Method and System for Dynamically Controlling the Drop of Game Items Based on a Game Player Portrait Model

Abstract

The present invention provides a method and system for dynamically controlling the dropping of game props based on a game player portrait model. The game behavior data of game players is disassembled and analyzed to obtain effective game behavior data, and a game behavior representation model is constructed based on this; based on the requirements of the game scenario, the attribute information of game props is determined, and a game prop pool matching the game scenario is designed based on this; based on the game behavior representation model, the game behavior characteristic information of game players in different game scenarios is obtained, and a game player portrait model is generated based on this; combining the game player portrait model and the game prop pool, a probability table between game behavior and prop dropping is generated to provide a basis for prop dropping; based on the game prop dropping records in the actual game process, the game prop dropping error data is determined, and the dropping weight distribution of the corresponding game props in the probability table is adjusted based on this, and a game prop dropping system based on game player behavior is established to improve the personalization and reliability of game prop dropping control.

Description

Technical Field

[0001] The present invention relates to the technical field of game development, and particularly to a method and system for dynamically controlling the dropping of game props based on a game player portrait model. Background Art

[0002] In casual game applications, a large number of game scenarios involve the dropping of game props. For example, when a game player successfully completes a corresponding task or clears a level in a game scenario, the game scenario will automatically drop corresponding game props as rewards, etc. This requires randomly selecting one or more game props from a reward pool. In order to balance the diversity of props among different game players, it is necessary to construct a random model for the dropping of game props for different game players. The existing random models have a single structure and a large amount of modification work. When designing the dropping of game props for different game players, the cost of model change is high, the update and iteration are complex, and it is impossible to balance the diverse and differential needs of the dropping of game props among different game players. It is impossible to establish a game prop dropping system based on game player behavior, reducing the personalization and reliability of game prop dropping control. Summary of the Invention

[0003] Aiming at the defects existing in the prior art, the present invention provides a method and system for dynamically controlling the dropping of game props based on a game player portrait model. The game behavior data of game players is disassembled and analyzed to obtain effective game behavior data, and based on this, a game behavior characterization model is constructed to accurately characterize the behavior patterns of game players; based on the requirements of the game scenario, the attribute information of game props is determined, and based on this, a game prop pool matching the game scenario is designed to ensure the practicability of game props; based on the game behavior characterization model, the game behavior characteristic information of game players in different game scenarios is obtained, and based on this, a game player portrait model is generated to accurately characterize the game behavior habits of game players; combining the game player portrait model and the game prop pool, a probability table between game behavior and prop dropping is generated to provide a basis for prop dropping; based on the game prop dropping records in the actual game process, the game prop dropping error data is determined, and based on this, the dropping weight distribution of the corresponding game props in the probability table is adjusted to perform dynamic feedback control on game prop dropping, establish a game prop dropping system based on game player behavior, and improve the personalization and reliability of game prop dropping control.

[0004] The present invention provides a method for dynamically controlling the dropping of game props based on a game player portrait model, including the following steps:

[0005] Step S1, obtain the game behavior data of game players, disassemble and analyze the game behavior data to obtain the effective game behavior data of the game players; then, based on all the effective game behavior data, construct a game behavior characterization model matching the game players;

[0006] Step S2, determine the game prop attribute information that matches the game scenario based on the game scenario requirements; design a game prop pool that matches the game scenario based on the game prop attribute information;

[0007] Step S3, obtain the game behavior characteristic information of the game player in different game scenarios based on the game behavior representation model; generate a game player portrait model based on the game behavior characteristic information; generate a probability table between the game behavior of the game player and the prop drop based on the game player portrait model and the game prop pool;

[0008] Step S4, obtain the game prop drop record of the game player in the actual game process, analyze the game prop drop record, and determine the game prop drop error data; adjust the drop weight distribution of the corresponding game props in the probability table based on the game prop drop error data.

[0009] In an embodiment disclosed in the present application, in the step S1, obtain the game behavior data of the game player, disassemble and analyze the game behavior data to obtain the effective game behavior data of the game player; then, based on all the effective game behavior data, construct a game behavior representation model that matches the game player, including:

[0010] Based on the identity information of the game player on the game client, obtain the game behavior data of the game player in the historical game process; disassemble the game behavior data to obtain all the game behavior data initiated by the game player in each game scenario, so as to generate several game behavior data sets corresponding one by one to all the game scenarios; obtain the occurrence frequency of each type of game behavior under the game behavior data set, and if the occurrence frequency is greater than or equal to the preset frequency threshold, determine the game behavior data of the corresponding type as the effective game behavior data;

[0011] Perform game scenario content and game behavior association recognition on the effective game behavior data corresponding to all game scenarios to obtain game scenario content and game behavior mapping data; perform neural network learning processing on the game scenario content and game behavior mapping data to construct a game behavior representation module that matches the game player.

[0012] In an embodiment disclosed in the present application, in the step S2, determine the game prop attribute information that matches the game scenario based on the game scenario requirements; design a game prop pool that matches the game scenario based on the game prop attribute information, including:

[0013] Analyze and process the requirements of the game scenario to obtain the visual adjustment requirements and game reward requirements of the game scenario during operation; based on the visual adjustment requirements and the game reward requirements, determine the visual attribute information and functional attribute information of game props that match the game scenario;

[0014] Based on the visual attribute information and functional attribute information of the game props, design corresponding game props; after performing visual differentiation processing on all the designed game props, integrate all the game props into a game prop pool.

