CN116522091A - Analysis method, system, equipment and medium of user information - Google Patents

Analysis method, system, equipment and medium of user information Download PDF

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Publication number
CN116522091A
CN116522091A CN202310512871.0A CN202310512871A CN116522091A CN 116522091 A CN116522091 A CN 116522091A CN 202310512871 A CN202310512871 A CN 202310512871A CN 116522091 A CN116522091 A CN 116522091A
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China
Prior art keywords
data table
user
user information
group
crowd
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CN202310512871.0A
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Chinese (zh)
Inventor
徐逸
赖飞
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Ctrip Travel Information Technology Shanghai Co Ltd
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Ctrip Travel Information Technology Shanghai Co Ltd
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Priority to CN202310512871.0A priority Critical patent/CN116522091A/en
Publication of CN116522091A publication Critical patent/CN116522091A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2219—Large Object storage; Management thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation

Abstract

The invention discloses a method, a system, equipment and a medium for analyzing user information, wherein the method comprises the following steps: acquiring user information of a user; constructing a first data table and a second data table according to the user information; the first data table comprises a plurality of labels under each code number and crowd categories, the crowd categories are set by a plurality of different labels, and the second data table comprises a plurality of evaluation indexes under each code number; and matching target indexes corresponding to each crowd category from the evaluation indexes based on the first data table and the second data table. The invention divides the label for distinguishing the user group and the evaluation index of the user, which are originally combined together, into two pieces in one table, and rapidly determines the group category of the user, thereby determining the target index of the group. When the labels of the users are changed, the influence on the second data table where the evaluation indexes are located is avoided, and the problem of dimension expansion under one table is solved.

