A method, device, medium and computer equipment for implementing IDMapping

Abstract

The present invention discloses a method, apparatus, medium, and computer equipment for implementing IDMapping. The method comprises: obtaining original user data; determining whether a user has a standardized user ID based on a user identifier; and if not, calling a unified distributed auto-increment sequence to generate a standardized user ID for the user; normalizing different identifiers of multiple users based on time and relationship strength to obtain a normalized multivariate relationship group; comparing the user creation time of different types of users in the multivariate relationship group, and using the standardized user ID of the user with the earliest user creation time as oneID to obtain an IDMapping; and synchronizing the IDMapping data to a k,v in-memory database. The present invention can provide an IDMapping with fast normalization speed and high result accuracy.

Description

Technical Field

[0001] The present invention relates to a method, device, medium and computer equipment for implementing IDMapping, and belongs to the technical field of data fusion. Background Art

[0002] With the development of information technology, the proliferation of applications within and outside organizations to meet people's diverse cultural needs has generated massive amounts of data. This data includes behavioral data from numerous different user identities. While seemingly isolated, these data points are interconnected. IDMapping technology aims to identify the behavior of the same user or subject from multiple data sources, massive amounts of data, and complex networks, and to connect fragmented user behavior characteristics across different applications or functional modules to create a complete and accurate user profile.

[0003] Existing IDMapping technologies are mainly divided into two schools. The first is to select complex normalization algorithms, which improves data accuracy. However, due to the complexity of the algorithms, computing performance bottlenecks become more prominent as the amount of data increases. Some technologies can only normalize T+1 offline data, and there is no good solution for updating real-time incremental data. The second is to select high-performance computing engines and key-value storage databases. This improves computing and query performance, but the accuracy of the calculation results is not high, and misidentification and missed identification are common. Summary of the Invention

[0004] The present invention aims to overcome the shortcomings of the prior art and provide a method, apparatus, medium, and computer device for implementing IDMapping, which can provide IDMapping with fast normalization speed and high accuracy. To achieve the above-mentioned object, the present invention is implemented by adopting the following technical solutions:

[0005] In a first aspect, the present invention provides a method for implementing IDMapping, comprising:

[0006] Obtain original user data; the original user data includes basic user information, which includes user ID, user creation time, and user relationship establishment time;

[0007] Determine whether the user has a standardized user ID based on the user ID. If not, call the unified distributed auto-increment sequence to generate a standardized user ID for the user.

[0008] Normalize the different identifiers of multiple users according to time and relationship strength to obtain a normalized multi-relation group. Compare the user creation time of different types of users in the multi-relation group, and use the standardized user ID of the user with the earliest user creation time as oneID to obtain IDMapping.

[0009] Synchronize IDMapping data to the k,v in-memory database.

[0010] In combination with the first aspect, optionally, the original user data also includes user behavior information and user business processing information used to establish a relationship between two users, and the user behavior information includes: device identification code, user registration information, customer account opening information, and user login information.

[0011] In conjunction with the first aspect, optionally, normalizing different identifiers of multiple users according to time and relationship strength to obtain a normalized multi-relation group includes:

[0012] Preset the weight of the relationship between two user identities;

[0013] Based on user behavior information and user business processing information, extract the two user identifiers with the most recent user relationship establishment time to establish a user binary relationship group;

[0014] Number the user binary relationship groups from 1 to n according to the preset relationship strength weights;

[0015] Start traversing the associations from binary relationship group 1 and splicing it with the next binary relationship group 2 to form a multi-relation group a. If the user type in binary relationship group 1 does not overlap with the user type in binary relationship group 2, multi-relation group a is a four-relation group. If the user type in binary relationship group 1 overlaps with the user type in binary relationship group 2, multi-relation group a is a three-relation group.

[0016] Take the multi-tuple relationship group a as the initial multi-tuple relationship group for traversal association, and form the multi-tuple relationship group b with the binary relationship group 3;

[0017] Split and merge the conflicting relationships of the multi-relation group b to obtain the multi-relation group c;

[0018] Repeat the splitting and merging of traversal associations and conflicting relationships until the traversal association of the binary relationship group n is completed, and a normalized multi-relation group is obtained.

[0019] In combination with the first aspect, optionally, splitting and merging conflicting relationships of the multi-relation group b to obtain the multi-relation group c includes:

[0020] If the associated binary relationship group 3 has only one user type or no user type is included in the initial multi-tuple relationship group, no conflict processing is required;

[0021] If all user types in the associated binary relationship group 3 are included in the initial multi-relationship group, determine the relationship strength weights between the two types of users in the binary relationship group 2 and the other types of users in the initial multi-relationship group. The user types with greater weights are grouped with the other user types in the initial multi-relationship group into group 1, and the user types with smaller weights are grouped separately into group 2.

[0022] In the multi-relationship group b, the duplicate records in group 1 are judged by the time when the user relationship is established with the user type with a smaller weight, and the records with a later user relationship establishment time are retained. The duplicate records in group 2 are handled in two cases. In the first case, if the user type of group 1 corresponding to group 2 is not duplicated, they are merged. In the second case, if the user type of group 1 corresponding to group 2 is duplicated, the user relationship establishment time is judged and compared with the user in group 1. The records with a later user relationship establishment time retain the relationship, and the records with an earlier user relationship establishment time disconnect the relationship between group 1 and group 2.

