Game data processing method and device, electronic equipment, computer readable storage medium and computer program product

Abstract

The invention provides a game data processing method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring game operation data of a target object in a current game level in real time, and determining a skill level and a behavior mode of the target object based on the game operation data; determining a first adjustment strategy for the game level content based on the skill level and the 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 next game level content based on the first adjustment strategy and the second adjustment strategy to obtain the updated next game level content. According to the invention, the flexibility of game level content adjustment and the retention rate of the target object can be improved.

Description

Technical Field

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

[0002] The advancement of artificial intelligence and machine learning technologies has provided technical support for adaptive adjustment of game difficulty. Adaptive adjustment of game difficulty means that the game system can dynamically adjust the game difficulty according to the ability and performance of the game user to maintain the challenge and fun of the game user during the game. With the development of games, game users are increasingly pursuing personalized gaming experience and expect the game to adapt to their own abilities and preferences. By adaptively adjusting the game difficulty, game users can always be in a moderately challenging state, thereby improving the game experience and satisfaction of game users.

[0003] In related technologies, a linear difficulty adjustment method is usually adopted, that is, as the game progresses, the game difficulty gradually increases. This method cannot adjust the game difficulty according to the actual performance of the game users. Although the skill level of the game users will be evaluated in the initial stage, it cannot follow up the changes in the skill level of the game users, resulting in low flexibility in adjusting the game level content and low retention rate of the target objects. Summary of the invention

[0004] The embodiments of the present application provide a game data processing method, device, electronic device, computer-readable storage medium and computer program product, which can improve the flexibility of game level content adjustment and the retention rate of target objects.

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

[0006] The present application provides a method for processing game data, the method comprising:

[0007] Acquire game operation data of a target object in a current game level in real time, and determine a skill level and behavior pattern of the target object based on the game operation data;

[0008] Determining a first adjustment strategy for game level content based on the skill level and the behavior pattern;

[0009] 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;

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

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

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

[0013] A second determination module, configured to determine a first adjustment strategy for game level content based on the skill level and the behavior pattern;

[0014] A third determination module is used to 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;

[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 updated content of the next game level.

[0016] An embodiment of the present application provides an electronic device, the electronic device comprising:

[0017] A memory for storing computer executable instructions or computer programs;

[0018] The processor is used to implement the game data processing method provided in the embodiment of the present application when executing the computer executable instructions or computer programs stored in the memory.

[0019] An embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions or a computer program for implementing the game data processing method provided in the embodiment of the present application when executed by a processor.

[0020] An embodiment of the present application provides a computer program product, including computer executable instructions or a computer program. When the computer executable instructions or the computer program are executed by a processor, the game data processing method provided by the embodiment of the present application is implemented.

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

[0022] By using the embodiment of the present application, the game operation data of the target object in the current game level is obtained in real time, and the skill level and behavior pattern of the target object are determined based on the game operation data, and then the first adjustment strategy for the game level content is determined based on the skill level and behavior pattern, which can collect the game operation data of the target object in real time, and analyze the skill level and behavior pattern of the target object, so as to dynamically adjust the game level content based on the skill level and behavior pattern, so as to achieve the matching of the game clearance level of the target object with the game level content, thereby improving the retention rate of the target object, and then obtain the game feedback data of the target object, and determine the second adjustment strategy for the game level content based on the game feedback data, so as to dynamically adjust the game level content based on the game feedback data of the target object, improve the satisfaction of the target object with the game level content, thereby improving the retention rate of the target object, and then adjust the next game level content based on the first adjustment strategy and the second adjustment strategy, and obtain the updated next game level content. In this way, the game level content can be dynamically adjusted based on the first adjustment strategy and the second adjustment strategy, so as to achieve the diversification of the game level content adjustment method and improve the flexibility of the game level content adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a schematic diagram of an application mode of the game data processing method provided in an embodiment of the present application;

[0024] Figure 2 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0025] Figure 3A is a first flow chart of the game data processing method provided by an embodiment of the present application;

[0026] Figure 3B is a second flow chart of the game data processing method provided in an embodiment of the present application;

[0027] Figure 3C This is a third flow chart of the game data processing method provided in an embodiment of the present application.

