Game task dynamic adjustment method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a game task dynamic adjustment method and device, electronic equipment and a storage medium, and relates to the technical field of game development. The method comprises the following steps: acquiring game behavior data and historical task performance data of a current player; generating a target game task meeting the task preference of the current player based on the game behavior data and a preset task structure template library; and based on the game behavior data and the historical task performance data, in combination with a pre-training task difficulty prediction model, difficulty parameter adjustment is performed on a target game task, so that the difficulty of the target game task is matched with the current player, and personalized generation of task contents and dynamic adjustment of the difficulty are realized. The individuation degree of the task is effectively improved, the task difficulty is accurately matched with the player ability, and negative experience caused by too simple or difficult tasks is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of game development, in particular to a game task dynamic adjustment method and device, electronic equipment and storage medium. BACKGROUND

[0002] In network games, especially open-world games, the task system is one of the core mechanisms to drive players to explore, improve activity and enhance immersion.

[0003] Traditional task systems mostly use preset tasks or tasks generated based on fixed rules, and the task content, type and difficulty are determined at the design stage, lacking the ability to dynamically adjust according to individual differences of players. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a game task dynamic adjustment method and device, electronic equipment and storage medium.

[0005] In order to achieve the above purpose, the technical solutions adopted by the embodiments of the present application are as follows: In a first aspect, the present application provides a game task dynamic adjustment method, which comprises: obtaining game behavior data and historical task performance data of a current player; generating a target game task meeting the task preference of the current player based on the game behavior data and a preset task structure template library; adjusting the difficulty parameters of the target game task based on the game behavior data and the historical task performance data, in combination with a pre-trained task difficulty prediction model, so that the difficulty of the target game task is adapted to the current player.

[0006] Optionally, the preset task structure template library comprises a plurality of task structure templates corresponding to different task types, and the step of generating the target game task of the current player based on the game behavior data and the preset task structure template library comprises: determining the adaptive task type of the current player according to the task preference of the current player; generating a target game task meeting the task preference of the current player based on the task structure template corresponding to the adaptive task type.

[0007] Optionally, the step of adjusting the difficulty parameters of the target game task based on the game behavior data and the historical task performance data, in combination with a pre-trained task difficulty prediction model, comprises: obtaining, by using the pre-training task difficulty prediction model, a predicted completion time of the target game task based on content of the target game task, the game behavior data, and the historical task performance data; determining, according to the predicted completion time, whether the difficulty of the target game task is suitable for the current player; in a case where it is determined that the difficulty of the target game task is not suitable for the current player, performing difficulty parameter adjustment on the target game task.

[0008] Optionally, the step of determining, according to the predicted completion time, whether the difficulty of the target game task is suitable for the current player comprises: if the predicted completion time is within a preset time range, it is determined that the difficulty of the target game task is suitable for the current player; if the predicted completion time is not within the preset time range, it is determined that the difficulty of the target game task is not suitable for the current player.

[0009] Optionally, the case where the difficulty of the target game task is not suitable for the current player comprises a first case and a second case, the first case being that the difficulty of the target game task is higher than the achievable task difficulty of the current player, and the second case being that the difficulty of the target game task is lower than the achievable task difficulty of the current player; the step of performing difficulty parameter adjustment on the target game task comprises: if the case where the difficulty of the target game task is not suitable for the current player is the first case, performing first preset adjustment on a task difficulty parameter of the target game task to reduce the difficulty of the target game task to the achievable task difficulty of the current player; if the case where the difficulty of the target game task is not suitable for the current player is the second case, performing second preset adjustment on the task difficulty parameter of the target game task to increase the difficulty of the target game task to the achievable task difficulty of the current player.

[0010] Optionally, the method further comprises: according to a background setting of the target game task, rendering a game environment suitable for content of the target game task when the current player performs the target game task.

[0011] Optionally, the game behavior data comprises position information, and the method further comprises: determining, according to the position information and an operation habit of the current player, a setting area of the target game task in a game space.

