CN110704817A - False real name identification method and device and computer readable storage medium - Google Patents
False real name identification method and device and computer readable storage medium Download PDFInfo
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Abstract
The invention provides a false real name identification method, a false real name identification device and a computer readable storage medium, and relates to the technical field of data processing, wherein the method comprises the following steps: analyzing the number allocation behavior of the channel provider to which the normal real-name number belongs to obtain the integral number allocation behavior characteristics of the channel provider to which the normal real-name number belongs; analyzing the number allocation behavior of a certain channel provider to obtain the number allocation behavior characteristics of the channel provider; and identifying whether the number placed by the channel provider is a false real-name number or not according to the difference between the number placing behavior characteristic of the channel provider and the integral number placing behavior characteristic.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a false real-name recognition method and apparatus, and a computer-readable storage medium.
Background
At present, the communication industry, the internet industry and the like adopt technical means such as identity card and life photo comparison, living body verification and the like to ensure the authenticity of a user for self-reporting identity information of the user. However, the inventor finds that in practical applications, many users verified by such techniques are still false real-name users.
Disclosure of Invention
The embodiment of the disclosure provides a false real name identification method, which can effectively identify false real name numbers.
According to an aspect of the embodiments of the present disclosure, there is provided a false real name recognition method, including: analyzing the number allocation behavior of the channel provider to which the normal real-name number belongs to obtain the integral number allocation behavior characteristics of the channel provider to which the normal real-name number belongs; analyzing the number allocation behavior of a certain channel provider to obtain the number allocation behavior characteristics of the channel provider; and identifying whether the number placed by the channel provider is a false real-name number or not according to the difference between the number placing behavior characteristic of the channel provider and the integral number placing behavior characteristic.
In some embodiments, the identifying whether the number placed by the channel provider is a false real-name number according to the difference between the number placing behavior feature of the channel provider and the overall number placing behavior feature includes: judging that the difference between the number allocation behavior characteristic of the channel trader and the integral number allocation behavior characteristic exceeds a preset difference; if the difference between the number allocation behavior characteristic of the channel trader and the integral number allocation behavior characteristic exceeds a preset difference, determining that the number allocated by the channel trader is a suspected false real-name number; analyzing the use behaviors of all suspected false real-name numbers to obtain the similarity of the use behaviors of different suspected false real-name numbers; and determining the number placed by the channel trader as a false real-name number under the condition that the similarity is greater than the preset similarity.
In some embodiments, if the difference between the number assignment behavior feature of the channel dealer and the overall number assignment behavior feature exceeds a preset difference and the duration is less than a preset time, the number assigned by the channel dealer is determined to be a suspected false real-name number.
In some embodiments, the similarity is determined by the coefficient of variation of all suspected false real name numbers.
In some embodiments, the number assignment behavior comprises at least one of: the number allocation time distribution, the number allocation quantity in unit time, and the matching degree of the identity cards corresponding to different numbers and the pictures shot on the number allocation site.
According to another aspect of the disclosed embodiments, there is provided a false real name recognition apparatus, including: the first analysis module is used for analyzing the number allocation behavior of the channel provider to which the normal real-name number belongs so as to obtain the integral number allocation behavior characteristics of the channel provider to which the normal real-name number belongs; the second analysis module is used for analyzing the number allocation behavior of a certain distributor to obtain the number allocation behavior characteristics of the distributor; and the identification module is used for identifying whether the number placed by the channel provider is a false real-name number or not according to the difference between the number placing behavior characteristic of the channel provider and the integral number placing behavior characteristic.
In some embodiments, the identification module is to: judging that the difference between the number allocation behavior characteristic of the channel trader and the integral number allocation behavior characteristic exceeds a preset difference; if the difference between the number allocation behavior characteristic of the channel trader and the integral number allocation behavior characteristic exceeds a preset difference, determining that the number allocated by the channel trader is a suspected false real-name number; analyzing the use behaviors of all suspected false real-name numbers to obtain the similarity of the use behaviors of different suspected false real-name numbers; and determining the number placed by the channel trader as a false real-name number under the condition that the similarity is greater than the preset similarity.
