CN113886393A

CN113886393A - Data processing method, data processing apparatus, storage medium, and electronic apparatus

Info

Publication number: CN113886393A
Application number: CN202111189187.0A
Authority: CN
Inventors: 陈新河; 曹鼎; 余晓睿; 张宾; 何家荣
Original assignee: Netease Hangzhou Network Co Ltd
Current assignee: Netease Hangzhou Network Co Ltd
Priority date: 2021-10-12
Filing date: 2021-10-12
Publication date: 2022-01-04

Abstract

The invention discloses a data processing method, a data processing device, a storage medium and an electronic device. The method comprises the following steps: determining first-level policy data in a policy tree, wherein the first-level policy data is used for indicating that first target data are pushed to a first pushing object in a first object set, and the first-level policy data comprises the first target data; determining second-level strategy data in the strategy tree, and determining a first sub-object set in the first object set, wherein the second-level strategy data is next-level strategy data of the first-level strategy data, and feedback information of a first pushing object in the first sub-object set to the first target data meets a preset feedback condition; and determining the first sub-object set as a second object set, wherein the second-level policy data is used for indicating that second target data is pushed to a second pushing object in the second object set, and the second-level policy data comprises the second target data. By the method and the device, the technical effect of automatically adjusting and iterating the push object to which the strategy is pushed according to the user feedback is achieved.

Description

Data processing method, data processing apparatus, storage medium, and electronic apparatus

Technical Field

The present invention relates to the field of computing, and in particular, to a data processing method, apparatus, storage medium, and electronic apparatus.

Background

Currently, when a user is touched, in order to formulate a corresponding user touch strategy, a technical focus is mostly focused on how to improve the richness and accuracy of a user tag, improve the multidimensional selection ability of tag combination, improve the timeliness of the tag and the touch, integrate the channel ability of multi-resource differentiation, integrate comprehensive publishing ability (such as full volume, gray scale, test publishing) and the like.

In addition, the strategies in the related art are all single pushing, namely stopping, and after the strategy reaches the user, the real feedback of the user is ignored, so that the pushing effect cannot be effectively monitored, and the pushing object pushed by the strategy cannot be automatically adjusted and iterated.

Aiming at the technical problem that the automatic adjustment and iteration of a push object to which a strategy is pushed cannot be carried out according to user feedback in the prior art, an effective solution is not provided at present.

Disclosure of Invention

The invention mainly aims to provide a data processing method, a data processing device, a storage medium and an electronic device, and at least solves the technical problem that automatic adjustment iteration cannot be performed on a push object to which a strategy is pushed according to user feedback.

In order to achieve the above object, according to an aspect of the present invention, there is provided a data processing method. The method can comprise the following steps: determining first-level policy data in a policy tree, wherein the first-level policy data is used for indicating that first target data are pushed to a first pushing object in a first object set, and the first-level policy data comprises the first target data; determining second-level strategy data in the strategy tree, and determining a first sub-object set in the first object set, wherein the second-level strategy data is next-level strategy data of the first-level strategy data, and feedback information of a first pushing object in the first sub-object set to the first target data meets a preset feedback condition; and determining the first sub-object set as a second object set, wherein the second-level policy data is used for indicating that second target data is pushed to a second pushing object in the second object set, and the second-level policy data comprises the second target data.

Optionally, the first-level policy data is used to indicate that when the behavior data of the first push object in the first object set satisfies the first trigger condition, the first target data is pushed to the first push object in the first object set, and the first-level policy data includes the first trigger condition; the second-level policy data is used for indicating that second target data is pushed to a second pushing object in the second object set when the behavior data of the second pushing object in the second object set meets a second trigger condition, and the second-level policy data comprises the second trigger condition.

Optionally, the method further comprises: under the condition that the first-level policy data is the first-level policy data of a policy tree, acquiring at least one target label, wherein each target label is used for representing information of a target field in a target log; performing logic combination on at least one target label to obtain a third trigger condition; determining a pushing object with behavior data meeting a third trigger condition as an original object set; a first set of objects is determined in the original set of objects.

Optionally, determining the first set of objects in the original set of objects comprises: determining the strategy attribute of the first-level strategy data as a first strategy attribute, and determining the original object set as a first pushing object set; or determining that the policy attribute of the first-level policy data is a second policy attribute, and then acquiring a first push object with behavior data meeting a first trigger condition in the original object set to obtain a first push object set, wherein the first-level policy data comprises the first trigger condition.

Optionally, the first-level policy data includes a preset feedback condition, and determining second-level policy data in the policy tree includes: and determining second-level strategy data corresponding to preset feedback conditions in the strategy tree.

Optionally, the first-level policy data includes a target logical relationship and a target value, and the method further includes: and determining that the feedback information meets the target value in the target logical relationship, and determining that the feedback information meets the preset feedback condition.

Optionally, the first level policy data includes a target time interval, and determining a first set of sub-objects in the first set of objects includes: a first set of child objects is determined in the first set of objects at a target time interval after pushing the first target data to the first push object in the first set of objects.

Optionally, determining a first set of sub-objects in the first set of objects comprises: determining, based on the first level policy data, that a first set of sub-objects is allowed to be determined in the first set of objects, then determining the first set of sub-objects in the first set of objects.

Optionally, the method further comprises: storing the strategy tree into a target database through a target interface provided by a server; from the target database, each level of policy data of the policy tree is invoked.

Optionally, the first set of sub-objects includes objects to which the first target data was successfully pushed.

In order to achieve the above object, according to another aspect of the present invention, there is also provided a data processing apparatus. The device also includes: the first determining unit is used for determining first-level policy data in the policy tree, wherein the first-level policy data is used for indicating that first target data are pushed to a first pushing object in a first object set, and the first-level policy data comprises the first target data; the second determining unit is used for determining second-level strategy data in the strategy tree, and then determining a first sub-object set in the first object set, wherein the second-level strategy data is next-level strategy data of the first-level strategy data, and feedback information of a first pushing object in the first sub-object set to the first target data meets a preset feedback condition; and a third determining unit, configured to determine the first set of sub-objects as a second set of objects, where the second-level policy data is used to indicate that second target data is pushed to a second push object in the second set of objects, and the second-level policy data includes the second target data.

In order to achieve the above object, according to another aspect of the present invention, there is also provided a computer-readable storage medium. The computer readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the computer readable storage medium controls a device to execute the data processing method according to the embodiment of the present invention.

In order to achieve the above object, according to another aspect of the present invention, an electronic device is provided. The electronic device comprises a memory in which a computer program is stored and a processor arranged to run the computer program to perform the data processing method of an embodiment of the invention.

