CN114896263A

CN114896263A - Method, system, electronic device and storage medium for determining target crowd

Info

Publication number: CN114896263A
Application number: CN202210313796.0A
Authority: CN
Inventors: 李可威
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2022-03-28
Filing date: 2022-03-28
Publication date: 2022-08-12

Abstract

The disclosure provides a method, a system, an electronic device and a storage medium for determining a target crowd, and relates to the technical field of big data, in particular to the technical field of user portrait. The specific implementation scheme is as follows: determining a target crowd circling requirement; generating a corresponding search statement according to the target crowd selection requirement; executing the search statement to obtain target crowd data meeting the target crowd selection requirement; and saving the target crowd data. The present disclosure enables efficient determination of target demographic data.

Description

Method, system, electronic device and storage medium for determining target crowd

Technical Field

The present disclosure relates to the field of big data technology, and more particularly, to the field of user portrait technology.

Background

With the continuous development of big data technology, big data-based applications and services are also expanded in various scenes. The user portrait service provides complete and rapid user group accurate description and label service for each service party based on a big data platform so as to support the requirements of accurate marketing and the like.

Information PUSH (PUSH) based on target population is a main application of fine operation, and has wide application scenes in scenes such as information application, media information popularization and the like. How to determine the target crowd data efficiently becomes a technical problem to be solved.

Disclosure of Invention

The present disclosure provides a method, system, electronic device and storage medium for determining a target population.

According to an aspect of the present disclosure, there is provided a method of determining a target population, comprising:

determining a target crowd circling requirement;

generating a corresponding search statement according to the target crowd selection requirement;

executing the search statement to obtain target crowd data meeting the target crowd circling requirement;

and storing the target crowd data.

According to another aspect of the present disclosure, there is provided a system for determining a target population, comprising:

the interface module is used for determining a target crowd selection requirement and generating a corresponding search statement according to the target crowd selection requirement;

the storage module is used for executing the search statement to obtain target crowd data meeting the target crowd selection requirement; and also for storing target population data.

According to another aspect of the present disclosure, there is provided an electronic device including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method for determining a target population.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the above-described method of determining a target population.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the above-described method of determining a target population.

Advantageous effects

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a schematic diagram of an application scenario according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of an implementation of a method of determining a target population according to an embodiment of the present disclosure;

FIG. 3 is an architectural diagram of a crowd bag system according to an embodiment of the disclosure;

FIG. 4 is a flow chart of an implementation of a method of determining a target population according to another embodiment of the present disclosure;

FIG. 5 is a flow chart of an implementation of determining a target population culling requirement according to an embodiment of the present disclosure;

FIG. 6 is a flow chart of an implementation of determining a target population and employing locking and unlocking according to an embodiment of the present disclosure;

FIG. 7 is a schematic diagram illustrating a locking manner in the process of creating a new target group according to an embodiment of the disclosure;

FIG. 8 is a schematic diagram of a process for generating target demographic data according to an embodiment of the present disclosure;

FIG. 9 is a schematic diagram of a process for updating target demographic data according to an embodiment of the present disclosure;

FIG. 10 is a schematic block diagram of a system for determining a target population according to an embodiment of the present disclosure;

FIG. 11 is a schematic block diagram of a system for determining a target population according to another embodiment of the present disclosure;

fig. 12 is a block diagram of an electronic device for implementing a method of determining a target population according to an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the technology of determining target population, two ideas are adopted in the related technology:

the first is to search the population using a mapping convention (MapReduce).

The second is to use a native search database implementation. For example: flexible search Server (elastic search), MongoDB database, Remote Dictionary service (Remote Dictionary Server), and logical computation combination.

Both of the above two methods have a serious problem that each time the target population circling requirement (the specific content of the target population circling requirement can be represented by the portrait label of the target population) changes, Structured Query Language (SQL) or Domain Specific Language (DSL) needs to be rewritten, which results in low efficiency of determining the target population and is prone to errors.

In order to solve the problem, the present disclosure provides a method for determining a target group, which can automatically convert a search request into a corresponding search statement, and reduce a use threshold.

The present disclosure provides a method for determining a target group, which may be applied to a data processing apparatus, for example, where the apparatus may be deployed in a situation executed by a terminal or a server or other processing devices, so as to achieve the determination of the target group. For example, the method may be applied to the application scenario shown in fig. 1, as shown in fig. 1, the application scenario may include a terminal 110 and a push server 120, taking an example that a device applying the method is deployed in the push server 120, a user may send a request for using the method for confirming a target crowd to the push server 120 through the terminal 110, the push server 120 may convert the request into a corresponding search statement, send the search statement to a search server 130 storing crowd data, and obtain target crowd information in the request from the search server 130 to perform an information push service on the target crowd.

The terminal 110 is connected to the push server 120 through a wireless network or a wired network. Optionally, the terminal 110 is a smartphone, a tablet, a laptop, a desktop computer, a smart watch, a vehicle-mounted terminal, etc., but is not limited thereto. The terminal 110 is installed and operated with an application program supporting the target population determining method.

The push server 120 and the searcher server 130 may be independent servers, or server clusters or distributed systems, or cloud servers providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, and big data and artificial intelligence platforms.

