WO2022152018A1

WO2022152018A1 - Method and device for identifying multiple accounts belonging to the same person

Info

Publication number: WO2022152018A1
Application number: PCT/CN2022/070277
Authority: WO
Inventors: 李佳璐
Original assignee: 北京沃东天骏信息技术有限公司; 北京京东世纪贸易有限公司
Priority date: 2021-01-14
Filing date: 2022-01-05
Publication date: 2022-07-21
Also published as: CN113779346A

Abstract

The present application discloses a method and device for identifying multiple accounts belonging to the same person. One specific embodiment of said method comprises: acquiring user information corresponding to and identification information involved in accounts in an account pair; determining whether the accounts in the account pair satisfy preset association information, wherein the preset association information is used for representing whether the accounts in the account pair have the possibility of belonging to the same person; in response to determining that the accounts in the account pair satisfy the preset association information, determining whether the accounts in the account pair satisfy a preset determination condition, wherein the preset determination condition is used for determining whether the accounts in the account pair belongs to the same person; in response to determining that the accounts in the account pair do not satisfy the preset determination condition, processing the user information and the identification information of the accounts in the account pair to obtain feature vectors corresponding to the account pair; and inputting the feature vectors into an identification model to determine whether the accounts in the account pair belong to the same person. The present application provides a method for identifying multiple accounts belonging to the same person, which improves the identification accuracy.

Description

Method and device for identifying one person with multiple accounts

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application claims the priority of the Chinese patent application filed on January 14, 2021 with the application number of 202110049322.5 and the invention titled "Method and Device for Identifying One Person and Multiple Accounts", the full text of which is by reference incorporated into this application.

technical field

The embodiments of the present application relate to the field of computer technologies, and in particular, to a method and an apparatus for identifying one person with multiple accounts.

Background technique

In e-commerce platforms, it is very common for the same person to have multiple accounts. For example, in addition to having a personal account on the JD.com platform, a user also has a corporate account for conducting corporate business. For another example, the user has registered multiple personal accounts on different devices.

SUMMARY OF THE INVENTION

The embodiments of the present application propose a method and device for identifying one person with multiple accounts.

In a first aspect, the embodiments of the present application provide a method for identifying one person with multiple accounts, including: acquiring user information and related identification information corresponding to an account in an account pair; determining whether the account in the account pair satisfies a predetermined Assuming association information, the preset association information is used to represent whether the accounts in the account pair have the possibility of belonging to the same person; in response to determining that the accounts in the account pair satisfy the preset association information, determine whether the accounts in the account pair satisfy the preset judgment. The preset judgment condition is used to determine whether the accounts in the account pair belong to the same person; in response to determining that the account in the account pair does not meet the preset judgment condition, the user information and identification information of the account in the account pair are processed. , obtain the feature vector corresponding to the account pair; input the feature vector into the pre-trained identification model to determine whether the accounts in the account pair belong to the same person, wherein the identification model is used to characterize whether the feature vector corresponding to the account pair and the account in the account pair are not Correspondence between judgment results belonging to the same person.

In some embodiments, the preset judgment condition includes a first judgment condition and a second judgment condition, the first judgment condition is used to determine that the accounts in the account pair belong to the same person, and the second judgment condition is used to determine that the accounts in the account pair do not belong to the same person. belong to the same person.

In some embodiments, the above-mentioned processing of the user information and identification information of the accounts in the account pair to obtain the feature vector corresponding to the account pair includes: processing the corresponding item information between the accounts in the account pair to obtain each The sub-feature vector corresponding to the corresponding item information; splicing each sub-feature vector to obtain the feature vector.

In some embodiments, the user information includes user portrait information, consumption habit information, and receipt information; the above-mentioned processing of the corresponding item information between the accounts in the account pair to obtain the sub-feature vector corresponding to each corresponding item information, including : According to the user information of the accounts in the account pair, the sub-feature vector representing the similarity of the corresponding item information between the accounts is determined.

In some embodiments, the above-mentioned processing of the corresponding item information between the accounts in the account pair to obtain the sub-feature vector corresponding to each corresponding item information includes: for each type of identification information, performing the following operations: according to The identification information of this type of the account in the account pair determines the sub-feature vector representing the number of intersection identification information involved in the accounts in the account pair; for each account in the account pair, according to the account number and the intersection identification information. The number of associations, and the number of associations with all identification information of this type, determine the sub-feature vector representing the attribution degree of the intersection identification information relative to the account number; determine the sub-feature vector representing the sum of the attribution degrees of the accounts in the account pair, and a sub-feature vector representing the difference between the belonging degrees of the accounts in the account pair; for each identification information in this type, according to the number of accounts associated with the identification information, determine the sub-feature vector representing the sharing degree of the identification information.

In some embodiments, the identification model is obtained by training in the following manner: obtaining user information and the involved identification information corresponding to each account in the account set; Multiple account pairs; screen out account pairs that meet the preset judgment conditions from the multiple account pairs, obtain multiple training account pairs, and set each training account pair according to the preset judgment conditions to characterize the account in the training account pair Whether it belongs to the label of the same person; process the user information and identification information of the account in each training account pair in multiple account pairs, and obtain the feature vector corresponding to the training account pair; use machine learning methods to train account pairs. The corresponding feature vector is used as the input, and the input training account pair is used as the expected output to train the initial recognition model to obtain the recognition model.

In a second aspect, an embodiment of the present application provides an apparatus for identifying one person with multiple accounts, including: an obtaining unit configured to obtain user information and related identification information corresponding to an account in an account pair; a unit configured to determine whether an account in an account pair satisfies preset association information, and the preset association information is used to represent whether the account in the account pair has the possibility of belonging to the same person; a second determining unit is configured to respond to determining The accounts in the account pair satisfy the preset association information, and determine whether the accounts in the account pair satisfy the preset judgment condition, wherein the preset judgment condition is used to determine whether the accounts in the account pair belong to the same person; the processing unit is configured to respond After it is determined that the account in the account pair does not meet the preset judgment condition, the user information and identification information of the account in the account pair are processed to obtain a feature vector corresponding to the account pair; the third determining unit is configured to input the feature vector into a preset The trained identification model determines whether the accounts in the account pair belong to the same person, wherein the identification model is used to represent the correspondence between the feature vector corresponding to the account pair and the judgment result of whether the accounts in the account pair belong to the same person.

