Game data processing methods, devices, electronic devices, computer-readable storage media, and computer program products

Abstract

This application provides a game data processing method, apparatus, electronic device, computer-readable storage medium, and computer program product. The method includes: acquiring game operation data of a target object in the current game level in real time, and determining the target object's skill level and behavior pattern based on the game operation data; determining a first adjustment strategy for the game level content based on the skill level and behavior pattern; acquiring game feedback data of the target object, and determining a second adjustment strategy for the game level content based on the game feedback data; and adjusting the content of the next game level based on the first and second adjustment strategies to obtain updated next game level content. This application improves the flexibility of game level content adjustment and the retention rate of the target object.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a game data processing method, apparatus, electronic device, computer-readable storage medium, and computer program product. Background Technology

[0002] Advances in artificial intelligence and machine learning technologies have provided technical support for adaptive difficulty adjustment in games. Adaptive difficulty adjustment refers to a game system's ability to dynamically adjust the game difficulty based on the player's skill and performance, maintaining challenge and enjoyment throughout the game. As games evolve, players increasingly seek personalized gaming experiences, expecting games to adapt to their abilities and preferences. Adaptive difficulty adjustment keeps players in a moderately challenging state, thereby enhancing their gaming experience and satisfaction.

[0003] In related technologies, a linear difficulty adjustment method is usually adopted, that is, the game difficulty gradually increases as the game progresses. This method cannot adjust the game difficulty according to the actual performance of the game user. Although the skill level of the game user is assessed in the initial stage, it cannot keep up with the changes in the skill level of the game user, resulting in low flexibility in adjusting the game level content and low retention rate of the target audience. Summary of the Invention

[0004] This application provides a game data processing method, apparatus, electronic device, computer-readable storage medium, and computer program product, which can improve the flexibility of adjusting game level content and the retention rate of target objects.

[0005] The technical solution of this application embodiment is implemented as follows:

[0006] This application provides a game data processing method, the method comprising:

[0007] The system acquires real-time game operation data of the target object in the current game level and determines the target object's skill level and behavior pattern based on the game operation data.

[0008] Based on the skill level and the behavior pattern, determine a first adjustment strategy for the game level content;

[0009] Obtain game feedback data from the target object, and based on the game feedback data, determine a second adjustment strategy for the game level content;

[0010] Based on the first adjustment strategy and the second adjustment strategy, the content of the next game level is adjusted to obtain the updated content of the next game level.

[0011] This application provides a game data processing device, including:

[0012] The first determining module is used to acquire game operation data of the target object in the current game level in real time, and determine the skill level and behavior pattern of the target object based on the game operation data;

[0013] The second determining module is used to determine a first adjustment strategy for the game level content based on the skill level and the behavior pattern;

[0014] The third determining module is used to acquire game feedback data of the target object and, based on the game feedback data, determine a second adjustment strategy for the game level content;

[0015] The content adjustment module is used to adjust the content of the next game level based on the first adjustment strategy and the second adjustment strategy to obtain the updated content of the next game level.

[0016] This application provides an electronic device, the electronic device comprising:

[0017] Memory is used to store executable instructions or computer programs.

[0018] When a processor executes computer-executable instructions or computer programs stored in the memory, it implements the game data processing method provided in the embodiments of this application.

[0019] This application provides a computer-readable storage medium storing computer-executable instructions or computer programs, which, when executed by a processor, implement the game data processing method provided in this application.

[0020] This application provides a computer program product, including computer-executable instructions or a computer program, which, when executed by a processor, implements the game data processing method provided in this application.

[0021] The embodiments of this application have the following beneficial effects:

[0022] By applying the embodiments of this application, game operation data of the target object in the current game level is acquired in real time. Based on the game operation data, the skill level and behavior pattern of the target object are determined. Then, based on the skill level and behavior pattern, a first adjustment strategy for the game level content is determined. This allows for real-time collection and analysis of the target object's game operation data, enabling dynamic adjustment of the game level content based on skill level and behavior pattern. This ensures that the target object's game completion level matches the game level content, thereby improving the target object's retention rate. Next, game feedback data of the target object is acquired, and a second adjustment strategy for the game level content is determined based on this data. This allows for dynamic adjustment of the game level content based on the target object's game feedback data, increasing the target object's satisfaction with the game level content and thus improving the target object's retention rate. Finally, based on the first and second adjustment strategies, the content of the next game level is adjusted to obtain the updated next game level content. Thus, dynamic adjustment of the game level content based on the first and second adjustment strategies diversifies the methods of adjusting game level content and improves the flexibility of game level content adjustment. Attached Figure Description

[0023] Figure 1 This is a schematic diagram illustrating the application mode of the game data processing method provided in the embodiments of this application;

[0024] Figure 2 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application;

[0025] Figure 3A This is a first flowchart illustrating the game data processing method provided in this application embodiment;

[0026] Figure 3B This is a second flowchart illustrating the game data processing method provided in the embodiments of this application;

[0027] Figure 3C This is a schematic diagram of the third process of the game data processing method provided in the embodiments of this application.

[0028] It should be noted that the terms "first" and "second" mentioned above are only used to distinguish between different options and do not represent the degree of superiority or inferiority of the options or their priority in the implementation process. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0031] In the following description, the terms "first, second, third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0032] In the implementation of this application, the collection and processing of relevant data should strictly comply with the requirements of relevant laws and regulations, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the personal information subject.

[0033] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0034] Unless otherwise defined, all technical and scientific terms used in the embodiments of this application have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the embodiments of this application is for the purpose of describing the embodiments of this application only and is not intended to limit this application.

[0035] Before providing a further detailed description of the embodiments of this application, the nouns and terms involved in the embodiments of this application will be explained, and the nouns and terms involved in the embodiments of this application shall be interpreted as follows.

