Target business crowd determination method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a target business crowd determination method and device, electronic equipment and a storage medium, and belongs to the technical field of data processing. The method comprises the following steps: obtaining a screening condition of a target business crowd, wherein the screening condition comprises at least one screening label; for each screening label, a target bitmap corresponding to a user label matched with the screening label is obtained from a pre-obtained first mapping relation, the first mapping relation comprises a plurality of user labels and a bitmap corresponding to each user label, and each user label corresponds to one attribute of a user; the bitmap corresponding to the user tag comprises a plurality of attribute identifiers, and each attribute identifier is used for describing the attribute of one user; and determining a target business crowd according to the attribute identifier in the target bitmap corresponding to each screening tag. According to the embodiment of the invention, the processing efficiency can be improved during screening, the screening efficiency of the target service crowd is improved, and subsequent targeted service pushing for the target service crowd is facilitated.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and device for determining a target business population, an electronic device, and a storage medium. Background Art

[0002] With the continuous development of information technology, financial institutions' systems contain a large number of users. Each user has his or her own user portrait. The user portrait is a label abstracted based on the user's basic attributes, behavioral habits and other information. The user portrait contains multiple user labels of the user.

[0003] When promoting their business, financial institutions need their business personnel to screen the target audience. The purpose of screening the target audience is to push suitable financial products to the target audience, thereby achieving accurate push. Crowd screening is the process of screening a group of users according to user tags. Currently, users are pre-associated with tags and stored in the database in the form of large wide tables.

[0004] However, using a large wide table to store tags makes the query timeliness of tags in the crowd screening process poor, resulting in a long screening process. Therefore, how to improve the processing efficiency of screening has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The main purpose of the embodiments of the present application is to provide a method and device for determining a target business population, an electronic device, and a storage medium, aiming to improve the efficiency of screening the target business population.

[0006] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for determining a target business population, the method comprising:

[0007] Acquire a screening condition for a target business population, wherein the screening condition includes at least one screening tag;

[0008] For each of the filtering tags, a target bitmap corresponding to a user tag matching the filtering tag is obtained from a pre-acquired first mapping relationship, wherein the first mapping relationship includes a plurality of user tags and a bitmap corresponding to each user tag, each user tag corresponds to an attribute of a user, and the bitmap corresponding to the user tag includes a plurality of attribute identifiers, each attribute identifier is used to describe the attribute of a user;

[0009] The target business population is determined according to the attribute identifier in the target bitmap corresponding to each of the screening tags.

[0010] In some embodiments, the screening condition further includes: a logic operation rule, the logic operation rule including at least one of an AND operation, an OR operation, and a NOT operation;

[0011] Determining the target business population according to the attribute identifier in the target bitmap corresponding to each of the screening tags includes:

[0012] Processing a plurality of target bitmaps corresponding to the plurality of filtering tags according to a logic operation rule to obtain a first target bitmap, wherein the plurality of target bitmaps correspond to the plurality of filtering tags one by one;

[0013] The target business group is determined according to the attribute identifier in the first target bitmap.

[0014] In some embodiments, the bitmap corresponding to the user tag further includes a plurality of user identifiers, the plurality of user identifiers correspond one-to-one to the plurality of attribute identifiers, the first target bitmap is stored in a first database, and the first database is a column storage database;

[0015] The determining the target service population according to the attribute identifier in the first target bitmap includes:

[0016] Obtaining a target identifier whose attribute identifier is a first preset identifier in the first target bitmap;

[0017] The target business group is determined according to the user identifier corresponding to the target identifier.

[0018] In some embodiments, the bitmap corresponding to the user tag further includes a plurality of user identifiers, and the plurality of user identifiers correspond one-to-one to the plurality of attribute identifiers;

[0019] After determining the target business population according to the attribute identifier in the target bitmap corresponding to each of the screening tags, the method further includes:

[0020] Obtaining a user ID of each user in the target business population, wherein the user ID is used to identify a unique user;

[0021] For each of the user identifiers, traverse the bitmap corresponding to each user tag in the first mapping relationship to obtain an attribute identifier corresponding to each of the user identifiers;

[0022] A description package of the target business group is generated according to the user identification of each user in the target business group and the attribute identification corresponding to the user identification.

[0023] In some embodiments, before acquiring, for each of the filtering tags, a target bitmap corresponding to a user tag matching the filtering tag from a pre-acquired first mapping relationship, the method further includes:

[0024] Obtain multiple user IDs, a user tag corresponding to each user ID, and an attribute value of the user tag;

[0025] constructing a second mapping relationship according to the multiple user identifiers, the user tag corresponding to each user identifier, and the attribute value of the user tag, wherein the second mapping relationship is stored in a second database;

[0026] Importing the second mapping relationship into a first database, the timeliness of data processing in the first database is higher than that in the second database, and the first database is the column storage database;

[0027] The second mapping relationship is bitmap converted to obtain the first mapping relationship, the first mapping relationship is stored in the first database, the attribute identifier includes a first preset identifier and a second preset identifier, the first preset identifier is used to indicate that the attribute value of the user tag exists, and the second preset identifier is used to indicate that the attribute value of the user tag does not exist.