In some embodiments, the identification module is configured to determine that the number placed by the distributor is a suspected false real-name number if the difference between the number placing behavior feature of the distributor and the overall number placing behavior feature exceeds a preset difference and the duration is less than a preset time.
In some embodiments, the similarity is determined by the coefficient of variation of all suspected false real name numbers.
In some embodiments, the number assignment behavior comprises at least one of: the number allocation time distribution, the number allocation quantity in unit time, and the matching degree of the identity cards corresponding to different numbers and the pictures shot on the number allocation site.
According to another aspect of the embodiments of the present disclosure, there is provided a false real name recognition apparatus including: a memory; and a processor coupled to the memory, the processor configured to perform the method of any of the above embodiments based on instructions stored in the memory.
According to yet another aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method according to any one of the embodiments described above.
In the embodiment of the disclosure, by analyzing the number allocation behavior of the channel provider to which the normal real-name number belongs and the number allocation behavior of a certain channel provider, the overall number allocation behavior characteristic and the number allocation behavior characteristic of the channel provider can be obtained, and further whether the number allocated by the channel provider is the false real-name number or not can be identified according to the difference between the overall number allocation behavior characteristic and the number allocation behavior characteristic of the channel provider.
The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow diagram of a false real-name identification method according to some embodiments of the present disclosure;
FIG. 2 is a schematic flow diagram of a false real-name identification method according to further embodiments of the present disclosure;
FIG. 3 is a schematic diagram of a false real name identification device, according to some embodiments of the present disclosure;
FIG. 4 is a schematic diagram of a false real name recognition device, according to further embodiments of the present disclosure;
FIG. 5 is a schematic diagram of a false real name recognition device according to further embodiments of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
FIG. 1 is a flow diagram of a false real name identification method according to some embodiments of the present disclosure.
In step 102, the number allocation behavior of the distributor to which the normal real-name number belongs is analyzed to obtain the overall number allocation behavior characteristics of the distributor to which the normal real-name number belongs.
In some embodiments, the number assignment behavior may include at least one of: the distribution of number placing time, the number of placed numbers in unit time and the difference between the matching degrees of the identity cards corresponding to different numbers and the pictures shot on the number placing site.
By analyzing the number allocation behaviors, the overall number allocation behavior characteristics corresponding to the distributor to which the normal real-name number belongs can be obtained, for example, the number allocation time distribution is relatively consistent (for example, follows normal distribution), the number allocation quantity in unit time is relatively close, and the matching degrees of the identity cards corresponding to different numbers and the pictures shot on the number allocation site are different.
In step 104, the number assignment behavior of a certain channel provider is analyzed to obtain the number assignment behavior characteristics of the channel provider.
For example, when a number is found to be suspicious, a distributor corresponding to the suspicious number may be found. And analyzing the number allocation behavior of the channel provider to obtain the number allocation behavior characteristics of the channel provider. The number allocation behavior characteristics can be, for example, the distribution situation of the number allocation time, the number of the numbers allocated in unit time is larger, and the matching degrees of the identity cards corresponding to different numbers and the pictures taken on the number allocation site are the same.
In step 106, according to the difference between the channel dealer's ranking behavior characteristics and the overall ranking behavior characteristics, it is identified whether the number paid by the channel dealer is a false real-name number.
For example, the overall number-placing time X conforms to a normal distribution, i.e., X obeys (μ, σ)2) Where μ is desired and σ is the standard deviation. The area within the horizontal axis interval (μ - σ, μ + σ) is 68.268949%, the probability P { | X- μ<σ }, 2 Φ (1) -1, 0.6826. The area in the horizontal axis interval (μ -1.96 σ, μ +1.96 σ) is 95.449974%, and the probability P { | X- μ |<2 σ ═ 2 Φ (2) -1 ═ 0.9544. The area in the horizontal axis interval (μ -2.58 σ, μ +2.58 σ) is 99.730020%, and the probability P { | X- μ |<3σ}＝2Φ(3)-1＝0.9974。
The basic idea of testing by small probability events, which typically means events with a probability of less than 5% occurring, and assumptions, is to consider that an event is almost impossible to occur in one trial. It can be seen that the probability that X falls outside (μ -2 σ, μ +2 σ) is less than five percent, and it is often assumed in practical terms that the corresponding event does not occur, and therefore, the interval (μ -2 σ, μ +2 σ) can be basically regarded as the actually possible value interval of the number discharge time X.