[0028] It should be pointed out that the above-mentioned "first" and "second" are only used to distinguish different solutions, and do not represent the degree of superiority or inferiority of the solutions or the priority in the implementation process. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0030] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be 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" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0032] The relevant data collection and processing in the embodiments of this application should be strictly in accordance with the requirements of relevant laws and regulations when applied in examples, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of authorization of laws and regulations and the personal information subject.

[0033] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0034] Unless otherwise defined, all technical and scientific terms used in the embodiments of the present application have the same meanings as those commonly understood by those skilled in the art. The terms used in the embodiments of the present application are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0035] Before further describing the embodiments of the present application in detail, the nouns and terms involved in the embodiments of the present application are explained. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations.

[0036] 1) Game difficulty adjustment: It is a mechanism by which the game system can automatically adjust the game difficulty level according to the ability, performance and preferences of the game users.

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

[0038] The embodiments of the present application provide a game data processing method, device, electronic device, computer-readable storage medium and computer program product, which can improve the flexibility of game level content adjustment and the retention rate of target objects.

[0039] The following describes an exemplary application of the electronic device provided by the embodiment of the present application. The electronic device provided by the embodiment of the present application can be implemented as various types of terminals such as a laptop computer, a tablet computer, a desktop computer, a set-top box, a smart phone, a smart speaker, a smart watch, a smart TV, and a vehicle-mounted terminal, and can also be implemented as a server. The following describes an exemplary application when the device is implemented as a server.

[0040] See also Figure 1 , Figure 1 Schematic diagram of the application mode of the game data processing method provided in the embodiment of the present application, for example, Figure 1 The server 200, the network 300 and the terminal 400 are involved. The terminal 400 is connected to the server 200 via the network 300. The network 300 can be a wide area network or a local area network, or a combination of the two.

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

[0042] In some embodiments, the server (e.g., server 200) may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or 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, content delivery networks (CDN), and big data and artificial intelligence platforms. The terminal 400 may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a car terminal, etc., but is not limited thereto. The terminal and the server may be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiments of the present application.

[0043] See also Figure 2 , Figure 2 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, the electronic device may be a terminal or a server, Figure 2 The electronic device 500 shown 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 achieve connection and communication between these components. In addition to the data bus, the bus system 440 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, the bus system 440 is not described in detail. Figure 2 Various buses are labeled 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., where 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 memory, hard disk drives, optical disk drives, etc. The memory 450 may optionally include one or more storage devices that are physically remote from the processor 410.

[0046] The memory 450 includes a volatile memory or a non-volatile memory, and may also include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory. ， The volatile memory may be a random access memory (ROM), and the volatile memory may be a random access memory (RAM). The memory 450 described in the embodiment of the present application is intended to include any suitable type of memory.

[0047] In some embodiments, memory 450 can store data to support various operations, examples of which include programs, modules, and data structures, or a subset or superset thereof, as exemplarily described below.

[0048] The operating system 451 includes system programs for processing various basic system services and performing hardware-related tasks, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic businesses and process 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 include: Bluetooth, Wireless Authentication (WiFi), and Universal Serial Bus (USB).

[0050] In some embodiments, the device provided in the embodiments of the present application can be implemented in software. Figure 2 A game data processing device 455 stored in a memory 450 is shown, which may be software in the form of a program or plug-in, 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 therefore may be arbitrarily combined or further split according to the functions implemented. 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 by the embodiment of the present application by running various computer executable instructions or computer programs. For example, computer executable instructions can be commands, machine instructions or software instructions at the microprogram level. The computer program can be a native program or software module in the operating system; it can be a local (Native) application (APPlicatio n, APP), that is, a program that needs to be installed in the operating system to run, such as a cloud computing APP or an instant messaging APP; it can also be a small program that can be embedded in any APP, that is, a program that can be run only by downloading it to a browser environment. In short, the above-mentioned computer executable instructions can be instructions in any form, and the above-mentioned computer program can be an application, module or plug-in in any form.

[0052] The game data processing method provided in the embodiment of the present application will be explained in combination with the exemplary application and implementation of the server device provided in the embodiment of the present application.

[0053] The following describes the game data processing method provided in an embodiment of the present application. For ease of understanding of the game data processing method provided in an embodiment of the present application, the following describes an application scenario of the game data processing method provided in an embodiment of the present 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 an embodiment of the present application can improve the flexibility of game level content adjustment and the retention rate of the target object.