[0028] In some embodiments, performing bitmap conversion on the second mapping relationship to obtain the first mapping relationship includes:

[0029] The second mapping relationship is bitmap-converted according to the type of the user tag to obtain the first mapping relationship, and the type of the user tag includes a numerical user tag and a character user tag.

[0030] In some embodiments, the obtaining of screening conditions for the target business population includes:

[0031] In response to a screening request for a target business population triggered by a screening platform, the screening request is parsed to obtain screening conditions for the target business population.

[0032] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a device for determining a target business population, the device comprising:

[0033] An acquisition module, used to acquire a screening condition of a target business population, wherein the screening condition includes at least one screening tag;

[0034] a processing module, configured to obtain, for each of the filtering tags, a target bitmap corresponding to a user tag matching the filtering tag from a pre-acquired first mapping relationship, wherein the first mapping relationship includes a plurality of user tags and a bitmap corresponding to each user tag, each user tag corresponds to an attribute of a user, and the bitmap corresponding to the user tag includes a plurality of attribute identifiers, each attribute identifier being used to describe the attribute of a user;

[0035] The processing module is further used to determine the target business population according to the attribute identifier in the target bitmap corresponding to each of the screening tags.

[0036] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0037] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0038] The target business population determination method and device, electronic device and storage medium proposed in the present application obtain screening conditions for the target business population, wherein the screening conditions include at least one screening tag. For each screening tag, a target bitmap corresponding to a user tag matching the screening tag is obtained from a pre-acquired first mapping relationship. Each user tag corresponds to an attribute of a user, and the bitmap corresponding to the user tag includes multiple attribute identifiers, each attribute identifier is used to describe an attribute of a user. The target business population is determined according to the attribute identifier in the target bitmap corresponding to each screening tag, and data is stored in a bitmap manner, which can improve processing efficiency during screening, improve the screening efficiency of the target business population, and facilitate subsequent targeted business push to the target business population. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of a method for determining a target business population provided by an embodiment of the present application;

[0040] Figure 2 yes Figure 1 Flow chart of step S103 in FIG.

[0041] Figure 3 It is a schematic diagram of the relationship between labels and bitmaps;

[0042] Figure 4 yes Figure 2 Flow chart of step S202 in FIG.

[0043] Figure 5 is another flow chart of the method for determining the target business population provided in the embodiment of the present application;

[0044] Figure 6 This is a schematic diagram of the platform and database provided in the embodiment of the present application. Figure 1 ;

[0045] Figure 7 is a schematic diagram of a first mapping relationship and a second mapping relationship;

[0046] Figure 8 This is a schematic diagram of the platform and database provided in the embodiment of the present application. Figure 2 ;

[0047] Fig. 9 is a schematic diagram of the structure of a device for determining a target business population provided in an embodiment of the present application;

[0048] Fig.10 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0052] First, some nouns involved in this application are analyzed:

[0053] User label is a standardized definition of user attributes, behaviors, preferences, etc., which is used to describe a group of users with the same characteristics.

[0054] User ID refers to the information used to identify the user and is the unique identifier of the user.

[0055] The user identification can be a device ID or a user ID, and the device ID refers to the logo of the device terminal used by the user.

[0056] When promoting their business, financial institutions need their business personnel to screen the target audience. The purpose of screening the target audience is to push suitable financial products to the target audience, thereby achieving accurate push. Crowd screening is the process of screening a group of users according to user tags. Currently, users are pre-associated with tags and stored in the database in the form of large wide tables.

[0057] However, the use of large and wide tables to store labels makes the query timeliness of labels in the crowd screening process poor, resulting in a long screening process.

[0058] Based on this, the embodiments of the present application provide a method and device for determining a target business population, an electronic device, and a storage medium, aiming to improve the efficiency of screening the target business population.

[0059] The target business population determination method provided in the embodiment of the present application relates to the field of artificial intelligence technology. The target business population determination method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the target business population determination method, etc., but is not limited to the above forms.

[0060] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0061] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0062] Figure 1 is a flow chart of a method for determining a target business population provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S103.

[0063] Step S101, obtaining screening conditions for a target business population, where the screening conditions include at least one screening tag.

[0064] In this embodiment, filtering conditions for the target business population are obtained, and the filtering conditions are triggered by business personnel based on a filtering platform, wherein the filtering conditions include at least one filtering tag. For example, if the business personnel selects user tags of gender and age, then the filtering tags include gender and age. The business personnel selects at least one user tag, and the user tag includes basic information, such as gender, age, region, occupation, marital status, etc.