[0015] In an embodiment disclosed in the present application, in the step S3, based on the game behavior characterization model, obtain the game behavior characteristic information of the game player in different game scenarios; based on the game behavior characteristic information, generate a game player portrait model; based on the game player portrait model and the game prop pool, generate a probability table between the game behavior of the game player and the prop drop, including:

[0016] Input the game rule information corresponding to different game scenarios into the game behavior characterization model to obtain the game behavior characteristic information of the game player under the game rule information corresponding to different game scenarios; wherein, the game behavior characteristic information includes the type of action behavior selected and implemented by the game player in the game scenario and the success rate of the action behavior execution; based on the game behavior characteristic information, construct a game player portrait model that can characterize the game behavior habits of the game player;

[0017] Based on the game player portrait model, determine the scoring rules corresponding to different action behaviors of the game player in different game scenarios; then, based on the scoring rules and the game prop pool, generate a corresponding prop drop probability mapping table for the game player when making different game behaviors in different game scenarios, and use this as the probability table between the game behavior of the game player and the prop drop.

[0018] In an embodiment disclosed in the present application, in the step S4, obtain the game prop drop record of the game player during the actual game process, analyze the game prop drop record, and determine the game prop drop error data; based on the game prop drop error data, adjust the drop weight distribution of the corresponding game props in the probability table, including:

[0019] Obtain the attribute information corresponding to the game prop drop event that occurs during the actual game process of the game player; wherein, the attribute information includes the type of game prop dropped corresponding to the game prop drop event and whether the game player successfully completes the corresponding game behavior; based on the attribute information, determine whether a game prop error drop event occurs currently, so as to generate game prop drop error data;

[0020] Analyze the incorrect data of the game item drops to determine the probability of an incorrect drop event for the corresponding game item; then, based on the probability of the incorrect drop event, adjust the drop weight allocation value of the corresponding game item in the probability table.

[0021] The present invention also provides a dynamic game item drop control system based on a game player portrait model, including:

[0022] A game behavior data disassembling and analyzing module, configured to obtain the game behavior data of a game player, disassemble and analyze the game behavior data, and obtain the effective game behavior data of the game player;

[0023] A game behavior characterization model construction module, configured to construct a game behavior characterization model matching the game player based on all the effective game behavior data;

[0024] A game item attribute determination module, configured to determine the game item attribute information matching the game scene based on the game scene requirements;

[0025] A game item pool design module, configured to design a game item pool matching the game scene based on the game item attribute information;

[0026] A player portrait model generation module, configured to obtain the game behavior characteristic information of the game player in different game scenes based on the game behavior characterization model; and generate a game player portrait model based on the game behavior characteristic information;

[0027] A prop drop probability table generation module, configured to generate a probability table between the game behavior of the game player and the prop drop based on the game player portrait model and the game item pool;

[0028] A game item incorrect drop identification module, configured to obtain the game item drop record of the game player during the actual game process, analyze the game item drop record, and determine the incorrect game item drop data;

[0029] A probability table correction module, configured to adjust the drop weight allocation of the corresponding game item in the probability table based on the incorrect game item drop data.

[0030] In an embodiment disclosed in the present application, the game behavior data disassembling and analyzing module is configured to obtain the game behavior data of a game player, disassemble and analyze the game behavior data, and obtain the effective game behavior data of the game player, including:

[0031] Based on the identity information of the game player on the game client, obtain the game behavior data of the game player during the historical game process; disassemble the game behavior data to obtain all the game behavior data initiated by the game player corresponding to each game scenario, so as to generate several game behavior data sets corresponding one by one to all game scenarios; obtain the occurrence frequency of each type of game behavior under the game behavior data set, and if the occurrence frequency is greater than or equal to the preset frequency threshold, determine the game behavior data of the corresponding type as valid game behavior data;

[0032] The game behavior characterization model construction module is used to construct a game behavior characterization model matching the game player based on all valid game behavior data, including:

[0033] Perform game scenario content and game behavior association recognition on the valid game behavior data corresponding to all game scenarios to obtain game scenario content and game behavior mapping data; perform neural network learning processing on the game scenario content and game behavior mapping data to construct a game behavior characterization module matching the game player.

[0034] In an embodiment disclosed in the present application, the game item attribute determination module is used to determine game item attribute information matching the game scenario based on the game scenario requirements, including:

[0035] Analyze and process the game scenario requirements to obtain the screen visual adjustment requirements and game reward requirements during the operation of the game scenario; based on the screen visual adjustment requirements and the game reward requirements, determine the game item visual attribute information and game item function attribute information matching the game scenario;

[0036] The game item pool design module is used to design a game item pool matching the game scenario based on the game item attribute information, including:

[0037] Design corresponding game items based on the game item visual attribute information and the game item function attribute information; after performing visual differentiation processing on all the designed game items, integrate all the game items into a game item pool.

[0038] In an embodiment disclosed in the present application, the player screen model generation module is used to obtain the game behavior feature information of the game player in different game scenarios based on the game behavior characterization model; based on the game behavior feature information, generate a game player portrait model, including:

[0039] Input the game rule information corresponding to different game scenarios into the game behavior characterization model to obtain the game behavior characteristic information of the game player under the game rule information corresponding to different game scenarios; wherein, the game behavior characteristic information includes the type of action behavior selected and implemented by the game player in the game scenario and the success rate of action behavior execution; based on the game behavior characteristic information, construct a game player portrait model that can characterize the game behavior habits of the game player.

[0040] The prop drop probability table generation module is used to generate a probability table between the game behavior of the game player and prop drops based on the game player portrait model and the game prop pool, including:

[0041] Based on the game player portrait model, determine the scoring rules corresponding to different action behaviors of the game player in different game scenarios; then, based on the scoring rules and the game prop pool, generate a corresponding prop drop probability mapping table for the game player when making different game behaviors in different game scenarios, and use this as the probability table between the game behavior of the game player and prop drops.

