expected standards of game performance, historical test data, and differences in the actual operating environment. The interval can be set to a fixed width, or it can be dynamically divided based on a certain statistical distribution (such as normal distribution). Performance change rate classification: Compare the performance change rate between each adjacent scene with the pre-divided interval to determine the interval range to which it belongs. Mapping to determine the jamming situation: By analyzing the distribution of classified data, intervals with large performance change rates can be identified, and these intervals are often associated with game jams. For example, if the classified data in a certain interval appears frequently, and the performance fluctuation represented by the interval exceeds the set threshold, then it can be considered that the target game is at risk of jamming when these scenes are switched.

[0177] For example, take the frame rate change rate as an example: the change rate is classified and distributed into different interval ranges to facilitate unified calculation and display; the interval range examples are as follows:

[0178] In the 12 intervals ['<-10%', '-10%~-8%', '-8%~-6%', '-6%~-4%', '-4%~-2%', '-2%~0%', '0~2%', '2%~4%', '4%~6%', '6%~8%', '8%~10%', '>10%'], the interval range is set based on 2% as the dimension increase or decrease, and the specific division dimension of the interval range can be adjusted according to the actual application. Specifically, the greater the change value of the division dimension of the interval range, the more serious the lag of the screened devices. That is, the greater the negative value change, the greater the lag.

[0179] One of the above technical solutions has the following advantages or beneficial effects: Improve the accuracy of analysis: By classifying the numerical intervals, the performance change rate can be converted into a format that is easier to understand and analyze, so as to more accurately identify the game's freezes. Optimize performance tuning strategies: The classification results can provide developers with targeted performance tuning suggestions. For example, if there is a lot of classified data in a certain interval, you can focus on the scene transitions corresponding to the interval, and improve the smoothness of the game by optimizing the code, reducing resource loading time, etc. Support continuous monitoring and improvement: The numerical interval classification method can be used as part of the game performance monitoring system to continuously track and analyze the performance changes of the game, and provide data support for subsequent improvements and optimizations.

[0180] In an implementation of the embodiment of the present invention, the performance change rate is marked with the scene time corresponding to the scene; and determining the jamming situation of the target game according to the interval range mapping of the classification data may include the following steps:

[0181] The interval range where classified data exists is taken as the target interval;

[0182] Determine the maximum number of performance change rates that are continuous in time sequence in the target interval according to the scenario time, and use the maximum number as the adjacent value of the target interval;

[0183] When the numerical range corresponding to the target interval reaches the first numerical threshold, and the adjacent value corresponding to the target interval reaches the second numerical threshold, it is determined that the jamming condition of the target game is continuously increasing jamming.

[0184] Specifically, in the process of further refining the association between the performance change rate and the game jamming situation, the embodiment of the present invention takes into account the numerical interval classification of the performance change rate, and also introduces the concept of scene time and adjacent values. Specifically, first mark the corresponding scene time for each performance change rate, so that the performance changes can be tracked in time series. Then, after determining the interval range (ie, the target interval) where the classified data exists, the continuity of the performance change rate in these intervals in time series is further analyzed, and this continuity is quantified by calculating adjacent values ​​(ie, the maximum number of continuous performance change rates in time series within the target interval). Finally, combined with the numerical range and adjacent values ​​of the target interval, rules for judging game jamming situations are formulated.

[0185] In practical applications, scene time marking: mark the corresponding scene time for each performance change rate, so as to accurately track performance changes in time series and provide information in the time dimension for subsequent analysis. Target interval determination: The interval range where there is classified data is determined as the target interval, and these intervals are the focus of analyzing game jams. Adjacent value calculation: within the target interval, calculate the maximum number of continuous performance change rates in time series, and this number is called adjacent values. Adjacent values ​​reflect the continuity and frequency of performance change rates within the target interval. Jam situation judgment: The embodiment of the present invention sets two numerical thresholds: the first numerical threshold is used to judge whether the numerical range of the target interval has reached the threshold that may cause jams; the second numerical threshold is used to judge whether the adjacent values ​​of the target interval have reached the threshold that may indicate continuous jams. When these two conditions are met at the same time, it is determined that the target game has a situation of continuous increasing jams within the target interval.

[0186] Exemplarily, the aforementioned interval range example is used for further explanation, with the average frame rate as the performance indicator value, and the frame rate change rate as the performance change rate as an example, and the calculation is performed according to this classification interval, where the classification refers to the specific number that lands in the interval. For example, if the average frame rate ratio is negative and the value is between '-6% and -4%', for example, there are 10 adjacent ratios that fall in this interval, then the value corresponding to this interval is 10. Similarly, each interval is the number of ratios that appear in this interval. Through quantitative comparison and verification of a large amount of data on the actual situation, it is concluded that the frame rate change rate is negative and is less than negative 0.04. The device is judged to be more and more stuck as it is played. Similarly, for other performance parameters such as memory change rate and CPU change rate, the above logic can also be used to determine the jam.

