Game strategy generation method and device, equipment and storage medium

Abstract

The application discloses a game strategy generation method and device, equipment and a storage medium. The method comprises the following steps: obtaining historical operation data of a player in a game and task data of a current to-be-executed task; determining operation characteristics, preference characteristics and skill characteristics of the player according to the historical operation data; and generating a game strategy for executing the to-be-executed task according to the operation characteristics, the preference characteristics, the skill characteristics and the task data. According to the scheme, the historical operation data of the player is analyzed to determine the operation characteristics, the preference characteristics and the skill characteristics of the player, and the game strategy is generated in combination with the task data of the current to-be-executed task, so that the game strategy can adapt to the actual operation habits and game ability of the player.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, device and storage medium for generating game strategies. Background Technology

[0002] As the complexity of video game gameplay continues to increase, game strategy has become a crucial factor influencing players' task completion efficiency, operational experience, and sustained engagement. For novice players, complex skill mechanics, task rules, and cooperative requirements can easily create a high learning curve, leading to confusion in the early stages of the game.

[0003] Existing game guidance methods typically rely on fixed tutorials, making it difficult to adjust in real time based on different players' skill levels, behavioral preferences, and task requirements. While this method can provide basic guidance, it suffers from insufficient adaptability and weak dynamic response capabilities in personalized strategy recommendation scenarios. Summary of the Invention

[0004] This application provides a game strategy generation method, apparatus, device, and storage medium, which solves the technical problems in the prior art where game strategies rely on fixed rules and are difficult to adapt to players' operating habits, gameplay preferences, and skill abilities. It realizes the generation of game strategies based on players' operating characteristics, preference characteristics, skill characteristics, and task data to be executed, thereby improving the personalization of game strategy generation and the task adaptation effect.

[0005] Firstly, this application provides a method for generating game strategies, including: Acquire the player's historical action data in the game, as well as the task data of the current pending task; The player's operational characteristics, preference characteristics, and skill characteristics are determined based on the historical operation data. A game strategy for executing the task to be performed is generated based on the operational characteristics, preference characteristics, skill characteristics, and task data.

[0006] Secondly, this application provides a game strategy generation apparatus, comprising: The acquisition module is configured to acquire the player's historical operation data in the game, as well as the task data of the current task to be performed; The feature module is configured to determine the player's operation features, preference features, and skill features based on the historical operation data; The strategy module is configured to generate a game strategy for executing the task to be executed based on the operation characteristics, the preference characteristics, the skill characteristics, and the task data.

[0007] Thirdly, this application provides a game strategy generation device, comprising: One or more processors; A memory that stores one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the game strategy generation method as described in the first aspect.

[0008] Fourthly, this application provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the game strategy generation method as described in the first aspect.

[0009] In this application, game strategies are generated based on players' historical operation data and task data. First, the player's operation characteristics, preference characteristics, and skill characteristics are determined, and then a matching game strategy is generated in combination with the current task to be performed, thereby improving the personalization and task adaptability of the game strategy. Attached Figure Description

[0010] Figure 1 This is a flowchart of a game strategy generation method provided in an embodiment of this application; Figure 2 This is a flowchart of a player feature determination method provided in an embodiment of this application; Figure 3 This is a flowchart of an operational feature determination method provided in an embodiment of this application; Figure 4 This is a flowchart of a method for determining preference features provided in an embodiment of this application; Figure 5 This is a flowchart of a game strategy determination method provided in an embodiment of this application; Figure 6 This is a flowchart of a game strategy update method provided in an embodiment of this application; Figure 7 This is a flowchart of an operational difference value calculation method provided in an embodiment of this application; Figure 8 This is a flowchart illustrating the steps of a game strategy generation method provided in an embodiment of this application; Figure 9 This is a structural block diagram of a game strategy generation device provided in an embodiment of this application; Figure 10 This is a schematic diagram of the structure of a game strategy generation device provided in an embodiment of this application. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as being processed sequentially, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. A process can be terminated when its operation is completed, but it may also have additional steps not included in the drawings. A process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0012] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0013] Currently, in modern game development and operation, players' operational abilities, behavioral preferences, and task comprehension directly impact the game experience and their willingness to continue participating. Traditional game guidance methods typically rely on fixed tutorials, preset strategies, and static prompts, making it difficult to personalize them according to different players' actual operating habits, skill levels, and task requirements. This can easily lead to high learning costs and low task execution efficiency for novice players, thereby affecting game retention and user experience.

[0014] In existing technologies, game strategy recommendations typically focus on task recommendation, lacking comprehensive analysis of player historical operation data, preference characteristics, and skill characteristics, making it difficult to generate execution strategies suitable for the current task in a timely manner. When players have different operational abilities, preference types, and task objectives, fixed strategies cannot meet individualized needs. Therefore, there is an urgent need for a method that can generate game strategies based on player historical operation data and current task data to improve the personalization, task adaptability, and player gaming experience of game strategy recommendations.