Description

Analysis method, system, equipment and medium of user information
Technical Field
The present invention relates to the field of big data processing, and in particular, to a method, a system, an apparatus, and a medium for analyzing user information.
Background
After the user is provided with the labels such as the star level, the member level, the experimental position or the APP version, sample trend analysis needs to be carried out on each label or a group formed by a plurality of labels in real time. When user data is analyzed conventionally, people are analyzed based on a set broad table. When the data volume is small, the dimension of the label is fixed and the label is not required to be modified, the crowd can be analyzed through the original setting wide table, and the requirement can be met; however, when the user data volume increases, the label dimension increases or the labels are frequently changed, the setting of the broad table needs to be performed with back-flushing of the basic data, or each sub-data in the setting of the broad table needs to be processed. It should be emphasized that when the comparison group experimental data and the experimental group experimental data exist in the setting broad table, the data volume to be calculated will be increased sharply, and once the multi-dimensional labels of the user and the crowd to which the user belongs need to be changed, the header of the original setting broad table needs to be changed, and the user needs to be analyzed again, so that the time consumption is long.
Disclosure of Invention
The invention aims to overcome the defects that in the prior art, in the face, all data are placed in a single wide table, the number of users is large, the number of dimension labels of the users is large, and the analysis of multi-dimension crowd is time-consuming and low in efficiency under the condition of frequent dimension switching, and provides a method, a system, equipment and a medium for analyzing user information.
The invention solves the technical problems by the following technical scheme:
as a first aspect of the present invention, the present invention provides an analysis method of user information, the analysis method including:
acquiring user information of a user; the user information comprises a code number, a plurality of labels and a plurality of evaluation indexes;
constructing a first data table and a second data table according to the user information; the first data table comprises a plurality of labels under each code number and crowd categories, the crowd categories are set by a plurality of different labels, and the second data table comprises a plurality of evaluation indexes under each code number;
and matching target indexes corresponding to each crowd category from the evaluation indexes based on the first data table and the second data table.
Preferably, the step of matching, based on the first data table and the second data table, the target index corresponding to each crowd category from the evaluation index specifically includes:
querying the coding numbers contained in each crowd category from the first data table;
and matching target indexes corresponding to each crowd category from the second data table according to the code numbers.
Preferably, the evaluation index includes at least one of user traffic, order rate, amount of delivery, and conversion rate;
and/or the tag includes at least two of a user age, a user device type, a member level, a user level, and a user gender.
Preferably, the tag includes a first tag and a second tag, and after the step of constructing a first data table and a second data table according to the user information, the analysis method further includes:
determining the total number of first users corresponding to the first label or the total number of second users corresponding to the second label;
comparing the total number of the first users or the total number of the second users with the total number of the actual users, and carrying out validity check on the first data table according to the comparison result.
Preferably, after the step of constructing the first data table and the second data table according to the user information, the analysis method further includes: correcting the crowd category based on the updated label;
and adjusting the first data table according to the corrected crowd category.
Preferably, the user includes a first user group and a second user group, and the step of matching, from the evaluation index, a target index corresponding to each crowd category based on the first data table and the second data table includes:
determining a first index group to be analyzed of a first user group according to the first data table and the second data table;
determining a second index group to be analyzed of a second user group according to the first data table and the second data table;
and analyzing the indexes to be analyzed of the same crowd category in the first user group and the second user group according to the first index group to be analyzed and the second index group to be analyzed.
Preferably, before the step of analyzing the to-be-analyzed index of the same crowd category in the first target user group and the second target user group according to the first to-be-analyzed index group and the second to-be-analyzed index group, the method includes:
acquiring a plurality of computing clusters and performance indexes corresponding to each computing cluster; the computing cluster corresponds to a department where the user information is located;
estimating the requirement performance of the to-be-analyzed indexes of the same crowd category in the first user group and the second user group;
determining a target computing cluster from the computing clusters according to the required performance and performance indexes corresponding to the computing clusters; the target computing cluster is used for analyzing indexes to be analyzed of the same crowd category in the first user group and the second user group.
As a second aspect of the present invention, the present invention provides an analysis system of user information, the analysis system including: the system comprises an information acquisition module, a data table construction module and an index matching module;
the information acquisition module is used for acquiring user information of a user; the user information comprises a code number, a plurality of labels and a plurality of evaluation indexes;
the data table construction module is used for constructing a first data table and a second data table according to the user information;
the first data table comprises a plurality of labels under each code number and crowd categories, the crowd categories are set by a plurality of different labels, and the second data table comprises a plurality of evaluation indexes under each code number;
the index matching module is used for matching target indexes corresponding to each crowd category from the evaluation indexes based on the first data table and the second data table.
As a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a method of analysing user information as in the first aspect of the invention when executing the computer program.
As a fourth aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the analysis method of user information as in the first aspect of the present invention.
The invention has the positive progress effects that: according to the method, the label for distinguishing the user group and the evaluation index of the user, which are originally combined together, are split into two tables, the group type of the user and the evaluation index of the corresponding group type are determined based on the two tables, the group type of the user is finally and rapidly determined, the target index of the group is finally determined, in addition, when the label of the user is changed, the influence on a second data table where the evaluation index is located is avoided, and the problem of dimensional expansion under one wide table is solved.
Drawings
Fig. 1 is a flow chart of a method for analyzing user information in embodiment 1 of the present invention.
Fig. 2 is a schematic structural diagram of a system for analyzing user information in embodiment 2 of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device according to embodiment 3 of the present invention.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention.