That is, the entire number placing time X is substantially between (μ -2 σ, μ +2 σ), and if the number placing time X of the channel trader falls outside (μ -2 σ, μ +2 σ), it can be considered that the number placed by the channel trader is abnormal, and may be a false real-name number.
For example, if the number of the channel dealer placed in the unit time is far greater than the channel dealer to which the normal real-name number belongs, it can be considered that the number placed by the channel dealer is abnormal, and may be a false real-name number.
For another example, the matching degrees of the identification cards corresponding to the different numbers of the channel trader and the pictures taken in the number placing field are the same, that is, the channel trader is likely to obtain different numbers by using the same identification card and the pictures taken in the number placing field. In this case, it is considered that the number placed by the distributor is abnormal and may be a false real-name number.
In the above embodiment, by analyzing the number allocation behavior of the channel provider to which the normal real-name number belongs and the number allocation behavior of a certain channel provider, the overall number allocation behavior characteristic and the number allocation behavior characteristic of the channel provider can be obtained, and further, whether the number allocated by the channel provider is the false real-name number or not can be identified according to the difference between the overall number allocation behavior characteristic and the number allocation behavior characteristic of the channel provider.
FIG. 2 is a flow diagram of a false real name identification method according to further embodiments of the present disclosure. The following steps, which are the same or similar to those of fig. 1, can be referred to the above description.
In step 202, the number allocation behavior of the distributor to which the normal real-name number belongs is analyzed to obtain the overall number allocation behavior characteristics of the distributor to which the normal real-name number belongs.
In step 204, the number assignment behavior of a certain channel provider is analyzed to obtain the number assignment behavior characteristics of the channel provider.
In step 206, it is determined that the difference between the channel dealer's ranking behavior characteristic and the overall ranking behavior characteristic exceeds a predetermined difference. If yes, go to step 208.
In other embodiments, if the difference between the channel dealer's number assignment behavior characteristic and the overall number assignment behavior characteristic exceeds a preset difference, it may be additionally determined whether a duration exceeding the preset difference is greater than a preset time, and if the duration is less than the preset time, it is determined that the number placed by the channel dealer is a suspected false real-name number. This may avoid misunderstanding certain better performing channel providers as placing exceptions.
For example, the difference between the number of.
In step 208, the number placed by the distributor is determined to be a suspected false real-name number.
In step 210, the usage behaviors of all suspected false real-name numbers are analyzed to obtain the similarity of the usage behaviors of different suspected false real-name numbers.
The usage behavior may be, for example, the number of calls, the duration of the call, and the traffic for all suspected false real-name numbers. The subsequent behaviors of the false real-name numbers are relatively similar, for example, the call times are equal, the call duration is basically close, and the traffic is fixed in a certain interval.
Therefore, by analyzing the use behaviors of all the suspected false real-name numbers, the similarity of the use behaviors of different suspected false real-name numbers can be obtained. In some embodiments, the similarity may be determined by the coefficient of variation of all suspected false real name numbers. The larger the coefficient of variation, the smaller the similarity; the smaller the coefficient of variation, the smaller the similarity.
In step 212, in the case that the similarity is greater than the preset similarity, it is determined that the number placed by the distributor is a false real-name number.
For example, in the case where the coefficient of variation is smaller than a preset coefficient of variation, the similarity may be considered to be larger than a preset similarity.
In the above embodiment, whether the number placed by the distributor is a suspected false real-name number can be determined by analyzing the number placing behavior of the distributor to which the normal real-name number belongs and the number placing behavior of a certain distributor, and whether the number placed by the distributor is a false real-name number can be determined by analyzing the use behaviors of all suspected false real-name numbers. This way the determined false real name number is made more accurate.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the device embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
FIG. 3 is a schematic diagram of a false real name recognition device according to some embodiments of the present disclosure. As shown in fig. 3, the apparatus of this embodiment includes a first analysis module 301, a second analysis module 302, and an identification module 303.