The embodiment of the present disclosure provides a method for determining a target group, and fig. 2 is a flowchart for implementing the method for determining a target group according to the present disclosure, including:

s201: determining a target crowd circling requirement;

s202: generating a corresponding search statement according to the target crowd selection requirement;

s203: executing the search statement to obtain target crowd data meeting the target crowd selection requirement;

s204: the target population data is saved.

After the target crowd circling requirement is determined, the corresponding search statement can be automatically generated, and the search statement is executed to obtain the target crowd data meeting the crowd circling requirement. Therefore, the code sentences do not need to be written manually when the target crowd is selected, and the efficiency and the accuracy of determining the target crowd are improved.

In some embodiments, the search statement may comprise an ElasticSearch DSL search statement.

The ElasticSearch is a distributed full-text search engine system based on a road-card (Lucene). An ElasticSearch cluster (cluster) may store data and retrieve the data stored in the ElasticSearch cluster upon receiving a request.

Therefore, the embodiment of the disclosure performs target crowd selection by using the search function of the elastic search cluster; after the target crowd circling requirement is determined, an ElasticSearch DSL search statement which can be identified and executed by the ElasticSearch cluster is automatically generated, and the circling of the target crowd is executed by the ElasticSearch cluster. Therefore, the method for determining the target crowd provided by the embodiment of the disclosure does not need to manually write the code sentences used by the elastic search cluster, can improve the efficiency and accuracy of determining the target crowd, reduces the use threshold, completely decouples the portrait label and the searching capability, and enables non-technical personnel such as operators and the like to easily determine the target crowd data.

The target crowd data can also be a crowd packet, and the crowd packet can contain a plurality of target individual data (such as identification information of target individuals) which are selected in a circle and meet the target crowd selection requirement. The embodiment of the present disclosure provides a system for determining a target crowd, which may be referred to as a crowd sourcing system; the crowd pack system provides a visual platform for a user, the crowd pack is abstracted into a vivid dragging type page, and the user can freely combine portrait labels of any number and any type on a crowd pack new page to create the crowd pack. The crowd packet system provided by the embodiment of the disclosure completely shields details such as searching, updating, uploading and downloading of the crowd packet at the bottom layer for the user, so that the use threshold of the crowd packet is reduced, and meanwhile, the crowd packet system has higher searching performance by means of a universal searching interface.

Fig. 3 is a schematic diagram of an architecture of a crowd-sourcing system according to the present disclosure, which, as shown in fig. 3, can be divided into three layers from top to bottom, including:

(one) an interface layer:

the visual and draggable crowd pack management page is provided, and functions of crowd pack creation, crowd list viewing, custom crowd uploading, similar crowd searching and the like are provided. The crowd package is further abstracted into a vivid dragging type page, a user can select the crowd page on the label, and any number of portrait labels and any type of portrait labels can be freely combined to newly build target crowd data.

As shown in fig. 3, a user may select and/or input portrait tags through a tag circle crowd page of the interface layer, and send a target crowd creation request requesting to perform crowd circle selection to obtain target crowd data.

As shown in fig. 3, the interface layer may display target crowd data generated by different users on a crowd list page, so as to implement multiplexing of the target crowd data, and the user may use the target crowd data generated by other users.

As shown in fig. 3, the interface layer provides a PUSH page, and the user can select to PUSH the target people in the target people data that has been generated and saved. For example, in order to realize refined push of the network disk service, an operator can input a portrait label of a target crowd of the network disk service through an interface layer, and the rear end of the crowd packet system realizes selection of the target crowd to obtain target crowd data of the network disk service; and then, an operator can indicate to push information to a target group of the network disk service through the interface layer, so that efficient and fine pushing is realized. In the whole process, the technical requirements on operators are low, the operators only need to drag, click, input and the like in the visual page of the interface layer, and codes do not need to be written.

(II) interface layer:

the interface layer can be an Elasticissearch-based universal search interface, further abstracts DSL, and completely decouples target population data establishment of the interface layer and underlying Elasticissearch DSL search. The universal interface is used for expanding and developing the portrait label, and bottom layer codes do not need to be changed when the portrait label of a target crowd is newly added on the interface layer. The interface layer can also provide a target crowd data management function, and the capabilities of creating, updating, deleting, multiplexing, downloading, updating and calling back of the target crowd data are realized, so that the target crowd data can be more conveniently multiplexed.

As shown in fig. 3, the tag circle human processor (Handler) of the interface layer may determine a target crowd circle selection requirement according to an instruction input by a user through the interface layer, generate a corresponding search statement according to the target crowd circle selection requirement, send the search statement to the storage layer through the tag circle human interface, and execute the search statement by the Elasticsearch cluster of the storage layer to perform crowd circle selection.

The user can also input user-defined target crowd data, as shown in fig. 3, the user-defined crowd processor of the interface layer can receive the target crowd data input by the user through the interface layer, and send the target crowd data to the storage layer through the crowd addition interface, and the storage layer stores the user-defined target crowd data.

The embodiment of the present disclosure may also update the target crowd data automatically at regular time, as shown in fig. 3, the timing update processor of the interface layer may automatically initiate the timing update, instruct the storage layer to update the target crowd data through the crowd update interface, and store the updated target crowd data.

