Game map generation method and terminal
By generating grid areas in the game map and updating task probabilities, the problem of disconnect between game map loading and task generation is solved, enabling real-time dynamic loading of the game map and task diversity, thus improving the flexibility and reliability of the game experience.
Patent Information
- Application Number
- CN202511681374.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-17
- Publication Date
- 2026-03-13
AI Technical Summary
In existing technologies, game map loading and task generation are disconnected, lacking a dynamic response mechanism, and failing to achieve real-time content adjustment and task diversity, thus affecting the continuity and immersion of the game experience.
By obtaining the current position of the virtual character, a grid area and its task probability are generated, tasks are configured, and the task probabilities of adjacent grids are updated when a task is triggered. The adaptiveness and interactivity of task generation and character behavior are enhanced by using influence parameters.
It enables real-time dynamic loading of game map content and task diversity, improves the flexibility and reliability of game map generation, and enhances the interactivity and immersion of players with the environment.
Smart Images
Figure CN121648557A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology, and in particular to a method and terminal for generating game maps. Background Technology
[0002] Existing technologies for generating world game maps suffer from the following limitations: Traditional map loading and task systems are typically independent, with map loading, task generation, and player real-time behavior disconnected, lacking dynamic response mechanisms and unable to adjust content instantly based on the current location. Secondly, common map management solutions, such as optimizing boundary movement in multiplayer scenes or stitching local radar maps to a global map, focus primarily on improving rendering performance or static terrain construction, failing to effectively couple the dynamic destruction and generation logic of land parcels with task probability distribution. Furthermore, existing task generation solutions typically generate tasks based on long-term behavioral profiles, relying on historical data analysis. They lack short-term response mechanisms that can influence and reshape the surrounding environment and task logic in real-time when a player triggers a task, thus hindering seamless dynamic loading of map content and flexible generation of diverse tasks. Summary of the Invention
[0003] The technical problem to be solved by the present invention is to provide a game map generation method and terminal that can realize real-time dynamic loading of game map content and task diversity, thereby improving the flexibility and reliability of game map generation.
[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for generating game maps, comprising the following steps: Obtain the current position of the virtual character, generate a grid area on the map with the current position as the center and the task probability of each grid, wherein the task probability is the probability of each type of task appearing, and configure a task for each grid according to the task probability of each grid. When the virtual character triggers a task corresponding to the grid where the current position is located, influence parameters are determined for the adjacent grids based on the task type of the triggered task and the task types of the adjacent grids where the current position is located. Update the task probability of the adjacent grid according to the influence parameter, and update the task of the adjacent grid according to the updated task probability.
[0005] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A game map generation terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the game map generation method described above.
[0006] The beneficial effects of this invention are as follows: This invention provides a game map generation method and terminal. By obtaining the current position of a virtual character, a grid area and the task probability of each grid are generated on the map with the current position as the center. Tasks are configured for each grid according to the task probability of each grid, ensuring real-time dynamic loading of game map content and task diversity. When the virtual character triggers the task corresponding to the grid where the current position is located, the influence parameters are determined for the adjacent grids according to the triggered task type and the task types of the adjacent grids where the current position is located. The task probabilities of the adjacent grids are updated according to the influence parameters. The tasks of the adjacent grids are updated according to the updated task probabilities. This enhances the adaptability and interactivity of task generation and virtual character behavior in the game map, thereby improving the flexibility and reliability of game map generation. Attached Figure Description
[0007] Figure 1 This is a flowchart of a game map generation method according to an embodiment of the present invention; Figure 2 This is a flowchart of another game map generation method according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating the task probability update process in a game map generation method according to an embodiment of the present invention. Figure 4 This is a schematic diagram of a game map generation terminal according to an embodiment of the present invention; Label Explanation: 1. A game map generation terminal; 2. Memory; 3. Processor. Detailed Implementation
[0008] To explain the technical content, objectives, and effects of the present invention in detail, the following description is provided in conjunction with the embodiments and accompanying drawings.