[0054] As mentioned above, the electronic device implementing the game data processing method of the embodiment of the present application may be a terminal, a server, or a combination of the two. Next, the game data processing method provided by the embodiment of the present application is described by taking the electronic device as a server as an example. Figure 3A , Figure 3A is a first flow chart of the game data processing method provided in the embodiment of the present application, which will be combined with Figure 3A The steps shown are explained.

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

[0056] Here, the target object can be a game user. Through the event listener in the game application, the game operation data of the target object in the current game level is obtained in real time. The game operation data includes game completion time data, game success rate data, and game level selection data. The skill level and behavior pattern of the target object are determined based on the game operation data. The skill level is used to measure the game clearance level of the target object. The behavior pattern refers to the habitual operations performed by the target object during the game clearance process, such as: game button habits in the game level.

[0057] In some embodiments, see Figure 3B , Figure 3B is a second flow chart of the game data processing method provided in an embodiment of the present application, Figure 3A In step 301 shown in the figure, "determining the skill level and behavior pattern of the target object based on the game operation data" can be achieved by Figure 3B Steps 3011 to 3012 are implemented as described 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, the game completion time data is used to represent the clearance time of the current game level. The game success rate data is used to represent the ratio of the number of times the game level is passed to the total number of times the game level is executed.

[0060] In some embodiments, determining the skill level of the target object based on the game completion time data and the game success rate data can be achieved by executing 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; based on the first level and the second level, determining the skill level of the target object.

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

[0062] The multiple reference success rate intervals include a first reference success rate interval, a second reference success rate interval, and a third reference success rate interval, and each reference success rate interval is preset. For example, the first reference success rate interval is set to 0-30%, the second reference success rate interval is set to 31-60%, and the third reference success rate interval is set to 61-100%. According to each reference success rate interval, the game user's game clearance level can be measured in the game clearance success rate dimension. The first reference success rate interval corresponds to the game user's first level game clearance level, the second reference success rate interval corresponds to the game user's second level game clearance level, and the third reference success rate interval corresponds to the game user's third level game clearance level.

[0063] Determine the target time interval corresponding to the game completion time data from multiple reference time intervals, that is, the target time interval is the reference time interval corresponding to the game completion time data. Determine the target success rate interval corresponding to the game success rate data from multiple reference success rate intervals, that is, the target success rate interval is the reference success rate interval corresponding to the game success rate data. Determine the first level corresponding to the target time interval, and determine the second level corresponding to the target success rate interval. The first level is the game clearance level level corresponding to the target object in the target time interval, and the second level is the game clearance level level corresponding to the target object in the target success rate interval. When determining the skill level of the target object based on the first level and the second level, the size of the game clearance level level can be compared between the first level and the second level, and the larger game clearance level level of the first level and the second level is determined as the skill level of the target object, or the smaller game clearance level level of the first level and the second level is determined as the skill level of the target object. In some embodiments, the game clearance level levels corresponding to the first level and the second level can also be averaged and rounded, and the skill level of the target object is determined according to the average rounding result.

[0064] For example, the target time interval is a first reference time interval of 10 to 15 minutes, and the first level corresponding to the target time interval is determined to be level 1. The target success rate interval is a second reference success rate interval of 61 to 100%, and the second level corresponding to the target success rate interval is determined to be level 3. The larger game clearance level between the first level and the second level is level 3, and level 3 can be determined as the skill level of the target object. The smaller game clearance level between the first level and the second level is level 1, and in some embodiments, level 1 can be determined as the skill level of the target object. The game clearance level level 1 corresponding to the first level and the game clearance level level 3 corresponding to the second level can also be averaged and rounded. If the average rounding result is 2, level 2 is determined as the skill level of the target object.

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

[0066] Here, the game level selection data includes game level special effects selection data and game level button selection data. The game level special effects selection data is used to characterize the game special effects selected by the target object during the game clearance process. The game level button selection data is used to characterize the button operations performed by the target object during the game clearance process. A distributed computing framework (such as Apache Hadoop or Spark) can be used to process the game level selection data to obtain the processed game level selection data, and a model trained with a machine learning library (such as TensorFlow or PyTorch) can be used to identify the processed game level selection data to determine the behavior pattern of the target object, that is, to determine the habitual operations performed by the target object during the game clearance process, such as: game special effects selection habits, game button habits.

