CN117076276A - User task triggering method and device and electronic equipment - Google Patents

Abstract

The embodiment of the application provides a user task triggering method, a device and electronic equipment, wherein the method acquires real-time embedded point data containing user information data in real time by utilizing a real-time calculation task, then processes access embedded point paths of the real-time embedded point data according to preset association rules such as association relation and time relation among the real-time embedded point data and the like according to the user information data of the user, so as to obtain user embedded point paths of all users, and if the user embedded point paths meet the preset business rules, the user data of the user are pushed to a preset seat system, and a target user task is triggered by the preset seat system. Compared with the prior art that the user task is triggered easily by the user buried point information data based on the offline state, the embodiment of the application has the advantages that the real-time buried point data generated by the user is fast reacted, the seat triggering system is triggered to trigger the corresponding target user task, and the use experience of the user can be better and faster ensured.

Description

User task triggering method and device and electronic equipment

Technical Field

The present application relates to the field of big data technologies, and in particular, to a user task triggering method and apparatus, and an electronic device.

Background

In the technical field of big data, the buried point technology is taken as a data acquisition method, and is essentially an event tracking technology, which is used for capturing, processing and distributing certain target event or behavior data and is commonly used for analyzing the event data. The agent system is used as a service system for targeted service according to events or behaviors triggered by users, and a buried point technology is often used for tracking the events triggered by the users so as to trigger corresponding user tasks to serve the users, so that the use experience of the users is improved.

However, the existing point burying technology for the seat system is often based on event data triggered by offline users, processes and classifies behaviors of the users, screens out target users meeting target classification rules, and then triggers user tasks corresponding to the target classification rules for the target users to serve the target users. Because the timeliness of the event data triggered by the offline user is poor, the user task triggered by the event data triggered by the offline user is easy to delay, and the use experience of the user is not guaranteed.

Disclosure of Invention

In view of the above, the embodiment of the application provides a user task triggering method, a device and electronic equipment, so as to solve the problem of poor timeliness of triggering user tasks by an agent system realized based on a buried point technology in the prior art.

In a first aspect, the present application provides a method for triggering a user task, where the method includes:

real-time buried point data are obtained in real time by utilizing a real-time computing task, wherein the real-time buried point data at least comprise user information data of each user;

processing the access buried point path according to the user information data of the users and a preset association rule to obtain the user buried point path of each user, wherein the preset association rule comprises: the association relation and time relation among the real-time buried data;

and if the user buried point path meets the preset business rule, pushing the user information data of the target user to a preset seat system so that the preset seat system triggers a target user task, wherein the target user is a user of which the user buried point data meets the preset business rule.

With reference to the first aspect, in a second possible embodiment, the real-time buried data further includes: user behavior data generated by each user, wherein the user behavior data consists of a plurality of buried point objects, the processing of the access buried point path is performed on the real-time buried point data according to a preset association rule so as to obtain a user buried point path of each user, and the processing comprises the following steps:

Analyzing the real-time buried point data of each user to obtain each buried point object in the real-time buried point data of each user, and storing each buried point object into a preset KV database according to a preset storage rule;

taking the ID of the user as a partition key value, and carrying out partition processing on each buried point object in the preset KV database according to a preset association rule so as to store the buried point object of each user into different buried point object lists;

and generating a user buried point path of each user according to a preset time interval based on the buried point objects in the buried point object list of each user.

With reference to the second possible embodiment of the first aspect, in a third possible embodiment, the generating, based on the embedded point objects in the embedded point object list of each user, a user embedded point path of each user according to a preset time interval includes:

and if the embedded point objects in the embedded point object list of the user are not updated within the preset time interval, generating a user embedded point path of the user based on each embedded point object in the embedded point object list of the user.

With reference to the first aspect, in a fourth possible embodiment, the method further includes:

Screening the user buried point path based on a preset business rule screening operator, and screening out target users meeting the preset business rule;

and pushing the user information data of the target user to the preset seat system according to the preset user task triggering time.

With reference to the first or fourth possible embodiment of the first aspect, in a fifth possible embodiment, the pushing the user information data of the target user to a preset seat system includes:

according to a preset interface document, assembling a user buried point path of the target user and user information data of the target user to obtain assembled buried point data;

pushing the assembled buried point data to the preset seat system so that the preset seat system triggers a target user task according to the assembled buried point data.

In a second aspect, the present application provides a user task trigger apparatus, the apparatus comprising:

the acquisition module is used for acquiring real-time buried point data in real time by utilizing a real-time calculation task, wherein the real-time buried point data at least comprises user information data of each user;

the user buried point path processing module is used for processing the access buried point path of the real-time buried point data according to the user information data of the user and preset association rules to obtain the user buried point path of each user, wherein the preset association rules comprise: the association relation and time relation among the real-time buried data;

And the data pushing module is used for pushing the user information data of the target user to a preset seat system if the user embedded point path meets the preset service rule so that the preset seat system triggers the target user task, wherein the target user is a user for which the user embedded point data meets the preset service rule.

