CN114238312A - User portrait determination method and device based on bitmap calculation - Google Patents
User portrait determination method and device based on bitmap calculation Download PDFInfo
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Abstract
The invention discloses a user portrait determination method and a device based on bitmap calculation, wherein the method comprises the following steps: determining a user representation query requirement; the user portrait query requirement is used for indicating at least two target portrait dimensions to be queried; determining at least two portrait bitmap data according to the user portrait query requirement; the portrait bitmap data is used for recording a plurality of user information meeting the target portrait dimensionality in a bitmap data form; calculating the portrait information of the target user according to the bitmap data of the at least two portraits; the target user representation information is used to indicate all user information that satisfies the at least two target representation dimensions. Therefore, the invention can utilize the low resource occupancy rate of the bitmap data to reduce the cost of user information storage on one hand, and can utilize the high efficiency of bitmap calculation to improve the efficiency of user portrait determination on the other hand.
Description
Technical Field
The invention relates to the technical field of user portrait calculation, in particular to a user portrait determination method and device based on bitmap calculation.
Background
User portrait data is one of the important data bases in user data analysis. User representation data is generally composed of a plurality of items of feature data of a specific group or object, such as frequent region, amount of consumption, or biometrics. In some cases, user representation data is needed to represent the user's data in multiple representation dimensions, such as users who have gone through both the A and B regions in 8 months, in which case, data mining or matching of multiple user feature data is needed to solve for such user representation data.
In the prior art, when solving such user portrait data related to multiple dimensions, portrait calculation is generally performed by adopting a specific data analysis mode adapted to an original data storage type of user characteristic data, such as user geographical location information and user tag records, but such a mode needs to occupy a large amount of storage space to store user characteristic data on one hand, and needs to occupy huge computing resources to implement data calculation on the other hand, and is low in efficiency and high in cost, and obviously cannot adapt to the current situation that the data volume is increasing day by day.
Accordingly, there is a need for a low-cost and efficient user portrait calculation method.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method and an apparatus for determining a user portrait based on bitmap calculation, which can utilize the low resource occupancy rate of bitmap data to reduce the cost of user information storage, and can utilize the high efficiency of bitmap calculation to improve the efficiency of user portrait determination.
In order to solve the above technical problem, a first aspect of the present invention discloses a method for determining a user portrait based on bitmap calculation, the method comprising:
determining a user representation query requirement; the user portrait query requirement is used for indicating at least two target portrait dimensions to be queried;
determining at least two portrait bitmap data according to the user portrait query requirement; the portrait bitmap data is used for recording a plurality of user information meeting the target portrait dimensionality in a bitmap data form;
calculating the portrait information of the target user according to the bitmap data of the at least two portraits; the target user representation information is used to indicate all user information that satisfies the at least two target representation dimensions.
As an alternative embodiment, in the first aspect of the invention, the target representation dimensions include one or more of a geographic location dimension, a geographic area dimension, a time dimension, and a facility dimension.
As an optional implementation manner, in the first aspect of the present invention, the determining at least two portrait bitmap data according to the user portrait query requirement includes:
determining all the target portrait dimensions to be queried according to the user portrait query requirement;
and determining image bitmap data corresponding to the target image dimension for each target image dimension.
As an optional implementation manner, in the first aspect of the present invention, the calculating target user portrait information according to the at least two portrait bitmap data includes:
performing bitmap intersection calculation on the at least two portrait bitmap data to determine intersection user information among all the portrait bitmap data;
and determining the intersection user information as target user portrait information.
As an alternative embodiment, in the first aspect of the present invention, each said target portrait dimension corresponds to a plurality of said portrait bitmap data corresponding to different user partitions; the calculating of the portrait information of the target user according to the bitmap data of the at least two portraits comprises the following steps:
for each user partition, determining all the portrait bitmap data corresponding to the user partition;
performing bitmap intersection calculation on all the portrait bitmap data corresponding to the user partition to determine intersection user information among all the portrait bitmap data;
and determining the intersection user information corresponding to all the user partitions as target user portrait information.
As an optional implementation manner, in the first aspect of the present invention, the partition manner of the user partition includes at least one of a partition based on a number dimension, a partition based on a gender dimension, a partition based on a standing region dimension, and a partition based on an age dimension.
As an optional implementation manner, in the first aspect of the present invention, before the determining the user representation query requirement, the method further includes:
acquiring a plurality of user information meeting any one of the target portrait dimensions;
and converting a plurality of user identifications corresponding to the plurality of user information into portrait bitmap data corresponding to the target portrait dimension based on a bitmap data conversion rule.
