CN111475268A - Task item distribution method, device and equipment and readable storage medium - Google Patents

Task item distribution method, device and equipment and readable storage medium Download PDF

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Publication number
CN111475268A
CN111475268A CN202010252190.1A CN202010252190A CN111475268A CN 111475268 A CN111475268 A CN 111475268A CN 202010252190 A CN202010252190 A CN 202010252190A CN 111475268 A CN111475268 A CN 111475268A
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task
target
behavior
target account
data
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CN202010252190.1A
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CN111475268B (en
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李亚楠
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Tencent Technology Shenzhen Co Ltd
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Tencent Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/85—Providing additional services to players
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/50—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by details of game servers
    • A63F2300/57—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by details of game servers details of game services offered to the player

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  • Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application discloses a task item distribution method, a task item distribution device, a task item distribution equipment and a readable storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring historical behavior data of a target account from a behavior database; matching the historical behavior data with a behavior feature table to obtain target behavior features; determining a task data set corresponding to the target behavior characteristics; at least one task item is selected from the task data set to be allocated to the target account. The historical behavior data of the target account is acquired and analyzed to obtain the corresponding target behavior characteristics, and the task item is selected to be distributed to the target account according to the target behavior characteristics, so that the task item distributed to the target account is more in line with the game habit of the target account, the problem that server resources are wasted due to the fact that the task item distributed to the target account is low in adaptation degree and cannot be completed is solved, and the utilization efficiency of server resources is improved.

Description

Task item distribution method, device and equipment and readable storage medium
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a task item distribution method, a task item distribution device, a task item distribution equipment and a readable storage medium.
Background
In applications such as games, a task reward mechanism is usually provided, that is, a certain reward is obtained in a manner of completing a task, such as: the game is usually provided with a daily task module, the daily task module comprises task items, and a player completes the task items through the game process and obtains rewards corresponding to the task items.
In the related art, when setting the task item, a fixed daily task list is set, and the completion progress of the task item in the daily task list is reset every day according to the update of the date, so that the daily goal of the player is to complete the task item in the daily task list.
When the task items are set in the above manner, because the task items in the daily task list are fixed, the game habits of the players cannot complete the task items in the daily task list, and the task items allocated to the players cannot be completed, which wastes server resources.
Disclosure of Invention
The embodiment of the application provides a method, a device and equipment for distributing task items and a readable storage medium, which can improve the utilization efficiency of server resources when distributing the task items to a target account. The technical scheme is as follows:
in one aspect, a method for allocating task items is provided, and the method includes:
acquiring historical behavior data of a target account from a behavior database, wherein the historical behavior data is generated in the process of program historical use of the target account;
matching the historical behavior data with a behavior feature table to obtain target behavior features corresponding to the target account, wherein the behavior feature table comprises behavior features used for representing program use characteristics;
determining a task data group corresponding to the target behavior characteristics, wherein the task data group comprises task items matched with the target behavior characteristics;
and selecting at least one task item from the task data group to be distributed to the target account.
In another aspect, an apparatus for distributing task items is provided, the apparatus including:
the acquisition module is used for acquiring historical behavior data of a target account from a behavior database, wherein the historical behavior data is generated by the target account in the process of program historical use;
the matching module is used for matching the historical behavior data with a behavior feature table to obtain target behavior features corresponding to the target account, and the behavior feature table comprises behavior features used for representing program use characteristics;
the determining module is used for determining a task data group corresponding to the target behavior characteristics, and the task data group comprises task items matched with the target behavior characteristics;
and the distribution module is used for selecting at least one task item from the task data group and distributing the task item to the target account.
In another aspect, a computer device is provided, which comprises a processor and a memory, wherein at least one instruction, at least one program, set of codes, or set of instructions is stored in the memory, and the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by the processor to implement the method for allocating task items as described in any of the embodiments of the present application.
In another aspect, a computer readable storage medium is provided, in which at least one instruction, at least one program, a set of codes, or a set of instructions is stored, which is loaded and executed by the processor to implement the method for allocating a task item as described in any of the embodiments of the present application.
In another aspect, a computer program product is provided, which when run on a computer causes the computer to perform the method of assigning task items as described in any of the embodiments of the present application above.
The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:
the historical behavior data of the target account is acquired and analyzed to obtain the corresponding target behavior characteristics, so that the task item is selected from the task data group corresponding to the target behavior characteristics and is distributed to the target account, the task item distributed to the target account is more in line with the game habit of the target account, the task item is suitable for the target account to complete, the problem that server resources are wasted due to the fact that the task item distributed to the target account is low in adaptation degree and cannot be completed is solved, and the utilization efficiency of the server resources is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a task list interface for a player account provided by an exemplary embodiment of the present application;
FIG. 2 is a diagram illustrating a correspondence between task data sets and behavior features provided by an exemplary embodiment of the present application;
fig. 3 is a block diagram of a terminal according to an exemplary embodiment of the present application;
FIG. 4 is a schematic illustration of an implementation environment provided by an exemplary embodiment of the present application;
FIG. 5 is a flow diagram of a method for assigning task items provided by an exemplary embodiment of the present application;
FIG. 6 is a flow chart of a method for assigning task items provided by another exemplary embodiment of the present application;
FIG. 7 is an overall flow diagram of task item allocation provided by an exemplary embodiment of the present application;
FIG. 8 is a flowchart of a method for assigning task items provided by another exemplary embodiment of the present application;
FIG. 9 is a flowchart of an assignment process for task items provided by an exemplary embodiment of the present application;
FIG. 10 is a block diagram of an apparatus for distributing task items according to an exemplary embodiment of the present application;
FIG. 11 is a block diagram of an apparatus for distributing task items according to another exemplary embodiment of the present application;
fig. 12 is a block diagram of a server according to an exemplary embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
First, terms referred to in the embodiments of the present application are briefly described:
task item: the task conditions are provided for the user account in the application program and used for exchanging the bonus resources, and illustratively, the player account exchanges the corresponding bonus resources by completing the task items in the game application program. Illustratively, in the game application program, the win-win task item is provided to the target account, that is, the target account participates in the virtual match-up in the game application program, and when the win-win frequency reaches the required frequency, the reward resource corresponding to the win-win task item can be obtained.
