CN109428910B

CN109428910B - Data processing method, device and system

Info

Publication number: CN109428910B
Application number: CN201710735375.6A
Authority: CN
Inventors: 刘贺; 黄泽芳; 徐军
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2017-08-24
Filing date: 2017-08-24
Publication date: 2021-03-23
Anticipated expiration: 2037-08-24
Also published as: CN109428910A

Abstract

The invention discloses a data processing method, a device and a system, aiming at any client, acquiring online behavior information acquired by the client; matching the online behaviors of the online behavior information with preset online behaviors corresponding to tasks in a preset task data table; if the matching with at least one task is successful, accumulating the metering value of the online behavior information into a preset behavior data table and an accumulated value of the preset online behavior corresponding to the online behavior information acquired by the client; and determining the tasks met by the online behavior information acquired by the client according to the judgment threshold value in the task data table and the accumulated value in the behavior data table. Compared with the prior art, the configurable task data table is arranged, so that when a new task is added, only the new task needs to be established in the task data table, the front end, the rear end, the client and the like do not need to be modified, and the workload is reduced when the new task is developed.

Description

Data processing method, device and system

Technical Field

The present invention relates to the field of network technologies, and in particular, to a data processing method, apparatus, and system.

Background

When a developer develops an online event, such as an online game, an online live broadcast, etc., it is generally necessary to set a plurality of online tasks, such as a group battle 10 times, a prize player 3 times, etc., according to the actual needs of the user. While participating in online activities (via a client installed on the terminal), the user may obtain the corresponding electronic resource (e.g., level 1 up, get gold 30, credit accumulation 100, etc.) by performing the corresponding online task. In order to accurately count the online tasks completed by the users and reasonably allocate electronic resources to the users, the following method is often adopted for counting:

taking live broadcasting as an example, suppose that live broadcasting includes three tasks, which are respectively: a. b and c, the steps for counting are as follows:

in the first step, the online behavior involved in task a is determined.

For example, the online behavior is continuous online, and the completion condition of task a is continuous online for 3 hours.

And secondly, acquiring online behavior information of a certain user aiming at the task a.

Still taking the above example as an example, it is assumed that the obtained online behavior information of the user for task a may be: the continuous on-line time was 2 hours.

And thirdly, accumulating the online behavior information of the user aiming at the task a into a database.

Still taking the above example as an example, assuming that before accumulation, the online behavior information of the user in the database may be: if the continuous online time is 1 hour, after the online behavior information of the second step is accumulated, the online behavior information of the user in the database may be: the continuous on-line time was 3 hours.

And fourthly, judging whether the online behavior information of the user meets the completion condition of the task a.

Still taking the above example as an example, it may be determined that the online behavior information of the user satisfies the completion condition of the task a.

It should be noted that, at this time, feedback information corresponding to the task a, such as reward issuance information, point change information, and the like, may also be sent to the user.

And fifthly, replacing the task a with a task b and a task c respectively, and repeating the first step to the fourth step to determine all the tasks completed by the user.

As can be seen from the above, in the conventional data processing method, if the online activity includes a plurality of tasks, the online behavior information needs to be acquired once for each task, which may reduce data processing efficiency, for example, the repeated acquisition of the same online behavior information may result in low statistical efficiency; moreover, since the tasks included in each online activity are pre-solidified in the program, when one or more tasks are newly added to the online activity, the corresponding program, such as the front-end, client-side, and backend programs, needs to be modified at multiple locations. In addition, even if the tasks are similar, new task development is performed separately for each task, and the repeated work is much. In summary, the method in the prior art may bring a large workload to developers, extend the development period of the new task, reduce the online speed of the new task, and make the later maintenance inconvenient.

That is to say, the existing data processing method has the problems of low statistical efficiency, large workload of new task development, long period, low online speed and the like.

Disclosure of Invention

The embodiment of the invention provides a data processing method, a device and a system, which are used for solving the problems of low statistical efficiency, large workload of new task development, long development period of a new task and low online speed of the new task in the existing data processing method.

The embodiment of the invention provides a data processing method, which comprises the following steps:

aiming at any client, acquiring online behavior information acquired by the client; the online behavior information is composed of an online behavior and a metric value corresponding to the online behavior;

matching the online behaviors of the online behavior information with preset online behaviors corresponding to tasks in a preset task data table; each task in the task data table corresponds to at least one preset on-line behavior, and each preset on-line behavior has a judgment threshold corresponding to the behavior; the judgment threshold is used for representing the completion condition of the corresponding task;

if the matching with at least one task is successful, accumulating the metering value into a preset behavior data table and an accumulated value of a preset on-line behavior corresponding to the on-line behavior information acquired by the client;

and determining the tasks completed by the client according to the judgment threshold value in the task data table and the accumulated value in the behavior data table.

Optionally, the obtaining of the online behavior information collected by the client includes:

acquiring online behavior information acquired by the client in real time; and/or the presence of a gas in the gas,

and acquiring online behavior information acquired by the client at set time intervals.

Further, before the online behavior information is respectively matched with each task in a preset task data table, the data processing method further includes:

acquiring at least one of the following attribute information: the current time attribute information, the geographic area attribute information of the client and the client attribute information corresponding to the client;

and for any acquired attribute information, determining that the attribute information is in a set range corresponding to the attribute information.

Specifically, determining the task completed by the client according to the judgment threshold in the task data table and the accumulated value in the behavior data table includes:

aiming at any task successfully matched, determining a preset on-line behavior corresponding to the task;

if the fact that the accumulated value of the behaviors on the preset line, collected by the client side, in the behavior data table is not smaller than the judgment threshold corresponding to the behaviors on the preset line is determined, it is determined that the behaviors on the preset line meet the completion condition;

and if all the preset on-line behaviors in the task meet the completion condition, determining that the task is a completed task.