In some embodiments, the processing unit includes: a processing subunit, configured to process the corresponding item information between the accounts in the account pair to obtain a sub-feature vector corresponding to each corresponding item information; a splicing unit, configured to Concatenate each sub-eigenvector to get the eigenvector.

In some embodiments, the user information includes user portrait information, consumption habit information, and receipt information; the processing subunit is further configured to: according to the user information of the accounts in the account pair, determine the information representing the corresponding items of information between the accounts. A sub-feature vector of similarity.

In some embodiments, the processing subunit is further configured to: for each type of identification information, perform the following operation: according to the type of identification information of the account in the account pair, determine that the accounts in the account pair are related to The sub-feature vector of the number of intersection identification information; for each account in the account pair, according to the number of associations between the account and the intersection identification information, and the number of associations with all identification information of this type, determine the representative intersection identification information. The sub-feature vector relative to the attribution degree of the account; determine the sub-feature vector representing the sum of the attribution degrees of the accounts in the account pair, and the sub-feature vector representing the difference between the attribution degrees of the accounts in the account pair; For each identification information of , a sub-feature vector representing the sharing degree of the identification information is determined according to the number of accounts associated with the identification information.

In some embodiments, the above-mentioned device further includes: a training unit, configured to obtain the recognition model by training in the following manner: acquiring user information corresponding to each account in the account set and the involved identification information; Set the account combination of the associated information as an account pair, and obtain multiple account pairs; screen out the account pairs that meet the preset judgment conditions from the multiple account pairs, obtain multiple training account pairs, and train each training account according to the preset judgment conditions. The account pair is set to indicate whether the accounts in the training account pair belong to the same person; the user information and identification information of the accounts in each training account pair in the multiple account pairs are processed to obtain the corresponding features of the training account pair Vector; using the machine learning method, the feature vector corresponding to the training account pair is used as input, and the input training account pair corresponding label is used as the expected output to train the initial recognition model to obtain the recognition model.

In a third aspect, embodiments of the present application provide a computer-readable medium on which a computer program is stored, wherein the computer program implements the method described in any implementation manner of the first aspect when the computer program is executed by a processor.

In a fourth aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a storage device, on which one or more programs are stored, when the one or more programs are processed by the one or more processors Execution causes one or more processors to implement a method as described in any implementation form of the first aspect.

The method and device for identifying one person with multiple accounts provided by the embodiments of the present application, by acquiring the user information corresponding to the account in the account pair and the involved identification information; The association information is used to represent whether the accounts in the account pair have the possibility of belonging to the same person; in response to determining that the accounts in the account pair meet the preset association information, determine whether the accounts in the account pair meet the preset judgment conditions, wherein the preset The judgment condition is used to determine whether the accounts in the account pair belong to the same person; in response to determining that the account in the account pair does not meet the preset judgment condition, the user information and identification information of the account in the account pair are processed, and the corresponding account pair is obtained. input the feature vector into the pre-trained identification model to determine whether the accounts in the account pair belong to the same person, wherein the identification model is used to characterize whether the feature vector corresponding to the account pair and the account in the account pair belong to the same person. The corresponding relationship between the results provides a method for identifying one person and multiple accounts, and improves the identification accuracy.

Description of drawings

Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

FIG. 1 is an exemplary system architecture diagram to which an embodiment of the present application may be applied;

2 is a flowchart of an embodiment of a method for identifying one person with multiple accounts according to the present application;

3 is a schematic diagram of an application scenario of the method for identifying one person with multiple accounts according to the present embodiment;

4 is a flow chart of another embodiment of a method for identifying one person with multiple accounts according to the present application;

5 is a structural diagram of an embodiment of an apparatus for identifying one person with multiple accounts according to the present application;

FIG. 6 is a schematic structural diagram of a computer system suitable for implementing the embodiments of the present application.

Detailed ways

The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the related invention, but not to limit the invention. In addition, it should be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

It should be noted that the embodiments in the present application and the features of the embodiments may be combined with each other in the case of no conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

FIG. 1 shows an exemplary architecture 100 to which the method and apparatus for identifying one person with multiple accounts of the present application can be applied.

As shown in FIG. 1 , the system architecture 100 may include

terminal devices

101 , 102 , and 103 , a network 104 and a server 105 . The communication connections among the

terminal devices

101 , 102 , and 103 constitute a topology network, and the network 104 is used to provide a medium for communication links between the

terminal devices

101 , 102 , and 103 and the server 105 . The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The

terminal devices

101, 102, and 103 may be hardware devices or software that support network connection for data interaction and data processing. When the

terminal devices

101, 102, and 103 are hardware, they can be various electronic devices that support network connection, information acquisition, interaction, display, processing and other functions, including but not limited to smart phones, tablet computers, e-book readers, Laptops and desktops, etc. When the

terminal devices

101, 102, and 103 are software, they can be installed in the electronic devices listed above. It can be implemented, for example, as multiple software or software modules for providing distributed services, or as a single software or software module. There is no specific limitation here.

The server 105 may be a server that provides various services, for example, a background processing server that obtains user information corresponding to the accounts associated with the

terminal devices

101, 102, and 103, the involved identification information, and determines one person and multiple accounts. The background processing server will need to determine whether two accounts belonging to the same person form an account pair, and obtain whether the account in the account pair is based on the triple identification of whether the preset association information is met, whether the preset judgment condition is met, and the identification model is used for identification. belong to the same person. As an example, the server 105 may be a cloud server.