In the data processing method of this embodiment, for a policy tree including multi-level policy data, a sub-object set in a push object set corresponding to the policy data is an object whose feedback information fed back to target data in the policy data meets a preset feedback condition, and the sub-object set can be used to determine an initial push object set of next-level policy data, and so on, thereby implementing automatic adjustment iteration on the push object set corresponding to the policy data, avoiding that all policies are terminated by single push, thereby solving the technical problem that automatic adjustment iteration cannot be performed on a push object pushed to by a policy according to user feedback, and achieving the technical effect of performing automatic adjustment iteration on the push object pushed to the policy according to user feedback.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:

fig. 1 is a block diagram of a hardware configuration of a mobile terminal of a data processing method according to an embodiment of the present invention;

FIG. 2 is a flow chart of a method of data processing according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of an interface for a business to formulate a user reach policy according to an embodiment of the present invention

FIG. 4 is a diagram illustrating an autonomous selection of user tag combinations to obtain a particular user grouping, according to an embodiment of the invention;

FIG. 5 is a schematic diagram of an interface of a policy combining module according to an embodiment of the invention;

FIG. 6 is a schematic diagram of an interface for determining nodes in real time according to an embodiment of the invention;

FIG. 7 is a schematic diagram of a policy making node according to an embodiment of the present invention;

FIG. 8 is a schematic diagram of a policy evaluation node according to an embodiment of the present invention;

FIG. 9 is a diagram illustrating an automatic evaluation and an automatic iteration based on policy push effects, according to an embodiment of the present invention;

FIG. 10 is a schematic diagram of a user grouping according to an embodiment of the present invention;

FIG. 11 is a schematic diagram of a policy tree configuration according to an embodiment of the present invention;

FIG. 12 is a diagram illustrating trigger conditions of a business definition policy according to an embodiment of the present invention;

FIG. 13 is a schematic diagram of a policy evaluation alternative triple relationship, according to an embodiment of the invention;

FIG. 14 is a schematic diagram of the logic of a policy evaluation in accordance with an embodiment of the present invention;

fig. 15 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention.

Detailed Description

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all 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 application.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

The method provided by the embodiment of the application can be executed in a mobile terminal, a computer terminal or a similar operation device. Taking the example of being operated on a mobile terminal, fig. 1 is a hardware structure block diagram of the mobile terminal of a data processing method according to an embodiment of the present invention. As shown in fig. 1, the mobile terminal may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and optionally may also include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

The memory 104 can be used for storing computer programs, for example, software programs and modules of application software, such as a computer program corresponding to a data processing method in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, so as to implement the above-mentioned method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.

In the embodiment, a data processing method operating in the mobile terminal is provided. Fig. 2 is a flow chart of a data processing method according to an embodiment of the present invention. As shown in fig. 2, the method may include the steps of:

step S202, first-level policy data is determined in the policy tree, wherein the first-level policy data is used for indicating that first target data are pushed to a first pushing object in a first object set, and the first-level policy data comprises the first target data.

In the technical solution provided by step S202 of the present invention, the policy tree may be referred to as a policy combining module, which includes multiple levels of policy data combined according to the target combination relationship. For any level of policy data of the policy tree, the policy data may include data formulated for implementing policy reach, may include target data pushed to the push object, where the target data is also push content, and may further include a trigger condition that needs to be satisfied by behavior data of the push object in order to trigger the pushing of the target data to the push object.

Optionally, for any level of policy data of the policy tree, it has corresponding policy tree nodes, which may include a real-time judgment node, a policy making node (policy node), and a policy evaluation node (effect feedback node). The real-time judgment node is configured to configure a trigger condition in the policy data, where the trigger condition is used to indicate that only when the behavior data meets the trigger condition, the target data in the policy data is triggered to be pushed to a pushing object generating the behavior data, and thus the trigger condition is a real-time trigger condition, which may be configured by combining a real-time tag, for example, when the behavior data of the pushing object is upgraded to 50+ in real time, it is determined that the behavior data meets the trigger condition, and the target data corresponding to 50+ in level may be pushed to the pushing object; the system comprises a strategy making node and a strategy channel, wherein the strategy channel can be used for making target data pushed by a pushed object, and can also be used for making a strategy channel, a test check and a release mode, wherein the strategy channel can refer to a target data pushing mode, such as a short message pushing mode, a mail pushing mode and the like, the test check can comprise inputting a test account number and a test server, and a flow for checking the legality of the target data, the release mode comprises a release opportunity, a deadline time and a time interval, and can be a full release mode, an AB test (attest) release mode and the like, so that the target data can be pushed to the pushed object; the effect feedback node, i.e. the policy evaluation node, may be configured to formulate a preset feedback condition, and may be configured to, after the target data is pushed to the pushing object, based on the preset feedback condition, evaluating feedback information of the target data by the push object to obtain a push object set meeting preset feedback conditions, wherein, the preset feedback condition may be a requirement of the policy evaluation condition, and the feedback information may be used to indicate a pushing effect of the target data in the policy data to the pushing object, that is, the user feedback information, for representing the real behavior feedback situation of the user, it may be regarded as a tag, for example, the policy evaluation condition may be whether there is login in the past 1 day, if the target data a is pushed to the push object, and 1 day after the target data a is pushed to the push object, if the push object is logged in, the push object meets the policy evaluation condition. It should be noted that, if the policy data is the ultimate policy data in the policy tree, the effect feedback node may not be configured, and the preset feedback condition is not formulated. The final stage strategy data means that the stage strategy data has no next stage strategy data, and strategy evaluation is not required.

The policy data of each level of the policy tree of this embodiment can be formulated by the above method. Optionally, the basic information of the policy tree may also be configured in this embodiment, where the basic information may include a name of the policy tree, a policy classification, a life cycle classification, and the like, for example, the name of the policy tree is a god drainage pushing test, the policy classification is a platform drainage or a god drainage, and the life cycle classification is an active period, which is not limited herein. The strategy tree is constructed by formulating the strategy tree nodes and the basic information to form a strategy combination relation.

After the policy tree is constructed, the policy tree enters an enabled state, and first-level policy data is determined in the multi-level policy data of the policy tree, where the first-level policy data includes first target data, and the first target push data is push content to be pushed to a first push object in a first object set, where the first push object in the first object set is already determined, so that the first target data can be determined to be pushed to the first push object in the first object set by the first-level policy data. Optionally, the embodiment may push the first target data to the first push object in the first object set according to a policy channel, a test verification manner, and a publishing manner formulated for the first-level policy data.

