Game reward distribution method and device based on player data, equipment and medium

Abstract

The embodiment of the invention discloses a game reward distribution method and device based on player data, equipment and a medium, and the method comprises the steps that when a game task is started, channel data of game channels in a game are obtained, a player competition group and task target data are determined according to the channel data, and the player competition group comprises multiple players; acquiring player information of each player, calculating task reference judgment factors of the players based on the player information and the task target data, and acquiring first task completion data of the player competition group and second task completion data of the players when the game task is finished, and determining a game reward of each player based on the task target data, the task reference judgment factor, the first task completion data and the second task completion data. According to the scheme, the game rewards of the players are adaptively distributed based on the data of the players, the final game rewards can be reasonably determined and dynamically adjusted, the game rewards better fit the expectation of the players, and the matching degree of the game rewards and the game experience of the players are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of game technology, and in particular to a method, device, equipment and medium for distributing game rewards based on player data. Background Art

[0002] At present, most games have a game reward mechanism. The game sets tasks, achievements and other conditions, allowing players to obtain corresponding rewards by meeting specific conditions. For example, players can obtain gold coins, props and other rewards by completing specific tasks or achieving certain achievements. By setting fixed rewards, it can be ensured that players receive the expected rewards when completing specific tasks or reaching a certain level, thereby maintaining the fairness and sustainability of the game, and maintaining player participation and the attractiveness of the game.

[0003] With the development of the game industry, the competition in the game market is becoming increasingly fierce, and the needs of players are constantly changing. Among the relevant game reward mechanisms, most of them adopt fixed income and a single calculation method to design game rewards. In this kind of game reward mechanism, the game rewards are too fixed and single, and the reward data cannot be dynamically adjusted to meet the expectations of players, which affects the players' gaming experience. Summary of the invention

[0004] The embodiments of the present application provide a method, apparatus, device and medium for distributing game rewards based on player data, which solves the problem that the game rewards in the existing game reward mechanism are fixed and single, lack flexibility, and cannot dynamically adjust the reward data to meet the expectations of players, thereby affecting the player's gaming experience. The method can reasonably determine and dynamically adjust the final game reward, so that the game reward is more in line with the player's expectations, improves the matching degree of the game reward, and optimizes the player's gaming experience.

[0005] In a first aspect, an embodiment of the present application provides a method for distributing game rewards based on player data, including: when a game task is started, obtaining channel data of a game channel in a game, determining a player competition group and corresponding task target data according to the channel data, wherein the player competition group includes a plurality of players;

[0006] Obtaining player information of each of the players in the player competition group;

[0007] Calculate each player's task benchmark judgment factor based on the player information and the task target data;

[0008] When the game task is completed, obtaining the first task completion data of the player competition group and the second task completion data of each of the players;

[0009] A game reward for each player in the player competition group is determined based on the task target data, the task reference judgment factor, the first task completion data, and the second task completion data.

[0010] Optionally, the channel data includes a player ranking list and player chat data, and determining the player competition group and corresponding task target data according to the channel data includes:

[0011] Calculating the task weight value of each player according to the player ranking list and the player chat data, and grouping the players in the game channel to obtain a plurality of competition groups based on the task weight value and preset grouping conditions;

[0012] The corresponding task target data is determined according to the sum of the task weight values ​​of each player in the competition group.

[0013] Optionally, the player chat data includes the number of times the player speaks and the player chat duration, and the step of calculating the task weight value of each player according to the player ranking list and the player chat data includes:

[0014] Determine the ranking weight of the corresponding player according to the ranking order of each player in the player ranking list, and calculate the activity weight of each player based on the number of times the player speaks and the chat time of the player;

[0015] The corresponding task weight value is calculated according to the ranking weight and the activity weight of each player.

[0016] Optionally, the player information includes the player level and reward information before a preset time, and the task benchmark judgment factor is calculated based on the player information and the task target data, including:

[0017] Determine a target level corresponding to the task target data, and determine a variable parameter value in a preset factor calculation formula based on the target level;

[0018] determining a player's reward evaluation value based on the reward information;

[0019] The player level, the variable parameter value and the reward evaluation value are substituted into the preset factor calculation formula to calculate the task benchmark judgment factor.

[0020] Optionally, determining a game reward for each player in the player competition group based on the task target data, the task benchmark judgment factor, the first task completion data, and the second task completion data includes:

[0021] Determine the task reward to be adjusted according to the task target data and the set task reward library;

[0022] Adjusting the task reward to be adjusted based on the first task completion data to obtain a total game reward;

[0023] The total game reward is distributed according to the task benchmark judgment factor of each player and the second task completion data to obtain the corresponding game reward.

[0024] Optionally, the first task completion data includes task completion time and ranking information, and the step of adjusting the task reward to be adjusted based on the first task completion data to obtain a total game reward includes:

[0025] Determine the corresponding preset standard comparison time according to the ranking information of the player competition group, calculate the difference between the task completion time of the player competition group and the corresponding preset standard comparison time, and query the corresponding preset adjustment reward mapping table according to the difference to obtain the adjustment reward of the player competition group, wherein the adjustment reward includes an upward adjustment reward and a downward adjustment reward;

[0026] The task reward to be adjusted is adjusted based on the adjusted reward to obtain a total game reward.

[0027] Optionally, the second task completion data includes a percentage of completed tasks, and the allocation of the total game reward according to the task benchmark judgment factor of each player and the second task completion data to obtain a corresponding game reward includes:

[0028] Determine the preset standard proportion corresponding to the task benchmark judgment factor of each player, compare the task completion proportion of each player with the corresponding preset standard proportion to obtain players who fail to meet the standard, players who meet the standard, and players who exceed the standard, and calculate the unmet proportion value of the unmet players and the excess proportion value of the players who exceed the standard;

[0029] The reward distribution weight of each player in the player competition group is determined according to the non-compliance ratio, the excess ratio and the task completion ratio, and the reward distribution of each player in the player competition group is performed according to the reward distribution weight and the total game reward to obtain the corresponding game reward.