[0067] In an embodiment of the present application, the skill level of the target object is determined based on the game completion time data and the game success rate data, and the behavior pattern of the target object is determined based on the game level selection data. The skill level and behavior pattern of the target object can be analyzed in real time based on the game operation data of the target object in the current game level, so as to facilitate the subsequent dynamic adjustment of the next game level content based on the skill level and behavior pattern.

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

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

[0070] In some embodiments, see Figure 3C , Figure 3C This is a third flow chart of the game data processing method provided in an embodiment of the present application. Figure 3A Step 302 shown in FIG. 1 can also be performed by Figure 3C Steps 3021 to 3023 are implemented as described in detail below.

[0071] In step 3021, multiple game subtasks included in the next game level content and the difficulty coefficient value of each game subtask are determined.

[0072] Here, each game level content may include multiple game subtasks, each game subtask corresponds to a difficulty coefficient value, and the difficulty coefficient value is used to represent the difficulty level of completing the game subtask. After the target object completes each game subtask in the current game level, multiple game subtasks included in the next game level content and the difficulty coefficient value of each game subtask are determined.

[0073] For example, taking a word assembly game as an example, each word in the game level content can be a game sub-task, and each word corresponds to a difficulty coefficient value; taking a horizontal action game as an example, each obstacle jumping task in the game level content can be a game sub-task, and each obstacle jumping task corresponds to a difficulty coefficient value.

[0074] In step 3022, based on the skill level, behavior pattern, and difficulty coefficient value of each game subtask, the plurality of game subtasks are divided into a first subtask set and a second subtask set.

[0075] Here, a difficulty model is constructed using statistical analysis or machine learning algorithms (such as linear regression, decision tree, neural network, etc.). The difficulty model can predict the difficulty coefficient value of the game subtask that matches the game clearance level of the target object based on the skill level and behavior pattern of the target object. According to the difficulty coefficient value of each game subtask, a sub-game task that matches the game clearance level of the target object is determined from multiple game subtasks, that is, a first game subtask, and a sub-game task that does not match the game clearance level of the target object is determined, that is, a second game subtask. Multiple first game subtasks are divided into a subtask set to obtain a first subtask set. Divide multiple second game subtasks into a subtask set to obtain a second subtask set, that is, each first game subtask in the first subtask set meets the matching conditions determined based on the skill level and the behavior pattern, and each second game subtask in the second subtask set does not meet the matching conditions determined based on the skill level and the behavior pattern, that is, the difficulty coefficient value of the second game subtask is higher than the difficulty coefficient value of the game subtask that matches the game clearance level of the target object, or the difficulty coefficient value of the second game subtask is lower than the difficulty coefficient value of the game subtask that matches the game clearance level of the target object.

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

[0077] Here, the game level library is a database for storing game subtasks in each game level content. Based on the skill level and behavior pattern of the target object, multiple third sub-game tasks matching the game clearance level of the target object are determined from the game level library, and the multiple third sub-game tasks are divided into a subtask set to obtain a third subtask set. The first adjustment strategy is to replace the second subtask set with the third subtask set.

[0078] In an embodiment of the present application, multiple game subtasks included in the next game level content and the difficulty coefficient value of each game subtask are determined, and based on the skill level, behavior pattern and the difficulty coefficient value of each game subtask, a first adjustment strategy for the game level content is determined. The game level content can be dynamically adjusted based on the skill level and behavior pattern, so that the game clearance level of the target object is matched with the game level content, thereby improving the retention rate of the target object.

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

[0080] Here, the historical behavior data includes the industry data and hobby data of the target object, and the preference information is used to characterize the target object's preference for selecting game levels. A game subtask matching the historical behavior data and preference information of the target object is determined from the game level library, i.e., the fourth game subtask, and the fourth game subtask is added to the next game level content.

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

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

[0083] Here, the game feedback data is the data of satisfaction feedback of the target object on the game level content. The game feedback data of the target object is obtained through a questionnaire in the game application and a sentiment analysis tool (such as voice analysis or text analysis). The second adjustment strategy is to determine the adjustment strategy for the next game level content based on the game feedback data of the target object.