With reference to the second aspect, in a second possible embodiment, the real-time buried data further includes: user behavior data generated by each user, wherein the user behavior data consists of a plurality of embedded point objects, and the user embedded point path processing module is specifically used for:

analyzing the real-time buried point data of each user to obtain each buried point object in the real-time buried point data of each user, and storing each buried point object into a preset KV database according to a preset storage rule;

taking the ID of the user as a partition key value, and carrying out partition processing on each buried point object in the preset KV database according to a preset association rule so as to store the buried point object of each user into different buried point object lists;

generating a user buried point path of each user according to a preset time interval based on the buried point objects in the buried point object list of each user;

The generating a user buried point path of each user according to a preset time interval based on the buried point objects in the buried point object list of each user includes:

and if the embedded point objects in the embedded point object list of the user are not updated within the preset time interval, generating a user embedded point path of the user based on each embedded point object in the embedded point object list of the user.

With reference to the second aspect, in a third possible embodiment, the apparatus further includes:

the screening module is used for screening the user buried point paths based on a preset business rule screening operator and screening out target users meeting the preset business rule;

the data pushing module is specifically configured to push the user information data of the target user to the preset seat system according to the trigger time of the preset user task.

With reference to the second aspect, in a fourth possible embodiment, the data pushing module is further configured to:

according to a preset interface document, assembling a user buried point path of the target user and user information data of the target user to obtain assembled buried point data;

pushing the assembled buried point data to the preset seat system so that the preset seat system triggers a target user task according to the assembled buried point data.

In a third aspect, the present application provides an electronic device, including:

a processor; and

a memory in which a program is stored,

wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the user task triggering method of the first aspect.

In a fourth aspect, the present application provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the user task triggering method of the first aspect.

The application has the beneficial effects that:

the embodiment of the application provides a user task triggering method, a device and electronic equipment, wherein the method acquires real-time embedded point data containing user information data in real time by utilizing a real-time calculation task, then processes access embedded point paths of the real-time embedded point data according to preset association rules such as association relation and time relation among the real-time embedded point data and the like according to the user information data of the user, so as to obtain user embedded point paths of all users, and if the user embedded point paths meet the preset business rules, the user data of the user are pushed to a preset seat system, and a target user task is triggered by the preset seat system. Compared with the prior art that the user task is triggered easily by the user buried point information data based on the offline state, the embodiment of the application has the advantages that the real-time buried point data generated by the user is fast reacted, the seat triggering system is triggered to trigger the corresponding target user task, and the use experience of the user can be better and faster ensured.

Drawings

Further details, features and advantages of the application are disclosed in the following description of exemplary embodiments with reference to the following drawings, in which:

FIG. 1 is a schematic diagram of a possible flow of a user task triggering method according to an embodiment of the present application;

FIG. 2 is a schematic diagram of another possible flow of a user task triggering method according to an embodiment of the present application;

FIG. 3 is a schematic diagram of another possible flow of a user task triggering method according to an embodiment of the present application;

fig. 4 is a schematic diagram of a possible application scenario of a user task triggering method according to an embodiment of the present application;

FIG. 5 is a schematic diagram of a possible logic structure of a user task trigger apparatus according to an embodiment of the present application;

fig. 6 is a schematic diagram of a possible logic structure of an electronic device according to an embodiment of the present application.

Detailed Description

Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While the application is susceptible of embodiment in the drawings, it is to be understood that the application may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided to provide a more thorough and complete understanding of the application. It should be understood that the drawings and embodiments of the application are for illustration purposes only and are not intended to limit the scope of the present application.

It should be understood that the various steps recited in the method embodiments of the present application may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the application is not limited in this respect.

The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below. It should be noted that the terms "first," "second," and the like herein are merely used for distinguishing between different devices, modules, or units and not for limiting the order or interdependence of the functions performed by such devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those skilled in the art will appreciate that "one or more" is intended to be construed as "one or more" unless the context clearly indicates otherwise.

In order to facilitate understanding of the user task triggering method, the device and the electronic equipment provided by the application, technical terms related to the method, the device and the electronic equipment are explained below:

burying: event Tracking (Event Tracking) techniques, related techniques for capturing, processing, and transmitting events for a particular user behavior or user trigger by a pointer, and implementations thereof.

Key nodes: based on behavior buried point data of a user, the mined page access behavior links which are high in user coverage rate and easy to cut into a session are used for realizing accurate positioning of the user triggering the access links, and the seat system adopts corresponding user tasks to efficiently serve the user according to positioning information. Wherein the positioning information is a node triggered by the positioning user, and is not the physical positioning of the user.