[0036] 1) Game difficulty adjustment: This is a mechanism in which the game system can automatically adjust the game difficulty level based on the game user's ability, performance, and preferences.

[0037] 2) Retention rate: This measures whether game users continue to use the game application after a certain period of time.

[0038] This application provides a game data processing method, apparatus, electronic device, computer-readable storage medium, and computer program product, which can improve the flexibility of adjusting game level content and the retention rate of target objects.

[0039] The following describes exemplary applications of the electronic devices provided in the embodiments of this application. These electronic devices can be implemented as various types of terminals such as laptops, tablets, desktop computers, set-top boxes, smartphones, smart speakers, smartwatches, smart TVs, and in-vehicle terminals, or as servers. The following will describe exemplary applications when the device is implemented as a server.

[0040] See Figure 1 , Figure 1 This is a schematic diagram illustrating the application mode of the game data processing method provided in the embodiments of this application, for example. Figure 1 The system involves server 200, network 300, and terminal 400. Terminal 400 is connected to server 200 through network 300, which can be a wide area network, a local area network, or a combination of both.

[0041] When the target object passes the current game level, server 200 acquires the target object's game operation data in real time within the current game level, and determines the target object's skill level and behavior pattern based on the game operation data; based on the skill level and behavior pattern, it determines a first adjustment strategy for the game level content; it acquires the target object's game feedback data, and determines a second adjustment strategy for the game level content based on the game feedback data; based on the first and second adjustment strategies, it adjusts the content of the next game level to obtain the updated next game level content. If server 200 receives a request to enter the next game level, it sends the updated next game level content to the terminal corresponding to the target object, which can be terminal 400.

[0042] In some embodiments, the server (e.g., server 200) can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal 400 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, in-vehicle terminal, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited in this embodiment.

[0043] See Figure 2 , Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may be a terminal or a server. Figure 2 The illustrated electronic device 500 includes at least one processor 410, a memory 450, and at least one network interface 420. The various components in the electronic device 500 are coupled together via a bus system 440. It is understood that the bus system 440 is used to implement communication between these components. In addition to a data bus, the bus system 440 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 2 The general labeled all buses as Bus System 440.

[0044] The processor 410 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0045] The memory 450 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state storage, hard disk drives, optical disk drives, etc. The memory 450 may optionally include one or more storage devices physically located away from the processor 410.

[0046] The memory 450 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory. ， The volatile memory can be random access memory (RAM). The memory 450 described in the embodiments of this application is intended to include any suitable type of memory.

[0047] In some embodiments, memory 450 is capable of storing data to support various operations, examples of which include programs, modules, and data structures or subsets or supersets thereof, as illustrated below.

[0048] Operating system 451 includes system programs for handling various basic system services and performing hardware-related tasks, such as the framework layer, core library layer, and driver layer, for implementing various basic business functions and handling hardware-based tasks.

[0049] The network communication module 452 is used to reach other electronic devices via one or more (wired or wireless) network interfaces 420, exemplary network interfaces 420 including Bluetooth, WiFi, and Universal Serial Bus (USB).

[0050] In some embodiments, the apparatus provided in this application can be implemented in software. Figure 2 A game data processing device 455 stored in memory 450 is shown. It can be software in the form of programs and plug-ins, including the following software modules: a first determination module 4551, a second determination module 4552, a third determination module 4553, and a content adjustment module 4554. These modules are logical and can therefore be arbitrarily combined or further divided according to the functions they implement. The functions of each module will be described below.

[0051] In some embodiments, the terminal or server can implement the game data processing method provided in this application by running various computer-executable instructions or computer programs. For example, computer-executable instructions can be microprogram-level commands, machine instructions, or software instructions. Computer programs can be native programs or software modules in an operating system; they can be native applications (APPs), i.e., programs that need to be installed in the operating system to run, such as cloud computing APPs or instant messaging APPs; or they can be applets that can be embedded in any APP, i.e., programs that only need to be downloaded to a browser environment to run. In summary, the aforementioned computer-executable instructions can be any form of instruction, and the aforementioned computer programs can be any form of application, module, or plugin.

[0052] The game data processing method provided in this application will be described in conjunction with exemplary applications and implementations of the server equipment provided in the embodiments of this application.

[0053] The following describes the game data processing method provided in the embodiments of this application. For example, to facilitate understanding of the game data processing method provided in the embodiments of this application, the following describes the application scenario of the game data processing method provided in the embodiments of this application. After a game user completes the current game level, it is necessary to adjust the content of the next game level for the game user. The game data processing method provided in the embodiments of this application can improve the flexibility of adjusting the game level content and the retention rate of the target object.

[0054] As mentioned above, the electronic device implementing the game data processing method of this application embodiment can be a terminal, a server, or a combination of both. The following description uses an electronic device as a server as an example to illustrate the game data processing method provided in this application embodiment. See also... Figure 3A , Figure 3A This is a first flowchart illustrating the game data processing method provided in this application embodiment, which will be combined with... Figure 3A The steps shown are explained.

[0055] In step 301, the game operation data of the target object in the current game level is acquired in real time, and the skill level and behavior pattern of the target object are determined based on the game operation data.

[0056] Here, the target audience can be game users. Through event listeners within the game application, real-time game operation data of the target audience in the current game level is acquired. This data includes game completion time, success rate, and level selection. Based on this data, the target audience's skill level and behavioral patterns are determined. The skill level measures the target audience's skill proficiency in the game. Behavioral patterns refer to the habitual actions performed by the target audience during gameplay, such as their button presses within each level.

[0057] In some embodiments, see Figure 3B , Figure 3B This is a second flowchart illustrating the game data processing method provided in this application embodiment. Figure 3A In step 301 shown, "determining the target object's skill level and behavior pattern based on game operation data" can be achieved through... Figure 3B Steps 3011 to 3012 are implemented, and will be explained in detail below.

