Game task adjustment method and device, server and storage medium

Abstract

The application provides a game task adjustment method and device, terminal equipment and medium, and relates to the technical field of games. The game task adjustment method comprises the following steps: obtaining task behavior data in a preset time period in a game, the task behavior data being used to represent behavior data generated by a player for multiple tasks in the game; performing statistical analysis and processing on the task behavior data to determine a to-be-adjusted task in the game from the multiple tasks; and adjusting the to-be-adjusted task according to a preset adjustment strategy corresponding to the to-be-adjusted task and game behavior data in the preset time period. The to-be-adjusted task is determined by performing statistical analysis and processing on the task behavior data, the to-be-adjusted task is automatically adjusted based on the preset adjustment strategy corresponding to the to-be-adjusted task and the game behavior data, manual adjustment is not required, the waste of human resources is reduced, and the adjustment of the task is more objective.

Description

Technical Field

[0001] This invention relates to the field of game technology, and more specifically, to a method, apparatus, server, and storage medium for adjusting tasks in a game. Background Technology

[0002] With the development of internet technology, the variety of games has also increased. In some games, tasks can be set and assigned to players, guiding them to try more gameplay options. Therefore, the design of tasks and task assignments has become a hot research topic.

[0003] In related technologies, a pre-set task pool is established, which can contain multiple tasks. Players are assigned tasks based on the tasks in the pool, and if a task is too difficult, the player can choose to replace it. To better guide players to try more gameplay options, the difficulty of the tasks in the task pool is manually adjusted to ensure consistency.

[0004] However, in related technologies, manually adjusting tasks wastes unnecessary human resources, and the adjustment process is rather subjective. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the prior art by providing a method, apparatus, server, and medium for adjusting tasks in games, thereby solving the problems of manual task adjustment in related technologies, which wastes unnecessary human resources and is relatively subjective.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows:

[0007] In a first aspect, embodiments of the present invention provide a method for adjusting tasks in a game, including:

[0008] Acquire task behavior data within a preset time period in the game, wherein the task behavior data is used to characterize the player's behavior data in response to multiple tasks in the game;

[0009] Based on the statistical analysis of the task behavior data, the tasks to be adjusted in the game are determined from the multiple tasks.

[0010] The task to be adjusted is adjusted according to the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within the preset time period. The game behavior data is used to characterize the behavior data related to the task to be adjusted generated by the player when not participating in the task in the game.

[0011] Optionally, the step of performing statistical analysis on the task behavior data to determine the tasks to be adjusted in the game from the multiple tasks includes:

[0012] Obtain the number of times each task is replaced in the task behavior data;

[0013] Based on the number of times each task is replaced, tasks whose number of replacements is greater than a first threshold and whose number of replacements is less than a second threshold are identified as the tasks to be adjusted.

[0014] Wherein, the first threshold is greater than the second threshold.

[0015] Optionally, the step of performing statistical analysis on the task behavior data to determine the tasks to be adjusted in the game from the multiple tasks includes:

[0016] Obtain the number of times each task was assigned and replaced from the task behavior data;

[0017] Based on the ratio of the number of times each task is replaced to the number of times it is assigned, tasks whose ratio is greater than a third threshold and whose ratio is less than a fourth threshold are identified as the tasks to be adjusted.

[0018] Optionally, the step of performing statistical analysis on the task behavior data to determine the tasks to be adjusted in the game from the multiple tasks includes:

[0019] Obtain the number of times each task was completed and assigned from the task behavior data;

[0020] Based on the ratio of the number of times each task is completed to the number of times it is assigned, tasks with a ratio greater than a fifth threshold and a ratio less than a sixth threshold are identified from the plurality of tasks as the tasks to be adjusted.

[0021] Optionally, adjusting the task to be adjusted based on the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within a preset time period includes:

[0022] Determine the task parameters corresponding to the task to be adjusted;

[0023] Obtain target behavior data that matches the task parameters from the game behavior data;

[0024] Based on the target behavior data, the task parameter values ​​in the task to be adjusted are adjusted.

[0025] Optionally, the step of adjusting the task parameter values ​​in the task to be adjusted based on the target behavior data includes:

[0026] Obtain the task expectation for the task to be adjusted, wherein the task expectation includes at least a first ratio representing the number of times the task to be adjusted was completed and the number of times it was assigned after the adjustment.

[0027] Based on the first ratio and the target behavior data, the task parameter values ​​in the task to be adjusted are adjusted.

[0028] Optionally, adjusting the task parameter values ​​in the task to be adjusted based on the first ratio and the target task data includes:

[0029] The target behavior data is sorted to generate a first sequence of data;

[0030] Based on the first ratio, a target behavior data is selected from the first sequence data as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

[0031] Optionally, selecting a target behavior data from the first sequence data as the task parameter value corresponding to the task to be adjusted based on the first ratio includes:

[0032] Determine the target behavior data in the first sequence data that corresponds to the first ratio;

[0033] The target behavior data is used as the task parameter value corresponding to the task to be adjusted.

[0034] Optionally, adjusting the task parameter values ​​in the task to be adjusted based on the first ratio and the target task data includes:

[0035] Obtain the average value of the target behavior data;

[0036] The product of the average value and the first ratio is used as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

[0037] Secondly, embodiments of the present invention also provide a task adjustment device for games, comprising:

[0038] The acquisition module is used to acquire task behavior data within a preset time period in the game. The task behavior data is used to characterize the behavior data generated by the player in response to multiple tasks in the game.

[0039] The determination module is used to perform statistical analysis and processing based on the task behavior data to determine the task to be adjusted in the game from the multiple tasks.

[0040] The adjustment module is used to adjust the task to be adjusted according to the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within the preset time period. The game behavior data is used to characterize the behavior data related to the task to be adjusted generated by the player when not participating in the task in the game.

[0041] Optionally, the determining module is specifically used to obtain the number of times each task is replaced in the task behavior data; based on the number of times each task is replaced, determine the tasks among the plurality of tasks whose number of replacements is greater than a first threshold and whose number of replacements is less than a second threshold as the tasks to be adjusted; wherein, the first threshold is greater than the second threshold.

[0042] Optionally, the determining module is specifically used to obtain the number of times each task is assigned and the number of times it is replaced in the task behavior data; based on the ratio of the number of times each task is replaced to the number of times it is assigned, determine from the plurality of tasks the tasks whose ratio is greater than a third threshold and whose ratio is less than a fourth threshold as the tasks to be adjusted.

[0043] Optionally, the determining module is specifically used to obtain the number of times each task is completed and the number of times it is assigned in the task behavior data; based on the ratio of the number of times each task is completed to the number of times it is assigned, determine from the plurality of tasks the tasks whose ratio is greater than a fifth threshold and whose ratio is less than a sixth threshold as the tasks to be adjusted.