[0187] One of the above technical solutions has the following advantages or beneficial effects: Improving the comprehensiveness of the analysis: By introducing the concepts of scene time and adjacent values, the present invention not only considers the numerical size of the performance change rate, but also considers its continuity and frequency in time series, so as to be able to more comprehensively analyze the game's stuttering. Optimizing the accuracy of stuttering judgment: Combining the two dimensions of numerical range and adjacent values ​​for stuttering judgment can more effectively identify those situations that may cause continuous stuttering, avoiding misjudgment or missed judgment. Supporting targeted performance tuning: By analyzing the specific information of the target interval (such as scene time, numerical range, adjacent values, etc.), developers can formulate performance tuning strategies more specifically, thereby improving the efficiency and effectiveness of tuning.

[0188] In order to better illustrate the technical solution of this embodiment, a specific example is proposed below. This example can be applied to the monitoring / testing scenario of the game program. The embodiment of the present invention first selects the performance data of each scene or level for analysis, and takes the frame rate and memory as the performance data analysis object as an example, that is, the change rate of the frame rate and the change rate of the memory are further used to infer the players or devices that are getting stuck more and more during the game operation. Among them, before performing data statistics, data cleaning can be performed first, focusing on eliminating some scenes or levels that cannot show performance, such as login scenes, hang-up scenes, etc., and / or, focusing on eliminating data of some additional factors, such as low power, weak network, etc.; it is also possible to filter the session (session control) data with battle times>=preset game times threshold, that is, based on the data of the user performing a certain scene or level multiple times, excluding the case of special data, that is, the more data copies of the same scene, the more representative it is. Furthermore, the average frame rate and average memory of each round of battle scene data can be counted, and the average value can be used as an important reference basis; then the frame rate change rate and memory change rate of the scene corresponding to each session control are calculated, and the calculation method is the ratio of the current dotting data (using the average value calculated in the above steps as a calculation reference) to the difference between the previous dotting data. Finally, by classifying the change rate, the frame rate and memory are classified and distributed to different intervals, which is convenient for unified calculation. The judgment logic is such as: the frame rate change rate is negative and less than negative 0.04, which is judged as a device that becomes more and more stuck as the game progresses; the memory change rate is positive and greater than 0.02, which is judged as a device that becomes more and more stuck as the game progresses. The embodiment of the present invention can use performance indicator data such as frame rate and memory to determine whether a user or device becomes more and more stuck as the game progresses, and by comparing the change rate of the calculated indicators, the user who becomes more and more stuck as the game progresses and the corresponding game scene can be accurately located.

[0189] Corresponding to the above method embodiment, the present invention also provides an embodiment of a game freeze judgment system. Figure 7 FIG. 2 is a schematic diagram showing the structure of a game freeze judgment system according to an embodiment of the present invention. Figure 7As shown, the game freeze judgment system 700 includes:

[0190] The acquisition module 701 is configured to obtain the performance data of each scene during the running process of the target game;

[0191] The statistics module 702 is configured to perform time series statistics comparison on the performance data to obtain the performance change rate between adjacent scenes;

[0192] The classification module 703 is configured to classify the performance change rate into numerical ranges to obtain the jamming situation of the target game.

[0193] In an implementation manner of the embodiment of the present invention, the system may further include:

[0194] The cleaning module is configured to clean the performance data and remove the first target scene and its corresponding performance data.

[0195] In an implementation manner of the embodiment of the present invention, the system may further include:

[0196] The first determination module is configured to determine the target scene as a first target scene when there is no battle scene in the target scene; the battle scene is determined based on the session control data corresponding to the target scene;

[0197] The second determination module is configured to determine the target scene as the first target scene when the operation parameters of the target terminal running the target game meet the preset threshold conditions during the operation of the target scene.

[0198] In an implementation manner of the embodiment of the present invention, the system may further include:

[0199] The screening module is configured to retain the second target scenario and its corresponding performance data.

[0200] In an implementation manner of the embodiment of the present invention, the system may further include:

[0201] The third determination module is configured to determine the target scene as the second target scene when the number of battle scenes in the target scene reaches a scene threshold; the number of battle scenes is determined based on the session control data corresponding to the target scene.

[0202] The above is a schematic scheme of a game jamming judgment system of this embodiment. It should be noted that the technical scheme of the game jamming judgment system and the technical scheme of the game jamming judgment method applied to the game jamming judgment system belong to the same concept, and the details not described in detail in the technical scheme of the game jamming judgment system can be found in the description of the technical scheme of the game jamming judgment method.

[0203] like Figure 8 As shown, Figure 8 The structure block diagram of an electronic device 800 provided according to an embodiment of the present invention is shown. The components of the electronic device 800 include but are not limited to a memory 810 and a processor 820. The processor 820 is connected to the memory 810 via a bus 830, and the database 850 is used to store data.

[0204] The electronic device 800 also includes an access device 840 that enables the electronic device 800 to communicate via one or more networks 860. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 840 may include one or more of any type of network interface (e.g., a network interface card (NIC)) that is wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a global interconnection for microwave access (Wi□MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, and the like.