For example, the matching relationship between the task item and the progress condition refers to the following table one:
watch 1
Task item Conditions of completion
Winning the game First number of rounds
(II) completing the game Second number of pairs
(III) connecting points Number of junctional win
(IV) complete the attack assistance Number of attacks
(V) obtaining the double killers Number of double kill
Combining the first table, when the number of winning hands reaches the first number of hands, determining to complete the task item (one); when the number of completed game pairs reaches a second game pair number, determining to complete a task item (II); when the number of the tie points reaches the tie point number, determining to complete the task item (III); when the number of times of completing the attack assistance reaches the attack assistance number, determining to complete a task Item (IV); and when the times of completing the double killing reaches the double killing times, determining to complete the task item (V).
Optionally, the task item corresponds to a task type, and illustratively, the task item may be a task item of a daily type or a task item of a challenge type. The task item of the daily type refers to a task item that is refreshed with a date as a refresh period, that is, a task item that is completed in a limited time with the date as a time limit, where the refresh period of the task item of the daily type may be one day or multiple days, which is not limited in the embodiment of the present application, and is described with one day as an example, for example: and allocating a first task item to the target account on 31 rd 3 month, and if the first task item is not completed on 31 rd 3 month, allocating a new task item to the target account on 1 st 4 month, wherein the first task item may be included in the new task item or may not be included in the new task item. The task items of the challenge type refer to accumulable task items, that is, after the task items of the challenge type are allocated to the player account, until the player account completes the task items, otherwise, the task items continuously exist in the existing task items of the player account, optionally, the number of the task items of the challenge type that each player can receive is limited in number, and when the number of the task items of the challenge type reaches the upper limit in number, the task items of the challenge type are not allocated to the player.
Referring to fig. 1, schematically, daily tasks assigned to a player account are displayed on a task list interface 100 of the player account, where the daily tasks include a task item 110, a task item 120, and a task item 130, a refresh period of the task item 110 is 1 day, a refresh period of the task item 120 and the task item 130 is 5 days, the current task item 110 is refreshed and reset after 13 hours, and the task item 120 and the task item 130 are refreshed and reset after 4 days.
Task data set: the task data group is a task group which classifies and summarizes task items according to the behavior characteristics, and optionally, each task data group can correspond to one behavior characteristic or a plurality of behavior characteristics. Optionally, the task data set includes task items matched with the behavior characteristics, so that after the behavior characteristics corresponding to the player account are determined, the task items can be determined from the task data set corresponding to the behavior characteristics and distributed to the player account.
Referring to fig. 2, schematically, the corresponding relationship between the task data set and the behavior feature is included, as shown in fig. 2, the single player task 211 corresponds to the behavior feature list 212; the middle player task 221 corresponds to the behavior feature middle way 222; wild player task 231 corresponds to behavior feature wild 232; the totipotent player tasks 241 correspond to a behavioral characteristic statement 242, a behavioral characteristic middle way 243, and a behavioral characteristic assist 244.
Optionally, the task item allocation method provided in this embodiment of the present application may be applied to at least one of the following scenarios:
firstly, in a game application program, acquiring historical behavior data of a player account in historical virtual battles, matching the historical behavior data with a behavior characteristic table to obtain target behavior characteristics corresponding to the player account, determining a task data group corresponding to the target behavior characteristics, selecting a task item from the task data group to be distributed to the player account, and enabling the player account to exchange game resource rewards in the game application program by completing the task item, wherein the game resource rewards comprise: game items, virtual gold coins, and the like;
secondly, acquiring historical behavior data of the user account in a historical shopping record in a shopping application program, matching the historical behavior data with a behavior feature table to obtain a target behavior feature corresponding to the user account in the shopping process, determining a task data group corresponding to the target behavior feature, and selecting a task item from the task data group to distribute to the user account;
optionally, the historical behavior data includes purchase times data, purchase amount data, browsing data, search data, and the like of the user account. The purchase frequency data is used for representing the number of times of ordering the user account in a target time period, the purchase amount data is used for representing the total amount of the ordering of the user account in the target time period, the browsing data is used for representing the time length, the number and the like of browsing products in the application program by the user account in the target time period, and the searching data is used for representing the number of times of searching the products by the target account in the target time period.
Illustratively, after obtaining historical behavior data of a user account in a historical shopping record, matching the historical behavior data with a behavior feature table, and if a target behavior feature of the user account obtained according to browsing data is a longer browsing time, determining a task data group corresponding to the target behavior feature, wherein the task data group includes task items corresponding to the target behavior feature, such as: the total browsing time reaches 1 hour, the number of browsed products reaches 20, and the browsing time of a single product reaches 3 minutes. Selecting task items from a task data group corresponding to the target behavior characteristics, and distributing the task items to user accounts, wherein the user accounts can exchange shopping resource rewards in a shopping application program by completing the task items, such as: voucher, full discount coupon, etc.
Thirdly, in the singing application program, acquiring historical behavior data of the user account in the historical singing or playing process, matching the historical behavior data with the behavior characteristic table to obtain target behavior characteristics corresponding to the user account in the singing or playing process, determining a task data group corresponding to the target behavior characteristics, and selecting task items from the task data group to distribute to the user account.
Optionally, the historical behavior data includes singing times data of the user account, property presentation data, work playing data, attention data, work release data, and the like. The song recording times of the user account in the target time period are represented by the singing times data, the prop presenting data are used for representing the number of props presented to other accounts by the user account in the target time period, the work playing data are used for representing the time length, the times and the like of the user account playing the published singing works in the target time period, the attention data are used for representing the times of the target account paying attention to other accounts in the target time period to establish the association relationship, and the work publishing data are used for representing the times of the user account publishing the recorded songs in the target time period.
Illustratively, after obtaining historical behavior data of a user account in a historical singing or playing process, matching the historical behavior data with a behavior feature table, obtaining target behavior features of the user account according to work release data, wherein the target behavior features are a large number of work releases, and determining a task data group corresponding to the target behavior features, wherein the task data group comprises task items corresponding to the target behavior features, and if: the work is released for 5 times, the single song is recorded and released for 2 times, and the work is received for 20 times of listening after being released. Selecting task items from a task data group corresponding to the target behavior characteristics, distributing the task items to a user account, and exchanging props in the singing application program by the user account by finishing the task items, wherein the props comprise: fresh flower props for giving away singing works of other accounts, tuning props for tuning the singing works of the user accounts and the like.