[0044] Optionally, the adjustment module is specifically used to determine the task parameters corresponding to the task to be adjusted; obtain target behavior data that matches the task parameters from the game behavior data; and adjust the task parameter values ​​in the task to be adjusted based on the target behavior data.

[0045] Optionally, the adjustment module is specifically used to obtain the task expectation for the task to be adjusted, the task expectation including at least a first ratio representing the number of times the task to be adjusted is completed and the number of times it is assigned after adjustment; and to adjust the task parameter values ​​in the task to be adjusted based on the first ratio and the target behavior data.

[0046] Optionally, the adjustment module is specifically used to sort the target behavior data to generate a first sequence data; based on the first ratio, select a target behavior data from the first sequence data as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

[0047] Optionally, the adjustment module is specifically used to determine the target behavior data in the first sequence data corresponding to the first ratio; and to use the target behavior data as the task parameter value corresponding to the task to be adjusted.

[0048] Optionally, the adjustment module is specifically used to obtain the average value of the target behavior data; and to use the product of the average value and the first ratio as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

[0049] Thirdly, embodiments of the present invention also provide a server, including: a memory and a processor, wherein the memory stores a computer program executable by the processor, and the processor executes the computer program to implement the game task adjustment method described in any of the first aspects above.

[0050] Fourthly, embodiments of the present invention also provide a storage medium storing a computer program, wherein when the computer program is read and executed, it implements the game task adjustment method described in any of the first aspects above.

[0051] The beneficial effects of this invention are as follows: This invention provides a method for adjusting tasks in a game. The method includes: acquiring task behavior data within a preset time period in the game, whereby the task behavior data characterizes the behavioral data generated by players in response to multiple tasks in the game; performing statistical analysis on the task behavior data to determine the task to be adjusted from among the multiple tasks; and adjusting the task to be adjusted based on a preset adjustment strategy corresponding to the task to be adjusted and the game behavior data within the preset time period. The game behavior data characterizes the task-related behavioral data generated by players when performing the task to be adjusted in the game. By performing statistical analysis on the task behavior data to determine the task to be adjusted, and by automatically adjusting the task based on the preset adjustment strategy and the game behavior data, no manual adjustment is required, reducing the waste of human resources and making the task adjustment more objective. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart illustrating a method for adjusting tasks in a game, provided by an embodiment of the present invention;

[0054] Figure 2 A flowchart illustrating a method for adjusting tasks in a game, provided by an embodiment of the present invention;

[0055] Figure 3 A flowchart illustrating a method for adjusting tasks in a game, provided by an embodiment of the present invention;

[0056] Figure 4 A flowchart illustrating a method for adjusting tasks in a game, provided by an embodiment of the present invention;

[0057] Figure 5 A flowchart illustrating a method for adjusting tasks in a game, provided by an embodiment of the present invention;

[0058] Figure 6 A flowchart illustrating a method for adjusting tasks in a game, provided by an embodiment of the present invention;

[0059] Figure 7 A flowchart illustrating a method for adjusting tasks in a game, provided by an embodiment of the present invention;

[0060] Figure 8 A flowchart illustrating a method for adjusting tasks in a game, provided by an embodiment of the present invention;

[0061] Figure 9 A flowchart illustrating a method for adjusting tasks in a game, provided by an embodiment of the present invention;

[0062] Figure 10 This is a schematic diagram of a task adjustment device in a game provided by an embodiment of the present invention;

[0063] Figure 11 This is a schematic diagram of the structure of a terminal device provided in an embodiment of the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0065] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0066] In the description of this application, it should be noted that if the terms "upper", "lower", etc. appear to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship that the product of this application is usually placed in, it is only for the convenience of describing this application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0067] Furthermore, the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Additionally, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0068] It should be noted that, where there is no conflict, the features in the embodiments of this application can be combined with each other.

[0069] With the development of internet technology, the variety of games has also increased. In some games, tasks can be set and assigned to players, guiding them to try more gameplay options. Therefore, the design and assignment of tasks has become a hot research topic. In this application, tasks can be understood as those assigned to players after they log in. There can be multiple tasks, and players can choose which to complete. Generally, completing a task will yield related rewards, etc., which are not limited here. Some games set daily tasks, tasks within a certain period, or tasks that need to be completed within a specified time, etc., which are also not limited here.

[0070] In related technologies, a pre-set task pool is established, which can contain multiple tasks. Players are assigned tasks based on these tasks. If a task is deemed too difficult, players can choose to replace it or abandon it. To better encourage players to explore more gameplay options, existing solutions manually adjust the difficulty of tasks in the task pool.

[0071] In view of the problems in related technologies, which involve manually adjusting tasks, wasting unnecessary human resources and having a subjective adjustment process, this application provides a task adjustment method in games. The method determines the task to be adjusted by statistical analysis of task behavior data, and automatically adjusts the task based on the preset adjustment strategy corresponding to the task and the game behavior data. This eliminates the need for manual adjustment, reduces the waste of human resources, and makes the adjustment of tasks more objective.

[0072] In this embodiment, the server can run at least one task pool. The tasks in each task pool can have different difficulties, while the tasks in the same task pool can have the same difficulty. The server can use the game task adjustment method provided in this embodiment to ensure that the tasks in the same task pool have the same or similar difficulty. In practical applications, tasks can be assigned to players from the same task pool or different task pools according to actual game needs.

[0073] For example, if a player needs to complete three tasks of varying difficulty—easy, medium, and difficult—they can be assigned easy tasks from a first task pool containing only easy tasks of the same difficulty. Alternatively, they can be assigned medium tasks from a second task pool containing only medium tasks of the same difficulty. Finally, they can be assigned difficult tasks from a third task pool containing only complex tasks of the same difficulty. When switching between tasks of the same difficulty, players can only switch from the corresponding task pool, preventing them from switching tasks across different difficulty levels. Furthermore, because tasks within the same task pool are of equal difficulty, it also prevents players from frequently switching between tasks within the same pool.

[0074] The following explanation uses the server as the executing entity to illustrate the game task adjustment method provided in the embodiments of this application.

[0075] Figure 1 This is a flowchart illustrating a method for adjusting tasks in a game, as provided in an embodiment of the present invention. Figure 1 As shown, the method may include:

[0076] S101. Obtain task behavior data within a preset time period in the game.

[0077] Among them, task behavior data is used to characterize the behavioral data generated by players in response to multiple tasks in the game.

[0078] In some implementations, players can perform actions on in-game tasks via a terminal that can communicate with a server. The server can then obtain task behavior data from multiple players over a preset historical time period. This server can be a cloud server.

[0079] In addition, there can be multiple players, and multiple players can log in and operate on their own terminals. Each terminal can communicate with the server through the game client.

[0080] S102. Perform statistical analysis and processing based on task behavior data to identify tasks in the game that need adjustment from multiple tasks.