Example 1
Referring to fig. 1, the present embodiment provides a method for analyzing user information, including:
s1, acquiring user information of a user; the user information comprises a code number, a plurality of labels and a plurality of evaluation indexes;
s2, constructing a first data table and a second data table according to user information;
the first data table comprises a plurality of labels under each code number and crowd categories, the crowd categories are set by a plurality of different labels, and the second data table comprises a plurality of evaluation indexes under each code number;
and S3, matching target indexes corresponding to each crowd category from the evaluation indexes based on the first data table and the second data table.
In this embodiment, the first data table and the second data table may travel a virtual column through the multi if function, the first data table and the second data table may be associated through the virtual column BY the code number, and then virtual fields in the virtual column may be aggregated BY the GROUP BY aggregation algorithm, so as to obtain crowd types under different labels.
In the method of the embodiment, the label for distinguishing the user group and the evaluation index of the user which are originally combined together are changed into two tables, the group type of the user and the evaluation index of the corresponding group type are determined based on the two tables, the group type of the user can be rapidly determined, the target index of the group is finally determined, the second data table where the evaluation index is located is not influenced when the label of the user is changed, and the problem of dimensional expansion under the original table is avoided.
In an optional embodiment, the step of matching the target index corresponding to each crowd category from the evaluation index based on the first data table and the second data table specifically includes:
inquiring the code number contained in each crowd category from a first data table;
and matching target indexes corresponding to each group of people from the second data table according to the code numbers.
In this embodiment, the number may be a user id (also referred to as a uid), a device id (also referred to as a CID), an identity protocol id (also referred to as a METAID), or the like.
In this embodiment, for example, the crowd category 1 includes code numbers 001-010, corresponding evaluation indexes are determined in the second data table according to the code numbers, and target indexes of the crowd category 1 are determined.
In an alternative embodiment, the evaluation index includes at least one of user traffic, rate of placement, amount of delivery, and conversion rate;
and/or the tag includes at least two of a user age, a user device type, a member level, a user level, and a user gender.
In this embodiment, the user traffic refers to the click volume of the user facing a certain scene (e.g., browsing merchandise, browsing scenic spots, browsing air tickets, browsing hotel rooms, etc.). Conversion refers to the probability that a user will collect, pay attention to, or become a paying user.
In an alternative embodiment, the tag includes a first tag and a second tag, and after the step of constructing the first data table and the second data table according to the user information, the analysis method further includes:
determining the total number of the first users corresponding to the first label or the total number of the second users corresponding to the second label;
comparing the total number of the first users or the total number of the second users with the total number of the actual users, and carrying out validity check on the first data table according to the comparison result.
In this embodiment, the first tag may be a sex tag, a tag of whether or not it is a member, or the like. Taking the gender label as an example, when the total number of users is 100, and the gender of 100 is not unknown, if the sum of the users with the labels of men and women is 100, the verification can be determined to pass. When the sum of the male and female users is not 100, the first data table is determined to be failed to verify (i.e. the validity verification is failed).
In an alternative embodiment, after the step of constructing the first data table and the second data table according to the user information, the analysis method further comprises: correcting the crowd category based on the updated label;
and adjusting the first data table according to the corrected crowd category.
In this embodiment, adjusting the first data table may include checking the user's tag, or improving the user's tag to classify the crowd category again.
In an alternative embodiment, the user includes a first user group and a second user group, and the step of matching the target index corresponding to each crowd category from the evaluation index based on the first data table and the second data table includes:
determining a first index group to be analyzed of the first user group according to the first data table and the second data table;
determining a second index group to be analyzed of a second user group according to the first data table and the second data table;
and analyzing the indexes to be analyzed of the same crowd category in the first user group and the second user group according to the first index group to be analyzed and the second index group to be analyzed.
In this embodiment, the first user group and the second user group may be two groups of people with different usage environments, for example, the first user group is an original app and the second user group is a new version app. At this time, it can be verified how the new version affects the index (e.g., the lower rate, the amount of the order) of the users (e.g., male vip users) of the same crowd category in the first user group and the second user group. In this embodiment, the index may further include a trend, a lift trend, a sample trend, and the like.
In an alternative embodiment, before the step of analyzing the indicators to be analyzed of the same crowd category in the first target user group and the second target user group according to the first indicator to be analyzed group and the second indicator to be analyzed group, the method includes:
acquiring a plurality of computing clusters and performance indexes corresponding to each computing cluster; the computing cluster corresponds to a department where the user information is located;
estimating the requirement performance of the indexes to be analyzed of the same crowd category in the first user group and the second user group;
determining a target computing cluster from the computing clusters according to the required performance and performance indexes corresponding to the computing clusters; the target computing cluster is used for analyzing indexes to be analyzed of the same crowd category in the first user group and the second user group.
In the present embodiment, the required performance may be calculated according to at least one of the number of facing users, the number of user tags, the number of people of users, the number of evaluation indexes, the calculation amount of single evaluation indexes by different departments (may also be simply referred to as BU).
In this embodiment, the data of different departments are calculated using different clusters, so that the data between the different departments is horizontally divided. And when a single server is down, all departments cannot calculate, so that the reliability is improved.
In this embodiment, each department may calculate, in the corresponding target computing cluster, data of the department including a user tag managed by the department and an evaluation index of the user.
And constructing a corresponding first data table and second data table in the target computing cluster.
In this embodiment, table 1 and table 2 are taken as examples, table 1 is not stored in the form of a bitmap, and table 2 is stored in the form of a bitmap. By storing in the form of a bitmap, the storage space in the computer can be reduced.
TABLE 1
Membership grade General member M1 2022-11-17
Membership grade General member M3 2022-11-17
Membership grade General member M4 2022-11-17