Further, after determining that the client has completed the task, the data processing method further includes:

sending feedback information corresponding to the tasks completed by the client to the client; the feedback information comprises any one or more of reward issuing information, point accumulation information and grade change information.

Accordingly, an embodiment of the present invention provides a data processing apparatus, including:

the acquisition module is used for acquiring online behavior information acquired by any client; the online behavior information is composed of an online behavior and a metric value corresponding to the online behavior;

the matching module is used for matching the online behaviors of the online behavior information with preset online behaviors corresponding to tasks in a preset task data table; each task in the task data table corresponds to at least one preset on-line behavior, and each preset on-line behavior has a judgment threshold corresponding to the behavior; the judgment threshold is used for representing the completion condition of the corresponding task;

the accumulation module is used for accumulating the metering value into a preset behavior data table and an accumulation value of a preset on-line behavior corresponding to the on-line behavior information acquired by the client if the matching with at least one task is successful;

and the statistical module is used for determining the tasks completed by the client according to the judgment threshold value in the task data table and the accumulated value in the behavior data table.

Optionally, the obtaining module is specifically configured to obtain online behavior information collected by the client in real time; and/or acquiring online behavior information acquired by the client at set time intervals.

Specifically, the obtaining module is further configured to obtain at least one of the following attribute information: the current time attribute information, the geographic area attribute information of the client and the client attribute information corresponding to the client;

and the determining module is used for determining that the attribute information is in a set range corresponding to the attribute information according to any acquired attribute information.

Specifically, the statistical module is specifically configured to determine, for any task that is successfully matched, a preset online behavior corresponding to the task; if the fact that the accumulated value of the behaviors on the preset line, collected by the client side in the behavior data table, is not smaller than the judgment threshold corresponding to the behaviors on the preset line is determined, the fact that the behaviors on the preset line meet the completion condition is determined; and if all the preset on-line behaviors in the task meet the completion condition, determining that the task is the completed task.

Further, the data processing apparatus further comprises a feedback module, wherein:

the feedback module is used for sending feedback information corresponding to the tasks completed by the client to the client after determining the tasks completed by the client; the feedback information comprises any one or more of reward issuing information, point accumulation information and grade change information.

Correspondingly, the embodiment of the present invention further provides a data processing system, including at least one client, a server, a task data table and a behavior data table, wherein:

any client in the at least one client is used for acquiring online behavior information and sending the online behavior information to the server; the online behavior information is composed of an online behavior and a metric value corresponding to the online behavior;

the server is used for matching the online behaviors of the acquired online behavior information with preset online behaviors corresponding to tasks in a preset task data table aiming at any client; if the matching with at least one task is successful, accumulating the metering value into a preset behavior data table and an accumulated value of a preset on-line behavior corresponding to the on-line behavior information acquired by the client; determining the tasks completed by the client according to the judgment threshold value in the task data table and the accumulated value in the behavior data table; each task in the task data table corresponds to at least one preset on-line behavior, and each preset on-line behavior has a judgment threshold corresponding to the behavior; the judgment threshold is used for representing the completion condition of the corresponding task.

Further, an embodiment of the present invention provides a computing device, including a memory and a processor, wherein:

the memory to store program instructions;

the processor is configured to call the program instruction stored in the memory, and execute the data processing method according to the obtained program.

In addition, an embodiment of the present invention further provides a computer storage medium, where the computer storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable the computer to execute the data processing method in the embodiment of the present invention.

The invention has the following beneficial effects:

the embodiment of the invention provides a data processing method, a device and a system, aiming at any client, acquiring online behavior information acquired by the client; the online behavior information is composed of an online behavior and a metric value corresponding to the online behavior; matching the online behaviors of the online behavior information with preset online behaviors corresponding to tasks in a preset task data table; each task in the task data table corresponds to at least one preset on-line behavior, and each preset on-line behavior has a judgment threshold corresponding to the behavior; the judgment threshold is used for representing the completion condition of the corresponding task; if the matching with at least one task is successful, accumulating the metering value into a preset behavior data table and an accumulated value of a preset on-line behavior corresponding to the on-line behavior information acquired by the client; and determining the tasks completed by the client according to the judgment threshold value in the task data table and the accumulated value in the behavior data table. Compared with the prior art, the configurable task data table is arranged in the embodiment of the invention, when a plurality of tasks are simultaneously aimed at, all tasks completed by the client can be quickly counted only by acquiring online behavior information once, so that the counting efficiency is improved; and when a new task is needed, only the new task needs to be established in the task data table, and the front end, the rear end, the client and the like do not need to be updated with any program, so that during the development of the new task, the development workload can be effectively reduced, the development period can be reduced, the online speed can be increased, and the later maintenance cost can be saved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used 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 invention, 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 schematic flow chart illustrating a first data processing method according to a first embodiment of the present invention;

fig. 2 is a schematic diagram illustrating an acquisition manner of a first uplink information according to a first embodiment of the present invention;

fig. 3 is a schematic diagram illustrating an acquisition manner of a second uplink information according to a first embodiment of the present invention;

fig. 4 is a schematic flowchart illustrating a second data processing method according to a first embodiment of the present invention;

fig. 5 is a schematic diagram illustrating an actual application of the data processing method according to the first embodiment of the present invention;

fig. 6 is a schematic flowchart illustrating a live scene-based data processing method according to a first embodiment of the present invention;

fig. 7 is a schematic view illustrating a page display of a live scene according to a first embodiment of the present invention;

fig. 8 is a schematic structural diagram of a data processing apparatus according to a second embodiment of the present invention;

FIG. 9 is a block diagram of a data processing system according to a second embodiment of the present invention;

FIG. 10 is a diagram illustrating an actual architecture of a data processing system according to a second embodiment of the present invention;

fig. 11 is a schematic structural diagram of a computing device provided in the third embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The first embodiment is as follows:

in order to solve the problems of low statistical efficiency, large workload of developing a new task, long development period of the new task, and low online speed of the new task in the conventional data processing method, a first embodiment of the present invention provides a data processing method, which is a flowchart of a first data processing method provided in the first embodiment of the present invention, as shown in fig. 1. The first data processing method in the embodiment of the invention can be applied to various application scenes such as online live broadcast, online games, online social contact and the like, and can be generally used for counting tasks completed by users in various application scenes.