[0023] In combination with the first aspect, optionally, it also includes incremental user relationship for IDMapping, specifically including:

[0024] Get the original incremental user data;

[0025] Determine whether a standardized user ID exists for the incremental user based on the incremental user's user ID. If not, call a unified distributed auto-increment sequence to generate a standardized user ID for the incremental user.

[0026] Determine whether the incremental user relationship exists in the IDMapping data; if so, no reorganization is required;

[0027] If it does not exist, IDMapping associates the incremental user binary relationship group, splits and merges the conflicting relationships of multiple incremental users according to the user relationship establishment time and the preset relationship strength weight, and realizes the partial reorganization of the multi-relationship group; compares the user creation time of different types of users in the reorganized multi-relationship group, and takes the standardized user ID of the user with the earliest user creation time as oneID to obtain the IDMapping of the incremental user relationship.

[0028] In combination with the first aspect, optionally, the user identification includes a device user ID, a registered user ID and a brokerage client ID, and the user identification is associated with the user login behavior and the business processing behavior.

[0029] In a second aspect, the present invention provides a system for implementing IDMapping, comprising:

[0030] Acquisition module: used to acquire original user data; the original user data includes basic user information, which includes user ID, user creation time and user relationship establishment time;

[0031] Standardized user ID generation module: used to determine whether the user has a standardized user ID based on the user identifier. If not, a unified distributed auto-increment sequence is called to generate a standardized user ID for the user.

[0032] ID relationship normalization module: This module is used to normalize the different identifiers of multiple users based on time and relationship strength to obtain a normalized multi-relation group. It then compares the user creation time of different types of users in the multi-relation group and uses the standardized user ID of the user with the earliest user creation time as oneID to obtain IDMapping.

[0033] Data synchronization module: used to synchronize IDMapping data to the k,v in-memory database.

[0034] In combination with the second aspect, optionally, a k,v memory database is also included to store IDMapping data.

[0035] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for implementing IDMapping described in the first aspect is implemented.

[0036] In a fourth aspect, the present invention provides a computer device, comprising:

[0037] a memory for storing instructions;

[0038] The processor is configured to execute the instructions so that the device performs operations to implement the method for implementing IDMapping as described in the first aspect.

[0039] Compared with the prior art, the IDMapping method, device, and storage medium provided by the embodiments of the present invention based on time and relationship strength have the following beneficial effects:

[0040] The present invention obtains original user data; the original user data includes basic user information, and the basic user information includes user identification, user creation time and user relationship establishment time; determines whether the user has a standardized user ID based on the user identification, and if not, calls a unified distributed auto-increment sequence to generate a standardized user ID for the user; normalizes different identifications of multiple users based on time and relationship strength to obtain a normalized multi-relationship group, compares the user creation time of different types of users in the multi-relationship group, and uses the standardized user ID of the user with the earliest user creation time as oneID to obtain IDMapping; the present invention provides an efficient normalization method, presets relationship strength weights, and saves relationship weight calculation time from massive data and complex relationship networks; based on user behavior information and user business processing information, extracts the two user identifications with the closest user relationship establishment time to establish a user binary relationship group, which can clean out weak relationships and invalid data to the greatest extent, reduce data volume, and reduce the complexity of the relationship network;

[0041] The present invention synchronizes IDMapping data to the k,v in-memory database; the present invention can complete the normalization processing of all user data in a short period of time, can update incremental user relationships in seconds, the calculation results are highly accurate, and the QPS of the IDMapping query service can reach more than 200,000. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flowchart of a method for implementing IDMapping provided in Example 1 of the present invention;

[0043] Figure 2 This is a schematic diagram of an application environment of a method for implementing IDMapping provided in Embodiments 1 and 2 of the present invention;

[0044] Figure 3 This is a flowchart of a method for implementing IDMapping provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0045] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0046] Example 1:

[0047] The application environment of the method for implementing IDMapping provided in this embodiment is as follows: Figure 2As shown. Terminal 102 communicates with server 103 via a network, and database server 101 communicates with server 103 via a network. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablet computers, and portable wearable devices. Server 103 and database server 101 can be independent servers or multiple servers forming a server cluster.

[0048] like Figure 1 As shown, this embodiment provides a method for implementing IDMapping, including:

[0049] Get raw user data;

[0050] Determine whether the user has a standardized user ID based on the user ID. If not, call the unified distributed auto-increment sequence to generate a standardized user ID for the user.

[0051] Normalize the different identifiers of multiple users according to time and relationship strength to obtain a normalized multi-relation group. Compare the user creation time of different types of users in the multi-relation group, and use the standardized user ID of the user with the earliest user creation time as oneID to obtain IDMapping.

[0052] Synchronize IDMapping data to the k,v in-memory database.

[0053] The specific steps include:

[0054] Step 1: Get the original user data.