Membership grade General member M6 2022-11-17
Membership grade Black diamond member M2 2022-11-17
Membership grade Black diamond member M5 2022-11-17
TABLE 2
Membership grade General member {M1,M3,M4,M6} 2022-11-17
Membership grade Black diamond member {M2,M5} 2022-11-17
In the above embodiment, the configuration of table 1 may be processed in advance into the configuration of table 2 by the crowd-sourcing method of SprakJob. The coded crowd pack is stored in a clickhouse (real-time data analysis database) in a bitmap bit array format, and the storage space occupied by the compressed crowd pack is smaller.
Furthermore, if a user's 64-bit METAID (an identity protocol ID) is used as the user's code number, since clickhouse is a C++ based big-end store and a small-end store of read data is not corresponding, this would result in the METAID and the original data being different from each other, which can be avoided by adapting a 64-bit packetized Java ARchive packet.
In the above embodiment, when a label of a user managed by a department changes, the changed information may be updated in a target cluster, the crowd category of the user is reclassified, and finally, the classified crowd category is broadcasted and updated to each cluster including the first data table in the form of a message queue. Thereby realizing the consistency of the crowd types of the users among the clusters.
According to the analysis method of the user information, the labels which are originally combined together and used for distinguishing the user groups and the evaluation indexes of the users are changed into two tables from one wide table, the group types of the users and the evaluation indexes of the corresponding group types are determined based on the two tables, the group types of the users can be determined quickly, the target indexes of the group can be determined finally, the second data table where the evaluation indexes are located cannot be influenced when the labels of the users are changed, and the problem of dimensional expansion under the original wide table is avoided.
Example 2
Referring to fig. 2, the present embodiment provides an analysis system for user information, the analysis system includes: an information acquisition module 201, a data table construction module 202 and an index matching module 203;
the information acquisition module 201 is configured to acquire user information of a user; the user information comprises a code number, a plurality of labels and a plurality of evaluation indexes;
the data table construction module 202 is configured to construct a first data table and a second data table according to user information;
the first data table comprises a plurality of labels under each code number and crowd categories, the crowd categories are set by a plurality of different labels, and the second data table comprises a plurality of evaluation indexes under each code number;
the index matching module 203 is configured to match, from the evaluation indexes, target indexes corresponding to each crowd category based on the first data table and the second data table.
In an alternative embodiment, the index matching module 203 is specifically configured to query the first data table for the code number included in each crowd category;
and matching target indexes corresponding to each group of people from the second data table according to the code numbers.
In an alternative embodiment, the evaluation index includes at least one of a user flow rate, a rate of order, a transaction amount, and a conversion rate;
and/or the tag includes at least two of a user age, a user device type, a member level, a user level, and a user gender.
In an alternative embodiment, the analysis system further comprises a checking module, wherein the checking module is used for determining a first total number of users corresponding to the first tag or a second total number of users corresponding to the second tag;
comparing the total number of the first users or the total number of the second users with the total number of the actual users, and carrying out validity check on the first data table according to the comparison result.
In an alternative embodiment, the verification module is further configured to modify the crowd category based on the updated tag;
and adjusting the first data table according to the corrected crowd category.
In an alternative embodiment, the index matching module 203 is further configured to determine a first index group to be analyzed of the first user group according to the first data table and the second data table;
determining a second index group to be analyzed of a second user group according to the first data table and the second data table;
and analyzing the indexes to be analyzed of the same crowd category in the first user group and the second user group according to the first index group to be analyzed and the second index group to be analyzed.
In an optional embodiment, the system further includes a performance evaluation module, where the performance evaluation module is configured to obtain a plurality of computing clusters and a performance index corresponding to each computing cluster; the computing cluster corresponds to a department where the user information is located;
estimating the requirement performance of the indexes to be analyzed of the same crowd category in the first user group and the second user group;
determining a target computing cluster from the computing clusters according to the required performance and performance indexes corresponding to the computing clusters; the target computing cluster is used for analyzing indexes to be analyzed of the same crowd category in the first user group and the second user group.
Through the system in this embodiment, the label for distinguishing the user crowd and the evaluation index of the user are changed from one wide table into two tables, the crowd category of the user and the evaluation index of the corresponding crowd category are determined based on the two tables, the crowd category of the user can be determined quickly, the target index of the crowd is determined finally, and the second data table where the evaluation index is located is not affected when the label of the user is changed, so that the problem of dimensional expansion under the original wide table is avoided.
Example 3
Fig. 3 is a schematic structural diagram of an electronic device according to the present embodiment. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the analysis method of user information of embodiment 1 when executing the program. The electronic device 30 shown in fig. 3 is only an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 3, the electronic device 30 may be embodied in the form of a general purpose computing device, which may be a server device, for example. Components of electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, a bus 33 connecting the different system components, including the memory 32 and the processor 31.
The bus 33 includes a data bus, an address bus, and a control bus.
Memory 32 may include volatile memory such as Random Access Memory (RAM) 321 and/or cache memory 322, and may further include Read Only Memory (ROM) 323.
Memory 32 may also include a program/utility 325 having a set (at least one) of program modules 324, such program modules 324 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 31 executes various functional applications and data processing, such as the analysis method of user information of embodiment 1 of the present invention, by running a computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface 35. Also, model-generating device 30 may also communicate with one or more networks, such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet, via network adapter 36. As shown, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the model-generating device 30, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present invention. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Example 4
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the analysis method of user information of embodiment 1.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the analysis method of user information implementing embodiment 1, when the program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device, partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.