In the embodiment of the present invention, the online behavior information collected by the client may generally be online behavior information of a user corresponding to the client, and the user corresponding to the client may generally be a user where a terminal installed with the client is located. It should be noted that the online behavior information may be online behaviors of the user, such as login account, transmission information, and behavior data of the reward anchor. Of course, the online behavior information may also include other information as long as it can represent the online status data of the user, and is not limited herein.

It should be noted that, in the embodiment of the present invention, the execution subject of the first data processing method may generally be a corresponding server, and the server may generally form a corresponding system with one or more clients. Wherein, the one or more clients may be APP (Application) on the corresponding terminal device, and the server may be a background server related to the APP, etc.; the server may be a server set individually or a server cluster set in batch, which is not described herein in detail.

Specifically, as shown in fig. 1, in the embodiment of the present invention, the first data processing method may include the following steps:

step 101: aiming at any client, acquiring online behavior information acquired by the client; the online behavior information is composed of an online behavior and a metric value corresponding to the online behavior;

for example, if the online behavior information is "bonus player 2 times," the "bonus player" may be the online behavior of the online behavior information, and "2" may be a measure value of the "bonus player" of the online behavior, which will not be described again.

Specifically, as shown in table 1, it is a possible structure table of online behavior information collected by the client in the embodiment of the present invention.

TABLE 1 Online behavior information Table

As can be seen from table 1, in the embodiment of the present invention, the online behaviors collected by the client may include all the online behaviors of the user corresponding to the client, and specifically may be all the online behaviors predefined and applicable to the corresponding application scenario. It should be noted that the predefined online behavior may be defined and stored in the task database after the requirement of the user is clarified by the corresponding management platform, which is not described herein again.

Because the online behavior information of the user to be acquired (that is, the online behavior information of the user corresponding to the client acquired by the client) may be different in different application scenarios, different online behaviors may be predefined for different application scenarios, so that when the online behavior of the user is acquired, only the online behavior information applicable to the application scenario may be acquired. The applicable online behavior may include a plurality of behaviors in each application scenario. Specifically, as shown in table 2, it is an online behavior table under different application scenarios in the embodiment of the present invention:

table 2 on-line behavior table under different application scenarios

As can be seen from table 2, the online behaviors involved in different application scenarios may be the same or different. For example, online behaviors such as "interaction," "sharing," "check-in" and the like may be applicable to live scenes, as well as game scenes and social scenes. On-line behaviors such as "reward anchor", "attention anchor", and the like may be applicable only to a live scene, and on-line behaviors such as "team battle", "team formation", "wild", and the like may be applicable only to a game scene, which is not limited in any way in the embodiment of the present invention.

Optionally, the obtaining of the online behavior information collected by the client may include:

receiving online behavior information which is uploaded by a client and acquired by the client; or,

and acquiring online behavior information acquired by the client.

That is to say, in the embodiment of the present invention, the online behavior information collected by the client may be obtained in the following two ways:

first, as shown in fig. 2, it is a schematic diagram of obtaining a first obtaining manner of online behavior information according to an embodiment of the present invention. Specifically, as shown in fig. 2, the first obtaining manner may be that a client collects online behavior information of a user, and sends the online behavior information to a corresponding server, so that the server obtains the online behavior information. The method can effectively reduce the load of the server and improve the efficiency of acquiring the online behavior information, thereby being taken as a preferred scheme;

second, as shown in fig. 3, it is a schematic diagram of a second obtaining manner of the online behavior information according to the embodiment of the present invention. Specifically, as shown in fig. 3, the second obtaining manner may be that the server directly collects the online behavior information collected by the client through a communication link between the server and the client. Although this method can also acquire the online behavior information of the user, it may be an alternative because it may place a small load on the server in a scenario where there are many users.

It should be noted that, if the application scenario has a very high requirement on real-time performance, a real-time acquisition mode may be generally adopted. However, this method usually brings a large load to the server and the client, and the implementation cost is high and the practicability is low. If the method is in an application scenario with a low real-time requirement, a mode of acquiring the data at set time intervals can be generally adopted, and although certain real-time needs to be abandoned, the load of the server and the client can be greatly reduced, and the implementation cost is low. In practical application, the advantages and disadvantages of the above two methods can be measured, and the obtaining method most suitable for practical situation is selected, which is not described in detail.

The set time period may be flexibly set according to an actual situation, for example, the set time period may be set to 1 minute, 1 hour, 1 day, and the like, as long as the actual acquisition requirement can be met, which is not limited in this embodiment of the present invention.

Preferably, the set duration may be a heartbeat interval between the client and the server.

The heartbeat interval represents a time interval between two continuous heartbeat packets sent by the client to the server, and can be flexibly set according to actual conditions. The heartbeat packet may generally be a heartbeat packet transmitted between a client and a server to characterize that the client is still online. Typically, the heartbeat packets may include two types of heartbeat packets, one is a heartbeat packet sent by the client to the server at every heartbeat interval, in such a way that the server is less loaded and therefore less costly. The other method is that the server sends a heartbeat inquiry packet to the client at intervals of heartbeat, and the client replies the heartbeat packet to the server after receiving the heartbeat inquiry packet. In practical application, any mode can be selected for sending the heartbeat packet as long as the server can be ensured to know the online state of the client according to the heartbeat packet.

More preferably, the acquiring the online behavior information collected by the client may include:

and acquiring the heartbeat packet carrying the online behavior information acquired by the client.