[0009] In massively multiplayer online role-playing games (MMORPGs), dynamic map generation and quest systems are crucial for enhancing player immersion and gameplay. Current solutions typically rely on predefined or procedurally generated game maps, resulting in a disconnect between map loading, quest generation, and real-time player behavior. This lack of dynamic response mechanisms prevents real-time content adjustments based on the player's current location. Furthermore, existing map management methods often focus on performance optimization, such as optimizing boundary movement in multiplayer scenes or constructing a global map by stitching together local radar images. While these methods have made progress in rendering efficiency and static terrain generation, they fail to effectively integrate the dynamic destruction and generation mechanisms of terrain tiles with the probability distribution of quest content. In addition, existing quest generation schemes generally rely on analyzing and profiling long-term player behavior data, lacking short-term mechanisms for real-time response and reshaping of surrounding quest logic and environmental states upon quest triggering. This limits the seamless dynamic loading of map content and the flexible generation of diverse quests, impacting the overall continuity and immersion of the game experience.
[0010] Please refer to Figure 1 This invention provides a method for generating game maps, including the following steps: Obtain the current position of the virtual character, generate a grid area on the map with the current position as the center and the task probability of each grid, wherein the task probability is the probability of each type of task appearing, and configure a task for each grid according to the task probability of each grid. When the virtual character triggers a task corresponding to the grid where the current position is located, influence parameters are determined for the adjacent grids based on the task type of the triggered task and the task types of the adjacent grids where the current position is located. Update the task probability of the adjacent grid according to the influence parameter, and update the task of the adjacent grid according to the updated task probability.
[0011] As can be seen from the above description, the beneficial effects of the present invention are as follows: by obtaining the current position of the virtual character, a grid area and the task probability of each grid are generated on the map with the current position as the center. In this embodiment, the grid area can be a nine-square grid area with the current position as the center, realizing the effective binding and real-time adjustment between map generation, task content and player's real-time position, ensuring the real-time dynamic loading of game map content and task diversity; when the virtual character triggers the task corresponding to the grid where the current position is located, the influence parameters are determined for the adjacent grids according to the task type of the triggered task and the task type of the adjacent grids of the current position grid, the task probability of the adjacent grids is dynamically updated based on the influence parameters, and the task is updated for the adjacent grids according to the updated task probability. By establishing a correlation mechanism between task types, the adaptability and interactivity of task generation and virtual character behavior in the game map are enhanced, thereby improving the flexibility and reliability of game map generation.
[0012] Furthermore, obtaining the current location of the virtual character also includes: Obtain the distance the virtual character has moved from its original position to its current position, and obtain the first visible area corresponding to the original position; If the moving distance exceeds the virtual character's field of vision threshold, obtain the second visible area corresponding to the current position, calculate the intersection area of the first visible area and the second visible area, and release the land resources in the area outside the intersection area of the first visible area.
[0013] As described above, by obtaining the movement distance of the virtual character from its original position to its current position and its corresponding first visible area, the character's displacement and visible field of view can be accurately quantified, providing an initial spatial benchmark for resource management. When the movement distance exceeds the field of view threshold (such as 0.5 times the field of view diameter), the second visible area is obtained, and the intersection area between the first and second visible areas is calculated to accurately identify the overlapping parts of the field of view, ensuring the accuracy of resource release. By releasing the land resources outside the intersection area in the first visible area, memory usage is effectively reclaimed, and system performance is optimized to support the stable operation of large-scale virtual environments.
[0014] Further, generating a grid area on the map centered on the current location and the task probability of each grid includes: Obtain the preset task type of each grid in the grid region, convert the preset task type of each grid into the corresponding encoded value, and construct an input vector based on the encoded values of all grids; The task probability of each grid in the grid region is calculated based on the input vector.
[0015] As described above, by converting the preset task type of each grid in the grid area into an encoded value and constructing an input vector, the preset task types in this embodiment can be: empty=0, combat=1, gathering=2, shop=3, worship=4. The nine encoded values corresponding to the grid area are arranged in spatial order to form a nine-dimensional input vector, realizing the standardization and vectorization of non-numerical data, improving data consistency and model input efficiency. If a grid has a configured task type, it is used as the preset task type; if no task type is configured in the grid, the default type is used as the preset task type. The task probability of each grid in the grid area is calculated based on the input vector, improving the accuracy and speed of configuring tasks for each grid, thereby improving the response efficiency of map content generation.
[0016] Furthermore, tasks are configured for each grid based on the task probability of each grid, including: For each grid located in the intersecting region within the grid area, the task type of the grid is determined by combining the original task probability of the grid and the task probability of the grid, and the task is configured according to the task type; For each grid located outside the intersecting region within the grid area, the task type of the grid is determined directly based on the task probability of the grid, and the task is configured.
