CN111475268B - Task item distribution method, device, equipment and readable storage medium - Google Patents

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

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CN111475268B
CN111475268B CN202010252190.1A CN202010252190A CN111475268B CN 111475268 B CN111475268 B CN 111475268B CN 202010252190 A CN202010252190 A CN 202010252190A CN 111475268 B CN111475268 B CN 111475268B
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task
target
behavior
target account
item
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CN111475268A (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, device and 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 feature; at least one task item is selected from the task data set and assigned to the target account. The historical behavior data of the target account is obtained, the historical behavior data is analyzed to obtain corresponding target behavior characteristics, task items are selected according to the target behavior characteristics and distributed to the target account, so that the task items distributed to the target account are more in accordance with game habits of the target account, the problem that server resources are wasted due to the fact that the task items distributed to the target account are low in adaptation degree and cannot be completed is avoided, and the utilization efficiency of the server resources is improved.

Description

Task item distribution method, device, 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, device and equipment and a readable storage medium.
Background
In applications such as games, a task rewarding mechanism is generally provided, that is, a certain reward is obtained in a manner of completing a task, for example: a daily task module is generally provided in a game, in which task items are included, and a player completes the task items through a game process and acquires rewards corresponding to the task items.
In the related art, when task items are set, a fixed daily task list is set, and the completion progress of the task items in the daily task list is reset every day according to the update of the date, so that the daily objective of a player is to complete the task items in the daily task list.
When the task items are set in the above manner, the task items in the daily task list are fixed, so that the game habit of the player cannot finish the task items in the daily task list, and the task items distributed to the player cannot be finished, thereby wasting server resources.
Disclosure of Invention
The embodiment of the application provides a task item distribution method, device and equipment and a readable storage medium, which can improve the utilization efficiency of server resources when task items are distributed to a target account. The technical scheme is as follows:
In one aspect, a method for distributing task items is provided, where the method includes:
acquiring historical behavior data of a target account from a behavior database, wherein the historical behavior data is generated in a program historical use process 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 for representing the use characteristics of the program;
determining a task data set corresponding to the target behavior feature, wherein the task data set comprises task items matched with the target behavior feature;
at least one task item is selected from the task data set and allocated to the target account.
In another aspect, there is provided an apparatus for distributing task items, the apparatus comprising:
the system comprises an acquisition module, a program history use module and a program history use module, wherein 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 in the program history use process of the target account;
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, wherein the behavior feature table comprises behavior features for representing the use characteristics of the program;
The determining module is used for determining a task data set corresponding to the target behavior characteristic, wherein the task data set comprises task items matched with the target behavior characteristic;
and the allocation module is used for selecting at least one task item from the task data set to be allocated to the target account.
In another aspect, a computer device is provided, where the computer device includes a processor and a memory, where the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, where the at least one instruction, the at least one program, the set of codes, or the set of instructions are loaded and executed by the processor to implement a method for assigning task items according to any of the embodiments of the present application.
In another aspect, a computer readable storage medium is provided, where at least one instruction, at least one program, a set of codes, or a set of instructions is stored, where the at least one instruction, the at least one program, the set of codes, or the set of instructions are loaded and executed by the processor to implement a method for assigning task items according to 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 task item allocation method according to any one of the embodiments of the present application described above.
The beneficial effects that technical scheme that this application embodiment provided include at least:
the historical behavior data of the target account is obtained, the historical behavior data is analyzed to obtain the corresponding target behavior characteristics, so that task items are selected from the task data set corresponding to the target behavior characteristics and distributed to the target account, the task items distributed to the target account are more in accordance with the game habit of the target account, the task items are completed by the target account, the problem that server resources are wasted due to the fact that the task items distributed to the target account are low in adaptation degree and cannot be completed is avoided, and the utilization efficiency of the server resources is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a task list interface of a player account provided in one exemplary embodiment of the present application;
FIG. 2 is a schematic diagram of correspondence between task data groups and behavioral characteristics 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 flowchart of a method for task item allocation provided by an exemplary embodiment of the present application;
FIG. 6 is a flow chart of a method of task item allocation provided by another exemplary embodiment of the present application;
FIG. 7 is a general flow chart of task item allocation provided by an exemplary embodiment of the present application;
FIG. 8 is a flow chart of a method of task item allocation provided by another exemplary embodiment of the present application;
FIG. 9 is a flowchart of a task item assignment process provided by an exemplary embodiment of the present application;
FIG. 10 is a block diagram of a task item distribution device provided in one exemplary embodiment of the present application;
FIG. 11 is a block diagram of a task item distribution device provided in 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
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
First, the terms involved in the embodiments of the present application will be briefly described:
Task item: the task conditions for exchanging the bonus resources are provided to the user account in the application program, and the player account is used for exchanging the corresponding bonus resources by completing the task items in the game application program. Illustratively, in the game application program, the continuous win task item is provided for the target account, that is, the target account participates in virtual fight in the game application program, and when the continuous win frequency reaches the required frequency, the bonus resource corresponding to the continuous win task item can be obtained.