In addition, the interface layer can also comprise a crowd deletion interface and the like, and the crowd deletion interface is used for deleting the saved target crowd data.

(III) storage layer:

the storage layer may use the Elasticissearch cluster to store the portrait tag data, and when storing, the Elasticissearch cluster may store values of various types of portrait tags for various dimensions, using an Identification (ID) of the target individual as a routing ID of the Elasticissearch. The storage layer can also use the Elasticissearch cluster to store the meta information (meta information) of the target crowd data, and high stability and high availability of the meta information of the target crowd data are realized by means of multi-node backup of the Elasticissearch cluster. The meta information of the target crowd data may include data such as a target crowd circling requirement for circling the target crowd data, and identification information of the target crowd data. In order to prevent the problems of data pollution and the like caused by concurrent threads, a distributed lock can be adopted to ensure the concurrency safety in the processes of creating, updating, deleting and the like of the meta-information of the target crowd data.

As shown in fig. 3, the storage layer may store the circled target crowd data by using an object storage module. The object storage can ensure extremely high stability, and high-speed target crowd data uploading is realized by utilizing the block uploading characteristic.

With the crowd-sourcing system introduced in the above embodiments, in some embodiments, as shown in fig. 4, the process of determining the target crowd-sourcing requirement in the embodiments of the present disclosure may include:

s401: receiving a target crowd new establishment request, wherein the target crowd new establishment request comprises at least one portrait label used for target crowd circling;

s402: and generating a target crowd circling requirement according to at least one portrait label for target crowd circling.

For example, a target group new building request is generated by adopting an interface layer according to a received user instruction, and the target group new building request is sent to an interface module;

and the interface module generates a target crowd circling requirement according to at least one portrait label in the target crowd newly-built request.

Through the process, the user can input the instruction through the visual interface when newly building the target crowd data without compiling codes for target crowd selection, so that the technical threshold for determining the target crowd data is reduced, and non-technical personnel can conveniently realize the target crowd selection.

The target crowd circling request may be stored in meta information (meta information) of the target crowd data. For example, meta information of the target crowd data may contain the target crowd circling requirement and identification information of the target crowd data. The meta-information of the target crowd data can be saved for subsequent use in updating the target crowd data.

Table 1 shows an example of the structure of meta information of the target crowd data, which may also be referred to as "crown _ package meta information", where as shown in table 1, the structure of the crown _ package meta information is as follows:

TABLE 1

Since a large amount of user data is designed in the crowd sourcing system, privacy and security are very important, the embodiments of the present disclosure may perform the authority setting at the crowd sourcing level based on the crowd _ package meta information, for example, record a creator and administrator list in the crowd _ package meta information, and only a user (such as a creator or an administrator) given authority may operate for important functions such as downloading and deleting.

In other embodiments, with the crowd-sourcing system described in the above embodiments, as shown in fig. 5, the process of determining the target crowd rounding requirement in the embodiment of the present disclosure may include:

s501: generating a target crowd updating request, wherein the target crowd updating request comprises identification information of target crowd data which is requested to be updated;

s502: determining meta information of the target crowd data requesting updating according to the identification information of the target crowd data requesting updating;

s503: and extracting the target crowd circling requirement of the target crowd data requested to be updated from the meta information of the target crowd data requested to be updated.

The above process may be applied to update target population data. For example, the interface layer actively initiates the update of the target crowd data, generates a target crowd update request, and then reads the meta information of the target crowd data from the storage layer, thereby extracting the target crowd circling requirement of the target crowd data requesting the update. According to the process, in the target crowd updating process, the target crowd selection requirement can be automatically obtained and translated into the corresponding search statement, so that manual compiling of search codes is avoided, and the use threshold is reduced.

The embodiment of the disclosure can realize real-time update of the target crowd data through the process or manually indicate the real-time update of the target crowd data, thereby ensuring that the target crowd data is always in the latest state and facilitating use of related personnel such as operation and the like.

In some embodiments, when new creation, update, deletion, and the like are performed on target crowd data, a distributed lock may be used to ensure concurrency security during operation.

Taking the new establishment of target crowd data as an example, a distributed lock can be adopted to ensure concurrency security in the new establishment process, and prevent the problems of data pollution of the crowd _ package meta information and the like caused by concurrent threads.

In some embodiments, the lock may be locked before generating the target crowd circling requirement and the meta information of the target crowd data, and unlocked after the operation is completed. For example, as shown in fig. 6, after receiving the target people new creation request in step S401, the method may further include:

s601: acquiring the target crowd identification in the target crowd new establishment request;

s602: and searching a preset index table by adopting the target crowd identification, taking the thread identification of the target crowd new establishment request as the thread identification corresponding to the target crowd identification under the condition that the thread identification corresponding to the target crowd identification does not exist in the index table, and storing the corresponding relation between the target crowd identification and the thread identification of the target crowd new establishment request in the index table. After that, the above step S402 may be further performed.