It should be noted that the above application scenarios are only illustrative examples, and the task item allocation method provided in the embodiment of the present application is applicable to determining task items according to historical behavior data of a target account and allocating the task items to the target account.
Optionally, in this embodiment of the present application, an example in which the assignment method of task items is applied to a game application scenario is described.
The terminal in the present application may be a desktop computer, a laptop portable computer, a mobile phone, a tablet computer, an e-book reader, an MP3(Moving Picture Experts Group Audio L eye III, mpeg compressed standard Audio layer 3) player, an MP4(Moving Picture Experts Group Audio L eye iv, mpeg compressed standard Audio layer 4) player, etc. an application supporting a virtual environment, such as an application supporting a three-dimensional virtual environment, is installed and operated in the terminal.
Fig. 3 shows a block diagram of an electronic device according to an exemplary embodiment of the present application. The electronic device 300 includes: an operating system 320 and application programs 322.
Operating system 320 is the base software that provides applications 322 with secure access to computer hardware.
Application 322 is an application that supports a virtual environment. Optionally, application 322 is an application that supports a three-dimensional virtual environment. The application 322 may be any one of a virtual reality application, a three-dimensional map program, a military simulation program, a TPS game, an FPS game, an MOBA game, and a multi-player gunfight type live game. The application 322 may be a stand-alone application, such as a stand-alone three-dimensional game program, or may be a network-connected application.
Fig. 4 shows a block diagram of a computer system provided in an exemplary embodiment of the present application. The computer system 400 includes: a first device 420, a server 440, and a second device 460.
The first device 420 is installed and operated with an application program supporting a virtual environment. The application program can be any one of a virtual reality application program, a three-dimensional map program, a military simulation program, a TPS game, an FPS game, an MOBA game and a multi-player gunfight living game. The first device 420 is a device used by a first user who uses the first device 420 to control a first virtual object located in a virtual environment for activities including, but not limited to: adjusting at least one of body posture, crawling, walking, running, riding, jumping, driving, picking, shooting, attacking, throwing. Illustratively, the first virtual object is a first virtual character, such as a simulated persona or an animated persona.
The first device 420 is connected to the server 440 through a wireless network or a wired network.
The server 440 includes at least one of a server, a plurality of servers, a cloud computing platform, and a virtualization center. The server 440 is used to provide background services for applications that support a three-dimensional virtual environment. Optionally, server 440 undertakes primary computing work and first device 420 and second device 460 undertakes secondary computing work; alternatively, server 440 undertakes secondary computing work and first device 420 and second device 460 undertakes primary computing work; alternatively, the server 440, the first device 420, and the second device 460 perform cooperative computing by using a distributed computing architecture.
The second device 460 is installed and operated with an application program supporting a virtual environment. The application program can be any one of a virtual reality application program, a three-dimensional map program, a military simulation program, an FPS game, an MOBA game and a multi-player gun battle type survival game. The second device 460 is a device used by a second user who uses the second device 460 to control a second virtual object located in the virtual environment to perform activities including, but not limited to: adjusting at least one of body posture, crawling, walking, running, riding, jumping, driving, picking, shooting, attacking, throwing. Illustratively, the second virtual object is a second virtual character, such as a simulated persona or an animated persona.
Optionally, the first virtual character and the second virtual character are in the same virtual environment. Alternatively, the first avatar and the second avatar may belong to the same team, the same organization, have a friend relationship, or have temporary communication rights. Alternatively, the first virtual character and the second virtual character may belong to different teams, different organizations, or two groups with enemy.
Alternatively, the applications installed on the first device 420 and the second device 460 are the same, or the applications installed on the two devices are the same type of application for different control system platforms. The first device 420 may generally refer to one of a plurality of devices, and the second device 460 may generally refer to one of a plurality of devices, and this embodiment is illustrated by the first device 420 and the second device 460. The device types of the first device 420 and the second device 460 are the same or different, and include: at least one of a game console, a desktop computer, a smartphone, a tablet, an e-book reader, an MP3 player, an MP4 player, and a laptop portable computer. The following embodiments are illustrated where the device is a desktop computer.
Those skilled in the art will appreciate that the number of devices described above may be greater or fewer. For example, the number of the devices may be only one, or several tens or hundreds, or more. The number and the type of the devices are not limited in the embodiments of the present application.
With reference to the above noun brief introduction and description of implementation environment, a method for distributing task items provided in this embodiment of the present application is described, fig. 5 is a flowchart of a method for distributing task items provided in an exemplary embodiment of the present application, taking application of this method in a server as an example, as shown in fig. 5, this method includes:
step 501, obtaining historical behavior data of the target account from a behavior database.
Optionally, the historical behavior data is data generated by the target account in the process of program historical use.
Alternatively, taking a game application as an example, the historical behavior data is data generated by the target account in the historical virtual battle. Optionally, the historical behavior data includes operation data, walking position data, attack success rate data, victory or defeat data, attack and kill data, attack assisting data, failure data, consumption data, duration data, and the like of the target account in the historical virtual battle, which is not limited in the embodiment of the present application. The operation data refers to game habits represented by operation triggered by the target account in the virtual battle; the position data refers to the area where the target account moves in the virtual fight; the attack success rate data refers to the number of times that the target account successfully attacks the enemy in the virtual fight and accounts for the proportion of the total number of times of attacks; the winning or losing data refers to the proportion of successful times in the virtual battle completed by the target account to the total times; the killing data refers to the times of killing the enemy in the virtual fight by the target account; the attack assisting data refers to the times of assisting teammates to kill the other party in the virtual battle; the failure data refers to the number of times that the target account is killed in the virtual battle; the consumption data refers to the consumption condition of the target account in the application program; the duration data refers to the game duration of the target account in the application program.