[0081] In some implementations, the server can perform statistical analysis on task behavior data to determine different types of tasks to be adjusted from multiple tasks.

[0082] It should be noted that different types of tasks to be adjusted can include at least one of the following: tasks that are easy to complete, tasks that are easy to replace, etc. Among them, tasks that are easy to complete can be overly simple tasks, and tasks that are easy to replace can be overly difficult tasks.

[0083] S103. Adjust the task to be adjusted based on the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within the preset time period.

[0084] Among them, game behavior data is used to characterize the behavioral data generated by players in the game that is related to the task to be adjusted. Game behavior data is the behavioral data generated by players when they are playing normally and not participating in tasks, and the game behavior data is related to the task content to be adjusted.

[0085] In some implementations, the server can adjust the task parameter values ​​in the task to be adjusted based on the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data related to the task to be adjusted within a preset time period, so as to obtain the adjusted task.

[0086] It should be noted that the task to be adjusted can be a task in a task pool, and the adjusted task can be of the same or similar difficulty level as other tasks in the task pool.

[0087] In addition, the preset time period can be one day or two days, or it can be set according to actual needs or experience. This application embodiment does not impose specific restrictions on this.

[0088] In summary, this invention provides a method for adjusting tasks in a game. The method includes: acquiring task behavior data within a preset time period in the game, whereby the task behavior data characterizes the behavioral data generated by players in response to multiple tasks in the game; performing statistical analysis on the task behavior data to determine the task to be adjusted from among the multiple tasks; and adjusting the task to be adjusted based on a preset adjustment strategy corresponding to the task to be adjusted and the game behavior data within the preset time period. The game behavior data characterizes the task-related behavioral data generated by players when performing the task to be adjusted. By performing statistical analysis on the task behavior data to determine the task to be adjusted, and by automatically adjusting the task based on the preset adjustment strategy and the game behavior data, manual adjustment is eliminated, reducing the waste of human resources and making task adjustments more objective.

[0089] Optionally, task behavior data may include: the type of task assigned to each logged-in player, the task replacement behavior, and the task completion status. Task completion status includes: whether the task was completed and the number of games played when the task was completed. For example, the record format may be "User a, Date, Task A, Completed / Replaced; User b, Date, Task B, Completed / Replaced".

[0090] For example, if the task to be adjusted can cause 1000 damage, the game behavior data related to the task to be adjusted can include: the cumulative damage caused in normal gameplay within a preset time period. This is just an example; depending on the task content to be adjusted, the game behavior data related to the task content can also include: the number of enemies killed in normal gameplay within a preset time period, the cumulative damage caused using a certain weapon, etc.

[0091] Figure 2 This is a flowchart illustrating a method for adjusting tasks in a game, as provided in an embodiment of the present invention. Figure 2 As shown, the process in S102 above, which involves statistical analysis of task behavior data to determine the tasks to be adjusted in the game from multiple tasks, includes:

[0092] S201. Obtain the number of times each task is replaced in the task behavior data.

[0093] The server can perform statistical analysis on the replacement behavior data in the task behavior data to obtain the number of times each task is replaced.

[0094] S202. Based on the number of times each task is replaced, identify the tasks that have been replaced more than the first threshold and those that have been replaced less than the second threshold as tasks to be adjusted.

[0095] The first threshold is greater than the second threshold.

[0096] In some implementations, the server determines whether the number of times each task has been replaced is greater than or equal to a first threshold. If the number of times a task has been replaced is greater than or equal to the first threshold, then the task is determined to be a task to be adjusted. The type of task to be adjusted can be an easily replaceable type.

[0097] In other implementations, the server determines whether the number of times each task is replaced is less than or equal to a second threshold. If the number of times a task is replaced is less than or equal to the second threshold, the task is determined to be a task to be adjusted. The type of task to be adjusted can be an easily completed type.

[0098] Of course, the server can also identify tasks that are easily replaced and need adjustment based on the completion behavior data in the task behavior data and the data of the player in the process of completing the task. These tasks have a replacement count of less than or equal to the second threshold and a number of execution rounds at the time of completion of the task greater than or equal to the seventh threshold. In other words, it can identify the tasks that are most often retained but have too many execution rounds at the time of completion.

[0099] In practical applications, tasks that are easily replaceable can be considered as more difficult tasks, and their difficulty should be reduced when adjusting them; tasks that are easily completed can be considered as less difficult tasks, and their difficulty should be increased when adjusting them.

[0100] Optional, Figure 3 This is a flowchart illustrating a method for adjusting tasks in a game, as provided in an embodiment of the present invention. Figure 3 As shown, the process in S102 above, which involves statistical analysis of task behavior data to determine the tasks to be adjusted in the game from multiple tasks, includes:

[0101] S301. Obtain the number of times each task is assigned and replaced from the task behavior data.

[0102] In this embodiment of the application, the server can perform statistical analysis on the task behavior data to obtain the number of times each task is assigned and the number of times it is replaced.

[0103] S302. Based on the ratio of the number of times each task is replaced to the number of times it is assigned, tasks with a ratio greater than the third threshold and a ratio less than the fourth threshold are identified as tasks to be adjusted.

[0104] Among them, the third threshold is greater than the fourth threshold.

[0105] In some implementations, the server can use the ratio of the number of times each task is replaced to the number of times it is assigned as the replacement rate of each task; the server can determine whether the replacement rate of each task is greater than or equal to a third threshold, and if so, determine that the task is a task to be adjusted, and determine that the type of the task to be adjusted is an easily replaceable type.

[0106] In addition, the server can determine whether the replacement rate of each task is less than or equal to the third threshold. If so, the task is determined to be a task to be adjusted. This task is not easy to be replaced, and the type of task to be adjusted is determined to be an easy-to-complete type.

[0107] Optional, Figure 4 This is a flowchart illustrating a method for adjusting tasks in a game, as provided in an embodiment of the present invention. Figure 4 As shown, the process in S102 above, which involves statistical analysis of task behavior data to determine the tasks to be adjusted in the game from multiple tasks, includes:

[0108] S401. Obtain the number of times each task is assigned and replaced from the task behavior data.

[0109] In this embodiment of the application, the server can perform statistical analysis on the task behavior data to obtain the number of times each task is assigned and the number of times it is replaced.

[0110] S402. Based on the ratio of the number of times each task is replaced to the number of times it is assigned, tasks with a ratio greater than the third threshold and a ratio less than the fourth threshold are identified as tasks to be adjusted.

[0111] The third threshold is greater than the fourth threshold.

[0112] In some implementations, the server can use the ratio of the number of times each task is replaced to the number of times it is assigned as the replacement rate of each task; the server can determine whether the replacement rate of each task is greater than or equal to a third threshold, and if so, determine that the task is a task to be adjusted, and determine that the type of the task to be adjusted is an easily replaceable type.