[0065] Step S102: for each filtering tag, obtain a target bitmap corresponding to a user tag matching the filtering tag from a pre-acquired first mapping relationship, wherein the first mapping relationship includes multiple user tags and a bitmap corresponding to each user tag, each user tag corresponds to an attribute of the user, and the bitmap corresponding to the user tag includes multiple attribute identifiers, each attribute identifier is used to describe an attribute of a user.

[0066] In this embodiment, for each screening tag, a target bitmap corresponding to a user tag matching the screening condition is obtained from the first mapping relationship obtained in advance. In order to save storage space and solve the time limit problem of query, a bitmap is used to store data, wherein the first mapping relationship includes multiple user tags and a bitmap corresponding to each tag, wherein the user tag is a standardized definition of user attributes, behaviors, preferences, etc., and is used to describe customer characteristics. Each user tag corresponds to an attribute of a user, such as age, education, gender, etc. The bitmap corresponding to the user tag includes multiple attribute identifiers, each attribute identifier is used to describe the attribute of a user, such as user tag A is male, and the bitmap corresponding to the user tag includes 2 attribute identifiers, including 0 and 1, and the attribute of a user is described by 0 or 1. Among them, the bitmap can be one of an efficient compressed bitmap (Roaring BitMap) or a bitmap (BitMap), wherein the compressed bitmap is a compressed data structure for storing bit data, which is mainly used in fields such as data analysis and database indexing. Its main advantage is that it can realize efficient union, intersection and difference operations of bit sequences, and it is also more advantageous than other data structures in terms of storage space.

[0067] Step S103, determining the target business population according to the attribute identifier in the target bitmap corresponding to each screening tag.

[0068] In this embodiment, the target business population is determined based on the attribute identifier in the target bitmap corresponding to each filtering tag. Specifically, user tag A is male, the attribute identifier of user a in the bitmap corresponding to user tag A is 1, and the attribute identifier of user b in the bitmap corresponding to user tag A is 0. The target business population is determined based on the attribute representation, and business is subsequently pushed to the target business population in a targeted manner.

[0069] Steps S101 to S103 shown in the embodiment of the present application are performed by obtaining filtering conditions for the target business population, wherein the filtering conditions include at least one filtering tag. For each filtering tag, a target bitmap corresponding to a user tag matching the filtering tag is obtained from a pre-acquired first mapping relationship. Each user tag corresponds to an attribute of a user, and the bitmap corresponding to the user tag includes multiple attribute identifiers, each attribute identifier is used to describe an attribute of a user. The target business population is determined according to the attribute identifier in the target bitmap corresponding to each filtering tag. Data is stored in the form of a bitmap instead of a wide table, which can improve processing efficiency during screening, improve the screening efficiency of the target business population, and facilitate subsequent targeted business push to the target business population.

[0070] See also Figure 2In some embodiments, the screening condition further includes: a logic operation rule, the logic operation rule includes at least one of an AND operation, an OR operation, and a NOT operation; step S103 may include but is not limited to steps S201 to S202:

[0071] Step S201 : Process multiple target bitmaps corresponding to multiple filter tags according to a logic operation rule to obtain a first target bitmap, wherein the multiple target bitmaps correspond to the multiple filter tags one by one.

[0072] In this embodiment, multiple target bitmaps correspond one-to-one to multiple filtering tags, and the multiple target bitmaps corresponding to the multiple filtering tags are processed according to logical operation rules. The logical operation rules are that the user triggers a filtering request through a filtering platform, and the filtering request carries filtering conditions. The filtering conditions include at least one filtering tag and logical operation rules. The logical operation rules include at least one of AND operation, OR operation, and NOT operation.

[0073] Alternatively, if the logical operation rule is an OR operation, see Figure 3 , the screening labels are user label A and user label B. The attribute corresponding to user label A is female, corresponding to target bitmap a, wherein target bitmap a includes 2 attribute identifiers, each of which is used to describe the attribute of a user, "0" is used to describe that the user is not female, and "1" is used to describe that the user is female. The attribute corresponding to user label B is undergraduate, corresponding to target bitmap b, wherein target bitmap b includes 2 attribute identifiers, each of which is used to describe the attribute of a user, "0" is used to describe that the user is not undergraduate, and "1" is used to describe that the user is undergraduate. Both target bitmap a and target bitmap b correspond to the attributes of user a, user b, user c, and user d. Through target bitmap a, it is determined that user a, user b, and user c are not female, and user c is female; through target bitmap b, it is determined that user a and user d are undergraduates, and user c and user d are not undergraduates. The target bitmap a and target bitmap b are processed according to the OR operation, that is, the union is calculated, and the first target bitmap of the union of target bitmap a and target bitmap b is obtained. The target business population is determined through the first target bitmap.

[0074] Step S202: determining a target business group according to the attribute identifier in the first target bitmap.