For example, the online behavior information collected by the client may be carried in the heartbeat packet, or the online behavior information may be used as the heartbeat packet, which not only meets the requirement that the server knows the online state of the client, but also meets the requirement that the server obtains the online behavior information collected by the client. Therefore, the server and the client can realize two requirements through one-time communication, thereby greatly saving transmission bandwidth and improving the acquisition efficiency of online behavior information.

Step 102: matching the online behaviors of the online behavior information with preset online behaviors corresponding to tasks in a preset task data table; each task in the task data table corresponds to at least one preset on-line behavior, and each preset on-line behavior has a judgment threshold corresponding to the behavior; the judgment threshold is used for representing the completion condition of the corresponding task.

Alternatively, as shown in table 3, it is a possible structure of the task data table described in the embodiment of the present invention.

TABLE 3 task database

Specifically, as can be seen from table 3, the task data table described in the embodiment of the present invention may generally include a plurality of tasks. When a new task needs to be established, only the new task needs to be defined in a configurable task data table, for example, a task including three on-line behaviors and corresponding judgment thresholds can be defined in the task data table. Therefore, when the subsequent statistics of the tasks completed by the client (namely the tasks completed by the user corresponding to the client) is carried out, the statistics can be directly carried out according to the obtained online behavior information without carrying out any modification on programs in a front-end server, the client, a back-end server and the like, such as version updating or development and the like, so that the development time of a new task can be saved, the development cost of the new task is reduced, and the online speed of the new task is improved.

For example, if a task in the task data table is composed of a preset on-line behavior and a judgment threshold thereof, the format of the task may be: (1) 5 times of reward, wherein reward can be a preset on-line behavior, and reward can be a judgment threshold value of 5; (2) giving a gift 10 times, wherein the gift giving can be a preset online behavior, and the gift giving 10 can be a judgment threshold; (3) paying attention to 3 items, wherein the 'attention' can be a preset on-line behavior, and the '3' can be a judgment threshold value; (4) the group wars for 9 times, wherein the group wars can be a preset on-line behavior, and the group wars for 9 times can be a judgment threshold; (5) and (3) interacting for 2 times, wherein the interaction can be a preset on-line behavior, and the interaction 2 can be a judgment threshold value.

For another example, if a task in the task data table is composed of two predetermined on-line behaviors and a determination threshold thereof, the format of the task may be: (1) and the online behavior exceeds 2 hours and is a member, wherein the online behavior and the member behavior can be preset online behaviors, and the online behavior and the member behavior can be judged to be a threshold value 2 and 1. At this time, it should be noted that the judgment threshold of the preset online behavior information "member" may be flexibly set according to the actual situation, for example, "yes" may be set to "1", and "no" may be set to "0", or "yes" may be set to "0", and "no" may be set to "1", and the like; of course, it should be noted that the judgment threshold of the online behavior "member" may be preset, and may also be determined according to a metering value of the online behavior "adding to a certain organization" collected by the client; (2) reward for 3 times and pay attention to 4 persons, wherein the reward and the attention can be online behaviors, and the reward and the attention can be judgment thresholds 3 and 4; (3) the new user is full of 100, wherein the new user and the recharge can be online behavior, and the '1 and 100' can be judgment thresholds. At this time, it should be noted that the judgment threshold of the online behavior information "new user" may be flexibly set according to actual situations, for example, "yes" may be set to "1", and "no" may be set to "0", or "yes" may be set to "0", and "no" may be set to "1".

It should be noted that, as shown in table 3, when a task in the task data table is composed of three or more preset on-line behaviors and determination threshold values thereof, each on-line behavior and determination threshold value thereof may also be determined in the above manner.

Further, as shown in fig. 4, it is a schematic flow chart of a second data processing method according to the embodiment of the present invention. Specifically, as shown in fig. 4, before the on-line behavior information is respectively matched with each task in a preset task data table, the data processing method may further include:

step 1011: acquiring at least one of the following attribute information: the current time attribute information, the geographic area attribute information of the client and the client attribute information corresponding to the client;

step 1012: and for any acquired attribute information, determining that the attribute information is in a set range corresponding to the attribute information.

It should be noted that the time attribute information may generally include time information such as year, month, day, week, holiday, time period, or combined time information composed of time information such as year, month, day, week, holiday, time period. For example, the number of the days can be 2017, 8, 2, 14, 6: 00-8: 00 afternoon, valentine's day, and the like; the geographic region attribute information of the client may generally include geographic region information such as country, region, province, etc. of the user corresponding to the client, or combined geographic region information composed of the geographic region information such as country, region, province, etc. For example, the material can be Chinese, Beijing, Qinghai, basalt area of Beijing, Zhongguancun area of Haihe area of Beijing, etc.; the client attribute information corresponding to the client may generally include user attribute information of a user corresponding to the client, such as information of gender, age, genus, and the like of the user, or combined information composed of information of gender, age, genus, and the like. For example, it may be 20 years old, female, girl of Malus, etc.

Specifically, as shown in table 4, it is a classification table of the attribute information described in the embodiment of the present invention.

TABLE 4 Attribute information Classification Table

It should be noted that, in different application scenarios, the attribute information may further include other information, for example, in a game scenario, the attribute information may further include uniform attribute information, such as north 3 area, north 74 area, and the like; in a live scene, the attribute information collected by the client may further include anchor information and audience information, which is not limited in this embodiment of the present invention.

As can be seen from the above, in the embodiment of the present invention, before performing task matching, it may also be determined whether the client is able to participate in the task (i.e., whether a user corresponding to the client is able to participate in the task), that is, whether the client satisfies a corresponding activity condition.

Taking a live broadcast scene as an example, assuming that a task needs to be executed on an episodic plot, after acquiring online behavior information (that is, online behavior information of a user corresponding to the client) acquired by the client, judging whether the current time is the episodic plot on the same day according to time attribute information, if so, performing task matching operation, otherwise, failing to execute the task;

or, taking a game scene as an example, assuming that a task can be executed only by a beijing user, after online behavior information acquired by the client is acquired, whether the geographic area where the client is located is the beijing is judged according to geographic area attribute information, if so, task matching operation is performed, otherwise, the task cannot be executed;

or, for example, in a social scenario, assuming that a task must be executed only by a female user, after online behavior information acquired by a client is acquired, it may be determined whether the user is a female user according to client attribute information, and if so, a task matching operation is performed, otherwise, the task cannot be executed.