[0113] In addition, the server can determine whether the replacement rate of each task is less than or equal to the third threshold. If so, the task is determined to be a task to be adjusted. The task to be adjusted has a low replacement rate, is not easy to be replaced, and is easy to complete. The type of task to be adjusted is determined to be the type that is easy to complete.

[0114] Optional, Figure 5 This is a flowchart illustrating a method for adjusting tasks in a game, as provided in an embodiment of the present invention. Figure 5 As shown, the process in S102 above, which involves statistical analysis of task behavior data to determine the tasks to be adjusted in the game from multiple tasks, includes:

[0115] S501. Obtain the number of times each task was completed and assigned from the task behavior data.

[0116] In this embodiment of the application, the server can perform statistical analysis on the task behavior data to obtain the number of times each task is completed and the number of times it is assigned.

[0117] S502. Based on the ratio of the number of times each task is completed to the number of times it is assigned, tasks with a ratio greater than the fifth threshold and tasks with a ratio less than the sixth threshold are identified as tasks to be adjusted.

[0118] The third threshold is greater than the fourth threshold.

[0119] In some implementations, the server can use the ratio of the number of times each task is completed to the number of times it is assigned as the completion rate of each task; the server can determine whether the completion rate of each task is greater than or equal to a fifth threshold, and if so, determine that the task is a task to be adjusted, and determine that the type of the task to be adjusted is an easy-to-complete type.

[0120] In addition, the server can determine whether the completion rate of each task is less than or equal to the sixth threshold. If so, the task is determined to be a task to be adjusted. The task to be adjusted has a low completion rate, is not easy to complete, and is easy to replace. The type of task to be adjusted is determined to be the type that is easy to complete.

[0121] In this embodiment of the application, the server can count the completion rate of all tasks and calculate a preset number of quantiles; determine a preset range based on the preset number of quantiles, and determine a fifth threshold and a third threshold based on the preset range.

[0122] For example, the preset number of quantiles can be three quantiles: the 25th quantile, the 50th quantile, and the 75th quantile, denoted as Q1, Q2, and Q3 respectively. Then the interquartile range can be Q3 - Q1, which is the difference between the upper and lower quartiles. The preset range can be Q3 + 1.5IQR to Q1 - 1.5IQR. Here, Q3 + 1.5IQR can be used as the fifth and third thresholds; Q1 - 1.5IQR can be used as the fourth and sixth thresholds, and IQR is the interquartile range.

[0123] Optional, Figure 6 This is a flowchart illustrating a method for adjusting tasks in a game, as provided in an embodiment of the present invention. Figure 6 As shown, the process of adjusting the task to be adjusted in S103 above, based on the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within a preset time period, may include:

[0124] S601. Determine the task parameters corresponding to the task to be adjusted.

[0125] Among them, the task parameters corresponding to the task to be adjusted can represent the task content of the task to be adjusted.

[0126] For example, the task parameters for the task to be adjusted could be "deal 500 damage", "kill 10 enemies", "attack enemies 4 times with ranged weapons", etc.

[0127] S602. Obtain target behavior data that matches the task parameters from the game behavior data.

[0128] In some implementations, the server can perform statistical analysis on the game behavior data of multiple players within a preset time period to obtain statistically analyzed game behavior data. From this data, target behavior data matching the task parameters can be extracted. During the statistical analysis process, the cumulative value for each player's game behavior data for each type can be calculated.

[0129] For example, within a preset time period, the statistically analyzed game behavior data may include: Player A dealing a total of 200 damage, Player B dealing a total of 1000 damage, and Player C dealing a total of 800 damage; Player A killing a total of 50 enemies, Player B killing a total of 150 enemies, and Player C killing a total of 80 enemies. When the task parameter for the task to be adjusted is dealing 500 damage, the target behavior data matching the task parameter will be obtained from the above-described statistically analyzed game behavior data, including: Player A dealing a total of 200 damage, Player B dealing a total of 1000 damage, and Player C dealing a total of 800 damage.

[0130] S603. Based on the target behavior data, adjust the task parameter values ​​in the task to be adjusted.

[0131] In this embodiment of the application, the server can process the target behavior data, obtain the processing result, and adjust the task parameter values ​​in the task to be adjusted according to the processing result.

[0132] For example, the target behavior data includes: Player A has dealt a total of 200 damage, Player B has dealt a total of 1000 damage, and Player C has dealt a total of 800 damage; based on this target behavior data, the task parameter value (dealing 500 damage) in the task to be adjusted is increased or decreased.

[0133] Optional, Figure 7 This is a flowchart illustrating a method for adjusting tasks in a game, as provided in an embodiment of the present invention. Figure 7 As shown, the step in S603 above, which adjusts the task parameter values ​​in the task to be adjusted based on the target behavior data, includes:

[0134] S701, Obtain the task expectation for the task to be adjusted.

[0135] The task expectation includes at least a first ratio representing the number of times the task to be adjusted was completed and the number of times it was assigned.

[0136] In the embodiments of this application, the first ratio of the number of times the task to be adjusted was completed to the number of times it was assigned can characterize the expected completion rate for the task to be adjusted after the adjustment.

[0137] It should be noted that the first ratio can be set according to actual needs, or the above-mentioned Q1-1.5IQR can be used as the first ratio, or it can be determined in other ways. This application embodiment does not impose specific restrictions on this.

[0138] S702. Based on the first ratio and the target behavior data, adjust the task parameter values ​​in the task to be adjusted.

[0139] In this embodiment of the application, based on the first ratio and the target behavior data, the task parameter values ​​in the task to be adjusted are adjusted so that the adjusted task can achieve the task expectation of the task to be adjusted.

[0140] Optional, Figure 8 This is a flowchart illustrating a method for adjusting tasks in a game, as provided in an embodiment of the present invention. Figure 8 As shown, the process of adjusting the task parameter values ​​in the task to be adjusted based on the first ratio and the target behavior data in S702 above may include:

[0141] S801. Sort the target behavior data to generate the first sequence data.

[0142] It should be noted that the server can sort the target behavior data in ascending order to generate the first sequence of data; or it can sort the target behavior data in descending order to generate the first sequence of data; this application embodiment does not impose specific limitations on this.

[0143] S802. Based on the first ratio, select a target behavior data from the first sequence data as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

[0144] Optionally, in S802 above, based on the first ratio, a target behavior data is selected from the first sequence data as the task parameter value corresponding to the task to be adjusted, in order to adjust the task parameter value in the task to be adjusted, including:

[0145] Obtain the average value of the target behavior data, and use the product of the average value and the first ratio as the task parameter value in the task to be adjusted.

[0146] This allows for adjusting or decreasing the task parameter values ​​in the task to be adjusted, either to reduce or increase the task difficulty.