Optionally, the historical behavior data of the target account is data generated by virtual fight of the target account in a historical time period; or the historical behavior data of the target account is data generated by the target account in a preset number of historical virtual battles; or the historical behavior data of the target account is data generated in all historical virtual battles of the target account, and the generation duration of the historical behavior data is not limited in the embodiment of the application.
Optionally, the behavior database of the server stores behavior data generated according to the historical virtual battles corresponding to each player account, the server obtains the behavior data corresponding to the target account from the behavior database according to the account identifier of the target account, and selects the historical behavior data meeting the condition requirements according to the screening conditions of the historical behavior data. Such as: selecting historical behavior data generated in a historical time period from the behavior data; or selecting historical behavior data generated in historical virtual battles with the latest preset number from the behavior data; or, the behavior data is determined as historical behavior data as a whole.
Step 502, matching the historical behavior data with the behavior feature table to obtain the target behavior feature corresponding to the target account.
Optionally, the behavior feature table includes behavior data for characterizing the program usage characteristics. Taking a game application as an example for explanation, the behavior feature table includes behavior data for representing the fighting characteristics.
Optionally, data analysis is performed on the historical behavior data, and matching is performed according to the analysis result and the behavior feature table to obtain the target behavior feature corresponding to the target account. Such as: the historical behavior data comprises position data of a target account, wherein the position data represents that 6 rounds of the target account in 8 rounds of historical virtual battles move towards the middle way, and the historical behavior data is matched with the behavior feature table to obtain target behavior features corresponding to the target account, including the middle way; and the historical behavior data comprises operation data of the target account, wherein the operation data represents that 5 rounds of the target account in 8 rounds of historical virtual battles are auxiliary players, and after the historical behavior data is matched with the behavior feature table, the target behavior features corresponding to the target account are obtained and comprise assistance.
Alternatively, the target behavior feature corresponding to the target account determined at a single time may include only one behavior feature, or may include a plurality of behavior features.
Step 503, determining a task data set corresponding to the target behavior characteristics.
Optionally, when the target behavior feature corresponding to the target account includes a behavior feature, determining a task data group corresponding to the target behavior feature; and when the target behavior characteristics corresponding to the target account comprise a plurality of behavior characteristics, determining task data sets respectively corresponding to the behavior characteristics.
Optionally, the task data sets are task sets obtained by classifying and summarizing the task items according to the behavior characteristics, and optionally, each task data set may correspond to one behavior characteristic or to a plurality of behavior characteristics. The task data group comprises task items matched with the target behavior characteristics, so that after the target behavior characteristics corresponding to the target account are determined, the task items can be determined from the task data group corresponding to the target behavior characteristics and distributed to the target account.
Optionally, the task data set is provided with n corresponding and matched behavior features, where n is a positive integer, and the task data set including the target behavior feature in the n behavior features is determined as the task data set corresponding to the target behavior feature.
Schematically, a description is given of a matching manner of the target behavior characteristics and the task data set:
firstly, determining a task data set with corresponding behavior characteristics including target behavior characteristics as a task data set corresponding to the target behavior characteristics;
illustratively, the target behavior characteristics comprise behavior characteristics A, the task data group 1 corresponds to the behavior characteristics A and the behavior characteristics B, the task data group 2 corresponds to the behavior characteristics B, and the task data group 3 corresponds to the behavior characteristics C and the characteristics A; determining the task data groups as a task data group 1 and a task data group 3 according to the target behavior characteristics;
or the target behavior characteristics comprise behavior characteristics A and behavior characteristics B, the task data group 1 corresponds to the behavior characteristics A and the behavior characteristics B, the task data group 2 corresponds to the behavior characteristics B, and the task data group 3 corresponds to the behavior characteristics C and the behavior characteristics A; the task data set determined according to the target behavior characteristics is the task data set 1.
Secondly, determining a task data set with corresponding behavior characteristics including at least one target behavior characteristic as a task data set corresponding to the target behavior characteristic;
illustratively, the target behavior characteristics comprise behavior characteristics A and behavior characteristics B, the task data group 1 corresponds to the behavior characteristics A and the behavior characteristics B, the task data group 2 corresponds to the behavior characteristics B, and the task data group 3 corresponds to the behavior characteristics C; the task data groups determined according to the target behavior characteristics are the task data group 1 and the task data group 2.
Thirdly, the behavior characteristics corresponding to the task data group are arranged in the order of the priority from high to low, the highest priority comprising the target behavior data is determined, and the task data group with the highest priority comprising the target behavior data is determined as the task data group corresponding to the target behavior characteristics;
illustratively, the target behavior characteristics include behavior characteristics a, the task data group 1 corresponds to behavior characteristics a (priority 1) and behavior characteristics B (priority 2), the task data group 2 corresponds to behavior characteristics B (priority 1), and the task data group 3 corresponds to behavior characteristics C (priority 1) and behavior characteristics a (priority 2); the task data set determined according to the target behavior characteristics is a task data set 1 (the priority level 1 comprises behavior characteristics A);
or the target behavior characteristics comprise behavior characteristics A and behavior characteristics B, the task data group 1 corresponds to the behavior characteristics A (priority 1) and the behavior characteristics B (priority 2), the task data group 2 corresponds to the behavior characteristics B (priority 1) and the behavior characteristics C (priority 2), and the task data group 3 corresponds to the behavior characteristics C (priority 1) and the behavior characteristics A (priority 2); the task data set determined according to the target behavior characteristics is a task data set 1 (the priority 1 includes the behavior characteristics a) and a task data set 2 (the priority 1 includes the behavior characteristics B).
Step 504, at least one task item is selected from the task data group and distributed to the target account.
Optionally, when at least one task item is selected from the task data group, the task items may be sequentially selected according to the order of the task items in the task data group, or the task items may be randomly selected from the task data group and allocated to the target account.
Optionally, the at least one task item may be distributed to the target account as a daily task, may also be distributed to the target account as a challenge task, may also be partially distributed as a daily task, and may be distributed to the target account as a challenge task.