[0030] In a second aspect, the embodiment of the present application further provides a game reward distribution device based on player data, comprising:

[0031] The acquisition module is used to obtain the channel data of the game channel in the game when the game task is started;

[0032] A competition data determination module, used to determine a player competition group and corresponding task target data according to the channel data, wherein the player competition group includes a plurality of players;

[0033] The acquisition module is further used to acquire player information of each player in the player competition group;

[0034] A factor determination module, used to calculate the task benchmark judgment factor of each player based on the player information and the task target data;

[0035] The acquisition module is further used to acquire the first task completion data of the player competition group and the second task completion data of each of the players when the game task is completed;

[0036] A reward determination module is used to determine a game reward for each player in the player competition group based on the task target data, the task benchmark judgment factor, the first task completion data and the second task completion data.

[0037] In a third aspect, an embodiment of the present application further provides a game reward distribution device based on player data, the device comprising:

[0038] one or more processors;

[0039] a storage device for storing one or more programs,

[0040] When the one or more programs are executed by the one or more processors, the one or more processors implement the game reward distribution method based on player data described in the embodiment of the present application.

[0041] In a fourth aspect, an embodiment of the present application further provides a storage medium storing computer executable instructions, which, when executed by a computer processor, are used to execute the game reward distribution method based on player data described in an embodiment of the present application.

[0042] In the embodiment of the present application, when a game task is started, the channel data of the game channel in the game is obtained, and the player competition group and the corresponding task target data are determined according to the channel data. The player competition group includes multiple players, and the player information of each player in the player competition group is obtained. The task benchmark judgment factor of each player is calculated based on the player information and the task target data. When the game task is completed, the first task completion data of the player competition group and the second task completion data of each player are obtained, and the game reward of each player in the player competition group is determined based on the task target data, the task benchmark judgment factor, the first task completion data, and the second task completion data. This scheme adaptively allocates the game rewards of players based on player data, solves the problem that the game rewards in the existing game reward mechanism are fixed and single, lack flexibility, and cannot dynamically adjust the reward data to meet the expectations of players, which affects the game experience of players. It can reasonably determine and dynamically adjust the final reward of the game, so that the game reward is more in line with the expectations of players, improves the matching degree of game rewards, and optimizes the game experience of players. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flowchart of a method for distributing game rewards based on player data provided in an embodiment of the present application;

[0044] Figure 2 A flowchart of another method for distributing game rewards based on player data provided in an embodiment of the present application;

[0045] Figure 3 A flowchart of another method for distributing game rewards based on player data provided in an embodiment of the present application;

[0046] Figure 4 A flowchart of another method for distributing game rewards based on player data provided in an embodiment of the present application;

[0047] Figure 5 A module structure diagram of a game reward distribution device based on player data provided in an embodiment of the present application;

[0048] Figure 6 A schematic diagram of the structure of a game reward distribution device based on player data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the embodiments of the present application, rather than to limit the embodiments of the present application. It should also be noted that, for ease of description, only parts related to the embodiments of the present application are shown in the accompanying drawings, rather than all structures.

[0050] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and or or" in the specification and claims represents at least one of the connected objects, and the character "or" generally indicates that the objects associated before and after are in an "or" relationship.

[0051] A game reward distribution method based on player data provided in an embodiment of the present application can be applied to scenarios where rewards need to be distributed to players during the game. For example, when the player's game task is completed and the game reward is settled, the reward can be distributed through the game reward distribution method based on player data provided in an embodiment of the present application. A game reward distribution method based on player data provided in an embodiment of the present application, the execution subject of each step can be a computer device, which refers to any electronic device with data calculation, processing and storage capabilities, such as mobile phones, PCs (Personal Computers), tablet computers and other terminal devices, and can also be servers and other devices, which are not limited in the embodiments of the present application.

[0052] Figure 1 A flowchart of a method for distributing game rewards based on player data provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, specifically including:

[0053] Step S101: when a game task is started, obtain channel data of a game channel in the game, determine a player competition group and corresponding task target data according to the channel data, wherein the player competition group includes a plurality of players.

[0054] Among them, the game task can be an activity assigned to the participating players to complete within a specified time during the game process. The game task can be started when the system time reaches a certain set time point, or it can be started when certain start conditions are met, such as the number of players participating in the game task reaches a certain number, or the game task is refreshed. It can also be that the player manually triggers the start of the game task. The game task usually needs to last for a period of time for the player to complete the corresponding task goal. During the start of the game task, the channel data of the game channel in the game is obtained accordingly. Among them, the game channel is a communication tool in a multiplayer online game, which is used for real-time communication and interaction between players in the channel. It can be a separate communication tool software, or it can be a game channel generated by a chat function module integrated in the game for communication and interaction between players. Among them, the channel data represents various types of data information generated in the game channel, and the player competition group can be a plurality of player groups with a competitive relationship, and each player competition group can contain multiple players. The task target data corresponding to the player competition group is used to characterize the target data of the task that the player competition group needs to complete in the game task. For example, recruiting 100 workers is a task target data in the recruitment game task. The competition groups of players are in a competitive relationship with each other, and may compete with each other to complete the task target data first. Exemplarily, a method of determining the player competition groups and the corresponding task target data may be: obtaining the communication frequency between the players in the game channel in the game, calculating the intimacy between the players according to the communication frequency between the players, grouping the players in the game channel based on the intimacy between the players and the preset grouping conditions to obtain multiple competition groups, and randomly selecting a preset task target data from the multiple preset task target data to determine as the task target data of each competition group.