[0015] To address the problem that traditional game strategies rely on fixed tutorials and struggle to adapt to individual player abilities and task requirements, this embodiment provides a game strategy generation method. This method acquires a player's historical in-game action data and the task data of the current pending task, and determines the player's action characteristics, preference characteristics, and skill characteristics based on the historical action data. Then, by combining these characteristics with the task data, a game strategy for executing the current pending task is generated. This solution can dynamically generate personalized strategies based on player history and task requirements, improving the adaptability, accuracy of guidance, and player experience of game strategies.

[0016] Figure 1 This is a flowchart of a game strategy generation method provided in an embodiment of this application. (Reference) Figure 1 The game strategy generation method specifically includes: S110: Obtain the player's historical operation data in the game, as well as the task data of the current task to be performed.

[0017] Historical operation data can be records of player behavior during gameplay, such as movement records, attack records, skill release records, item usage records, task completion records, and interaction behavior records. Task data for the current task to be executed can be information related to the task that the player needs to complete in the current game scene, such as task objectives, task type, task difficulty, task constraints, enemy unit information, map environment information, and task reward information.

[0018] In one embodiment, the method for obtaining a player's historical operation data in the game can be: obtaining the player's corresponding player identifier, reading the historical operation record corresponding to the player identifier in the game server, and organizing the historical operation record in chronological order to obtain the player's historical operation data in the game.

[0019] In one embodiment, the method for obtaining the task data of the currently pending task can be: reading the task configuration file in the current game process, extracting the task objective, task map, task difficulty, enemy configuration, time limit and completion conditions corresponding to the currently pending task from the task configuration file, and using the above information as task data.

[0020] By following the steps above, we can obtain basic information about players' historical game behavior and current tasks, providing a data foundation for subsequently determining players' operational characteristics, preference characteristics, skill characteristics, and generating game strategies adapted to the current task.

[0021] S120. Determine the player's operation characteristics, preference characteristics, and skill characteristics based on the historical operation data.

[0022] Among them, operation features can be used to characterize a player's operation habits and operation stability, preference features can be used to characterize a player's preference for gameplay, characters, items, task types and interaction methods, and skill features can be used to characterize a player's ability to use game skills, hit rate and combo ability.

[0023] Optionally, Figure 2 This is a flowchart of a player feature determination method provided in an embodiment of this application. (Reference) Figure 2 The specific methods for determining the player's characteristics include: S1201. Extract operation input records, skill release records, and game interaction records from the historical operation data.

[0024] Among them, the operation input record can be used to represent the player's input of basic operations such as movement, view, attack, jump, defense or confirmation; the skill release record can be used to represent the time, target, location, skill type and release result of the player's skill release during the game; and the game interaction record can be used to represent the interaction behavior between the player and game characters, task objects, teammates, enemy units, NPCs, props and scene elements.

[0025] In one embodiment, the method for extracting operation input records from historical operation data can be as follows: read the input event log in the historical operation data, filter the operation event log according to the operation event type to obtain movement commands, view adjustment commands, normal attack commands, defense commands, jump commands, and item use commands, and sort the filtered input events according to the occurrence time to obtain operation input records.

[0026] In one embodiment, the method for extracting skill release records from historical operation data can be: reading the skill event log in the historical operation data, extracting fields such as skill identifier, skill release time, skill release location, skill target, skill cooldown status, skill hit result, and skill damage result, and combining the above fields to form skill release records.

[0027] In one embodiment, the method for extracting game interaction records from historical operation data can be: reading the interaction event logs in the historical operation data, filtering the interaction behaviors between players and task objectives, teammates, enemy units, NPCs, scene mechanisms and game props, and recording the interaction object, interaction type, interaction time and interaction result to obtain game interaction records.

[0028] Through the above steps, historical operation data can be broken down into operation input records, skill release records, and game interaction records, enabling players' basic operation behaviors, skill usage behaviors, and game interaction behaviors to be analyzed separately, providing a structured data foundation for subsequently determining players' operation characteristics, preference characteristics, and skill characteristics.

[0029] S1202. Determine the player's operation characteristics based on the operation input record.

[0030] Among them, operation characteristics can be used to characterize a player's operation habits, operation frequency, operation stability, and operation response capability.

[0031] Optionally, Figure 3 This is a flowchart of an operational feature determination method provided in an embodiment of this application. (Reference) Figure 3 The method for determining operational features specifically includes: S12021. Generate an operation input sequence based on the operation input record, wherein the operation input sequence includes multiple operation input events arranged in time.

[0032] Among them, an operation input event can be a specific player input behavior, such as a movement input event, an attack input event, a defense input event, a jump input event, a view adjustment input event, an item use input event, and a confirmation operation input event; an operation input sequence can be a time sequence data formed by arranging multiple operation input events according to their occurrence time, used to reflect the player's continuous operation process during the game.