In summary, according to the method for allocating task items provided in this embodiment, historical behavior data of a target account is obtained, and the historical behavior data is analyzed to obtain corresponding target behavior characteristics, so that task items are selected from a task data group corresponding to the target behavior characteristics and allocated to the target account, and thus the task items allocated to the target account better conform to the game habit of the target account, the task items are suitable for the target account to complete the task items, the problem that server resources are wasted due to the fact that the task items allocated to the target account have a low degree of adaptation is avoided, and the utilization efficiency of the server resources is improved.
In an alternative embodiment, the task data group is provided with n corresponding matching behavior features, fig. 6 is a flowchart of a task item distribution method provided in another exemplary embodiment of the present application, which is described by taking the method as an example for being applied in a server, and as shown in fig. 6, the method includes:
step 601, obtaining historical behavior data of the target account from the behavior database.
Optionally, the historical behavior data of the target account is data generated by virtual fight of the target account in a historical time period; or the historical behavior data of the target account is data generated by the target account in a preset number of historical virtual battles; or the historical behavior data of the target account is data generated in all historical virtual battles of the target account, and the generation duration of the historical behavior data is not limited in the embodiment of the application.
Optionally, the time for acquiring the historical behavior data of the target account may be preset, or may be triggered according to a timer, for example: and sending historical behavior data of the acquired target account at 0 point every day by taking the date as an updating period according to a set timer, and distributing task items to the target account according to the historical behavior data.
Step 602, matching the historical behavior data with the behavior feature table to obtain the target behavior feature corresponding to the target account.
Optionally, data analysis is performed on the historical behavior data, and matching is performed according to the analysis result and the behavior feature table to obtain the target behavior feature corresponding to the target account.
Alternatively, the target behavior feature corresponding to the target account determined at a single time may include only one behavior feature, or may include a plurality of behavior features.
Step 603, determining a task data set including the target behavior characteristics in the n behavior characteristics as a task data set corresponding to the target behavior characteristics.
Optionally, when the target behavior feature corresponding to the target account includes a behavior feature, determining a task data group corresponding to the target behavior feature; and when the target behavior characteristics corresponding to the target account comprise a plurality of behavior characteristics, determining task data sets respectively corresponding to the behavior characteristics.
Optionally, the task data set is provided with n corresponding and matched behavior features, where n is a positive integer, and the task data set including the target behavior feature in the n behavior features is determined as the task data set corresponding to the target behavior feature.
Schematically, a description is given of a matching manner of the target behavior characteristics and the task data set:
firstly, determining a task data set with corresponding behavior characteristics including target behavior characteristics as a task data set corresponding to the target behavior characteristics;
secondly, determining a task data set with corresponding behavior characteristics including at least one target behavior characteristic as a task data set corresponding to the target behavior characteristic;
thirdly, the behavior characteristics corresponding to the task data group are arranged in the order of the priority from high to low, the highest priority comprising the target behavior data is determined, and the task data group with the highest priority comprising the target behavior data is determined as the task data group corresponding to the target behavior characteristics.
Optionally, the n behavior features are arranged in sequence from high to low in priority, the target behavior feature is sequentially matched with the n behavior features in priority sequence, a target priority which is matched with the target behavior feature at first is determined, and the task data group with the target priority including the target behavior feature is used as the task data group corresponding to the target behavior feature.
Illustratively, the target behavior characteristics include one behavior characteristic and a plurality of behavior characteristics, which are respectively explained as follows:
one, the target behavior feature includes a behavior feature
Schematically, the target behavior feature corresponding to the target account is behavior feature a, the task data group 1 corresponds to behavior feature a (priority 1) and behavior feature B (priority 2), the task data group 2 corresponds to behavior feature B (priority 1), and the task data group 3 corresponds to behavior feature C (priority 1) and behavior feature a (priority 2); when the target behavior characteristics are matched with the behavior characteristics corresponding to each task data group according to the priority sequence, firstly matching the priority 1 of the task data group 1 with the behavior characteristics A, and determining the task data group 1 with the priority 1 including the behavior characteristics A as the task data group corresponding to the target account;
second, the target behavior feature includes a plurality of behavior features (two behavior features are exemplified here)
Illustratively, the target behavior characteristics include behavior characteristics a and behavior characteristics B, the task data group 1 corresponds to the behavior characteristics a (priority 1) and the behavior characteristics B (priority 2), the task data group 2 corresponds to the behavior characteristics B (priority 1) and the behavior characteristics C (priority 2), and the task data group 3 corresponds to the behavior characteristics C (priority 1) and the behavior characteristics a (priority 2); when the target behavior characteristics are matched with the behavior characteristics corresponding to each task data group according to the priority sequence, firstly matching the priority 1 of the task data group 1 with the behavior characteristics A, and determining the task data group 1 with the priority 1 including the behavior characteristics A and the task book data group 2 with the priority 1 including the behavior characteristics B as the task data group corresponding to the target account.
And step 604, selecting at least one task item from the task data group to distribute to the target account.
Optionally, when at least one task item is selected from the task data group, the task items may be sequentially selected according to the order of the task items in the task data group, or the task items may be randomly selected from the task data group and allocated to the target account.
Optionally, the at least one task item may be distributed to the target account as a daily task, may also be distributed to the target account as a challenge task, may also be partially distributed as a daily task, and may be distributed to the target account as a challenge task. Optionally, the task type of the task item to be allocated may be determined according to the task allocation requirement, and the task item of the corresponding type is determined from the task data group for allocation; or directly selecting task items from the task data group to distribute, and distributing the task items to the corresponding task list according to the task types of the task items.
In summary, according to the method for allocating task items provided in this embodiment, historical behavior data of a target account is obtained, and the historical behavior data is analyzed to obtain corresponding target behavior characteristics, so that task items are selected from a task data group corresponding to the target behavior characteristics and allocated to the target account, and thus the task items allocated to the target account better conform to the game habit of the target account, the task items are suitable for the target account to complete the task items, the problem that server resources are wasted due to the fact that the task items allocated to the target account have a low degree of adaptation is avoided, and the utilization efficiency of the server resources is improved.
According to the method provided by the embodiment, after n behavior characteristics corresponding to the task data group are arranged according to the priority, the n behavior characteristics are matched with the target behavior characteristics according to the arrangement sequence, so that the task data group with the high-priority behavior characteristics meeting the requirements is determined as the task data group corresponding to the target account, the task item selected from the task data group has higher adaptation degree with the target account, the problem that server resources are wasted due to the fact that the task item allocated to the target account has lower adaptation degree and cannot be completed is solved, and the utilization efficiency of the server resources is improved.