It should be noted that the server may be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or can be implemented as a single server. When the server is software, it can be implemented as a plurality of software or software modules (for example, software or software modules for providing distributed services), or can be implemented as a single software or software module. There is no specific limitation here.

It should also be noted that the method for identifying one person with multiple accounts provided by the embodiments of the present disclosure may be executed by a server, a terminal device, or a server and a terminal device in cooperation with each other. Correspondingly, each part (for example, each unit, sub-unit, module, sub-module) included in the device for identifying one person with multiple accounts can be all set in the server, or all can be set in the terminal device, and can also be set in the server respectively. and terminal equipment.

It should be understood that the numbers of terminal devices, networks and servers in FIG. 1 are merely illustrative. There can be any number of terminal devices, networks and servers according to implementation needs. When the electronic device on which the method for identifying one person and multiple accounts is executed does not need to perform data transmission with other electronic devices, the system architecture may only include the electronic device on which the method for identifying multiple accounts is executed (for example, server or terminal device).

Continuing to refer to FIG. 2 , a flow 200 of an embodiment of a method for identifying one person with multiple accounts is shown, including the following steps:

Step 201: Obtain the user information and the involved identification information corresponding to the accounts in the account pair.

In this embodiment, the execution body of the method for identifying one person with multiple accounts (for example, the server in FIG. 1 ) can obtain the user information corresponding to the accounts in the account pair from a remote or locally through a wired connection or a wireless connection. , the identification information involved.

The account number may be various types of accounts, such as a Jingdong account, a Taobao account, a QQ account, and the like. The two accounts in the account pair are accounts that need to be determined whether they belong to the same person. For each account in the account pair, the above-mentioned execution body may obtain the user information corresponding to the account and the involved identification information.

User information represents any information of the user that can be obtained based on the account. For example, the user information includes registration information filled in by the user when registering an account. The identification information represents any type of hardware identification information and software identification information associated with the account. For example, the identification information includes a device ID (Identity document, identification number), an Open ID (open identification number) and a Union ID (joint identification number) of the applet, and the like. Among them, the device ID includes but is not limited to IMEI (International Mobile Equipment Identity, International Mobile Equipment Identity) of Android system, AID (Andriod ID, identity identifier of Andriod system), IOS (mobile operating system developed by Apple) IDFA (Identifier for Advertising, advertising identifier), Open UDID (Unique Device Identifier, the unique device identifier of the device), etc. In this embodiment, when the account has logged in the hardware device corresponding to the hardware identification information, or has logged in the software corresponding to the software identification information, the identification information is considered to be identification information involved or associated with the account.

As an example, the account is a JD account, and the above-mentioned execution entity may collect user information and identification information corresponding to the account from various business channels of the entire JD e-commerce platform.

Step 202: Determine whether the accounts in the account pair satisfy preset association information.

In this embodiment, the above-mentioned execution body can determine whether the account in the account pair satisfies the preset association information. The preset association information is used to represent whether the accounts in the account pair have the possibility of belonging to the same person.

The preset association information can be specifically set according to the accounts in the account pair. As an example, the preset association information may be that at least one of the identification information related to the two accounts has the same identification information. For example, two accounts have logged into the same device, or two accounts have been connected to the same wireless hotspot.

It can be understood that there is generally a correlation between two accounts belonging to the same person. The association between accounts of the same person can be represented by preset association information. When the two accounts in the account pair satisfy the preset association information, the above-mentioned execution body may determine that the accounts in the account pair have the possibility of belonging to the same person; when the two accounts in the account pair do not satisfy the preset association information, the above-mentioned The execution subject can determine that the accounts in the account pair do not have the possibility of belonging to the same person.

Step 203 , in response to determining that the accounts in the account pair satisfy the preset association information, determine whether the accounts in the account pair satisfy the preset determination condition.

In this embodiment, the above-mentioned execution body may determine whether the account in the account pair satisfies the preset determination condition in response to determining that the account in the account pair satisfies the preset association information. The preset judgment condition is used to determine whether the accounts in the account pair belong to the same person.

The preset judgment condition may be specifically set according to the accounts in the account pair. As an example, the account is a user account of an e-commerce platform, and the preset determination condition may be whether the registration information between the accounts is the same. Specifically, when the ID numbers in the registration information of the two accounts are different, it can be determined that the two accounts do not belong to the same person.

In some optional implementations of this embodiment, the preset determination condition includes a first determination condition and a second determination condition. The first determination condition is used to determine that the accounts in the account pair belong to the same person. When the accounts in the account pair satisfy the first judgment condition, it can be determined that the accounts in the account pair belong to the same person; however, when the accounts in the account pair do not meet the first judgment condition, it cannot be determined that the accounts in the account pair do not belong to the same person. same person. The second judgment condition is used to determine that the accounts in the account pair do not belong to the same person. When the accounts in the account pair meet the second judgment condition, it can be determined that the accounts in the account pair do not belong to the same person; however, when the accounts in the account pair do not belong to the same person; When the second determination condition is not met, it cannot be determined that the accounts in the account pair belong to the same person.

Continuing to take the account of the user account of the e-commerce platform as an example, the first judgment condition is that the phone numbers in the highest frequency receiving information corresponding to the account are the same, and the users corresponding to the account have the same gender and the same age. When it is determined that the phone numbers in the highest frequency receiving information corresponding to the accounts in the account pair are the same, and the users corresponding to the accounts have the same gender and age, it is determined that the accounts in the account pair belong to the same person; When at least one of the phone number in the account receipt information, the gender and age of the user corresponding to the account are different, it cannot be determined that the accounts in the account pair do not belong to the same person, and there is still a possibility that they belong to the same person. The second determination condition is that the accounts in the account pair are accounts corresponding to each member of the registered family accounts, or the ID numbers in the registration information of the accounts in the account pair are different. The family account is a plurality of accounts associated with family relationships, and a plurality of the accounts have been authenticated as family relationships.