[0058] In step 3011, the skill level of the target object is determined based on the game completion time data and the game success rate data.

[0059] Here, game completion time data represents the time taken to complete the current game level. Game success rate data represents the ratio of the number of times a game level is completed to the total number of times a game level is executed.

[0060] In some embodiments, determining the skill level of a target object based on game completion time data and game success rate data can be achieved by performing the following steps: obtaining multiple reference time intervals and multiple reference success rate intervals corresponding to the current game level; determining the target time interval corresponding to the game completion time data from the multiple reference time intervals, and determining the target success rate interval corresponding to the game success rate data from the multiple reference success rate intervals; determining a first level corresponding to the target time interval, and determining a second level corresponding to the target success rate interval; and determining the skill level of the target object based on the first level and the second level.

[0061] Here, the current game level can correspond to multiple reference time intervals and multiple reference success rate intervals. These reference time intervals include a first reference time interval, a second reference time interval, and a third reference time interval, each of which is preset. For example, the first reference time interval is set to 10-15 minutes, the second reference time interval to 5-9 minutes, and the third reference time interval to 0-4 minutes. Based on each reference time interval, the user's game completion level can be measured along the game completion time dimension. The first reference time interval corresponds to the user's first-level game completion level, the second reference time interval corresponds to the user's second-level game completion level, and the third reference time interval corresponds to the user's third-level game completion level.

[0062] Multiple reference success rate intervals are included, comprising a first reference success rate interval, a second reference success rate interval, and a third reference success rate interval, each of which is pre-set. For example, the first reference success rate interval is set to 0-30%, the second reference success rate interval to 31-60%, and the third reference success rate interval to 61-100%. Based on each reference success rate interval, the game user's game completion level can be measured along the game completion success rate dimension. The first reference success rate interval corresponds to the user's first-level game completion level, the second reference success rate interval corresponds to the user's second-level game completion level, and the third reference success rate interval corresponds to the user's third-level game completion level.

[0063] The target time interval is determined from multiple reference time intervals, corresponding to the game completion time data; that is, the target time interval is the reference time interval corresponding to the game completion time data. Similarly, the target success rate interval is determined from multiple reference success rate intervals, corresponding to the game success rate data; that is, the target success rate interval is the reference success rate interval corresponding to the game success rate data. A first level is determined corresponding to the target time interval, and a second level is determined corresponding to the target success rate interval. The first level is the target object's game completion level within the target time interval, and the second level is the target object's game completion level within the target success rate interval. When determining the target object's skill level based on the first and second levels, the game completion level of the first and second levels can be compared. The larger of the two levels can be determined as the target object's skill level, or the smaller of the two levels can be determined as the target object's skill level. In some embodiments, the game completion level corresponding to the first and second levels can be averaged, and the target object's skill level can be determined based on the averaged result.

[0064] For example, if the target time interval is a first reference time interval of 10-15 minutes, the first level corresponding to the target time interval is determined to be Level 1. If the target success rate interval is a second reference success rate interval of 61-100%, the second level corresponding to the target success rate interval is determined to be Level 3. The larger of the first and second levels is Level 3, which can be determined as the skill level of the target. The smaller of the first and second levels is Level 1, which can be determined as the skill level of the target in some embodiments. Alternatively, the game completion level 1 corresponding to the first level and the game completion level 3 corresponding to the second level can be averaged and rounded down. If the average is rounded down to 2, then Level 2 can be determined as the skill level of the target.

[0065] Continue to refer to Figure 3B In step 3012, the behavior pattern of the target object is determined based on the game level selection data.

[0066] Here, game level selection data includes game level effect selection data and game level button selection data. Game level effect selection data characterizes the game effects selected by the target object during game completion. Game level button selection data characterizes the button operations performed by the target object during game completion. Distributed computing frameworks (such as Apache Hadoop or Spark) can be used to process the game level selection data, resulting in processed game level selection data. A model trained using machine learning libraries (such as TensorFlow or PyTorch) can then be used to identify the processed game level selection data, determining the target object's behavioral patterns, i.e., identifying the target object's habitual operations during game completion, such as game effect selection habits and game button operation habits.

[0067] In this embodiment, the skill level of the target object is determined based on game completion time data and game success rate data, and the behavior pattern of the target object is determined based on game level selection data. It can analyze the skill level and behavior pattern of the target object in real time based on the game operation data of the target object in the current game level, which is convenient for dynamically adjusting the content of the next game level based on the skill level and behavior pattern.

[0068] Continue to refer to Figure 3A In step 302, a first adjustment strategy for the game level content is determined based on skill level and behavior pattern.

[0069] Here, the first adjustment strategy is based on the target object's skill level and behavior patterns to determine the adjustment strategy for the content of the next game level.

[0070] In some embodiments, see Figure 3C , Figure 3C This is a schematic diagram of the third process of the game data processing method provided in the embodiments of this application. Figure 3A Step 302 shown in the figure can also be achieved by... Figure 3C Steps 3021 to 3023 are implemented, and will be explained in detail below.

[0071] In step 3021, the multiple game sub-tasks included in the next game level and the difficulty coefficient value of each game sub-task are determined.

[0072] Here, each game level can include multiple game sub-tasks, each with a corresponding difficulty coefficient value. The difficulty coefficient value represents the level of difficulty in completing the game sub-task. After the target object completes each game sub-task in the current game level, the multiple game sub-tasks included in the next game level and the difficulty coefficient value of each game sub-task are determined.

[0073] For example, in a word assembly game, each word in the game level can be a sub-task, and each word corresponds to a difficulty level. In a side-scrolling action game, each obstacle-jumping task in the game level can be a sub-task, and each obstacle-jumping task corresponds to a difficulty level.