[0147] Optional, Figure 9 This is a flowchart illustrating a method for adjusting tasks in a game, as provided in an embodiment of the present invention. Figure 9 As shown, the process in S802 above, which selects a target behavior data from the first sequence data as the task parameter value corresponding to the task to be adjusted based on the first ratio, and adjusts the task parameter value in the task to be adjusted, may include:

[0148] S901. Determine the target behavior data in the first sequence data that corresponds to the first ratio.

[0149] In some implementations, if there is target behavior data at a first ratio among the N target behavior data of the first sequence data, the target behavior data at the first ratio can be the Mth target behavior data, that is, the first ratio is M / N, then the Mth target behavior data is the target behavior data corresponding to the first ratio.

[0150] In other implementations, if there is no target behavior data at the first ratio among the N target behavior data of the first sequence data, then the two target behavior data adjacent to the first ratio are determined, and either of the two target behavior data is taken as the target behavior data corresponding to the first ratio; or, the average value of the two target behavior data is taken as the target behavior data corresponding to the first ratio.

[0151] S902. Take the target behavior data at the first ratio as the task parameter value corresponding to the task to be adjusted.

[0152] The server can use the parameter value of the target behavior data at the first ratio as the task parameter value corresponding to the task to be adjusted.

[0153] It should be noted that for both easily completed and easily replaceable tasks to be adjusted, the processes described in S901 and S902 above can be used to adjust the tasks so that the completion rate of the adjusted tasks reaches the desired completion rate.

[0154] In this embodiment of the application, for the type of task to be adjusted that is easily replaced, the parameter value of the target behavior data at the first ratio is used as the task parameter value corresponding to the task to be adjusted, which can reduce the difficulty of the task to be adjusted; for the type of task to be adjusted that is easily completed, the parameter value of the target behavior data at the first ratio is used as the task parameter value corresponding to the task to be adjusted, which can increase the difficulty of the task to be adjusted.

[0155] For example, if the task to be adjusted is identified as "causing 10,000 damage" as an easily replaceable type, the first sequence of data could include: Player A has caused a cumulative total of 200 damage, Player B has caused a cumulative total of 500 damage, Player C has caused a cumulative total of 600 damage, Player D has caused a cumulative total of 650 damage, Player E has caused a cumulative total of 700 damage, Player F has caused a cumulative total of 720 damage, Player G has caused a cumulative total of 800 damage, Player H has caused a cumulative total of 820 damage, and Player I has caused a cumulative total of 1,000 damage. If the first ratio is 1 / 3, then the target behavior data at the first ratio is Player C causing a cumulative total of 600 damage. Therefore, 600 damage is used as the task parameter value corresponding to the task to be adjusted, and the adjusted task is "causing 600 damage," thus reducing the difficulty of the task to be adjusted.

[0156] For example, if "kill 5 enemies" is identified as an easily completed task to be adjusted, the first sequence of data may include: Player A has killed a total of 16 enemies, Player B has killed a total of 15 enemies, Player C has killed a total of 14 enemies, Player D has killed a total of 12 enemies, Player E has killed a total of 12 enemies, Player F has killed a total of 11 enemies, Player G has killed a total of 10 enemies, Player H has killed a total of 7 enemies, Player I has killed a total of 4 enemies, and Player J has killed a total of 3 enemies. If the first ratio is 1 / 3, then the two target behavior data adjacent to the first ratio include player G killing 14 enemies and player D killing 12 enemies. In this case, killing 14 enemies or killing 12 enemies can be used as the task parameter value corresponding to the task to be adjusted. The adjusted task is "kill 14 enemies", which increases the difficulty of the task to be adjusted. Alternatively, the average of player G killing 14 enemies and player D killing 12 enemies can be calculated, and killing 13 enemies can be used as the task parameter value corresponding to the task to be adjusted.

[0157] Of course, depending on the task to be adjusted, the server may reduce the damage value caused by a certain weapon required in the task, or reduce the distance the virtual character is required to move in the task, or reduce the number of enemies killed by a single skill in the task. This application embodiment does not impose specific limitations on this.

[0158] Optionally, the adjustment of the task to be adjusted in step S103, based on the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within a preset time period, may further include:

[0159] Determine the average number of games a player plays to complete the task to be adjusted based on game behavior data; determine the preset parameters based on the average number of games played.

[0160] In some implementations, the server can determine the distribution data of the average number of games required to complete different game objectives based on game behavior data; determine the average number of games required to complete the task to be adjusted based on the distribution data; and adjust the task to be adjusted so that the number of games required for players to complete the adjusted task can be controlled within the average number of games, so that players can complete the task within the normal number of games per day, without completing it too quickly or requiring players to play too many games to complete the task.

[0161] For example, if a task to be adjusted is "to cause X damage per game", then the distribution of the damage caused by the player in each game can be calculated based on the game behavior data. Using this probability distribution, the average number of games a player needs to play to complete the task can be calculated for different damage numbers.

[0162] In this embodiment, the sample characteristics of multiple players, the sample task behavior data within a preset time period, and the effective active duration of the samples within a preset time period can be used to train a model according to a preset machine learning algorithm to obtain a preset recognition model.

[0163] In this embodiment, the sample characteristics of multiple players may include at least one of the following: character level, ranking tier, frequently used heroes (to facilitate the construction of a continuous function, this feature can be expanded into N features such as "Hero A usage rate", "Hero B usage rate", etc., where N is the number of selectable heroes in the game), recharge amount, game duration, average daily active time, etc. Different characteristic information can be set for different games.

[0164] The sample task behavior data mentioned above may include: the type of task assigned to each logged-in player, task replacement behavior, and task completion status. Task completion status includes: whether the task was completed, and the number of games played to complete the task.

[0165] Specifically, character level and ranking tier are the latest values ​​at the time of feature recording; recharge amount and game time are the cumulative values ​​at the time of feature recording; frequently used heroes and average daily active time are the "average over the past X days" at the time of feature recording, where X can be determined according to the business scenario. For example, X can be 7 to fully cover an active cycle (player behavior shows a high degree of periodicity on a weekly basis). Data is recorded once for each active time period of each player.

[0166] Additionally, the effective active time within the preset period is calculated as the player's actual active time in the game, i.e., the player's online time minus inactive time (such as AFK or temporary absence). For example, it can be calculated based on a player's total in-game time for a given day. Data is recorded once for each player's active day.

[0167] In some implementations, during training, for each player among multiple players, if the player's active duration within a first preset time period is greater than or equal to the average active duration, then the tasks completed by the player within the first preset time period are designated as positive guidance tasks to be adjusted; if the player's active duration within a second preset time period is less than the average active duration, then the tasks replaced by the player within the second preset time period are designated as negative guidance tasks to be adjusted. Optionally, the first preset time period and the second preset time period can be daily, weekly, or monthly.

[0168] During training, each player's preset time period can constitute labeled data: the player's own characteristics, the task to be adjusted, and the type of positive or negative guidance. Each player's preset time period may generate multiple labeled data points (e.g., the player replaced one task or completed three tasks), or it may generate no labeled data. A large amount of labeled data can be collected over a period of time. The data format can be "a series of features representing the player type, task name, and label (positive guidance / negative guidance)". A large amount of labeled data can be used as the training dataset.