[0012] In a second aspect, the present application provides a game task dynamic adjustment device, and the method comprises: an acquisition module, configured to acquire game behavior data of a current player and historical task performance data; a generation module, configured to generate a target game task meeting a task preference of the current player based on the game behavior data and a preset task structure template library; an adjustment module, configured to adjust a difficulty parameter of the target game task based on the game behavior data and the historical task performance data in combination with a pre-trained task difficulty prediction model, so that the difficulty of the target game task is adapted to the current player.

[0013] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores machine executable instructions capable of being executed by the processor, and the processor can execute the machine executable instructions to implement the game task dynamic adjustment method of the first aspect.

[0014] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the game task dynamic adjustment method of the first aspect.

[0015] The game task dynamic adjustment method, device, electronic device and storage medium provided by the embodiments of the present application can acquire game behavior data of a current player and historical task performance data, generate a target game task meeting a task preference of the current player based on the game behavior data and a preset task structure template library, and adjust a difficulty parameter of the target game task based on the game behavior data and the historical task performance data in combination with a pre-trained task difficulty prediction model, so that the difficulty of the target game task is adapted to the current player. Since the embodiments of the present application realize the personalized generation of task content and the dynamic adjustment of difficulty by combining player behavior analysis, a task structure template library and a task difficulty prediction model, the personalized degree of the task is effectively improved, and the task difficulty is accurately matched with the player ability, thereby avoiding negative experience caused by too simple or difficult tasks.

[0016] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are described in detail below, and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0018] Figure 1 A structural schematic block diagram of an electronic device provided by an embodiment of the present application is shown; Figure 2 A flowchart of a game task dynamic adjustment method provided by an embodiment of the present application is shown Figure One ; Figure 3 A flowchart of a game task dynamic adjustment method provided by an embodiment of the present application is shown Figure Two ; Figure 4 A flowchart of a game task dynamic adjustment method provided by an embodiment of the present application is shown Figure Three ; Figure 5 A functional module block diagram of a game task dynamic adjustment device provided by an embodiment of the present application is shown.

[0019] Icon: 100-electronic device; 110-memory; 120-processor; 130-communication module; 200-game task dynamic adjustment device; 201-obtaining module; 202-generating module; 203-adjusting module. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations.

[0021] Therefore, the detailed description of the embodiments of the present application provided in the accompanying drawings below is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0022] It should be noted that the terms "first", "second" and the like are used merely to distinguish one entity or action from another, without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0023] Reference will now be made to the drawings, in which Figure 1 is a block diagram of an electronic device 100. The electronic device 100 includes a memory 110, a processor 120, and a communication module. The memory 110, the processor 120, and the communication module 130 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, the elements can be electrically connected to each other through one or more communication buses or signal lines.

[0024] The memory 110 is configured to store programs or data. The memory 110 can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read only memory (PROM), an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM), and the like.

[0025] The processor 120 is configured to read / write data or programs stored in the memory 110 and perform corresponding functions.

[0026] The communication module 130 is configured to establish a communication connection between the electronic device 100 and other communication terminals through the network, and to receive and transmit data through the network.

[0027] It should be understood that Figure 1 The structure shown is only a structural schematic diagram of the electronic device 100. The electronic device 100 can also include more or fewer components than those shown in Figure 1 or have structures different from Figure 1different configurations are shown. Figure 1 The components shown in the middle can be implemented in hardware, software, or a combination thereof.

[0028] Please refer to Figure 2 The game task dynamic adjustment method provided by the embodiment of the application comprises steps S101-S103.

[0029] S101, game behavior data and historical task performance data of a current player are acquired.

[0030] In the embodiment of the application, acquiring game behavior data and historical task performance data of a current player is a key basic step for realizing task dynamic adjustment and personalized task generation.

[0031] The game behavior data of the current player includes but is not limited to real-time behavior information of the player in the game, for example: the area where the player is currently located, the moving path, the fighting behavior (such as the frequency of using skills, the fighting time length, the type and quantity of monsters killed), the interaction behavior (such as the interaction frequency and type with other players or NPCs), the task acceptance and completion condition (such as the task type, the completion time, the failure times), the resource acquisition and consumption condition (such as the gold coins, the props, the experience acquisition and use) and the like. The behavior data is collected through a real-time communication interface between the game client and the server, and is stored in a player behavior log database for subsequent analysis.