Seat system: a user service system is similar to a customer service system and is used for serving users, and particularly presumes possible needs of the users according to events triggered by the users through a buried point technology, and then provides corresponding and timely services for the users through the seat system.

Kafka: a high throughput distributed publish-subscribe messaging system for processing all action flow data of a user in an application or web site. The action stream data of the user includes: web browsing, searching, interaction with other users, and so forth.

Flink: a computing framework and distributed processing engine for stateful computation of unbounded data and bounded data. The Flink real-time computing task is designed in various common data clusters, and particularly, the computing is executed at the memory execution speed and any scale.

ListState: a data state on a data stream. For example, assuming that an input data stream is stored in a split Key manner according to id as a Key value, so as to form a KeyedStream, all data with id 1 in the KeyedStream share a data state, and all data with id 1 can access or update the data state, so that each Key value Key corresponds to an own state.

MapState: for storing one state type of key-value pairs and can be used in subsequent computations.

The Filter operator is filtering and screening, and outputs all result sets meeting judgment conditions.

Flap: a line of the data stream is logically or regularly split into 0 or more lines of outputs.

Keyed Process: for processing the data stream after the packet. Each time data enters an operator, internal logic processing is triggered and a timer function is provided.

As described in the background art, the existing point burying technology for the agent system generally performs processing classification on the behavior of the user based on the offline data table, screens out the user conforming to the service rule, pushes the user conforming to the service rule to the agent system, and triggers the corresponding user task by the agent system, which tends to cause the user task triggered by the agent system to have a certain timeliness, thereby affecting the user experience.

In view of this, in order to ensure timeliness of triggering user tasks by the agent system, the application provides a user task triggering method, a device and an electronic device, wherein the user task triggering method is applied to any electronic device with a user task triggering function, including but not limited to: personal mobile terminals, computers, servers, and the like.

In a first aspect, as shown in fig. 1, a method for triggering a user task according to an embodiment of the present application includes the following steps:

s11, acquiring real-time buried point data in real time by utilizing a real-time calculation task, wherein the real-time buried point data at least comprises user information data of each user.

S12, accessing buried point path processing is carried out on the real-time buried point data according to user information data of users and preset association rules so as to obtain user buried point paths of all the users;

the preset association rule comprises the following steps: the association relationship and time relationship between the real-time buried data.

S13, if the user buried point path meets a preset service rule, pushing user information data of the target user to a preset seat system so that the preset seat system triggers a target user task;

the target user is a user whose embedded point data meets a preset business rule.

According to the embodiment of the application, the real-time buried point data containing the user information data is obtained in real time by utilizing the real-time calculation task, and access buried point path processing is performed on the real-time buried point data according to the user information data of the users and preset association rules such as association relation and time relation among the real-time buried point data, so that the user buried point paths of all the users are obtained. If the user buried point path meets the preset business rule, pushing the user data of the user to a preset seat system, and triggering a target user task by the preset seat system. Therefore, compared with the prior art that the target user task is triggered based on the offline user embedded point information data, the embodiment of the application is based on the real-time embedded point data generated by the user to quickly respond, and the triggering agent system triggers the corresponding target user task, so that services can be better and faster provided for the user, and the use experience of the user is effectively ensured.

The above steps S11 to S13 will be described in detail below:

in executing step S11, the real-time computing task refers to a computing task for running in the distributed processing computing framework. Common distributed processing computing frameworks include, among others, a fly framework, a Spark Streaming framework, a Storm framework, and the like. For the sake of brevity in description, the following real-time computing tasks are described by taking the Flink real-time computing task as an example, and the real-time computing tasks under other frameworks only need to be automatically expanded based on the Flink real-time computing task provided by the application, so that the application is not repeated one by one.

When executing step S11, the real-time computing task is utilized to acquire real-time buried point data in real time, which means that the real-time computing task captures and processes the behavior data generated by the user in real time. Specifically, when step S11 is performed, the real-time embedded point information of the client may be subscribed to by Kafka to obtain the real-time embedded point data generated by the user. The real-time embedded point information at least comprises basic information of a user and behavior data of the user. Wherein the basic information of the user includes user information of the user side, such as a user name (or user ID) of the user. The behavior data of the user includes: which pages the user has accessed, the order in which the user accessed the pages, the time the user accessed the pages, etc.

In addition, in the embodiment of the present application, in the process of executing step S11, a Mysql database at the back end of the client may be connected through JDBC (Java DataBase Connectivity, java database connection) service, where the Mysql database stores the friend relationship of the client user, and the friend relationship of each service system is refreshed regularly, so that when step S13 is executed subsequently, relevant reminding information is pushed to the friends of the user.