[0075] In this embodiment, the attribute identifier in the first target bitmap is obtained, and further reference is made to Figure 3 , the attribute identifier of user a is 1, the attribute identifier of user b is 0, the attribute identifier of user c is 1, and the attribute identifier of user d is 1. The target business population can be determined according to the attribute identifiers in the first target bitmap.

[0076] See also Figure 4In some embodiments, step S202 may include but is not limited to steps S401 to S402:

[0077] Step S401: Obtain a target identifier whose attribute identifier is a first preset identifier in a first target bitmap.

[0078] In this embodiment, the attribute identifier includes a first preset identifier and a second preset identifier, the first preset identifier is set to "1", and the second preset identifier is set to "0". Figure 3 , search the first target bitmap for an identifier whose attribute identifier is a first preset identifier, which is a target identifier, that is, search for an identifier whose attribute identifier is "1".

[0079] Step S402: determining a target business group according to the user identifier corresponding to the target identifier.

[0080] Continue to see Figure 3 The attribute identifiers of user a, user c, and user d are the identifiers of the first preset identifier, all of which are target identifiers and belong to the target business population. User b is neither a female nor a college graduate, and the attribute identifier of user b is the second preset identifier, which is a non-target identifier. User b does not belong to the target population. The target business population is determined based on the user identifier corresponding to the target identifier, that is, based on user a, user c, and user d.

[0081] Figure 5 is another flow chart of the method for determining the target business population provided in the embodiment of the present application. Figure 5 The method may include but is not limited to steps S501 to S506:

[0082] Step S501, obtaining screening conditions for a target business population, where the screening conditions include at least one screening tag.

[0083] In this embodiment, filtering conditions for the target business population are obtained, and the filtering conditions are triggered by business personnel based on a filtering platform, wherein the filtering conditions include at least one filtering tag. For example, if the business personnel selects user tags of gender and age, then the filtering tags include gender and age. The business personnel selects at least one user tag, and the user tag includes basic information, such as gender, age, region, occupation, marital status, etc.

[0084] Step S502: For each filtering tag, a target bitmap corresponding to a user tag matching the filtering tag is obtained from a pre-acquired first mapping relationship, wherein the first mapping relationship includes multiple user tags and a bitmap corresponding to each user tag, each user tag corresponds to an attribute of the user, and the bitmap corresponding to the user tag includes multiple attribute identifiers, each attribute identifier is used to describe an attribute of a user.

[0085] In this embodiment, for each screening tag, a target bitmap corresponding to a user tag matching the screening condition is obtained from the pre-acquired first mapping relationship. In order to save storage space and solve the time limit problem of query, a bitmap is used to store data, wherein the first mapping relationship includes multiple user tags and a bitmap corresponding to each tag, wherein the user tag is a standardized definition of user attributes, behaviors, preferences, etc., and is used to describe customer characteristics. Each user tag corresponds to an attribute of the user, such as age, education, gender, etc. The bitmap corresponding to the user tag includes multiple attribute identifiers, each of which is used to describe the attributes of a user. For example, if user tag A is male, the bitmap corresponding to the user tag includes two attribute identifiers, including 0 and 1, and 0 and 1 are used to describe the attributes of a user.

[0086] Step S503: determine the target business population according to the attribute identifier in the target bitmap corresponding to each screening tag.

[0087] In this embodiment, the target business population is determined based on the attribute identifier in the target bitmap corresponding to each filtering tag. Specifically, user tag A is male, the attribute identifier of user a in the bitmap corresponding to user tag A is 1, and the attribute identifier of user b in the bitmap corresponding to user tag A is 0. The target business population is determined based on the attribute representation, and business is subsequently pushed to the target business population in a targeted manner.

[0088] Step S504: obtaining a user ID of each user in the target business population, where the user ID is used to identify a unique user.

[0089] In this embodiment, the bitmap corresponding to the user tag also includes multiple user identifiers, and the multiple user identifiers correspond to the multiple attribute identifiers one by one. Figure 6 The first target bitmap is stored in the first database, and the first database is used to store the bitmap. The user identification of each user in the target business population is obtained. The user identification is unique and is used to identify a unique user. The user identification can be a user name, a mobile phone number, a user ID, etc.

[0090] Step S505: for each user identifier, traverse the bitmap corresponding to each user tag in the first mapping relationship to obtain the attribute identifier corresponding to each user identifier.

[0091] In this embodiment, for each user identifier, the bitmap corresponding to each user tag in the first mapping relationship is traversed to obtain the attribute identifier corresponding to each user identifier, and obtain the age, gender, education level, etc. of each user.

[0092] Step S506: Generate a description package of the target business group according to the user ID of each user in the target business group and the attribute ID corresponding to the user ID.