[0032] The historical task performance data refers to the relevant records of the player in completing or not completing a task in the past period of time in the game process, including the task completion rate, the task failure times, the average task completion time, the task type preference (such as whether to prefer to complete a fighting task or an exploration task), the task abandonment rate, the task difficulty feedback (such as whether the task is considered too difficult or too easy) and the like. The data can be obtained by analyzing the task log database of the player, and is modeled and analyzed in combination with the player level, the growth curve and the like dimensions to evaluate the current game proficiency and interest tendency of the player.

[0033] In a possible implementation, the above data is acquired and processed in the following manner: (1) a real-time data acquisition module is deployed on the game server side, which is responsible for receiving player behavior event streams from the client in real time, and performing analysis and structured processing.

[0034] (2) a player behavior log database is deployed, which is used to store structured behavior data and task data, and supports efficient query and analysis; (3) a behavior feature extraction module is used to extract features from the original behavior data, and generate a feature vector for input of a machine learning model, such as player activity, preferred task type, fighting ability growth curve and the like; (4) Adopting the combination of incremental update and timing refreshing, the latest behavior and task performance of the player are ensured, and the real-time performance and accuracy of the task adjustment are improved.

[0035] S102, based on the game behavior data and the preset task structure template library, a target game task meeting the task preference of the current player is generated.

[0036] The "preset task structure template library" refers to a set of structured task templates defined by the system in advance, each template containing configurable parameters of elements such as task type, task target, task location, task reward, task trigger condition, and task background story.

[0037] The task templates can be classified into multiple categories according to the task type, including but not limited to: (1) Exploration type task: such as "finding hidden locations", "collecting specific items", etc. (2) Battle type task: such as "defeating specific monsters", "protecting NPCs from attacks", etc. (3) Puzzle type task: such as "solving mechanical puzzles", "completing specific dialogue choices", etc. (4) Interaction type task: such as "helping NPCs complete requests", "collaborating with other players to complete goals", etc. (5) Plot advancement type task: such as "advancing the main or side plot according to plot clues", etc.

[0038] In each task template, key parameters such as task difficulty, target quantity, and task location can be dynamically adjusted based on player data to achieve personalized generation of task content.

[0039] Before generating the target game task, first, the task preference modeling is performed based on the game behavior data of the player, including identifying the preference degree of the player for different types of tasks according to the historical task performance data of the player (such as task completion rate, task type preference, task failure times, task completion time, etc.); evaluating the current game ability level of the player based on the player level, battle data, and task completion success rate; combining real-time environmental information such as the current area of the player, time, weather, and task progress to determine the suitable task type and trigger condition.

[0040] After completing the above modeling analysis, the embodiment of the application will perform the following steps S102-1~S102-2.

[0041] S102-1, according to the task preference of the current player, the adaptive task type of the current player is determined.

[0042] S102-2, based on the task structure template corresponding to the adaptive task type, a target game task meeting the task preference of the current player is generated.

[0043] In the embodiments of the present application, first, the matching task type is screened from the task structure template library according to the player's preference; then the task difficulty, target quantity, task location and other parameters are dynamically adjusted according to the player's ability evaluation result; then the background setting is added to the task in combination with the current game environment (such as weather, time, regional background story, etc.), the immersion is enhanced, and the triggering mechanism of the task is determined, so as to obtain the target game task that meets the current player's task preference.

[0044] The content of the target game task includes: task type (such as exploration type, battle type, puzzle type, etc.), task target description (such as defeating a specified number of monsters, collecting specific items, etc.), task location information (such as map area, terrain complexity, etc.), task reward setting (such as experience, gold coins, props, etc.), and task precondition (such as level requirement, plot progress, etc.).

[0045] S103, based on the game behavior data and the historical task performance data, the difficulty parameter of the target game task is adjusted by combining the pre-trained task difficulty prediction model, so that the difficulty of the target game task is adapted to the current player.

[0046] In the embodiments of the present application, the dynamic adjustment of the task difficulty is a key technical link to improve the player's game experience and enhance the adaptability and challenge of the task. By combining the real-time game behavior data and the historical task performance data of the player, and with the help of the pre-trained task difficulty prediction model, the difficulty parameters in the generated target game task are intelligently adjusted, so that the task difficulty is matched with the ability level and task completion habit of the current player, thereby improving the playability and completion rate of the task.