Claims (10)

1. A method of analyzing user information, the method comprising:
acquiring user information of a user; the user information comprises a code number, a plurality of labels and a plurality of evaluation indexes;
constructing a first data table and a second data table according to the user information; the first data table comprises a plurality of labels under each code number and crowd categories, the crowd categories are set by a plurality of different labels, and the second data table comprises a plurality of evaluation indexes under each code number;
and matching target indexes corresponding to each crowd category from the evaluation indexes based on the first data table and the second data table.
2. The method of analyzing user information according to claim 1, wherein the step of matching target indexes corresponding to each of the crowd categories from the evaluation indexes based on the first data table and the second data table specifically comprises:
querying the coding numbers contained in each crowd category from the first data table;
and matching target indexes corresponding to each crowd category from the second data table according to the code numbers.
3. The method for analyzing user information according to claim 1, wherein the evaluation index includes at least one of a user flow rate, a deposit rate, a transaction amount, and a conversion rate;
and/or the tag includes at least two of a user age, a user device type, a member level, a user level, and a user gender.
4. The method of analyzing user information according to claim 1, wherein the tag includes a first tag and a second tag, and after the step of constructing a first data table and a second data table from the user information, the method of analyzing further comprises:
determining the total number of first users corresponding to the first label or the total number of second users corresponding to the second label;
comparing the total number of the first users or the total number of the second users with the total number of the actual users, and carrying out validity check on the first data table according to the comparison result.
5. The method of analyzing user information according to claim 1, wherein after the step of constructing a first data table and a second data table from the user information, the method of analyzing further comprises: correcting the crowd category based on the updated label;
and adjusting the first data table according to the corrected crowd category.
6. The method of analyzing user information according to claim 1, wherein the user includes a first user group and a second user group, and the step of matching a target index corresponding to each of the crowd categories from the evaluation index based on the first data table and the second data table includes:
determining a first index group to be analyzed of the first user group according to the first data table and the second data table;
determining a second index group to be analyzed of the second user group according to the first data table and the second data table;
and analyzing the indexes to be analyzed of the same crowd category in the first user group and the second user group according to the first index group to be analyzed and the second index group to be analyzed.
7. The method of claim 6, wherein before the step of analyzing the indicators to be analyzed of the same crowd category in the first target user group and the second target user group according to the first indicator group to be analyzed and the second indicator group to be analyzed, the method further comprises:
acquiring a plurality of computing clusters and performance indexes corresponding to each computing cluster; the computing cluster corresponds to a department where the user information is located;
estimating the requirement performance of the to-be-analyzed indexes of the same crowd category in the first user group and the second user group;
determining a target computing cluster from the computing clusters according to the required performance and performance indexes corresponding to the computing clusters; the target computing cluster is used for analyzing indexes to be analyzed of the same crowd category in the first user group and the second user group.
8. A system for analyzing user information, the system comprising: the system comprises an information acquisition module, a data table construction module and an index matching module;
the information acquisition module is used for acquiring user information of a user; the user information comprises a code number, a plurality of labels and a plurality of evaluation indexes;
the data table construction module is used for constructing a first data table and a second data table according to the user information;
the first data table comprises a plurality of labels under each code number and crowd categories, the crowd categories are set by a plurality of different labels, and the second data table comprises a plurality of evaluation indexes under each code number;
the index matching module is used for matching target indexes corresponding to each crowd category from the evaluation indexes based on the first data table and the second data table.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of analyzing user information according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of analyzing user information according to any of claims 1 to 7.
CN202310512871.0A 2023-05-08 2023-05-08 Analysis method, system, equipment and medium of user information Pending CN116522091A (en)

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