[0074] In step 3022, based on skill level, behavior pattern, and difficulty coefficient value of each game sub-task, multiple game sub-tasks are divided into a first sub-task set and a second sub-task set.

[0075] Here, statistical analysis or machine learning algorithms (such as linear regression, decision trees, neural networks, etc.) are used to construct a difficulty model. This model can predict the difficulty coefficient values ​​of game sub-tasks that match the target's skill level and behavioral patterns. Based on the difficulty coefficient values ​​of each game sub-task, the sub-game tasks that match the target's skill level (i.e., the first game sub-tasks) are determined from multiple game sub-tasks, and the sub-game tasks that do not match the target's skill level (i.e., the second game sub-tasks) are determined. These multiple first game sub-tasks are then divided into a sub-task set, resulting in the first sub-task set. Multiple second game subtasks are divided into a subtask set to obtain the second subtask set. That is, each first game subtask in the first subtask set satisfies the matching conditions determined based on skill level and behavior pattern, while each second game subtask in the second subtask set does not satisfy the matching conditions determined based on skill level and behavior pattern. In other words, the difficulty coefficient of the second game subtask is higher than the difficulty coefficient of the game subtask matching the target's game completion level, or the difficulty coefficient of the second game subtask is lower than the difficulty coefficient of the game subtask matching the target's game completion level.

[0076] In step 3023, based on skill level and behavior pattern, a third sub-task set is determined from the game level library, and the second sub-task set is replaced by the third sub-task set as the first adjustment strategy.

[0077] Here, the game level library is a database used to store the game sub-tasks within each game level. Based on the target object's skill level and behavior patterns, multiple third sub-game tasks matching the target object's game completion level are identified from the game level library. These multiple third sub-game tasks are then divided into a sub-task set, resulting in a third sub-task set. The first adjustment strategy is to replace the second sub-task set with the third sub-task set.

[0078] In this embodiment, the multiple game sub-tasks included in the next game level and the difficulty coefficient value of each game sub-task are determined. Based on skill level, behavior pattern and the difficulty coefficient value of each game sub-task, a first adjustment strategy for the game level content is determined. This strategy can dynamically adjust the game level content based on skill level and behavior pattern, so as to match the target user's game completion level with the game level content, thereby improving the target user's retention rate.

[0079] In some embodiments, the following steps may also be performed: obtaining historical behavior data and preference information of the target object; determining a fourth game sub-task from the game level library based on the historical behavior data and preference information; and adding the fourth game sub-task to the content of the next game level.

[0080] Here, historical behavioral data includes industry data and interest data of the target audience, while preference information is used to characterize the target audience's preferences for selecting game levels. A game sub-task matching the target audience's historical behavioral data and preference information is determined from the game level library; this is the fourth game sub-task, and it is added to the next game level.

[0081] In this embodiment, the content of the next game level is adjusted based on historical behavior data and preference information, thereby providing game users with a personalized gaming experience and improving user satisfaction and retention.

[0082] Continue to refer to Figure 3A In step 303, game feedback data of the target object is obtained, and a second adjustment strategy for the game level content is determined based on the game feedback data.

[0083] Here, game feedback data refers to the target audience's satisfaction feedback on the game level content. This data is obtained through in-app surveys and sentiment analysis tools (such as voice or text analysis). The second adjustment strategy is based on this game feedback data to determine adjustments for the next game level.

[0084] In some embodiments, determining a second adjustment strategy for game level content based on game feedback data can be achieved by performing the following steps: determining the target object's satisfaction with each game level content based on game feedback data; identifying game level content with a satisfaction level higher than a preset threshold as first target game level content, and determining a first target sub-game task corresponding to the first target game level content from the game level library; identifying game level content with a satisfaction level lower than a preset threshold as second target game level content, and determining the adjustment strategy of replacing the second target sub-game task corresponding to the second target game level content with the first target sub-game task as the second adjustment strategy.

[0085] Here, based on game feedback data obtained from in-game surveys and sentiment analysis tools, the target audience's satisfaction with each game level can be determined. A preset threshold is set for satisfaction with game level content. For each game level, if the target audience's satisfaction with a game level is higher than the preset threshold, it indicates high satisfaction, and that game level is designated as the first target game level. If the target audience's satisfaction with a game level is lower than the preset threshold, it indicates low satisfaction, and that game level is designated as the second target game level. The first target sub-game task corresponding to the first target game level is determined from the game level library, and the second target sub-game task corresponding to the second target game level is determined. The second adjustment strategy is to replace the second target sub-game task in the next game level with the first target sub-game task.

[0086] In this embodiment of the application, a second adjustment strategy for the game level content is determined based on game feedback data. This strategy can dynamically adjust the game level content based on the game feedback data of the target audience, thereby improving the target audience's satisfaction with the game level content and thus increasing the target audience's retention rate.

[0087] Continue to refer to Figure 3A In step 304, the content of the next game level is adjusted based on the first adjustment strategy and the second adjustment strategy to obtain the updated content of the next game level.

[0088] Here, A / B testing and the multi-armed bandit algorithm can be used to test the retention rate of game users for the first and second adjustment strategies, respectively, in order to select the better adjustment strategy to adjust the content of the next game level.

[0089] In some embodiments, adjusting the content of the next game level based on a first adjustment strategy and a second adjustment strategy to obtain updated content can be achieved by performing the following steps: performing retention rate tests on the first adjustment strategy and the second adjustment strategy respectively to obtain a first retention rate and a second retention rate; when the first retention rate is lower than the second retention rate, adjusting the content of the next game level based on the first adjustment strategy to obtain updated content; when the first retention rate is higher than the second retention rate, adjusting the content of the next game level based on the second adjustment strategy to obtain updated content.