In step S602, if the thread identifier corresponding to the target group identifier does not exist in the index table, it indicates that no other user is currently operating the target group corresponding to the target group identifier, and in this case, the user initiating the target group new request may lock the target group corresponding to the target group identifier. Since the thread identifier can uniquely represent the user who initiated the target group new request, the method shown in step S602 may be adopted, that is, the thread identifier of the target group new request is taken as the thread identifier corresponding to the target group identifier, and the corresponding relationship between the target group identifier and the thread identifier of the target group new request is stored in the index table. The index table stores the corresponding relationship between the target crowd identification and one thread identification, and then indicates that the target crowd corresponding to the target crowd identification is locked by the user (or client) corresponding to the thread identification.

As shown in fig. 6, after the step S402, the method may further include:

s603: and storing the meta information of the target crowd data, wherein the meta information of the target crowd data comprises the target crowd circling requirement and the identification information of the target crowd data.

After the meta information of the target crowd data is saved, the target crowd may be unlocked, for example, in step S604 shown in fig. 6, the thread identifier corresponding to the target crowd identifier is deleted from the index table. After the target group is deleted, the target group corresponding to the target group identifier is restored to an unlocked state, and then other users (clients) can lock the target group and execute operations according to instructions after locking.

As can be seen from the locking and unlocking processes, when a user operates the target data, the target data is locked, and after the operation is completed, the target data is unlocked, so that other users can be prevented from operating the target data at the same time, and concurrency errors are avoided.

Fig. 7 is a schematic diagram of a locking manner in a process of creating a new target group (group of people package), according to an embodiment of the present disclosure, including the following steps:

in a first step, a user (e.g., user 1) enters a new request for crowd package 1.

Secondly, requesting locking by a corresponding crowd packet system instance (such as the crowd packet system instance 1), for example, searching an index table from an elastic search cluster, and determining whether a thread ID corresponding to the crowd packet 1 exists in the index table, wherein the thread ID corresponding to the crowd packet 1 is different from the thread ID of the user 1; if crowd package 1 has a corresponding thread ID that is different from the thread ID of user 1, it indicates that crowd package 1 has been locked by other users. If not, indicating that the crowd package 1 is not locked, the user 1 can lock the crowd package 1. Assuming the crowd package 1 has not been locked by other users, the third step continues.

And thirdly, the user 1 successfully locks the crowd packet 1.

Fourthly, the crowd packet system example 1 creates a crowd packet 1 and returns a success message of creating the crowd packet to the user 1.

As shown in fig. 7, assuming that another user (e.g., user 2) also inputs a new request of the crowd package 1, and the corresponding crowd package system instance (e.g., crowd package system instance 2) requests locking, for example, an index table is searched from the elastic search cluster, since the crowd package 1 is locked by the user 1 and the thread ID corresponding to the crowd package 1 is stored in the index table (the thread ID is the thread ID of the user 1), it can be determined that the crowd package 1 and the thread ID corresponding to the crowd package 1 are different from the thread ID of the user 2, the crowd package 1 cannot be locked, and the crowd package system instance 2 returns a new failure message to the user 2.

The embodiment of the disclosure can adopt the following specific working procedures to lock and unlock the target population.

(1) And creating an index table for locking operation, wherein the index table can be identified by a target crowd, and if one target crowd is locked by a user, the index table stores the identification of the target crowd and the thread ID of the user. If a target crowd is unlocked, the thread ID corresponding to the identification of the target crowd can be deleted from the index table.

The storage environment of the index table may be established in the following manner, for example, 1 main fragment and 3 sub-fragments are set by using the following codes to store the index table, and a refresh period when data is written into the index table is set to 10 ms.

(2) And a locking script is newly established and used when a user locks.

An example of a locking script is as follows:

in the above example, the name of the locking script is document-lock. With this locking script, it can be determined whether the thread ID (ctx. _ source. process _ ID in the above example) that attempts locking is different from the thread ID (param. process _ ID in the above example) corresponding to the target group; if not, then the target crowd has been locked by other users, then an error prompt is returned, such as 'available locked by other threads' in the above example; otherwise, the user is allowed to lock the target population, perform a null operation (e.g., perform ctx. op ═ noop' in the above example)

(3) And newly establishing an unlocking script for use in unlocking.

An example of an unlock script is as follows:

in the above example, the name of the unlock script is document-unlock. With this unlocking script, it can be determined whether the thread ID (ctx. _ source. process _ ID in the above example) attempting to unlock is different from the thread ID (params. process _ ID in the above example) corresponding to the target group; if not, then the target crowd is indicated to have been locked by other users, then an error prompt is returned, such as 'available locked by other threads' in the above example; otherwise, the user is allowed to unlock the target group, perform a null operation (e.g., perform ctx. op ═ noop' in the above example)

(4) If thread 1 is to lock the target population, the locking script described above may be invoked. For example, with a request that the incoming process _ id is 49d92f4ddd6164663ed8fe915d915dc9, indicating that the lock is to be applied to the crowd, aeaf05e3-408c-42af-9ade-64cf28e2fc041 is the thread that requested the lock. After the request is successful, the thread is indicated to successfully lock the crowd packet, and operations such as creating the crowd packet, updating the crowd packet, deleting the crowd packet and the like can be performed.