The second aspect of the embodiments of the present invention discloses a user portrait determination device based on bitmap calculation, where the device includes:
a requirement determining module for determining a user portrait query requirement; the user portrait query requirement is used for indicating at least two target portrait dimensions to be queried;
the bitmap determining module is used for determining bitmap data of at least two portrait according to the user portrait query requirement; the portrait bitmap data is used for recording a plurality of user information meeting the target portrait dimensionality in a bitmap data form;
the portrait calculation module is used for calculating portrait information of a target user according to the at least two portrait bitmap data; the target user representation information is used to indicate all user information that satisfies the at least two target representation dimensions.
As an alternative embodiment, in the second aspect of the invention, the target representation dimensions include one or more of a geographic location dimension, a geographic area dimension, a time dimension, and a facility dimension.
As an optional implementation manner, in the second aspect of the present invention, the determining a specific manner of at least two portrait bitmap data by the bitmap determining module according to the user portrait query requirement includes:
determining all the target portrait dimensions to be queried according to the user portrait query requirement;
and determining image bitmap data corresponding to the target image dimension for each target image dimension.
As an optional implementation manner, in the second aspect of the present invention, a specific manner of the portrait calculation module calculating the portrait information of the target user according to the at least two portrait bitmap data includes:
performing bitmap intersection calculation on the at least two portrait bitmap data to determine intersection user information among all the portrait bitmap data;
and determining the intersection user information as target user portrait information.
As an alternative embodiment, in the second aspect of the present invention, each said target portrait dimension corresponds to a plurality of said portrait bitmap data corresponding to different user zones; the bitmap determination module calculates the specific mode of the portrait information of the target user according to the bitmap data of the at least two portraits, and the bitmap determination module comprises the following steps:
for each user partition, determining all the portrait bitmap data corresponding to the user partition;
performing bitmap intersection calculation on all the portrait bitmap data corresponding to the user partition to determine intersection user information among all the portrait bitmap data;
and determining the intersection user information corresponding to all the user partitions as target user portrait information.
As an optional implementation manner, in the second aspect of the present invention, the partition manner of the user partition includes at least one of a partition based on a number dimension, a partition based on a gender dimension, a partition based on a standing region dimension, and a partition based on an age dimension.
As an optional implementation manner, in the second aspect of the present invention, the apparatus further includes a bitmap generation module, configured to perform the following steps, acquiring a plurality of user information satisfying any one of the target portrait dimensions, and converting a plurality of user identifiers corresponding to the plurality of user information into portrait bitmap data corresponding to the target portrait dimension based on a bitmap data conversion rule.
In a third aspect, the present invention discloses another apparatus for determining a user portrait based on bitmap calculation, the apparatus comprising:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute part or all of the steps of the user portrait determination method based on bitmap calculation disclosed in the first aspect of the present invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the embodiment of the invention discloses a user portrait determination method and device based on bitmap calculation, wherein the method comprises the following steps: determining a user representation query requirement; the user portrait query requirement is used for indicating at least two target portrait dimensions to be queried; determining at least two portrait bitmap data according to the user portrait query requirement; the portrait bitmap data is used for recording a plurality of user information meeting the target portrait dimensionality in a bitmap data form; calculating the portrait information of the target user according to the bitmap data of the at least two portraits; the target user representation information is used to indicate all user information that satisfies the at least two target representation dimensions. Therefore, the embodiment of the invention can store the user information of the specific portrait dimension by using the bitmap data and determine the user portrait information by using a bitmap calculation mode, thereby reducing the cost of storing the user information by using the low resource occupancy rate of the bitmap data on one hand and improving the efficiency of determining the user portrait by using the high efficiency of the bitmap calculation on the other hand.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flowchart illustrating a method for determining a user portrait based on bitmap calculation according to an embodiment of the present invention.
FIG. 2 is a schematic structural diagram of a user portrait determination apparatus based on bitmap calculation according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of another user portrait determination apparatus based on bitmap calculation according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or apparatus that comprises a list of steps or elements is not limited to those listed but may alternatively include other steps or elements not listed or inherent to such process, method, product, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The invention discloses a user portrait determining method and device based on bitmap calculation, which can save user information of specific portrait dimensions by using bitmap data and determine the user portrait information by using a bitmap calculation mode, thereby reducing the cost of user information storage by using the low resource occupancy rate of the bitmap data on one hand and improving the efficiency of user portrait determination by using the high efficiency of bitmap calculation on the other hand. The following are detailed below.