[0055] In one embodiment, the channel data includes a player ranking list and player chat data. The player ranking list can be a ranking list of data that represents the player's ability to complete game tasks, such as a player's score ranking list. Player chat data is used to characterize the data generated when players chat in the game channel. Another way to determine the player competition group and the corresponding task target data can also be: calculate the task weight value of each player based on the player ranking list and the player chat data, group the players in the game channel based on the task weight value of each player and the preset grouping conditions to obtain multiple competition groups, and determine the corresponding task target data based on the sum of the task weight values ​​of each player in the competition group, wherein the task weight value can be the importance of the player for completing the task. By grouping the competition groups and determining the task target data through the player's ranking and chat data, it is possible to ensure the equality of strength between the competition groups and the adaptability of the task difficulty, and improve the player's game experience and satisfaction.

[0056] Step S102: Obtain player information of each player in the player competition group, and calculate a task benchmark judgment factor for each player based on the player information and the task target data.

[0057] Among them, the player information is used to characterize the various relevant data information of the player himself, such as the player's game level, game time, etc. The task benchmark judgment factor can be a factor for judging the difficulty of the player to complete the game task, which is used to determine the difficulty of the player to complete the task target data. After determining the player competition group and the corresponding task target data, the player information of each player in the player competition group is obtained, and the task benchmark judgment factor of each player is calculated based on the player information and task target data of each player. In one embodiment, the preset judgment factor calculation formula associated with the task target data is determined, and the player game level in each player information is respectively input into the corresponding position in the preset judgment factor calculation formula to obtain the task benchmark judgment factor of each player. In one embodiment, the player information includes the player level and the reward information before the preset time. The player level is used to characterize the level of the player divided according to a specific standard, such as the level divided according to the amount of the player recharge. The reward information is used to characterize the information of the reward obtained by the player by completing the task. Optionally, a method for calculating a task benchmark judgment factor may be: determining a target level corresponding to task target data, determining a variable parameter value in a preset factor calculation formula based on the target level corresponding to the task target data, determining a player's reward evaluation value based on the player's reward information before a preset time, and substituting the player's player level, variable parameter value, and reward evaluation value into the preset factor calculation formula to calculate the task benchmark judgment factor. By analyzing and calculating the task benchmark judgment factor based on the task target data, the player's player level, and the reward information before a preset time, the rationality and accuracy of the task benchmark judgment factor can be improved.

[0058] Step S103: When the game task is completed, obtain the first task completion data of the player competition group and the second task completion data of each of the players, and determine the game reward for each player in the player competition group based on the task target data, the task benchmark judgment factor, the first task completion data and the second task completion data.

[0059] Among them, the end of the game task means that the player's participation in the game task is not in the opening stage, for example, reaching a certain set time point or triggering the end of the game task according to the task completion status of each competition group. Among them, the first task completion data of the player competition group represents the relevant data of the player competition group completing the game task, and the second task completion data represents the relevant data of the player completing the game task. The game reward can be the reward data obtained by the player after completing the game task, such as props, points and other game rewards. After the game task is completed, the first task completion data of each player competition group and the second task completion data of each player in each player competition group will be settled. Among them, when determining the game reward of each player, the aforementioned task target data, task benchmark judgment factor, first task completion data and second task completion data are comprehensively determined.

[0060] Optionally, a method for determining the game reward for each player in a player competition group may be: querying the task target data corresponding to the associated to-be-allocated reward, determining the reward allocation ratio for each competition group according to the first task completion data of each competition group, determining the group reward for each competition group based on the reward allocation ratio for each competition group and the to-be-allocated reward, determining the group reward allocation ratio for each player according to the task benchmark judgment factor for each player in the competition group and the first task completion data, and determining the game reward for each player based on the group reward allocation ratio and the group reward for each player in the competition group.

[0061] Optionally, another way to determine the game reward for each player in the player competition group may be: determine the task reward to be adjusted based on the task target data and the set task reward library, adjust the task reward to be adjusted based on the first task completion data of the player competition group to obtain the total game reward, and distribute the total game reward according to the task benchmark judgment factor of each player and the second task completion data to obtain the corresponding game reward. Determining the game reward of each player by the task completion data of the player competition group, the task benchmark judgment factor of the player, and the task completion data can make the game reward more reasonable and more in line with the expectations of the players.

[0062] As can be seen from the above, when the game task is turned on, the channel data of the game channel in the game is obtained, and the player competition group and the corresponding task target data are determined according to the channel data. The player competition group includes multiple players, and the player information of each player in the player competition group is obtained. The task benchmark judgment factor of each player is calculated based on the player information and the task target data. When the game task is completed, the first task completion data of the player competition group and the second task completion data of each player are obtained, and the game reward of each player in the player competition group is determined based on the task target data, the task benchmark judgment factor, the first task completion data, and the second task completion data. This scheme adaptively allocates the game rewards of players through player data, solves the problem that the game rewards in the existing game reward mechanism are fixed and single, lack flexibility, and cannot dynamically adjust the reward data to meet the expectations of players, which affects the game experience of players. It can reasonably determine and dynamically adjust the final reward of the game, so that the game reward is more in line with the expectations of players, improves the matching degree of game rewards, and optimizes the game experience of players.

[0063] Figure 2 A flowchart of another method for distributing game rewards based on player data provided in an embodiment of the present application is provided, which provides an optional specific method for determining player competition groups and task target data, such as Figure 2 As shown, specifically including:

[0064] Step S201: When a game task is enabled, the channel data of the game channel in the game is obtained, and the task weight value of each player is calculated according to the player ranking list and the player chat data.

[0065] The channel data includes a player ranking list and player chat data, and the task weight value of each player is calculated based on the player ranking list and player chat data in the channel data. Optionally, the player chat data includes the number of times the player speaks and the length of the player chat. A method for calculating the task weight value may be: determining the ranking weight of the corresponding player based on the ranking order of each player in the player ranking list, calculating the activity weight of each player based on the number of times each player speaks and the length of the player chat, and calculating the corresponding task weight value based on the ranking weight and activity weight of each player. The number of times a player speaks is used to represent the number of times a player makes a speech in a game channel, the length of the player chat is used to represent the length of time a player chats with other players in a game channel, the ranking weight may be the importance of the player's ranking, and the activity weight may be the importance of the player's activity in the game channel.