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

[0085] Here, based on the questionnaire in the game application and the game feedback data obtained by the sentiment analysis tool, the target object's satisfaction with each game level content can be determined. A preset threshold is set for the satisfaction with the game level content. For each game level content, when the target object's satisfaction with a game level content is higher than the preset threshold, it indicates that the target object is highly satisfied with the game level content, and the game level content is determined as the first target game level content. When the target object's satisfaction with a game level content is lower than the preset threshold, it indicates that the target object is less satisfied with the game level content, and the game level content is determined as the second target game level content. The first target sub-game task corresponding to the first target game level content and the second target sub-game task corresponding to the second target game level content are determined from the game level library. The second adjustment strategy is to replace the second target sub-game task in the next game level content with the first target sub-game task.

[0086] In an embodiment of the present application, a second adjustment strategy for the game level content is determined based on the game feedback data, and the game level content can be dynamically adjusted based on the game feedback data of the target object, thereby improving the target object's satisfaction with the game level content, thereby improving the retention rate of the target object.

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

[0088] Here, the control experiment A / B test and the multi-armed bandit algorithm (Multi-Armed Band it) can be used to test the retention rate of game users for the first adjustment strategy and the second adjustment strategy respectively, so as to select a better adjustment strategy to adjust the content of the next game level.

[0089] In some embodiments, 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, which can be achieved by executing the following steps: performing retention rate tests on the first adjustment strategy and the second adjustment strategy respectively, and obtaining the first retention rate and the second retention rate accordingly; 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 the updated content of the next game level; 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 the updated content of the next game level.

[0090] Here, the retention rate test is used to evaluate the retention of game users in the game application. The test period of the retention rate test can be one week or one month. The initial number of game users is determined, and the ratio of the number of game users who continue to use the game application during the test period to the initial number of game users is calculated, and the ratio result is determined as the retention rate. The first adjustment strategy is used to adjust the game level content, that is, the game level content is adjusted based on the skill level and behavior pattern of the game user, and the first retention rate of the game user under the first adjustment strategy is calculated. The second adjustment strategy is used to adjust the game level content, that is, the game level content is adjusted based on the game feedback data of the game user, and the second retention rate of the game user 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 the game user under the second adjustment strategy is higher, and the next game level content is adjusted based on the second adjustment strategy to obtain the updated next game level content. When the first retention rate is higher than the second retention rate, it indicates that the retention rate of the game user under the first adjustment strategy is higher, and the next game level content is adjusted based on the first adjustment strategy to obtain the updated next game level content.

[0091] In the embodiment of the present application, retention rate tests are performed on the first adjustment strategy and the second adjustment strategy respectively, and the first retention rate and the second retention rate are obtained correspondingly. 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 the game level content adjustment method and improving the adaptability and flexibility of the game level content adjustment.

[0092] In some embodiments, the following steps can 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 next game level content 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 next game level content is stored.

[0093] Here, when the target object passes the current game level, if a request to enter the next game level is received, that is, the first request, the representational state transfer application interface RESTful API or the communication protocol WebSockets and other technologies can be used to send the updated next game level content to the terminals of different platforms corresponding to the target object, so as to achieve cross-platform synchronization of the game level content. When it is determined that the target object has passed the current game level, if a request to exit the game is received, that is, the second request, a cloud service (such as AWS or Azure) is used to store the updated next game level content, so as to wait for the target object to re-enter the game application and execute the clearance process of the next game level content.

[0094] In an embodiment of the present 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 that the game progress and the game level content are set on different platform terminals corresponding to the target object and update synchronously, thereby improving the game user's gaming experience and thus improving the retention rate of the target object.

[0095] In some embodiments, the game data processing method provided by the embodiment of the present application can be applied in the field of cloud technology. The game operation data of the target object in the current game level is obtained in real time on the cloud platform, and the skill level and behavior pattern of the target object are determined based on the game operation data, and then based on the skill level and behavior pattern, the first adjustment strategy for the game level content is determined, and the cloud server can collect the game operation data of the target object in real time, and analyze the skill level and behavior pattern of the target object, so as to dynamically adjust the game level content based on the skill level and behavior pattern, so as to match the game clearance level of the target object with the game level content, thereby improving the retention rate of the target object, and then obtain the game feedback data of the target object, and determine the second adjustment strategy for the game level content based on the game feedback data, and the cloud server can dynamically adjust the game level content based on the game feedback data of the target object, improve the satisfaction of the target object with the game level content, thereby improving the retention rate of the target object, and then adjust the next game level content based on the first adjustment strategy and the second adjustment strategy, and obtain the updated next game level content. In this way, the cloud server can dynamically adjust the game level content based on the first adjustment strategy and the second adjustment strategy, thereby achieving diversification of game level content adjustment methods and improving flexibility of game level content adjustment.