Step S204, determining second-level strategy data in the strategy tree, and determining a first sub-object set in the first object set, wherein feedback information of a first pushing object in the first sub-object set to the first target data meets a preset feedback condition.

In the technical solution provided in step S204 of the present invention, it may be determined whether the first-level policy data has second-level policy data in the policy tree, where the second-level policy data is the next-level policy data of the first-level policy data. If the first-level policy data is judged to have the second-level policy data, and the second-level policy data is automatically started, the first sub-object set can be further determined in the first object set.

In this embodiment, after the first target data in the first level policy data is pushed to the first push object in the first set of objects, the first push object in the first object set can feed back the first target data to obtain feedback information, it may be determined whether the feedback information meets the preset feedback condition corresponding to the first-level policy data defined in the policy tree, for example, the preset feedback condition may be whether there is a login in the past 1 day or not, if the first target data is pushed to the first pushing object in the first object set, the feedback information may be 1 day after the first target data is pushed to the first pushing object, and the first pushing object is logged in, it may be determined that the feedback information satisfies a preset feedback condition, and then the first push object of which the feedback information meets the preset feedback condition is classified into the first sub-object set, so that the purpose of performing strategy evaluation on the first-level strategy data is achieved.

Optionally, the embodiment may upload the first object set to a distributed file system (HDFS) for storage, may store the first object set in a data warehouse tool (Hive), may deploy a policy evaluation program, and perform feedback on the first target data in the first-level policy data through the first object set to obtain feedback information, thereby determining which objects in the first object set satisfy a preset feedback condition for the feedback information of the first target data, so as to determine the first sub-object set.

Optionally, in this embodiment, if the second-level policy data is not determined in the policy tree, it may be stated that the first-level policy data is the final policy data of the policy tree.

Optionally, in this embodiment, the policy data in the policy tree may set a field to identify whether it is a final policy, for example, the field is stream _ id _ next, if this field is empty, it is determined that the policy data has no next-level policy data, and it is the final policy data, otherwise, the policy data has the next-level policy data.

Step S206, determining the first sub-object set as a second object set, where the second-level policy data is used to indicate that second target data is pushed to a second pushing object in the second object set, and the second-level policy data includes the second target data.

In the technical solution provided in step S206 of the present invention, after determining the first set of sub-objects in the first set of objects, the first set of sub-objects may be determined as a second set of objects, where the second set of objects is an initial set of push objects for the second-level policy data, and may include all push objects to which second target data in the second-level policy data is to be pushed, that is, an initial push list of the second-level policy data. The second target push data is also push content to be pushed to a second push object in a second object set, where the second object set is determined by a first sub-object set aiming at the first-level policy data, so that the second target data can be determined to be pushed to the second push object in the second object set by the second-level policy data. Optionally, the embodiment may push the second target data to the second push object in the second object set according to a policy channel, a test verification manner, and a publishing manner formulated for the second-level policy data.

Optionally, in this embodiment, third-level policy data may also be determined in the policy tree, and then a second sub-object set is determined in the second object set, where feedback information of a second push object to second target data in the second sub-object set meets a preset feedback condition corresponding to the second-level policy data.

Optionally, in this embodiment, it is determined whether the second-level policy data has third-level policy data in the policy tree, where the third-level policy data is next-level policy data of the second-level policy data. If the second-level policy data is judged to have third-level policy data, a second set of sub-objects may be further determined in the second set of objects.

Optionally, in this embodiment, if the third-level policy data is not determined in the policy tree, it may be stated that the second-level policy data is the final policy data of the policy tree.

In this embodiment, after the second target data in the second-level policy data is pushed to the second pushing object in the second object set, the second pushing object in the second object set may feed back the second target data to obtain feedback information, and may determine whether the feedback information meets the preset feedback condition corresponding to the second-level policy data formulated in the policy tree, and if the feedback information meets the preset feedback condition, the second pushing object whose feedback information meets the preset feedback condition is classified into the second child object set.

Optionally, the second set of sub-objects is determined as a third set of objects, wherein the third level policy data is used to indicate that third target data is pushed to a third push object in the third set of objects, and the third level policy data includes the third target data.

The third object set of this embodiment is an initial push object set for the third-level policy data, and the third-level policy data may include third target data pushed to the third object set, where the third target push data is also push content pushed to a third push object in the third object set, where the third object set is determined by a second sub-object set for the second-level policy data, and thus the third target data pushed to the third push object in the third object set may be determined by the third-level policy data. Optionally, the embodiment may push the third target data to a third push object in the third object set according to a policy channel, a test verification manner, and a publishing manner formulated for the third-level policy data.

Optionally, in this embodiment, the initial pushed object sets corresponding to the policy data of each level in the policy tree are sequentially determined by the above method until the final policy data in the policy tree, so that a multi-round pushing mechanism is implemented, user feedback information is effectively monitored, and the purpose of automatically adjusting and iterating the object to which the target data in the policy data is pushed is achieved according to the feedback information.

Each level of policy data in this embodiment may be iteratively performed according to the above method, so as to determine an initial push object set corresponding to each level of policy data.

Through the above steps S202 to S206 in the present application, first-level policy data is determined in a policy tree, where the first-level policy data is used to indicate that first target data is pushed to a first push object in a first object set, and the first-level policy data includes the first target data; determining second-level strategy data in the strategy tree, and determining a first sub-object set in the first object set, wherein the second-level strategy data is next-level strategy data of the first-level strategy data, and feedback information of a first pushing object in the first sub-object set to the first target data meets a preset feedback condition; and determining the first sub-object set as a second object set, wherein the second-level policy data is used for indicating that second target data is pushed to a second pushing object in the second object set, and the second-level policy data comprises the second target data. That is to say, for a policy tree including multi-level policy data, a sub-object set in a push object set corresponding to the policy data is an object whose feedback information fed back to target data in the policy data meets a preset feedback condition, and the sub-object set can be used for determining an initial push object set of next-level policy data, and so on, thereby implementing automatic adjustment iteration on the push object set corresponding to the policy data, avoiding that the policies are all completed as soon as a single push is performed, solving the technical problem that automatic adjustment iteration cannot be performed on a push object to which the policies are pushed according to user feedback, and achieving the technical effect of performing automatic adjustment iteration on the push object to which the policies are pushed according to user feedback.

The above method of this embodiment is further described below.