[0093] In this embodiment, in a certain user tag, if the attribute identifier corresponding to the user identifier is the first preset identifier, it means that the user belongs to this user tag and there is an attribute value corresponding to this user tag, then the attribute value of the user tag is obtained according to the user tag corresponding to the attribute identifier, and if the attribute identifier corresponding to the user identifier is the second preset identifier, then the user does not belong to this user tag and there is no attribute value corresponding to this user tag. According to the user identifiers of each user in the target business population and the attribute identifiers corresponding to the user identifiers, the user identifier of the user whose attribute identifier corresponding to the user identifier is the first preset identifier is obtained, according to the user identifiers of each user in the target business population and the attribute identifiers corresponding to the user identifiers, the attribute value of the user tag is determined, and the description package of the target business population is generated according to the user identifiers of each user in the target business population and the attribute values ​​of each user.

[0094] In some embodiments, step S102 may also include but is not limited to the following steps:

[0095] Acquire multiple user identifiers, user tags corresponding to each user identifier, and attribute values ​​of the user tags; construct a second mapping relationship based on the multiple user identifiers, the user tags corresponding to each user identifier, and the attribute values ​​of the user tags, and the second mapping relationship is stored in a second database; import the second mapping relationship into a first database, the timeliness of data processing in the first database is higher than that in the second database, and the first database is a column storage database; perform bitmap conversion on the second mapping relationship to obtain a first mapping relationship, and the first mapping relationship is stored in the first database, and the attribute identifier includes a first preset identifier and a second preset identifier, the first preset identifier is used to indicate that the attribute value of the user tag exists, and the second preset identifier is used to indicate that the attribute value of the user tag does not exist.

[0096] In this embodiment, multiple user identifiers, user tags corresponding to each user identifier, and attribute values ​​of the user tags are obtained, for example, the user tags of user a include education level and purchased insurance products, and the attribute values ​​are undergraduate degree and product a. A second mapping relationship is constructed based on multiple users, user tags corresponding to each user identifier, and attribute values ​​of the user tags, and the second mapping relationship is stored in the second database in the form of a table.

[0097] See also Figure 6 The target business population determination system includes: a platform, a first database and a second database. The user triggers a screening request through the platform. The data in the first database is processed more efficiently than that in the second database. The first database is a column storage database. The second mapping relationship is imported into the first database, and the second mapping relationship is converted into a bitmap to obtain a first mapping relationship. The first mapping relationship is stored in the first database.

[0098] See also Figure 7 , the left side is the second mapping relationship, which is stored in the form of a table, and the right side is the first mapping relationship. The fields of the second mapping relationship table include gender and education level. The same field can correspond to multiple different values. Multiple user tags are set to convert the fields in the first mapping relationship. For example, the gender in the first mapping relationship table is converted, and the user tags corresponding to gender are set to 2, user tag A (male) and user tag B (female). The attribute values ​​of Zhao a, Li b, and Yang d in the second mapping relationship table are male. In the bitmap a corresponding to user tag A (male), the attribute identifier "1" is used to indicate the gender of Zhao a, Li b, and Yang d. The attribute value of Wang c is female. In user tag A (male), the attribute identifier "0" is used to indicate the gender of Wang c; and in the bitmap b corresponding to user tag B (female), the attribute identifier "1" is used to indicate the gender of Wang c, and the attribute identifier "0" is used to indicate the gender of Zhao a, Li b, and Yang d.

[0099] Continue to see Figure 7 , the education level in the second mapping relationship table is converted, and the user tags corresponding to the education level are set to user tag C (undergraduate), user tag D (college), and Figure 7 For the user tag E (high school) not shown in the figure, the attribute values ​​of Zhao A and Wang C in the second mapping relationship table are bachelor's degree. In the bitmap c corresponding to the user tag C (bachelor's degree), the attribute identifier "1" is used to represent the academic qualifications of Zhao A and Wang C. The attribute value of Li B is high school, and the attribute value of Yang D is college degree. In the user tag C (bachelor's degree), the attribute identifier "0" is used to represent the academic qualifications of Yang D. In the bitmap d corresponding to the user tag D (college), the attribute identifier "1" is used to represent the academic qualifications of Yang D, and the attribute identifier "0" is used to represent the academic qualifications of Zhao A, Wang C and Li B.

[0100] In the above-mentioned first mapping relationship, the attribute identifier includes a first preset identifier (i.e., "1" in the above text) and a second preset identifier (i.e., "0" in the above text), the first preset identifier is used to indicate that the attribute value of the user tag exists, and the second preset identifier is used to indicate that the attribute value of the user tag does not exist.

[0101] In some embodiments, performing bitmap conversion on the second mapping relationship to obtain the first mapping relationship includes:

[0102] According to the type of the user tag, the second mapping relationship is bitmap-converted to obtain the first mapping relationship, and the type of the user tag includes a numerical type user tag and a character type user tag.

[0103] In this embodiment, the types of user tags include user tags of numerical type and user tags of character type, wherein the bitmap stored in the second database includes a first bitmap and a second bitmap, and data is imported into different bitmaps according to the types of user tags. Specifically, the numerical type tags are imported into the first bitmap, which is Float64 Bitmap, the character type tags are imported into the second bitmap, which is String Bitmap, and the array type tags are split according to the expanded basic types. The second mapping relationship is converted into a bitmap according to the type to obtain the first mapping relationship, and different types of user tags are imported into different bitmaps for subsequent use.