Illustratively, such as when performing step S11, the real-time computing task receives a message from user A that 4 of the clients 1, 2, 3, and 4 are sending to user A in the buddy list of user A. The user A has 10 friends in total, and the 10 friends comprise a client 1 and a client 2. At this time, if the behavior data generated by the client 1 or the client 2 indicates that the client 1 or the client 2 has a risk of accessing the unknown link, the preset seat system triggers a risk early warning task by pushing the user information of the user a and the user information of the client 1 or the client 2 to the preset seat system, so as to send a risk early warning prompt to the user a, and inform the user a of a potential risk of suffering from telecommunication fraud in advance.

When step S12 is executed, the information data of the user may be a user ID, where the user ID may be identity information of the user, or may be an account number opened when the user uses the client, and the specific form of the present application is not limited. Because the number of users of one client is huge, when executing step S12, the user ID is taken as the basis of the data splitting process, and the behavior data generated by the same user ID is gathered and combined into a buried point path.

Specifically, as shown in fig. 2, in one possible embodiment, the real-time embedded point data of the user includes user behavior data, where the user behavior data is composed of a plurality of embedded point objects, and the step S12 may further be implemented by:

s121, analyzing the real-time buried point data of each user to obtain each buried point object in the real-time buried point data of each user, and storing each buried point object into a preset KV database according to a preset storage rule;

the preset KV database is a preset Key-Value Key Value database.

S122, taking the ID of the user as a partition key value, and carrying out partition processing on each buried point object in a preset KV database according to a preset association rule so as to store the buried point objects of each user into different buried point object lists;

S123, generating a user buried point path of each user according to a preset time interval based on the buried point objects in the buried point object list of each user.

In step S121, the real-time buried point data subscribed from Kafka may be analyzed by using a flap operator in a flap frame to generate a normalized buried point object. By way of example, assume that there are three users: after the buried point information data of the user A, the user B and the user C enter the Kafka message queue, a native flap operator of a task is calculated in real time through a fly, the buried point data are converted into buried point objects from a long character string data, and then the buried point objects are stored in a preset KV database according to a storage rule of a preset Key-Value Key Value mapping relation. In the embodiment of the application, the embedded point object refers to character string data with physical meaning in the embedded point data, and if the behavior data in the embedded point data of the user includes a page accessed by the user and time when the user accesses the page, the page accessed by the user and the time when the user accesses the page, and the time when the user accesses the page are taken as one embedded point object respectively.

Specifically, a Key is used as an index basis, and the embedded point object is stored in a Value corresponding to the Key Value. For example, if the user behavior data generated by the user a includes: page1 accessed by the user, the user accesses Time Time1 corresponding to Page1, and user behavior data of user A is identified by ID of user A: 0001A is used as a Key index basis and is respectively stored in a Value corresponding to the Key, namely Key 0001A-Value Page1 and Key 1.

In another possible embodiment, the user-generated embedded point objects belonging to the same embedded point object type may be stored in the same data table by taking the type of the embedded point object as a Key. For example, each embedded point object belonging to the access Page may be stored in the same data table according to which Page is used as the index base of Key, and different embedded point objects correspond to different Value values.

When executing step S122, the user ID may be used as a partition key value, and the embedded point objects belonging to the same user ID are subjected to partition processing, that is, the embedded point objects are respectively stored into different partitions according to a preset association rule. The method may be such that each embedded point object belonging to the same user ID is stored in the same embedded point object list in the order of time from early to late according to the generation time of the embedded point object as a ranking criterion. Alternatively, the embedded objects may be sorted according to importance levels of the embedded objects, and the embedded objects belonging to the same user ID may be stored in the same embedded object list in order of importance levels from low to high.

For example, assuming that there are a user a, a user B, and a user C, after the user a, the user B, and the user C start to have embedded point information enter the Kafka message queue, the three users are stored into three different embedded point object lists ListState through a Keyed Process operation: litstate-A, litstate-B, litstate-C.

When executing step S123, the timer is used to segment the buried point objects in each buried point object list based on different buried point object lists, and each buried point object list only continuously receives the buried point object data generated by the user of the user ID corresponding to the buried point object list according to the time interval set by the timer.

For example, taking the foregoing list of embedded point objects of the user a, the user B and the user C as an example, the embedded point information of the subsequent user a, the user B and the user C will only enter the corresponding ListState, that is, each ListState only retains the information of one user.

In one possible embodiment, if the embedded point object in the embedded point object list of a certain user is not updated within a preset time interval, a user embedded point path of the user is generated based on each embedded point object in the embedded point object list of the user.

Since the client is used by the user for many times rather than 24 hours, the client is used intermittently, if the preset time interval is set too wide, the embedded point object list of the user is always in an updated state, so in the embodiment of the application, the preset time interval should be designed according to historical user access records or according to practical experience. In order to ensure the user experience, a setting of 5 min/time interval may be recommended.