Example one
Referring to fig. 1, fig. 1 is a flow chart illustrating a method for determining a user portrait based on bitmap calculation according to an embodiment of the present invention. The user representation determining method described in fig. 1 may be applied to a user representation computing chip, a computing terminal, or a computing server (where the computing server may be a local server or a cloud server). As shown in fig. 1, the user portrait determination method based on bitmap calculation may include the following operations:
101. a user representation query requirement is determined.
Optionally, the user portrait query requirement may be determined according to information input by the user, for example, a visual interface is provided for the user to select portrait dimensions to be queried, or may be automatically generated according to a data input requirement of the algorithm model, for example, when a user ID transmitted by a specific algorithm model and corresponding portrait dimensions to be queried are received, the user portrait query requirement is generated.
Optionally, the user representation query requirement is used to indicate at least two target representation dimensions to be queried. Optionally, the target portrait dimensions may include one or more of a geographic location dimension, a geographic area dimension, a time dimension, and a facility dimension, for example, the user portrait query requirement may indicate that all users who want to query to appear in area a in month 20XX 8, and then two target portrait dimensions indicated by the user portrait query requirement are respectively the time dimension corresponding to month 20XX 8 and the geographic area dimension corresponding to area a, and for example, the user portrait query requirement may indicate that all users who want to query to appear in both positions of longitude and latitude coordinates 113.261996,23.12725 and a CCC subway station, and then two target portrait dimensions indicated by the user portrait query requirement are respectively the geographic location dimension corresponding to coordinates of longitude and latitude 113.261996, and a position of 23.12725 and the facility dimension corresponding to the CCC subway station.
Optionally, the geographic location dimension may be a two-dimensional location dimension, a three-dimensional location dimension, or a coordinate location dimension in a specific coordinate system, such as a longitude and latitude location dimension. Optionally, the geographic region dimension may be an administrative region dimension of a country, province, city, district, or the like. Optionally, the time dimension may include one or more of years, months, days, hours, minutes, and seconds. Alternatively, the facilities in the facility dimension may be POI (Point of interest) or AOI (area of interest), and the types thereof include but are not limited to: sports leisure facilities, catering facilities, life service facilities, science and education culture facilities, shopping facilities, transportation facilities, national government facilities, accommodation facilities, business facilities of enterprises, health care/health care facilities, public facilities, and the like.
102. At least two portrait bitmap data are determined based on user portrait query requirements.
Optionally, the portrait bitmap data is used to record a plurality of user information satisfying the target portrait dimensions in the form of bitmap data. Alternatively, the representation bitmap data may correspond to one target representation dimension, e.g., it may include user information for all users in area A, or it may correspond to multiple target representation dimensions, e.g., it may include user information for all users who appear in area A in month 8 of 20 XX.
103. And calculating the portrait information of the target user according to the bitmap data of at least two portraits.
Optionally, the target user representation information is used to indicate all user information that satisfies at least two target representation dimensions. Optionally, intersection operation or union operation may be performed on at least two portrait bitmap data according to a requirement, so as to calculate portrait information of the target user. Here, since the intersection calculation based on the bitmap data is an operation at the bit level of the finest granularity in the computer, and is faster than other operations, a high calculation effect can be obtained.
Therefore, the embodiment of the invention can store the user information of the specific portrait dimension by using the bitmap data and determine the user portrait information by using a bitmap calculation mode, thereby reducing the cost of storing the user information by using the low resource occupancy rate of the bitmap data on one hand and improving the efficiency of determining the user portrait by using the high efficiency of the bitmap calculation on the other hand.
As an optional implementation, the determining at least two portrait bitmap data according to the user portrait query requirement in step 102 includes:
determining all target portrait dimensions to be queried according to a user portrait query requirement;
for each target image dimension, image bitmap data corresponding to the target image dimension is determined.
Optionally, the user representation query request may be in a text format, for example, a text received from a user input, such as "query all users appearing in region a in month 8 of 20 XX", thus determining all target representation dimensions to be queried may be by identifying text keywords in the user representation query request through a keyword recognition algorithm model to identify target representation dimensions therein.
Optionally, for each target image dimension, one or more image bitmap data corresponding to each target image dimension may be determined in a preset image bitmap database in a tag index manner.