The first analysis module 301 is configured to analyze the number allocation behavior of the distributor to which the normal real-name number belongs, so as to obtain the overall number allocation behavior characteristic of the distributor to which the normal real-name number belongs. The number assignment behavior may include at least one of: the number allocation time distribution, the number allocation quantity in unit time, and the matching degree of the identity cards corresponding to different numbers and the pictures shot on the number allocation site.
The second analysis module 302 is configured to analyze the number allocation behavior of a certain distributor to obtain the number allocation behavior characteristics of the certain distributor.
The identifying module 303 is configured to identify whether the number placed by the channel provider is a false real-name number according to a difference between the number placing behavior feature of the channel provider and the overall number placing behavior feature.
In the above embodiment, by analyzing the number allocation behavior of the channel provider to which the normal real-name number belongs and the number allocation behavior of a certain channel provider, the overall number allocation behavior characteristic and the number allocation behavior characteristic of the channel provider can be obtained, and further, whether the number allocated by the channel provider is the false real-name number or not can be identified according to the difference between the overall number allocation behavior characteristic and the number allocation behavior characteristic of the channel provider.
In order to improve the accuracy of the determined false real-name number, in some embodiments, the identifying module 303 is configured to determine that the difference between the number assignment behavior characteristic of the channel provider and the overall number assignment behavior characteristic exceeds a preset difference; if the difference between the number allocation behavior characteristic of the channel trader and the overall number allocation behavior characteristic exceeds a preset difference, determining the number allocated by the channel trader as a suspected false real-name number; analyzing the use behaviors of all suspected false real-name numbers to obtain the similarity of the use behaviors of different suspected false real-name numbers; and under the condition that the similarity is greater than the preset similarity, determining that the number put by the channel trader is a false real-name number.
In some embodiments, the similarity may be determined by the coefficient of variation of all suspected false real name numbers.
In some embodiments, the identifying module 303 is configured to determine that the number placed by the distributor is a suspected false real-name number if the difference between the number placing behavior feature of the distributor and the overall number placing behavior feature exceeds a preset difference and the duration is less than a preset time.
FIG. 4 is a schematic diagram of a false real name recognition device according to further embodiments of the present disclosure. As shown in fig. 4, the apparatus of this embodiment includes a memory 401 and a processor 402.
The memory 401 may be a magnetic disk, flash memory, or any other non-volatile storage medium. The memory 401 is used for storing instructions corresponding to the method of any one of the foregoing embodiments. Coupled to memory 401, processor 402 may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 402 is used to execute instructions stored in the memory 401.
FIG. 5 is a schematic diagram of a false real name recognition device according to further embodiments of the present disclosure. As shown in fig. 5, the apparatus 500 of this embodiment includes a memory 501 and a processor 502.
The processor 502 is coupled to the memory 501 by a BUS (BUS) 503. The system 500 may also be connected to an external storage device 505 through a storage interface 504 to call external data, and may also be connected to a network or an external computer system (not shown) through a network interface 506.
The present disclosure also provides a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method of any of the preceding embodiments.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. 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.
Thus far, the present disclosure has been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (12)
1. A false real name identification method, comprising:
analyzing the number allocation behavior of the channel provider to which the normal real-name number belongs to obtain the integral number allocation behavior characteristics of the channel provider to which the normal real-name number belongs;
analyzing the number allocation behavior of a certain channel provider to obtain the number allocation behavior characteristics of the channel provider;
and identifying whether the number placed by the channel provider is a false real-name number or not according to the difference between the number placing behavior characteristic of the channel provider and the integral number placing behavior characteristic.
2. The method of claim 1, wherein the identifying whether the number placed by the distributor is a false real-name number according to the difference between the number placing behavior feature of the distributor and the overall number placing behavior feature comprises:
judging that the difference between the number allocation behavior characteristic of the channel trader and the integral number allocation behavior characteristic exceeds a preset difference;
if the difference between the number allocation behavior characteristic of the channel trader and the integral number allocation behavior characteristic exceeds a preset difference, determining that the number allocated by the channel trader is a suspected false real-name number;
analyzing the use behaviors of all suspected false real-name numbers to obtain the similarity of the use behaviors of different suspected false real-name numbers;
and determining the number placed by the channel trader as a false real-name number under the condition that the similarity is greater than the preset similarity.