For an exemplary matching relationship between task items and progress conditions, please refer to the following table one:
list one
Task item Completion condition
(one) winning the game First game number
(II) completion of the office Second pair number of rounds
(III) continuous winning Number of consecutive peptides
(IV) completion of the attack Number of attacks
(V) obtaining double kills Number of double kills
As can be seen from the above table one, when the number of winning games reaches the first number of games, determining to complete the task item (one); determining to complete the task item (II) when the number of completed games reaches the second number of games; when the number of the continuous peptides reaches the number of the continuous peptides, determining to complete the task item (III); when the number of times of completing the attack reaches the attack number, determining to complete the task Item (IV); and determining to finish the task item (five) when the number of times of double killing is up to the number of times of double killing.
Optionally, the task items correspond to task types, which are illustrative, and the task items can be daily task items or challenge task items. The daily task item refers to a task item refreshed by taking a date as a refresh period, that is, a task item completed in a limited time by taking a date as a time limit, where the daily task item refresh period may be one day or multiple days, which is not limited in the embodiment of the present application, and is described by taking one day as an example, for example, as follows: and (3) distributing a first task item to the target account on the 31 rd of 3 months, and if the target account does not complete the first task item on the 31 rd of 3 months, distributing a new task item to the target account on the 1 st of 4 months, wherein the new task item may or may not comprise the first task item. The challenge type task items refer to task items which can be accumulated, namely after the challenge type task items are distributed 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 challenge type task items which can be received by each player is at the upper limit of the number, and when the number of the challenge type task items reaches the upper limit of the number, the challenge type task items are not distributed to the player.
Referring to fig. 1, a daily task assigned to a player account is displayed on a task list interface 100 of the player account, wherein the daily task includes 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 sets are used for classifying and summarizing task items according to behavior characteristics, and each task data set can be used for corresponding to one behavior characteristic or a plurality of behavior characteristics. Optionally, the task data set includes task items matched with the behavior features, so that after determining the behavior features corresponding to the player account, the task items can be determined from the task data set corresponding to the behavior features and distributed to the player account.
Referring to fig. 2, a correspondence relationship between a task data set and a behavior feature is shown in fig. 2, where a previous player task 211 corresponds to a behavior feature previous bill 212; middle player task 221 corresponds to behavior feature middle 222; the wild player task 231 corresponds to the behavioral characteristic wild 232; the all-round player task 241 corresponds to a behavioral characteristics upper sheet 242, a behavioral characteristics middle path 243, and behavioral characteristics assistance 244.
Optionally, the task item allocation method provided in the embodiment of the present application may be applied to at least one of the following scenarios:
firstly, in a game application program, historical behavior data of a player account in a historical virtual fight is obtained, the historical behavior data is matched with a behavior feature table to obtain target behavior features corresponding to the player account, a task data set corresponding to the target behavior features is determined, task items are selected from the task data set to be distributed to the player account, and the player account can exchange game resource rewards in the game application program by completing the task items, such as: game props, virtual gold coins, etc.;
secondly, in the shopping application program, acquiring historical behavior data of the user account in a historical shopping record, matching the historical behavior data with a behavior feature table to obtain target behavior features corresponding to the user account in the shopping process, determining a task data set corresponding to the target behavior features, and selecting task items from the task data set to be distributed to the user account;
optionally, the historical behavior data includes purchase times data, purchase amount data, browsing data, searching data and the like of the user account. The purchase time data are used for representing the number of times of the user account in the target time period, the purchase amount data are used for representing the total amount of the user account in the target time period, the browsing data are used for representing the duration, the number and the like of the user account in the target time period for browsing the products in the application program, and the search data are used for representing the number of times of the target account in the target time period for searching the products.
Schematically, after the historical behavior data of the user account in the historical shopping record is obtained, the historical behavior data is matched with a behavior feature table, if the target behavior feature of the user account is obtained according to browsing data, and the browsing time is long, a task data set corresponding to the target behavior feature is determined, wherein the task data set comprises task items corresponding to the target behavior feature, such as: the total browsing duration reaches 1 hour, the number of browsed products reaches 20, and the number of browsed individual products reaches 3 minutes. Selecting a task item from a task data set corresponding to the target behavior feature, and distributing the task item to a user account, wherein the user account can exchange shopping resource rewards in a shopping application program by completing the task item, such as: coupon, full coupon, etc.
Thirdly, in the singing application program, historical behavior data of the user account in the historical singing or playing process is obtained, the historical behavior data is matched with a behavior feature table, target behavior features corresponding to the user account in the singing or playing process are obtained, a task data set corresponding to the target behavior features is determined, and task items are selected from the task data set and distributed to the user account.
Optionally, the historical behavior data includes singing frequency data of a user account, prop giving data, work playing data, attention data, work release data and the like. The singing frequency data is used for representing the song recording frequency of the user account in a target time period, the property giving data is used for representing the number of properties given to other accounts by the user account in the target time period, the work playing data is used for representing the time length, the frequency and the like of playing the released singing works by the user account in the target time period, the attention data is used for representing the frequency of paying attention to other accounts by the target account in the target time period so as to establish an association relationship, and the work release data is used for representing the frequency of releasing the recorded songs by the user account in the target time period.