(5) And after the thread 1 finishes the transaction, releasing the distributed lock of the crowd packet, and calling the unlocking script. For example, the following request is used to delete the thread ID corresponding to the crowd packet, indicating that the distributed lock of the crowd packet is released.

Some implementations that employ distributed locks to ensure concurrency security during the target population data establishment process are introduced above. In the process of updating the target crowd data, the same mode can be adopted, the target crowd is locked before updating, and the target crowd is unlocked after updating is completed, so that concurrence safety in the updating process is ensured. The locking and unlocking modes in the updating process can refer to the locking and unlocking modes in the target crowd data establishing process, and are not described herein again.

After the above locking and unlocking manners are described, the generation and updating processes of the target crowd data are described in an overall manner with reference to fig. 8 and 9.

Fig. 8 is a schematic diagram of a generation process of target crowd data according to an embodiment of the present disclosure. As shown in fig. 8, the generation of the target population data may include the steps of:

s801: the user selects portrait tags on the interface layer, logically combines the selected portrait tags, and clicks to submit.

S802: and checking the interface layer, for example, repeatedly checking the name of the target crowd, checking the range of the specific numerical value of the portrait label selected by the user, checking the type of the portrait label and the like. The interface layer can calculate the target crowd name input by the user by adopting an MD5 information abstraction Algorithm (MD5Message-Digest Algorithm), and the calculation result is used as a Key value (Key) of the target crowd, so that the uniqueness of the target crowd name is ensured.

S803: and adding a distributed lock on the target crowd by the interface layer to establish meta information of the target crowd data.

S804: the interface layer translates meta information of the target crowd data into an ElasticSearch DSL statement, and query optimization is carried out in the translation process. Specific implementations of query optimization and translation are described in detail below.

S805: and the ElasticSearch cluster of the storage layer adopts the ElasticSearch DSL statement to perform crowd selection to obtain target crowd data.

S806: and uploading the target crowd data obtained by circle selection to an interface layer by the Elasticissearch cluster of the storage layer, and storing the target crowd data to an object storage module by the interface layer.

S807: and the interface layer carries out distributed unlocking on the target crowd.

Fig. 9 is a schematic diagram of an update process of target crowd data according to an embodiment of the present disclosure. As shown in fig. 9, the update of the target population data may include the steps of:

s901: and executing instantiated scheduling by the interface layer, and initiating the updating of the generated target crowd data.

S902: and the interface layer adds a distributed lock to the target crowd to acquire meta information of the target crowd data.

S903: the interface layer translates meta information of the target crowd data into an ElasticSearch DSL statement, and query optimization is carried out in the translation process.

S904: and the ElasticSearch cluster of the storage layer adopts the ElasticSearch DSL statement to perform crowd selection again to obtain updated target crowd data.

S905: and uploading the target crowd data obtained by re-selection to an interface layer by the Elasticissearch cluster of the storage layer, and storing the target crowd data to an object storage module by the interface layer.

S906: and the interface layer performs distributed unlocking on the target crowd.

In addition to enabling target crowd selection by the Elasticsearch cluster, the embodiments of the present disclosure also support uploading of custom target crowd data by users. The creator of the target crowd can describe the information of the target crowd data so as to provide reference for other users and facilitate the multiplexing of the target crowd data.

The embodiment of the disclosure supports a plurality of different users to establish target crowd data in the crowd system, and the crowd system can provide crowd lists of the target crowd data established by all different users. This crowd list may support the following functions:

1) checking the name of the target crowd;

2) and describing a target population. The creator provides information describing the crowd package and provides reference for other people to reuse the crowd package.

3) Daily updates are initiated. After the system is started, the crowd packet is updated every day at regular time, so that the crowd packet is always up-to-date. The embodiment of the present disclosure may set that only the creator and/or administrator has the right to click to open the update.

4) Daily renewal was stopped. After the update is stopped, the crowd packets are not updated every day, the crowd packets which are not used can be stopped from being updated, and the back-end computing resources are saved. Embodiments of the present disclosure may set that only the creator and/or administrator may click on the pause.

5) The crowd packet is deleted. The embodiment of the disclosure can set that only the creator and/or administrator has the authority to delete the crowd package.

6) Set crowd packet types such as: a tag crowd bag, a similar crowd bag, and an uploading crowd bag.

7) The crowd bag state is set, so that the user can conveniently judge whether the crowd bag is in place. If the crowd pack state is divided into 4 types: initializing the people in the circle, completing the initializing the people in the circle, updating the people in the circle and completing the updating of the people in the circle.

By setting the crowd list to support the functions, the authority management based on the crowd packet level can be realized, and the safety of user data is ensured, if only the user endowed with the authority can operate the crowd packet.

The embodiment of the disclosure can translate the target crowd circling requirement by adopting the interface layer of the crowd packet system to obtain the corresponding elastic search DSL statement. For example, in some embodiments, the target group selection requirement may be translated by a preset interaction protocol and a format requirement of the DSL search statement of the flexible search server, so as to obtain a search statement (e.g., an elastic search DSL statement) corresponding to the target group selection requirement. Because the portrait label grows rapidly, the adoption of the secondary interface protocol can realize high expansibility, and can complete the expansion under the condition of not changing codes aiming at the newly added portrait label, thereby improving the system efficiency. In addition, in the process of translating into the elastic search DSL statement, the embodiment of the present disclosure may further perform optimization of the elastic search DSL statement to improve the elastic search performance.