When it is determined that the accounts in the account pair do not belong to the accounts corresponding to each member of the family account, or it is not determined that the ID numbers in the registration information of the accounts in the account pair are different, at this time, it cannot be determined that the accounts in the account pair are belong to the same person.

It can be understood that it may not be possible to determine whether the accounts in the account pair belong to the same person through the preset judgment conditions. That is, the accounts in the account pair neither satisfy the first judgment condition and cannot be determined to belong to the same person, nor do they meet the second judgment condition and cannot be determined to not belong to the same person.

Step 204, in response to determining that the account in the account pair does not meet the preset judgment condition, process the user information and identification information of the account in the account pair to obtain a feature vector corresponding to the account pair.

In this embodiment, in response to determining that the account in the account pair does not meet the preset judgment condition, the execution body may process the user information and identification information of the account in the account pair to obtain a feature vector corresponding to the account pair.

As an example, the above-mentioned execution body may digitize the user information and identification information of the accounts in the account pair based on the same standard, and use the digitized vector as the feature vector corresponding to the account pair.

In some optional implementation manners of this embodiment, the foregoing execution body performs the foregoing step 204 in the following manner:

First, the corresponding item information between the accounts in the account pair is processed to obtain a sub-feature vector corresponding to each corresponding item information. The corresponding item information represents information corresponding to the accounts in the account pair. For example, an account pair includes an account x and an account y, and the registration information corresponding to the account x and the registration information corresponding to the account y may be regarded as corresponding items of information. The above-mentioned execution body may digitize each group of corresponding item information to obtain a sub-feature vector corresponding to the corresponding item information.

Second, concatenate each sub-eigenvector to get the eigenvector.

As an example, the above-mentioned execution body may splicing each sub-feature vector based on a preset sequence to obtain a feature vector.

In some optional implementation manners of this embodiment, the user information corresponding to the account includes user portrait information, consumption habit information, and receipt information. Regarding the user information, the execution body may determine a sub-feature vector representing the similarity of the corresponding item information between the accounts according to the user information of the accounts in the account pair.

It can be understood that when the accounts in the account pair belong to the same person, the similarity of the user information corresponding to the accounts in the account pair is relatively high; when the accounts in the account pair do not belong to the same person, the user information corresponding to the accounts in the account pair is relatively high. the similarity is low.

Taking the receipt information as an example, the above-mentioned execution entity can obtain the proportion of the number of orders in which the receipt information overlaps between the accounts in the account pair by statistics. For the recipient's name, the number of orders for account x is o _x , the number of orders for account y is o _y , and the number of orders for account x and account y with the same recipient name is o _xy . The above executive body obtains the ratio of the overlapping recipient names in the order of account x r _x =o _xy /o _x , and obtains the ratio of the overlapping recipient names in the order of account y r _y =o _xy / o _y . The proportion of orders with overlapping receipt information depicts the possibility that the receipt information is the account holder's information. The higher the proportion, the more likely the receipt information is the account holder's information. The similarity between the two accounts is similar. higher degree.

For the information that can be classified in the user information, such as gender, purchasing power, and common payment methods, the similarity between the accounts in the account pair is distinguished by a specific numerical value. Taking gender as an example, when the gender of account x and account y are both non-empty and the same, the similarity is determined to be 1; when the gender of account x and account y is empty, the similarity is determined to be 0; The gender of account y and account y are both non-null and different, and the similarity is determined to be -1.

In some optional implementations of this embodiment, for each type of identification information, the above-mentioned execution body may perform the following operations:

First, according to the type of identification information of the accounts in the account pair, a sub-feature vector representing the quantity of intersection identification information involved in the accounts in the account pair is determined.

As an example, the identification information of this type is the IMEI information of the Android system. The identification information of this type of account x in the account pair includes A, B, C, and D, and the identification information of this type of account y in the account pair includes B, C, D, and E, then the account x, The intersection identification information of y is B, C, and D, and the number is 3. Further, the above-mentioned execution body can determine the sub-feature vector representing the number of intersection identification information involved in the accounts in the account pair

Second, for each account in the account pair, according to the number of associations between the account and the intersection identification information, and the association times with all identification information of the type, determine the degree of belonging of the intersection identification information relative to the account. sub-feature vector.

In this implementation, each time an account logs in to the hardware device corresponding to the hardware identification information or the software corresponding to the software identification information, it can be determined that the account is associated with the hardware device corresponding to the hardware identification information or the software corresponding to the software identification information.

Continue to take this type of identification information as the IMEI information of the Android system as an example, the IMEI set of the device logged in by account x is:

The IMEI set of the devices logged in by account y is:

Then the set of IMEIs of the same devices logged in by account x and account y is:

where m and n are both positive integers. Note that the total number of times account x has logged in on the IMEI is

but

Among them, x _i is the number of login times of account x on the device corresponding to imei _i . Similarly, record the total number of times account y has logged in on the IMEI as

but

Among them, y _i is the number of login times of account y on the device corresponding to imei _i . Determine the total number of logins of account x and account y on the same IMEI, respectively:

and

but

We define the attribution of IMEI to account x as

The attribution of IMEI to account y is defined as

Intuitively, for accounts x and y in an account pair, the degree of attribution of IMEI to account x is the ratio of the number of logins of account x on the same IMEI of the two accounts to the number of logins of account x on all IMEIs; IMEI The degree of attribution to account y is the ratio of the number of logins of account y on the same IMEI of the two accounts to the number of logins of account y on all IMEIs. The greater the ID belonging degree, the more likely the device corresponding to the ID is owned by the user corresponding to the account. Correspondingly, a small ID attribution usually means that the account may only log in to the device corresponding to the ID once occasionally, and there is no strong belonging relationship.

Third, a sub-feature vector representing the sum of the attribution degrees of the accounts in the account pair, and a sub-feature vector representing the difference between the attribution degrees of the accounts in the account pair are determined.