[0033] In one embodiment, the operation input sequence can be generated by: extracting the event identifier, operation type, occurrence time, duration and operation parameters corresponding to each operation input event from the operation input record, and sorting the multiple operation input events in ascending order according to the occurrence time of each operation input event to obtain the operation input sequence.

[0034] Through the above steps, scattered operation input records can be organized into an operation input sequence arranged by time, giving the player's historical operation behavior a clear temporal structure, and providing a data foundation for subsequent analysis of player operation habits, operation rhythm and operation stability based on the operation input sequence.

[0035] S12022. Determine the player's operation frequency and operation area distribution based on the operation input sequence.

[0036] Among them, operation frequency can be used to represent the number of times a player performs an operation input per unit of time, and operation area distribution can be used to represent the concentration of player operations in the operation area of ​​the screen.

[0037] In one embodiment, the operation frequency can be determined by: obtaining the number of operation input events contained in the operation input sequence, obtaining the length of the historical time period corresponding to the operation input sequence, and dividing the number of operation input events by the length of the historical time period to obtain the player's operation frequency. The specific calculation formula is as follows:

[0038] in, For operating frequency, The number of input events for the operation. The length of the historical period.

[0039] In one embodiment, the method for determining the operation area distribution may be: extracting the operation position corresponding to each operation input event from the operation input sequence, mapping the operation position to multiple preset areas, and calculating the proportion of operation input events in each area to obtain the operation area distribution.

[0040] Through the above steps, the player's operation frequency and operation area distribution can be determined based on the operation input sequence, so that the player's operation activity level and operation space habits can be quantified and expressed, providing a basis for subsequent determination of the player's operation characteristics and generation of game strategies adapted to the player's operation style.

[0041] S12023. Determine the player's operation characteristics based on the operation frequency and the operation area distribution.

[0042] In one embodiment, the way to determine a player's operation characteristics based on operation frequency and operation area distribution is to determine the area with the highest proportion in the operation area distribution as the player's main operation area, and to determine the player's operation characteristics by combining operation frequency and main operation area.

[0043] Through the above steps, the player's operation frequency and operation area distribution can be converted into operation features that can be used for strategy generation. This allows the system to identify the player's operational activity level and main operation space bias, providing a basis for generating game strategies that conform to the player's operation habits and capabilities.

[0044] S1203. Determine the player's preference characteristics based on the skill release records and game interaction records.

[0045] Optionally, Figure 4 This is a flowchart of a preference feature determination method provided in an embodiment of this application. (Reference) Figure 4 The method for determining these preference features specifically includes: S12031. Determine the player's skill type preference based on the skill release record.

[0046] Skill type preference can be used to characterize a player's preference for different skill categories, such as preference for ranged attack skills, melee attack skills, defensive skills, control skills, healing skills, and movement skills.

[0047] In one embodiment, determining a player's skill type preference based on skill release records can be achieved by: extracting skill identifiers corresponding to each skill release from the skill release records; determining the skill type of each skill release based on the established correspondence between skill identifiers and skill types; counting the number of releases of each skill type within a historical time period; dividing the number of releases of each skill type by the total number of skill releases to obtain the release percentage corresponding to that skill type; and identifying skill types with release percentages exceeding a threshold as the player's skill type preference. For example, if the release percentage of ranged attack skills is the highest, it can be determined that the player has a preference for ranged attack skills; if the release percentages of control and defensive skills are relatively high, it can be determined that the player has a preference for control and defensive skills.

[0048] Through the above steps, we can statistically analyze players' usage tendencies for different skill types based on skill release records, and identify the skill types used more frequently as players' skill type preferences. This provides a basis for subsequently combining game interaction records to determine preference characteristics and generate game strategies that conform to players' skill usage habits.

[0049] S12032. Determine the player's interaction type preference based on the game interaction records.

[0050] Interaction type preference can be used to characterize a player's preference for different interaction methods, such as preference for mission progression, combat confrontation, cooperation, resource collection, and scene exploration.

[0051] In one embodiment, determining a player's interaction type preference based on game interaction records can be achieved by: extracting the interaction objects, actions, and scenarios corresponding to each interaction from the game interaction records; determining the interaction type of each interaction based on a pre-defined correspondence between interaction objects, actions, and types; counting the occurrences of each interaction type within a historical time period; dividing the occurrences of each interaction type by the total number of interactions to obtain the interaction percentage for that type; and identifying interaction types with percentages exceeding a threshold as the player's interaction type preference. For example, a high percentage of interaction between a player and quest NPCs, quest items, and quest trigger areas indicates a preference for quest progression; a high percentage of interaction between a player and enemy units indicates a preference for combat; and a high percentage of interaction involving assistance, healing, and cooperation with teammates indicates a preference for teamwork.

[0052] Through the above steps, we can determine a player's behavioral tendencies in different interaction types based on their historical game interaction behavior. This allows us to quantify a player's preferences for task progression, combat, cooperation, resource collection, and scene exploration, providing a basis for subsequently determining preference characteristics by combining skill type preferences and generating game strategies that conform to player behavior habits.