For example, in the related art, there are several main ways for the ElasticSearch: range (range search), terms (exact match), match (fuzzy match), best _ not (exclude).

Abstract ES universal search interface protocol & examples are as follows:

{

"business",// must pass, label _ cross indicates that the label circles the crowd pack.

The package _ name, teacher, must pass, crowd package name, support Chinese, letters, numbers, underlines, other characters will be filtered out.

"package _ descriptor": teacher user crowd,// must pass, crowd package description information.

"user": xxx ",// must pass, creator

"only _ show _ number": 0",// optional, and the tag is passed in to indicate that only the number of circled hits is returned, and no crowd pack is created.

5000,// optional, which when introduced into the label means that the restricted population circles the upper limit, in units of units.

"range_number":[

{ "name": label "," value ": 0,0.02] },// numerical range screening.

"not_range_number":[

{ "name": label "," value ": 0,1000] },// exclusion range screening.

Range _ string [ { "name": last _ active _ day. value ], "value": 2021-12-01","2021-12-31"] } ],// string range screening.

Range _ string _ last _ days [ { "name": label "," value ": [ -30,0] } ]// string exact match.

"not _ blocks _ string" [ { "name": "label", "value": [ "xxx", "xxx" ] } ],// exclude strings from exact match.

"terms _ number" [ { "name": label "," value ": xxx ],// number exact match.

"match": { "name": label "," value ": [" xxx "," xxx "] } ],// string fuzzy match.

"not match" [ { "name": label "," value ":" [ "xxx" ] } ],// exclude string fuzzy matches.

"exist _ labels" [ "label" ],// a label is present.

"not _ exists _ labels" [ "labels" ],// label exclusions.

As can be seen from the above example, since the format of the target group circle selection requirement meets the preset interaction protocol, when performing DSL language translation, the target group circle selection requirement can be translated according to the interaction protocol and the format requirement of the ElasticSearch DSL statement, so as to obtain the search statement corresponding to the target group circle selection requirement.

In some embodiments, the translation process may include:

(1) detecting whether a target crowd selection requirement meets a first specification, wherein the first specification comprises at least one of a format specification and a search range specification;

(2) formatting the target crowd selection requirement under the condition that the target crowd selection requirement meets the first specification;

for example, the relative dates in string range filter-relative days version range _ string _ last _ days are converted to absolute dates. And combining the character string arrays in the fuzzy matching match into character strings, thereby facilitating the fuzzy matching of the elastic search.

(3) And (4) translating the formatted target population selection requirement to obtain an ElasticSearch DSL statement.

For example, range _ number, range _ string _ last _ days are converted to an elastic search range (range) search range search in the elastic search DSL language, and term _ string, not _ term _ string are converted to a term exact match search in the elastic search DSL language. Converting the match, not _ match into an ElasticSearch fuzzy matching (match) search in ElasticSearch DSL language. The exist _ labels, not _ exist _ labels are converted to an elastic search presence (exist) search in the elastic search DSL language.

In the translation process, query optimization can be realized, and the problem of unreasonable range search results in the related technology is solved. For example, in the related art, the Elasticsearch server is used to search for users with a consumption amount less than or equal to 1000 yuan, and only target groups with consumption amounts (i.e. consumption amounts greater than 0) and with amounts less than 1000 yuan can be returned, but target groups without consumption amounts (i.e. consumption amounts equal to 0) cannot be returned; such crowd circling results are clearly unreasonable. The interactive protocol designed in the embodiment of the present disclosure can eliminate the target group with the consumption amount of 1000 to infinity by screening the "not _ range _ number" through the exclusion range, so as to select users (including users with the consumption amount of 0) with the consumption amount of less than or equal to 1000 yuan in a circle

In addition, in the translation process, a vernier paging request can be used for carrying out search optimization on the elastic search DSL statement to obtain a search result. The vernier paging search can ensure that the memory of large data volume search does not overflow and avoid deep paging.

In some embodiments, translating the target population selection requirement may include:

translating the target crowd circling requirements according to the search optimization standard; the search optimization criteria include: the computing operation in the course of the lean search and/or the content returned by the lean search.

For example, instead of mut, filtering (filter) is used to ignore the relevance scores and cache the query results as much as possible to improve performance. If the parameter _ source is set to be negative (false), the content returned by the search is simplified, so that the bandwidth occupation is saved, and the search performance is improved.

In summary, the method for determining the target crowd provided by the embodiment of the disclosure can completely decouple the service index and the crowd packet searching capability through the universal interface, and when a new tag needs to be added to the crowd packet human interface, due to the universality of the protocol, zero change of a back-end code can be realized, so that the crowd packet use threshold is greatly reduced, and the crowd packet has the advantages of simplicity, easiness in use, high efficiency, openness to expansion and the like.

Fig. 10 is a schematic block diagram of a system for determining a target population according to an embodiment of the present disclosure. A system 1000 for identifying a target population as shown in fig. 10, comprising:

the interface module 1010 is used for determining a target crowd selection requirement and generating a corresponding search statement according to the target crowd selection requirement;

the storage module 1020 is configured to execute the search statement to obtain target crowd data meeting the target crowd circling requirement; and also for storing target population data.