[0066] In one embodiment, the preset order interval into which the ranking order of each player in the player ranking list falls is determined, and the preset ranking weight corresponding to the preset order interval is determined as the ranking weight of the corresponding player, and the speech activity and duration activity corresponding to the number of player speeches and the player communication time of each player are determined, and the player's speech activity and duration activity are added to obtain the player's total activity, and the preset activity weight associated with the player's total activity is determined as the player's activity weight, and the ranking weight and activity weight of each player are multiplied by the preset ranking factor and the preset activity factor respectively, and added to obtain the corresponding task weight value. An exemplary example may be that player a ranks 7th in the player ranking list, the number of times the player speaks is 20, the player communication time is 15 minutes, the preset ranking factor is 0.6, the preset activity factor is 0.4, the preset order intervals include interval 1 [1, 5], interval 2 (5, 10], interval 3 (10, 15], interval 4 (15, 20], and the corresponding preset ranking weights are 0.4, 0.3, 0.2, and 0.1, respectively. The preset order interval into which player a's ranking falls is interval 2, that is, player a's ranking weight is 0.3. The query results show that the number of times the player speaks is 20, and the corresponding speech activity is 45, and the corresponding time activity is 30 for the player communication time of 15 minutes. Then, player a's total activity is 75, and the associated preset activity weight of 75 is 0.5. The ranking weight and activity weight of player a are multiplied and superimposed with the preset ranking factor and the preset activity factor respectively to obtain a task weight value of player a of 0.38.

[0067] In another embodiment, the task weight value may also be calculated by determining the ranking times of each player in the player ranking list, calculating the activity of each player according to the number of player speeches and the duration of player communication, and inputting the ranking times and activity of each player into a preset task weight value calculation formula for calculation to obtain the task weight value of each player, wherein the preset task weight value calculation formula includes multiple fixed parameters.

[0068] Step S202: grouping the players in the game channel based on the task weight value and preset grouping conditions to obtain a plurality of competition groups, and determining corresponding task target data according to the sum of the task weight values ​​of the players in the competition groups.

[0069] The preset grouping condition may be a condition for presetting the competitive grouping of each player. In one embodiment, the preset grouping condition is that the sum of the task weight values ​​of each player in each competitive group is equal and the number of players is the same, and the task weight value of each player is substituted into the preset grouping condition to obtain multiple competitive groups, and the corresponding preset target mapping table is queried according to the sum of the task weight values ​​of the competitive groups to obtain the corresponding task target data. A simplified example may be that the game channel includes 9 players, namely player 1, player 2, player 3, player 4, player 5, player 6, player 7, player 8, and player 9, and the corresponding task weight values ​​are 0.3, 0.6, 0.4, 0.8, 0.5, 0.9, 0.8, 0.2, and 0.6, respectively. In order to make the sum of the task weight values ​​of each player in each competition group equal and the number of players the same, player 1, player 2, and player 4 are determined as one competition group, player 3, player 5, and player 7 are determined as one competition group, and player 6, player 8, and player 9 are determined as one competition group. The sum of the task weight values ​​of each competition group is 1.7. The preset target mapping table is exemplarily shown in the following table:

[0070] The sum of task weights Mission target data (0，1] Recruit 50 people (1，2] Recruit 100 people (2，3] Recruit 150 people

[0071] The above preset target mapping table describes the mapping relationship between the sum of task weights and task target data, which is specifically described as follows: when the sum of task weight values ​​is within the interval (0, 1], the task target data is to recruit 50 people; when the sum of task weight values ​​is within the interval (1, 2], the task target data is to recruit 100 people; when the sum of task weight values ​​is within the interval (2, 3], the task target data is to recruit 150 people. The sum of the current task weights of each competitive group is 1.7, and the interval it falls into is (1, 2], so it is determined that the task target data of each competitive group is to recruit 100 people.

[0072] In another embodiment, a specific method for determining player competition groups and corresponding task target data may be: arranging each player in order of task weight value from large to small, and dividing the arranged players equally according to a preset number to obtain multiple player division groups, randomly selecting one player from each player division group for combination, until all players are combined to obtain multiple competition groups, calculating the average value of the sum of the task weight values ​​of each player in each competition group, and querying the corresponding preset target mapping table according to the average value of the sum of the task weight values ​​to obtain the corresponding task target data.

[0073] Step S203: Obtain player information of each player in the player competition group, and calculate a task benchmark judgment factor for each player based on the player information and the task target data.

[0074] Step S204: When the game task is completed, obtain the first task completion data of the player competition group and the second task completion data of each of the players, and determine the game reward for each player in the player competition group based on the task target data, the task benchmark judgment factor, the first task completion data and the second task completion data.

[0075] As can be seen from the above, the task weight value of each player is calculated based on the player ranking list and the player chat data, and the players in the game channel are grouped based on the task weight value of each player and the preset grouping conditions to obtain multiple competition groups, and the corresponding task target data is determined according to the sum of the task weight values ​​of each player in the competition group. This solution groups the competition groups and determines the task target data based on the player ranking and chat data, which can ensure the equality of strength between the competition groups and the adaptability of the task difficulty, and improve the game experience and satisfaction of the players.

[0076] Figure 3 A flowchart of another method for distributing game rewards based on player data provided in an embodiment of the present application is provided, which provides an optional specific method for calculating the task benchmark judgment factor, such as Figure 3 As shown, specifically including:

[0077] Step S301: When a game task is started, obtain channel data of a game channel in the game, and determine a player competition group and corresponding task target data according to the channel data, wherein the player competition group includes multiple players.

[0078] Step S302, obtaining the player information of each player in the player competition group, determining the target level corresponding to the task target data, determining the variable parameter value in the preset factor calculation formula based on the target level, determining the player's reward evaluation value based on the reward information, and substituting the player level, the variable parameter value and the reward evaluation value into the preset factor calculation formula to calculate the task benchmark judgment factor.