Step 103: and if the matching with at least one task is successful, accumulating the metering value into a preset behavior data table and an accumulated value of a preset on-line behavior corresponding to the on-line behavior information acquired by the client.

It should be noted that the behavior data table may generally include behavior data of a plurality of clients (i.e., behavior data of users corresponding to the plurality of clients, respectively), and the behavior data of each client may be distributed in a row or a column, which is not described herein again.

Specifically, as shown in table 5, it is one possible structure of the behavior data table described in the embodiment of the present invention.

TABLE 5 behavior data sheet

Specifically, as can be seen from table 5, the behavior data table in the embodiment of the present invention may generally include a plurality of types of behavior data collected by a plurality of clients, and each of the behavior data tables may generally include a preset online behavior and a cumulative value corresponding to the preset online behavior. In addition, in a general case, the type of the behavior on the preset line in the behavior data table may be the same as the type of the behavior on the preset line in the task data table, that is, which behaviors on the preset line exist in the task data table and which behaviors on the preset line exist in the behavior data table. If a new task is set in the task data table, after the server obtains the information of the on-line behavior corresponding to the new task, the server can establish behavior data containing the on-line behavior and the accumulated value thereof in the behavior data table.

For example, a format of a certain behavior data in the behavior data table may be: (1) reward 3 times, wherein the reward can be a preset on-line behavior, and the accumulated value can be a reward 3; (2) the group battle is carried out for 20 times, wherein the group battle can be a preset on-line behavior, and the 20 can be an accumulated value; (3) grade 26, wherein. The "level" can be a predetermined on-line behavior, and the "26" can be an accumulated value.

For example, when a new task needs to be established, that is, 200 fans are needed, a new task may be added to the task data table, where "being attended" may be a preset on-line behavior of the new task, and "200" may be a judgment threshold of the preset on-line behavior. Then, when acquiring the online behavior information acquired by the client, the server also acquires the concerned quantity of the client, for example, the concerned quantity that can be acquired to the client is 10. Then, the server may establish new behavior data in the behavior data table, where "the focused" may be a preset on-line behavior of the new behavior data, and "10" may be an accumulated value of the preset on-line behavior, which is not described in detail herein.

It should be noted that the task data table and the behavior data table in the embodiment of the present invention may be respectively configured in corresponding databases, and for example, the task data table may be configured in a CDB (Cloud Database), and the behavior data table may be configured in a CKV (Cloud Key Value). Of course, it should be noted that the task data table and the behavior data table may also be configured in the same database, and this is not limited in this embodiment of the present invention.

For example, it is assumed that an application scene is a live scene, and the acquired online behavior information collected by a certain client may include: the continuous online time is 2 hours, the main broadcast is rewarded for 3 times, the main broadcast is concerned for 3 times, the interaction is carried out for 20 times, and the task data table is determined to comprise two tasks which are respectively as follows: (1) and (2) interacting for 100 times, determining that the 'reward anchor' in the online behavior information collected by the client can be matched with the 'reward anchor' in the preset online behavior in the task (1), and the 'interaction' in the online behavior information can be matched with the 'interaction' in the preset online behavior in the task (2).

Further, it is assumed that the behavior data collected by the client in the behavior data table includes: the reward anchor is played for 4 times and the interaction is performed for 50 times, then the reward 3 times and the interaction 20 times can be accumulated into the reward anchor 4 times and the interaction 50 times, namely after accumulation, the behavior data collected by the client in the behavior data table can be reward 7 times and interaction 70.

Step 104: and determining the tasks completed by the client according to the judgment threshold value in the task data table and the accumulated value in the behavior data table.

It should be noted that the task completed by the client may generally be a task completed by a user corresponding to the client, and is not described herein again.

Specifically, determining the task completed by the client according to the judgment threshold in the task data table and the accumulated value in the behavior data table may include:

For example, assume that the task data table includes 3 tasks, which are respectively: (1) 5 times of reward anchor, (2) 3 times of attention anchor, (3) 100 times of interaction, and the on-line behavior information collected by the client in the behavior data table is assumed to comprise: if the reward anchor is played 7 times and the interaction is 70 times, the task completed by the client can be determined to be task (1).

Further, after determining that the task of the client has been completed, the data processing method may further include:

For example, still taking the above example as an example, assuming that the task satisfied by the online behavior information acquired by the client is "reward 5 times", and the feedback information corresponding to the "reward 5 times" task is 10 bonus coins and 100 bonus points, the account information of the client (i.e. the account information of the user corresponding to the client) may be correspondingly changed according to the feedback information, for example, the number of the coins in the account information of the client is increased by 10, and the bonus points in the account information of the client is increased by 100 points, which is not described in detail herein.

It should be noted that, in the embodiment of the present invention, in one statistics, only when the cumulative value of the online behavior reaches the judgment threshold for the first time, the corresponding feedback information is sent to the client, for example, after the user corresponding to the client wins 5 times, the reward is issued to the client, and even if the cumulative value of "wining" is greater than "5", the reward is not continued any more in the following process, thereby avoiding the problem of repeated rewards, and ensuring the accuracy of the reward.

As can be seen from the above, in the embodiment of the present invention, the data processing method may generally include three steps, as shown in fig. 5, which is a schematic actual flow chart of the data processing method in the embodiment of the present invention. Specifically, as shown in fig. 5, in the embodiment of the present invention, the data processing method may actually include several steps of collecting online behavior information, matching tasks, counting completed tasks, and calculating points (or issuing rewards).