[0205] In one embodiment of the present invention, the above components of the electronic device 800 and Figure 8 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 8 The electronic device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of the present invention. Those skilled in the art can add or replace other components as needed.

[0206] The electronic device 800 may be any type of stationary or mobile electronic device, including a mobile computer or mobile electronic device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable electronic device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary electronic device such as a desktop computer or a PC. The electronic device 800 may also be a mobile or stationary server.

[0207] Among them, the processor 820 is used to execute computer executable instructions of the game freeze judgment method.

[0208] The above is a schematic scheme of an electronic device of this embodiment. It should be noted that the technical scheme of the electronic device and the technical scheme of the above-mentioned game jamming judgment method belong to the same concept, and the details not described in detail in the technical scheme of the electronic device can be referred to the description of the technical scheme of the above-mentioned game jamming judgment method.

[0209] An embodiment of the present invention further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned game freeze judgment method is implemented.

[0210] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are implemented to be located in one place, or may also be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0211] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0212] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions under the shared conditions without violating the spirit of the present invention. These equivalent deformations or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for determining game freeze, characterized in that: The following steps are involved: Get the performance data of each scene during the target game running process; Performing time series statistical comparison on the performance data to obtain the performance change rate between adjacent scenes; The performance change rate is classified into numerical ranges to obtain the jamming situation of the target game.

2. The method according to claim 1, characterized in that Before the step of performing time series statistical comparison on the performance data, the method further comprises the following steps: The performance data is cleaned to remove the first target scene and the corresponding performance data.

3. The method according to claim 2, characterized in that The method further comprises the following steps: When the target scene does not have a battle scene, determining the target scene as the first target scene; the battle scene is determined based on the session control data corresponding to the target scene; When the target scenario is running, if the running parameters of the target terminal running the target game meet the preset threshold conditions, the target scenario is determined as the first target scenario.

4. The method according to claim 1, characterized in that Before the step of performing time series statistical comparison on the performance data, the method further comprises the following steps: The second target scene and the corresponding performance data are retained.

5. The method according to claim 4, characterized in that The method further comprises the following steps: When the number of battle scenes in the target scene reaches a scene threshold, the target scene is determined as the second target scene; the number of battle scenes is determined based on the session control data corresponding to the target scene.

6. The method according to claim 1, characterized in that The performing time series statistical comparison on the performance data to obtain the performance change rate between adjacent scenes comprises the following steps: Performing statistical analysis on the performance data to obtain a performance indicator value for each of the scenarios; The performance indicator values ​​are compared and analyzed based on a time series order to obtain the performance change rate between adjacent scenes.

7. The method according to claim 6, characterized in that The performance data includes instantaneous performance values ​​collected by multiple sampling nodes during the operation of the scenario; the statistical analysis of the performance data to obtain the performance index value of each scenario includes the following steps: Averaging the instantaneous performance values ​​collected by all the sampling nodes in the scene to obtain a performance average value; The performance average value corresponding to each scenario is used as the performance indicator value.

8. The method according to claim 6, characterized in that The comparing and analyzing the performance indicator values ​​based on the time sequence to obtain the performance change rate between adjacent scenes includes the following steps: Arrange the performance indicator values ​​corresponding to all the scenarios in chronological order to obtain an indicator set; Taking the first performance indicator value in the indicator set as the first indicator value; Taking the performance indicator value next to the first indicator value in the indicator set as the second indicator value; Obtaining the performance change rate between the corresponding adjacent scenes according to the ratio of the difference between the second index value and the first index value to the first index value; The second indicator value is used as the first indicator value, and the step of using the next performance indicator value of the first indicator value in the indicator set as the second indicator value is returned to be executed until the performance change rate between all adjacent scenes is obtained.

9. The method according to claim 1, characterized in that: The step of classifying the performance change rate into numerical intervals to obtain the jamming condition of the target game includes the following steps: Classifying the performance change rates between all adjacent scenes into pre-divided interval ranges; The jamming condition of the target game is determined based on the interval range mapping where the classification data exists.

10. The method according to claim 9, characterized in that The performance change rate is marked with the scene time corresponding to the scene; and determining the jamming condition of the target game according to the interval range mapping with the classification data includes the following steps: The interval range where the classified data exists is used as the target interval; Determine the maximum number of the performance change rates that are consecutive in time sequence in the target interval according to the scene time, and use the maximum number as an adjacent value of the target interval; When the numerical range corresponding to the target interval reaches a first numerical threshold, and the adjacent value corresponding to the target interval reaches a second numerical threshold, it is determined that the jamming condition of the target game is continuously increasing jamming.

11. A game freeze judgment system, characterized in that: include: A collection module, configured to obtain performance data of each scene during the running process of the target game; A statistical module is configured to perform time series statistical comparison on the performance data to obtain a performance change rate between adjacent scenes; The classification module is configured to classify the performance change rate into numerical intervals to obtain the jamming situation of the target game.

12. An electronic device, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the game stuttering judgment method as described in any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the game freeze determination method as described in any one of claims 1 to 10.

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