Therefore, by implementing the optional implementation mode, all target portrait dimensions to be inquired can be determined according to user portrait inquiry requirements, and then portrait bitmap data corresponding to the target portrait dimensions is determined for each target portrait dimension, so that all portrait bitmap data to be used for calculation can be accurately determined according to the user portrait inquiry requirements, and then efficient and low-cost user portrait calculation can be realized on the basis.
As an optional implementation manner, the calculating the target user portrait information according to at least two portrait bitmap data in the above steps includes:
performing bitmap intersection calculation on at least two portrait bitmap data to determine intersection user information among all portrait bitmap data;
and determining the intersection user information as the target user portrait information.
Optionally, the bitmap intersection calculation for at least two portrait bitmap data may be performed by calling a corresponding computer function instruction.
Therefore, by implementing the optional implementation mode, bitmap intersection calculation can be performed on at least two portrait bitmap data to determine intersection user information among all portrait bitmap data, so that target user portrait information can be accurately determined, and efficient and low-cost user portrait calculation is realized.
As an alternative embodiment, there may be multiple portrait bitmap data for each target portrait dimension corresponding to different user zones. Accordingly, the step 103 of calculating the image information of the target user according to at least two image bitmap data comprises:
for each user partition, determining all portrait bitmap data corresponding to the user partition;
performing bitmap intersection calculation on all portrait bitmap data corresponding to the user partition to determine intersection user information among all portrait bitmap data;
and determining intersection user information corresponding to all the user partitions as target user portrait information.
Optionally, the partition manner of the user partition includes at least one of a partition based on a number dimension, a partition based on a gender dimension, a partition based on a standing region dimension, and a partition based on an age dimension. Optionally, the union of the intersection user information corresponding to all the user partitions may be determined as the target user portrait information.
Optionally, the portrait bitmap data of different user partitions are stored in different storage locations or different data spaces, for example, the portrait bitmap data of different user partitions may be stored in different shards (boards) of the database, so as to implement distributed computing.
In a specific embodiment, a certain number of some users may be divided into a area a and other users may be divided into B areas based on a certain partition manner, for example, based on a number dimension, and data of the a area and the B area may be stored in the a slice and the B slice, respectively. When calculating the user information meeting the specific portrait dimension, respectively finding out the A portrait bitmap data and the B portrait bitmap data meeting the specific portrait dimension from all the portrait bitmap data of the A slice and the B slice, then calculating the intersection user information of all the A portrait bitmap data in the A slice to determine the A user portrait data meeting the specific portrait dimension in the users in the A area, then calculating the intersection user information of all the B portrait bitmap data in the B slice to determine the B user portrait data meeting the specific portrait dimension in the users in the B area, and then determining the union of the A user portrait data and the B user portrait data as the final user portrait data.
Therefore, by implementing the optional implementation mode, intersection user information corresponding to all user partitions can be calculated to determine target user portrait information, so that independent operations in partitions and all partitions can be performed on data based on the idea of distributed calculation under the condition of huge data volume, the portrait operation efficiency can be effectively improved, the production cost is reduced, the cluster hardware performance can be utilized to the maximum extent, the operation efficiency is greatly improved, and high-efficiency and low-cost user portrait calculation is realized.
As an optional implementation manner, before determining the user representation query requirement in step 101, the method further includes:
acquiring a plurality of user information meeting any target portrait dimension;
and converting a plurality of user identifications corresponding to a plurality of user information into portrait bitmap data corresponding to the target portrait dimension based on a bitmap data conversion rule.
In this embodiment, the portrait bitmap data records a plurality of user information in a bitmap (bitmap) data form, that is, a manner of converting a plurality of user identifiers corresponding to the plurality of user information into portrait bitmap data corresponding to the target portrait dimension based on a bitmap data conversion rule may be a manner of storing the user identifiers of all the user information as keys (keys) in the bitmap data, and then determining a value (value) of the key corresponding to the user information satisfying any target portrait dimension among all the user information as a first specific value, such as 1, and determining a value of the key corresponding to the user information not satisfying the target portrait dimension among all the user information as a second specific value, such as 0, so as to obtain final portrait bitmap data.