[0104] In some embodiments, step S101 may also include but is not limited to the following steps:

[0105] In response to a screening request for a target business population triggered by a screening platform, the screening request is parsed to obtain screening conditions for the target business population.

[0106] In this embodiment, the user selects the user tag that he wants to filter in the interface of the filtering platform and clicks the filtering button, thereby triggering a filtering request for the target business population, parsing the filtering request, and obtaining the filtering conditions for the target business population. The filtering request includes: at least one filtering tag and a logical operation rule. For each filtering condition, the corresponding target bitmap is obtained, and the target business population is further determined based on the attribute identifier in the target bitmap corresponding to each filtering tag, so as to push business to the target business population in a targeted manner.

[0107] The following is an example of the method for determining the target business population provided in the embodiment of the present application. Figure 8 The target business population determination system provided by the present application is shown, and the system includes: a platform and a server backend, the server backend includes a first database and a second database, the platform is communicated with the backend, and the first database and the second database are communicated with each other. The first database can be a Clickhouse database, a column storage database, and the second database can be a hive database.

[0108] Among them, the hive database stores the second mapping relationship, obtains multiple user identifiers (including the customer's user identifier and the agent's user identifier), the user tag corresponding to each user identifier, and the attribute value corresponding to the user tag, and obtains the insurance policy. The above data is stored in the form of a hive table, and the customer's user tag, the agent's user tag and the insurance policy are associated through ID-mapping. The string or discrete value is converted into a continuous value type. The conversion of discrete data helps to improve the resource utilization of Bitmap (i.e. the bitmap above). Among them, the insurance policy includes basic information, claims, preservation and other tags. The customer's user tags include basic information, customer online and offline behavior, customer service, product holding and other tags. The agent's user tags include basic information, personal development, interpersonal network, work ability and other tags. The second mapping relationship is constructed based on the customer's user identifier, the agent's user identifier, the user tag corresponding to each user identifier and the attribute value of the user tag, and the second mapping relationship is imported into the Clickhouse database.

[0109] Specifically, the second mapping relationship is bitmap converted to obtain the first mapping relationship, the first mapping relationship is stored in the Clickhouse database, the first mapping relationship includes a first bitmap and a second bitmap, and the second mapping relationship is bitmap converted according to the type of user tags, wherein the numerical type tags are imported into the first bitmap, the first bitmap is Float64Bitmap, and the character type tags are imported into the second bitmap, the second bitmap is String Bitmap.

[0110] Specifically, business personnel use the platform to combine tags to select, analyze, and compare, etc., and use the built-in Bitmap function of the Clickhouse database to implement logical operations such as AND or NOT, and improve query performance to 2-5s. Specifically, business personnel select pre-set user tags (i.e., filter tags in the above text) and logical operation rules in the platform interface, click Confirm, and the filter request is triggered. The server backend receives the filter request, parses the filter request, and obtains the filter conditions, which include at least one filter tag and a logical operation rule. For each filtering tag, a target bitmap corresponding to a user tag matching the filtering tag is obtained from a pre-acquired first mapping relationship, wherein the first mapping relationship includes multiple user tags and a bitmap corresponding to each user tag, each user tag corresponds to an attribute of a user, and the bitmap corresponding to the user tag includes multiple attribute identifiers, each attribute identifier is used to describe an attribute of a user; according to the attribute identifier in the target bitmap corresponding to each filtering tag, a crowd package ID list table (i.e., the target business crowd mentioned above) is determined, specifically, multiple target bitmaps corresponding to multiple filtering tags are processed according to logical operation rules to obtain a crowd package bitmap (i.e., the first icon bitmap mentioned above), and multiple target bitmaps are mapped to multiple filtering tags. One-to-one correspondence; determine the target business population according to the attribute identifier in the population package bitmap; obtain the target identifier whose attribute identifier in the population package bitmap is the first preset identifier, the user identifier corresponding to the target identifier, determine the population package ID list table, and further obtain the user identifier of each user in the population package ID list table, and the user identifier is used to identify the unique user; for each user identifier, traverse the bitmap corresponding to each user label in the first mapping relationship to obtain the attribute identifier corresponding to each user identifier; generate a population package table (i.e., the description package of the target business population mentioned above) according to the user identifier of each user in the target business population and the attribute identifier corresponding to the user identifier, and store the population package data in the hive database for downstream retrieval. Through RoaringBitMap, the deduplication calculation time of the circle is improved from minutes to within 15s, reducing memory usage by 70%, and the query rate per second (QPS) is increased from an average of 20 to an average of 500, and the QPS of some queries is increased to 1000+, which greatly reduces hardware costs. It supports the association permissions of multiple primary keys of customers, policies, and agents. The performance is improved from 30 minutes for large table association to within 15 seconds. It no longer needs to open a wide table, and the labeling time is improved by more than several hours, saving 70% of storage resources. It supports label selection based on historical partition tables, eliminating the need for repeated processing and reducing the labor and hardware costs of label processing.In the above process, generating the crowd package only ID list table does not require high timeliness, and needs to output a large amount of label data, which requires a large amount of memory. The output label is obtained by exporting the Bitmap object of the list to the hive database and associating it with the label table through hive sql.