3. The method of claim 2, wherein if the difference between the number assignment behavior feature of the distributor and the overall number assignment behavior feature exceeds a preset difference and the duration is less than a preset time, determining that the number assigned by the distributor is a suspected false real-name number.
4. The method of claim 2, wherein the similarity is determined by the coefficient of variation of all suspected false real name numbers.
5. The method of any of claims 1-4, wherein the number assignment behavior comprises at least one of: the number allocation time distribution, the number allocation quantity in unit time, and the matching degree of the identity cards corresponding to different numbers and the pictures shot on the number allocation site.
6. A false real name recognition device comprising:
the first analysis module is used for analyzing the number allocation behavior of the channel provider to which the normal real-name number belongs so as to obtain the integral number allocation behavior characteristics of the channel provider to which the normal real-name number belongs;
the second analysis module is used for analyzing the number allocation behavior of a certain distributor to obtain the number allocation behavior characteristics of the distributor;
and the identification module is used for identifying whether the number placed by the channel provider is a false real-name number or not according to the difference between the number placing behavior characteristic of the channel provider and the integral number placing behavior characteristic.
7. The apparatus of claim 6, wherein the identification module is to:
judging that the difference between the number allocation behavior characteristic of the channel trader and the integral number allocation behavior characteristic exceeds a preset difference;
if the difference between the number allocation behavior characteristic of the channel trader and the integral number allocation behavior characteristic exceeds a preset difference, determining that the number allocated by the channel trader is a suspected false real-name number;
analyzing the use behaviors of all suspected false real-name numbers to obtain the similarity of the use behaviors of different suspected false real-name numbers;
and determining the number placed by the channel trader as a false real-name number under the condition that the similarity is greater than the preset similarity.
8. The apparatus of claim 7, wherein the identification module is configured to determine that the number placed by the distributor is a suspected false real-name number if the difference between the number placement behavior feature of the distributor and the overall number placement behavior feature exceeds a preset difference and the duration is less than a preset time.
9. The apparatus of claim 7, wherein the similarity is determined by a coefficient of variation of all suspected false real name numbers.
10. The apparatus of any of claims 6-9, wherein the number assignment behavior comprises at least one of: the number allocation time distribution, the number allocation quantity in unit time, and the matching degree of the identity cards corresponding to different numbers and the pictures shot on the number allocation site.
11. A false real name recognition device comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of any of claims 1-5 based on instructions stored in the memory.
12. A computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method of any one of claims 1-5.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5627886A (en) * | 1994-09-22 | 1997-05-06 | Electronic Data Systems Corporation | System and method for detecting fraudulent network usage patterns using real-time network monitoring |
| CN101770626A (en) * | 2010-01-11 | 2010-07-07 | 中国联合网络通信集团有限公司 | Method, device and system for identifying agents with card-laundering behavior |
| CN101803323A (en) * | 2007-02-26 | 2010-08-11 | 艾利森电话股份有限公司 | A method and apparatus for monitoring client behaviour |
| CN102542393A (en) * | 2010-12-27 | 2012-07-04 | 中国移动通信集团山东有限公司 | Method, system and device for realizing real-name registration of user identity information |
-
2018
- 2018-07-09 CN CN201810741581.2A patent/CN110704817A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5627886A (en) * | 1994-09-22 | 1997-05-06 | Electronic Data Systems Corporation | System and method for detecting fraudulent network usage patterns using real-time network monitoring |
| CN101803323A (en) * | 2007-02-26 | 2010-08-11 | 艾利森电话股份有限公司 | A method and apparatus for monitoring client behaviour |
| CN101770626A (en) * | 2010-01-11 | 2010-07-07 | 中国联合网络通信集团有限公司 | Method, device and system for identifying agents with card-laundering behavior |
| CN102542393A (en) * | 2010-12-27 | 2012-07-04 | 中国移动通信集团山东有限公司 | Method, system and device for realizing real-name registration of user identity information |
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Application publication date: 20200117 |