Specifically, as can be seen from fig. 5, it is assumed that the online behavior information collected by a certain client is: the new users, the members, the continuous online time of 3 hours, the reward anchor of 4 times, the attention anchor of 4 times, and the interaction of 100 times, and the tasks included in the task database are respectively: (1) the new user is continuously online for 2 hours, (2) the member pays attention to the anchor for 2 times, (3) the online time is 5 hours, (4) the anchor is rewarded for 4 times, (5) the new user gives a gift for the anchor for 7 times, and (6) the interaction is performed for 100 times, if the original accumulated value (before the accumulation) of each online behavior in the behavior data table is 0, the tasks completed by the client can be determined as follows: (1) new users are online for 2 hours, (2) members pay attention to the anchor for 2 times, (4) the main anchor is rewarded for 4 times, and (6) the interaction is performed for 100 times.

After determining that each task of the client has been completed, the user corresponding to the client may be further identified, such as point accumulation and reward issue. As shown in fig. 5, 10 credits and 10 coins that satisfy task (1), 2 credits, 2 gift packages, and 11 medals that satisfy task (2), 15 credits and 5 speaking privileges that satisfy task (4), and 5 credits and 2 entry privileges that satisfy task (6) are sent to the client.

In addition, it should be noted that, in the embodiment of the present invention, the point information of the client (i.e., the point information of the user corresponding to the client) may also be stored in a corresponding point data table, where the point data table may be configured in a corresponding database, such as Redis. Of course, it should be noted that the integral data table may also be configured in the same database as the task data table and the behavior data table, which is not described in detail herein.

In the following, a live broadcast scenario is taken as an example to describe the data processing method in the embodiment of the present invention in detail, and specifically, as shown in fig. 6, it is a schematic flow diagram of the data processing method based on the live broadcast scenario in the embodiment of the present invention. As can be seen from fig. 6, the live scene-based data processing method may include the following steps:

step 601: starting;

step 602: and aiming at any client, acquiring online behavior information acquired by the client.

Wherein the on-line behavior information is comprised of an on-line behavior and a metric corresponding to the on-line behavior.

Step 603: and judging whether to traverse each task in the task data table, if so, executing step 609, and if not, executing step 604.

Step 604: and judging whether the online behaviors in the online behavior information are matched with preset online behaviors in each task in the task data table, if so, executing the step 605, and if not, executing the step 604.

The matching means that the online behavior in the online behavior information is consistent with the preset online behavior in the task, and details of the online behavior and the preset online behavior are omitted here, if the online behavior is the number of times of the rewarding anchor, the number of times of the team battle, and the like.

Step 605: and judging whether to traverse the behaviors on each preset line in the task or not aiming at any task in the task data table, if so, executing step 609, and if not, executing step 606.

Step 606: and accumulating the metering value of each online behavior matched with the preset online behavior of each task in the task data table into the accumulated value of the corresponding preset online behavior of the client in the behavior data table.

Step 607: and judging whether the accumulated value of the on-line behaviors meets a corresponding judgment threshold value or not aiming at any preset on-line behaviors in the behavior data table, if so, executing a step 608, and if not, executing a step 605.

Step 608: and counting and outputting all tasks met by the online behavior information acquired by the client.

Step 609: and (6) ending.

It should be noted that the data processing method based on the live broadcast scene in the embodiment of the present invention may be applicable to not only the anchor user in the live broadcast scene but also the audience user in the live broadcast scene, and a front-end interface thereof may be generally as shown in fig. 7, which is a schematic front-end interface diagram of the data processing method based on the live broadcast scene in the embodiment of the present invention. As can be appreciated from FIG. 7, a host may be included in the front-end interface; viewers, such as viewer A, viewer B, viewer C, etc. shown in FIG. 7; interactive information, such as "support!shown in FIG. 7! "," Spreader! "and the like; a send message button, a record video button, a send private letter button, a present button, a share button, and more buttons.

The embodiment one of the invention provides a data processing method, aiming at any client, acquiring online behavior information acquired by the client; the online behavior information is composed of an online behavior and a metric value corresponding to the online behavior; matching the online behaviors of the online behavior information with preset online behaviors corresponding to tasks in a preset task data table; each task in the task data table corresponds to at least one preset on-line behavior, and each preset on-line behavior has a judgment threshold corresponding to the behavior; the judgment threshold is used for representing the completion condition of the corresponding task; if the matching with at least one task is successful, accumulating the metering value into a preset behavior data table and an accumulated value of a preset on-line behavior corresponding to the on-line behavior information acquired by the client; and determining the tasks completed by the client according to the judgment threshold value in the task data table and the accumulated value in the behavior data table. Compared with the prior art, the configurable task data table is arranged in the embodiment of the invention, when a plurality of tasks are simultaneously aimed at, all completed tasks of the client can be quickly counted only by acquiring online behavior information once, so that the counting efficiency is improved; and when a new task is needed, only the new task needs to be established in the task data table, and the front end, the rear end, the client and the like do not need to be updated with any program, so that during the development of the new task, the development workload can be effectively reduced, the development period can be reduced, the online speed can be increased, and the later maintenance cost can be saved.

Example two:

based on the same inventive concept as that of the first embodiment of the present invention, a second embodiment of the present invention provides a data processing apparatus, as shown in fig. 8, which is a schematic structural diagram of the data processing apparatus described in the second embodiment of the present invention. The same points can be referred to the content described in the first embodiment of the present invention, and the description in this embodiment is omitted. Specifically, as shown in fig. 8, the data processing apparatus according to the embodiment of the present invention may include:

the acquiring module 81 is configured to acquire, for any client, online behavior information acquired by the client; the online behavior information is composed of an online behavior and a metric value corresponding to the online behavior;

a matching module 82, configured to match an online behavior of the online behavior information with a preset online behavior corresponding to each task in a preset task data table; each task in the task data table corresponds to at least one preset on-line behavior, and each preset on-line behavior has a judgment threshold corresponding to the behavior; the judgment threshold is used for representing the completion condition of the corresponding task;

the accumulation module 83 is configured to, if the matching with the at least one task is successful, accumulate the measurement value into a preset behavior data table and an accumulated value of a preset online behavior corresponding to the online behavior information acquired by the client;

the statistic module 84 may determine the tasks completed by the client according to the judgment threshold in the task data table and the accumulated value in the behavior data table.