Referring to fig. 7, schematically, an overall flowchart of task item allocation provided in an exemplary embodiment of the present application is shown, as shown in fig. 7, in the process, the process includes: determining the distribution time 710 of the task items, when the distribution time 710 is reached, obtaining the characteristics 720 of the player, searching the corresponding task groups 730 according to the characteristics 720 of the player, obtaining a random task pool 740 corresponding to the task groups, selecting the task items from the task pool 740, judging whether the task list of the player is full, when the task list is full, not distributing the task items, and when the task list is not full, distributing the task items.
In an alternative embodiment, when selecting a task item from a task data set for distribution, the task item is selected by generating a random number, fig. 8 is a flowchart of a distribution method for the task item provided in another exemplary embodiment of the present application, which is described by taking an example that the method is applied to a server, as shown in fig. 8, the method includes:
step 801, obtaining historical behavior data of the target account from the behavior database.
Optionally, the historical behavior data of the target account is data generated by virtual fight of the target account in a historical time period; or the historical behavior data of the target account is data generated by the target account in a preset number of historical virtual battles; or the historical behavior data of the target account is data generated in all historical virtual battles of the target account, and the generation duration of the historical behavior data is not limited in the embodiment of the application.
And step 802, matching the historical behavior data with the behavior characteristic table to obtain target behavior characteristics corresponding to the target account.
Optionally, data analysis is performed on the historical behavior data, and matching is performed according to the analysis result and the behavior feature table to obtain the target behavior feature corresponding to the target account.
Alternatively, the target behavior feature corresponding to the target account determined at a single time may include only one behavior feature, or may include a plurality of behavior features.
Step 803, determining a task data set corresponding to the target behavior characteristics.
Schematically, a description is given of a matching manner of the target behavior characteristics and the task data set:
firstly, determining a task data set with corresponding behavior characteristics including target behavior characteristics as a task data set corresponding to the target behavior characteristics;
secondly, determining a task data set with corresponding behavior characteristics including at least one target behavior characteristic as a task data set corresponding to the target behavior characteristic;
thirdly, the behavior characteristics corresponding to the task data group are arranged in the order of the priority from high to low, the highest priority comprising the target behavior data is determined, and the task data group with the highest priority comprising the target behavior data is determined as the task data group corresponding to the target behavior characteristics.
Step 804, obtaining the random number in the range of the random number.
Optionally, a weight value is correspondingly set for a task item in the task data set, a first weight sum of the task item in the task data set is determined, and a range between the target value and the first weight sum is determined as a random number range. Such as: the target value is 1, a range between 1 and the first weight sum is determined as a random number range.
Illustratively, the task data set includes a task item 1, a task item 2, and a task item 3, where a weight value of the task item 1 is 3, a weight value of the task item 2 is 4, and a weight value of the task item 3 is 6, then the first weight sum is 13, and the random number range is 1 to 13.
Step 805, determining a corresponding random task item in the task data group according to the random number.
Optionally, traversing the task items in the task data set, calculating a second weight sum of the traversed task items in the traversing process, stopping traversing in response to the second weight sum and the obtained numerical value of the random number, and determining the last task item obtained by traversing as a random task item.
Illustratively, the task data set includes a task item 1, a task item 2 and a task item 3, wherein the weight value of the task item 1 is 3, the weight value of the task item 2 is 4, the weight value of the task item 3 is 6, and a random number is determined to be 5 within a random number range, so that the task item in the task data set is traversed, firstly, the task item 1 is traversed to obtain a second weight sum of 3, and if the random number is not reached to 5, the task item 2 is traversed to obtain a second weight sum of 7, and the random number is reached to 5, so that the task item 2 is determined to be a random task item.
Step 806, assigning the random task item to the target account.
Optionally, when allocating the random task item to the target account, first determining a task type of the random task item, and determining an allocation rule corresponding to the task type, so as to allocate the random task item to the target account according to the allocation rule.
Optionally, the task types include a daily type and a challenge type, that is, the task items include a daily task and a challenge task, and when the random task item is a daily task, the random task item is distributed according to a distribution rule corresponding to the daily type; and when the random task item is the challenge task, distributing according to the distribution rule corresponding to the challenge type.
Optionally, when the random task item corresponds to the daily type and the existing task item of the target account includes the random task item, the random task item is distributed to the target account in a manner of resetting the completion progress data of the random task item; and in response to the fact that the random task item corresponds to the challenge type and the existing task items of the target account include the random task item, discarding the random task item.
Illustratively, the random task item is implemented to win the game task, such as: when the player wins the game in an accumulated manner to reach 5 games, the game winning task is completed, and the game winning task is realized as a daily-type task item as an example: when the winning game task is determined to be a task item distributed to a target account, firstly, determining whether the current existing task item of the target account contains the winning game task or not, wherein 1, the current existing task item of the target account contains the winning game task, and the completion progress is 2/5, resetting the winning game task to 0/5 and distributing the winning game task to the target account; 2. if the current task item of the target account does not contain the winning game task, the winning game task is directly distributed to the target account.
Illustratively, the random task item is implemented to accomplish an office task, such as: when the player completes the game in an accumulative way to 20 hands, the game task is completed, and the task item with the game task being realized as a challenge type is taken as an example for explanation: when the completion of the office alignment task is determined to be the task item distributed to the target account, firstly, whether the completion office alignment task is contained in the current existing task item of the target account is determined, 1, the completion office alignment task is contained in the current existing task item of the target account, and the completion progress is 8/20, the completion office alignment task is discarded, and the completion progress of the current completion office alignment task of the target account is reserved; 2. if the current task item of the target account does not contain the task for completing the game, the task for completing the game is directly distributed to the target account.
Optionally, the weight value corresponding to each task item determines the priority of the task item appearing at random, that is, in the task data set, the task items are arranged in sequence from high to low according to the weight value.