[0096] The following describes an exemplary application of the game data processing method provided in an embodiment of the present application in a scenario of adjusting the difficulty of a word game level.

[0097] Existing games use a linear difficulty adjustment method, that is, as the game progresses, the game difficulty gradually increases. This method usually does not take into account the actual performance of the game users, but presets a game difficulty curve. Community-driven difficulty adjustment is also usually adopted, that is, game users are allowed to submit and rate game levels in the community. More difficult game levels are usually created by more experienced game users. In addition, the skill level of game users is usually assessed only once at the initial stage of the game or at certain important stages, and the game difficulty is set accordingly.

[0098] In the process of adjusting the difficulty of game levels, the related technologies have the following problems:

[0099] 1) Static difficulty setting: It provides a preset static game difficulty level, and cannot adjust the game difficulty according to the actual performance of the game user. Although the skill level of the game user will be evaluated in the initial stage, it cannot follow up the change of the game user's skill level. As the game user becomes more proficient in the game, 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, game users of different skill levels may feel that the game is too easy or too difficult, which may lead to the loss of game users due to mismatched game difficulty.

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

[0102] In view of the problems existing in the related technologies, the embodiments of the present application propose a game data processing method. Compared with the related technologies, the method uses big data technology and machine learning algorithms to analyze the game behavior data of game users in real time, and dynamically adjusts the game difficulty to optimize the game user experience. It can not only improve the playability and challenge of the game, but also enhance the immersion and satisfaction of game users, while reducing the user churn rate caused by mismatched game difficulty, and provide game developers and game 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 users’ gaming behavior data in real time, it is possible to identify the skill levels of game users at different stages and dynamically adjust the game difficulty;

[0104] 2) Personalized gaming experience: Provide customized gaming experience based on each game user’s historical performance and preferences, thereby improving game user satisfaction and retention rate;

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

[0106] The product side of the embodiment of the present application involves an improved human-computer interaction solution for gaming software, and the specific implementation steps include data collection, data analysis, difficulty adjustment, content generation, user feedback, and cross-platform synchronization.

[0107] Data collection is done through the game user behavior tracking module. The system collects game user's game operation data in real time, such as the number of key presses, game time, error rate, and speed of completing game levels. Data analysis is done through the data analysis engine to process the collected data and use machine learning algorithms to identify the skill level and game style of game users. Difficulty adjustment is done through the game difficulty adjustment algorithm to dynamically adjust the game difficulty according to the data analysis results to increase or decrease the complexity of the game level. For example, if the game user completes multiple game levels in a row and quickly, the system will increase the difficulty of the game level; if the game user encounters difficulties in a certain game level, the system will reduce the difficulty of the game level.

[0108] Content generation is to generate customized game content based on the historical behavior data and preferences of game users through a personalized content generator, such as custom-designed word game levels. User feedback is the system collecting feedback from game users through the user interface, such as satisfaction surveys, to further optimize the difficulty adjustment algorithm. Cross-platform synchronization is to ensure that the game progress and game difficulty settings of game users on different devices can be synchronized.

[0109] The technical side of the embodiment of the present application involves changes in the game data processing flow. The game data processing flow starts with the game user behavior tracking module, collects the game user's game operation data, and then processes the game operation data through the data analysis engine to obtain the game data analysis results. The difficulty adjustment algorithm adjusts the game difficulty according to the game data analysis results. The network architecture diagram of the embodiment of the present application can achieve cross-platform data synchronization. The change in the data processing flow is reflected in the transition from static difficulty setting to dynamic difficulty adjustment, and the expansion from a single difficulty level to multi-dimensional difficulty control.

[0110] Below, taking the word game level as an example, the specific implementation steps are illustrated.

[0111] The data collection module is used to collect game user behavior data through event listeners in the game, including but not limited to the speed of completing words, number of attempts, accuracy, number of bonus words found (game users use the letters provided in the game level to combine words that actually exist but do not belong to this game level. By collecting additional discovered words, game users can obtain additional game rewards), etc. Data collection complies with privacy protection and data security standards to ensure the security of game user information.