[0079] The player information may be the player level and the reward information before a preset time. The target level is used to characterize the difficulty level of completing the task target data. The preset factor calculation formula may be a preset formula for calculating the task benchmark judgment factor. The variable parameter value is used to characterize the parameter value in the preset factor calculation formula that can change with the target level of the task target data. The reward evaluation value may be a value obtained by evaluating the player's reward information before a preset time. The higher the reward evaluation value, the higher the value of the player's reward information.

[0080] Optionally, the reward information includes one or more reward types and corresponding reward values. A method for calculating a task benchmark judgment factor may be: querying a preset target level mapping table according to the task target data to obtain the corresponding target level, determining the variable parameter values ​​in multiple preset factor calculation formulas associated with the target level, determining the to-be-determined reward evaluation value corresponding to the reward value in the player's reward information before a preset time, and when there are multiple reward types in the reward information, determining the preset weight value for each reward type, multiplying each to-be-determined reward evaluation value with the corresponding preset weight value and superimposing them to obtain the player's reward evaluation value, and substituting the player level, variable parameter value and reward evaluation value in the player information into the preset factor calculation formula to calculate the task benchmark judgment factor.

[0081] Exemplarily, the preset target level mapping table is shown in the following table:

[0082] Mission target data Target level Recruit (0,50] people Level 1 Recruit (50, 100] people Level 2 Recruit (100, 150] people Level 3

[0083] The above preset target level mapping table describes the mapping relationship between task target data and target level. Specifically, when the task target data is to recruit (0, 50] people, the corresponding target level is level one; when the task target data is to recruit (50, 100] people, the corresponding target level is level two; when the task target data is to recruit (100, 150] people, the corresponding target level is level three.

[0084] Optionally, the current task target data is to recruit 50 people. The above preset target level mapping table is queried to obtain the corresponding target level of level 1. The variable parameter value 1 associated with level 1 is 100, the variable parameter value 2 is 2, the variable parameter value 3 is 0.3, the variable parameter value 4 is 0.04, and the variable parameter value 5 is 0.1. The player level in the player information of player a is level 5. The point reward value in the reward information before the preset time is 200, and the experience reward value is 50. The query obtains that the point reward value of 200 corresponds to the to-be-determined reward evaluation value of 60, and the experience reward value of 50 corresponds to The reward evaluation value is 80, the preset weight corresponding to the point reward is 0.3, and the preset weight corresponding to the experience reward value is 0.7. Then, each reward evaluation value to be determined is multiplied by the corresponding preset weight value and superimposed to obtain a reward evaluation value of 74 for player a. The preset factor calculation formula is (reward evaluation value / variable parameter 1+variable parameter 3) / variable parameter 2+(player level*variable parameter 4+variable parameter 5). Substitute the player level, variable parameter value and reward evaluation value in the player information into the preset factor calculation formula to obtain the task benchmark judgment factor of player a, which is 0.82.

[0085] Another way to determine the task benchmark judgment factor may be: query the preset target level mapping table according to the task target data to obtain the corresponding target level, obtain the preset variable parameter value to be adjusted, determine the adjustment value associated with the target level corresponding to the task target data, adjust the variable parameter value to be adjusted according to the adjustment value to obtain the variable parameter value in the preset factor calculation formula, input the player's reward information before the preset time into the trained reward evaluation model to obtain the player's reward evaluation value, and substitute the player level, variable parameter value and reward evaluation value in the player information into the preset factor calculation formula to calculate the task benchmark judgment factor.

[0086] Step S303: When the game task is completed, obtain the first task completion data of the player competition group and the second task completion data of each of the players, and determine the game reward for each player in the player competition group based on the task target data, the task benchmark judgment factor, the first task completion data and the second task completion data.

[0087] As can be seen from the above, the sum of the player information of each player in the player competition group is obtained, the target level corresponding to the task target data is determined, the variable parameter value in the preset factor calculation formula is determined based on the target level corresponding to the task target data, the player's reward evaluation value is determined based on the reward information in the player information, and the player level, variable parameter value and reward evaluation value in the player information are substituted into the preset factor calculation formula to calculate the task benchmark judgment factor. This solution can improve the rationality and accuracy of the task benchmark judgment factor by analyzing the task target data, the player's player level and the reward information before the preset time to calculate the task benchmark judgment factor.

[0088] Figure 4 A flowchart of another method for distributing game rewards based on player data provided in an embodiment of the present application is provided, which provides a specific method for distributing game rewards as an optional method, such as Figure 4 As shown, specifically including:

[0089] Step S401: When a game task is started, obtain channel data of a game channel in the game, and determine a player competition group and corresponding task target data according to the channel data, wherein the player competition group includes multiple players.

[0090] Step S402: Obtain player information of each player in the player competition group, and calculate a task benchmark judgment factor for each player based on the player information and the task target data.

[0091] Step S403: When the game task is completed, obtain the first task completion data of the player competition group and the second task completion data of each player.

[0092] Step S404, determine the task reward to be adjusted according to the task target data and the set task reward library, adjust the task reward to be adjusted based on the first task completion data to obtain the total game reward, and distribute the total game reward according to the task benchmark judgment factor of each player and the second task completion data to obtain the corresponding game reward.

[0093] Among them, the task reward library is used to represent a database storing a variety of task rewards, and the task reward library can be used to determine the rewards to be adjusted. The task rewards to be adjusted can be the task rewards corresponding to each competitive group that need to be further adjusted, and the total game reward can be the total reward used to distribute game rewards to each player in the competitive group. In one embodiment, the task rewards to be adjusted for each ranking order of the competitive group corresponding to the task target data are queried in the task reward library, and the respective task rewards to be adjusted are adjusted based on the first task completion data of each competitive group to obtain the corresponding total game reward, and the total game reward of the competitive group is distributed according to the task benchmark judgment factor of each player and the second task completion data to obtain the corresponding game reward. Optionally, the first task completion data includes task completion time and ranking information, wherein the task completion time is used to characterize the length of time for the competition group to complete the game task, and the ranking information may be the order in which the various competition groups complete the game tasks. A method for determining the total game reward may be: determining a corresponding preset standard comparison time based on the ranking information of the player competition group, calculating the difference between the task completion time of the player competition group and the corresponding preset standard comparison time, and querying the corresponding preset adjustment reward mapping table based on the difference to obtain an adjusted reward for the player competition group, wherein the adjusted reward includes an upward adjustment reward and a downward adjustment reward, and adjusting the task reward to be adjusted based on the adjusted reward of the player competition group to obtain the total game reward.