As an alternative implementation manner, the first-level policy data is used to indicate that when the behavior data of the first push object in the first object set satisfies the first trigger condition, the first-level policy data pushes the first target data to the first push object in the first object set, and the first-level policy data includes the first trigger condition; the second-level policy data is used for indicating that second target data is pushed to a second pushing object in the second object set when the behavior data of the second pushing object in the second object set meets a second trigger condition, and the second-level policy data comprises the second trigger condition.

The first-level policy data in the policy tree may make a real-time judgment node, for example, if the real-time judgment node is made for the first-level policy data, the first-level policy data may include a first trigger condition, where the first trigger condition is used to indicate that, when the behavior data of the first push object in the first object set satisfies the first trigger condition, the first push object whose behavior data satisfies the first trigger condition is triggered to push the first target data.

In this embodiment, the first trigger condition is essentially what relationship the real-time tag (value of a field of a real-time log) has, and what value is satisfied. For example, the target log may be a role synchronization log roleSrc, the target field may be a replay field, a level field, an account field, and a gate assignment field of the user, and if the first trigger condition is that the replay field is player-upgrade, and the level field is 45 (i.e., upgraded to 45 levels), and the account field is a certain value, and the gate assignment field is 12, a first set of push objects satisfying the first trigger condition is obtained.

In this embodiment, if a real-time determination node is formulated for the second-level policy data, the second-level policy data may include a second trigger condition, where the second trigger condition is used to indicate that when the behavior data of the second push object in the second object set satisfies the second trigger condition, the second push object whose behavior data satisfies the second trigger condition is triggered to push the second target data to the second push object whose behavior data satisfies the second trigger condition.

Optionally, the first-level policy data in the policy tree may not make a real-time judgment node, for example, if the real-time judgment node is not made for the first-level policy data, the first-level policy data may not include the first trigger condition, so that the first object set may be directly determined as the push object set of the first target data. If no real-time judgment node is established for the second-level policy data, the second-level policy data may not include the second trigger condition, so that the second object set may be directly determined as the push object set of the second target data.

As an optional implementation, the method further comprises: under the condition that the first-level policy data is the first-level policy data of a policy tree, acquiring at least one target label, wherein each target label is used for representing information of a target field in a target log; performing logic combination on at least one target label to obtain a third trigger condition; determining a pushing object with behavior data meeting a third trigger condition as an original object set; a first set of objects is determined in the original set of objects.

In this embodiment, the target tag (user tag) may be a key value pair in a target log, a key in the key value pair may be a target field, a median of the key value pair may be information of the target field, and the information of the target field may be a value of the target field, where the target log may be a log generated during a game application running process, and thus the target tag may be used to represent a logged role level, a VIP level, a logged number of days, a logged application channel, a logged application version, a logged operating system, a corresponding value, and the like, where no specific limitation is made here, and the target tag may be used for user clustering and may also be used for policy evaluation. Optionally, the target log in this embodiment may be a real-time log, and thus the target tag may be a real-time tag. Alternatively, the target tag of this embodiment may be referred to as a user tag, tag data.

In the embodiment, the target label can embody the characteristics of the user behavior, and the construction of the target label is an important link for realizing the fine operation of the user. Taking a game application as an example, the player personality characteristics and the game behavior characteristics can be described by constructing the target tags, such as player level, VIP level, accumulated online time, accumulated consumption, the number of days of last login, the date of last login and the like.

Optionally, in this embodiment, the at least one target tag may be generated by using a big data computing engine (Hadoop, Spark, etc.) according to a certain period, for example, at least one target tag is computed every day.

In this embodiment, the logic combination of the at least one target tag to obtain the third trigger condition may be that the at least one target tag is combined according to the target logic to obtain the third trigger condition, where the third trigger condition is a condition for triggering the pushed object to be included in the original object set, for example, only when the behavior data satisfies the third trigger condition, the pushed object whose behavior data satisfies the third trigger condition is triggered to be included in the original object set. The target logic may refer to a combination rule of at least one target tag, and may be what value the value of the target field in the target tag satisfies in a relationship, where the relationship may be "═ a", "| a! The target field may be a plurality of target tags including, not included, inside, not inside, and the like, and when the number of target tags whose values satisfy a certain relationship in a target field is plural, the plural target tags may be combined in one or more of logical and, logical or, logical not, and the like.

For example, the target field of the first target tag is the last login date, the value of the target field is the specific date value of the last login date, the account VIP level of the target field of the second target tag is the specific level value of the account VIP level, and the target fields are combined according to the target logic to obtain the third trigger condition, which may be (the first target tag is the last login date, the specific date value of the last login date is 20210421, and the specific date value of the last login date is less than 20210713) and (the third target tag is the account level, the specific level value of the account VIP level is VIP 5). The specific date value of the last login date of the push object b is obtained as 20210710, the VIP level of the account is 7, the specific date value 20210710> of the last login date is 20210421, the specific level value 20210710 of the last login date is less than 20210713, and the VIP level value 7> is 5, so that the push object b can be classified into the original object set.

Optionally, this embodiment may determine, in the user clustering module, the original object set based on the at least one target label. Optionally, the embodiment may convert the at least one target tag into a Structured Query Language (SQL) and submit the SQL to an open source distributed Query engine (presto ad hoc Query framework) for computation, so as to generate a specific original object set, which may also be referred to as a user cluster list and a policy list.

After the original object set is obtained, the original object set may be determined as a first object set, that is, the original object set is determined as an initial push object set of the first-level policy data.

As an alternative embodiment, determining the first set of objects in the original set of objects includes: determining the strategy attribute of the first-level strategy data as a first strategy attribute, and determining the original object set as a first pushing object set; or determining that the policy attribute of the first-level policy data is a second policy attribute, and then acquiring a first push object with behavior data meeting a first trigger condition in the original object set to obtain a first push object set, wherein the first-level policy data comprises the first trigger condition.

In this embodiment, the policy attributes of the first level policy data may include a first policy attribute and a second policy attribute. Optionally, when the real-time judgment node is not configured to the first-level policy data, the policy attribute of the first-level policy attribute is the first policy attribute, that is, the first-level policy data is offline policy data, at this time, an original object set (an initial push object set of the first-level policy data) may be directly determined as a first push object set, and the first push object set is sent to a push service, and first target data in the first-level policy data is pushed to the first push object set through the push service.