[0047] Among them, the "pre-trained task difficulty prediction model" is a regression prediction model constructed based on supervised learning, and the training process is based on a large amount of historical player behavior data and corresponding task completion label data.

[0048] The model training process can be as follows: First, collect a large amount of historical behavior data and task completion records of players; perform preprocessing operations such as cleaning, normalization, feature selection, etc. on the data; construct training samples, each sample including a player feature vector and a corresponding task label.

[0049] Then, use the least square method or gradient descent method to train the linear regression model and optimize the model parameters.

[0050] Next, use mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), etc. to evaluate the performance of the model; improve the generalization ability of the model through cross-validation; according to the actual application situation, introduce regularization (such as L1 / L2 regularization) to prevent overfitting.

[0051] After the model training is completed, it is deployed to the game server side as an online inference model, and supports regular model updates to adapt to changes in player behavior patterns.

[0052] In a possible implementation, the label value of each sample can be a task completion difficulty, and the output of the pre-trained task difficulty prediction model can be a "task difficulty score". By comparing the predicted difficulty score with a preset task difficulty benchmark value, the difficulty parameters of the target game task are dynamically adjusted, including but not limited to: the number of task objectives (such as the number of monsters to be defeated, the number of items to be collected), enemy strength (such as monster level, attack power, defense power), time limit (such as the time limit for completing the task), spatial complexity (such as the complexity of the terrain of the task location, the accessibility of the path), and reward settings (such as reward experience value, item value).

[0053] If the predicted difficulty score is higher than the player's ability threshold, the task difficulty is reduced, for example, the number of enemies is reduced, and the time limit for completing the task is extended.

[0054] If the predicted difficulty score is lower than the player's ability threshold, the task difficulty is increased, for example, the level of the enemy is increased, and the complexity of the task objective is increased.

[0055] If the predicted difficulty score is in the moderate range, the task difficulty is maintained or slightly adjusted to increase the challenge.

[0056] In another possible implementation, the label value of each sample can be a task completion time, and the output of the trained task difficulty prediction model can be a "task completion time". Accordingly, the implementation process of step S103 can include: S103-1, using the pre-trained task difficulty prediction model, obtaining the predicted completion time of the target game task based on the content of the target game task, the game behavior data, and the historical task performance data.

[0057] S103-2, determining whether the difficulty of the target game task is adapted to the current player according to the predicted completion time.

[0058] After the model outputs the predicted completion time, if the predicted completion time is within the preset time range, it is determined that the difficulty of the target game task is adapted to the current player; if the predicted completion time is not within the preset time range, it is determined that the difficulty of the target game task is not adapted to the current player.

[0059] In the embodiments of the present application, in order to realize accurate matching of task difficulty and player ability, after generating the target game task and predicting the completion time, the adaptability between the task difficulty and the current player is further judged by setting a preset time range.

[0060] The "preset time range" can be a reasonable time interval preset according to the game task design standard, the average level of the player group, and the characteristics of the task type, for measuring the expected time range of the player to complete the task. The setting of the time range can include the following ways: (1) Based on the task type setting, that is, the average completion time of different types of tasks is different, such as exploration type tasks usually take a long time, and battle type tasks are relatively controllable, and independent time ranges can be set for each type of task; (2) Based on the player level or ability interval setting, that is, different preset time ranges are set for players in different level segments to more accurately match the player's ability; (3) Dynamic adjustment mechanism, that is, according to the game version update, the change of the overall player behavior data, and the like, the system regularly optimizes and updates the preset time range model.

[0061] Exemplarily, the preset time range of the battle type task can be 3-8 minutes; the preset time range of the exploration type task can be 5-12 minutes; and the preset time range of the puzzle type task can be 4-10 minutes.

[0062] If the predicted completion time is within the preset time range, it means that the target game task is appropriate for the current player, neither too simple to lack interest nor too difficult to cause frustration, so the difficulty of the target game task is determined to be matched with the current player.

[0063] If the predicted completion time is shorter than the lower limit of the preset time range, it means that the target game task is too simple for the current player, lacking challenge, which may lead to a decrease in the player's interest in the task, so the difficulty of the target game task is determined to be mismatched with the current player.