In a particular embodiment, a Roaring Bitmap data structure may be employed to store a plurality of user information satisfying a target representation dimension. Wherein the Roaring Bitmap data structure divides the 32-bit INT type data into 216Each Chunk corresponds to the upper 16 bits of an integer and uses a Container (Container) to store the lower 16 bits of a value. The Roaring Bitmap stores the containers in a dynamic array as a primary index. Wherein the container uses two different configurations: array Container (Array Container) and Bitmap Container (Bitmap Container). The array container stores sparse data, and the bitmap container stores dense data. If the number of integers in a container is less than 4096, values are stored using the array container. If it is larger than 4096, the value is stored using a bitmap container.
Therefore, by implementing the optional implementation mode, a plurality of user identifications corresponding to a plurality of user information which can meet any target portrait dimension are converted into portrait bitmap data corresponding to the target portrait dimension based on a bitmap data conversion rule, so that the user information of a specific portrait dimension can be stored by using the bitmap data, the cost of storing the user information is reduced by using the low resource occupancy rate of the bitmap data, and the efficient and low-cost user portrait calculation can be realized based on the subsequent operation.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a user portrait determination apparatus based on bitmap calculation according to an embodiment of the present invention. The user representation determining apparatus described in fig. 2 may be applied to a user representation computing chip, a computing terminal, or a computing server (where the computing server may be a local server or a cloud server). As shown in fig. 2, the user portrait determination apparatus based on bitmap calculation may include:
a requirement determination module 201 for determining a user representation query requirement.
Optionally, the user portrait query requirement may be determined according to information input by the user, for example, a visual interface is provided for the user to select portrait dimensions to be queried, or may be automatically generated according to a data input requirement of the algorithm model, for example, when a user ID transmitted by a specific algorithm model and corresponding portrait dimensions to be queried are received, the user portrait query requirement is generated.
Optionally, the user representation query requirement is used to indicate at least two target representation dimensions to be queried. Optionally, the target portrait dimensions may include one or more of a geographic location dimension, a geographic area dimension, a time dimension, and a facility dimension, for example, the user portrait query requirement may indicate that all users who want to query to appear in area a in month 20XX 8, and then two target portrait dimensions indicated by the user portrait query requirement are respectively the time dimension corresponding to month 20XX 8 and the geographic area dimension corresponding to area a, and for example, the user portrait query requirement may indicate that all users who want to query to appear in both positions of longitude and latitude coordinates 113.261996,23.12725 and a CCC subway station, and then two target portrait dimensions indicated by the user portrait query requirement are respectively the geographic location dimension corresponding to coordinates of longitude and latitude 113.261996, and a position of 23.12725 and the facility dimension corresponding to the CCC subway station.
Optionally, the geographic location dimension may be a two-dimensional location dimension, a three-dimensional location dimension, or a coordinate location dimension in a specific coordinate system, such as a longitude and latitude location dimension. Optionally, the geographic region dimension may be an administrative region dimension of a country, province, city, district, or the like. Optionally, the time dimension may include one or more of years, months, days, hours, minutes, and seconds. Alternatively, the facilities in the facility dimension may be POI (Point of interest) or AOI (area of interest), and the types thereof include but are not limited to: sports leisure facilities, catering facilities, life service facilities, science and education culture facilities, shopping facilities, transportation facilities, national government facilities, accommodation facilities, business facilities of enterprises, health care/health care facilities, public facilities, and the like.
And the bitmap determining module 202 is used for determining at least two portrait bitmap data according to the user portrait query requirement.
Optionally, the portrait bitmap data is used to record a plurality of user information satisfying the target portrait dimensions in the form of bitmap data. Alternatively, the representation bitmap data may correspond to one target representation dimension, e.g., it may include user information for all users in area A, or it may correspond to multiple target representation dimensions, e.g., it may include user information for all users who appear in area A in month 8 of 20 XX.
And the portrait calculation module 203 is used for calculating portrait information of the target user according to the at least two portrait bitmap data.
Optionally, the target user representation information is used to indicate all user information that satisfies at least two target representation dimensions. Optionally, intersection operation or union operation may be performed on at least two portrait bitmap data according to a requirement, so as to calculate portrait information of the target user. Here, since the intersection calculation based on the bitmap data is an operation at the bit level of the finest granularity in the computer, and is faster than other operations, a high calculation effect can be obtained.
Therefore, the embodiment of the invention can store the user information of the specific portrait dimension by using the bitmap data and determine the user portrait information by using a bitmap calculation mode, thereby reducing the cost of storing the user information by using the low resource occupancy rate of the bitmap data on one hand and improving the efficiency of determining the user portrait by using the high efficiency of the bitmap calculation on the other hand.