[0042] In an embodiment disclosed in the present application, the game prop incorrect drop identification module is used to obtain the game prop drop record of the game player during the actual game process, analyze the game prop drop record, and determine the game prop drop error data, including:

[0043] Obtain the attribute information corresponding to the game prop drop event that occurs during the actual game process of the game player; wherein, the attribute information includes the type of game prop dropped corresponding to the game prop drop event and whether the game player successfully completes the corresponding game behavior; based on the attribute information, determine whether an incorrect game prop drop event occurs currently, so as to generate the game prop drop error data.

[0044] The probability table correction module is used to adjust the drop weight distribution of the corresponding game props in the probability table based on the game prop drop error data, including:

[0045] Analyze the game prop drop error data to determine the probability of the corresponding game prop having an incorrect drop event; then, based on the probability of the incorrect drop event, adjust the drop weight distribution value of the corresponding game prop in the probability table.

[0046] Compared with the prior art, the game item dynamic drop control method and system based on the game player portrait model disassemble and analyze the game behavior data of game players to obtain effective game behavior data, and build a game behavior representation model based on this to accurately represent the behavior patterns of game players; based on the requirements of the game scenario, determine the game item attribute information, and design a game item pool that matches the game scenario based on this to ensure the practicality of the game items; based on the game behavior representation model, obtain the game behavior characteristic information of game players in different game scenarios, and generate a game player portrait model based on this to accurately represent the game behavior habits of game players; combine the game player portrait model and the game item pool to generate a probability table between game behavior and item drop to provide a basis for item drop; based on the game item drop records in the actual game process, determine the game item drop error data, and adjust the drop weight distribution of the corresponding game items in the probability table based on this to perform dynamic feedback control on the game item drop, establish a game item drop system based on the game player behavior, and improve the personalization and reliability of the game item drop control.

[0047] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification, claims, and drawings.

[0048] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 It is a schematic flowchart of the game item dynamic drop control method based on the game player portrait model provided by the present invention.

[0051] Figure 2 It is a schematic framework diagram of the game item dynamic drop control system based on the game player portrait model provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0053] Refer to Figure 1 , which is a schematic flowchart of a game item dynamic dropping control method based on a game player portrait model provided by an embodiment of the present invention. The game item dynamic dropping control method based on the game player portrait model includes:

[0054] Step S1, obtain the game behavior data of the game player, disassemble and analyze the game behavior data to obtain the effective game behavior data of the game player; then, based on all the effective game behavior data, construct a game behavior characterization model matching the game player;

[0055] Step S2, based on the game scene requirements, determine the game item attribute information matching the game scene; based on the game item attribute information, design a game item pool matching the game scene;

[0056] Step S3, based on the game behavior characterization model, obtain the game behavior characteristic information of the game player in different game scenes; based on the game behavior characteristic information, generate a game player portrait model; based on the game player portrait model and the game item pool, generate a probability table between the game behavior of the game player and the item drop;

[0057] Step S4, obtain the game item drop record of the game player in the actual game process, analyze the game item drop record to determine the game item drop error data; based on the game item drop error data, adjust the drop weight distribution of the corresponding game items in the probability table.

[0058] The beneficial effects of the above technical solution are as follows: The method for dynamically controlling the dropping of game props based on the game player portrait model disassembles and analyzes the game behavior data of game players to obtain effective game behavior data, constructs a game behavior representation model based on this, and accurately represents the behavior patterns of game players; based on the requirements of the game scenario, determines the attribute information of game props, and designs a game prop pool that matches the game scenario based on this to ensure the practicality of game props; based on the game behavior representation model, obtains the game behavior characteristic information of game players in different game scenarios, generates a game player portrait model based on this, and accurately represents the game behavior habits of game players; combines the game player portrait model and the game prop pool to generate a probability table between game behavior and prop dropping, providing a basis for prop dropping; based on the game prop dropping records in the actual game process, determines the game prop dropping error data, and adjusts the dropping weight distribution of the corresponding game props in the probability table based on this to perform dynamic feedback control on game prop dropping, establish a game prop dropping system based on game player behavior, and improve the personalization and reliability of game prop dropping control.

[0059] Preferably, in step S1, obtain the game behavior data of game players, disassemble and analyze the game behavior data to obtain the effective game behavior data of the game players; then, based on all the effective game behavior data, construct a game behavior representation model that matches the game players, including:

[0060] Based on the identity information of game players on the game client, obtain the game behavior data of game players in the historical game process; disassemble the game behavior data to obtain all the game behavior data initiated by game players corresponding to each game scenario, thereby generating several game behavior data sets corresponding one by one to all game scenarios; obtain the occurrence frequency of each type of game behavior under the game behavior data set. If the occurrence frequency is greater than or equal to the preset frequency threshold, determine the game behavior data of the corresponding type as effective game behavior data;

[0061] Perform game scenario content and game behavior association recognition on the effective game behavior data corresponding to all game scenarios to obtain game scenario content and game behavior mapping data; perform neural network learning processing on the game scenario content and game behavior mapping data to construct a game behavior representation module that matches the game players.