[0064] If the predicted completion time exceeds the upper limit of the preset time range, it means that the task is too difficult for the current player, which may lead to an increase in the task failure rate and the player loss rate, so the difficulty of the target game task is determined to be mismatched with the current player.

[0065] S103-3, in the case where the difficulty of the target game task is determined to be mismatched with the current player, the difficulty parameter of the target game task is adjusted.

[0066] It can be understood that when the difficulty of the target game task is determined to be matched with the current player, the current task parameters of the target game task are maintained unchanged, and the target game task is pushed to the current player at an appropriate time.

[0067] When the difficulty of the target game task is determined to be mismatched with the current player, the key difficulty parameters such as the task target quantity, the enemy strength, and the time limit will be dynamically adjusted according to the player behavior data and the task content.

[0068] Further, the adjustment process of adjusting the difficulty parameter of the target game task includes the following two cases: Case one, if the situation that the difficulty of the target game task is not suitable for the current player is the first case, the first preset adjustment is performed on the task difficulty parameter of the target game task to reduce the difficulty of the target game task to the task difficulty that the current player can complete.

[0069] Case two, if the situation that the difficulty of the target game task is not suitable for the current player is the second case, the second preset adjustment is performed on the task difficulty parameter of the target game task to increase the difficulty of the target game task to the task difficulty that the current player can complete.

[0070] Among them, the situation that the difficulty of the target game task is not suitable for the current player includes the first case and the second case, the first case is that the difficulty of the target game task is higher than the task difficulty that the current player can complete, and the second case is that the difficulty of the target game task is lower than the task difficulty that the current player can complete.

[0071] When the predicted completion time exceeds the upper limit of the preset time range, it can be determined that the difficulty of the target game task is too high for the current player, which belongs to the "first case", that is, the task difficulty is higher than the task difficulty that the player can complete.

[0072] This case can be manifested as: the player level is low, the enemy strength of the task is set too high; the number of task targets is too large, which exceeds the current ability of the player; the time limit of the task is too short, resulting in a large completion pressure; the task location path is complex, which exceeds the existing navigation or operation ability; the task reward is insufficient to motivate the player to invest.

[0073] In order to adapt the task difficulty to the current player's ability, the first preset adjustment is performed according to the "first case", and the adjustment process can include: (1) Reduce enemy strength: reduce the number of enemy units in the task; reduce the level, attack power or defense power of the enemy units; increase the predictability of the enemy AI behavior to facilitate the player to cope.

[0074] (2) Reduce the number of task targets: reduce the number of monsters to be defeated; reduce the number of items to be collected; shorten the task path or the number of target points.

[0075] (3) Extend the time limit of the task: increase the time limit for completing the task; cancel the time limit or provide a time extension reward prompt.

[0076] (4) Optimize the task location and path: adjust the task target to the vicinity of the current area where the player is located; optimize the task path design to reduce complex terrain or obstacles; increase navigation prompts or guiding props.

[0077] (5) Strengthen the task assistance mechanism: provide temporary gain effect (such as attack power promotion, defense bonus); push task prompts or NPC assistance information; increase the recovery props or skills that can be used in the middle of the task.

[0078] When the predicted completion time is lower than the lower limit of the preset time range, it can be determined that the target game task is too low in difficulty for the current player, which belongs to the "second case", that is, the task difficulty is lower than the current player's task completion difficulty.

[0079] This case may be manifested as: the player's level is high, and the enemy strength of the task is too low; the number of task targets is too small, lacking challenge; the time limit for the task is loose, and the completion process is too easy; the task location path is simple, lacking exploration; the task reward is not attractive enough, resulting in low interest of the player.

[0080] In order to adapt the task difficulty to the current player's ability, the second preset adjustment is performed according to the "second case", and the adjustment process can include: (1) Increase enemy strength: increase the number of enemy units in the task; increase the level, attack power or defense power of the enemy units; introduce more challenging enemy types or special abilities.

[0081] (2) Increase the number of task targets: increase the number of monsters to be defeated; increase the number or type of items to be collected; increase the task path nodes or exploration targets.