Continuing from the above example, the sum of the attribution degrees of the accounts in the account pair is

and

The difference between the attribution degrees of the accounts in the account pair is

and

poor.

Fourth, for each identification information in the type, according to the number of accounts associated with the identification information, a sub-feature vector representing the sharing degree of the identification information is determined.

The degree of sharing of the identification information is: the number of accounts that have logged in the identification information. It can be understood that the higher the sharing degree of the identification information, the more likely the device corresponding to the identification information is a public device.

Step 205: Input the feature vector into the pre-trained recognition model to determine whether the accounts in the account pair belong to the same person.

In this embodiment, the above-mentioned execution body may input the feature vector into the pre-trained recognition model to determine whether the accounts in the account pair belong to the same person. The identification model is used to represent the correspondence between the feature vector corresponding to the account pair and the determination result of whether the accounts in the account pair belong to the same person.

The recognition model can be any network model with recognition function, including but not limited to a convolutional neural network model, a recurrent neural network model, and a residual neural network model.

Continuing to refer to FIG. 3 , FIG. 3 is a schematic diagram 300 of an application scenario of the method for identifying one person with multiple accounts according to this embodiment. In the application scenario of FIG. 3 , the account pair includes an e-commerce account x and an e-commerce account y. The server 301 first obtains from the database server 302 the user information and the involved identification information respectively corresponding to the account x and the account y in the account pair. Then, the server determines whether there is at least one identical identification information (that is, preset association information) in the identification information related to the account x and the account y in the account pair. The preset association information is used to represent whether the account x and the account y in the account pair have the possibility of belonging to the same person. Then, in response to determining that the account x and the account y in the account pair have at least one identical identification information (including the second identification and the third identification), the server 301 determines whether the account x and the account y in the account pair satisfy the requirements in the recipient information The phone number of the account is the same, the gender and age of the user corresponding to the account are the same, and the ID number in the registration information of the account in the account pair is different (that is, the preset judgment condition). The preset judgment condition is used to determine whether the accounts in the account pair belong to the same person. Then, in response to determining that the account x and the account y in the account pair do not meet the preset determination conditions, the user information and identification information of the account in the account pair are processed to obtain a feature vector corresponding to the account pair. Finally, the server 301 inputs the feature vector into the pre-trained recognition model, and determines that the account x and the account y in the account pair do not belong to the same person. The identification model is used to represent the correspondence between the feature vector corresponding to the account pair and the determination result of whether the accounts in the account pair belong to the same person.

In the method provided by the above-mentioned embodiments of the present disclosure, by acquiring the user information corresponding to the account in the account pair and the involved identification information, it is determined whether the account in the account pair satisfies the preset association information, and the preset association information is used to represent the account. Whether the accounts in the pair have the possibility of belonging to the same person; in response to determining that the accounts in the pair of accounts satisfy the preset association information, determine whether the accounts in the pair of accounts satisfy the preset judgment condition, wherein the preset judgment condition is used to determine the account number Whether the accounts in the pair belong to the same person; in response to determining that the accounts in the pair of accounts do not meet the preset judgment conditions, the user information and identification information of the accounts in the pair of accounts are processed to obtain the feature vector corresponding to the pair of accounts; the feature vector Input the pre-trained identification model to determine whether the accounts in the account pair belong to the same person, wherein the identification model is used to represent the corresponding relationship between the feature vector corresponding to the account pair and the judgment result of whether the accounts in the account pair belong to the same person, Thus, a method for identifying one person with multiple accounts is provided, and the identification accuracy is improved.

In some optional implementations of this embodiment, the recognition model is obtained by training in the following manner:

First, obtain the user information and the involved identification information corresponding to each account in the account set.

In this implementation manner, the account set includes a large number of accounts. The manner of acquiring the user information corresponding to the account and the involved identification information can be performed with reference to the manner of step 201, and details are not described herein again.

Second, the accounts in the account set that satisfy the preset association information are combined into account pairs to obtain multiple account pairs.

It can be understood that only accounts that satisfy the preset association information have the possibility of belonging to the same person, and when there is a possibility of belonging to the same person, the accounts can be combined into account pairs.

Third, screen out the account pairs that meet the preset judgment conditions from the multiple account pairs, obtain multiple training account pairs, and set each training account pair according to the preset judgment conditions to indicate whether the account in the training account pair belongs to Same person label.

In this implementation manner, the preset judgment condition can determine the account pair that belongs to the same person and the account pair that does not belong to the same person. For account pairs belonging to the same person, the above-mentioned execution entity may set a label that the accounts in the account pair belong to the same person, and use such account pairs as positive samples. For account pairs that do not belong to the same person, the above-mentioned execution entity may set a label that the accounts in the account pair do not belong to the same person, and use such account pairs as negative samples.

Fourth, the user information and identification information of the accounts in each training account pair in the multiple account pairs are processed to obtain a feature vector corresponding to the training account pair.

In this implementation manner, the manner of information processing can be performed with reference to the manner of step 204, and details are not described herein again.

Fifth, using the machine learning method, the feature vector corresponding to the training account pair is used as the input, and the label corresponding to the input training account pair is used as the expected output to train the initial recognition model to obtain the recognition model.

In this implementation manner, the above-mentioned execution subject can cyclically train the initial model through a large number of training account pairs, and complete the training of the initial model in response to reaching the preset end condition. The preset end condition may be, for example, that the loss function of the model converges and the number of training times reaches a certain number of times.

In some optional implementation manners of this embodiment, the above-mentioned execution body may also perform information processing based on user granularity for multiple accounts belonging to the same person. Among them, the user granularity representation is in units of users, not accounts. As an example, the information processing may be, for example, information push. When account x and account y belong to the same person, the information is pushed with the user to which account x and account y belong as the object of information push. Specifically, information push may be performed for an account of the user. After one account of the user receives the push information, information is not pushed to another account of the user. It can be understood that information processing based on user granularity can improve user experience.