Specifically, in the embodiment of the present application, the Timer refers to a Timer method in the link calculation framework, and the embedded point object generated by the user is recorded by modifying the logic flow in the Timer into a logic flow according to a preset time interval. Exemplary:

for example, the user a accesses different pages of the client at different time points, and the generated records include the following:

t1 user A has accessed Page A at 8:01;

t2 user A has accessed page B at 8:02;

t3 user A has accessed Page A at 8:03;

t4. user a has accessed page C at 8:05;

t5 user A has accessed page D at 8:18;

t6. user a has accessed page F at 8:19;

t7. user a has accessed page a at 8:20;

t8. user A accessed page F at 8:29.

It can be seen that, in the process of T1 to T4, each behavior of the user corresponds to one buried object, where the time interval between each buried object is less than 5min, but the time interval between each buried object is more than 5min between T4 to T5. At this time, waiting is performed at a preset time interval in T4, and if the user a is still generating behavior data within the preset time interval, a new buried point object is generated, and the buried point object list is updated. However, if no new buried object is generated more than 5 minutes after T4, a user buried path a is generated with the buried object between T1 and T4. Similarly, the buried object between T5 and T7 generates a user buried path B.

On the basis, the preset time interval is taken as a cutting evidence of the user buried point path, when the user buried point path is received once, for example, after the user buried point path A is received, the new message sent by the user A is not received by 8:10, at the moment, the user buried point path A is pushed to a preset seat system, or whether user information of the user A and information of the user buried point path A are pushed to the preset seat system is further judged based on whether the user buried point path A meets a preset service rule or not.

In the embodiment of the application, if the embedded point object in the embedded point object list of one user is not updated within the preset time interval, the user is indicated to stop the use of the client in the time interval, and no new behavior data is naturally generated, and no embedded point object is generated. In order to avoid the influence of too long time on user experience caused by not triggering user tasks, in the embodiment of the application, when the embedded point objects are not updated within a preset time interval, a user embedded point path ListState is generated based on each embedded point object in the current embedded point object list according to the time relationship between the embedded point objects or the association relationship between the embedded point objects. In the embodiment of the application, the buried point paths which are sequenced in time sequence can completely show which actions are specifically generated by the user during the use of the client.

According to the embodiment of the application, the real-time buried point data of different users are analyzed to obtain the buried point objects contained in the behavior data of the different users, then the buried point objects belonging to the same user are stored into the same data partition according to the user ID, and then the buried point objects in each data partition are sorted according to the association relation or the time relation among the buried point objects to obtain a complete behavior chain.

If the embedded point objects in the user embedded point object list are not updated for a long time, the user is indicated to leave the client, at this time, a complete embedded point path is generated according to each ordered embedded point object in the user object list, and further, the user can know what behaviors are specifically generated in the time when using the client, so that the next execution step S13 determines whether to provide corresponding services for the user based on the behaviors of the user, and further, timeliness of the seat system is effectively ensured.

As an implementation manner, the buried data of the user may be stored separately from the service data, where the buried data is stored in a preset KV database in the manner of the steps S121 and S122, and the service data is stored in the Mysql database. The service data in Mysql data is stored in a MapState state type. Thus, the embedded point data and the business data are not mutually interfered, and the subsequent generation of the user embedded point path based on the embedded point data is facilitated.

Illustratively, for example, service 1 is a fruit sales service, the service data store of which is stored in the Mysql database in the Ms1 state type, and in addition to this, all users (Q, W, E, R) using the fruit sales service are also stored in Ms1 of the Mysql database, where Q represents user Q, W represents user W, and so on, Q, W, E, R are visible as the user's ID. Similarly, if service 2 is a vegetable sales service, the service data store of the vegetable sales service is stored in the Mysql database in the Ms2 state type, wherein all users (a, S, F, G) using the vegetable sales service are also stored in Ms2, and wherein a, S, F, G can be used as user IDs. Wherein there is no intersection between the traffic data between the different traffic lines.

When executing step S11, the service data stored in the Mysql database at the back end of the client may also be obtained by means of a real-time computing task, so as to obtain which services the user triggers. For example, in the whole access process of the users T1 to T8, the user a accesses the page a, the page B, and the page C (in which, the embedded point object is generated based on the page: pageA, pageB, pageC) in the process of T1 to T4, if the embedded point message of the user a is not received for 5min, the embedded point object of the user a is generated into the embedded point path according to the time sequence at 8 points and 10 points: pageA- > PageB- > PageC. And then screening the buried point path to judge whether the buried point path of the user meets a preset service rule.

In one possible embodiment, as shown in fig. 3, the buried point path of the user may be screened by:

s21, screening the user buried point paths of the users based on a preset business rule screening operator, and screening out target users meeting the preset business rule;

s22, pushing user information data of the target user to the preset seat system according to the preset user task trigger time.