Optionally, when the first-level policy data is configured with the real-time judgment node, the first-level policy data includes a first trigger condition, the policy attribute of the first-level policy attribute is a second policy attribute, that is, the first-level policy data is real-time policy data, and at this time, a first push object whose behavior data meets the first trigger condition is obtained in the original object set, so as to obtain a first push object set, which is a real-time policy list, and then the first push object set is sent to the push service, and the first push object set is pushed with the first target data in the first-level policy data through the push service. Optionally, the embodiment may read the game log stream in real time by a real-time policy detection module based on an open source processing framework (Flink), obtain a first set of push objects satisfying a first trigger condition of the first-level policy data, and send the first set of push objects to the push service through an open source processing platform (Kafka), so as to push the first target data in the first-level policy data to the first set of push objects.

It should be noted that, in the embodiment, for the initial push object sets of policy data at each level in the policy tree, by determining the policy attribute of the policy data at each level, when the policy data is offline policy data, the initial push object sets can be directly sent to the push service; when the policy data is real-time policy data, the push object whose initial push object centralized behavior data meets the trigger condition in the policy data can be sent to the push service.

As an optional implementation manner, the determining the second-level policy data in the policy tree includes: and determining second-level strategy data corresponding to preset feedback conditions in the strategy tree.

In this embodiment, for any level of policy data of the policy tree, the policy data may include target data to be pushed to the push object, and in addition to a trigger condition that needs to be satisfied by behavior data of the push object in order to trigger the target data to be pushed to the push object, the policy data may further include a preset feedback condition to perform policy evaluation on feedback information that the push object feeds back the received target data. In this embodiment, the first-level policy data may be at a next level in the policy tree, and there may be a plurality of second-level policy data, where different second-level policy data correspond to different preset feedback conditions, and this embodiment may determine the second-level policy data corresponding to the preset feedback conditions.

For example, the current one-stage policy data is policy data a, the next-stage policy data is policy data B and policy data C, and the preset feedback condition may be: if yes, it is indicated that after the policy data a reaches the pushing object, the pushing object is logged in, that is, a reflowing user, so that the pushing object meeting the preset feedback condition can be used as a list of the policy data B, and for the reflowing user, the target data in the policy data B can be pushed to the pushing object meeting the preset feedback condition, for example, the target data in the policy data B is a reflowing welfare gift package.

For another example, the preset feedback condition may be: if the policy data a is not logged in the past 1 day, it indicates that the policy data a is not logged in after touching the push object, that is, the push object is a user without backflow and meets the preset feedback condition, so that the push object meeting the preset feedback condition can be used as a list of the policy data C, and for the user without backflow, the target data in the policy data C can be pushed to the push object meeting the preset feedback condition, for example, the target data in the policy data B is stronger short message push or data for customer service to actively intervene in a maintenance system.

As an optional implementation, the first-level policy data includes a target logical relationship and a target value, and the method further includes: and determining that the feedback information meets the target value in the target logical relationship, and determining that the feedback information meets the preset feedback condition.

In this embodiment, when configuring the policy tree, three-element relationship information may be configured in the first-level policy data of the policy tree, where the three-element relationship information includes a target logical relationship and a target value, so that when performing policy evaluation on the feedback information, it may be determined whether the feedback information satisfies the target value in the target logical relationship, and if it is determined that the feedback information satisfies the target value in the target logical relationship, it may be determined that the feedback information meets a preset feedback condition, that is, it is determined that the feedback information meets the policy evaluation condition.

As an alternative embodiment, the first-level policy data includes a target time interval, and the determining a first set of sub-objects in the first set of objects includes: a first set of child objects is determined in the first set of objects at a target time interval after pushing the first target data to the first push object in the first set of objects.

In this embodiment, when configuring the policy tree, a target time interval may be configured in the first-level policy data of the policy tree, so that feedback information for feeding back the first target data by the first push object in the first object set may be obtained at the target time interval after the first target data in the first-level policy data is pushed to the first object set, and a first sub-object set where the feedback information meets a preset feedback condition is determined from the first object set.

As an alternative embodiment, determining a first set of sub-objects in a first set of objects includes: determining, based on the first level policy data, that a first set of sub-objects is allowed to be determined in the first set of objects, then determining the first set of sub-objects in the first set of objects.

In this embodiment, when configuring the policy tree, whether to allow determining whether the feedback information meets the preset feedback condition may be configured in the first-level policy data of the policy tree, that is, whether the first-level policy data needs to perform effect feedback evaluation may be detected. If the first-level policy data allows determining whether the feedback information meets the preset feedback condition, a first sub-object set may be determined in the first object set, where the feedback information of the first sub-object set to the first target data in the first policy data meets the preset feedback condition.

As an optional implementation, the method further comprises: storing the strategy tree into a target database through a target interface provided by a server; from the target database, each level of policy data of the policy tree is invoked.

In this embodiment, the front-end client may write the relevant configuration information of the policy tree of the service configuration into the target database via the target interface provided by the back-end server, for example, store the configuration information through the policy tree table and the policy table of the target database, so that each level of policy data may be called from the target database later when the policy tree is used.

As an alternative embodiment, the first set of sub-objects includes objects to which the first target data was successfully pushed.

After the first target data in the first-level policy data is pushed to the first object set, if the next-level policy data of the first-level policy data is determined in the policy tree, all objects to which the first target data is successfully pushed, that is, a push success list, may be obtained, and then, among all objects to which the first target data is successfully pushed, the first sub-object set is further determined, so that the first sub-object set of the embodiment includes objects to which the first target data is successfully pushed, that is, policy evaluation is performed in a set of objects to which the first target data in the first-level policy data is successfully received.

For example, the next-level policy data B of the policy data a is, in the push success list of the object to which the target data is successfully pushed in the policy data a, an object list in which the feedback information of the target data meets the preset feedback condition set by the policy data a is determined, and is used as the initial push object set of the next-level policy data B.

In the related art, most strategies are terminated by a single push in user touch. However, in this embodiment, the object for pushing the target data in the next-level policy data can be subjected to policy evaluation and automatic iterative adjustment based on the feedback information of the target data in the first-level policy data by the pushing object, so that a multi-round pushing mechanism is established for the feedback information of different pushing objects, the technical problem that the pushing object to which the policy is pushed cannot be subjected to automatic adjustment and iteration according to the user feedback is solved, the technical effect of performing automatic adjustment and iteration on the pushing object to which the policy is pushed according to the user feedback is achieved, the response rate, the conversion rate, the payment rate and the like of the user contact are further effectively improved, and the enterprise is enabled.

The technical solutions of the embodiments of the present invention are further described below by way of examples with reference to preferred embodiments, and specifically, by way of examples with game applications.