[0082] (3) Shorten the time limit for the task: shorten the task completion time limit to increase the time pressure; increase the time reward mechanism to encourage rapid task completion.

[0083] (4) Increase the complexity of the task location and path: adjust the task target to a more distant or complex terrain area; increase obstacles, mechanisms or path branches; reduce navigation prompts to increase exploration and puzzle elements.

[0084] (5) Strengthen the task challenge mechanism: increase the additional challenge conditions of the task (such as completion without damage, completion with limited skills, etc.); introduce a time-limited boss or surprise enemy mechanism; increase the task failure penalty mechanism to increase the seriousness of completion.

[0085] Understandably, the adjusted target game task will again undergo difficulty adaptability verification to ensure that its predicted completion time falls within the preset time range, thereby achieving precise matching of difficulty and player ability.

[0086] In order to enhance the immersion and narrative of the task, please refer to Figure 3 The game task dynamic adjustment method provided by the embodiment of the application further includes step S104.

[0087] S104, according to the background setting of the target game task, rendering a game environment matching the content of the target game task when the current player executes the target game task.

[0088] The "background setting of the target game task" refers to the non-functional description information of the task story background, theme style, event situation, etc. assigned during task design. For example: the task type is "missing merchant in a snowstorm", and the corresponding task background setting is snow weather, emphasizing the harsh environment and time urgency; the task type is "night raid enemy camp", and the corresponding task background setting is night combat, emphasizing concealment and dark environment; the task type is "mysterious ritual of the altar", and the corresponding task background setting is an ancient temple, emphasizing the mysterious atmosphere and ritual sense.

[0089] During the task generation or triggering stage, key environmental elements in the task background setting, such as weather, time, location, atmosphere, NPC behavior, etc., are extracted and used as input parameters for environmental rendering.

[0090] After obtaining the task background setting, the environmental parameters in the current game world are dynamically adjusted through the game engine to generate a game scene matching the task content. This includes the following aspects: (1) Weather system linkage, that is, if the task background is "snowstorm", "thunderstorm" or other specific weather events, corresponding weather effects are dynamically generated in the task triggering area. Weather changes can affect player visibility, movement speed, enemy behavior, etc., enhancing task challenge and immersion.

[0091] (2) Time and lighting system adjustment, that is, if the task background is "night", "dusk" or other time settings, automatically adjust the scene lighting parameters to simulate the visual atmosphere of the corresponding time. Combined with dynamic shadows, fog effects, halos, etc. rendering technology, enhance the realism of the scene.

[0092] (3) Terrain and scene element changes, that is, if the task background is "temple ruins", "abandoned village" or other special scenes, corresponding terrain models and decorations can be temporarily loaded or activated, such as temporarily generating a mysterious altar or hidden entrance in a normal plain map.

[0093] (4) NPC behavior and dialogue adaptation, that is, during task execution, relevant NPCs can adjust their behavior logic and dialogue content according to the task background, such as in the "missing merchant" task, passing NPCs will mention the missing event, increasing the authenticity of the task atmosphere.

[0094] (5) Sound and music rendering, that is, according to the task background, play adaptive background music and environmental sound effects, such as "mysterious task" playing deep background music, "battle task" playing tense battle sound effects, enhancing emotional guidance.

[0095] In the embodiment of the application, real-time rendering of environment changes can be completed by the game client and the server in cooperation, consistency can be maintained in the multiplayer game, and after the task is completed, the environment parameters are restored to the original state, so that the normal game experience of other tasks or normal game experience is ensured.

[0096] To this end, refer to Figure 4 The game task dynamic adjustment method provided by the embodiment of the application further includes step S105.

[0097] S105, determining a setting area of the target game task in the game space according to the position information and the operation habit of the current player.

[0098] The "player position information" refers to real-time coordinates of the current player in the game world, a region type (such as a main city, a wild area, a copy, a water area, etc.) where the player is located, a moving direction and a speed, and the like geographical state data.

[0099] The position information can be used to determine a current game environment feature of the player, for example, whether the player is in a safe area (such as a main city), whether the player is in a high exploration value area (such as a ruin or a secret passage), whether the player is in an area with a high player density (such as a task hotspot area or a resource-rich area), and whether the player is close to a task recommendation area or a task chain connection point.