[0053] S12033. Determine the player's preference characteristics based on the skill type preference and interaction type preference.

[0054] In one embodiment, determining a player's preference characteristics based on skill type preference and interaction type preference can be achieved by combining skill type preference and interaction type preference. For example, when the skill type preference is a ranged attack skill preference and the interaction type preference is a combat confrontation preference, it can be determined that the player has a ranged output preference characteristic; when the skill type preference is a healing skill preference and the interaction type preference is a cooperative interaction preference, it can be determined that the player has a support / cooperative interaction preference characteristic.

[0055] Through the above steps, players' preferences in skill usage and game interaction can be integrated into a unified preference feature. This allows the system to identify the gameplay style, task execution method, and interaction strategy that is more suitable for the player, providing a basis for generating personalized game strategies by combining operation features, skill features, and task data.

[0056] S1204. Generate a skill release sequence based on the skill release record, and determine the player's skill characteristics based on the skill release sequence.

[0057] Among them, the skill release sequence can be multiple skill release events arranged in chronological order, which can be used to reflect the player's skill usage rhythm, skill combination habits and skill execution ability during the game; skill characteristics can be used to characterize the player's skill usage frequency, skill hit rate, skill connection ability and skill mastery.

[0058] In one embodiment, the method for generating a skill release sequence based on skill release records may be as follows: extract the skill identifier, skill release time, skill target, and skill release result corresponding to each skill release behavior from the skill release records, and sort the multiple skill release behaviors according to the skill release time to obtain the skill release sequence.

[0059] In one embodiment, the method for determining a player's skill characteristics based on a skill release sequence can be as follows: count the number of skill release events and the number of skill hit events in the skill release sequence, divide the number of skill release events by the length of the historical time period to obtain the skill release frequency; determine the skill connection interval by the time interval between adjacent skill release events; divide the number of skill hit events by the number of skill release events to obtain the skill hit rate, and combine the skill release frequency, skill connection interval, and skill hit rate to determine the player's skill characteristics.

[0060] Through the above steps, skill release records can be organized into a skill release sequence with a time order. Based on the skill release sequence, the player's skill characteristics in terms of skill release frequency, skill hit effect, skill connection rhythm, and skill combination ability can be determined. This provides a basis for generating game strategies that are adapted to the player's abilities by combining operation characteristics, preference characteristics, and task data.

[0061] S130. Generate a game strategy for executing the task to be executed based on the operation characteristics, the preference characteristics, the skill characteristics, and the task data.

[0062] Game strategies can include operational suggestions, skill release suggestions, route selection suggestions, and resource allocation suggestions to guide players in performing tasks.

[0063] In one embodiment, the game strategy can be generated by inputting operation features, preference features, skill features, and task data into a large language model, and then using the large language model to match and analyze player abilities, player preferences, and task requirements to obtain a game strategy suitable for the player to perform the task.

[0064] Through the above steps, personalized game strategies can be generated based on the player's operational characteristics, preference characteristics, skill characteristics, and task data of the current task to be performed. This means that game strategies no longer rely solely on fixed tutorials or general recommendations, but can be dynamically generated by combining the player's actual operational ability, gameplay preferences, and skill mastery, thereby improving the adaptability, feasibility, and task completion efficiency of game strategies.

[0065] Optionally, Figure 5 This is a flowchart illustrating a game strategy determination method provided in an embodiment of this application. (Reference) Figure 5 The specific methods for determining the game strategy include: S1301. Generate multiple candidate game strategies for executing the task to be executed based on the task data.

[0066] Among them, candidate game strategies can be a variety of optional execution schemes generated based on task data, such as offensive strategies, defensive strategies, detour exploration strategies, resource priority strategies, cooperative advancement strategies, and skill burst strategies.

[0067] In one embodiment, generating multiple candidate game strategies based on task data can be achieved by parsing the task objective, task type, and task constraints in the task data, inputting the task objective, task type, and task constraints into a large language model, and generating multiple candidate game strategies corresponding to different execution paths.

[0068] Through the above steps, multiple optional task execution plans can be generated based on the task data of the current task to be executed, providing a strategic foundation for subsequently selecting the final game strategy suitable for the player from multiple candidate game strategies by combining the player's operation characteristics, preference characteristics and skill characteristics.

[0069] S1302. Determine the strategy fit score of each candidate game strategy based on the operational characteristics, the preference characteristics, and the skill characteristics.

[0070] Among them, the strategy fit score can be used to represent the degree of matching between candidate game strategies and players' personal abilities and behavioral habits.