[0062] The beneficial effects of the above technical solution are as follows: The game behaviors implemented by different game players in the same game are not the same, resulting in different success situations of clearing the game for different game players. In order to provide game item drop rewards specifically applicable to different game players, it is necessary to identify the game behaviors of game players. For this purpose, based on the identity information of game players on the game client, the game behavior data of game players in the historical game process is obtained; the game behavior data is disassembled to obtain all the game behavior data initiated by game players corresponding to each game scenario, thereby generating several game behavior data sets corresponding one by one to all game scenarios. In this way, each game behavior data set can accurately and comprehensively reflect the game behavior status of game players corresponding to each game scenario. Then, obtain the occurrence frequency of each type of game behavior under the game behavior data set. If the occurrence frequency is greater than or equal to the preset frequency threshold, the game behavior data of the corresponding type is determined as valid game behavior data. In this way, the game behaviors habitually made by game players in the game scenario can be accurately extracted, providing a reliable data source for the subsequent extraction of the game behavior habits presented by game players. Additionally, perform game scenario content and game behavior association recognition on the valid game behavior data corresponding to all game scenarios to obtain game scenario content and game behavior mapping data, and construct a game behavior representation module matching the game player through neural network learning, so that the game behavior representation module can accurately associate the game scenario content with the game behaviors made by the game player.

[0063] Preferably, in step S2, based on the game scenario requirements, determine the game item attribute information matching the game scenario; based on the game item attribute information, design a game item pool matching the game scenario, including:

[0064] Analyze and process the game scenario requirements to obtain the visual adjustment requirements and game reward requirements during the operation of the game scenario; based on the visual adjustment requirements and the game reward requirements of the game scenario, determine the game item visual attribute information and game item function attribute information matching the game scenario;

[0065] Based on the game item visual attribute information and the game item function attribute information, design corresponding game items; after performing visual differentiation processing on all the designed game items, integrate all the game items into the game item pool.

[0066] The beneficial effects of the above technical solution are as follows: The scene contents of different game scenes under the game are not the same. The visual effects of the pictures and the prop rewards corresponding to the corresponding game behaviors of game players in different game scenes are also different. In order to adapt to the operation of different game scenes, the requirements of the game scenes are analyzed and processed to obtain the visual adjustment requirements of the pictures and the game reward requirements during the operation of the game scenes. In this way, the visual attribute information and the functional attribute information of game props that match the game scenes can be accurately determined, so as to design the visual appearance and prop functions of game props subsequently and ensure that they match the current game scene. After performing visual differentiation processing on all the designed game props, all the game props are integrated into a game prop pool, which can avoid the same game props for different game behaviors in the same game scene and improve the uniqueness of the props in the game prop pool.

[0067] Preferably, in this step S3, based on the game behavior representation model, obtain the game behavior characteristic information of game players in different game scenes; based on the game behavior characteristic information, generate a game player portrait model; based on the game player portrait model and the game prop pool, generate a probability table between the game behaviors of game players and prop drops, including:

[0068] Input the game rule information corresponding to different game scenes into the game behavior representation model to obtain the game behavior characteristic information of game players under the game rule information corresponding to different game scenes; wherein, the game behavior characteristic information includes the type of action behavior selected and implemented by the game player in the game scene and the success rate of action behavior execution; based on the game behavior characteristic information, construct a game player portrait model that can represent the game behavior habits of the game player.

[0069] Based on the game player portrait model, determine the scoring rules corresponding to different action behaviors of the game player in different game scenes; then, based on the scoring rules and the game prop pool, generate a corresponding prop drop probability mapping table when the game player makes different game behaviors in different game scenes, and use this as the probability table between the game behaviors of the game player and prop drops.

[0070] The beneficial effects of the above technical solution are as follows: Input the game rule information corresponding to different game scenarios into the game behavior characterization model to obtain the game behavior characteristic information of the game player under the game rule information corresponding to different game scenarios. In this way, it is possible to screen the game action behaviors and their execution success rates of the game player during the game process, thereby providing reliable and rich data support for constructing a game player portrait model that matches the game player. Additionally, based on the game player portrait model, determine the scoring rules corresponding to different action behaviors of the game player in different game scenarios. The scoring rules can be, but are not limited to, the rules for the game client to give corresponding score values to the action behaviors after the game player makes corresponding action behaviors in the corresponding game scenarios, realizing the quantitative evaluation of each action behavior. Also, based on the scoring rules and the game item pool, generate a mapping table of the item drop probabilities corresponding to different game behaviors of the game player in different game scenarios, which is used as the probability table between the game player's game behaviors and item drops. This probability table is used to represent the probability of item drops corresponding to different game behaviors of the game player in different game scenarios, thereby providing a basis for game item drop control.

[0071] Preferably, in step S4, obtain the game item drop records of the game player during the actual game process, analyze the game item drop records, and determine the game item drop error data; based on the game item drop error data, adjust the drop weight distribution of the corresponding game items in the probability table, including:

[0072] Obtain the attribute information corresponding to the game item drop event that occurs during the actual game process of the game player; wherein, the attribute information includes the type of game item dropped corresponding to the game item drop event and whether the game player successfully completes the corresponding game behavior; based on the attribute information, determine whether a game item error drop event occurs currently, thereby generating game item drop error data;

[0073] Analyze the game item drop error data to determine the probability of the corresponding game item having an error drop event; then, based on the probability of the error drop event, adjust the drop weight distribution value of the corresponding game item in the probability table.

[0074] The beneficial effects of the above technical solution are as follows: Obtain the attribute information corresponding to the game item drop event that occurs during the actual game process of the game player. Through this attribute information, characterize the type of game item dropped when the game item drop event occurs and whether the game player successfully completes the corresponding game behavior, so as to accurately determine whether a game item mis-drop event occurs currently, generate game item drop error data based on this, and accurately determine the error event that occurs when controlling game item drops according to this probability table. Additionally, analyze the game item drop error data to determine the probability of the corresponding game item having a mis-drop event, adjust the drop weight allocation value of the corresponding game item in this probability table based on this, accurately establish a game item drop system based on the game player's behavior, and improve the personalization and reliability of game item drop control.