As can be seen from the above description of the buried object list, only one user information is stored in each ListState, and it can be understood that each ListState is just a Key: the Value data table, key is the unique identification ID generated by the user when the app is registered, and Value is the buried point path of the user. In one possible embodiment, the preset business rule may be: if one or a plurality of target embedded point objects exist in the embedded point objects in the embedded point path Value of the user, the embedded point path of the user is indicated to meet the preset business rule.

For example, if there are 5 client accesses by the client today, specifically user a, user B, user C, user D, user E. When step S11 is executed, a total of 5 clients using service 1 are respectively: user A, user B, user C, user E and user F match through the key of ListState and the ID obtained from Mysql, and the hit is pushed to service line 1, so that user distribution is realized, and users belonging to different services are distinguished.

In another possible embodiment, the preset business rule may be: if the embedded point object in the embedded point path Value of the user does not contain one or more target embedded point objects, the embedded point path of the user is indicated to meet the preset business rule.

In a specific example, if the target embedded point object is PageC, matching is performed through a key of ListSate and an ID obtained from Myql, and if the embedded point object of the user A includes PageA and PageB, but does not include the target embedded point object PageC, it indicates that the user A does not access the page C, and at this time, the user A can be considered to satisfy a preset business rule.

When step S21 is executed, the preset business rule screening operator refers to an operation function taking a preset business rule as a screening condition. The specific preset service screening operators are set according to actual service requirements, and the generated service rule screening operators are different according to different service users and different service requirements. In the embodiment of the application, the preset business rule screening operator can be obtained by logic modification based on a Filter operator under the Flink framework. After the step S21 is executed to screen out the target users satisfying the preset business rules, the target users are marked and saved.

When executing step S22, the preset user task trigger time is set according to the actual service requirement, and the obtained user task trigger methods are different for different service users and different service requirements. The task trigger time of the user can be set based on the emergency degree of the service, wherein the task trigger time duration of the user and the emergency degree of the service are in positive correlation, and the more the service is emergency, the shorter the corresponding task trigger time of the user is, the less the service is emergency, and the longer the corresponding task trigger time of the user is.

According to the embodiment of the application, the preset service rule screening operator is designed according to the actual service requirement, so that the buried point path of the user is split, the target user meeting the preset service rule is screened out, the data volume required to be processed by the preset seat system is reduced, the processing efficiency of the preset seat system is ensured, and the processing timeliness of the preset seat system is ensured.

In another possible embodiment, when step S13 is performed or step S22 is performed, pushing the user information data of the target user to the preset seat system includes:

s31, assembling the user buried point path of the target user and the user information data of the target user according to a preset interface document to obtain assembled buried point data;

And S32, pushing the assembled embedded point data to a preset seat system so that the preset seat system triggers a target user task according to the assembled embedded point data.

In step S31, the preset interface document essentially is a configuration file set according to the connection relationship between each service and the back-end database, where the configuration file defines the communication interface between each front-end service system and the back-end database. When executing step S31, the communication interface between the front-end service system and the back-end database may be obtained by reading the preset interface document, and then the user buried point path of the target user and the user information data of the target user are assembled to obtain an assembled buried point data. Specifically, the user buried point path, the preset business rule hit by the user and the user information data of the user are combined into a complete data. For example, the user ID of the user a, the preset service rule (assumed to be service rule 1) that the user a conforms to, and the buried point path (PageA- > PageB- > PageC) of the user are spliced into the following data in an end-to-end manner:

user A_Business rule 1_PageA- > PageB- > PageC

And then pushing the piece of data to a preset seat system in a Post request mode. In one possible embodiment, information such as the ID of the user, the rule hit by the user, and the like may be combined into a json dictionary format, and then pushed to the preset seat system through a Post request.

In a possible embodiment, when executing step S32, the assembled data may be pushed to the service system corresponding to the service rule, then the service system triggers related settings, the service system notifies or issues the related settings to the preset seat system in a message or task mode, and after the preset seat system receives the related settings, the preset seat system triggers the target user task according to the received task configuration information.

For example, when the preset agent system receives that the user a accesses the fruit sales service system, the latest fruit preferential short message is pushed to the user a, so as to provide the price information reference for the user a, and further help the user a make a decision when purchasing the fruit.

According to the embodiment of the application, the buried point information data generated by the user is obtained in real time through a real-time calculation task, the buried point information data is cached in a preset KV database to obtain a buried point object list of the user, whether the user leaves or not is judged according to a preset time interval by means of a timer, and if the user leaves, a user buried point path of the user is generated according to the buried point object list of the user and the association rule among buried point objects.

And combining the designed preset business rules to judge whether the user meets the preset business rules or not, and judging whether the user needs further service or not. If the user information is met, the user information is pushed to a preset agent system, the agent system triggers corresponding target user tasks to the user, so that the user notification is realized at the moment of arrival with high throughput and low time delay, the user task triggering delay can be considered, and the use experience of the user is effectively ensured.