Schematically, after the historical behavior data of the user account in the historical singing or playing process is obtained, the historical behavior data is matched with a behavior feature table, if the target behavior feature of the user account obtained according to the work release data is that the work release number is large, a task data set corresponding to the target behavior feature is determined, wherein the task data set comprises task items corresponding to the target behavior feature, such as: the work is released for 5 times, the single song is recorded and released for 2 times, and 20 listening times are received after the work is released. Selecting task items from task data sets corresponding to target behavior characteristics, and distributing the task items to user accounts, wherein the user accounts can exchange props in singing application programs by completing the task items, such as: fresh flower property for giving away to singing works of other accounts, tuning property for tuning the works singed by user accounts, etc.
It should be noted that the above application scenario is merely an illustrative example, and the task item allocation method provided in the embodiment of the present application is applicable to any scenario in which a task item is determined by historical behavior data of a target account and allocated to the target account.
Optionally, in the embodiment of the present application, an example in which the task item allocation method is applied to a game application scenario is described.
The terminals in this application may be desktop computers, laptop portable computers, cell phones, tablet computers, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III, moving picture experts compression standard audio layer 3) players, MP4 (Moving Picture Experts Group Audio Layer IV, moving picture experts compression standard audio layer 4) players, and the like. The terminal has installed and running therein an application supporting a virtual environment, such as an application supporting a three-dimensional virtual environment. The application may be any one of a virtual reality application, a three-dimensional map application, a Third person shooter game (TPS), a First person shooter game (FPS), a multiplayer online tactical game (Multiplayer Online Battle Arena Games, MOBA). Alternatively, the application may be a stand-alone application, such as a stand-alone three-dimensional game, or a network-connected application.
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 underlying software that provides applications 322 with secure access to computer hardware.
The application 322 is an application supporting a virtual environment. Alternatively, 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 TPS game, an FPS game, a MOBA game, and a multiplayer warfare survival game. The application 322 may be a stand-alone application, such as a stand-alone three-dimensional game, or a network-connected application.
FIG. 4 illustrates 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 installs and runs an application supporting a virtual environment. The application may be any one of a virtual reality application, a three-dimensional map program, a TPS game, an FPS game, a MOBA game, and a multiplayer warfare survival game. The first device 420 is a device used by a first user to control a first virtual object located in a virtual environment to perform activities including, but not limited to: adjusting at least one of body posture, crawling, walking, running, riding, jumping, driving, picking up, shooting, attacking, throwing. Illustratively, the first virtual object is a first virtual character, such as an emulated persona or a cartoon persona.
The first device 420 is connected to the server 440 via 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 supporting a three-dimensional virtual environment. Optionally, the server 440 takes on primary computing work, and the first device 420 and the second device 460 take on secondary computing work; alternatively, the server 440 performs the secondary computing job and the first device 420 and the second device 460 perform the primary computing job; alternatively, the server 440, the first device 420 and the second device 460 may perform collaborative computing using a distributed computing architecture.
The second device 460 installs and runs an application supporting a virtual environment. The application may be any one of a virtual reality application, a three-dimensional map program, an FPS game, a MOBA game, and a multiplayer gunfight survival game. The second device 460 is a device used by a second user that 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 up, shooting, attacking, throwing. Illustratively, the second virtual object is a second virtual character, such as an emulated persona or a cartoon persona.
Optionally, the first avatar and the second avatar 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 avatar and the second avatar may belong to different teams, different organizations, or two parties with hostility.
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 refer broadly to one of a plurality of devices and the second device 460 may refer broadly to one of a plurality of devices, the present embodiment being illustrated with only 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 the device types include: at least one of a game console, a desktop computer, a smart phone, a tablet computer, an electronic book reader, an MP3 player, an MP4 player, and a laptop portable computer. The following embodiments are illustrated with the device being a desktop computer.
Those skilled in the art will appreciate that the number of devices described above may be greater or lesser. Such as the above-mentioned devices may be only one, or the above-mentioned devices may be several tens or hundreds, or more. The number of devices and the types of devices are not limited in the embodiments of the present application.
In connection with the description of the noun introduction and the implementation environment, a method for distributing task items provided in an embodiment of the present application is described, and fig. 5 is a flowchart of a method for distributing task items provided in an exemplary embodiment of the present application, where the method is applied to a server, for example, as shown in fig. 5, and the method includes:
step 501, historical behavior data of a target account is obtained from a behavior database.
Optionally, the historical behavior data is data generated by the target account in a program historical use process.
Alternatively, taking a game application as an example, the historical behavior data is data generated in a historical virtual fight by the target account. Optionally, the historical behavior data includes operation data, running data, attack success rate data, win-lose data, attack-kill data, attack-assist data, failure data, consumption data, duration data, and the like of the target account in the historical virtual fight, which is not limited in the embodiment of the present application. The operation data refers to game habits characterized by the operation triggered by the target account in the virtual fight; the running data refers to an area where the target account moves in 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 attack; the winning data refers to the proportion of the successful times in the virtual combat completed by the target account to the total times; the killing data refers to the number of times that the target account number kills the enemy in the virtual fight; the attack-helping data refers to the number of times that the target account assists teammates to attack and kill the other party in the virtual fight; the failure data refers to the number of times the target account is knocked out in the virtual fight; the consumption data refers to the consumption condition of the target account in the application program; the time length data refers to the game time length condition 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 combat; or, the historical behavior data of the target account is data generated in all the historical virtual combat of the target account, and the generation time of the historical behavior data is not limited in the embodiment of the application.