[0017] As described above, for each grid cell located in the intersecting region (i.e., a grid cell within the intersection region between the first and second visible regions, centered on the current position), the task type is determined by combining the original task probability of the grid cell and the task probability of the grid cell itself. Tasks are then configured based on these task types, effectively integrating multi-source information and improving the accuracy and adaptability of task generation. For each grid cell located outside the intersecting region, the task type is directly determined and tasks are configured based on the task probability of the grid cell. This approach simplifies the computation process and improves processing efficiency and response speed.
[0018] Further, updating the task probabilities of the adjacent grids according to the influence parameters, and updating the tasks for the adjacent grids according to the updated task probabilities, includes: Calculate the task update probability of the adjacent grid based on the task probability of the adjacent grid and the influence parameter:
[0019] In the formula, T k This indicates the task type of the grid where the virtual character is currently located; m is an index variable used to iterate through all possible grids affected by T. k The task type of the adjacent grid affected; T m This indicates the specific task type specified by the index variable m; P new (T m ) indicates that the task type T is in the adjacent grid. m Task update probability; P base (T m ) indicates that the task type T is in the adjacent grid. m Task probability; clamp [0,1] This represents a clamping function used to ensure that the calculation result does not exceed the range [0,1]; α k→m The task type T represents the grid where the virtual character is currently located. k For task type T m Parameters affecting probability; The task update probability is used to determine the task type of the adjacent grid and to configure the task.
[0020] As described above, the task update probability of adjacent grids is calculated by substituting the task probability and influencing parameters into the formula, thus achieving objectivity and consistency in the task update probability of adjacent grids, effectively avoiding subjective bias. The task update probability is then used to redetermine the task type of adjacent grids and configure tasks, enabling dynamic and real-time updates of the task status of adjacent grids. This enhances the adaptability and interactivity of task generation and virtual character behavior in the game map, thereby improving the flexibility and reliability of game map generation.
[0021] Please refer to Figure 2 Another embodiment of the present invention provides a game map generation terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the game map generation method described above.
[0022] The game map generation method and terminal described above are applicable to game map loading and task generation, enabling real-time dynamic loading of game map content and task diversity, thus improving the flexibility and reliability of game map generation. The following is a detailed description of the implementation methods: Please refer to Figure 1 and Figure 2 One embodiment of the present invention is as follows: A method for generating game maps, comprising the following steps: S0. Obtain the current position of the virtual character, obtain the distance the virtual character has moved from its original position to its current position, and obtain the first visible area corresponding to the original position; If the moving distance exceeds the virtual character's field of vision threshold, obtain the second visible area corresponding to the current position, calculate the intersection area of the first visible area and the second visible area, and release the land resources in the area outside the intersection area of the first visible area.
[0023] In this embodiment, the current position of the virtual character is monitored in real time, and its movement distance relative to its original position is calculated. Simultaneously, based on the original position and the virtual character's field of view diameter, a first visible area is determined. When the movement distance exceeds a field of view threshold (e.g., 0.5 times the field of view diameter), a resource management operation is triggered. Specifically, a second visible area formed based on the current position is obtained, and the intersection area between the first and second visible areas is calculated. For all land resources covered within the first visible area, precise resource release is performed. This means retaining the land within the intersection area while destroying and reclaiming the texture memory, collision data, and task script instances occupied by land outside the intersection area within the first visible area, thereby achieving optimized management of memory and computing resources.
[0024] S1. Obtain the current position of the virtual character, generate a grid area on the map with the current position as the center and the task probability of each grid, wherein the task probability is the probability of each type of task appearing, and configure a task for each grid according to the task probability of each grid, specifically including: S1.1-S1.2.
[0025] S1.1. Generate a grid area on the map centered on the current location and the task probability of each grid, including: Obtain the preset task type of each grid in the grid region, convert the preset task type of each grid into the corresponding encoded value, and construct an input vector based on the encoded values of all grids; The task probability of each grid in the grid region is calculated based on the input vector.