As an optional implementation, the bitmap determination module 202 determines at least two specific ways of representing bitmap data according to the user representation query requirement, including:
determining all target portrait dimensions to be queried according to a user portrait query requirement;
for each target image dimension, image bitmap data corresponding to the target image dimension is determined.
Optionally, the user representation query request may be in a text format, for example, a text received from a user input, such as "query all users appearing in region a in month 8 of 20 XX", thus determining all target representation dimensions to be queried may be by identifying text keywords in the user representation query request through a keyword recognition algorithm model to identify target representation dimensions therein.
Optionally, for each target image dimension, one or more image bitmap data corresponding to each target image dimension may be determined in a preset image bitmap database in a tag index manner.
Therefore, by implementing the optional implementation mode, all target portrait dimensions to be inquired can be determined according to user portrait inquiry requirements, and then portrait bitmap data corresponding to the target portrait dimensions is determined for each target portrait dimension, so that all portrait bitmap data to be used for calculation can be accurately determined according to the user portrait inquiry requirements, and then efficient and low-cost user portrait calculation can be realized on the basis.
As an optional embodiment, the portrait calculation module 203 calculates the portrait information of the target user according to at least two portrait bitmap data, including:
performing bitmap intersection calculation on at least two portrait bitmap data to determine intersection user information among all portrait bitmap data;
and determining the intersection user information as the target user portrait information.
Optionally, the bitmap intersection calculation for at least two portrait bitmap data may be performed by calling a corresponding computer function instruction.
Therefore, by implementing the optional implementation mode, bitmap intersection calculation can be performed on at least two portrait bitmap data to determine intersection user information among all portrait bitmap data, so that target user portrait information can be accurately determined, and efficient and low-cost user portrait calculation is realized.
As an alternative embodiment, there may be multiple portrait bitmap data for each target portrait dimension corresponding to different user zones. Correspondingly, the bitmap determination module 202 calculates the specific manner of the portrait information of the target user according to the bitmap data of at least two portraits, including:
for each user partition, determining all portrait bitmap data corresponding to the user partition;
performing bitmap intersection calculation on all portrait bitmap data corresponding to the user partition to determine intersection user information among all portrait bitmap data;
and determining intersection user information corresponding to all the user partitions as target user portrait information.
Optionally, the partition manner of the user partition includes at least one of a partition based on a number dimension, a partition based on a gender dimension, a partition based on a standing region dimension, and a partition based on an age dimension. Optionally, the union of the intersection user information corresponding to all the user partitions may be determined as the target user portrait information.
Optionally, the portrait bitmap data of different user partitions are stored in different storage locations or different data spaces, for example, the portrait bitmap data of different user partitions may be stored in different shards (boards) of the database, so as to implement distributed computing.
In a specific embodiment, a certain number of some users may be divided into a area a and other users may be divided into B areas based on a certain partition manner, for example, based on a number dimension, and data of the a area and the B area may be stored in the a slice and the B slice, respectively. When calculating the user information meeting the specific portrait dimension, respectively finding out the A portrait bitmap data and the B portrait bitmap data meeting the specific portrait dimension from all the portrait bitmap data of the A slice and the B slice, then calculating the intersection user information of all the A portrait bitmap data in the A slice to determine the A user portrait data meeting the specific portrait dimension in the users in the A area, then calculating the intersection user information of all the B portrait bitmap data in the B slice to determine the B user portrait data meeting the specific portrait dimension in the users in the B area, and then determining the union of the A user portrait data and the B user portrait data as the final user portrait data.
Therefore, by implementing the optional implementation mode, intersection user information corresponding to all user partitions can be calculated to determine target user portrait information, so that independent operations in partitions and all partitions can be performed on data based on the idea of distributed calculation under the condition of huge data volume, the portrait operation efficiency can be effectively improved, the production cost is reduced, the cluster hardware performance can be utilized to the maximum extent, the operation efficiency is greatly improved, and high-efficiency and low-cost user portrait calculation is realized.
As an optional implementation manner, the apparatus further includes a bitmap generation module, configured to obtain multiple pieces of user information that satisfy any target portrait dimension, and convert multiple pieces of user identifiers corresponding to the multiple pieces of user information into portrait bitmap data corresponding to the target portrait dimension based on a bitmap data conversion rule.