[0075] Refer to Figure 2 , which is a schematic framework diagram of a game item dynamic drop control system based on a game player portrait model provided by an embodiment of the present invention. The game item dynamic drop control system based on the game player portrait model includes:

[0076] A game behavior data decomposition and analysis module, which is used to obtain the game behavior data of the game player, decompose and analyze the game behavior data, and obtain the effective game behavior data of the game player;

[0077] A game behavior characterization model construction module, which is used to construct a game behavior characterization model that matches the game player based on all effective game behavior data;

[0078] A game item attribute determination module, which is used to determine the game item attribute information that matches the game scene based on the game scene requirements;

[0079] A game item pool design module, which is used to design a game item pool that matches the game scene based on the game item attribute information;

[0080] A player portrait model generation module, which is used to obtain the game behavior characteristic information of the game player in different game scenes based on the game behavior characterization model; generate a game player portrait model based on the game behavior characteristic information;

[0081] A prop drop probability table generation module, which is used to generate a probability table between the game behavior of the game player and prop drops based on the game player portrait model and the game item pool;

[0082] A game item mis-drop identification module, which is used to obtain the game item drop record of the game player during the actual game process, analyze the game item drop record, and determine the game item drop error data;

[0083] A probability table correction module, which is used to adjust the distribution of the dropping weights of the corresponding game items in the probability table based on the game item dropping error data.

[0084] The beneficial effects of the above technical solution are as follows: The game item dynamic dropping control system based on the game player portrait model disassembles and analyzes the game behavior data of game players to obtain effective game behavior data, and constructs a game behavior representation model based on this to accurately represent the behavior patterns of game players; based on the requirements of the game scenario, determine the attribute information of game items, and design a game item pool that matches the game scenario based on this to ensure the practicality of game items; based on the game behavior representation model, obtain the game behavior characteristic information of game players in different game scenarios, and generate a game player portrait model based on this to accurately represent the game behavior habits of game players; combine the game player portrait model and the game item pool to generate a probability table between game behavior and item dropping, providing a basis for item dropping; based on the game item dropping records in the actual game process, determine the game item dropping error data, and adjust the distribution of the dropping weights of the corresponding game items in the probability table based on this to perform dynamic feedback control on game item dropping, establish a game item dropping system based on game player behavior, and improve the personalization and reliability of game item dropping control.

[0085] Preferably, the game behavior data disassembly and analysis module is used to obtain the game behavior data of game players, disassemble and analyze the game behavior data, and obtain the effective game behavior data of the game players, including:

[0086] Based on the identity information of the game player on the game client, obtain the game behavior data of the game player in the historical game process; disassemble the game behavior data to obtain all the game behavior data initiated by the game player corresponding to each game scenario, so as to generate several game behavior data sets corresponding one by one to all game scenarios; obtain the occurrence frequency of each type of game behavior under the game behavior data set, and if the occurrence frequency is greater than or equal to the preset frequency threshold, determine the game behavior data of the corresponding type as effective game behavior data.

[0087] The game behavior representation model construction module is used to construct a game behavior representation model that matches the game player based on all effective game behavior data, including:

[0088] Perform game scenario content and game behavior association recognition on the effective game behavior data corresponding to all game scenarios to obtain game scenario content and game behavior mapping data; perform neural network learning processing on the game scenario content and game behavior mapping data to construct a game behavior representation module that matches the game player.

[0089] The beneficial effects of the above technical solution are as follows: The gaming behaviors implemented by different game players in the same game are not the same, resulting in different success situations in clearing the game for different game players. In order to provide game item drop rewards specifically applicable to different game players, it is necessary to identify the gaming behaviors of game players. For this purpose, based on the identity information of game players on the game client, the gaming behavior data of game players in the historical gaming process is obtained; the gaming behavior data is disassembled to obtain all the gaming behavior data initiated by game players corresponding to each game scenario, thereby generating several gaming behavior data sets corresponding one by one to all game scenarios. In this way, each gaming behavior data set can accurately and comprehensively reflect the gaming behavior state of game players in the game scenario corresponding to each game scenario. Then, the occurrence frequency of each type of gaming behavior under the gaming behavior data set is obtained. If the occurrence frequency is greater than or equal to the preset frequency threshold, the gaming behavior data of the corresponding type is determined as valid gaming behavior data. In this way, the gaming behaviors habitually made by game players in the game scenario can be accurately extracted, providing a reliable data source for the subsequent extraction of the gaming behavior habits presented by game players. Additionally, the gaming scenario content and gaming behavior association recognition are performed on the valid gaming behavior data corresponding to all game scenarios to obtain the mapping data of the gaming scenario content and gaming behavior, and a gaming behavior representation module matching the game player is constructed through neural network learning, enabling the gaming behavior representation module to accurately associate the gaming scenario content with the gaming behaviors made by the game player.

[0090] Preferably, the game item attribute determination module is used to determine the game item attribute information matching the game scenario based on the game scenario requirements, including:

[0091] Analyze and process the game scenario requirements to obtain the visual adjustment requirements and game reward requirements of the game scenario during operation; based on the visual adjustment requirements and the game reward requirements of the game scenario, determine the visual attribute information and game item function attribute information of the game item matching the game scenario;

[0092] The game item pool design module is used to design a game item pool matching the game scenario based on the game item attribute information, including:

[0093] Design corresponding game items based on the visual attribute information and game item function attribute information of the game item; after performing visual differentiation processing on all the designed game items, integrate all the game items into the game item pool.