Optionally, the behavior data generated according to the historical virtual combat is stored in the behavior database of the server corresponding to each player account, the server obtains the behavior data corresponding to the target account from the behavior database according to the account identification of the target account, and the historical behavior data meeting the condition requirements is selected according to the screening condition of the historical behavior data. Such as: selecting historical behavior data generated in a historical time period from the behavior data; or, selecting the historical behavior data generated in the last preset number of historical virtual combat from the behavior data; or, the behavior data is determined as a whole as historical behavior data.
Step 502, matching the historical behavior data with a behavior feature table to obtain a target behavior feature corresponding to the target account.
Optionally, behavior data for characterizing the usage characteristics of the program is included in the behavior characteristics table. Taking a game application as an example, the behavior feature table includes behavior data for characterizing the fight characteristics.
Optionally, data analysis is performed on the historical behavior data, and matching is performed with the behavior feature table according to an analysis result to obtain target behavior features corresponding to the target account. Such as: the historical behavior data comprises the running data of the target account, wherein the running data represents that the target account runs to a middle road in 6 of 8 historical virtual fights, and after the historical behavior data is matched with the behavior feature table, the target behavior feature corresponding to the target account comprises the middle road; the historical behavior data comprises operation data of the target account, wherein the operation data represents that 5 of the target account in 8 historical virtual combat is an auxiliary player, and after the historical behavior data is matched with the behavior feature table, the target behavior feature corresponding to the target account is obtained and comprises auxiliary.
Optionally, the target behavior feature corresponding to the target account determined at a 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 feature.
Optionally, when one behavior feature is included in the target behavior features corresponding to the target account, determining a task data set corresponding to the target behavior feature; when the target behavior features corresponding to the target account comprise a plurality of behavior features, determining task data sets corresponding to the behavior features respectively.
Optionally, the task data sets are task sets that classify and summarize task items according to behavior features, and optionally, each task data set may correspond to one behavior feature or may correspond to a plurality of behavior features. The task data set 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 set corresponding to the target behavior characteristics and distributed to the target account.
Optionally, the task data set is provided with n behavior features which are correspondingly matched, 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.
Illustratively, the matching modes of the target behavior characteristics and the task data set are described as follows:
Firstly, determining a task data group comprising target behavior characteristics in corresponding behavior characteristics as a task data group corresponding to the target behavior characteristics;
schematically, the target behavior features include behavior feature a, behavior feature a corresponding to task data set 1, behavior feature B corresponding to task data set 2, behavior feature B corresponding to task data set 3, behavior feature C corresponding to task data set 3, and behavior feature a; the task data group determined according to the target behavior characteristics is a task data group 1 and a task data group 3;
or the target behavior characteristics comprise behavior characteristics A and behavior characteristics B, wherein 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 group determined according to the target behavior feature is the task data group 1.
Secondly, determining a task data group comprising at least one target behavior feature in the corresponding behavior features as a task data group corresponding to the target behavior feature;
schematically, the target behavior features comprise a behavior feature A and a behavior feature B, the task data set 1 corresponds to the behavior feature A, the task data set 2 corresponds to the behavior feature B, and the task data set 3 corresponds to the behavior feature C; the task data groups determined according to the target behavior characteristics are task data group 1 and task data group 2.
Thirdly, arranging the behavior characteristics corresponding to the task data sets in a priority order from high to low, determining the highest priority comprising target behavior data, and determining the task data set comprising the target behavior data in the highest priority as the task data set corresponding to the target behavior characteristics;
schematically, the target behavior features include behavior feature a, task data set 1 corresponds to behavior feature a (priority 1), behavior feature B (priority 2), task data set 2 corresponds to behavior feature B (priority 1), task data set 3 corresponds to behavior feature C (priority 1), and behavior feature a (priority 2); the task data group determined according to the target behavior feature is task data group 1 (behavior feature a is included in priority 1);
or the target behavior features comprise behavior features A and behavior features B, the task data set 1 corresponds to the behavior features A (priority 1) and the behavior features B (priority 2), the task data set 2 corresponds to the behavior features B (priority 1) and the behavior features C (priority 2), and the task data set 3 corresponds to the behavior features C (priority 1) and the behavior features A (priority 2); the task data group determined from the target behavior feature is the task data group 1 (the behavior feature a is included in the priority 1) and the task data group 2 (the behavior feature B is included in the priority 1).
At step 504, at least one task item is selected from the task data set to be assigned to the target account.
Optionally, when at least one task item is selected from the task data set, the task items may be sequentially selected according to the arrangement order of the task items in the task data set, or the task items may be randomly selected from the task data set and allocated to the target account, and the selection manner of the task items is not limited in this embodiment.
Optionally, the at least one task item may be assigned to the target account as a daily task, may be assigned to the target account as a challenge task, may be assigned to the target account as a daily task in part, and may be assigned to the target account as a challenge task in other parts.
In summary, according to the task item distribution method provided by the embodiment, by acquiring the historical behavior data of the target account and analyzing the historical behavior data, the corresponding target behavior characteristics are obtained, so that the task item is selected from the task data set corresponding to the target behavior characteristics to be 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 completed by the target account, the problem that the server resource is wasted due to the fact that the task item distributed to the target account is low in adaptation degree and cannot be completed is avoided, and the utilization efficiency of the server resource is improved.