In this embodiment, the portrait bitmap data records a plurality of user information in a bitmap (bitmap) data form, that is, a manner of converting a plurality of user identifiers corresponding to the plurality of user information into portrait bitmap data corresponding to the target portrait dimension based on a bitmap data conversion rule may be a manner of storing the user identifiers of all the user information as keys (keys) in the bitmap data, and then determining a value (value) of the key corresponding to the user information satisfying any target portrait dimension among all the user information as a first specific value, such as 1, and determining a value of the key corresponding to the user information not satisfying the target portrait dimension among all the user information as a second specific value, such as 0, so as to obtain final portrait bitmap data.
In a particular embodiment, a Roaring Bitmap data structure may be employed to store a plurality of user information satisfying a target representation dimension. Wherein the Roaring Bitmap data structure divides the 32-bit INT type data into 216Each Chunk corresponds to the upper 16 bits of an integer and uses a Container (Container) to store the lower 16 bits of a value. The Roaring Bitmap stores the containers in a dynamic array as a primary index. Wherein the container uses two different configurations: array Container (Array Container) and Bitmap Container (Bitmap Container). The array container stores sparse data, and the bitmap container stores dense data. If the number of integers in a container is less than 4096, values are stored using the array container. If it is larger than 4096, the value is stored using a bitmap container.
Therefore, by implementing the optional implementation mode, a plurality of user identifications corresponding to a plurality of user information which can meet any target portrait dimension are converted into portrait bitmap data corresponding to the target portrait dimension based on a bitmap data conversion rule, so that the user information of a specific portrait dimension can be stored by using the bitmap data, the cost of storing the user information is reduced by using the low resource occupancy rate of the bitmap data, and the efficient and low-cost user portrait calculation can be realized based on the subsequent operation.
EXAMPLE III
Referring to fig. 3, fig. 3 is a schematic diagram illustrating an apparatus for determining a user portrait based on bitmap calculation according to an embodiment of the present invention. The user representation determining apparatus described in fig. 3 may be applied to a user representation computing chip, a computing terminal, or a computing server (where the computing server may be a local server or a cloud server). As shown in fig. 3, the user portrait determination apparatus based on bitmap calculation may include:
a memory 301 storing executable program code;
a processor 302 coupled to the memory 301;
wherein, the processor 302 calls the executable program code stored in the memory 301 for executing the steps of the bitmap calculation-based user portrait determination method described in the first embodiment or the second embodiment.
Example four
The embodiment of the invention discloses a computer-readable storage medium which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the user portrait determination method based on bitmap calculation described in the first embodiment or the second embodiment.
EXAMPLE five
An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps of the bitmap calculation-based user representation determination method described in the first or second embodiment.
While certain embodiments of the present disclosure have been described above, other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily have to be in the particular order shown or in sequential order to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus, device, and non-volatile computer-readable storage medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and in relation to the description, reference may be made to some portions of the description of the method embodiments.
The apparatus, the device, the nonvolatile computer readable storage medium, and the method provided in the embodiments of the present specification correspond to each other, and therefore, the apparatus, the device, and the nonvolatile computer storage medium also have similar advantageous technical effects to the corresponding method.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, the present specification embodiments may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Finally, it should be noted that: the method and apparatus for determining a user portrait based on bitmap calculation disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, and are only used for illustrating the technical solutions of the present invention, rather than for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for user portrait determination based on bitmap computing, the method comprising:
determining a user representation query requirement; the user portrait query requirement is used for indicating at least two target portrait dimensions to be queried;
determining at least two portrait bitmap data according to the user portrait query requirement; the portrait bitmap data is used for recording a plurality of user information meeting the target portrait dimensionality in a bitmap data form;
calculating the portrait information of the target user according to the bitmap data of the at least two portraits; the target user representation information is used to indicate all user information that satisfies the at least two target representation dimensions.
2. A bitmap computation-based user representation determination method as claimed in claim 1 wherein the target representation dimensions comprise one or more of a geographic location dimension, a geographic area dimension, a time dimension and a facility dimension.
3. A method of bitmap computation-based user representation determination as recited in claim 1 wherein said determining at least two representation bitmap data based on said user representation query request comprises:
determining all the target portrait dimensions to be queried according to the user portrait query requirement;
and determining image bitmap data corresponding to the target image dimension for each target image dimension.
4. A bitmap calculation-based user representation determination method as claimed in claim 3 wherein said calculating target user representation information from said at least two representation bitmap data comprises:
performing bitmap intersection calculation on the at least two portrait bitmap data to determine intersection user information among all the portrait bitmap data;
and determining the intersection user information as target user portrait information.