[0026] In this embodiment, the grid area is a 3x3 grid area centered on the current location. The specific implementation process of generating the grid area on the map is as follows: obtain the preset task type of each grid in the grid area, convert the preset task type of each grid into a preset encoding value (e.g., empty=0, combat=1, gathering=2, shop=3, worship=4), arrange these 9 encoding values in spatial order to form a 9-dimensional input vector; input the 9-dimensional input vector into the pre-trained plot generation model, which is a neural grid model, including an input layer (containing 9 nodes to receive the input vector), a hidden layer (containing 256 nodes and using the ReLU activation function and Dropout=0.2 strategy to prevent overfitting), and an output layer (45 nodes). After model calculation, output a 45-dimensional vector, which represents the task probability of each grid in the grid area, that is, the probability of occurrence of each of the 9 positions in the entire 3x3 grid corresponding to 5 task types. Determine a task for each plot according to the task probability, thereby completing the instantiation of the new plot content.
[0027] S1.2. Configure tasks for each grid according to the task probability of each grid, including: For each grid located in the intersecting region within the grid area, the task type of the grid is determined by combining the original task probability of the grid and the task probability of the grid, and the task is configured according to the task type; For each grid located outside the intersecting region within the grid area, the task type of the grid is determined directly based on the task probability of the grid, and the task is configured.
[0028] In this embodiment, for each grid located within the intersecting region (i.e., a grid within the intersection region between the first and second visible regions within a nine-square grid centered on the current position), the task type is determined by combining the original task probability of the grid and the task probability of the grid output by the plot generation model. Tasks are then configured according to the task type, effectively integrating multi-source information and improving the accuracy and adaptability of task generation. For each grid located outside the intersecting region, the task type is directly determined and the task is configured based on the task probability of the grid output by the plot generation model. This approach simplifies the calculation process and improves processing efficiency and response speed.
[0029] S2. When the virtual character triggers a task corresponding to the grid where its current position is located, an influence parameter is determined for the adjacent grids based on the task type of the triggered task and the task types of the adjacent grids. The task probability of the adjacent grids is updated based on the influence parameter, and the task for the adjacent grids is updated based on the updated task probability. Specifically, this includes: Calculate the task update probability of the adjacent grid based on the task probability of the adjacent grid and the influence parameter:
[0030] In the formula, T k This indicates the task type of the grid where the virtual character is currently located (i.e., the type of task the player is currently performing); ； m is an index variable used to iterate through all possible objects affected by the current task T. k The task type of the adjacent grid affected; T m Indicates the specific task type specified by index m; P new (T m ) indicates that the task type T is in the adjacent grid. m Task update probability (i.e., task type T) m The updated probability); P base (T m ) indicates that the task type T is in the adjacent grid. m Task probability (i.e., task type T) md (baseline probability); clamp [0,1] This is a clamping function to ensure that the calculation result falls within the range [0, 1]; α k→m The task type T represents the network where the virtual character is currently located. k For task type T m The probability-affected parameter (i.e., the currently executing task type T) k For task type T m (Probability influence parameters); The task update probability is used to determine the task type of the adjacent grid and to configure the task.
[0031] Please refer to Figure 3 In this embodiment, when a virtual character triggers a task corresponding to its current grid location, a 0.5-second delay timer is started. After the timer expires, the task probabilities of the eight adjacent grids surrounding the current grid location are adjusted. Specifically, the corresponding influence parameters are queried from a preset parameter table based on the task type of the triggered task and the task types of the adjacent grids. This parameter is defined in the interval [-0.5, +0.5] and represents the probability influence of the currently executing task type k (e.g., k=4 "worship") on the surrounding task types m (e.g., m=1 "combat"). When, it means that executing task k will increase the probability of encountering task m in the surrounding area (positive correlation, such as being more likely to encounter plot events after worshipping gods), when When, it means that executing task k will reduce the probability of encountering task m in the surrounding area (negative correlation, such as avoiding combat after worshiping a god). When the time is right, it means there is no impact; the task probability (i.e., the original task probability P of the adjacent grid) will be used. base (T m Substitute the influencing parameters into the calculation formula. Calculate the task update probability of adjacent grid cells, where the clamping function = clamp [0,1] Ensure the final result is not affected by multiplication by a coefficient. However, if the probability exceeds a reasonable range [0,1], for example, if the calculation result is greater than 1, then take 1; if the calculation result is less than 0, then take 0; use the task update probability to determine the task type of adjacent grids and configure tasks, realize the dynamic and real-time update of the task status of adjacent grids, enhance the adaptability and interactivity of task generation and virtual character behavior in the game map, thereby improving the flexibility and reliability of game map generation.