[0071] In one embodiment, the strategy fit score can be determined as follows: The strategy operation requirements, strategy preference types, and strategy skill requirements corresponding to the candidate game strategies are obtained separately; the strategy operation requirements are matched with the player's operation characteristics to obtain an operation fit score; the strategy preference types are matched with the player's preference characteristics to obtain a preference fit score; the strategy skill requirements are matched with the player's skill characteristics to obtain a skill fit score; and the operation fit score, preference fit score, and skill fit score are weighted and fused to obtain the strategy fit score. The specific calculation formula is as follows:

[0072] in, To adapt the score to the strategy, To adapt the score for operation, To adapt scores to preferences, Skill-based score, , and These are the weights corresponding to the operation adaptation score, preference adaptation score, and skill adaptation score, respectively. The weight of each score can be set according to the type of task to be performed. For example, in combat tasks, the weight of skill adaptation score can be higher, while in exploration tasks, the weight of preference adaptation score and operation adaptation score can be higher.

[0073] Through the above steps, the suitability of multiple candidate game strategies can be evaluated based on the player's operational characteristics, preference characteristics, and skill characteristics. This ensures that each candidate game strategy has a corresponding strategy suitability score, providing a quantitative basis for selecting the most suitable game strategy for the player to perform the task.

[0074] S1303. The candidate game strategy with the highest strategy fit score is determined as the game strategy for executing the task to be executed.

[0075] Among them, game strategy can be the final strategic plan used to guide players in performing the current task to be performed.

[0076] In one embodiment, the game strategy can be determined by: obtaining the strategy fit score corresponding to each candidate game strategy, comparing multiple strategy fit scores, determining the strategy fit score with the highest value, and determining the candidate game strategy corresponding to the strategy fit score as the game strategy for executing the task to be executed.

[0077] By following the steps above, the strategy that best matches the player's operational characteristics, preference characteristics, and skill characteristics can be selected from multiple candidate game strategies. This avoids using fixed or generalized task execution suggestions, thereby improving the fit between the game strategy and the player's individual abilities and current task requirements, and increasing the efficiency and success rate of completing the task to be executed.

[0078] Optionally, Figure 6 This is a flowchart illustrating a game strategy update method provided in an embodiment of this application. (Reference) Figure 6 The specific methods for updating the game strategy include: S140. Obtain the player's real-time operation data.

[0079] Real-time operation data refers to the player's actions generated in real-time during the current game, reflecting the player's current operational status, rhythm, and behavioral changes while performing a task. Real-time operation data can include current movement input data, attack input data, skill release data, camera adjustment data, item usage data, interaction behavior data, current location data, current combat status data, and current task progress data.

[0080] In one embodiment, the method for obtaining the player's real-time operation data can be: reading the player's operation event log in the current game scene from the game client in real time, extracting fields such as operation type, operation time, operation location, operation object and operation result, and determining the extracted data as the player's real-time operation data.

[0081] Through the above steps, real-time operation data of players during the current task execution can be obtained, enabling the system to grasp the player's current operation status and task execution performance, providing a data foundation for subsequent adjustments to game strategies based on real-time operation data, judgment of strategy execution deviations, or generation of dynamic prompt information.

[0082] S150. Calculate the operation difference value based on the real-time operation data and the historical operation data.

[0083] Among them, the operation difference value can be used to characterize the degree of deviation of a player's current operation state from historical operation habits, historical operation abilities, and historical behavior patterns.

[0084] Optionally, Figure 7 This is a flowchart illustrating a method for calculating operational difference values ​​provided in an embodiment of this application. (Reference) Figure 7 The specific method for calculating the difference value in this operation includes: S1501. Determine the historical behavior feature vector based on the historical operation data.

[0085] Among them, the historical behavior feature vector can be a multi-dimensional numerical feature extracted from historical operation data, used to characterize the player's operation frequency, skill usage habits, interaction behavior tendencies, movement methods and task execution performance in the historical game process.

[0086] In one embodiment, determining the historical behavior feature vector based on historical operation data can be achieved by extracting multiple historical behavior indicators from the historical operation data and arranging these indicators according to a preset indicator order to obtain the historical behavior feature vector. Historical behavior indicators may include historical operation frequency, historical skill release frequency, historical skill hit rate, historical interaction frequency, historical movement distance, historical number of hits, historical task completion rate, and historical item usage frequency, etc. The historical behavior feature vector can be represented as follows:

[0087] in, For historical behavior feature vectors, These are different historical behavior indicators extracted from historical operational data. For example, It can represent the frequency of historical operations. It can represent the frequency of historical skill usage. It can represent the historical skill hit rate. It can represent the frequency of historical interactions.

[0088] Through the above steps, players' historical operation data can be converted into historical behavior feature vectors of a unified dimension, so that players' historical operation habits and historical behavior capabilities can be numerically expressed. This provides a benchmark for subsequent determination of real-time behavior feature vectors based on real-time operation data and calculation of the operation difference value between real-time behavior feature vectors and historical behavior feature vectors.

[0089] S1502. Determine the real-time behavior feature vector based on the real-time operation data.

[0090] Among them, the real-time behavior feature vector can be a multi-dimensional numerical feature extracted from real-time operation data, used to characterize the player's real-time operation frequency, real-time skill usage, real-time interaction behavior, real-time movement status and current task execution performance in the current task.