In order to exert the life cycle value of the user to the maximum extent, a great number of scenes can be set in the operation process of the game user so as to reach the user, so that the user can be conveniently contacted with the user for a long time, for example, platform drainage, game maintenance, promotion of activity promotion, user loss recall and the like, and the reaching way can include but is not limited to short messages, mails, telephones, application push and the like.

The user label can embody the characteristics of user behaviors, and the establishment of the user label is an important link for realizing refined operation of the user. Taking a game application as an example, a user tag is also a gamer tag, and the user tag can be constructed to depict the personal characteristics and game behavior characteristics of a player, for example: player rating, VIP rating, cumulative online time, cumulative consumption, last 7/30 log-in days, last log-in date, etc.

Based on the user tags, the service can independently perform tag combination to obtain specific user groups, and further formulate corresponding user reach strategies. The core process of business making the reach strategy mainly comprises the following parts: the method comprises the steps of defining crowd, configuring content (configuring push channels and push content), testing and publishing, and the like, as shown in figure 3. Fig. 3 is a schematic diagram of an interface for a service to formulate a user reach policy according to an embodiment of the present invention, where the configuration content may include configuring a push channel and push content, the test may include inputting a test account and a test server, and the publishing may include a publishing manner, such as a publishing opportunity, deadline, and time interval.

However, even if the same group of users are selected based on the same user tag combination circle, the touch channels, touch modes, touch time, and document contents preferred by the users are different.

In the related art, most of technical focus points are focused on how to improve the richness and accuracy of user tags, improve the multidimensional selection capability of tag combination, improve the timeliness of tags and reach, integrate the channel capability of multi-resource differentiation, and integrate comprehensive release capability (such as full-scale, gray scale and attest release).

However, in this embodiment, automatic evaluation and automatic iteration strategies may be performed based on the strategy pushing effect, and a multi-round pushing mechanism may be established for different groups and different user feedback information, so as to improve the user response rate, the conversion rate, the payment rate, and the like.

The scheme of this embodiment is further exemplified below.

The user grouping module and the service combination user tag may generate a user grouping list based on a presto ad hoc query framework, as shown in fig. 4. Fig. 4 is a schematic diagram of autonomously selecting a user tag combination to obtain a specific user group according to an embodiment of the present invention. As shown in fig. 4, the update mode may be update every day, a combination rule of the user tag may be set, and what value the value of the target field in the user tag satisfies with a certain logical relationship may be set, for example, the value of the target field in the user tag 1 > is 1.

The policy combination module, also called a policy tree, may be used to make a policy combination iteration scheme, may make different policies for the same group of users, set different evaluation conditions of the policies, automatically iterate the policies based on the policy pushing effect, and establish a multi-round pushing mechanism based on the real behavior feedback of the users, as shown in fig. 5. Fig. 5 is a schematic diagram of an interface of a policy combination module according to an embodiment of the present invention, which may configure basic information of a policy tree, such as configuring a name, a policy classification, a life cycle classification, and selecting a group of the policy tree, and may further configure nodes of the policy tree, including a start node, a level determination node, a god sprite push node, an unregistered secondary push node, a god secondary push node, and a god short message push node.

Optionally, the nodes of the policy tree of this embodiment mainly include: the method comprises a real-time judgment node, a strategy making node and a strategy evaluation node. The level judgment node in fig. 5 belongs to a real-time judgment node, the god sprite push node belongs to a policy node, the unregistered secondary push node belongs to a policy evaluation node, the god secondary push node belongs to a policy node, and the god short message push node belongs to a policy node.

And if the real-time trigger condition is configured, the pushing is triggered only when the user trigger condition is met. As shown in fig. 6. Fig. 6 is a schematic diagram of an interface of a real-time node according to an embodiment of the present invention, where the real-time node may include information such as a node name, a node description, a maximum release amount, and a combination rule.

Based on the obtained user group, the strategy node can make a corresponding reach strategy by the service, which mainly comprises the following parts: configuring a push channel and push content of a policy, testing verification, and issuing, as shown in fig. 7, where fig. 7 is a schematic diagram of a policy making node according to an embodiment of the present invention, and may further include an instruction list, instruction content, whether to judge online, and frequency control.

The effect feedback node, which may also be referred to as a policy evaluation node, is configured to perform evaluation based on the policy evaluation condition set by the service after the policy pushing and after a specified time interval, so as to obtain a user list meeting the requirement of the evaluation condition, as shown in fig. 8. Fig. 8 is a schematic diagram of a policy evaluation node according to an embodiment of the present invention, which may include a node name, a feedback interval, a node description, a combination rule, and the like. And satisfying the evaluation requirement to obtain a user list, and taking the user list as an initial push list of the next-level policy.

And the push service is used for receiving the strategy list and performing user touch according to the file, channel and release mode of the strategy configuration.

Fig. 9 is a schematic diagram of automatic evaluation and automatic iteration based on policy push effect according to an embodiment of the present invention. As shown in fig. 9, the following steps may be included:

step S1, tag data of each game of the users is generated every day by using big data computing engines such as hadoop and spark (the tags can be used for user grouping and also as policy evaluation).

Step S2, the service may obtain a specific user group based on the user label combination on the front-end page, for example, the back-end may forward the condition configured by the user to SQL, which is delivered to the ad hoc computing engine presto for computation, and generate a user list as a policy list of the primary policy. Fig. 10 is a schematic diagram of a user grouping according to an embodiment of the present invention. As shown in fig. 10, the service wants to circle out a VIP high-level user group lost for 7-90 days, so as to adopt some recall strategies to recall high-value users, which can be obtained by combining the following user tags: the last login date is 20210421, the last login date is 20210713, and the account VIP rating is 5.

Step S3, the service configures the policy tree in the front-end page, that is, performs policy combination, as shown in fig. 5.

And S3.1, configuring basic information of the strategy tree, wherein the user cluster generated in the step 2 can be configured at the selected clustering position.

Step S3.2, a strategy tree is constructed, and three types of node information are mainly required to be configured: and judging nodes, strategy nodes and effect feedback nodes in real time.

Step S3.3, it is determined in real time that the node may or may not be configured, and if configured, the push may be triggered only when the real-time behavior of the user meets the trigger condition, and as shown in fig. 6, the push may be performed only when the real-time upgrade > is equal to 50 for the user.

The real-time judgment node of the embodiment is used for distinguishing a real-time strategy or an offline strategy, and can be divided into the offline strategy and the real-time strategy according to whether a real-time trigger condition is configured. The off-line strategy is to directly send the strategy list to a push service for pushing; the real-time policy can be that a Flink-based real-time policy detection module reads the game log stream in real time to obtain a real-time policy list meeting policy requirements, and the real-time policy list is sent to the push service through kafka.