[0032] This embodiment also provides the following specific application scenarios, taking forest exploration as an example: The virtual character initially resides in grid G22, which has no mission. Within its surrounding field of vision (a 3x3 grid), there are G21 (combat mission) and G23 (gathering mission). When the virtual character moves right to G23, if the movement distance exceeds a threshold, the land resources (including textures, colliders, and script resources) on the left (G11, G12, G13) that have moved out of the field of vision are destroyed. Using the new location as the center, the land generation model is invoked to reconstruct a 3x3 grid (covering G22, G23, G24, etc., where the intersecting areas are (G12, G22, G32, G13, G23, G33), and the newly generated area is (G14, G24, G34)). The model outputs the mission probability for all grids. For example, if it predicts that the new grid G24 has a 40% probability of generating a shop mission, then grid G24 is instantiated as a shop mission.
[0033] When the virtual character begins collecting at G23, a 0.5-second delay mechanism (to prevent frequent refreshes) initiates probability linkage. It reads the currently executed task type (i.e., a collection task, type code 2), traverses the 8 grids surrounding the current position G23, and updates the task probabilities of the adjacent 8 grids according to a preset parameter table. Taking the newly generated G24 grid as an example, its original shop task baseline probability P... base The percentage for (stores) is 40%. Since the current task is a data collection task, the coefficient α for the store task (m=3) is... 2→3 The value is 0 (meaning that, according to the table, the probability of the collection task does not affect the probability of the store task), therefore P new (Shop) = clamp(40% * (1 + 0)) = 40%, which remains unchanged. Simultaneously, the system will also calculate the probability of G24's acquisition task, assuming its baseline acquisition task probability P. base (Collection) = 35%, Influencing parameter α 2→2 If P is -0.3, then P new (Collection) = clamp(35% * (1 + (-0.3))) = 24.5%. Similarly, update the probabilities of other task types on grid G24. Since the shop task has the highest probability among all task types, the task on grid G24 is determined to be a shop task. After the virtual character finishes the collection task on G23, it moves right to G24 to execute the generated shop task.
[0034] In this way, every action of the virtual character (movement or task execution) will change the game map content in real time, forming a dynamic virtual environment that is both infinitely expansive and intrinsically interconnected.
[0035] In summary, the game map generation method provided by this invention obtains the current position of a virtual character, generates a grid area on the map centered on the current position, and assigns a task probability to each grid. Based on the task probability of each grid, tasks are configured for each grid, ensuring real-time dynamic loading of game map content and task diversity. When the virtual character triggers a task corresponding to the grid where the current position is located, influence parameters are determined for the adjacent grids based on the triggered task type and the task types of the adjacent grids. The task probabilities of the adjacent grids are updated based on the influence parameters, and tasks are updated for the adjacent grids based on the updated task probabilities. This enhances the adaptability and interactivity of task generation and virtual character behavior in the game map, thereby improving the flexibility and reliability of game map generation.
[0036] According to another aspect of the invention, Figure 4 This is a schematic diagram illustrating a game map generation terminal according to an embodiment of the present invention. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of a game map generation method as described above.
[0037] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.
Claims
1. A method for generating game maps, characterized in that, Including the following steps: Obtain the current position of the virtual character, generate a grid area on the map with the current position as the center and the task probability of each grid, wherein the task probability is the probability of each type of task appearing, and configure a task for each grid according to the task probability of each grid. When the virtual character triggers a task corresponding to the grid where the current position is located, influence parameters are determined for the adjacent grids based on the task type of the triggered task and the task types of the adjacent grids where the current position is located. Update the task probability of the adjacent grid according to the influence parameter, and update the task of the adjacent grid according to the updated task probability.
2. The game map generation method according to claim 1, characterized in that, Obtaining the current location of a virtual character also includes: Obtain the distance the virtual character has moved from its original position to its current position, and obtain the first visible area corresponding to the original position; If the moving distance exceeds the virtual character's field of vision threshold, obtain the second visible area corresponding to the current position, calculate the intersection area of the first visible area and the second visible area, and release the land resources in the area outside the intersection area of the first visible area.