[0094] An exemplary example may be that there are 5 competition groups in total, the task completion time of competition group a is 1 hour, the ranking number is 3, the preset standard comparison time associated with the ranking number 3 is 50 minutes, the current task target data is to recruit 100 people, and the reward to be adjusted for the first competition group that completes the recruitment of 100 people in the task reward library is 500 points, the reward to be adjusted for the second competition group is 400 points, the reward to be adjusted for the third competition group is 300 points, the reward to be adjusted for the fourth competition group is 200 points, and the reward to be adjusted for the fifth competition group is 100 points. Then the reward to be adjusted corresponding to competition group a is 300 points, and the preset adjustment reward mapping table is shown in the following table as an example:

[0095] Difference Adjustment Rewards (0, +5]min -20 points (+5, +10]min -40 points (+10, +15]min -60 points (-5,0]min +20 points (-10, -5]min +40 points (-15, -10]min +60 points

[0096] The above preset adjustment reward mapping table describes the mapping relationship between the difference between the task completion time of the competition group and the corresponding preset standard comparison time and the adjustment reward. "+" represents that the task completion time of competition group a is longer than the preset standard comparison time, and "-" represents that the task completion time of competition group a is shorter than the preset standard comparison time. Among them, the difference between the task completion time of competition group a and the corresponding preset standard comparison time is +10 minutes. The above preset adjustment reward mapping table is queried to obtain the corresponding adjustment reward of -40 points. Based on the adjustment reward of competition group a, the corresponding task reward to be adjusted is adjusted to obtain a total game reward of 260 points.

[0097] Optionally, the second task completion data includes a percentage of completed tasks. A method for distributing and processing the total game reward may be: determining the preset standard percentage corresponding to the task benchmark judgment factor of each player, comparing the percentage of completed tasks of each player with the corresponding preset standard percentage to obtain players who do not meet the standard, players who meet the standard, and players who exceed the standard, calculating the percentage of unmet players and the percentage of excess players, determining the reward distribution weights of each player in the player competition group according to the percentage of unmet players, the percentage of excess players, and the percentage of completed tasks, and distributing rewards to each player in the player competition group according to the reward distribution weights and the total game reward to obtain the corresponding game reward. Among them, the preset standard percentage is used to represent the standard percentage of game task content that players should complete, the percentage of completed tasks is used to represent the percentage of game task content that players complete, the unmet players are used to represent players whose percentage of completed game task content does not meet the standard, the players who exceed the standard are used to represent players who complete game tasks in excess, the unmet percentage value is used to represent the percentage of game content that players do not complete, and the excess percentage value is used to represent the percentage of game content that players complete in excess.

[0098] In one embodiment, the deduction weight value and the sum of the deduction weight values ​​of the players who fail to meet the standard are determined according to the non-meeting ratio value, the players who exceed the standard are ranked from high to low according to the excess ratio value, and the allocation ratio of the players who exceed the standard is determined according to the ranking, the allocation ratio is multiplied by the sum of the deduction weight values ​​to obtain the additional weight value of each player who exceeds the standard, the task completion ratio of the players who fail to meet the standard is subtracted from the corresponding deduction weight value to obtain the reward allocation weight of each player who fails to meet the standard, the task completion ratio of the players who exceed the standard is added to the corresponding additional weight value to obtain the reward allocation weight of each player who exceeds the standard, and the task completion ratio of the players who meet the standard is determined as the reward allocation weight of each player who meets the standard.

[0099] A simplified example may be that there are 6 players in competition group a, and according to the task benchmark judgment factor of each player, the preset standard proportions corresponding to players 1, 2...6 are 30%, 5%, 20%, 10%, 15%, and 20% respectively, and the completed task proportions corresponding to players 1, 2...6 are 20%, 10%, 27%, 8%, 15%, and 20% respectively. The completed task proportions of players 1 and 4 are less than the corresponding preset standard proportions, the completed task proportions of players 2, 3, and 5 are greater than the corresponding preset standard proportions, and the completed task proportions of players 5 and 6 are equal to the corresponding preset standard proportions, that is, players 1 and 4 are unsatisfactory players, and the corresponding unsatisfactory proportions are 10% and 2% respectively; players 2 and 3 are over-satisfactory players, and the corresponding over-satisfactory proportions are 5% and 7% respectively; players 5 and 6 are qualified players, and the unsatisfactory proportions of the unsatisfactory players are converted into weighted forms and determined as corresponding deduction weight values, that is, players The deduction weight value of player 1 is 0.1, the deduction weight value of player 4 is 0.02, and the sum of the deduction weight values ​​is 0.12. According to the excess ratio, the players who have exceeded the target are ranked from high to low as player 3-player 2. According to this ranking, the allocation ratio of player 3 is determined to be 0.6, and the allocation ratio of player 2 is 0.4. The allocation ratios of player 3 and player 2 are multiplied by the sum of the deduction weight values ​​to obtain the additional weight value of player 3 is 0.072, and the additional weight value of player 2 is 0.048. Player 1 and player 4 are multiplied by the sum of the deduction weight values. The completed task percentage of players 3 and 2 is subtracted from the corresponding deduction weight value to obtain the reward distribution weight of player 1, which is 0.1, and the reward distribution weight of player 4 is 0.06. The completed task percentage of players 3 and 2 is added with the corresponding additional weight value to obtain the reward distribution weight of player 3, which is 0.342, and the reward distribution weight of player 2 is 0.148. The completed task percentage of players 5 and 6 is converted into weight form to obtain the reward distribution weight of player 5, which is 0.15, and the reward distribution weight of player 6 is 0.2.