Step S3.4, the policy node may have to make a corresponding reach policy, mainly make a reach channel content manner of the policy, and the like, and may mainly include configuring policy channels and contents, testing verification, and publishing manners, as shown in fig. 7.

Step S3.5, the effect feedback node may perform evaluation based on the policy evaluation condition set by the service after the policy push is formulated and after a time interval, to obtain a user list meeting the requirement of the evaluation condition, as shown in fig. 8.

If there is a next-level policy node, there must be an evaluation condition for configuring the policy, and if the policy is a final policy, no configuration is required.

For example, assuming that the push content in the policy data a is pushed, the following policy evaluation may be set for the next day, where the policy evaluation condition is: if yes, it indicates that the part of users are logged in after the policy a is reached, so that the part of users can be used as a list of policy data B to push the pushed content of the policy data B for users with a backflow, for example, a backflow welfare gift package; policy evaluation conditions can also be set, and whether login is performed in the past 1 day or not indicates that the part of users still do not log in after the policy data a is reached, so that the policy data C can be used as a list of the policy data C, and push content of the policy data C is pushed for users without backflow, for example, stronger short message push or customer service active intervention maintenance is performed.

Step S3.6, the basic flow established for one round of policy is described above, and the next-level policy may be continuously established according to the above steps S3.3 to S3.5 to form a policy combination relationship, i.e. a policy tree, as shown in fig. 11, where fig. 11 is a schematic diagram of a policy tree configuration according to an embodiment of the present invention, which includes a real-time judgment node, a policy node, and an effect feedback node, for example, the real-time judgment node (node name 1), the policy node (policy name 1), the effect feedback node (node name 1), the real-time judgment node (node name 1), the policy node (policy name 1), the policy node (node name 1), the effect feedback node (node name 2), and the policy node (policy name 2) are sequentially executed, and the real-time judgment node (node name 2) and the policy node (node name 2) may also be sequentially executed, The policy node (policy name 2), the effect feedback node (node name 2), and the policy node (policy name 2), and may also sequentially execute other real-time judgment nodes (node names) and policy nodes (policy names).

Step S4, the front end writes the policy tree related configuration of the service configuration into the policy tree table and the policy table of the database for storage through the interface provided by the back end.

Step S5, the strategy tree enters into the starting state, all the first-level strategies carry out strategy calculation to obtain the list, push the list and generate the list.

And S5.1, dividing into an offline strategy and a real-time strategy according to whether the real-time trigger condition is configured.

And S5.2, directly sending the list file to a pushing service for pushing by using an offline strategy.

And S5.3, reading the game log stream in real time by a real-time strategy detection module based on the Flink to obtain a real-time strategy list meeting the strategy requirement, and sending the real-time strategy list to a pushing service through kafka for pushing.

Fig. 12 is a schematic diagram of a trigger condition of a service definition policy according to an embodiment of the present invention. As shown in fig. 12, in policy making/policy configuration, a group of people may be defined, a configuration real-time trigger condition may be selected, a grouping may be selected, and the configuration trigger condition is that what logical relationship and what value are satisfied for a real-time tag (a value of a certain field of a certain real-time log), and a service may be combined.

And step S5.4, after the push service acquires the strategy list, the push service can carry out push touch according to the channel, the content and the release mode of the service configuration, and meanwhile, the successful push list of each strategy is reserved.

The embodiment can be used for marking the label based on the real behavior of the user to obtain the user label.

In step S6, all lists for which the policy push is successful are uploaded to the HDFS for storage, and stored in a policy evaluation configuration table (Hive table) for policy evaluation.

The user list meeting the requirement of the evaluation condition in this embodiment is established on the list of successful policy push, and if the next-level policy of the policy a is the policy B, the policy a pushes the successful list + the evaluation condition meeting the requirement of the policy a is the user list of which the policy a meets the requirement of the evaluation condition, so as to be used by the next-level policy B.

In step S7, the user has some behavior feedback after being touched, and the feedback condition index of the user' S real behavior can be regarded as a label. The embodiment can deploy a policy evaluation program, detect which policies need effect feedback evaluation every day, associate tag data of users according to a policy push list and policy evaluation configuration, and check which users meet policy evaluation requirements.

And S7.1, performing policy evaluation configuration by the policy evaluation module, and evaluating at a proper time according to the evaluation condition set by the policy to obtain a user list meeting the evaluation condition requirement. As shown in fig. 13, where fig. 13 is a schematic diagram of a policy evaluation selectable triple relationship according to an embodiment of the present invention, in a policy evaluation node, a feedback interval may be configured, and a combination rule is set, which is essentially what logical relationship a certain label satisfies what value.

Step S7.2, the presto association policy push list may be used to associate the tag data of the user (which tags are used may be known from the policy evaluation configuration), and the policy list meeting the requirement is screened.

In step S8, the policy list after evaluation (evaluation user) obtained in step S7 is used as a next-level policy list (initial push list of next-level policies).

The embodiment takes the user list meeting the requirement of the evaluation condition as the initial push list of the next-level policy. FIG. 14 is a schematic diagram of the logic of a policy evaluation in accordance with an embodiment of the present invention. As shown in fig. 14, the policy 1 push list includes a plurality of push objects, each push object has last 3 days (3d) of login days, and the policy evaluation condition may be: the login day number > of the last 3 days is 1 (that is, there is login after push), and assuming that the next-level policy of policy 1 is policy 2, the initial push list of policy 2 is the push target of which the login day number > of the last 3 days in the push list of policy 1 is 1.

And step S9, automatically starting the next-level policy, calculating the policy to obtain the list, pushing the list and generating the list, as shown in step S5.

Step S10, for the next level policy, the above steps S5 to S9 are repeated until the policy is the final policy, i.e., there is no next level policy.

For example, assume that policy a- > B- > C, that is, the policy list obtained after the evaluation of the policy a at the previous stage can be used as the initial push list of the policy B at the next stage, the policy list will become effective, the policy B is generated (step S5), the data corresponding to the policy B is pushed to the generated policy list, a push success list of the policy B can be obtained (step S6), the policy B is subjected to policy evaluation according to the policy evaluation condition with respect to the push success list (step S7), and the policy list meeting the policy evaluation condition is determined as the initial push list of the policy C (step S8).

As shown in fig. 5 or fig. 11, the policy at the bottom of the graph belongs to the final policy, and the policy has no next-level policy, and does not need to perform policy evaluation to obtain a policy list for the next-level policy to use.