[0100] The "operation habit" refers to a behavior preference formed by the player in the game process, including but not limited to the following dimensions: an action path preference, that is, whether the player is inclined to follow a main line, to freely explore, or to repeatedly tour a certain area; a region activity level, that is, a staying time in different map areas and a task completion frequency; a task acceptance habit, that is, when and where the player usually accepts a task and whether the player prefers automatic triggering or manual taking; a social behavior preference, that is, whether the player often interacts with NPCs and whether the player is inclined to complete a task in a team.

[0101] After the position information and the operation habit of the current player are obtained, the setting area of the target game task in the game space can be determined through the following steps: 1. Region adaptability evaluation: whether the surrounding region is suitable as a task trigger point is evaluated according to the current position of the player; region screening is performed in combination with a task type (such as a battle type task suitable for a wild area and a puzzle type task suitable for a ruin area).

[0102] 2. Player path prediction: a moving direction and a possible passing region of the player in a future period of time are predicted based on a historical moving track and a behavior model of the player; the task is set on a predicted travel path of the player, so as to improve a task taking rate.

[0103] 3. Task distribution balance control: dynamically adjust the task setting area in combination with the current task density of each area in the game world and the player distribution; avoid excessive concentration of tasks in a hot area; prevent player congestion, server load, and other problems; guide players to areas with less exploration value and improve the utilization rate of the overall game world.

[0104] 4. Personalized area recommendation: for players who prefer exploration, tasks are generated in unexplored areas or hidden locations; for players who prefer socializing, tasks are generated in NPC-intensive areas or public task points; for players who prefer combat, tasks are generated in monster-intensive or combat hotspot areas.

[0105] 5. Task trigger area distribution: bind the determined task setting area with the task content and send it to the client through the task push module; support automatic triggering or manual pickup of tasks in the area, determined by player preference settings.

[0106] Compared with the prior art, the embodiments of the present application have the following beneficial effects: (1) Based on player task preferences, behavior data, and historical task performance, dynamically select and generate tasks that meet the interests of players from the task structure template library, avoid repeated and low-matching task push, and improve the acceptance rate and completion intention of players.

[0107] (2) Use a pre-trained task difficulty prediction model to predict the task completion time based on the player's current ability and task content, and compare it with the preset time range to determine whether the task difficulty is suitable; and when it is not suitable, adjust the parameters (such as adjusting the enemy strength, target quantity, time limit, terrain complexity, etc.) according to the two cases of "difficulty too high" or "difficulty too low", to ensure that the task challenge matches the player's ability and avoid frustration or boredom.

[0108] (3) Through the linkage rendering of task background setting and game environment (such as weather, lighting, NPC behavior, sound effects, etc.), enhance the narrative and immersion of the task, and improve the overall game experience.

[0109] (4) Combine real-time location information and operation habits (such as path preferences, area activity levels, etc.) to dynamically determine the task trigger area, improve the natural trigger rate of the task and the willingness of the player to accept it, and avoid the task from being disconnected from the player's current behavior.

[0110] (5) Dynamically adjust the task trigger area to guide players to unexplored or low-density areas, improve map utilization, and avoid player congestion and server load imbalance caused by task concentration in hot areas.

[0111] To perform the corresponding steps in the above embodiments and various possible methods, an implementation of a game task dynamic adjustment device 200 is given below. Further, please refer to... Figure 5 , Figure 5 This is a functional block diagram of a game task dynamic adjustment device 200 provided in an embodiment of the present invention. It should be noted that the basic principle and technical effects of the game task dynamic adjustment device 200 provided in this embodiment are the same as those in the above embodiments. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above embodiments. The game task dynamic adjustment device 200 includes: The acquisition module 201 is used to acquire the current player's game behavior data and historical task performance data.

[0112] The generation module 202 is used to generate target game tasks that meet the current player's task preferences based on game behavior data and a preset task structure template library.

[0113] The adjustment module 203 is used to adjust the difficulty parameters of the target game task based on game behavior data and historical task performance data, combined with a pre-trained task difficulty prediction model, so that the difficulty of the target game task is adapted to the current player.