[0112] Data analysis is done through big data analysis engines, using distributed computing frameworks (such as Apache Hadoop or Spark) to process large data sets. Machine learning libraries (such as TensorFlow or PyTorch) are used to train models to identify game users' skill levels (low-level skill level, medium-level skill level, and high-level skill level) and behavior patterns (game users' habit of clicking and dragging a word, game level selection).

[0113] Difficulty adjustment is to maintain a multi-level word library, and each game difficulty level corresponds to a different word set in the word library. According to the skill level and behavior pattern of the game users output by the big data analysis engine, the words in each word set in the word library are adjusted in real time, and the words can be selected from the corresponding word set through a random algorithm with a small variance to form the subsequent game levels. By providing more sophisticated game difficulty control, the diversity and freshness of the game level content are ensured.

[0114] The generation of personalized content is based on the collected historical behavior data and preferences of game users (including whether the preferred words are verb or noun type, whether they like to challenge long words or short words, reward words for discovery, etc.), and the big data analysis engine is used to determine the industry and interests of the game users, and then some related words are added to certain game levels (such as those related to the science field, the game field, etc.), so that game users can keep the game fresh and familiar.

[0115] The user feedback mechanism is used to collect game feedback data from game users through in-game questionnaires and sentiment analysis tools (such as voice or text analysis). The collected game feedback data is used to train a reward model, which can evaluate the quality of game content based on the feedback of game users. For example, if the game user is highly satisfied with a certain game level, the reward model will output a higher score for the words in the game level to a certain extent. Then, through the PPO reinforcement learning algorithm, the reward model will learn to generate the optimal strategy for the game level of the corresponding difficulty level in the process of constantly trying to generate new game levels. The trained reward model will then be 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 in the data collection module, analysis of players' interests and hobbies, preferred words, and other data), and then uses technologies such as RESTful API or WebSockets to achieve real-time synchronization of game progress and game difficulty settings on different platform devices.

[0117] System monitoring and optimization is to track the performance of the game data processing process and the health status of the system by implementing a real-time monitoring system. Use A / B testing and multi-armed bandit algorithms to test the difficulty adjustment strategy based on game user behavior and user feedback, and then make a comprehensive comparison based on online data (including the overall game time, retention rate, clearance rate, game product ratings, etc.) to select a better difficulty adjustment strategy to adjust the next game level content for game users.

[0118] In the embodiments of the present application, the data collection method can be diversified. In addition to the number of keystrokes and game time, the game user's game operation data can also include the game user's physiological data, such as heart rate changes, to more comprehensively evaluate the game user's tension and involvement. The game user's economic data in the game can also be used as a factor in adjusting the game difficulty, such as adjusting the game difficulty according to the game user's game resource ownership. The game difficulty can also be adjusted according to the game user's emotions and behavior patterns, such as evaluating the game user's emotional state through voice recognition or facial expression analysis.

[0119] In the embodiment of the present application, data can be collected across different games. Based on the performance of the game user in other games, the skill level of the game user in the current game can be predicted through big data engine analysis, so as to adjust the difficulty of the current game. At the same time, the time curve of the game user's skill level improvement in other games can also be analyzed, and the difficulty of subsequent levels of the current game can be pre-adjusted accordingly.

[0120] In the embodiment of the present application, an AI coach training mode can be developed, in which the AI ​​coach provides real-time feedback and suggestions based on the actual performance of the game user to help the game user improve his skill level.

[0121] In the application scenario of the difficulty adjustment of the word game level mentioned above, the playability and challenge of the game are improved through real-time data analysis and dynamic adjustment of the game difficulty. Game users of different skill levels can find game level challenges that suit them, which enhances the immersion and satisfaction of game users and reduces the loss of players due to the game being too simple or difficult. Through personalized game content generation, a more personalized and adaptable gaming experience is provided to game users. Cross-platform data synchronization ensures the consistency of the gaming experience of game users on different devices and improves the convenience of the game. In addition, game developers can rely on the system to adjust the difficulty of the game, reducing the need to design multiple game versions for game levels of different difficulty levels and improving the efficiency of game development.

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

[0123] In some embodiments, the game operation data includes game completion time data, game success rate data, and game level selection data. The first determination 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 determine the behavior pattern of the target object based on the game level selection data.

[0124] In some embodiments, the first determination module 4551 is also used to obtain 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; based on the first level and the second level, determine the skill level of the target object.