In summary, according to the method for allocating task items provided in this embodiment, historical behavior data of a target account is obtained, and the historical behavior data is analyzed to obtain corresponding target behavior characteristics, so that task items are selected from a task data group corresponding to the target behavior characteristics and allocated to the target account, and thus the task items allocated to the target account better conform to the game habit of the target account, the task items are suitable for the target account to complete the task items, the problem that server resources are wasted due to the fact that the task items allocated to the target account have a low degree of adaptation is avoided, and the utilization efficiency of the server resources is improved.
According to the method provided by the embodiment, the task items are randomly determined from the task data group and distributed to the target account in the mode of determining the random number, only the task items in the task data group need to be traversed, and the weighted values are added, so that the algorithm is simple and convenient, the extra space is not occupied, the time complexity is 0(n), and the task item selection efficiency is high.
Fig. 9 is a flowchart of a task item allocation process provided in an exemplary embodiment of the present application, and as shown in fig. 9, the process includes:
the task item distribution time is determined according to the trigger 910, and when the trigger condition of the trigger 910 is reached, the task type 920 is judged through the data configuration 930.
When the task type 920 belongs to the daily type 940, determining that the task item of the daily type is allocated to the target account from the task data set, judging whether to start a precondition 941 for task item allocation, when the precondition 941 is started, judging whether the precondition is completed, and when the precondition is completed, allocating the task item to the target account.
When the task type 920 belongs to the challenge type 950, a task item of the challenge type is determined from the task data set and is allocated to the target account.
Fig. 10 is a block diagram of a task item allocation apparatus according to an exemplary embodiment of the present application, where as shown in fig. 10, the apparatus includes:
an obtaining module 1010, configured to obtain historical behavior data of a target account from a behavior database, where the historical behavior data is data generated by the target account in a historical program use process;
a matching module 1020, configured to match the historical behavior data with a behavior feature table to obtain a target behavior feature corresponding to the target account, where the behavior feature table includes behavior features used for characterizing program usage characteristics;
a determining module 1030, configured to determine a task data set corresponding to the target behavior feature, where the task data set includes task items matched with the target behavior feature;
the allocating module 1040 is configured to select at least one task item from the task data set to allocate to the target account.
In an optional embodiment, the task data group is provided with n corresponding matched behavior characteristics, wherein n is a positive integer;
the determining module 1030 is further configured to determine the task data set including the target behavior feature in the n behavior features as the task data set corresponding to the target behavior feature.
In an alternative embodiment, n of the behavior characteristics are arranged in order of priority from high to low;
as shown in fig. 11, the determining module 1030 includes:
a matching unit 1031, configured to match the target behavior feature with the n behavior features in order of priority;
a determining unit 1032, configured to determine a target priority that is first correspondingly matched with the target behavior feature; and taking the task data group with the target priority including the target behavior characteristics as the task data group corresponding to the target behavior characteristics.
In an optional embodiment, the obtaining module 1010 is further configured to obtain a random number within a range of random numbers;
the determining module 1030 is further configured to determine a corresponding random task item in the task data group according to the random number;
the allocating module 1040 is further configured to allocate the random task item to the target account.
In an optional embodiment, the task items in the task data group are correspondingly provided with weight values;
the determining module 1030 is further configured to determine a first weighted sum of the task items in the task data group; determining a range between a target value and the first weighted sum as the random number range.
In an optional embodiment, the determining module 1030 is further configured to traverse the task items in the task data set, and calculate a second weighted sum for the traversed task items in the traversal process; and stopping traversing in response to the second weight and reaching the numerical value of the random number, and determining the last task item obtained by traversing as the random task item.
In an optional embodiment, the determining module 1030 is further configured to determine a task type of the random task item; determining an allocation rule corresponding to the task type;
the distributing module 1040 is further configured to distribute the random task item to the target account according to the distribution rule.
In an alternative embodiment, the task types include a daily type and a challenge type;
the allocating module 1040 is further configured to, in response to that the random task item corresponds to the daily type and an existing task item of the target account includes the random task item, allocate the random task item to the target account in a manner of resetting completion progress data of the random task item;
the allocating module 1040 is further configured to, in response to that the random task item corresponds to the challenge type and an existing task item of the target account includes the random task item, discard the random task item.
In summary, the distribution device for task items provided in this embodiment obtains the corresponding target behavior characteristics by obtaining the historical behavior data of the target account and analyzing the historical behavior data, so as to select the task item from the task data group corresponding to the target behavior characteristics to distribute to the target account, so that the task item distributed to the target account better conforms to the game habit of the target account, the task item is suitable for the target account to complete the task item, the problem that server resources are wasted because the task item distributed to the target account has a low degree of adaptation is avoided, and the utilization efficiency of the server resources is improved.
It should be noted that: the task item allocation apparatus provided in the foregoing embodiment is only illustrated by dividing the functional modules, and in practical applications, the function allocation may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the task item allocation device and the task item allocation method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments for details, and are not described herein again.
Fig. 12 shows a schematic structural diagram of a server according to an exemplary embodiment of the present application. Specifically, the method comprises the following steps:
the server 1200 includes a Central Processing Unit (CPU) 1201, a system Memory 1204 including a Random Access Memory (RAM) 1202 and a Read Only Memory (ROM) 1203, and a system bus 1205 connecting the system Memory 1204 and the CPU 1201. The server 1200 also includes a basic input/output System (I/O System) 1206 that facilitates transfer of information between devices within the computer, and a mass storage device 1207 for storing an operating System 1213, application programs 1214, and other program modules 1215.
The basic input/output system 1206 includes a display 1208 for displaying information and an input device 1209, such as a mouse, keyboard, etc., for user input of information. Wherein a display 1208 and an input device 1209 are connected to the central processing unit 1201 through an input-output controller 1210 coupled to the system bus 1205. The basic input/output system 1206 may also include an input/output controller 1210 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, input-output controller 1210 also provides output to a display screen, a printer, or other type of output device.
The mass storage device 1207 is connected to the central processing unit 1201 through a mass storage controller (not shown) connected to the system bus 1205. The mass storage device 1207 and its associated computer-readable media provide non-volatile storage for the server 1200. That is, the mass storage device 1207 may include a computer-readable medium (not shown) such as a hard disk or Compact disk Read Only Memory (CD-ROM) drive.