[0094] The beneficial effects of the above technical solution are as follows: The scene contents of different game scenes under the game are not the same. The visual effects of the pictures and the prop rewards corresponding to the corresponding game behaviors of game players in different game scenes are also different. In order to adapt to the operation of different game scenes, the game scene requirements are analyzed and processed to obtain the picture visual adjustment requirements and game reward requirements during the operation of the game scene. In this way, the visual attribute information and game prop function attribute information of the game props matching the game scene can be accurately determined, so as to design the visual appearance and prop functions of the game props subsequently and ensure that they match the current game scene. After performing visual differentiation processing on all the designed game props, all the game props are integrated into a game prop pool, which can avoid the same game props for different game behaviors in the same game scene and improve the uniqueness of the props in the game prop pool.

[0095] Preferably, the player picture model generation module is used to obtain the game behavior characteristic information of game players in different game scenes based on the game behavior representation model; based on the game behavior characteristic information, a game player portrait model is generated, including:

[0096] Input the game rule information corresponding to different game scenes into the game behavior representation model to obtain the game behavior characteristic information of game players under the game rule information corresponding to different game scenes; wherein, the game behavior characteristic information includes the type of action behavior selected and implemented by the game player in the game scene and the success rate of action behavior execution; based on the game behavior characteristic information, a game player portrait model capable of representing the game behavior habits of the game player is constructed.

[0097] The prop drop probability table generation module is used to generate a probability table between the game behaviors of game players and prop drops based on the game player portrait model and the game prop pool, including:

[0098] Based on the game player portrait model, determine the scoring rules corresponding to different action behaviors of the game player in different game scenes; then, based on the scoring rules and the game prop pool, generate a corresponding prop drop probability mapping table for the game player when performing different game behaviors in different game scenes, and use this as the probability table between the game behaviors of the game player and prop drops.

[0099] The beneficial effects of the above technical solution are as follows: By inputting the game rule information corresponding to different game scenarios into the game behavior representation model, the game behavior characteristic information of the game player under the game rule information corresponding to different game scenarios can be obtained. In this way, the game actions and their execution success rates of the game player during the game process can be screened, providing reliable and rich data support for constructing a game player portrait model that matches the game player. Additionally, based on this game player portrait model, the scoring rules corresponding to different action behaviors of the game player in different game scenarios are determined. The scoring rules can be, but are not limited to, the rules for the game client to give corresponding score values to the action behaviors after the game player makes the corresponding action behaviors in the corresponding game scenarios, realizing the quantitative evaluation of each action behavior. Also, based on this scoring rule and the game item pool, a mapping table of the item drop probabilities corresponding to different game behaviors of the game player in different game scenarios is generated. This is used as the probability table between the game behaviors and item drops of the game player, which is used to represent the probabilities of item drops corresponding to different game behaviors of the game player in different game scenarios, thus providing a basis for game item drop control.

[0100] Preferably, the game item incorrect drop identification module is used to obtain the game item drop records of the game player during the actual game process, analyze the game item drop records, and determine the game item drop error data, including:

[0101] Obtain the attribute information corresponding to the game item drop event that occurs during the actual game process of the game player; wherein, the attribute information includes the type of game item dropped corresponding to the game item drop event and whether the game player successfully completes the corresponding game behavior; based on this attribute information, determine whether an incorrect game item drop event occurs currently, thereby generating game item drop error data;

[0102] The probability table correction module is used to adjust the drop weight distribution of the corresponding game items in the probability table based on the game item drop error data, including:

[0103] Analyze the game item drop error data to determine the probability of the corresponding game item having an incorrect drop event; then, based on the probability of the incorrect drop event, adjust the drop weight distribution value of the corresponding game item in the probability table.

[0104] The beneficial effects of the above technical solution are as follows: Obtain the attribute information corresponding to the game item drop event during the actual game process of the game player. Through this attribute information, characterize the type of game item dropped corresponding to the game item drop event and whether the game player successfully completes the corresponding game behavior, so as to accurately judge whether a game item mis-drop event occurs currently, generate game item drop error data based on this, and accurately determine the error event that occurs when controlling game item drops according to this probability table. Additionally, analyze the game item drop error data, determine the probability of the corresponding game item having a mis-drop event, adjust the drop weight allocation value of the corresponding game item in this probability table accordingly, accurately establish a game item drop system based on the behavior of game players, and improve the personalization and reliability of game item drop control.

[0105] In the actual control of game item drops, it can be implemented through the following specific embodiments:

[0106] Suppose a task reward in a game needs to randomly distribute 3 - 5 item rewards to players, and the available item types include 100 different types. These 100 different types of items have different usage effects. Due to different usage scenarios and values, it is necessary to obtain items of different values according to the player's behavior in the entire game and drop multiple items from them. Below, the user behavior and item drops in this game scenario will be simulated.

[0107] Now start the design and configuration of the item extraction random model.

[0108] Step 1, first create a behavior portrait for the user. For example, define behaviors such as killing, activities, tasks, etc. Among them, the login score is 0 - 5 points for the number of kills, 0 - 5 points for the duration of a single kill, 0 - 5 points for the kill time, and 0 - 5 points for the kill interval; the activity score is 0 - 5 points for the number of activities, 0 - 5 points for the activity duration, 0 - 5 points for the activity participation rate, and 0 - 5 points for the activity leaderboard; the task score is 0 - 5 points for the task completion rate and 0 - 5 points for the task time consumption.