[0090] Here, retention rate testing is used to evaluate the retention of game users within the game application. The test period for retention rate testing can be one week or one month. An initial number of game users is determined, and the ratio of the number of game users who continue using the game application during the test period to the initial number of game users is calculated. This ratio is determined as the retention rate. A first adjustment strategy is used to adjust the game level content, which is based on the skill level and behavioral patterns of game users. The first retention rate under the first adjustment strategy is calculated. A second adjustment strategy is used to adjust the game level content, which is based on game user feedback data. The second retention rate under the second adjustment strategy is calculated. When the first retention rate is lower than the second retention rate, it indicates that the retention rate of game users under the second adjustment strategy is higher. Therefore, the next game level content is adjusted based on the second adjustment strategy, resulting in an updated next game level. When the first retention rate is higher than the second retention rate, it indicates that the retention rate of game users under the first adjustment strategy is higher. Therefore, the next game level content is adjusted based on the first adjustment strategy, resulting in an updated next game level.

[0091] In this embodiment, retention rate tests are performed on the first adjustment strategy and the second adjustment strategy respectively to obtain the first retention rate and the second retention rate. Based on the first retention rate and the second retention rate, the optimal adjustment strategy is determined from the first adjustment strategy and the second adjustment strategy, thereby realizing the diversification of game level content adjustment methods and improving the adaptability and flexibility of game level content adjustment.

[0092] In some embodiments, the following steps may also be performed: when it is determined that the target object has passed the current game level, if a first request to enter the next game level is received, the updated content of the next game level is sent to the terminal corresponding to the target object; when it is determined that the target object has passed the current game level, if a second request to exit the game is received, the updated content of the next game level is stored.

[0093] Here, when the target object passes the current game level, if it receives a request to enter the next game level (the first request), it can use technologies such as a RESTful API (Representative State Transfer Application Programming Interface) or WebSockets to send the updated next game level content to the target object's terminals on different platforms, achieving cross-platform synchronization of game level content. When it is determined that the target object has passed the current game level, if it receives a request to exit the game (the second request), it uses cloud services (such as AWS or Azure) to store the updated next game level content, waiting for the target object to re-enter the game application and execute the process of passing the next game level.

[0094] In this embodiment of the application, by sending the updated next game level content to the terminal corresponding to the target object, or storing the updated next game level content, it is possible to ensure the synchronous update of game progress and game level content settings on different platform terminals corresponding to the target object, thereby improving the game experience of game users and thus improving the retention rate of the target object.

[0095] In some embodiments, the game data processing method provided in this application can be applied to the field of cloud technology. The method involves acquiring game operation data of a target object in the current game level in real time on a cloud platform, determining the target object's skill level and behavior pattern based on the game operation data, and then determining a first adjustment strategy for the game level content based on the skill level and behavior pattern. The cloud server can collect the target object's game operation data in real time and analyze the target object's skill level and behavior pattern, thereby dynamically adjusting the game level content based on the skill level and behavior pattern to match the target object's game completion level with the game level content, thus improving the target object's retention rate. Next, the method acquires the target object's game feedback data and determines a second adjustment strategy for the game level content based on the game feedback data. The cloud server can dynamically adjust the game level content based on the target object's game feedback data, improving the target object's satisfaction with the game level content, thereby improving the target object's retention rate. Finally, based on the first and second adjustment strategies, the next game level content is adjusted to obtain the updated next game level content. In this way, the cloud server can dynamically adjust the game level content based on the first and second adjustment strategies, thereby diversifying the ways to adjust the game level content and improving the flexibility of adjusting the game level content.

[0096] The following will describe an exemplary application of the game data processing method provided in the embodiments of this application in the scenario of adjusting the difficulty of word game levels.

[0097] Existing games often employ linear difficulty adjustments, where the difficulty gradually increases as the game progresses. This approach typically doesn't consider the actual performance of the players, instead pre-setting a difficulty curve. Another common approach is community-driven difficulty adjustment, where users submit and rate game levels in the community, with more difficult levels usually created by more experienced players. Furthermore, game difficulty is usually set based on an assessment of the players' skill level only at the beginning or certain key stages of the game.

[0098] The related technologies have the following problems in adjusting the difficulty of game levels:

[0099] 1) Static difficulty settings: Provides preset static game difficulty levels. The game difficulty cannot be adjusted according to the actual performance of the game user. Although it will assess the skill level of the game user in the initial stage, it cannot keep up with the changes in the skill level of the game user. As the game user becomes more proficient, the set game difficulty may no longer be challenging for the game user.

[0100] 2) Lack of personalized gaming experience: Due to the uniformity of game difficulty settings, gamers of different skill levels may find the game too easy or too difficult, resulting in user churn due to the mismatch between game difficulty and difficulty.

[0101] 3) Low game development efficiency: Game developers need to design and test multiple game versions for different difficulty levels, which reduces game development efficiency.

[0102] This application proposes a game data processing method to address the problems existing in related technologies. Compared with related technologies, this method utilizes big data technology and machine learning algorithms to analyze game user behavior data in real time and dynamically adjust the game difficulty to optimize the user experience. This not only improves the game's playability and challenge but also enhances user immersion and satisfaction, while reducing user churn caused by mismatched game difficulty. It provides game developers and users with a more intelligent, flexible, and personalized game difficulty adjustment solution, thereby improving the overall game experience and technological progress in the game industry. The main improvements include the following:

[0103] 1) Real-time data analysis: By collecting and analyzing game user behavior data in real time, it is possible to identify the skill level of game users at different stages and dynamically adjust the game difficulty;

[0104] 2) Personalized gaming experience: Provide customized gaming experiences based on each user's historical performance and preferences to improve user satisfaction and retention rates;

[0105] 3) Reduce game development costs: The system can automatically adjust the game difficulty, so game developers do not need to design multiple game versions for each difficulty level, thereby reducing game development costs and time.