[0111] In addition, business personnel can perform cross-object selection through the platform. When using cross-object selection, for example, when selecting customers by insurance policy, they only need to use the mapping relationship table between the customer and insurance policy table to convert the insurance policy's Bitmap into the customer's Bitmap, and then perform an AND operation with the customer's own selected Bitmap.

[0112] See also Fig. 9 , the embodiment of the present application also provides a target business population determination device 90, which can implement the above-mentioned target business population determination method, and the device includes: an acquisition module 901 and a processing module 902;

[0113] Among them, the acquisition module 901 is used to obtain the screening conditions of the target business population, and the screening conditions include at least one screening tag. The processing module 902 is used to obtain, for each screening tag, a target bitmap corresponding to a user tag that matches the screening tag from a pre-acquired first mapping relationship, wherein the first mapping relationship includes multiple user tags and a bitmap corresponding to each user tag, each user tag corresponds to an attribute of the user, and the bitmap corresponding to the user tag includes multiple attribute identifiers, each attribute identifier is used to describe an attribute of a user. The processing module 902 is also used to determine the target business population according to the attribute identifier in the target bitmap corresponding to each screening tag.

[0114] In some embodiments, the processing module 902 is further used to process multiple target bitmaps corresponding to multiple filtering tags according to logical operation rules to obtain a first target bitmap, and the multiple target bitmaps correspond one-to-one to the multiple filtering tags; and determine the target business population according to the attribute identifier in the first target bitmap.

[0115] In some embodiments, the acquisition module 901 is further used to acquire a target identifier whose attribute identifier is a first preset identifier in the first target bitmap. The processing module 902 is further used to determine a target service group according to a user identifier corresponding to the target identifier.

[0116] In some embodiments, the acquisition module 901 is also used to obtain the user identifier of each user in the target business population, and the user identifier is used to identify a unique user; the processing module 902 is also used to traverse the bitmap corresponding to each user tag in the first mapping relationship for each user identifier, and obtain the attribute identifier corresponding to each user identifier; based on the user identifier of each user in the target business population and the attribute identifier corresponding to the user identifier, a description package of the target business population is generated.

[0117] In some embodiments, the acquisition module 901 is also used to obtain multiple user identifiers, user tags corresponding to each user identifier, and attribute values ​​of the user tags. The processing module 902 is also used to construct a second mapping relationship based on multiple user identifiers, user tags corresponding to each user identifier, and attribute values ​​of the user tags, and the second mapping relationship is stored in the second database; the second mapping relationship is imported into the first database, the timeliness of data processing in the first database is higher than that of the second database, and the first database is a column storage database; the second mapping relationship is bitmap converted to obtain the first mapping relationship, and the first mapping relationship is stored in the first database, and the attribute identifier includes a first preset identifier and a second preset identifier, the first preset identifier is used to indicate that the attribute value of the user tag exists, and the second preset identifier is used to indicate that the attribute value of the user tag does not exist.

[0118] In some embodiments, the processing module 902 is further used to perform bitmap conversion on the second mapping relationship according to the type of the user tag to obtain the first mapping relationship, and the type of the user tag includes a numeric type user tag and a character type user tag.

[0119] In some embodiments, the acquisition module 901 is further configured to respond to a screening request for a target business population triggered by a screening platform, parse the screening request, and obtain screening conditions for the target business population.

[0120] The specific implementation of the target business population determination device is substantially the same as the specific implementation of the target business population determination method described above, and will not be described in detail herein.

[0121] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned target service population determination method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0122] See also Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0123] The processor 1001 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0124] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1002, and the processor 1001 calls and executes the target business population determination method of the embodiment of this application;

[0125] Input / output interface 1003, used to implement information input and output;

[0126] The communication interface 1004 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0127] A bus 1005 , which transmits information between various components of the device (e.g., the processor 1001 , the memory 1002 , the input / output interface 1003 , and the communication interface 1004 );

[0128] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0129] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned method for determining a target business population is implemented.

[0130] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0131] The target business population determination method, target business population determination device, electronic device and storage medium provided by the embodiments of the present application obtain screening conditions for the target business population, wherein the screening conditions include at least one screening tag. For each screening tag, a target bitmap corresponding to a user tag matching the screening tag is obtained from a pre-acquired first mapping relationship. Each user tag corresponds to an attribute of a user, and the bitmap corresponding to the user tag includes multiple attribute identifiers, each attribute identifier is used to describe an attribute of a user. The target business population is determined according to the attribute identifier in the target bitmap corresponding to each screening tag, and data is stored in a bitmap manner, which can improve processing efficiency during screening, improve the screening efficiency of the target business population, and facilitate subsequent targeted business push to the target business population.