In an alternative embodiment, the task data set is provided with n behavior features that are correspondingly matched, and fig. 6 is a flowchart of a task item allocation method provided in another exemplary embodiment of the present application, where the method is applied to a server, and illustrated in fig. 6, and the method includes:
step 601, historical behavior data of a target account is obtained from a 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 combat; or, the historical behavior data of the target account is data generated in all the historical virtual combat of the target account, and the generation time of the historical behavior data is not limited in the embodiment of the application.
Optionally, the timing of acquiring the historical behavior data of the target account may be preset, or may be triggered according to a timer, for example: according to the set timer, the historical behavior data of the target account is obtained by taking the date as the updating period, and the task items are distributed 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 with the behavior feature table according to an analysis result to obtain target behavior features corresponding to the target account.
Optionally, the target behavior feature corresponding to the target account determined at a 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 feature from the n behavior features as a task data set corresponding to the target behavior feature.
Optionally, when one behavior feature is included in the target behavior features corresponding to the target account, determining a task data set corresponding to the target behavior feature; when the target behavior features corresponding to the target account comprise a plurality of behavior features, determining task data sets corresponding to the behavior features respectively.
Optionally, the task data set is provided with n behavior features which are correspondingly matched, 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.
Illustratively, the matching modes of the target behavior characteristics and the task data set are described as follows:
firstly, determining a task data group comprising target behavior characteristics in corresponding behavior characteristics as a task data group corresponding to the target behavior characteristics;
secondly, determining a task data group comprising at least one target behavior feature in the corresponding behavior features as a task data group corresponding to the target behavior feature;
thirdly, the behavior characteristics corresponding to the task data sets are arranged according to the priority from high to low, the highest priority comprising the target behavior data is determined, and the task data set comprising the target behavior data in the highest priority is determined to be the task data set corresponding to the target behavior characteristics.
Optionally, the n behavior features are arranged according to the priority from high to low, the target behavior features and the n behavior features are sequentially matched according to the priority order, the target priority which is firstly matched with the target behavior features correspondingly is determined, and the task data set which comprises the target behavior features in the target priority is used as the task data set which corresponds to the target behavior features.
Illustratively, the target behavior feature includes one behavior feature and a plurality of behavior features, respectively, which are described as follows:
1. The target behavior feature comprises a behavior feature
Schematically, the target behavior features corresponding to the target account are behavior feature a, the task data set 1 corresponds to behavior feature a (priority 1), behavior feature B (priority 2), the task data set 2 corresponds to behavior feature B (priority 1), and the task data set 3 corresponds to behavior feature C (priority 1) and behavior feature a (priority 2); when the target behavior characteristic is matched with the behavior characteristic corresponding to each task data group according to the priority order, firstly matching the priority 1 of the task data group 1 with the behavior characteristic A, and determining the task data group 1 comprising the behavior characteristic A in the priority 1 as the task data group corresponding to the target account;
2. the target behavioral characteristics include a plurality of behavioral characteristics (two behavioral characteristics are illustrated herein)
Schematically, the target behavior features include a behavior feature a and a behavior feature B, the task data set 1 corresponds to the behavior feature a (priority 1), the behavior feature B (priority 2), the task data set 2 corresponds to the behavior feature B (priority 1), the behavior feature C (priority 2), and the task data set 3 corresponds to the behavior feature C (priority 1) and the behavior feature a (priority 2); when the target behavior feature is matched with the behavior feature corresponding to each task data group according to the priority order, firstly matching the priority 1 of the task data group 1 with the behavior feature A, and determining the task data group 1 including the behavior feature A in the priority 1 and the task book data group 2 including the behavior feature B in the priority 1 as the task data group corresponding to the target account.
At step 604, at least one task item is selected from the task data set to be assigned to the target account.
Optionally, when at least one task item is selected from the task data set, the task items may be sequentially selected according to the arrangement order of the task items in the task data set, or the task items may be randomly selected from the task data set and allocated to the target account, and the selection manner of the task items is not limited in this embodiment.
Optionally, the at least one task item may be assigned to the target account as a daily task, may be assigned to the target account as a challenge task, may be assigned to the target account as a daily task in part, and may be assigned to the target account as a challenge task in other parts. Optionally, according to task allocation requirements, determining task types of task items to be allocated, and determining task items of corresponding types from a task data set to be allocated; the task items can also be directly selected from the task data set to be distributed, and the task items can be distributed to the corresponding task list according to the task types of the task items.
In summary, according to the task item distribution method provided by the embodiment, by acquiring the historical behavior data of the target account and analyzing the historical behavior data, the corresponding target behavior characteristics are obtained, so that the task item is selected from the task data set corresponding to the target behavior characteristics to be 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 completed by the target account, the problem that the server resource is wasted due to the fact that the task item distributed to the target account is low in adaptation degree and cannot be completed is avoided, and the utilization efficiency of the server resource is improved.
According to the method provided by the embodiment, n behavior features corresponding to the task data sets are sequentially arranged according to the priority, and then the n behavior features are matched with the target behavior features according to the arrangement order, so that the task data set with high priority and meeting the requirements is determined to be the task data set corresponding to the target account, the task item selected from the task data set is higher in 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 cannot be completed due to lower adaptation degree is avoided, and the utilization efficiency of the server resources is improved.