Continuing to refer to Fig. 4, a schematic flow 400 of an embodiment of the method for identifying one person with multiple accounts according to the present application is shown, including the following steps:

Step 401: Obtain the user information and the involved identification information corresponding to the accounts in the account pair.

Step 402: Determine whether the accounts in the account pair satisfy the preset association information.

The preset association information is used to represent whether the accounts in the account pair have the possibility of belonging to the same person.

Step 403 , in response to determining that the account in the account pair satisfies the preset association information, determine whether the account in the account pair satisfies the first determination condition.

The first determination condition is used to determine that the accounts in the account pair belong to the same person. When the accounts in the account pair meet the first determination condition, it is proved that the accounts in the account pair belong to the same person. It should be noted that when the accounts in the account pair do not meet the first determination condition, it cannot be proved that the accounts in the account pair do not belong to the same person.

Step 404, in response to determining that the account in the account pair does not satisfy the first determination condition, determine whether the account in the account pair meets the second determination condition.

The second determination condition is used to determine that the accounts in the account pair do not belong to the same person. When the accounts in the account pair meet the second determination condition, it proves that the accounts in the account pair do not belong to the same person. It should be noted that when the accounts in the account pair do not satisfy the second determination condition, it cannot be proved that the accounts in the account pair belong to the same person.

Step 405, in response to determining that the account in the account pair does not meet the first judgment condition and the second judgment condition, perform the following operations:

Step 4051 , according to the user information of the accounts in the account pair, determine a sub-feature vector representing the similarity of the corresponding item information between the accounts.

Step 4052, for each type of identification information, perform the following operations:

Step 40521: Determine, according to the type of identification information of the accounts in the account pair, a sub-feature vector representing the quantity of intersection identification information involved in all the accounts in the account pair.

Step 40522, for each account in the account pair, according to the number of associations between the account and the intersection identification information, and the association times with all identification information of this type, determine the degree of belonging that characterizes the intersection identification information relative to the account. sub-feature vector.

Step 40523: Determine a sub-feature vector representing the sum of the attribution degrees of the accounts in the account pair, and a sub-feature vector representing the difference between the attribution degrees of the accounts in the account pair.

Step 40524: For each identification information in the type, according to the number of accounts associated with the identification information, determine a sub-feature vector representing the sharing degree of the identification information.

Step 406, splicing each sub-feature vector to obtain a feature vector.

Step 407: Input the feature vector into the pre-trained recognition model to determine whether the accounts in the account pair belong to the same person.

The identification model is used to represent the correspondence between the feature vector corresponding to the account pair and the determination result of whether the accounts in the account pair belong to the same person.

It can be seen from this embodiment that, compared with the embodiment corresponding to FIG. 2 , the process 400 of the method for identifying one person with multiple accounts in this embodiment specifically describes the processing process of the feature vector and whether the account of the account pair belongs to The determination process of the same person further improves the recognition accuracy.

Continuing to refer to FIG. 5 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of an apparatus for identifying one person with multiple accounts, and the apparatus embodiment corresponds to the method embodiment shown in FIG. 2 . , the device can be specifically applied to various electronic devices.

As shown in FIG. 5 , the device for identifying one person with multiple accounts includes: an obtaining unit 501, configured to obtain user information corresponding to an account in the account pair and the involved identification information; a first determining unit 502, configured by is configured to determine whether the account in the account pair satisfies the preset association information, and the preset association information is used to represent whether the account in the account pair has the possibility of belonging to the same person; the second determining unit 503 is configured to respond to determining the account pair. The accounts in the pair of accounts satisfy the preset association information, and it is determined whether the accounts in the account pair meet the preset judgment conditions, wherein the preset judgment conditions are used to determine whether the accounts in the account pair belong to the same person; the processing unit 504 is configured to respond to It is determined that the account in the account pair does not meet the preset judgment condition, and the user information and identification information of the account in the account pair are processed to obtain a feature vector corresponding to the account pair; the third determining unit 505 is configured to input the feature vector in advance. The trained identification model determines whether the accounts in the account pair belong to the same person, wherein the identification model is used to represent the correspondence between the feature vector corresponding to the account pair and the judgment result of whether the accounts in the account pair belong to the same person.

In some embodiments, the processing unit 504 includes: a processing subunit (not shown in the figure), configured to process the corresponding item information between the accounts in the account pair to obtain the subfeature corresponding to each corresponding item information vector; a splicing unit (not shown in the figure), configured to splicing each sub-feature vector to obtain a feature vector.

In some embodiments, the user information includes user portrait information, consumption habit information and receipt information; the processing subunit (not shown in the figure) is further configured to: determine the representative account according to the user information of the account in the account pair A sub-feature vector of the similarity between the corresponding item information.

In some embodiments, the processing subunit (not shown in the figure) is further configured to: for each type of identification information, perform the following operations: determine the representation according to the type of identification information of the account in the account pair The sub-feature vector of the number of intersection identification information involved in the accounts in the account pair; for each account in the account pair, according to the number of associations between the account and the intersection identification information, and the association with all identification information of this type determine the sub-feature vector representing the attribution degree of the intersection identification information relative to the account; determine the sub-feature vector representing the sum of the attribution degrees of the accounts in the account pair, and the sub-feature vector representing the difference between the attribution degrees of the accounts in the account pair Feature vector; for each identification information in this type, according to the number of accounts associated with the identification information, determine the sub-feature vector representing the sharing degree of the identification information.

In some embodiments, the above-mentioned apparatus further includes: a training unit (not shown in the figure), configured to obtain an identification model by training in the following manner: acquiring the user information corresponding to each account in the account set, the involved identification information ; Combining the accounts meeting the preset association information in the account set into account pairs to obtain multiple account pairs; screening out account pairs that meet the preset judgment conditions from the multiple account pairs to obtain multiple training account pairs, and according to the preset Set the judgment condition to set a label for each training account pair to indicate whether the accounts in the training account pair belong to the same person; process the user information and identification information of the account in each training account pair in the multiple account pairs, and obtain The feature vector corresponding to the training account pair; using the machine learning method, the feature vector corresponding to the training account pair is used as input, and the label corresponding to the input training account pair is used as the expected output to train the initial recognition model to obtain the recognition model.