[0109] Step 2, define the scoring status of multiple items consistent with the user behavior. For example, for item 1, when the kill score is greater than 10 points, the activity score is greater than 5 points, and the task score is greater than 15 points, this item will be dropped. The drop weight of this item is 0.3 for kill, 0.1 for activity, and 0.8 for task, and calculate the drop pool weight of this item.

[0110] Step 3, according to the behavior item score table of 100 items, obtain 20 item pools that meet the user behavior, and calculate the drop weight of each item in the 20 item pools.

[0111] Step 4, drop the list of items that meet the drop conditions in a small - scale item pool, that is, randomly select 3 - 5 game items according to the weight in 20 dynamically generated game item pools.

[0112] During the actual extraction process of players, different behaviors will dynamically form different player characteristics, enriching the playability of players.

[0113] It can be seen that, compared with the traditional probability distribution model, this random probability model has high self - adaptability and scalability, and can generate different gameplay methods according to the user behavior pattern.

[0114] From the content of the above - mentioned embodiments, the method and system for dynamically controlling the dropping of game props based on the game player portrait model disassemble and analyze the game behavior data of game players to obtain effective game behavior data, and thus construct a game behavior representation model to accurately represent the behavior patterns of game players; based on the game scene requirements, determine the game prop attribute information, and thus design a game prop pool that matches the game scene to ensure the practicality of game props; based on the game behavior representation model, obtain the game behavior characteristic information of game players in different game scenes, and thus generate a game player portrait model to accurately represent the game behavior habits of game players; combine the game player portrait model and the game prop pool to generate a probability table between game behavior and prop dropping to provide a basis for prop dropping; based on the game prop dropping records in the actual game process, determine the game prop dropping error data, and thus adjust the dropping weight distribution of the corresponding game props in the probability table to perform dynamic feedback control on the game prop dropping, establish a game prop dropping system based on game player behavior, and improve the personalization and reliability of game prop dropping control.

[0115] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A method for controlling the dynamic drop of game props based on a game player portrait model, characterized in that: It includes the following steps: Step S1, obtaining game behavior data of a game player, disassembling and analyzing the game behavior data to obtain valid game behavior data of the game player; and then constructing a game behavior representation model matching the game player based on all valid game behavior data; Step S2, based on the game scene requirements, determine the game prop attribute information that matches the game scene; Based on the game prop attribute information, design a game prop pool that matches the game scene; Step S3, based on the game behavior characterization model, obtaining game player's game behavior feature information in different game scenarios; based on the game behavior feature information, generating a game player portrait model; based on the game player portrait model and the game prop pool, generating a probability table between the game player's game behavior and prop drop; Step S4, obtaining a game item drop record of a game player during an actual game process, analyzing the game item drop record, and determining game item drop error data; Based on the game item drop error data, adjusting the drop weight distribution of the corresponding game item in the probability table; Wherein, in the step S3, based on the game behavior representation model, game behavior feature information of game players in different game scenarios is obtained; based on the game behavior feature information, a game player portrait model is generated, including: The game rule information corresponding to different game scenes is input into the game behavior characterization model to obtain the game behavior feature information of the game players under the game rule information corresponding to different game scenes; wherein the game behavior feature information includes the action behavior type and the action behavior execution success rate selected by the game players in the game scenes; based on the game behavior feature information, a game player portrait model capable of characterizing the game behavior habits of the game players is constructed.

2. The method for controlling the dynamic drop of game props based on the game player portrait model according to claim 1, characterized in that: In the step S1, the game behavior data of the game player is obtained, and the game behavior data is analyzed to obtain the effective game behavior data of the game player; Based on all valid game behavior data, a game behavior representation model matching the game player is constructed, including: Based on the identity information of the game player on the game terminal, the game behavior data of the game player in the historical game process is obtained; the game behavior data is disassembled to obtain all the game behavior data initiated by the game player in each game scene, thereby generating a number of game behavior data sets corresponding to all the game scenes one by one; the occurrence frequency of each type of game behavior under the game behavior data set is obtained, and if the occurrence frequency is greater than or equal to a preset frequency threshold, the corresponding type of game behavior data is determined as valid game behavior data; The game scene content and game behavior association identification is performed on the valid game behavior data corresponding to all game scenes to obtain game scene content and game behavior mapping data; the game scene content and game behavior mapping data are processed by neural network learning to construct a game behavior representation module that matches the game player.

3. The method for controlling the dynamic drop of game props based on the game player portrait model according to claim 1, characterized in that: In step S2, based on the game scene requirements, game prop attribute information matching the game scene is determined; Based on the game props attribute information, a game prop pool matching the game scene is designed, including: Analyze and process the game scene requirements to obtain the screen visual adjustment requirements and game reward requirements of the game scene during operation; determine the game prop visual attribute information and game prop functional attribute information that match the game scene based on the screen visual adjustment requirements and the game reward requirements; Based on the visual attribute information of the game props and the functional attribute information of the game props, corresponding game props are designed; after visual differentiation processing is performed on all the designed game props, all the game props are integrated into a game prop pool.

4. The method for controlling the dynamic drop of game props based on the game player portrait model according to claim 1, characterized in that: In the step S3, based on the game player portrait model and the game prop pool, a probability table between the game player's game behavior and the prop drop is generated, including: Based on the game player portrait model, the scoring rules corresponding to the different actions performed by the game player in different game scenes are determined; then based on the scoring rules and the game prop pool, a prop drop probability mapping table corresponding to the game player performing different game behaviors in different game scenes is generated, which serves as a probability table between the game player's game behavior and prop drop.