That is, the data processing apparatus according to the second embodiment of the present invention may include: the system comprises an acquisition module, a matching module, an accumulation module and a statistic module, wherein the acquisition module is used for acquiring online behavior information acquired by any client, the matching module is used for matching the online behavior of the online behavior information with preset online behaviors corresponding to all tasks in a preset task data table, the accumulation module is used for accumulating the metering value into the preset behavior data table and the accumulated value of the preset online behaviors corresponding to the online behavior information acquired by the client if the online behavior information is successfully matched with at least one task, and the statistic module is used for determining the tasks completed by the client according to a judgment threshold value in the task data table and the accumulated value in the behavior data table. Compared with the prior art, the configurable task data table is arranged in the embodiment of the invention, when a plurality of tasks are simultaneously aimed at, all completed tasks of the client can be quickly counted only by acquiring online behavior information once, so that the counting efficiency is improved; and when a new task is needed, only the new task needs to be established in the task data table, and the front end, the rear end, the client and the like do not need to be updated with any program, so that during the development of the new task, the development workload can be effectively reduced, the development period can be reduced, the online speed can be increased, and the later maintenance cost can be saved.

Specifically, the obtaining module 81 may be specifically configured to obtain online behavior information collected by the client in real time; and/or acquiring online behavior information acquired by the client at set time intervals.

Further, the data processing apparatus may further include a determination module 85, wherein:

the obtaining module 81 may be further configured to obtain at least one of the following attribute information: the current time attribute information, the geographic area attribute information of the client and the client attribute information corresponding to the client;

the determining module 85 may be configured to determine that, for any one of the acquired attribute information, the attribute information is within a setting range corresponding to the attribute information.

Specifically, the statistical module 84 may be specifically configured to, for any task that is successfully matched, determine a preset online behavior corresponding to the task; if the fact that the accumulated value of the behaviors on the preset line, collected by the client side in the behavior data table, is not smaller than the judgment threshold corresponding to the behaviors on the preset line is determined, the fact that the behaviors on the preset line meet the completion condition is determined; and if all the preset on-line behaviors in the task meet the completion condition, determining that the task is the completed task.

Further, the data processing apparatus may further comprise a feedback module 86, wherein:

the feedback module 86 may be configured to send feedback information corresponding to the task completed by the client to the client after determining that the task completed by the client is completed; the feedback information comprises any one or more of reward issuing information, point accumulation information and grade change information.

Correspondingly, an embodiment of the present invention further provides a data processing system, as shown in fig. 9, which is a schematic structural diagram of the data processing system in the embodiment of the present invention. Specifically, as shown in fig. 9, the data processing system may include at least one client 91 (such as 911-91N shown in fig. 9), a server 92, a task data table 93, and a behavior data table 94, wherein:

any client 91 of the at least one client may be configured to collect online behavior information and send the online behavior information to the server 92; the online behavior information is composed of an online behavior and a metric value corresponding to the online behavior;

the server 92 may be configured to match, for any client, an online behavior of the acquired online behavior information with a preset online behavior corresponding to each task in a preset task data table 93; if the matching with at least one task is successful, accumulating the metering value into a preset behavior data table 94 and an accumulated value of a preset on-line behavior corresponding to the on-line behavior information acquired by the client; determining the tasks completed by the client according to the judgment threshold value in the task data table 93 and the accumulated value in the behavior data table 94; each task in the task data table 93 corresponds to at least one preset on-line behavior, and each preset on-line behavior has a corresponding judgment threshold; the judgment threshold is used for representing the completion condition of the corresponding task.

It should be noted that the server 92 may be a server set separately or a server cluster set in batch, which is not described herein again.

Further, as shown in fig. 10, it is a schematic diagram of an actual structure of the data processing system according to the embodiment of the present invention. Specifically, as can be seen from fig. 10, the data processing system may further include a management platform 95 and a points data table 96, wherein:

the management platform 95 may be configured to establish a task according to a requirement, and store the established task in the task data table 93;

the point data table 96 is used for storing the point information of the client when the server 92 counts the update of the point of the client.

It should be noted that the point information may include the number of points and a ranking list of the points. The point ranking list can comprise a daily point ranking list, a weekly point ranking list, a latest 7-day ranking list, a total ranking list and the like.

An embodiment of the present invention provides a data processing apparatus and system, which may include: the system comprises an acquisition module, a matching module, an accumulation module and a statistic module, wherein the acquisition module is used for acquiring online behavior information acquired by any client, the matching module is used for matching the online behavior of the online behavior information with preset online behaviors corresponding to all tasks in a preset task data table, the accumulation module is used for accumulating the metering value into the preset behavior data table and the accumulated value of the preset online behaviors corresponding to the online behavior information acquired by the client if the online behavior information is successfully matched with at least one task, and the statistic module is used for determining the tasks completed by the client according to a judgment threshold value in the task data table and the accumulated value in the behavior data table. Compared with the prior art, the configurable task data table is arranged in the embodiment of the invention, when a plurality of tasks are simultaneously aimed at, all completed tasks of the client can be quickly counted only by acquiring online behavior information once, so that the counting efficiency is improved; and when a new task is needed, only the new task needs to be established in the task data table, and the front end, the rear end, the client and the like do not need to be updated with any program, so that during the development of the new task, the development workload can be effectively reduced, the development period can be reduced, the online speed can be increased, and the later maintenance cost can be saved.