In this embodiment, the obtaining unit in the device for identifying one person with multiple accounts obtains the user information corresponding to the account in the account pair and the involved identification information; the first determining unit determines whether the account in the account pair satisfies the preset association information, the preset association information is used to represent whether the accounts in the account pair have the possibility of belonging to the same person; the second determination unit determines whether the accounts in the account pair satisfy the preset association information in response to determining whether the accounts in the account pair meet the preset association information. Set a judgment condition, wherein, the preset judgment condition is used to determine whether the accounts in the account pair belong to the same person; the processing unit responds to determining that the account in the account pair does not meet the preset judgment condition, the user information of the account in the account pair, user information, The identification information is processed to obtain the feature vector corresponding to the account pair; the third determining unit inputs the feature vector into the pre-trained identification model to determine whether the accounts in the account pair belong to the same person, wherein the identification model is used to represent the corresponding features of the account pair The corresponding relationship between the vector and the determination result of whether the accounts in the account pair belong to the same person provides a device for identifying one person and multiple accounts, and improves the identification accuracy.

Referring next to FIG. 6 , it shows a schematic structural diagram of a computer system 600 suitable for implementing the devices of the embodiments of the present application (eg,

devices

101 , 102 , 103 , and 105 shown in FIG. 1 ). The device shown in FIG. 6 is only an example, and should not impose any limitations on the functions and scope of use of the embodiments of the present application.

As shown in FIG. 6, a computer system 600 includes a processor (eg, CPU, central processing unit) 601 that can be loaded into a random access memory (RAM) according to a program stored in a read only memory (ROM) 602 or from a storage section 608 The program in 603 executes various appropriate actions and processes. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The processor 601 , the ROM 602 and the RAM 603 are connected to each other through a bus 604 . An input/output (I/O) interface 605 is also connected to bus 604 .

The following components are connected to the I/O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc. ; and a communication section 609 including a network interface card such as a LAN card, a modem, and the like. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I/O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 610 as needed so that a computer program read therefrom is installed into the storage section 608 as needed.

In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the method illustrated in the flowchart. In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 609 and/or installed from the removable medium 611 . When the computer program is executed by the processor 601, the above-mentioned functions defined in the method of the present application are performed.

It should be noted that the computer-readable medium of the present application may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), fiber optics, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device . Program code embodied on a computer readable medium may be transmitted using any suitable medium including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for performing the operations of the present application may be written in one or more programming languages, including object-oriented programming languages—such as Java, Smalltalk, C++, and also conventional procedures, or a combination thereof programming language - such as "C" or a similar programming language. The program code may execute entirely on the client computer, partly on the client computer, as a stand-alone software package, partly on the client computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the client computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider via Internet connection).

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logical functions for implementing the specified functions executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented in dedicated hardware-based systems that perform the specified functions or operations , or can be implemented in a combination of dedicated hardware and computer instructions.

The units involved in the embodiments of the present application may be implemented in a software manner, and may also be implemented in a hardware manner. The described unit may also be provided in the processor, for example, it may be described as: a processor including an acquisition unit, a first determination unit, a second determination unit, a processing unit and a third determination unit. Among them, the names of these units do not constitute a limitation of the unit itself under certain circumstances. For example, the third determination unit can also be described as "input the feature vector into the pre-trained recognition model, and determine whether the account in the account pair is not. Units belonging to the same person".

As another aspect, the present application also provides a computer-readable medium. The computer-readable medium may be included in the device described in the above embodiments, or may exist alone without being assembled into the device. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the device, the computer equipment: obtains the user information corresponding to the account in the account pair and the involved identification information; determines Whether the accounts in the account pair satisfy the preset association information, the preset association information is used to represent whether the accounts in the account pair have the possibility of belonging to the same person; in response to determining that the accounts in the account pair satisfy the preset association information, determine the account pair Whether the accounts in the account pair meet the preset judgment conditions, wherein the preset judgment conditions are used to determine whether the accounts in the account pair belong to the same person; in response to determining that the accounts in the account pair do not meet the preset judgment conditions, the account in the account pair is determined. The user information and identification information of the account pair are processed to obtain the feature vector corresponding to the account pair; the feature vector is input into the pre-trained identification model to determine whether the accounts in the account pair belong to the same person, wherein the identification model is used to represent the corresponding features of the account pair. The correspondence between the vector and the determination result of whether the accounts in the account pair belong to the same person.

The above description is only a preferred embodiment of the present application and an illustration of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solution formed by the specific combination of the above technical features, and should also cover the above technical features or Other technical solutions formed by any combination of its equivalent features. For example, a technical solution is formed by replacing the above-mentioned features with the technical features disclosed in this application (but not limited to) with similar functions.

Claims

A method for identifying one person with multiple accounts, comprising:

Obtain the user information and the involved identification information corresponding to the accounts in the account pair;

determining whether the accounts in the account pair satisfy preset association information, where the preset association information is used to represent whether the accounts in the account pair have the possibility of belonging to the same person;

In response to determining that the account in the account pair satisfies the preset association information, it is determined whether the account in the account pair satisfies a preset judgment condition, wherein the preset judgment condition is used to determine the account in the account pair. Whether the account belongs to the same person;

In response to determining that the account in the account pair does not meet the preset judgment condition, processing the user information and identification information of the account in the account pair to obtain a feature vector corresponding to the account pair; and