3. The game map generation method according to claim 1, characterized in that, Generate a grid area on the map centered at the current location, along with the task probability for each grid, including: Obtain the preset task type of each grid in the grid region, convert the preset task type of each grid into the corresponding encoded value, and construct an input vector based on the encoded values of all grids; The task probability of each grid in the grid region is calculated based on the input vector.
4. The game map generation method according to claim 2, characterized in that, Assign tasks to each grid based on the task probability of each grid, including: For each grid located in the intersecting region within the grid area, the task type of the grid is determined by combining the original task probability of the grid and the task probability of the grid, and the task is configured according to the task type; For each grid located outside the intersecting region within the grid area, the task type of the grid is determined directly based on the task probability of the grid, and the task is configured.
5. A game map generation method according to claim 4, characterized in that, Updating the task probability of the adjacent grid according to the influence parameter, and updating the task of the adjacent grid according to the updated task probability, includes: Calculate the task update probability of the adjacent grid based on the task probability of the adjacent grid and the influence parameter: In the formula, T k This indicates the task type of the grid where the virtual character is currently located; m is an index variable used to iterate through all possible grids affected by T. k The task type of the adjacent grid affected; T m This indicates the specific task type specified by the index variable m; P new (T m ) indicates that the task type T is in the adjacent grid. m Task update probability; P base (T m ) indicates that the task type T is in the adjacent grid. m Task probability; clamp [0,1] This represents a clamping function used to ensure that the calculation result does not exceed the range [0,1]; α k→m The task type T represents the grid where the virtual character is currently located. k For task type T m Parameters affecting probability; The task update probability is used to determine the task type of the adjacent grid and to configure the task.
6. A game map generation terminal, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it performs the following steps: Obtain the current position of the virtual character, generate a grid area on the map with the current position as the center and the task probability of each grid, wherein the task probability is the probability of each type of task appearing, and configure a task for each grid according to the task probability of each grid. When the virtual character triggers a task corresponding to the grid where the current position is located, influence parameters are determined for the adjacent grids based on the task type of the triggered task and the task types of the adjacent grids where the current position is located. Update the task probability of the adjacent grid according to the influence parameter, and update the task of the adjacent grid according to the updated task probability.
7. A game map generation terminal according to claim 6, characterized in that, Obtaining the current location of a virtual character also includes: Obtain the distance the virtual character has moved from its original position to its current position, and obtain the first visible area corresponding to the original position; If the moving distance exceeds the virtual character's field of vision threshold, obtain the second visible area corresponding to the current position, calculate the intersection area of the first visible area and the second visible area, and release the land resources in the area outside the intersection area of the first visible area.
8. A game map generation terminal according to claim 6, characterized in that, Generate a grid area on the map centered at the current location, along with the task probability for each grid, including: Obtain the preset task type of each grid in the grid region, convert the preset task type of each grid into the corresponding encoded value, and construct an input vector based on the encoded values of all grids; The task probability of each grid in the grid region is calculated based on the input vector.
9. A game map generation terminal according to claim 7, characterized in that, Assign tasks to each grid based on the task probability of each grid, including: For each grid located in the intersecting region within the grid area, the task type of the grid is determined by combining the original task probability of the grid and the task probability of the grid, and the task is configured according to the task type; For each grid located outside the intersecting region within the grid area, the task type of the grid is determined directly based on the task probability of the grid, and the task is configured.
10. A game map generation terminal according to claim 9, characterized in that, Updating the task probability of the adjacent grid according to the influence parameter, and updating the task of the adjacent grid according to the updated task probability, includes: Calculate the task update probability of the adjacent grid based on the task probability of the adjacent grid and the influence parameter: In the formula, T k This indicates the task type of the grid where the virtual character is currently located; m is an index variable used to iterate through all possible grids affected by T. k The task type of the adjacent grid affected; T m This indicates the specific task type specified by the index variable m; P new (T m ) indicates that the task type T is in the adjacent grid. m Task update probability; P base (T m ) indicates that the task type T is in the adjacent grid. m Task probability; clamp [0,1] This represents a clamping function used to ensure that the calculation result does not exceed the range [0,1]; α k→m The task type T represents the grid where the virtual character is currently located. k For task type T m Parameters affecting probability; The task update probability is used to determine the task type of the adjacent grid and to configure the task.