[0125] In some embodiments, the second determination module 4552 is also used 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 the skill level, 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, each first game sub-task in the first sub-task set meets the matching condition determined based on the skill level and the behavior pattern, and each second game sub-task in the second sub-task set does not meet the matching condition determined based on the skill level and the behavior pattern; based on the skill level and the 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.

[0126] In some embodiments, the second determination module 4552 is also used to obtain historical behavior data and preference information of the target object; based on the historical behavior data and preference information, determine the fourth game subtask from the game level library, and add the fourth game subtask to the next game level content.

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

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

[0129] In some embodiments, the content adjustment module 4554 is also used to, 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, send the updated next game level content 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, store the updated next game level content.

[0130] The embodiment of the present application provides a computer program product, which includes computer executable instructions or computer programs, and the computer executable instructions or computer programs are stored in a computer-readable storage medium. The processor of the electronic device reads the computer executable instructions or computer programs from the computer-readable storage medium, and the processor executes the computer executable instructions or computer programs, so that the electronic device executes the game data processing method described in the embodiment of the present application.

[0131] The present application embodiment provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions or computer programs are stored. When the computer-executable instructions or computer programs are executed by a processor, the processor will be caused to execute the game data processing method provided by the present application embodiment, for example, Figure 3A The game data processing method 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 storage, optical disk, or CD-ROM; or it may be various devices including one or any combination of the above memories.

[0133] In some embodiments, computer executable instructions may be in the form of a program, software, software module, script 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 a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0134] As an example, computer-executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0135] As an example, computer executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed at multiple sites and interconnected by a communication network.

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

Claims

1. A method for processing game data, characterized in that: The method comprises: Acquire game operation data of a target object in a current game level in real time, and determine a skill level and behavior pattern of the target object based on the game operation data; Determining a first adjustment strategy for game level content based on the skill level and the behavior pattern; 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; Based on the first adjustment strategy and the second adjustment strategy, the content of the next game level is adjusted to obtain updated content of the next game level.

2. The method according to claim 1, characterized in that The game operation data includes game completion time data, game success rate data, and game level selection data. The determining of the skill level and behavior pattern of the target object based on the game operation data includes: Determine the skill level of the target object based on the game completion time data and the game success rate data; A behavior pattern of the target object is determined based on the game level selection data.

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

4. The method according to claim 1, characterized in that: The determining a first adjustment strategy for game level content based on the skill level and the behavior pattern includes: Determining a plurality of game subtasks included in the next game level content and a difficulty coefficient value of each of the game subtasks; Based on the skill level, the behavior pattern, and the difficulty coefficient value of each of the game subtasks, the plurality of game subtasks are divided into a first subtask set and a second subtask set, each first game subtask in the first subtask set meets a matching condition determined based on the skill level and the behavior pattern, and each second game subtask in the second subtask set does not meet the matching condition determined based on the skill level and the behavior pattern; Based on the skill level and the behavior pattern, a third subtask set is determined from a game level library, and the second subtask set is replaced by the third subtask set as the first adjustment strategy.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Obtaining historical behavior data and preference information of the target object; Based on the historical behavior data and the preference information, a fourth game subtask is determined from a game level library, and the fourth game subtask is added to the next game level content.

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

7. The method according to any one of claims 1 to 4, characterized in that: The adjusting the next game level content based on the first adjustment strategy and the second adjustment strategy to obtain updated next game level content includes: Performing retention rate tests on the first adjustment strategy and the second adjustment strategy respectively, and obtaining a first retention rate and a second retention rate accordingly; When the first retention rate is lower than the second retention rate, adjusting the next game level content based on the first adjustment strategy to obtain updated next game level content; When the first retention rate is higher than the second retention rate, the next game level content is adjusted based on the second adjustment strategy to obtain updated next game level content.

8. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: When it is determined that the target object has passed the current game level, if a first request to enter a next game level is received, sending the updated next game level content to a 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 next game level content is stored.

9. A game data processing device, characterized in that: The device comprises: A first determination module is used to obtain game operation data of a target object in a current game level in real time, and determine a skill level and behavior pattern of the target object based on the game operation data; A second determination module, configured to determine a first adjustment strategy for game level content based on the skill level and the behavior pattern; A third determination module is used to 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; 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 updated content of the next game level.

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