Example three:

a third embodiment of the present invention provides a computing device, as shown in fig. 11, which is a schematic structural diagram of the computing device in the third embodiment of the present invention. The computing device may be specifically a desktop computer, a portable computer, a smart phone, a tablet computer, a Personal Digital Assistant (PDA), and the like. Specifically, the computing device according to the embodiment of the present invention may include a Central Processing Unit (CPU) 1101, a memory 1102, an input device 1103, an output device 1104, and the like, where the input device 1103 may include a keyboard, a mouse, a touch screen, and the like, and the output device 1104 may include a Display device, such as a Liquid Crystal Display (LCD), a Cathode Ray Tube (CRT), and the like.

The memory 1102 may include Read Only Memory (ROM) and Random Access Memory (RAM), and provides the central processor 1101 with program instructions and data stored in the memory 1102. In an embodiment of the present invention, the memory 1102 may be used to store a program of a data processing method.

By calling the program instructions stored in the memory 1102, the central processing unit 1101 may be configured to perform the following steps according to the obtained program instructions: aiming at any client, acquiring online behavior information acquired by the client; the online behavior information is composed of an online behavior and a metric value corresponding to the online behavior; matching the online behaviors of the online behavior information with preset online behaviors corresponding to tasks in a preset task data table; each task in the task data table corresponds to at least one preset on-line behavior, and each preset on-line behavior has a judgment threshold corresponding to the behavior; the judgment threshold is used for representing the completion condition of the corresponding task; if the matching with at least one task is successful, accumulating the metering value into a preset behavior data table and an accumulated value of a preset on-line behavior corresponding to the on-line behavior information acquired by the client; and determining the tasks completed by the client according to the judgment threshold value in the task data table and the accumulated value in the behavior data table.

Example four:

a fourth embodiment of the present invention provides a computer storage medium, which is used to store computer program instructions for the computing device, and which includes a program for executing the data processing method.

The computer storage media may be any available media or data storage device that can be accessed by a computer, including, but not limited to, magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND FLASH), Solid State Disks (SSDs)), etc.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus (device), or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims

1. A data processing method, comprising:

if the matching with at least one task is successful, accumulating the metering value into a preset behavior data table and an accumulated value of a preset on-line behavior corresponding to the on-line behavior information acquired by the client; the behavior data table comprises one or more preset on-line behaviors corresponding to a plurality of clients and an accumulated value corresponding to the preset on-line behaviors, and the task data table and the behavior data table comprise the same preset on-line behaviors;

aiming at any task successfully matched, determining a preset on-line behavior corresponding to the task; if the fact that the accumulated value of the behaviors on the preset line, collected by the client side, in the behavior data table is not smaller than the judgment threshold corresponding to the behaviors on the preset line is determined, it is determined that the behaviors on the preset line meet the completion condition; and if all the preset on-line behaviors in the task meet the completion condition, determining that the task is a completed task.

2. The data processing method of claim 1, wherein obtaining online behavior information collected by the client comprises:

3. The data processing method of claim 1, wherein before the on-line behavior information is respectively matched with tasks in a preset task data table, the data processing method further comprises:

4. The data processing method of claim 1, wherein after determining that the client has completed the task, the data processing method further comprises:

5. A data processing apparatus, comprising:

the accumulation module is used for accumulating the metering value into a preset behavior data table and an accumulation value of a preset on-line behavior corresponding to the on-line behavior information acquired by the client if the matching with at least one task is successful; the behavior data table comprises one or more preset on-line behaviors corresponding to a plurality of clients and an accumulated value corresponding to the preset on-line behaviors, and the task data table and the behavior data table comprise the same preset on-line behaviors;

the statistical module is used for determining a preset on-line behavior corresponding to any task which is successfully matched; if the fact that the accumulated value of the behaviors on the preset line, collected by the client side, in the behavior data table is not smaller than the judgment threshold corresponding to the behaviors on the preset line is determined, it is determined that the behaviors on the preset line meet the completion condition; and if all the preset on-line behaviors in the task meet the completion condition, determining that the task is a completed task.

6. The data processing apparatus of claim 5,

the acquisition module is specifically used for acquiring online behavior information acquired by the client in real time; and/or acquiring online behavior information acquired by the client at set time intervals.

7. The data processing apparatus of claim 5, wherein the data processing apparatus further comprises a determination module, wherein:

the obtaining module is further configured to obtain at least one of the following attribute information: the current time attribute information, the geographic area attribute information of the client and the client attribute information corresponding to the client;

8. The data processing apparatus of claim 5, wherein the data processing apparatus further comprises a feedback module, wherein:

9. A data processing system comprising at least one client, a server, a task data table, and a behavior data table, wherein:

the server is used for matching the online behaviors of the acquired online behavior information with preset online behaviors corresponding to tasks in a preset task data table aiming at any client; if the matching with at least one task is successful, accumulating the metering value into a preset behavior data table and an accumulated value of a preset on-line behavior corresponding to the on-line behavior information acquired by the client; and determining a preset on-line behavior corresponding to any task which is successfully matched; if the fact that the accumulated value of the behaviors on the preset line, collected by the client side, in the behavior data table is not smaller than the judgment threshold corresponding to the behaviors on the preset line is determined, it is determined that the behaviors on the preset line meet the completion condition; if all the preset on-line behaviors in the task meet the completion condition, determining the task to be a completed task;

each task in the task data table corresponds to at least one preset on-line behavior, and each preset on-line behavior has a judgment threshold corresponding to the behavior; the judgment threshold is used for representing the completion condition of the corresponding task; the behavior data table comprises one or more preset online behaviors corresponding to the plurality of clients and an accumulated value corresponding to the preset online behaviors, and the task data table and the behavior data table comprise the same preset online behaviors.

10. A computing device comprising a memory and a processor, wherein:

the memory to store program instructions;

the processor is used for calling the program instructions stored in the memory and executing the data processing method of any one of claims 1 to 4 according to the obtained program.

11. A computer storage medium having stored thereon computer-executable instructions for causing a computer to perform the data processing method of any one of claims 1 to 4.