[0106] The product side of this application relates to an improved human-computer interaction scheme for game software. The specific implementation steps include data collection, data analysis, difficulty adjustment, content generation, user feedback, and cross-platform synchronization.

[0107] Data collection is achieved through a game user behavior tracking module, where the system collects real-time game operation data such as key presses, game time, error rate, and speed of completing game levels. Data analysis involves a data analysis engine processing the collected data and using machine learning algorithms to identify the game user's skill level and play style. Difficulty adjustment is achieved through a game difficulty adjustment algorithm that dynamically adjusts the game difficulty based on data analysis results, increasing or decreasing the complexity of game levels. For example, if a game user completes multiple game levels quickly and consecutively, the system will increase the difficulty of those levels; if a game user encounters difficulty on a particular level, the system will decrease the difficulty.

[0108] Content generation utilizes a personalized content generator to create customized game content based on players' historical behavior data and preferences, such as custom-designed word game levels. User feedback is collected through the user interface, including satisfaction surveys, to further optimize the difficulty adjustment algorithm. Cross-platform synchronization ensures that game progress and difficulty settings are synchronized across different devices.

[0109] The technical aspects of this application's embodiments involve changes to the game data processing flow. The game data processing flow begins with a game user behavior tracking module, which collects game user operation data. This data is then processed by a data analysis engine to obtain game data analysis results. The difficulty adjustment algorithm adjusts the game difficulty based on these results. The network architecture diagram of this application's embodiments enables cross-platform data synchronization. The changes in the data processing flow are reflected in the shift from static difficulty settings to dynamic difficulty adjustments, and the expansion from a single difficulty level to multi-dimensional difficulty control.

[0110] Below, we will use a word game level as an example to illustrate the specific implementation steps.

[0111] The data collection module collects user behavior data through in-game event listeners, including but not limited to the speed of completing words, number of attempts, accuracy, and the number of reward words discovered (users use letters provided in game levels to create words that actually exist but do not belong to the current level; by collecting additional discovered words, users can obtain extra game rewards). Data collection adheres to privacy protection and data security standards to ensure the security of user information.

[0112] Data analysis utilizes big data analytics engines and distributed computing frameworks (such as Apache Hadoop or Spark) to process large-scale datasets. Machine learning libraries (such as TensorFlow or PyTorch) are used to train models to identify game users' skill levels (low, medium, and high) and behavioral patterns (user habits such as clicking and dragging words, and choosing game levels).

[0113] Difficulty adjustment involves maintaining a multi-layered vocabulary database, with each game difficulty level corresponding to a different set of words within the database. Based on the skill levels and behavioral patterns of game users analyzed by a big data analytics engine, the words in each set of the vocabulary database are adjusted in real time. Words are selected from the corresponding sets using a random algorithm with low variance to form subsequent game levels. By providing more refined game difficulty control, the diversity and freshness of game level content are ensured.

[0114] Personalized content is generated based on collected historical behavioral data and preferences of game users (including whether preferred words are verbs or nouns, whether they prefer challenging themselves with long or short words, and the reward words they discover). A big data analysis engine is used to determine the industry and interests of game users, and then relevant words (such as those related to science or gaming) are added to certain game levels to keep game users feeling both engaged and familiar with the game.

[0115] The user feedback mechanism collects game user feedback data through in-game surveys and sentiment analysis tools (such as voice or text analysis). This collected feedback data is used to train a reward model that evaluates the quality of game content based on user feedback. For example, if a user is highly satisfied with a particular game level, the reward model will assign a higher score to the words in that level. Then, through the PPO reinforcement learning algorithm, the reward model learns the optimal strategy for generating game levels of corresponding difficulty levels as it continuously attempts to generate new game levels. The trained reward model is subsequently applied to the actual game level generation process.

[0116] Cross-platform synchronization uses cloud services (such as AWS or Azure) to store game user data (including relevant game operation data from the data collection module, analysis of player interests, preferred words, etc.), and then uses technologies such as RESTful APIs or WebSockets to achieve real-time synchronization of game progress and game difficulty settings on different platform devices.

[0117] System monitoring and optimization involves implementing a real-time monitoring system to track the performance of the game data processing flow and the health of the system. A / B testing and the Multi-Armed Bandit algorithm are used to test difficulty adjustment strategies based on user behavior and user feedback. Then, online data (including overall game time, retention rate, completion rate, and game product ratings) are comprehensively compared to select the optimal difficulty adjustment strategy for adjusting the content of the next game level for users.

[0118] The data collection methods in this application embodiment can be diversified. In addition to the number of key presses and game time, the game user's operation data can also include physiological data, such as heart rate changes, to more comprehensively assess the user's level of tension and engagement. The user's in-game economic data can also be used as a factor in adjusting the game difficulty, for example, adjusting the difficulty based on the user's available game resources. Furthermore, the game difficulty can be adjusted based on the user's emotions and behavioral patterns, such as assessing the user's emotional state through voice recognition or facial expression analysis.

[0119] In this embodiment, data can be collected across different games. Based on the performance of game users in other games, the skill level of the game user in the current game can be predicted through big data engine analysis, thereby adjusting the difficulty of the current game. At the same time, the time curve of the game user's skill level progress in other games can also be analyzed to make corresponding pre-adjustments to the difficulty of subsequent levels in the current game.

[0120] In this embodiment of the application, an AI coach training mode can be developed. The AI ​​coach provides real-time feedback and suggestions based on the actual performance of the game user, helping the game user improve their skill level.