[0169] In practical applications, the server can identify the task to be adjusted for the target player based on the target player's own characteristic information using a preset recognition model, and determine the type of the task to be adjusted as a guidance type.

[0170] It should be noted that the target player can be any player who needs task guidance. The input to the preset recognition model can include: the target player's own characteristic information. The output of the preset recognition model can be the guidance type for the target player regarding the task to be adjusted, where the guidance type can be positive or negative.

[0171] The target player's own characteristic information may include at least one of the following: character level, ranking rank, frequently used heroes (to facilitate the construction of a continuous function, this feature can be expanded into N features such as "Hero A usage rate", "Hero B usage rate", etc., where N is the number of available heroes in the game), recharge amount, game time, average daily active time, etc. Different characteristic information can be set for different games.

[0172] In some implementations, the target player's own characteristic information is used as input to a preset recognition model. The preset recognition model can then output the task to be adjusted for the target player, as well as the type of the task to be adjusted, namely, the task to be adjusted for the target player with positive guidance and / or the task to be adjusted with negative guidance.

[0173] Optionally, when adjusting tasks of the positive guidance type and tasks of the negative guidance type, for the target player, the probability of the positive guidance type tasks appearing can be reduced, and the probability of the negative guidance type tasks appearing can be increased.

[0174] In some implementations, the weight of tasks of the positive guidance type can be increased by a preset multiple, and the weight of tasks of the positive guidance type can be decreased by a preset multiple. For example, assuming there are four tasks in total, with equal weights, the probability of each task appearing for the target player is 25%. If the weight ratio is 2:1:1:1, then the probability of each task appearing for the target player is 40%, 20%, 20%, and 20%, respectively.

[0175] Optionally, based on the target player's game behavior data and task behavior data, identify tasks with different game behavior data and task behavior data as the target player's tasks to be adjusted, and determine the type of the tasks to be adjusted as a guidance type.

[0176] The target players can be those who need quest guidance. The guidance type can be positive or negative.

[0177] In some implementations, the server can determine the target player's in-game behavior habits based on the target player's game behavior data, and classify tasks that are different from the target player's in-game behavior habits as tasks to be adjusted for negative guidance, and tasks that are the same as the target player's in-game behavior habits as tasks to be adjusted for positive guidance.

[0178] It should be noted that the task to be adjusted is determined individually for each player, and different players may have different tasks to be adjusted.

[0179] For example, a task could be to deal damage using a certain long-range virtual weapon. For player A, who is used to using long-range weapons frequently, and player B, who almost never uses long-range weapons, this task could be used as a negative guidance type of adjustment task for player A, increasing the probability of negative guidance type adjustment tasks appearing, in order to guide player A to complete the task of using the long-range virtual weapon.

[0180] Optionally, based on the task to be adjusted of the type of guidance and the target player's game behavior data, adjust the probability of the task appearing in the target player's game.

[0181] Specifically, by analyzing the target players' game behavior data, their in-game habits can be identified. For these target players, the probability of positively guided adjustment tasks appearing can be reduced, while the probability of negatively guided adjustment tasks appearing can be increased.

[0182] In some implementations, when the task to be adjusted (guided type) differs from the target player's in-game behavior (i.e., a task to be adjusted with negative guidance), the probability of the task appearing for the target player can be increased; when the task to be adjusted (guided type) is the same as the target player's in-game behavior (i.e., a task to be adjusted with positive guidance), the probability of the task appearing for the target player can be maintained or decreased.

[0183] For example, for player A who frequently uses ranged weapons, the probability of encountering missions that involve dealing damage using a mid-to-long-range virtual weapon can be increased. After this adjustment, if players' willingness to perform various missions is relatively even, then for a player who prefers to use only melee virtual weapons, the probability of them being randomly assigned a ranged virtual weapon mission can be appropriately increased.

[0184] It should be noted that the server can directly adjust the task to be adjusted, or it can first present the result to be adjusted through a data monitoring dashboard for the game designer to decide whether to apply it; this application embodiment does not impose specific limitations on this.

[0185] In the application embodiment, the server can automatically, procedurally, and in a standardized manner adjust tasks to maximize player participation and incentives (participation refers to the task completion rate; incentives refer to guiding players who are accustomed to a certain fixed gameplay to try other gameplay through tasks, thereby increasing the game's lifespan).

[0186] In summary, the embodiments of the present invention provide a method for adjusting tasks in a game. The method determines the task to be adjusted by statistical analysis of task behavior data, and automatically adjusts the task based on the preset adjustment strategy corresponding to the task and the game behavior data, without the need for manual adjustment, thereby reducing the waste of human resources and making the adjustment of tasks more objective.

[0187] Furthermore, optimizing task participation and incentives based on real data improves game lifespan and player experience. Daily tasks can be analyzed and optimized based on data, avoiding the subjectivity of human judgment. An automated, streamlined, and standardized analysis system has been built, avoiding the need for continuous human investment in optimizing previous manual adjustments with game version updates. The obtained model training results can provide guidance for the design of daily tasks in future games of this type.

[0188] The following describes the game task adjustment device, terminal equipment, and storage medium used to implement the game task adjustment method provided in this application. For the specific implementation process and technical effects, please refer to the relevant content of the game task adjustment method mentioned above, which will not be repeated below.

[0189] Figure 10 This is a schematic diagram of a task adjustment device in a game provided by an embodiment of the present invention, as shown below. Figure 10 As shown, the device may include:

[0190] The acquisition module 1001 is used to acquire task behavior data within a preset time period in the game. The task behavior data is used to characterize the behavior data generated by the player in response to multiple tasks in the game.

[0191] The determination module 1002 is used to perform statistical analysis and processing based on the task behavior data to determine the task to be adjusted in the game from the multiple tasks.

[0192] The adjustment module 1003 is used to adjust the task to be adjusted according to the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within the preset time period. The game behavior data is used to characterize the behavior data related to the task to be adjusted generated by the player when not participating in the task in the game.

[0193] Optionally, the determining module 1002 is specifically used to obtain the number of times each task is replaced in the task behavior data; based on the number of times each task is replaced, determine the tasks among the plurality of tasks whose number of replacements is greater than a first threshold and whose number of replacements is less than a second threshold as the tasks to be adjusted; wherein, the first threshold is greater than the second threshold.

[0194] Optionally, the determining module 1002 is specifically used to obtain the number of times each task is assigned and the number of times it is replaced in the task behavior data; based on the ratio of the number of times each task is replaced to the number of times it is assigned, determine from the plurality of tasks the tasks whose ratio is greater than a third threshold and whose ratio is less than a fourth threshold as the tasks to be adjusted.