[0100] From the above, it can be seen that the task reward to be adjusted is determined according to the task target data and the set task reward library, the task reward to be adjusted is adjusted based on the first task completion data of the player competition group to obtain the total game reward, and the total game reward is distributed and processed according to the task benchmark judgment factor of each player and the second task completion data to obtain the corresponding game reward. In the above method, the game reward of each player is determined by the task completion data of the player competition group, the player's task benchmark judgment factor and the task completion data, which can make the game reward more reasonable and more in line with the expectations of the players.

[0101] Figure 5This is a module structure diagram of a game reward distribution device based on player data provided in an embodiment of the present application. The system is used to execute a game reward distribution method based on player data provided in the above embodiment, and has functional modules and beneficial effects corresponding to the execution method. Figure 5 As shown, the system specifically includes:

[0102] The acquisition module 101 is used to acquire the channel data of the game channel in the game when the game task is started;

[0103] A competition data determination module 102, for determining a player competition group and corresponding task target data according to the channel data, wherein the player competition group includes a plurality of players;

[0104] The acquisition module 101 is further used to acquire player information of each player in the player competition group;

[0105] A factor determination module 103, configured to calculate a task benchmark judgment factor for each player based on the player information and the task target data;

[0106] The acquisition module 101 is further used to acquire the first task completion data of the player competition group and the second task completion data of each of the players when the game task is completed;

[0107] The reward determination module 104 is used to determine the game reward of each player in the player competition group based on the task target data, the task benchmark judgment factor, the first task completion data and the second task completion data.

[0108] It can be seen from the above scheme that when a game task is started, the channel data of the game channel in the game is obtained, and the player competition group and the corresponding task target data are determined based on the channel data. The player competition group includes multiple players, and the player information of each player in the player competition group is obtained. The task benchmark judgment factor of each player is calculated based on the player information and the task target data. When the game task is completed, the first task completion data of the player competition group and the second task completion data of each player are obtained, and the game reward of each player in the player competition group is determined based on the task target data, the task benchmark judgment factor, the first task completion data, and the second task completion data. This scheme adaptively allocates the game rewards of players through player data, solves the problem that the game rewards in the existing game reward mechanism are fixed and single, lack flexibility, and cannot dynamically adjust the reward data to meet the expectations of players, which affects the game experience of players. It can reasonably determine and dynamically adjust the final reward of the game, so that the game reward is more in line with the expectations of players, improves the matching degree of game rewards, and optimizes the game experience of players.

[0109] In a possible embodiment, the competition data determination module 102 is specifically configured to:

[0110] Calculating the task weight value of each player according to the player ranking list and the player chat data, and grouping the players in the game channel to obtain a plurality of competition groups based on the task weight value and preset grouping conditions;

[0111] The corresponding task target data is determined according to the sum of the task weight values ​​of each player in the competition group.

[0112] In a possible embodiment, the competition data determination module 102 is further configured to:

[0113] Determine the ranking weight of the corresponding player according to the ranking order of each player in the player ranking list, and calculate the activity weight of each player based on the number of times the player speaks and the chat time of the player;

[0114] The corresponding task weight value is calculated according to the ranking weight and the activity weight of each player.

[0115] In a possible embodiment, the factor determination module 103 is specifically configured to:

[0116] Determine a target level corresponding to the task target data, and determine a variable parameter value in a preset factor calculation formula based on the target level;

[0117] determining a player's reward evaluation value based on the reward information;

[0118] The player level, the variable parameter value and the reward evaluation value are substituted into the preset factor calculation formula to calculate the task benchmark judgment factor.

[0119] In a possible embodiment, the reward determination module 104 is specifically configured to:

[0120] Determine the task reward to be adjusted according to the task target data and the set task reward library;

[0121] Adjusting the task reward to be adjusted based on the first task completion data to obtain a total game reward;

[0122] The total game reward is distributed according to the task benchmark judgment factor of each player and the second task completion data to obtain the corresponding game reward.

[0123] In a possible embodiment, the reward determination module 104 is further configured to:

[0124] Determine the corresponding preset standard comparison time according to the ranking information of the player competition group, calculate the difference between the task completion time of the player competition group and the corresponding preset standard comparison time, and query the corresponding preset adjustment reward mapping table according to the difference to obtain the adjustment reward of the player competition group, wherein the adjustment reward includes an upward adjustment reward and a downward adjustment reward;

[0125] The task reward to be adjusted is adjusted based on the adjusted reward to obtain a total game reward.

[0126] In a possible embodiment, the reward determination module 104 is further configured to:

[0127] Determine the preset standard proportion corresponding to the task benchmark judgment factor of each player, compare the task completion proportion of each player with the corresponding preset standard proportion to obtain players who fail to meet the standard, players who meet the standard, and players who exceed the standard, and calculate the unmet proportion value of the unmet players and the excess proportion value of the players who exceed the standard;

[0128] The reward distribution weight of each player in the player competition group is determined according to the non-compliance ratio, the excess ratio and the task completion ratio, and the reward distribution of each player in the player competition group is performed according to the reward distribution weight and the total game reward to obtain the corresponding game reward.

[0129] Figure 6 A schematic diagram of the structure of a game reward distribution device based on player data provided in an embodiment of the present application, such as Figure 6 As shown, the device includes a processor 201, a memory 202, an input device 203 and an output device 204; the number of processors 201 in the device can be one or more. Figure 6 A processor 201 is taken as an example; the processor 201, memory 202, input device 203 and output device 204 in the device can be connected by a bus or other means. Figure 6 The example of connecting via a bus is taken. The memory 202, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions or modules corresponding to a method for distributing game rewards based on player data in an embodiment of the present application. The processor 201 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 202, that is, implements the above-mentioned method for distributing game rewards based on player data. The input device 203 can be used to receive input digital or character information, and generate key signal input related to user settings and function control of the device. The output device 204 may include a display device such as a display screen.