[0121] In the aforementioned application scenario of adjusting the difficulty of word game levels, real-time data analysis and dynamic adjustment of game difficulty enhance the game's playability and challenge. Players of different skill levels can find game levels suitable for their needs, increasing user immersion and satisfaction, and reducing player churn due to games being too easy or too difficult. Personalized game content generation provides users with a more personalized and adaptable gaming experience. Cross-platform data synchronization ensures a consistent gaming experience across different devices, improving convenience. Furthermore, game developers can rely on the system to adjust game difficulty, reducing the need to design multiple game versions for different difficulty levels and improving development efficiency.

[0122] The following description continues to illustrate the exemplary structure of the game data processing device 455 provided in this application embodiment as a software module. In some embodiments, such as... Figure 2 As shown, the software modules stored in the game data processing device 455 in the memory 450 may include: a first determining module 4551, used to acquire game operation data of the target object in the current game level in real time, and determine the skill level and behavior pattern of the target object based on the game operation data; a second determining module 4552, used to determine a first adjustment strategy for the game level content based on the skill level and behavior pattern; a third determining module 4553, used to acquire game feedback data of the target object, and determine a second adjustment strategy for the game level content based on the game feedback data; and a content adjustment module 4554, used to adjust the content of the next game level based on the first adjustment strategy and the second adjustment strategy to obtain the updated content of the next game level.

[0123] In some embodiments, the game operation data includes game completion time data, game success rate data, and game level selection data. The first determining module 4551 is also used to determine the skill level of the target object based on the game completion time data and the game success rate data; and to determine the behavior pattern of the target object based on the game level selection data.

[0124] In some embodiments, the first determining module 4551 is further configured to acquire multiple reference time intervals and multiple reference success rate intervals corresponding to the current game level; determine the target time interval corresponding to the game completion time data from the multiple reference time intervals, and determine the target success rate interval corresponding to the game success rate data from the multiple reference success rate intervals; determine the first level corresponding to the target time interval, and determine the second level corresponding to the target success rate interval; and determine the skill level of the target object based on the first level and the second level.

[0125] In some embodiments, the second determining module 4552 is further configured to determine multiple game sub-tasks included in the next game level content and the difficulty coefficient value of each game sub-task; based on skill level, behavior pattern and the difficulty coefficient value of each game sub-task, divide the multiple game sub-tasks into a first sub-task set and a second sub-task set, wherein each first game sub-task in the first sub-task set satisfies the matching conditions determined based on the skill level and the behavior pattern, and each second game sub-task in the second sub-task set does not satisfy the matching conditions determined based on the skill level and the behavior pattern; based on skill level and behavior pattern, determine a third sub-task set from the game level library, and replace the second sub-task set with the third sub-task set as the first adjustment strategy.

[0126] In some embodiments, the second determining module 4552 is further configured to acquire historical behavior data and preference information of the target object; based on the historical behavior data and preference information, determine a fourth game sub-task from the game level library, and add the fourth game sub-task to the content of the next game level.

[0127] In some embodiments, the third determining module 4553 is further configured to determine the target object's satisfaction with each game level content based on game feedback data; determine game level content with a satisfaction level higher than a preset threshold as the first target game level content; determine the first target sub-game task corresponding to the first target game level content from the game level library; determine game level content with a satisfaction level lower than the preset threshold as the second target game level content; and determine the adjustment strategy of replacing the second target sub-game task corresponding to the second target game level content with the first target sub-game task as the second adjustment strategy.

[0128] In some embodiments, the content adjustment module 4554 is further configured to perform retention rate tests on the first adjustment strategy and the second adjustment strategy respectively, and obtain the first retention rate and the second retention rate accordingly; when the first retention rate is lower than the second retention rate, the content of the next game level is adjusted based on the first adjustment strategy to obtain the updated content of the next game level; when the first retention rate is higher than the second retention rate, the content of the next game level is adjusted based on the second adjustment strategy to obtain the updated content of the next game level.

[0129] In some embodiments, the content adjustment module 4554 is further configured to, when determining that the target object has passed the current game level, send the updated next game level content to the terminal corresponding to the target object if a first request to enter the next game level is received; and when determining that the target object has passed the current game level, store the updated next game level content if a second request to exit the game is received.

[0130] This application provides a computer program product, which includes computer-executable instructions or a computer program stored in a computer-readable storage medium. The processor of an electronic device reads the computer-executable instructions or computer program from the computer-readable storage medium and executes the computer-executable instructions or computer program, causing the electronic device to perform the game data processing method described in this application.

[0131] This application provides a computer-readable storage medium storing computer-executable instructions or a computer program. When the computer-executable instructions or the computer program are executed by a processor, the processor will execute the game data processing method provided in this application. For example, ... Figure 3A The game data processing method is shown.

[0132] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EP ROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a variety of devices including one or any combination of the above-mentioned memories.

[0133] In some embodiments, computer-executable instructions may take the form of programs, software, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.

[0134] As an example, computer-executable instructions may, but do not necessarily, correspond to files in a file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple co-located files (e.g., files that store one or more modules, subroutines, or code sections).

[0135] As an example, computer-executable instructions can be deployed to execute on a single electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed across multiple locations and interconnected via a communication network.

[0136] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of this application are included within the scope of protection of this application.