[0091] In one embodiment, determining the real-time behavior feature vector based on real-time operation data can be achieved by extracting multiple real-time behavior indicators from the real-time operation data and arranging these indicators in the same order as the historical behavior feature vectors to obtain the real-time behavior feature vector. Real-time behavior indicators may include real-time operation frequency, real-time skill release frequency, real-time skill hit rate, real-time interaction frequency, real-time movement distance, real-time number of hits, real-time number of item uses, and current task progress, etc. The real-time behavior feature vector can be represented as:

[0092] in, This is a real-time behavioral feature vector. These are different real-time behavior metrics extracted from real-time operational data. For example, It can represent the real-time operating frequency. It can represent the real-time skill release frequency. It can represent real-time skill hit rate. It can indicate the frequency of real-time interaction.

[0093] Through the above steps, the real-time operation data of the player during the current task execution process can be converted into a real-time behavior feature vector with the same dimension as the historical behavior feature vector. This allows the player's current operation state to be numerically expressed, providing data basis for subsequent calculation of the operation difference value between the real-time behavior feature vector and the historical behavior feature vector, judging the degree of deviation of strategy execution, and dynamically adjusting the game strategy.

[0094] S1503. Calculate the Euclidean distance between the historical behavior feature vector and the real-time behavior feature vector to obtain the operation difference value.

[0095] Euclidean distance can be used to measure the degree of difference between two feature vectors in a multidimensional feature space.

[0096] In one embodiment, the operation difference value can be calculated as follows: extract historical behavior feature values ​​and real-time behavior feature values ​​for corresponding positions according to the same feature dimension, calculate the difference between feature values ​​of each dimension, square each difference, accumulate the squared differences corresponding to multiple feature dimensions, and perform a square root operation on the accumulated result to obtain the Euclidean distance between the historical behavior feature vector and the real-time behavior feature vector, and determine this Euclidean distance as the operation difference value. The specific calculation formula is as follows:

[0097] in, To manipulate the difference value, Indicates the first A real-time behavioral feature value, Indicates the first Each historical behavioral characteristic value, Indicates the number of feature dimensions.

[0098] Through the above steps, Euclidean distance can be used to quantitatively compare historical behavior feature vectors and real-time behavior feature vectors, and obtain operation difference values ​​that can be used to judge the degree of deviation of the player's current operation. This provides a basis for subsequent judgments on whether to adjust the game strategy, generate operation prompts, or update the strategy adaptation results based on the operation difference values.

[0099] S160. If the operation difference value is greater than the set difference value threshold, generate an updated game strategy for the task to be executed based on the real-time operation data and the task data.

[0100] The difference threshold can be a preset judgment benchmark used to determine whether the game strategy needs to be adjusted. For example, the difference threshold can be set to 0.3, and updating the game strategy can be a task execution plan that is regenerated when the player's current operation state changes significantly.

[0101] In one embodiment, the method for generating an updated game strategy based on real-time operation data and task data can be as follows: extract the player's current operation frequency, current skill release status, current interaction behavior, current position, current health, current resource quantity, and current task progress from the real-time operation data, and input the extracted data and task data together into a large language model to generate an updated game strategy.

[0102] Through the above steps, when there is a significant difference between the player's current operation and historical operation, the game strategy can be dynamically generated and updated based on real-time operation data and task data. This allows the game strategy to be adjusted as the player's current execution state changes, avoiding mismatch between the original strategy and the player's real-time operation ability and task progress, thereby improving the real-time adaptability of the game strategy and the success rate of task completion.

[0103] Optionally, Figure 8 This is a flowchart illustrating the steps of a game strategy generation method provided in an embodiment of this application. (Reference) Figure 8 The game strategy generation method specifically includes: S201. Obtain historical operation data.

[0104] In one embodiment, historical operation data of the player during the game is obtained. The historical operation data may include the player's movement operation records, attack operation records, skill release records, skill hit records, task completion records, and game interaction records, etc., to reflect the player's operation habits and behavior in the historical game process.

[0105] S202. Analyze the player's operational characteristics, preference characteristics, and skill characteristics.

[0106] In one embodiment, player behavior is analyzed based on historical operation data to determine the player's operation characteristics, preference characteristics, and skill characteristics. Operation characteristics can represent the player's operation frequency, accuracy, and activity level; preference characteristics can represent the player's preferred task types, combat styles, and interaction methods; and skill characteristics can represent the player's skill release frequency, skill hit rate, and skill usage habits.

[0107] S203, Personalized strategy generation.

[0108] In one embodiment, a personalized game strategy is generated based on the player's operational characteristics, preference characteristics, and skill characteristics, combined with the task data of the current task to be performed, to adapt to the player's current abilities and game preferences. For example, an offensive strategy can be generated for players with high operational activity, a strategy with more auxiliary prompts can be generated for players with low skill hit rate, and a ranged combat strategy can be generated for players who prefer ranged attacks.

[0109] S204, Real-time adjustment strategy.