Input the feature vector into a pre-trained identification model to determine whether the accounts in the account pair belong to the same person, wherein the identification model is used to represent whether the feature vector corresponding to the account pair and the account in the account pair belong to the same person The corresponding relationship between the judgment results.
The method according to claim 1, wherein the preset judgment condition includes a first judgment condition and a second judgment condition, the first judgment condition is used to determine that the accounts in the account pair belong to the same person, the The second determination condition is used to determine that the accounts in the account pair do not belong to the same person.
The method according to claim 1, wherein the processing of the user information and identification information of the accounts in the account pair to obtain a feature vector corresponding to the account pair, comprising:

processing the corresponding item information between the accounts in the account pair to obtain a sub-feature vector corresponding to each corresponding item information; and

The eigenvectors are obtained by concatenating each sub-eigenvector.
The method according to claim 3, wherein the user information includes user portrait information, consumption habit information and receipt information;

The processing of the corresponding item information between the accounts in the account pair to obtain the sub-feature vector corresponding to each corresponding item information, including:

According to the user information of the accounts in the account pair, a sub-feature vector representing the similarity of the corresponding item information between the accounts is determined.
The method according to claim 3, wherein the processing of the corresponding item information between the accounts in the account pair to obtain the sub-feature vector corresponding to each corresponding item information, comprising:

For each type of identification information, do the following:

According to the type of identification information of the accounts in the account pair, determine a sub-feature vector representing the number of intersection identification information involved in the accounts in the account pair;

For each account in the account pair, according to the number of associations between the account and the intersection identification information, and the association times with all identification information of the type, determine the relationship between the intersection identification information and the account. The sub-feature vector of the attribution degree;

determining a sub-feature vector representing the sum of the degrees of belonging of the accounts in the pair of accounts, and a sub-feature vector representing the difference in the degrees of belonging of the accounts in the pair of accounts; and

For each identification information in this type, according to the number of accounts associated with the identification information, a sub-feature vector representing the sharing degree of the identification information is determined.
The method according to any one of claims 1-5, wherein the recognition model is obtained by training in the following manner:

Obtain the user information and the involved identification information corresponding to each account in the account set;

combining the accounts satisfying the preset association information in the account set into account pairs to obtain multiple account pairs;

Screen out the account pairs that meet the preset judgment conditions from the multiple account pairs, obtain multiple training account pairs, and set each training account pair according to the preset judgment conditions to characterize the training account pair. Whether the account belongs to the same person's tag;

processing the user information and identification information of the account in each training account pair in the plurality of account pairs to obtain a feature vector corresponding to the training account pair; and

Using the machine learning method, take the training account pair corresponding feature vector as input, and take the input training account pair corresponding label as the expected output, train the initial recognition model, and obtain the recognition model.
A device for identifying one person with multiple accounts, comprising:

an obtaining unit, configured to obtain the user information and the involved identification information corresponding to the account in the account pair;

a first determining unit, configured to determine whether the accounts in the account pair satisfy preset association information, and the preset association information is used to represent whether the accounts in the account pair have the possibility of belonging to the same person;

The second determination unit is configured to, in response to determining that the account in the account pair satisfies the preset association information, determine whether the account in the account pair satisfies a preset judgment condition, wherein the preset judgment condition is determined by using To determine whether the accounts in the account pair belong to the same person;

a processing unit, configured to process the user information and identification information of the account in the account pair in response to determining that the account in the account pair does not meet the preset judgment condition, and obtain the corresponding feature of the account pair vector; and

The third determination unit is configured to input the feature vector into a pre-trained identification model, and determine whether the accounts in the account pair belong to the same person, wherein the identification model is used to represent the feature vector corresponding to the account pair and the account number The correspondence between the judgment results of whether the accounts in the pair belong to the same person.
The device according to claim 7, wherein the preset judgment condition includes a first judgment condition and a second judgment condition, the first judgment condition is used to determine that the accounts in the account pair belong to the same person, the The second determination condition is used to determine that the accounts in the account pair do not belong to the same person.
The apparatus of claim 7, wherein the processing unit comprises:

a processing subunit, configured to process the corresponding item information between the accounts in the account pair to obtain a sub-feature vector corresponding to each corresponding item information; and

The stitching unit is configured to stitch each sub-feature vector to obtain the feature vector.
The device according to claim 9, wherein the user information includes user portrait information, consumption habit information and receipt information;

The processing subunit is further configured to:

According to the user information of the accounts in the account pair, a sub-feature vector representing the similarity of the corresponding item information between the accounts is determined.
The apparatus of claim 9, wherein the processing subunit is further configured to:

For each type of identification information, do the following:

According to the type of identification information of the accounts in the account pair, determine a sub-feature vector representing the number of intersection identification information involved in the accounts in the account pair;

For each account in the account pair, according to the number of associations between the account and the intersection identification information, and the association times with all identification information of the type, determine the relationship between the intersection identification information and the account. The sub-feature vector of the attribution degree;

determining a sub-feature vector representing the sum of the degrees of belonging of the accounts in the pair of accounts, and a sub-feature vector representing the difference in the degrees of belonging of the accounts in the pair of accounts; and

For each identification information in this type, according to the number of accounts associated with the identification information, a sub-feature vector representing the sharing degree of the identification information is determined.
The apparatus according to any one of claims 7-11, further comprising: a training unit configured to obtain the recognition model by training in the following manner:

Obtain the user information and the involved identification information corresponding to each account in the account set;

combining the accounts satisfying the preset association information in the account set into account pairs to obtain multiple account pairs;

Screen out the account pairs that meet the preset judgment conditions from the multiple account pairs, obtain multiple training account pairs, and set each training account pair according to the preset judgment conditions to characterize the training account pair. Whether the account belongs to the same person's tag;

processing the user information and identification information of the account in each training account pair in the plurality of account pairs to obtain a feature vector corresponding to the training account pair; and

Using the machine learning method, the feature vector corresponding to the training account pair is used as the input, and the label corresponding to the input training account pair is used as the expected output to train the initial recognition model to obtain the recognition model.
A computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1-6.
An electronic device comprising:

one or more processors;

a storage device on which one or more programs are stored,

The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.