In some possible embodiments, the search statement comprises an elastic search server domain specific language DSL search statement.

Fig. 11 is a schematic block diagram of a system for determining a target population according to another embodiment of the present disclosure. A system 1100 for identifying a target population as shown in fig. 11, comprising:

the system comprises an interface module 1010, a storage module 1020 and an interface interaction module 1130, wherein the interface module 1010 further comprises a distributed lock processing sub-module 1011, a generation sub-module 1012 and a business processing sub-module 1013, and the storage module 1020 further comprises a meta information storage sub-module 1021.

In some possible implementations, the interface interaction module 1130 is configured to: generating a target crowd new building request according to the received instruction, and sending the target crowd new building request to an interface module; the target crowd newly-built request comprises at least one portrait label used for target crowd circling;

the interface module 1010 is configured to: and generating a target crowd circling requirement according to at least one portrait label for target crowd circling.

In some possible embodiments, the target people new creation request further includes a target people identification;

the distributed lock processing sub-module 1011 is configured to:

acquiring a target crowd identification in a target crowd new establishment request; searching a preset index table by adopting the target crowd identification, taking the thread identification of the target crowd new establishment request as the thread identification corresponding to the target crowd identification under the condition that the thread identification corresponding to the target crowd identification does not exist in the index table, and storing the corresponding relation between the target crowd identification and the thread identification of the target crowd new establishment request in the index table; the generation submodule is further used for indicating to generate a target crowd selection requirement;

the generation submodule 1012 is configured to:

and generating a target crowd circling requirement according to at least one portrait label for target crowd circling according to the instruction of the distributed lock processing sub-module 1011.

In some possible embodiments, the meta information storage sub-module 1021 is configured to:

and storing the meta information of the target crowd data, wherein the meta information of the target crowd data comprises the target crowd circling requirement and the identification information of the target crowd data.

In some possible embodiments, the distributed lock processing submodule 1011 is further configured to:

after the meta information of the target crowd data is saved in the meta information storage sub-module 1021, the thread identifier corresponding to the target crowd identifier is deleted from the index table.

In some possible embodiments, the business processing sub-module 1013 is configured to generate a target population update request, where the target population update request includes identification information of target population data requested to be updated; determining meta information of the target crowd data requesting updating according to the identification information of the target crowd data requesting updating; and extracting the target crowd circling requirement of the target crowd data requested to be updated from the meta information of the target crowd data requested to be updated.

In some possible embodiments, the format of the target group selection requirement satisfies a preset interaction protocol;

the interface module 1010 is configured to translate the target crowd selection requirement according to a preset interaction protocol and a format requirement of a DSL search statement of the flexible search server, so as to obtain a search statement corresponding to the target crowd selection requirement.

In some possible embodiments, the interface module 1010 is configured to:

detecting whether the target crowd selection requirement meets a first specification, wherein the first specification comprises at least one of a format specification and a search range specification;

formatting the target crowd selection requirement under the condition that the target crowd selection requirement meets a first specification;

and translating the formatted target crowd selection requirement to obtain a search sentence corresponding to the target crowd selection requirement.

In some possible embodiments, the interface module 1010 is configured to:

translating the target crowd selection requirement according to the search optimization standard; the search optimization criteria include: the computing operation in the course of the lean search and/or the content returned by the lean search.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the customs of public sequences.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

FIG. 12 shows a schematic block diagram of an example electronic device 1200, which can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 12, the apparatus 1200 includes a computing unit 1201 which can perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)1202 or a computer program loaded from the storage unit 12012 into a Random Access Memory (RAM) 1203. In the RAM 1203, various programs and data required for the operation of the device 1200 may also be stored. The computing unit 1201, the ROM 1202, and the RAM 1203 are connected to each other by a bus 1204. An input/output (I/O) interface 1205 is also connected to bus 1204.

Various components in the device 1200 are connected to the I/O interface 1205 including: an input unit 1206 such as a keyboard, a mouse, or the like; an output unit 1207 such as various types of displays, speakers, and the like; a storage unit 1208, such as a magnetic disk, optical disk, or the like; and a communication unit 1209 such as a network card, modem, wireless communication transceiver, etc. The communication unit 1209 allows the device 1200 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 1201 may be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 1201 performs the various methods and processes described above, such as a method of determining a target population. For example, in some embodiments, the method of determining a target population may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 1208. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 1200 via the ROM 1202 and/or the communication unit 1209. When the computer program is loaded into the RAM 1203 and executed by the computing unit 1201, one or more steps of the above described method of determining a target population may be performed. Alternatively, in other embodiments, the computing unit 1201 may be configured by any other suitable means (e.g., by means of firmware) to perform the method of determining the target population.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes 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 codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. 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 this disclosure, 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. A 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.

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 a pointing device (e.g., a mouse or a 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 can 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, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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 clients and servers. A client and server are generally 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. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method of identifying a target population, comprising:

determining the target population circling requirement;

executing the search statement to obtain target crowd data meeting the target crowd selection requirement;

and saving the target crowd data.