[0110] In one embodiment, during the player's execution of a task, real-time operation data of the player is acquired, and the player's current operation state is determined based on this data. When there is a significant difference between the player's real-time operation state and their historical operation state, the generated personalized game strategy is adjusted to obtain an updated game strategy, enabling the game strategy to adapt to the player's real-time operation performance and task execution status.

[0111] Based on the above embodiments, Figure 9 This is a structural block diagram of a game strategy generation device provided in an embodiment of this application. (Reference) Figure 9 The game strategy generation device provided in this embodiment specifically includes: an acquisition module 11, a feature module 12, and a strategy module 13.

[0112] The acquisition module 11 is configured to acquire the player's historical operation data in the game and the task data of the current task to be executed; the feature module 12 is configured to determine the player's operation features, preference features and skill features based on the historical operation data; and the strategy module 13 is configured to generate a game strategy for executing the task to be executed based on the operation features, preference features, skill features and task data.

[0113] Based on the above embodiments, the feature module 12 includes: a data extraction unit configured to extract operation input records, skill release records, and game interaction records from the historical operation data; an operation feature unit configured to determine the player's operation features based on the operation input records; a preference feature unit configured to determine the player's preference features based on the skill release records and game interaction records; and a skill feature unit configured to generate a skill release sequence based on the skill release records and determine the player's skill features based on the skill release sequence.

[0114] Based on the above embodiments, the operation feature unit includes: an operation sequence subunit configured to generate an operation input sequence based on the operation input record, the operation input sequence including multiple operation input events arranged in time; an operation frequency and region subunit configured to determine the player's operation frequency and operation region distribution based on the operation input sequence; and an operation feature subunit configured to determine the player's operation features based on the operation frequency and operation region distribution.

[0115] Based on the above embodiments, the preference feature unit includes: a skill preference subunit, configured to determine the player's skill type preference based on the skill release record; an interaction preference subunit, configured to determine the player's interaction type preference based on the game interaction record; and a preference feature subunit, configured to determine the player's preference feature based on the skill type preference and the interaction type preference.

[0116] Based on the above embodiments, the strategy module 13 includes: a candidate strategy unit configured to generate multiple candidate game strategies for executing the task to be executed based on the task data; an adaptation score unit configured to determine the strategy adaptation score of each candidate game strategy based on the operation characteristics, the preference characteristics, and the skill characteristics; and a strategy determination unit configured to determine the candidate game strategy with the highest strategy adaptation score as the game strategy for executing the task to be executed.

[0117] Based on the above embodiments, the game strategy generation device further includes: a real-time data module configured to acquire the player's real-time operation data; a difference calculation module configured to calculate an operation difference value based on the real-time operation data and the historical operation data; and a strategy update module configured to generate an updated game strategy for the task to be executed based on the real-time operation data and the task data when the operation difference value is greater than a set difference value threshold.

[0118] Based on the above embodiments, the difference calculation module includes: a historical vector unit configured to determine a historical behavior feature vector based on the historical operation data; a real-time vector unit configured to determine a real-time behavior feature vector based on the real-time operation data; and a difference calculation unit configured to calculate the Euclidean distance between the historical behavior feature vector and the real-time behavior feature vector to obtain an operation difference value.

[0119] The game strategy generation device provided in this application embodiment, through the collaborative processing of the acquisition module 11, the feature module 12, and the strategy module 13, achieves an automated process from acquiring historical player operation data and analyzing player behavior characteristics to generating personalized game strategies. This device can determine game strategies that match the player's operating habits, game preferences, and skill level based on the player's historical performance in the game and the task requirements of the current task to be performed, thereby improving the personalization and task adaptability of the generated game strategies. Specifically, the acquisition module 11 acquires the player's historical operation data in the game and the task data of the current task to be performed; the feature module 12 determines the player's operation characteristics, preference characteristics, and skill characteristics based on the historical operation data; and the strategy module 13 generates a game strategy for performing the task to be performed based on the operation characteristics, preference characteristics, skill characteristics, and task data. Through the cooperation of these modules, the device avoids the problem that fixed game strategies are difficult to adapt to the differences in the operating abilities and preferences of different players, making the generated game strategies more consistent with the player's actual operating state and task execution requirements, thus improving the accuracy and effectiveness of the game strategy recommendations.

[0120] The game strategy generation apparatus provided in this application embodiment can be used to execute the game strategy generation method provided in the above embodiment, and has corresponding functions and beneficial effects.

[0121] Figure 10 This is a schematic diagram of the structure of a game strategy generation device provided in an embodiment of this application, with reference to... Figure 10 The game strategy generation device includes a processor 21, a memory 22, a communication device 23, an input device 24, and an output device 25. The number of processors 21 and the number of memories 22 in the game strategy generation device can be one or more. The processor 21, memory 22, communication device 23, input device 24, and output device 25 of the game strategy generation device can be connected via a bus or other means.