[0195] Optionally, the determining module 1002 is specifically used to obtain the number of times each task is completed and the number of times it is assigned in the task behavior data; based on the ratio of the number of times each task is completed to the number of times it is assigned, determine from the plurality of tasks the tasks whose ratio is greater than a fifth threshold and whose ratio is less than a sixth threshold as the tasks to be adjusted.

[0196] Optionally, the adjustment module 1002 is specifically used to determine the task parameters corresponding to the task to be adjusted; obtain target behavior data that matches the task parameters from the game behavior data; and adjust the task parameter values ​​in the task to be adjusted based on the target behavior data.

[0197] Optionally, the adjustment module 1002 is specifically used to obtain the task expectation for the task to be adjusted, the task expectation including at least a first ratio representing the number of times the task to be adjusted is completed and the number of times it is assigned after adjustment; and to adjust the task parameter values ​​in the task to be adjusted based on the first ratio and the target behavior data.

[0198] Optionally, the adjustment module 1002 is specifically used to sort the target behavior data to generate a first sequence data; based on the first ratio, select a target behavior data from the first sequence data as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

[0199] Optionally, the adjustment module 1002 is specifically used to determine the target behavior data corresponding to the first ratio in the first sequence data; and to use the target behavior data corresponding to the first ratio as the task parameter value corresponding to the task to be adjusted.

[0200] The adjustment module 1002 is specifically used to obtain the average value of the target behavior data; and to use the product of the average value and the first ratio as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

[0201] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0202] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0203] Figure 11 This is a schematic diagram of the structure of a terminal device provided in an embodiment of the present invention, such as... Figure 11 As shown, the terminal device includes: processor 1101 and memory 1102.

[0204] The memory 1102 is used to store programs, and the processor 1101 calls the programs stored in the memory 1102 to execute the above method embodiments.

[0205] For example, the method may include:

[0206] Acquire task behavior data within a preset time period in the game, wherein the task behavior data is used to characterize the player's behavior data in response to multiple tasks in the game;

[0207] Based on the statistical analysis of the task behavior data, the tasks to be adjusted in the game are determined from the multiple tasks.

[0208] The task to be adjusted is adjusted according to the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within the preset time period. The game behavior data is used to characterize the behavior data related to the task to be adjusted generated by the player when not participating in the task in the game.

[0209] Optionally, the step of performing statistical analysis on the task behavior data to determine the tasks to be adjusted in the game from the multiple tasks includes:

[0210] Obtain the number of times each task is replaced in the task behavior data;

[0211] Based on the number of times each task is replaced, tasks whose number of replacements is greater than a first threshold and whose number of replacements is less than a second threshold are identified as the tasks to be adjusted.

[0212] Wherein, the first threshold is greater than the second threshold.

[0213] Optionally, the step of performing statistical analysis on the task behavior data to determine the tasks to be adjusted in the game from the multiple tasks includes:

[0214] Obtain the number of times each task was assigned and replaced from the task behavior data;

[0215] Based on the ratio of the number of times each task is replaced to the number of times it is assigned, tasks whose ratio is greater than a third threshold and whose ratio is less than a fourth threshold are identified as the tasks to be adjusted.

[0216] Optionally, the step of performing statistical analysis on the task behavior data to determine the tasks to be adjusted in the game from the multiple tasks includes:

[0217] Obtain the number of times each task was completed and assigned from the task behavior data;

[0218] Based on the ratio of the number of times each task is completed to the number of times it is assigned, tasks with a ratio greater than a fifth threshold and a ratio less than a sixth threshold are identified from the plurality of tasks as the tasks to be adjusted.

[0219] Optionally, adjusting the task to be adjusted based on the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within a preset time period includes:

[0220] Determine the task parameters corresponding to the task to be adjusted;

[0221] Obtain target behavior data that matches the task parameters from the game behavior data;

[0222] Based on the target behavior data, the task parameter values ​​in the task to be adjusted are adjusted.

[0223] Optionally, the step of adjusting the task parameter values ​​in the task to be adjusted based on the target behavior data includes:

[0224] Obtain the task expectation for the task to be adjusted, wherein the task expectation includes at least a first ratio representing the number of times the task to be adjusted was completed and the number of times it was assigned after the adjustment.

[0225] Based on the first ratio and the target behavior data, the task parameter values ​​in the task to be adjusted are adjusted.

[0226] Optionally, adjusting the task parameter values ​​in the task to be adjusted based on the first ratio and the target task data includes:

[0227] The target behavior data is sorted to generate a first sequence of data;

[0228] Based on the first ratio, a target behavior data is selected from the first sequence data as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

[0229] Optionally, selecting a target behavior data from the first sequence data as the task parameter value corresponding to the task to be adjusted based on the first ratio includes:

[0230] Determine the target behavior data in the first sequence data that corresponds to the first ratio;

[0231] The target behavior data is used as the task parameter value corresponding to the task to be adjusted.

[0232] Optionally, adjusting the task parameter values ​​in the task to be adjusted based on the first ratio and the target task data includes:

[0233] Obtain the average value of the target behavior data;

[0234] The product of the average value and the first ratio is used as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

[0235] In summary, by statistically analyzing task behavior data to determine the tasks to be adjusted, and then automatically adjusting the tasks based on the preset adjustment strategies and game behavior data corresponding to the tasks to be adjusted, the system eliminates the need for manual adjustments, reduces the waste of human resources, and makes the adjustments to the tasks more objective.

[0236] Optionally, the present invention also provides a program product, such as a computer-readable storage medium, including a program that, when executed by a processor, is used to perform the above-described method embodiments.

[0237] For example, the method may include:

[0238] Acquire task behavior data within a preset time period in the game, wherein the task behavior data is used to characterize the player's behavior data in response to multiple tasks in the game;

[0239] Based on the statistical analysis of the task behavior data, the tasks to be adjusted in the game are determined from the multiple tasks.

[0240] The task to be adjusted is adjusted according to the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within the preset time period. The game behavior data is used to characterize the behavior data related to the task to be adjusted generated by the player when not participating in the task in the game.

[0241] Optionally, the step of performing statistical analysis on the task behavior data to determine the tasks to be adjusted in the game from the multiple tasks includes:

[0242] Obtain the number of times each task is replaced in the task behavior data;

[0243] Based on the number of times each task is replaced, tasks whose number of replacements is greater than a first threshold and whose number of replacements is less than a second threshold are identified as the tasks to be adjusted.

[0244] Wherein, the first threshold is greater than the second threshold.

[0245] Optionally, the step of performing statistical analysis on the task behavior data to determine the tasks to be adjusted in the game from the multiple tasks includes:

[0246] Obtain the number of times each task was assigned and replaced from the task behavior data;

[0247] Based on the ratio of the number of times each task is replaced to the number of times it is assigned, tasks whose ratio is greater than a third threshold and whose ratio is less than a fourth threshold are identified as the tasks to be adjusted.