Referring to FIG. 7, an overall flow chart of task item allocation provided by an exemplary embodiment of the present application is shown, as shown in FIG. 7, and includes: determining a task item release time 710, acquiring a player characteristic 720 when the release time 710 is reached, searching a corresponding task group 730 according to the player characteristic 720, acquiring a random task pool 740 corresponding to the task group, selecting a task item from the task pool 740, judging whether a task list of a player is full, not distributing the task item when the task list is full, and distributing the task item when the task list is not full.
In an alternative embodiment, when task items are selected from the task data group to be allocated, the task items are selected by generating random numbers, and fig. 8 is a flowchart of a task item allocation method according to another exemplary embodiment of the present application, and the method is applied to a server, for example, and as shown in fig. 8, the method includes:
step 801, historical behavior data of a target account is obtained from a 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 combat; or, the historical behavior data of the target account is data generated in all the historical virtual combat of the target account, and the generation time of the historical behavior data is not limited in the embodiment of the application.
Step 802, 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 with the behavior feature table according to an analysis result to obtain target behavior features corresponding to the target account.
Optionally, the target behavior feature corresponding to the target account determined at a 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 feature.
Illustratively, the matching modes of the target behavior characteristics and the task data set are described as follows:
firstly, determining a task data group comprising target behavior characteristics in corresponding behavior characteristics as a task data group corresponding to the target behavior characteristics;
secondly, determining a task data group comprising at least one target behavior feature in the corresponding behavior features as a task data group corresponding to the target behavior feature;
thirdly, the behavior characteristics corresponding to the task data sets are arranged according to the priority from high to low, the highest priority comprising the target behavior data is determined, and the task data set comprising the target behavior data in the highest priority is determined to be the task data set corresponding to the target behavior characteristics.
Step 804, obtaining a random number in the random number range.
Optionally, the task items in the task data set are correspondingly provided with weight values, a first weight sum of the task items 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: and if the target value is 1, determining a range between 1 and the first weight sum as a random number range.
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 then the first weight sum is 13, and the random number ranges from 1 to 13.
Step 805, determining a corresponding random task item in the task data group according to the random number.
Optionally, traversing task items in the task data set, calculating a second weight sum for the traversed task items in the traversing process, responding to the second weight sum to obtain the numerical value of the random number, stopping traversing, and determining the last task item obtained by traversing as a random task item.
Schematically, the task data set includes task item 1, task item 2 and task item 3, where the weight value of task item 1 is 3, the weight value of task item 2 is 4, the weight value of task item 3 is 6, determining that the random number is 5 in the random number range, traversing task item 1 first to obtain a second weight sum of 3, traversing task item 2 to obtain a second weight sum of 7 to reach the random number of 5, so that task item 2 is determined as a random task item.
Step 806, assigning the random task item to the target account.
Optionally, when the random task item is allocated to the target account, the task type of the random task item is first determined, and an allocation rule corresponding to the task type is determined, so that the allocation rule allocates the random task item to the target account.
Optionally, the task type includes a daily type and a challenge type, that is, the task item includes a daily task and a challenge task, and when the random task item is a daily task, the task item is distributed according to a distribution rule corresponding to the daily type; when the random task item is a challenge task, the allocation rule corresponding to the challenge type is used for allocation.
Optionally, in response to the random task item corresponding to the daily type, and when the existing task item of the target account includes the random task item, distributing the random task item to the target account in a manner of resetting completion progress data of the random task item; in response to the random task item corresponding to the challenge type, and including the random task item in an existing task item of the target account, discarding the random task item.
Illustratively, the random task item is implemented to win a game task, such as: when the player cumulatively wins the game to 5 games, the winning game task is completed, and the task item of which the winning game task is realized as a daily type is taken as an example for explanation: when determining that the winning game task is a task item distributed to the target account, firstly determining whether the current existing task item of the target account contains the winning game task, 1, resetting the winning game task to 0/5 if the current existing task item of the target account contains the winning game task and the completion progress is 2/5, and distributing the winning game task to the target account; 2. and if the current existing task item of the target account does not contain the winning game task, directly distributing the winning game task to the target account.
Illustratively, the random task items are implemented to complete a game task, such as: when the player cumulatively completes the game to 20 games, the completed game task is completed, and the task item with the challenge type is taken as an example for explanation of the completion of the game task: when the task item allocated to the target account is determined as the completion of the target account, firstly determining whether the current existing task item of the target account contains the completion target account, 1, discarding the completion target account if the current existing task item of the target account contains the completion target account and the completion progress is 8/20, and reserving the completion progress of the current completion target account; 2. and if the current existing task item of the target account does not contain the completed office task, the completed office task is directly distributed to the target account.
Optionally, the corresponding weight value of each task item determines the priority of the task item when it appears randomly, that is, in the task data set, the task items are arranged in the order from high to low according to the weight value.
In summary, according to the task item distribution method provided by the embodiment, by acquiring the historical behavior data of the target account and analyzing the historical behavior data, the corresponding target behavior characteristics are obtained, so that the task item is selected from the task data set corresponding to the target behavior characteristics to be 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 completed by the target account, the problem that the server resource is wasted due to the fact that the task item distributed to the target account is low in adaptation degree and cannot be completed is avoided, and the utilization efficiency of the server resource is improved.