[0122] The memory 22, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the game strategy generation method in any embodiment of this application (e.g., the acquisition module 11, feature module 12, and strategy module 13 in the game strategy generation apparatus). The memory 22 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application required for a function; the data storage area may store data created based on the use of the device, etc. Furthermore, the memory 22 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0123] The communication device 23 is used for data transmission.

[0124] The processor 21 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 22, thereby realizing the game strategy generation method described above.

[0125] Input device 24 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 25 may include display devices such as a display screen.

[0126] The game strategy generation device provided above can be used to execute the game strategy generation method provided in the above embodiments, and has corresponding functions and beneficial effects.

[0127] This application embodiment also provides a storage medium containing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to execute a game strategy generation method. The game strategy generation method includes: acquiring historical operation data of a player in a game and task data of a current task to be executed; determining the player's operation characteristics, preference characteristics, and skill characteristics based on the historical operation data; and generating a game strategy to execute the task to be executed based on the operation characteristics, preference characteristics, skill characteristics, and task data.

[0128] Storage medium—any type of memory device or storage device. The term "storage medium" is intended to include: mounting media, such as CD-ROM, floppy disk, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. Storage medium may also include other types of memory or combinations thereof. Furthermore, storage medium may reside in a first computer system in which a program is executed, or it may reside in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media residing in different locations (e.g., in different computer systems connected via a network). Storage medium may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0129] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the game strategy generation method described above, but can also execute related operations in the game strategy generation method provided in any embodiment of this application.

[0130] The game strategy generation apparatus, storage medium, and game strategy generation device provided in the above embodiments can execute the game strategy generation method provided in any embodiment of this application. For technical details not described in detail in the above embodiments, please refer to the game strategy generation method provided in any embodiment of this application.

[0131] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application. The scope of this application is determined by the scope of the claims.

Claims

1. A method for generating game strategies, characterized in that, include: Acquire the player's historical action data in the game, as well as the task data of the current pending task; The player's operational characteristics, preference characteristics, and skill characteristics are determined based on the historical operation data. A game strategy for executing the task to be performed is generated based on the operational characteristics, preference characteristics, skill characteristics, and task data.

2. The game strategy generation method according to claim 1, characterized in that, The step of determining the player's operational characteristics, preference characteristics, and skill characteristics based on the historical operational data includes: Extract operation input records, skill release records, and game interaction records from the historical operation data; The player's operational characteristics are determined based on the operational input records; The player's preference characteristics are determined based on the skill release records and game interaction records; A skill release sequence is generated based on the skill release record, and the player's skill characteristics are determined based on the skill release sequence.

3. The game strategy generation method according to claim 2, characterized in that, Determining the player's operational characteristics based on the operational input record includes: An operation input sequence is generated based on the operation input record, the operation input sequence including multiple operation input events arranged in time; The player's operation frequency and operation area distribution are determined based on the operation input sequence; The player's operational characteristics are determined based on the operational frequency and the operational area distribution.

4. The game strategy generation method according to claim 2, characterized in that, The process of determining the player's preference characteristics based on the skill release records and game interaction records includes: The player's skill type preference is determined based on the skill release records; The player's interaction type preference is determined based on the game interaction records; The player's preference characteristics are determined based on the skill type preference and interaction type preference.

5. The game strategy generation method according to any one of claims 1-4, characterized in that, The step of generating a game strategy for executing the task to be executed based on the operational features, the preference features, the skill features, and the task data includes: Based on the task data, generate multiple candidate game strategies for executing the task to be executed; The strategy fit score of each candidate game strategy is determined based on the operational characteristics, the preference characteristics, and the skill characteristics. The candidate game strategy with the highest strategy fit score is determined as the game strategy for executing the task to be performed.

6. The game strategy generation method according to any one of claims 1-4, characterized in that, After generating the game strategy for executing the task based on the operational features, preference features, skill features, and task data, the process further includes: Obtain the player's real-time operation data; Calculate the operation difference value based on the real-time operation data and the historical operation data; If the operational difference value is greater than a set difference value threshold, an updated game strategy for the task to be executed is generated based on the real-time operational data and the task data.

7. The game strategy generation method according to claim 6, characterized in that, The step of calculating the operation difference value based on the real-time operation data and the historical operation data includes: Determine the historical behavior feature vector based on the historical operation data; Determine the real-time behavior feature vector based on the real-time operation data; Calculate the Euclidean distance between the historical behavior feature vector and the real-time behavior feature vector to obtain the operation difference value.

8. A game strategy generation device, characterized in that, include: The acquisition module is configured to acquire the player's historical operation data in the game, as well as the task data of the current task to be performed; The feature module is configured to determine the player's operation features, preference features, and skill features based on the historical operation data; The strategy module is configured to generate a game strategy for executing the task to be executed based on the operation characteristics, the preference characteristics, the skill characteristics, and the task data.

9. A game strategy generation device, characterized in that, include: One or more processors; A memory that stores one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the game strategy generation method as described in any one of claims 1-7.

10. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the game strategy generation method as described in any one of claims 1-7.

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