[0248] Optionally, the step of performing statistical analysis on the task behavior data to determine the tasks to be adjusted in the game from the multiple tasks includes:

[0249] Obtain the number of times each task was completed and assigned from the task behavior data;

[0250] Based on the ratio of the number of times each task is completed to the number of times it is assigned, tasks with a ratio greater than a fifth threshold and a ratio less than a sixth threshold are identified from the plurality of tasks as the tasks to be adjusted.

[0251] Optionally, adjusting the task to be adjusted based on the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within a preset time period includes:

[0252] Determine the task parameters corresponding to the task to be adjusted;

[0253] Obtain target behavior data that matches the task parameters from the game behavior data;

[0254] Based on the target behavior data, the task parameter values ​​in the task to be adjusted are adjusted.

[0255] Optionally, the step of adjusting the task parameter values ​​in the task to be adjusted based on the target behavior data includes:

[0256] Obtain the task expectation for the task to be adjusted, wherein the task expectation includes at least a first ratio representing the number of times the task to be adjusted was completed and the number of times it was assigned after the adjustment.

[0257] Based on the first ratio and the target behavior data, the task parameter values ​​in the task to be adjusted are adjusted.

[0258] Optionally, adjusting the task parameter values ​​in the task to be adjusted based on the first ratio and the target task data includes:

[0259] The target behavior data is sorted to generate a first sequence of data;

[0260] Based on the first ratio, a target behavior data is selected from the first sequence data as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

[0261] Optionally, selecting a target behavior data from the first sequence data as the task parameter value corresponding to the task to be adjusted based on the first ratio includes:

[0262] Determine the target behavior data in the first sequence data that corresponds to the first ratio;

[0263] The target behavior data is used as the task parameter value corresponding to the task to be adjusted.

[0264] Optionally, adjusting the task parameter values ​​in the task to be adjusted based on the first ratio and the target task data includes:

[0265] Obtain the average value of the target behavior data;

[0266] The product of the average value and the first ratio is used as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

[0267] In summary, by statistically analyzing task behavior data to determine the tasks to be adjusted, and then automatically adjusting the tasks based on the preset adjustment strategies and game behavior data corresponding to the tasks to be adjusted, the system eliminates the need for manual adjustments, reduces the waste of human resources, and makes the adjustments to the tasks more objective.

[0268] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0269] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0270] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0271] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0272] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for adjusting tasks in a game, characterized in that, include: Acquire task behavior data within a preset time period in the game, wherein the task behavior data is used to characterize the player's behavior data in response to multiple tasks in the game; Based on the statistical analysis of the task behavior data, the tasks to be adjusted in the game are determined from the multiple tasks. The task to be adjusted is adjusted according to the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within the preset time period. The game behavior data is used to characterize the behavior data related to the task to be adjusted generated by the player when not participating in the task in the game. The step of adjusting the task to be adjusted according to the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within a preset time period includes: Determine the task parameters corresponding to the task to be adjusted; Obtain target behavior data that matches the task parameters from the game behavior data; Obtain the task expectation for the task to be adjusted, wherein the task expectation includes at least a first ratio representing the number of times the task to be adjusted was completed and the number of times it was assigned after the adjustment. Based on the first ratio and the target behavior data, the task parameter values ​​in the task to be adjusted are adjusted.

2. The method according to claim 1, characterized in that, The step of performing statistical analysis and processing based on the task behavior data to determine the tasks in the game that need adjustment from the multiple tasks includes: Obtain the number of times each task is replaced in the task behavior data; Based on the number of times each task is replaced, tasks whose number of replacements is greater than a first threshold and whose number of replacements is less than a second threshold are identified as the tasks to be adjusted. Wherein, the first threshold is greater than the second threshold.

3. The method according to claim 1, characterized in that, The step of performing statistical analysis and processing based on the task behavior data to determine the tasks in the game that need adjustment from the multiple tasks includes: Obtain the number of times each task was assigned and replaced from the task behavior data; Based on the ratio of the number of times each task is replaced to the number of times it is assigned, tasks whose ratio is greater than a third threshold and whose ratio is less than a fourth threshold are identified as the tasks to be adjusted.

4. The method according to claim 1, characterized in that, The step of performing statistical analysis and processing based on the task behavior data to determine the tasks in the game that need adjustment from the multiple tasks includes: Obtain the number of times each task was completed and assigned from the task behavior data; Based on the ratio of the number of times each task is completed to the number of times it is assigned, tasks with a ratio greater than a fifth threshold and a ratio less than a sixth threshold are identified from the plurality of tasks as the tasks to be adjusted.

5. The method according to claim 1, characterized in that, The step of adjusting the task parameter values ​​in the task to be adjusted based on the first ratio and the target behavior data includes: The target behavior data is sorted to generate a first sequence of data; Based on the first ratio, a target behavior data is selected from the first sequence data as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

6. The method according to claim 5, characterized in that, The step of selecting a target behavior data from the first sequence data as the task parameter value corresponding to the task to be adjusted based on the first ratio includes: Determine the target behavior data in the first sequence data that corresponds to the first ratio; The target behavior data is used as the task parameter value corresponding to the task to be adjusted.

7. The method according to claim 1, characterized in that, The step of adjusting the task parameter values ​​in the task to be adjusted based on the first ratio and the target behavior data includes: Obtain the average value of the target behavior data; The product of the average value and the first ratio is used as the task parameter value corresponding to the task to be adjusted, so as to adjust the task parameter value in the task to be adjusted.

8. A task adjustment device in a game, characterized in that, include: The acquisition module is used to acquire task behavior data within a preset time period in the game. The task behavior data is used to characterize the behavior data generated by the player in response to multiple tasks in the game. The determination module is used to perform statistical analysis and processing based on the task behavior data to determine the task to be adjusted in the game from the multiple tasks. The adjustment module is used to adjust the task to be adjusted according to the preset adjustment strategy corresponding to the task to be adjusted and the game behavior data in the game within the preset time period. The game behavior data is used to characterize the behavior data related to the task to be adjusted generated by the player when not participating in the task in the game. The adjustment module is specifically used to determine the task parameters corresponding to the task to be adjusted; obtain target behavior data that matches the task parameters from the game behavior data; and obtain the task expectation for the task to be adjusted, wherein the task expectation includes at least a first ratio representing the number of times the task to be adjusted has been completed and the number of times it has been assigned after the adjustment. Based on the first ratio and the target behavior data, the task parameter values ​​in the task to be adjusted are adjusted.

9. A server, characterized in that, include: A memory and a processor, wherein the memory stores a computer program executable by the processor, and the processor executes the computer program to implement the game task adjustment method according to any one of claims 1-7.

10. A storage medium, characterized in that, The storage medium stores a computer program, which, when read and executed, implements the game task adjustment method according to any one of claims 1-7.

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