According to the method provided by the embodiment, the task items are randomly distributed to the target account number from the task data set in a mode of determining the random number, only the task items in the task data set are required to be traversed and the weight values are added, the algorithm is simple, no extra space is occupied, the time complexity is 0 (n), and the task item selection efficiency is high.
Illustratively, FIG. 9 is a flowchart of a task item allocation process provided by an exemplary embodiment of the present application, as shown in FIG. 9, which includes:
the task type 920 is determined by the data configuration 930 when the trigger condition of the trigger 910 is reached, according to the trigger 910 determining the task item allocation timing.
When the task type 920 belongs to the daily type 940, task items of the daily type are determined to be distributed to the target account from the task data set, whether the precondition 941 for task item distribution is started is judged, when the precondition 941 is started, whether the precondition is completed is judged, and when the precondition is completed, the task items are distributed 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 to be assigned to the target account.
FIG. 10 is a block diagram of a task item distribution device according to an exemplary embodiment of the present application, as shown in FIG. 10, including:
An obtaining module 1010, configured to obtain, from a behavior database, historical behavior data of a target account, where the historical behavior data is data generated in a program history using process of the target account;
the matching module 1020 is 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 a behavior feature for characterizing a usage characteristic of a program;
a determining module 1030, configured to determine a task data set corresponding to the target behavior feature, where the task data set includes a task item that matches the target behavior feature;
an allocation module 1040, configured to select at least one task item from the task data set to be allocated to the target account.
In an optional embodiment, the task data set is provided with n behavior features which are correspondingly matched, and n is a positive integer;
the determining module 1030 is further configured to determine, as the task data set corresponding to the target behavior feature, the task data set including the target behavior feature from among n behavior features.
In an alternative embodiment, n of the behavioral characteristics are ranked 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 n behavior features in order of priority;
a determining unit 1032 configured to determine a target priority that is first matched with the target behavior feature; and taking the task data group comprising the target behavior characteristic in the target priority as the task data group corresponding to the target behavior characteristic.
In an alternative embodiment, the obtaining module 1010 is further configured to obtain a random number in a random number range;
the determining module 1030 is further configured to determine a corresponding random task item in the task data set according to the random number;
the allocation module 1040 is further configured to allocate the random task item to the target account.
In an alternative 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 weight sum of the task items in the task data set; a range between a target value and the first weight sum is determined 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 weight sum for the traversed task items during the traversing; and stopping traversing in response to the second weight and the value of the random number, and determining the last task item obtained through traversing as the random task item.
In an alternative 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 allocation module 1040 is further configured to allocate the random task item to the target account according to the allocation rule.
In an alternative embodiment, the task types include a daily type and a challenge type;
the allocation module 1040 is further configured to allocate the random task item to the target account in a manner of resetting completion progress data of the random task item in response to the random task item corresponding to the daily type, where an existing task item of the target account includes the random task item;
the allocation module 1040 is further configured to discard the random task item in response to the random task item corresponding to the challenge type, where the random task item is included in an existing task item of the target account.
In summary, the task item distribution device provided in this embodiment obtains the corresponding target behavior feature 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 set corresponding to the target behavior feature to distribute to the target account, so that the task item distributed to the target account better accords with the game habit of the target account, is suitable for completing the task item by the target account, avoids the problem that the task item distributed to the target account cannot be completed due to low adaptation degree, and wastes server resources, and improves the utilization efficiency of server resources.
It should be noted that: the task item allocation device provided in the above embodiment is only exemplified by the division of the above functional modules, and in practical application, the above functional allocation may be performed 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 distribution device and the task item distribution method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the task item distribution device and the task item distribution method are detailed in the method embodiments, which 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 following is said:
the server 1200 includes a central processing unit (Central Processing Unit, CPU) 1201, a system Memory 1204 including a random access Memory (Random Access Memory, RAM) 1202 and a Read Only Memory (ROM) 1203, and a system bus 1205 connecting the system Memory 1204 and the central processing unit 1201. The server 1200 also includes a basic input/output system (Input Output System, I/O system) 1206, which helps to transfer information between various 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 both the display 1208 and the input device 1209 are coupled to the central processing unit 1201 via an input-output controller 1210 coupled to a system bus 1205. The basic input/output system 1206 can 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, the 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. Mass storage device 1207 and its associated computer-readable media provide non-volatile storage for server 1200. That is, the mass storage device 1207 may include a computer readable medium (not shown), such as a hard disk or compact disc read-only memory (Compact Disc Read Only Memory, CD-ROM) drive.
Computer readable media may include computer storage media and communication media without loss of generality. 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 (Erasable Programmable Read Only Memory, EPROM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read Only Memory, EEPROM), flash memory or other solid state memory technology, CD-ROM, digital versatile disks (Digital Versatile Disc, 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 recognize that computer storage media are not limited to the ones described above. 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 by being connected to a remote computer on a network, such as the Internet. That is, the server 1200 may be connected to the network 1212 through a network interface unit 1211 coupled to the system bus 1205, or alternatively, the network interface unit 1211 may be used to connect to other types of networks or remote computer systems (not shown).
The memory also includes one or more programs, one or more programs stored in the memory and configured to be executed by the CPU.
The embodiment of the application also provides a computer device, which comprises a processor and a memory, wherein at least one instruction, at least one section of program, code set or instruction set is stored in the memory, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor to realize the task item allocation method provided by the method embodiments.