[0130] The embodiment of the present application also provides a storage medium containing computer executable instructions, wherein the computer executable instructions are used to execute a game reward distribution method based on player data when executed by a computer processor, the method comprising:

[0131] When a game task is started, channel data of a game channel in the game is obtained, and a player competition group and corresponding task target data are determined according to the channel data, wherein the player competition group includes a plurality of players;

[0132] Obtaining player information of each of the players in the player competition group;

[0133] Calculate each player's task benchmark judgment factor based on the player information and the task target data;

[0134] When the game task is completed, obtaining the first task completion data of the player competition group and the second task completion data of each of the players;

[0135] A game reward for each player in the player competition group is determined based on the task target data, the task reference judgment factor, the first task completion data, and the second task completion data.

[0136] It is worth noting that in the above-mentioned embodiment of a game reward distribution method system based on player data, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present application.

[0137] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made to the present application without departing from the protection scope of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for distributing game rewards based on player data, characterized in that: The method comprises: When a game task is started, channel data of a game channel in the game is obtained, and a player competition group and corresponding task target data are determined according to the channel data, wherein the player competition group includes a plurality of players; Obtaining player information of each of the players in the player competition group; Calculate each player's task benchmark judgment factor based on the player information and the task target data; When the game task is completed, obtaining the first task completion data of the player competition group and the second task completion data of each of the players; A game reward for each player in the player competition group is determined based on the task target data, the task reference judgment factor, the first task completion data, and the second task completion data.

2. The method for distributing game rewards based on player data according to claim 1, characterized in that: The channel data includes a player ranking list and player chat data, and the determining of a player competition group and corresponding task target data according to the channel data includes: Calculating the task weight value of each player according to the player ranking list and the player chat data, and grouping the players in the game channel to obtain a plurality of competition groups based on the task weight value and preset grouping conditions; The corresponding task target data is determined according to the sum of the task weight values ​​of each player in the competition group.

3. The method for distributing game rewards based on player data according to claim 2, characterized in that: The player chat data includes the number of times the player speaks and the player chat duration, and the task weight value of each player is calculated according to the player ranking list and the player chat data, including: Determine the ranking weight of the corresponding player according to the ranking order of each player in the player ranking list, and calculate the activity weight of each player based on the number of times the player speaks and the chat time of the player; The corresponding task weight value is calculated according to the ranking weight and the activity weight of each player.

4. The method for distributing game rewards based on player data according to claim 1, characterized in that: The player information includes the player level and the reward information before a preset time. The task benchmark judgment factor calculated based on the player information and the task target data includes: Determine a target level corresponding to the task target data, and determine a variable parameter value in a preset factor calculation formula based on the target level; determining a player's reward evaluation value based on the reward information; The player level, the variable parameter value and the reward evaluation value are substituted into the preset factor calculation formula to calculate the task benchmark judgment factor.

5. The method for distributing game rewards based on player data according to claim 1, characterized in that: The determining of a game reward for each player in the player competition group based on the task target data, the task benchmark judgment factor, the first task completion data, and the second task completion data includes: Determine the task reward to be adjusted according to the task target data and the set task reward library; Adjusting the task reward to be adjusted based on the first task completion data to obtain a total game reward; The total game reward is distributed according to the task benchmark judgment factor of each player and the second task completion data to obtain the corresponding game reward.

6. The method for distributing game rewards based on player data according to claim 5, characterized in that: The first task completion data includes task completion time and ranking information, and the adjusting the task reward to be adjusted based on the first task completion data to obtain the total game reward includes: Determine the corresponding preset standard comparison time according to the ranking information of the player competition group, calculate the difference between the task completion time of the player competition group and the corresponding preset standard comparison time, and query the corresponding preset adjustment reward mapping table according to the difference to obtain the adjustment reward of the player competition group, wherein the adjustment reward includes an upward adjustment reward and a downward adjustment reward; The task reward to be adjusted is adjusted based on the adjusted reward to obtain a total game reward.

7. The method for distributing game rewards based on player data according to claim 5, characterized in that: The second task completion data includes a percentage of completed tasks, and the allocation of the total game reward according to the task benchmark judgment factor of each player and the second task completion data to obtain the corresponding game reward includes: Determine the preset standard proportion corresponding to the task benchmark judgment factor of each player, compare the task completion proportion of each player with the corresponding preset standard proportion to obtain players who fail to meet the standard, players who meet the standard, and players who exceed the standard, and calculate the unmet proportion value of the unmet players and the excess proportion value of the players who exceed the standard; The reward distribution weight of each player in the player competition group is determined according to the non-compliance ratio, the excess ratio and the task completion ratio, and the reward distribution of each player in the player competition group is performed according to the reward distribution weight and the total game reward to obtain the corresponding game reward.

8. A device for distributing game rewards based on player data, characterized in that: include: The acquisition module is used to obtain the channel data of the game channel in the game when the game task is started; A competition data determination module, used to determine a player competition group and corresponding task target data according to the channel data, wherein the player competition group includes a plurality of players; The acquisition module is further used to acquire player information of each player in the player competition group; A factor determination module, used to calculate the task benchmark judgment factor of each player based on the player information and the task target data; The acquisition module is further used to acquire the first task completion data of the player competition group and the second task completion data of each of the players when the game task is completed; A reward determination module is used to determine a game reward for each player in the player competition group based on the task target data, the task benchmark judgment factor, the first task completion data and the second task completion data.

9. A device for distributing game rewards based on player data, the device comprising: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enables the one or more processors to implement the game reward distribution method based on player data as described in any one of claims 1-7.

10. A storage medium storing computer executable instructions, wherein the computer executable instructions are used to perform the game reward distribution method based on player data as described in any one of claims 1 to 7 when executed by a computer processor.

Images