5. The method for controlling the dynamic drop of game props based on the game player portrait model according to claim 1, characterized in that: In the step S4, a game item drop record of a game player during an actual game process is obtained, the game item drop record is analyzed, and game item drop error data is determined; Based on the game item drop error data, adjusting the drop weight distribution of the corresponding game item in the probability table includes: Acquire attribute information corresponding to a game item drop event that occurs during an actual game process; wherein the attribute information includes the type of game item that is dropped when the game item drop event occurs and whether the game player successfully completes the corresponding game behavior; based on the attribute information, determine whether a game item error drop event currently occurs, thereby generating game item error drop data; The game item drop error data is analyzed to determine the probability of an error drop event occurring for the corresponding game item; and based on the probability of the error drop event occurring, the drop weight distribution value of the corresponding game item in the probability table is adjusted.

6. A game prop dynamic drop control system based on a game player portrait model, characterized in that: include: A game behavior data disassembly and analysis module is used to obtain game behavior data of game players, disassemble and analyze the game behavior data, and obtain effective game behavior data of the game players; A game behavior representation model building module, used to build a game behavior representation model matching the game player based on all valid game behavior data; A game prop attribute determination module is used to determine the game prop attribute information that matches the game scene based on the game scene requirements; A game prop pool design module, used to design a game prop pool matching the game scene based on the game prop attribute information; A player screen model generation module is used to obtain game player behavior feature information in different game scenarios based on the game behavior representation model; and generate a game player portrait model based on the game behavior feature information; A prop drop probability table generation module, used to generate a probability table between a game player's game behavior and prop drop based on the game player portrait model and the game prop pool; A game item error drop recognition module is used to obtain game item drop records of game players during actual game play, analyze the game item drop records, and determine game item drop error data; A probability table correction module, used to adjust the drop weight distribution of the corresponding game props in the probability table based on the game prop drop error data; The player screen model generation module is used to obtain game player behavior feature information in different game scenarios based on the game behavior representation model; and generate a game player portrait model based on the game behavior feature information, including: The game rule information corresponding to different game scenes is input into the game behavior characterization model to obtain the game behavior feature information of the game players under the game rule information corresponding to different game scenes; wherein the game behavior feature information includes the action behavior type and the action behavior execution success rate selected by the game players in the game scenes; based on the game behavior feature information, a game player portrait model capable of characterizing the game behavior habits of the game players is constructed.

7. The game prop dynamic drop control system based on the game player portrait model as claimed in claim 6, characterized in that: The game behavior data disassembly and analysis module is used to obtain the game behavior data of the game player, disassemble and analyze the game behavior data, and obtain the effective game behavior data of the game player, including: Based on the identity information of the game player on the game terminal, the game behavior data of the game player in the historical game process is obtained; the game behavior data is disassembled to obtain all the game behavior data initiated by the game player in each game scene, thereby generating a number of game behavior data sets corresponding to all the game scenes one by one; the occurrence frequency of each type of game behavior under the game behavior data set is obtained, and if the occurrence frequency is greater than or equal to a preset frequency threshold, the corresponding type of game behavior data is determined as valid game behavior data; The game behavior representation model construction module is used to construct a game behavior representation model matching the game player based on all valid game behavior data, including: The game scene content and game behavior association identification is performed on the valid game behavior data corresponding to all game scenes to obtain game scene content and game behavior mapping data; the game scene content and game behavior mapping data are processed by neural network learning to construct a game behavior representation module that matches the game player.

8. The game prop dynamic drop control system based on the game player portrait model as claimed in claim 6, characterized in that: The game prop attribute determination module is used to determine the game prop attribute information matching the game scene based on the game scene requirements, including: Analyze and process the game scene requirements to obtain the screen visual adjustment requirements and game reward requirements of the game scene during operation; determine the game prop visual attribute information and game prop functional attribute information that match the game scene based on the screen visual adjustment requirements and the game reward requirements; The game prop pool design module is used to design a game prop pool matching the game scene based on the game prop attribute information, including: Based on the visual attribute information of the game props and the functional attribute information of the game props, corresponding game props are designed; after visual differentiation processing is performed on all the designed game props, all the game props are integrated into a game prop pool.

9. The game prop dynamic drop control system based on the game player portrait model as claimed in claim 6, characterized in that: The item drop probability table generation module is used to generate a probability table between the game player's game behavior and the item drop based on the game player portrait model and the game item pool, including: Based on the game player portrait model, the scoring rules corresponding to the different actions performed by the game player in different game scenes are determined; then based on the scoring rules and the game prop pool, a prop drop probability mapping table corresponding to the game player performing different game behaviors in different game scenes is generated, which serves as a probability table between the game player's game behavior and prop drop.

10. The game prop dynamic drop control system based on the game player portrait model as claimed in claim 6, characterized in that: The game props error drop identification module is used to obtain the game props drop records of the game players in the actual game process, analyze the game props drop records, and determine the game props drop error data, including: Acquire attribute information corresponding to a game item drop event that occurs during an actual game process; wherein the attribute information includes the type of game item that is dropped when the game item drop event occurs and whether the game player successfully completes the corresponding game behavior; based on the attribute information, determine whether a game item error drop event currently occurs, thereby generating game item error drop data; The probability table correction module is used to adjust the drop weight distribution of the corresponding game props in the probability table based on the game prop drop error data, including: The game item drop error data is analyzed to determine the probability of an error drop event occurring for the corresponding game item; and based on the probability of the error drop event occurring, the drop weight distribution value of the corresponding game item in the probability table is adjusted.

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