Claims

1. A game data processing method, characterized in that, The method includes: The system acquires real-time game operation data of a target object in the current game level, including game completion time data, game success rate data, and game level selection data; determines the target object's skill level based on the game completion time data and the game success rate data; and determines the target object's behavioral pattern based on the game level selection data, whereby the behavioral pattern refers to the habitual operations performed by the target object during game completion. Determine the multiple game sub-tasks included in the next game level, and the difficulty coefficient value of each game sub-task, wherein the difficulty coefficient value is used to characterize the difficulty of completing the game sub-task; Based on the skill level, the behavior pattern, and the difficulty coefficient value of each game sub-task, multiple game sub-tasks are divided into a first sub-task set and a second sub-task set. Each first game sub-task in the first sub-task set satisfies the matching conditions determined based on the skill level and the behavior pattern, while each second game sub-task in the second sub-task set does not satisfy the matching conditions determined based on the skill level and the behavior pattern. The difficulty coefficient value is obtained by matching the skill level, behavior pattern, and the target object's game completion level. Based on the skill level and the behavior pattern, a third sub-task set is determined from the game level library. The third sub-task set consists of multiple third sub-game tasks that match the target object's game completion level. Replacing the second sub-task set with the third sub-task set is determined as the first adjustment strategy. Obtain game feedback data of the target object, and determine a second adjustment strategy for the game level content based on the game feedback data; Retention rate tests were performed on the first adjustment strategy and the second adjustment strategy respectively to obtain the first retention rate and the second retention rate. When the first retention rate is lower than the second retention rate, the content of the next game level is adjusted based on the first adjustment strategy to obtain the updated content of the next game level. When the first retention rate is higher than the second retention rate, the content of the next game level is adjusted based on the second adjustment strategy to obtain the updated content of the next game level. The retention rate test employs a controlled A / B test and a multi-armed slot machine algorithm.

2. The method according to claim 1, characterized in that, Determining the skill level of the target object based on the game completion time data and the game success rate data includes: Obtain multiple reference time intervals and multiple reference success rate intervals corresponding to the current game level; The target time interval corresponding to the game completion time data is determined from the plurality of reference time intervals, and the target success rate interval corresponding to the game success rate data is determined from the plurality of reference success rate intervals; Determine the first level corresponding to the target time interval, and determine the second level corresponding to the target success rate interval; Based on the first level and the second level, the skill level of the target object is determined.

3. The method according to any one of claims 1 to 2, characterized in that, The method further includes: Obtain the historical behavior data and preference information of the target object; Based on the historical behavior data and the preference information, a fourth game sub-task is determined from the game level library and added to the content of the next game level.

4. The method according to any one of claims 1 to 2, characterized in that, The determination of a second adjustment strategy for game level content based on the game feedback data includes: Based on the game feedback data, determine the target object's satisfaction with the content of each game level; The game level content with a satisfaction level higher than a preset threshold is determined as the first target game level content, and the first target sub-game task corresponding to the first target game level content is determined from the game level library; The game level content with a satisfaction level lower than the preset threshold is identified as the second target game level content, and the adjustment strategy of replacing the second target sub-game task corresponding to the second target game level content with the first target sub-game task is identified as the second adjustment strategy.

5. The method according to any one of claims 1 to 2, characterized in that, The step of adjusting the content of the next game level based on the first adjustment strategy and the second adjustment strategy to obtain the updated content of the next game level includes: Retention rate tests were performed on the first adjustment strategy and the second adjustment strategy respectively to obtain the first retention rate and the second retention rate. When the first retention rate is lower than the second retention rate, the content of the next game level is adjusted based on the first adjustment strategy to obtain the updated content of the next game level. When the first retention rate is higher than the second retention rate, the content of the next game level is adjusted based on the second adjustment strategy to obtain the updated content of the next game level.

6. The method according to any one of claims 1 to 2, characterized in that, The method further includes: When it is determined that the target object has passed the current game level, if a first request to enter the next game level is received, the updated content of the next game level is sent to the terminal corresponding to the target object. When it is determined that the target object has passed the current game level, if a second request to exit the game is received, the updated content of the next game level is stored.

7. A game data processing device, characterized in that, The device includes: The first determining module is used to acquire, in real time, the game operation data of the target object in the current game level, the game operation data including game completion time data, game success rate data and game level selection data; determine the skill level of the target object based on the game completion time data and game success rate data; and determine the behavior pattern of the target object based on the game level selection data, the behavior pattern referring to the habitual operations performed by the target object during the game level completion process; The second determining module is used to determine multiple game sub-tasks included in the next game level and the difficulty coefficient value of each game sub-task, wherein the difficulty coefficient value is used to characterize the difficulty level of completing the game sub-task; based on the skill level, the behavior pattern, and the difficulty coefficient value of each game sub-task, the multiple game sub-tasks are divided into a first sub-task set and a second sub-task set, wherein each first game sub-task in the first sub-task set satisfies the matching conditions determined based on the skill level and the behavior pattern, and each second game sub-task in the second sub-task set does not satisfy the matching conditions determined based on the skill level and the behavior pattern; wherein the difficulty coefficient value is obtained by matching the skill level, the behavior pattern, and the target object's game completion level; based on the skill level and the behavior pattern, a third sub-task set is determined from the game level library, wherein the third sub-task set consists of multiple third sub-game tasks that match the target object's game completion level, and the second sub-task set is replaced by the third sub-task set as the first adjustment strategy; The third determining module is used to acquire game feedback data of the target object and, based on the game feedback data, determine a second adjustment strategy for the game level content; The content adjustment module is used to perform retention rate tests on the first adjustment strategy and the second adjustment strategy respectively, and obtain the first retention rate and the second retention rate accordingly; When the first retention rate is lower than the second retention rate, the content of the next game level is adjusted based on the first adjustment strategy to obtain the updated content of the next game level. When the first retention rate is higher than the second retention rate, the content of the next game level is adjusted based on the second adjustment strategy to obtain the updated content of the next game level. The retention rate test employs a controlled A / B test and a multi-armed slot machine algorithm.

8. An electronic device, characterized in that, include: Memory, used to store executable instructions; A processor, when executing executable instructions stored in the memory, implements the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores executable instructions for implementing the method of any one of claims 1 to 6 when executed by a processor.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the method described in any one of claims 1 to 6.

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