Without loss of generality, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash Memory or other solid state Memory technology, CD-ROM, Digital Versatile Disks (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage, or other magnetic storage devices. Of course, those skilled in the art will appreciate that computer storage media is not limited to the foregoing. The system memory 1204 and mass storage device 1207 described above may be collectively referred to as memory.
According to various embodiments of the present application, the server 1200 may also operate as a remote computer connected to a network through a network, such as the Internet. That is, the server 1200 may be connected to the network 1212 through a network interface unit 1211 connected to the system bus 1205, or the network interface unit 1211 may be used to connect to other types of networks or remote computer systems (not shown).
The memory further includes one or more programs, and the one or more programs are stored in the memory and configured to be executed by the CPU.
Embodiments of the present application further provide a computer device, where the computer device includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or a set of instructions, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the method for allocating task items provided by the foregoing method embodiments.
Embodiments of the present application further provide a computer-readable storage medium, on which at least one instruction, at least one program, a code set, or a set of instructions is stored, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the method for allocating task items provided by the above method embodiments.
Optionally, the computer-readable storage medium may include: ROM, RAM, Solid State Drives (SSD), or optical disks, etc. The Random Access Memory may include a resistive Random Access Memory (ReRAM) and a Dynamic Random Access Memory (DRAM). The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (15)

1. A method for assigning task items, the method comprising:
acquiring historical behavior data of a target account from a behavior database, wherein the historical behavior data is generated in the process of program historical use of the target account;
matching the historical behavior data with a behavior feature table to obtain target behavior features corresponding to the target account, wherein the behavior feature table comprises behavior features used for representing program use characteristics;
determining a task data group corresponding to the target behavior characteristics, wherein the task data group comprises task items matched with the target behavior characteristics;
and selecting at least one task item from the task data group to be distributed to the target account.
2. The method of claim 1, wherein the task data groups are provided with n corresponding matching behavioral features, n being a positive integer;
the determining of the task data group corresponding to the target behavior feature includes:
and determining the task data group comprising the target behavior characteristics in the n behavior characteristics as the task data group corresponding to the target behavior characteristics.
3. The method of claim 2, wherein n of the behavior features are arranged in order of priority from high to low;
the determining the task data group including the target behavior characteristics in the n behavior characteristics as the task data group corresponding to the target behavior characteristics includes:
matching the target behavior characteristics with the n behavior characteristics in sequence according to the priority order;
determining a target priority which is correspondingly matched with the target behavior characteristics at first;
and taking the task data group with the target priority including the target behavior characteristics as the task data group corresponding to the target behavior characteristics.
4. The method according to any one of claims 1 to 3, wherein the selecting at least one task item from the task data set to be allocated to the target account comprises:
acquiring a random number within a random number range;
determining a corresponding random task item in the task data group according to the random number;
and distributing the random task item to the target account.
5. The method according to claim 4, wherein the task items in the task data group are correspondingly provided with weight values;
before acquiring the random number in the random number range, the method further comprises:
determining a first weighted sum of the task items in the task data set;
determining a range between a target value and the first weighted sum as the random number range.
6. The method of claim 4, wherein determining a corresponding random task item in the task data set according to the random number comprises:
traversing the task items in the task data set, and calculating a second weight sum of the traversed task items in the traversing process;
and stopping traversing in response to the second weight and reaching the numerical value of the random number, and determining the last task item obtained by traversing as the random task item.
7. The method of claim 4, wherein the assigning the random task item to the target account number comprises:
determining a task type of the random task item;
determining an allocation rule corresponding to the task type;
and distributing the random task items to the target account according to the distribution rule.
8. The method of claim 7, wherein the task types include a daily type and a challenge type;
the allocating the random task item to the target account according to the allocation rule comprises:
responding to the fact that the random task item corresponds to the daily type and the existing task item of the target account comprises the random task item, and distributing the random task item to the target account in a mode of resetting completion progress data of the random task item;
and in response to the fact that the random task item corresponds to the challenge type and the existing task item of the target account comprises the random task item, discarding the random task item.
9. The method according to any one of claims 1 to 3,
the target account is an account registered in a game application program, the historical behavior data is data generated by the target account in historical virtual combat, and the behavior characteristic table comprises the behavior characteristics used for representing combat characteristics.
10. An apparatus for distributing task items, the apparatus comprising:
the acquisition module is used for acquiring historical behavior data of a target account from a behavior database, wherein the historical behavior data is generated by the target account in the process of program historical use;
the matching module is used for matching the historical behavior data with a behavior feature table to obtain target behavior features corresponding to the target account, and the behavior feature table comprises behavior features used for representing program use characteristics;
the determining module is used for determining a task data group corresponding to the target behavior characteristics, and the task data group comprises task items matched with the target behavior characteristics;
and the distribution module is used for selecting at least one task item from the task data group and distributing the task item to the target account.
11. The apparatus of claim 10, wherein the task data groups are configured with n behavior features that are correspondingly matched, where n is a positive integer;
the determining module is further configured to determine the task data set including the target behavior feature in the n behavior features as the task data set corresponding to the target behavior feature.
12. The apparatus of claim 11, wherein n of the behavior features are arranged in order of priority from high to low;
the determining module includes:
the matching unit is used for sequentially matching the target behavior characteristics with the n behavior characteristics according to the priority order;
the determining unit is used for determining a target priority which is correspondingly matched with the target behavior characteristics at first; and taking the task data group with the target priority including the target behavior characteristics as the task data group corresponding to the target behavior characteristics.
13. The apparatus according to any one of claims 10 to 12, wherein the acquiring module is further configured to acquire a random number within a range of random numbers;
the determining module is further configured to determine a corresponding random task item in the task data set according to the random number;
the distribution module is further configured to distribute the random task item to the target account.
14. A computer device comprising a processor and a memory, the memory having stored therein at least one instruction, at least one program, set of codes or set of instructions, which is loaded and executed by the processor to implement a method of assigning task items according to any one of claims 1 to 9.
15. A computer-readable storage medium, having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement a method of assigning task items according to any one of claims 1 to 9.
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