Optionally, according to the upper and lower dependency relationships of the policies, each policy may have a field called "strategy _ id _ next" when stored in the database, and if the field is empty, it identifies that the policy has no next-level policy, and is the final policy.

It should be noted that step S3 is to describe how the service configures the policy combination, and step S3.5 is only for the configuration of the effect feedback node, and at this time, the evaluation is not really performed yet. After being configured in step S3, it is written into the database in step S4, step S5 actually pushes, and then the actual evaluation procedure is performed in steps S6 and S7.

Through the technical scheme of the embodiment, automatic evaluation and automatic iterative adjustment of strategies can be performed based on the strategy pushing effect, a multi-round pushing mechanism is established according to feedback of different groups and different users, the response rate, the conversion rate, the payment rate and the like of the users can be effectively improved, and the enterprises can be energized.

The embodiment of the invention also provides a data processing device. It should be noted that the data processing apparatus may be configured to execute the data processing method according to the embodiment of the present invention.

Fig. 15 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention. As shown in fig. 15, the data processing apparatus 150 may include: a first determination unit 151, a second determination unit 152, and a third determination unit 153.

A first determining unit 151, configured to determine first-level policy data in the policy tree, where the first-level policy data is used to indicate that first target data is pushed to a first push object in the first object set, and the first-level policy data includes the first target data.

A second determining unit 152, configured to determine second-level policy data in the policy tree, and then determine a first sub-object set in the first object set, where the second-level policy data is next-level policy data of the first-level policy data, and feedback information of the first push object in the first sub-object set to the first target data meets a preset feedback condition.

A third determining unit 153, configured to determine the first set of sub-objects as a second set of objects, where the second-level policy data is used to indicate that second target data is pushed to a second push object in the second set of objects, and the second-level policy data includes the second target data.

In the data processing apparatus in this embodiment, for a policy tree including multi-level policy data, a sub-object set in a push object set corresponding to the policy data is an object whose feedback information fed back to target data in the policy data meets a preset feedback condition, and the sub-object set can be used to determine an initial push object set of next-level policy data, and so on, thereby implementing automatic adjustment iteration on the push object set corresponding to the policy data, avoiding that all policies are terminated by single push, thereby solving the technical problem that automatic adjustment iteration cannot be performed on a push object pushed to by a policy according to user feedback, and achieving the technical effect of performing automatic adjustment iteration on the push object pushed to the policy according to user feedback.

The embodiment of the invention also provides a computer readable storage medium. The computer readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the computer readable storage medium controls a device to execute the data processing method according to the embodiment of the present invention.

Optionally, in this embodiment, the computer-readable storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.

Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.

Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.

It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data processing method, comprising:

determining first-level policy data in a policy tree, wherein the first-level policy data is used for representing that first target data is pushed to a first pushing object in a first object set, and the first-level policy data comprises the first target data;

determining second-level strategy data in the strategy tree, and determining a first sub-object set in the first object set, wherein the second-level strategy data is next-level strategy data of the first-level strategy data, and feedback information of a first pushing object in the first sub-object set to the first target data meets a preset feedback condition;

determining the first sub-object set as a second object set, wherein the second-level policy data is used for representing that second target data is pushed to a second pushing object in the second object set, and the second-level policy data comprises the second target data.

2. The method of claim 1,

the first-level policy data is used for indicating that the first target data is pushed to a first pushing object in the first object set when the behavior data of the first pushing object in the first object set meets a first trigger condition, and the first-level policy data comprises the first trigger condition;

the second-level policy data is used for indicating that the second target data is pushed to a second pushing object in the second object set when behavior data of the second pushing object in the second object set meets a second trigger condition, and the second-level policy data includes the second trigger condition.

3. The method of claim 1, further comprising:

under the condition that the first-level policy data is the first-level policy data of the policy tree, acquiring at least one target label, wherein each target label is used for representing information of a target field in a target log;

performing logic combination on the at least one target label to obtain a third trigger condition;

determining the pushing object with behavior data meeting the third triggering condition as an original object set;

determining the first set of objects in the original set of objects.

4. The method of claim 3, wherein determining the first set of objects in the original set of objects comprises:

determining the policy attribute of the first-level policy data as a first policy attribute, and determining the original object set as the first pushing object set; or

And if the policy attribute of the first-level policy data is determined to be a second policy attribute, acquiring the first pushed object with behavior data meeting a first trigger condition in the original object set to obtain the first pushed object set, wherein the first-level policy data comprises the first trigger condition.

5. The method of claim 1, wherein the first level policy data comprises the preset feedback condition, and wherein determining second level policy data in the policy tree comprises:

and determining the second-stage strategy data corresponding to the preset feedback condition in the strategy tree.

6. The method of claim 1, wherein the first level policy data comprises a target logical relationship and a target value, the method further comprising:

and if the feedback information meets the target value in the target logic relationship, determining that the feedback information meets the preset feedback condition.

7. The method of claim 1, wherein the first level policy data comprises a target time interval, and wherein determining a first set of sub-objects in the first set of objects comprises:

determining the first set of sub-objects in the first set of objects at the target time interval after pushing the first target data to a first push object in the first set of objects.

8. The method of claim 1, wherein determining a first set of sub-objects in the first set of objects comprises:

determining, based on the first level policy data, to allow determination of the first set of sub-objects in the first set of objects, then determining the first set of sub-objects in the first set of objects.

9. The method according to any one of claims 1 to 8, further comprising:

storing the strategy tree into a target database through a target interface provided by a server;

and calling each level of strategy data of the strategy tree from the target database.

10. The method of any of claims 1-8, wherein the first set of sub-objects comprises objects to which the first target data was successfully pushed.

11. A data processing apparatus, comprising:

a first determining unit, configured to determine first-level policy data in a policy tree, where the first-level policy data is used to represent that first target data is pushed to a first push object in a first object set, and the first-level policy data includes the first target data;

a second determining unit, configured to determine a first sub-object set in the first object set if second-level policy data is determined in the policy tree, where the second-level policy data is next-level policy data of the first-level policy data, and feedback information of a first target data by a first push object in the first sub-object set meets a preset feedback condition;

a third determining unit, configured to determine the first set of sub-objects as a second set of objects, where the second-level policy data is used to indicate that second target data is pushed to a second push object in the second set of objects, and the second-level policy data includes the second target data.

12. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, controls an apparatus in which the computer-readable storage medium is located to carry out the method of any one of claims 1 to 10.

13. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to be executed by the processor to execute the computer program to perform the method of any of claims 1 to 10.