[0114] Optionally, the above modules can be stored in the form of software or firmware. Figure 1 The memory 110 shown is either stored in or embedded in the operating system (OS) of the electronic device 100, and can be used by... Figure 1 The processor 120 executes the program. Meanwhile, the data and program code required to execute the above modules can be stored in the memory 110.

[0115] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can also be implemented by other manners. The apparatus embodiments described above are merely illustrative, for example, the flowcharts and block diagrams in the drawings show the possible implementation architecture, function and operation of the apparatus, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logic function. It should also be noted that in some alternative implementation manners, the functions noted in the blocks can also occur in different order from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can also be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for executing the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0116] In addition, each functional module in the embodiments of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0117] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the present application or the part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, an electronic device 100, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0118] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for dynamically adjusting game tasks, characterized in that, The method includes: Acquire current player's game behavior data and historical mission performance data; Based on the game behavior data and the preset task structure template library, generate target game tasks that meet the current player's task preferences; Based on the game behavior data and the historical task performance data, and combined with the pre-trained task difficulty prediction model, the difficulty parameters of the target game task are adjusted to make the difficulty of the target game task suitable for the current player.

2. The game task dynamic adjustment method as described in claim 1, characterized in that, The preset task structure template library includes multiple task structure templates corresponding to different task types. The step of generating the current player's target game task based on the game behavior data and the preset task structure template library includes: Based on the current player's task preferences, determine the appropriate task type for the current player; Based on the task structure template corresponding to the adapted task type, a target game task that satisfies the current player's task preferences is generated.

3. The game task dynamic adjustment method as described in claim 1, characterized in that, The step of adjusting the difficulty parameters of the target game task based on the game behavior data and the historical task performance data, combined with the pre-trained task difficulty prediction model, includes: Using the pre-trained task difficulty prediction model, based on the content of the target game task, the game behavior data, and the historical task performance data, the predicted completion time of the target game task is obtained. Based on the predicted completion time, determine whether the difficulty of the target game task is suitable for the current player; If it is determined that the difficulty of the target game task is not suitable for the current player, the difficulty parameter of the target game task is adjusted.

4. The game task dynamic adjustment method as described in claim 3, characterized in that, The step of determining whether the difficulty of the target game task is suitable for the current player based on the predicted completion time includes: If the predicted completion time is within a preset time range, then the difficulty of the target game task is determined to be suitable for the current player; If the predicted completion time is not within the preset time range, then it is determined that the difficulty of the target game task is not suitable for the current player.

5. The game task dynamic adjustment method as described in claim 3, characterized in that, The situation where the difficulty of the target game task is not suitable for the current player includes a first situation and a second situation. The first situation is that the difficulty of the target game task is higher than the difficulty of the task that the current player can complete. The second situation is that the difficulty of the target game task is lower than the difficulty of the task that the current player can complete. The step of adjusting the difficulty parameters of the target game task includes: If the first situation occurs when the difficulty of the target game task is not suitable for the current player, then the task difficulty parameter of the target game task is adjusted in the first preset way to reduce the difficulty of the target game task to a level that the current player can complete. If the second situation occurs when the difficulty of the target game task is not suitable for the current player, then the task difficulty parameter of the target game task is adjusted in a second preset manner to increase the difficulty of the target game task to a level that the current player can complete.

6. The game task dynamic adjustment method as described in claim 1, characterized in that, The method further includes: Based on the background settings of the target game task, when the current player performs the target game task, a game environment adapted to the content of the target game task is rendered.

7. The game task dynamic adjustment method as described in claim 1, characterized in that, The game behavior data includes location information, and the method further includes: Based on the location information and the current player's operating habits, determine the setting area of ​​the target game task in the game space.

8. A device for dynamically adjusting game tasks, characterized in that, The method includes: The acquisition module is used to acquire current player game behavior data and historical task performance data; The generation module is used to generate target game tasks that meet the current player's task preferences based on the game behavior data and the preset task structure template library; The adjustment module is used to adjust the difficulty parameters of the target game task based on the game behavior data and the historical task performance data, combined with the pre-trained task difficulty prediction model, so that the difficulty of the target game task is adapted to the current player.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, the processor executing the machine-executable instructions to implement the game task dynamic adjustment method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the game task dynamic adjustment method as described in any one of claims 1-7.

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