In order to facilitate understanding of the user task triggering method provided by the present application, a possible process flow diagram as shown in fig. 4 may be combined, and the triggering of the user task may be completed by the following flow:

1) The real-time buried point data of the APP is subscribed in real time through Kafka, basic information of a user and behavior operation of the user are extracted, and then a buried point data stream is constructed through Kafka, so that a buried point character string (namely Source in the figure) is generated. The specific implementation process may refer to the description of step S11, which is not repeated here.

2) And splitting the buried character string into a plurality of rows of data according to logic or rules by a filtering conversion operator in the flap to obtain a plurality of rows of buried objects. And inputting the buried point object obtained by conversion to different Keyed processes. The specific implementation process may refer to the description of step S11, which is not repeated here.

3) The Keyed Process sorts the input buried point objects into a buried point path list arranged in time sequence by means of a timer. The specific implementation process may refer to the descriptions related to the foregoing steps S121 to S123, which are not repeated here.

4) Combining the input buried point path list with the association relation of the user system by using the Keyed Process, determining the user to which each buried point path list obtained in the third step belongs, and pushing the service data to different Fliter preset service rule screening operators. Screening by Fliter, screening out target users, and marking and storing. And then asynchronously pushing the screened target users to a service system by means of an asynchronous operator, and then solving the user requirements. The specific implementation process may refer to the descriptions of steps S21 to S32, and will not be repeated here.

In a second aspect, as shown in fig. 5, the present application provides a user task trigger apparatus, the apparatus 500 comprising:

the acquisition module 501 is configured to acquire real-time buried point data in real time by using a real-time computing task, where the real-time buried point data at least includes user information data of each user;

the user buried point path processing module 502 is configured to access the buried point path processing to the real-time buried point data according to user information data of the user and preset association rules to obtain a user buried point path of each user, where the preset association rules include: the association relationship and time relationship between the real-time buried data;

The data pushing module 503 is configured to push user information data of the target user to the preset seat system if the user embedded point path meets the preset service rule, so that the preset seat system triggers the target user task, where the target user is a user whose user embedded point data meets the preset service rule.

With reference to the second aspect, in a second possible embodiment, the real-time buried data further includes: user behavior data generated by each user, a user buried point path processing module 502, specifically configured to:

analyzing the real-time buried point data of each user to obtain each buried point object in the real-time buried point data of each user, and storing each buried point object into a preset KV database according to a preset storage rule;

based on the ID of the user as a partition key value, partitioning the buried point objects in a preset KV database according to a preset association rule so as to store the buried point objects of the users into different buried point object lists;

generating a user buried point path of each user according to a preset time interval based on the buried point objects in the buried point object list of each user;

based on the buried point objects in the buried point object list of each user, generating a user buried point path of each user according to a preset time interval, including:

If the embedded point objects in the embedded point object list of the user are not updated within the preset time interval, generating a user embedded point path of the user based on each embedded point object in the embedded point object list of the user.

With reference to the second aspect, in a third possible embodiment, the apparatus 500 further includes:

the screening module 504 is configured to screen the user buried point path based on a preset service rule screening operator, and screen out a target user that meets the preset service rule;

the data pushing module 503 is specifically configured to push, according to a preset user task trigger time, user information data of the target user to a preset seat system.

With reference to the second aspect, in a fourth possible embodiment, the data pushing module 503 is further configured to:

according to a preset interface document, assembling a user buried point path of a target user and user information data of the target user to obtain assembled buried point data;

pushing the assembled embedded point data to a preset seat system so that the preset seat system triggers a target user task according to the assembled embedded point data.

The names of messages or information interacted between the devices in the embodiments of the present application are for illustrative purposes only and are not intended to limit the scope of such messages or information.

The exemplary embodiment of the application also provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores a computer program executable by the at least one processor for causing the electronic device to perform a method according to an embodiment of the application when executed by the at least one processor.

The exemplary embodiments of the present application also provide a non-transitory computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is for causing the computer to perform a method according to an embodiment of the present application.

The exemplary embodiments of the application also provide a computer program product comprising a computer program, wherein the computer program, when being executed by a processor of a computer, is for causing the computer to perform a method according to an embodiment of the application.

Referring to fig. 6, a block diagram of an electronic device 600 that may be a server or a client of the present application will now be described, which is an example of a hardware device that may be applied to aspects of the present application. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.

As shown in fig. 6, the electronic device 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

A number of components in the electronic device 600 are connected to the I/O interface 605, including: an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 may be any type of device capable of inputting information to the electronic device 600, and the input unit 606 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device. The output unit 607 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage unit 604 may include, but is not limited to, magnetic disks, optical disks. The communication unit 609 allows the electronic device 600 to exchange information/data with other devices through a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above. For example, in some embodiments, the foregoing user task triggering method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 600 via the ROM 602 and/or the communication unit 609. In some embodiments, the computing unit 601 may be configured to perform the aforementioned user task triggering method by any other suitable means (e.g., by means of firmware).