2. The method of claim 1, wherein the search statement comprises an elastic search server domain specific language DSL search statement.

3. The method of claim 1 or 2, wherein the determining target population circle requirements comprises:

receiving a target crowd newly-built request, wherein the target crowd newly-built request comprises at least one portrait label used for target crowd circling;

and generating the target crowd circling requirement according to the at least one portrait label for target crowd circling.

4. The method of claim 3, wherein the target people new request further includes a target people identification;

after the receiving of the new request of the target group, the method further includes:

acquiring the target crowd identification in the target crowd new establishment request;

searching a preset index table by adopting the target crowd identification, taking the thread identification of the target crowd new establishment request as the thread identification corresponding to the target crowd identification under the condition that the thread identification corresponding to the target crowd identification does not exist in the index table, and storing the corresponding relation between the target crowd identification and the thread identification of the target crowd new establishment request in the index table;

and continuing to execute the step of generating the target crowd circling requirement according to the at least one portrait label for target crowd circling.

5. The method of claim 4, further comprising:

6. The method of claim 5, after saving the meta information of the target demographic data, further comprising:

and deleting the thread identification corresponding to the target crowd identification from the index table.

7. The method of claim 5 or 6, wherein the determining target population circle requirements comprises:

generating a target crowd updating request, wherein the target crowd updating request comprises identification information of target crowd data which is requested to be updated;

determining meta information of the target crowd data requesting updating according to the identification information of the target crowd data requesting updating;

and extracting the target crowd circling requirement of the target crowd data requested to be updated from the meta information of the target crowd data requested to be updated.

8. The method of claim 2, wherein the target population circle requirement format satisfies a pre-defined interaction protocol;

generating a corresponding flexible search server DSL search statement according to the target crowd selection requirement, wherein the generating of the corresponding flexible search server DSL search statement comprises: and translating the target crowd selection requirement according to the preset interaction protocol and the format requirement of the DSL search statement of the elastic search server to obtain the search statement corresponding to the target crowd selection requirement.

9. The method of claim 8, wherein translating the target population selection requirement to obtain the search statement corresponding to the target population selection requirement comprises:

formatting the target crowd selection requirement under the condition that the target crowd selection requirement meets the first specification;

translating the formatted target crowd selection requirement to obtain a search statement corresponding to the target crowd selection requirement.

10. The method of claim 8 or 9, wherein said translating said target population selection requirements comprises:

translating the target crowd selection requirement according to a search optimization standard; the search optimization criteria include: the computing operation in the course of the lean search and/or the content returned by the lean search.

11. A system for determining a target population, comprising:

the storage module is used for executing the search statement to obtain target crowd data meeting the target crowd selection requirement; and also for saving the target population data.

12. The system of claim 11, wherein the search statement comprises an elastic search server domain specific language DSL search statement.

13. The system of claim 11 or 12, further comprising: the interface interaction module is used for generating a target crowd new building request according to the received instruction and sending the target crowd new building request to the interface module; the target crowd newly-built request comprises at least one portrait label used for target crowd circling;

the interface module is used for generating the target crowd circling requirement according to the at least one portrait label used for target crowd circling.

14. The system of claim 13, wherein the target people new request further includes a target people identification;

the interface module includes:

the distributed lock processing sub-module is used for acquiring the target crowd identification in the target crowd new establishment request; searching a preset index table by adopting the target crowd identification, taking the thread identification of the target crowd new establishment request as the thread identification corresponding to the target crowd identification under the condition that the thread identification corresponding to the target crowd identification does not exist in the index table, and storing the corresponding relation between the target crowd identification and the thread identification of the target crowd new establishment request in the index table; the generation submodule is further used for indicating to generate a target crowd selection requirement;

and the generation sub-module is used for generating the target crowd selection requirement according to the at least one portrait label used for target crowd selection according to the instruction of the distributed lock processing sub-module.

15. The system of claim 14, wherein the storage module comprises:

and the meta information storage sub-module is used for storing the meta information of the target crowd data, and the meta information of the target crowd data comprises the target crowd circling requirement and the identification information of the target crowd data.

16. The system of claim 15, wherein,

the distributed lock processing submodule is further configured to delete the thread identifier corresponding to the target crowd identifier from the index table after the meta information of the target crowd data is saved by the meta information storage submodule.

17. The system of claim 15 or 16, wherein the interface module further comprises:

the service processing submodule is used for generating a target crowd updating request, and the target crowd updating request comprises identification information of target crowd data which is requested to be updated; determining meta information of the target crowd data requesting updating according to the identification information of the target crowd data requesting updating; and extracting the target crowd circling requirement of the target crowd data requested to be updated from the meta information of the target crowd data requested to be updated.

18. The system of claim 12, wherein the target population circle requirement is formatted to satisfy a pre-established interaction protocol;

and the interface module is used for translating the target crowd selection requirement according to the preset interaction protocol and the format requirement of the DSL search statement of the flexible search server to obtain the search statement corresponding to the target crowd selection requirement.

19. The system of claim 18, wherein the interface module is to:

20. The system of claim 18 or 19, wherein the interface module is to:

21. An electronic device, comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.

22. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-10.

23. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-10.