Embodiments of the present application further provide a computer readable storage medium having at least one instruction, at least one program, a code set, or an instruction set stored thereon, where the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the task item allocation method provided by the foregoing method embodiments.
Alternatively, the computer-readable storage medium may include: ROM, RAM, solid state disk (Solid State Drives, SSD), or optical disk, etc. The random access memory may include resistive random access memory (Resistance Random Access Memory, reRAM) and dynamic random access memory (Dynamic Random Access Memory, DRAM), among others. The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages 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 for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments is merely exemplary in nature and is in no way intended to limit the invention, since it is intended that all modifications, equivalents, improvements, etc. that fall within the spirit and scope of the invention.

Claims (13)

1. A method of task item allocation, the method comprising:
acquiring historical behavior data of a target account from a behavior database, wherein the historical behavior data is generated in a program historical use process 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 for representing the use characteristics of the program;
classifying and summarizing task items according to the behavior characteristics to obtain a task data set, wherein the task data set is provided with n behavior characteristics which are correspondingly matched, and n is a positive integer;
determining task data sets including the target behavior characteristics in n behavior characteristics as task data sets corresponding to the target behavior characteristics, wherein the task data sets corresponding to the target behavior characteristics include task items matched with the target behavior characteristics;
and selecting at least one task item from a task data set corresponding to the target behavior characteristic, and distributing the task item to the target account.
2. The method of claim 1, wherein n of the behavioral characteristics are ranked in order of priority from high to low;
the determining the task data set including the target behavior feature from the n behavior features as the task data set corresponding to the target behavior feature includes:
sequentially matching the target behavior characteristics with n behavior characteristics according to a priority order;
Determining a target priority which is matched with the target behavior characteristic correspondingly at first;
and taking the task data group comprising the target behavior characteristics in the target priority as a task data group corresponding to the target behavior characteristics.
3. The method according to claim 1 or 2, wherein selecting at least one task item from the task data set corresponding to the target behavior feature to be allocated to the target account includes:
acquiring a random number in a random number range;
determining a corresponding random task item in a task data set corresponding to the target behavior characteristic according to the random number;
and distributing the random task item to the target account.
4. A method according to claim 3, wherein the task items in the task data set are correspondingly provided with weight values;
before the random number is acquired in the random number range, the method further comprises the following steps:
determining a first weight sum of the task items in the task data set;
a range between a target value and the first weight sum is determined as the random number range.
5. A method according to claim 3, wherein said determining a corresponding random task item from said random number in a task data set corresponding to said target behavioral characteristics comprises:
Traversing the task items in the task data set, and calculating a second weight sum for the traversed task items in the traversing process;
and stopping traversing in response to the second weight and the value of the random number, and determining the last task item obtained through traversing as the random task item.
6. The method of claim 3, wherein the assigning the random task item to the target account comprises:
determining a task type of the random task item;
determining an allocation rule corresponding to the task type;
and distributing the random task item to the target account according to the distribution rule.
7. The method of claim 6, wherein the task types include a daily type and a challenge type;
the distributing the random task item to the target account according to the distribution rule comprises the following steps:
distributing the random task item to the target account in a mode of resetting completion progress data of the random task item in response to the random task item corresponding to the daily type and the random task item being included in an existing task item of the target account;
And discarding the random task item in response to the random task item corresponding to the challenge type and the random task item being included in the existing task item of the target account.
8. A method according to claim 1 or 2, characterized in that,
the target account is an account registered in a game application program, the historical behavior data is data generated by the target account in a historical virtual fight, and the behavior characteristic table comprises the behavior characteristic used for representing the fight characteristic.
9. A task item distribution device, the device comprising:
the system comprises an acquisition module, a program history use module and a program history use module, wherein 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 in the program history use process of the target account;
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, wherein the behavior feature table comprises behavior features for representing the use characteristics of the program;
the determining module is used for classifying and summarizing task items according to the behavior characteristics to obtain a task data set, wherein the task data set is provided with n behavior characteristics which are correspondingly matched, and n is a positive integer; determining task data sets including the target behavior characteristics in n behavior characteristics as task data sets corresponding to the target behavior characteristics, wherein the task data sets corresponding to the target behavior characteristics include 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 set corresponding to the target behavior characteristic and distributing the task item to the target account.
10. The apparatus of claim 9, wherein n of the behavioral characteristics are arranged in a priority order from high to low;
the determining module includes:
the matching unit is used for sequentially matching the target behavior characteristics with n behavior characteristics according to a priority order;
a determining unit, configured to determine a target priority that is first correspondingly matched with the target behavior feature; and taking the task data group comprising the target behavior characteristics in the target priority as a task data group corresponding to the target behavior characteristics.
11. The apparatus according to claim 9 or 10, wherein the acquisition module is further configured to acquire a random number within a random number range;
the determining module is further used for determining a corresponding random task item in the task data set corresponding to the target behavior characteristic according to the random number;
the allocation module is further configured to allocate the random task item to the target account.
12. A computer device comprising a processor and a memory, wherein the memory has stored therein at least one program that is loaded and executed by the processor to implement the method of task item allocation of any one of claims 1 to 8.
13. A computer readable storage medium having stored therein at least one program loaded and executed by a processor to implement the task item allocation method of any one of claims 1 to 8.
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