Program code for carrying out methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

Claims (11)

1. A method for triggering a user task, the method comprising:

real-time buried point data are obtained in real time by utilizing a real-time computing task, wherein the real-time buried point data at least comprise user information data of each user;

processing the access buried point path according to the user information data of the users and a preset association rule to obtain the user buried point path of each user, wherein the preset association rule comprises: the association relation and time relation among the real-time buried data;

and if the user embedded point path meets the preset business rule, pushing user information data of a target user to a preset seat system so that the preset seat system triggers a target user task, wherein the target user is a user of which the user embedded point data meets the preset business rule.

2. The method of claim 1, wherein the real-time buried data further comprises: user behavior data generated by each user, wherein the user behavior data consists of a plurality of buried point objects, the processing of the access buried point path is performed on the real-time buried point data according to a preset association rule so as to obtain a user buried point path of each user, and the processing comprises the following steps:

analyzing the real-time buried point data of each user to obtain each buried point object in the real-time buried point data of each user, and storing each buried point object into a preset KV database according to a preset storage rule;

taking the ID of the user as a partition key value, and carrying out partition processing on each buried point object in the preset KV database according to a preset association rule so as to store the buried point object of each user into different buried point object lists;

and generating a user buried point path of each user according to a preset time interval based on the buried point objects in the buried point object list of each user.

3. The method according to claim 2, wherein the generating a user buried point path for each user according to a preset time interval based on the buried point objects in the buried point object list for each user includes:

And if the embedded point objects in the embedded point object list of the user are not updated within the preset time interval, generating a user embedded point path of the user based on each embedded point object in the embedded point object list of the user.

4. The method according to claim 1, wherein the method further comprises:

screening the user buried point path based on a preset business rule screening operator, and screening out target users meeting the preset business rule;

and pushing the user information data of the target user to the preset seat system according to the preset user task triggering time.

5. The method according to claim 1 or 4, wherein pushing the user information data of the target user to a preset seating system comprises:

according to a preset interface document, assembling a user buried point path of the target user and user information data of the target user to obtain assembled buried point data;

pushing the assembled buried point data to the preset seat system so that the preset seat system triggers a target user task according to the assembled buried point data.

6. A user task trigger apparatus, the apparatus comprising:

The acquisition module is used for acquiring real-time buried point data in real time by utilizing a real-time calculation task, wherein the real-time buried point data at least comprises user information data of each user;

the user buried point path processing module is used for processing the access buried point path of the real-time buried point data according to the user information data of the user and preset association rules to obtain the user buried point path of each user, wherein the preset association rules comprise: the association relation and time relation among the real-time buried data;

and the data pushing module is used for pushing the user information data of the target user to a preset seat system if the user embedded point path meets the preset service rule so that the preset seat system triggers the target user task, wherein the target user is a user for which the user embedded point data meets the preset service rule.

7. The apparatus of claim 6, wherein the real-time buried data further comprises: user behavior data generated by each user, wherein the user behavior data consists of a plurality of embedded point objects, and the user embedded point path processing module is specifically used for:

analyzing the real-time buried point data of each user to obtain each buried point object in the real-time buried point data of each user, and storing each buried point object into a preset KV database according to a preset storage rule;

Taking the ID of the user as a partition key value, and carrying out partition processing on each buried point object in the preset KV database according to a preset association rule so as to store the buried point object of each user into different buried point object lists;

generating a user buried point path of each user according to a preset time interval based on the buried point objects in the buried point object list of each user;

the generating a user buried point path of each user according to a preset time interval based on the buried point objects in the buried point object list of each user includes:

and if the embedded point objects in the embedded point object list of the user are not updated within the preset time interval, generating a user embedded point path of the user based on each embedded point object in the embedded point object list of the user.

8. The apparatus of claim 6, wherein the apparatus further comprises:

the screening module is used for screening the user buried point paths based on a preset business rule screening operator and screening out target users meeting the preset business rule;

the data pushing module is specifically configured to push, according to a preset user task trigger time, user information data of the target user to the preset seat system.

9. The apparatus of claim 6 or 8, wherein the data pushing module is further configured to:

according to a preset interface document, assembling a user buried point path of the target user and user information data of the target user to obtain assembled buried point data;

pushing the assembled buried point data to the preset seat system so that the preset seat system triggers a target user task according to the assembled buried point data.

10. An electronic device, the electronic device comprising:

a processor; and

a memory in which a program is stored,

wherein the program comprises instructions which, when executed by the processor, cause the processor to perform the method according to any of claims 1-5.

11. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-5.