5. A bitmap computation-based user representation determination method as claimed in claim 3 wherein each said target representation dimension corresponds to a plurality of said representation bitmap data corresponding to different user partitions; the calculating of the portrait information of the target user according to the bitmap data of the at least two portraits comprises the following steps:
for each user partition, determining all the portrait bitmap data corresponding to the user partition;
performing bitmap intersection calculation on all the portrait bitmap data corresponding to the user partition to determine intersection user information among all the portrait bitmap data;
and determining the intersection user information corresponding to all the user partitions as target user portrait information.
6. A bitmap computation-based user representation determination method as claimed in claim 5, wherein the partitioning of the user partition comprises at least one of a number dimension-based partition, a gender dimension-based partition, a standing zone dimension-based partition and an age dimension-based partition.
7. A method of bitmap computation-based user representation determination as recited in claim 1, wherein prior to said determining a user representation query requirement, said method further comprises:
acquiring a plurality of user information meeting any one of the target portrait dimensions;
and converting a plurality of user identifications corresponding to the plurality of user information into portrait bitmap data corresponding to the target portrait dimension based on a bitmap data conversion rule.
8. A user portrait determination apparatus based on bitmap computing, the apparatus comprising:
a requirement determining module for determining a user portrait query requirement; the user portrait query requirement is used for indicating at least two target portrait dimensions to be queried;
the bitmap determining module is used for determining bitmap data of at least two portrait according to the user portrait query requirement; the portrait bitmap data is used for recording a plurality of user information meeting the target portrait dimensionality in a bitmap data form;
the portrait calculation module is used for calculating portrait information of a target user according to the at least two portrait bitmap data; the target user representation information is used to indicate all user information that satisfies the at least two target representation dimensions.
9. A user portrait determination apparatus based on bitmap computing, the apparatus comprising:
a memory storing executable program code;
a processor coupled with the memory;
the processor invokes the executable program code stored in the memory to perform the bitmap calculation-based user representation determination method of any one of claims 1-7.
10. A computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the bitmap calculation-based user representation determination method according to any one of claims 1 to 7.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002088979A1 (en) * | 2001-05-02 | 2002-11-07 | Bitstream Inc. | Methods and systems for displaying media in scaled manner and/or in orientation |
| US20160234434A1 (en) * | 2015-02-09 | 2016-08-11 | Hisense Mobile Communications Technology Co., Ltd. | Image data processing method and apparatus |
| CN108415978A (en) * | 2018-02-09 | 2018-08-17 | 北京腾云天下科技有限公司 | User tag storage method, user's portrait computational methods and computing device |
| CN111708785A (en) * | 2020-06-27 | 2020-09-25 | 广州云莫凡信息科技有限公司 | Big data mining processing method and device and cloud platform |
| CN111782686A (en) * | 2020-05-13 | 2020-10-16 | 北京三快在线科技有限公司 | User data query method and device, electronic equipment and storage medium |
| CN113379477A (en) * | 2020-03-10 | 2021-09-10 | 阿里巴巴集团控股有限公司 | Data processing method and device and computing device |
| CN113468226A (en) * | 2021-06-24 | 2021-10-01 | 北京达佳互联信息技术有限公司 | Service processing method, device, electronic equipment and storage medium |
-
2021
- 2021-11-26 CN CN202111425521.8A patent/CN114238312A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002088979A1 (en) * | 2001-05-02 | 2002-11-07 | Bitstream Inc. | Methods and systems for displaying media in scaled manner and/or in orientation |
| US20160234434A1 (en) * | 2015-02-09 | 2016-08-11 | Hisense Mobile Communications Technology Co., Ltd. | Image data processing method and apparatus |
| CN108415978A (en) * | 2018-02-09 | 2018-08-17 | 北京腾云天下科技有限公司 | User tag storage method, user's portrait computational methods and computing device |
| CN113379477A (en) * | 2020-03-10 | 2021-09-10 | 阿里巴巴集团控股有限公司 | Data processing method and device and computing device |
| CN111782686A (en) * | 2020-05-13 | 2020-10-16 | 北京三快在线科技有限公司 | User data query method and device, electronic equipment and storage medium |
| CN111708785A (en) * | 2020-06-27 | 2020-09-25 | 广州云莫凡信息科技有限公司 | Big data mining processing method and device and cloud platform |
| CN113468226A (en) * | 2021-06-24 | 2021-10-01 | 北京达佳互联信息技术有限公司 | Service processing method, device, electronic equipment and storage medium |
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