[0132] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0133] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0134] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0136] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0137] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0138] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0139] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0141] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0142] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for determining a target business population, characterized in that: The method comprises: Acquire a screening condition for a target business population, wherein the screening condition includes at least one screening tag; For each of the filtering tags, a target bitmap corresponding to a user tag matching the filtering tag is obtained from a pre-acquired first mapping relationship, wherein the first mapping relationship includes a plurality of user tags and a bitmap corresponding to each user tag, each user tag corresponds to an attribute of a user, and the bitmap corresponding to the user tag includes a plurality of attribute identifiers, each attribute identifier is used to describe the attribute of a user; The target business population is determined according to the attribute identifier in the target bitmap corresponding to each of the screening tags.

2. The method according to claim 1, characterized in that The screening condition further includes: a logic operation rule, wherein the logic operation rule includes at least one of an AND operation, an OR operation, and a NOT operation; Determining the target business population according to the attribute identifier in the target bitmap corresponding to each of the screening tags includes: Processing a plurality of target bitmaps corresponding to the plurality of filtering tags according to a logic operation rule to obtain a first target bitmap, wherein the plurality of target bitmaps correspond to the plurality of filtering tags one by one; The target business group is determined according to the attribute identifier in the first target bitmap.

3. The method according to claim 2, characterized in that The bitmap corresponding to the user tag also includes multiple user identifiers, the multiple user identifiers correspond one-to-one to the multiple attribute identifiers, the first target bitmap is stored in a first database, and the first database is a column storage database; The determining the target service population according to the attribute identifier in the first target bitmap includes: Obtaining a target identifier whose attribute identifier is a first preset identifier in the first target bitmap; The target business group is determined according to the user identifier corresponding to the target identifier.

4. The method according to claim 1, characterized in that: The bitmap corresponding to the user tag also includes multiple user identifiers, and the multiple user identifiers correspond to the multiple attribute identifiers one by one; After determining the target business population according to the attribute identifier in the target bitmap corresponding to each of the screening tags, the method further includes: Obtaining a user ID of each user in the target business population, wherein the user ID is used to identify a unique user; For each of the user identifiers, traverse the bitmap corresponding to each user tag in the first mapping relationship to obtain an attribute identifier corresponding to each of the user identifiers; A description package of the target business group is generated according to the user identification of each user in the target business group and the attribute identification corresponding to the user identification.

5. The method according to claim 1, characterized in that Before acquiring, for each of the filter tags, a target bitmap corresponding to a user tag matching the filter tag from a pre-acquired first mapping relationship, the method further includes: Obtain multiple user IDs, a user tag corresponding to each user ID, and an attribute value of the user tag; constructing a second mapping relationship according to the multiple user identifiers, the user tag corresponding to each user identifier, and the attribute value of the user tag, wherein the second mapping relationship is stored in a second database; Importing the second mapping relationship into a first database, the timeliness of data processing in the first database is higher than that in the second database, and the first database is the column storage database; The second mapping relationship is bitmap converted to obtain the first mapping relationship, the first mapping relationship is stored in the first database, the attribute identifier includes a first preset identifier and a second preset identifier, the first preset identifier is used to indicate that the attribute value of the user tag exists, and the second preset identifier is used to indicate that the attribute value of the user tag does not exist.

6. The method according to claim 5, characterized in that The performing bitmap conversion on the second mapping relationship to obtain the first mapping relationship includes: The second mapping relationship is bitmap-converted according to the type of the user tag to obtain the first mapping relationship, and the type of the user tag includes a numerical user tag and a character user tag.

7. The method according to any one of claims 1 to 6, characterized in that: The screening conditions for obtaining the target business population include: In response to a screening request for a target business population triggered by a screening platform, the screening request is parsed to obtain screening conditions for the target business population.

8. A device for determining a target business population, characterized in that: The device comprises: An acquisition module, used to acquire a screening condition of a target business population, wherein the screening condition includes at least one screening tag; a processing module, configured to obtain, for each of the filtering tags, a target bitmap corresponding to a user tag matching the filtering tag from a pre-acquired first mapping relationship, wherein the first mapping relationship includes a plurality of user tags and a bitmap corresponding to each user tag, each user tag corresponds to an attribute of a user, and the bitmap corresponding to the user tag includes a plurality of attribute identifiers, each attribute identifier being used to describe the attribute of a user; The processing module is further used to determine the target business population according to the attribute identifier in the target bitmap corresponding to each of the screening tags.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method for determining a target business population as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for determining a target business population according to any one of claims 1 to 7 is implemented.

Images