[0055] Raw user data includes basic user information, user behavior information, and user business processing information. Basic user information includes user ID, user creation time, and user relationship establishment time. User IDs include device user ID (deviceID), registered user ID (userID), and brokerage customer ID (custID). User IDs are associated through user login and business processing behavior. User behavior information includes: device identification code, user registration information, customer account opening information, and user login information.

[0056] The original user data is transmitted through the network to Figure 1 104 servers.

[0057] Step 2: Determine whether the user has a standardized user ID based on the user identifier. If not, call a unified distributed auto-increment sequence to generate a standardized user ID for the user.

[0058] Standardized user ID is globally unique.

[0059] An example of the generated standardized user ID is shown in Table 1 below:

[0060] Table 1 User standardized ID table

[0061] stdid usertype userno crt_time stdid usertype userno crt_time 1 01 s1 2022 / 8 / 1 10:00 13 02 r5 2022 / 8 / 13 10:00 2 01 s2 2022 / 8 / 2 10:00 14 02 r6 2022 / 8 / 14 10:00 3 01 s3 2022 / 8 / 3 10:00 15 02 r7 2022 / 8 / 15 10:00 4 01 s5 2022 / 8 / 4 10:00 16 02 r8 2022 / 8 / 16 10:00 5 01 s6 2022 / 8 / 5 10:00 17 02 r10 2022 / 8 / 17 10:00 6 01 s7 2022 / 8 / 6 10:00 18 03 q1 2022 / 8 / 18 10:00 7 01 s8 2022 / 8 / 7 10:00 19 03 q2 2022 / 8 / 19 10:00 8 01 s9 2022 / 8 / 8 10:00 20 03 q3 2022 / 8 / 20 10:00 9 02 r1 2022 / 8 / 9 10:00 21 03 q4 2022 / 8 / 21 10:00 10 02 r2 2022 / 8 / 10 10:00 22 03 q6 2022 / 8 / 22 10:00 11 02 r3 2022 / 8 / 11 10:00 23 03 q8 2022 / 8 / 23 10:00 12 02 r4 2022 / 8 / 12 10:00 24 03 q9 2022 / 8 / 24 10:00

[0062] Step 3: Normalize the different identifiers of multiple users according to time and relationship strength to obtain a normalized multi-relation group. Compare the user creation time of different types of users in the multi-relation group, and use the standardized user ID of the user with the earliest user creation time as oneID to obtain IDMapping.

[0063] Step 3.1: Preset the weight of the relationship between the two user IDs and number them from large to small according to the weight.

[0064] The user relationships are shown in Table 2-4 below:

[0065] Table 2 Brokerage customer ID (custid) and registered user ID (userid) User relationship table cust_user

[0066] custid cust_user_conn_ts userid q1 2022 / 9 / 3 8:00 r2 q4 2022 / 9 / 4 8:00 r4 q3 2022 / 9 / 9 8:00 r3 q6 2022 / 9 / 11 8:00 r6 q8 2022 / 9 / 14 8:00 r7

[0067] Table 3 Device user ID (deviceid) and brokerage customer ID (custid) user relationship table device_cust

[0068] deviceid device_cust_conn_ts custid s1 2022 / 9 / 2 8:00 q1 s2 2022 / 9 / 5 8:00 q2 s6 2022 / 9 / 8 8:00 q6 s8 2022 / 9 / 13 8:00 q8 s9 2022 / 9 / 16 8:00 q9

[0069] Table 4 Device user ID (deviceid) and registered user ID (userid) user relationship table device_user

[0070]

[0071]

[0072] The relationship weights are shown in Table 5 below:

[0073] Table 5 Relationship weights

[0074] num tb weight 1 cust_user 0.5 2 device_cust 0.25 3 device_user 0.25

[0075] Step 3.2: Based on the user behavior information and user business processing information, extract the two user identifiers with the most recent user relationship establishment time to establish a user binary relationship group.

[0076] Step 3.2.1: Number the user binary relationship groups from 1 to n according to the preset relationship strength weights from large to small.

[0077] In this embodiment, n is 3.

[0078] Step 3.2.2: Start traversing the associations from binary relationship group 1 and concatenate it with the next binary relationship group 2 to form a multi-relation group a. If the user type in binary relationship group 1 does not overlap with the user type in binary relationship group 2, multi-relation group a is a four-relation group. If the user type in binary relationship group 1 overlaps with the user type in binary relationship group 2, multi-relation group a is a three-relation group.

[0079] Step 3.2.3: Use the multi-tuple relationship group a as the initial multi-tuple relationship group for traversal association, and form the multi-tuple relationship group b with the binary relationship group 3, as shown in Table 6 below.

[0080] Table 6 Multivariate relationship groups Table 6

[0081] devceid device_cust_conn_ts custid device_user_conn_ts userid cust_user_conn_ts s1 2022 / 9 / 2 8:00 q1 r2 2022 / 9 / 3 8:00 s2 2022 / 9 / 5 8:00 q2 s6 2022 / 9 / 8 8:00 q6 r6 2022 / 9 / 11 8:00 s8 2022 / 9 / 13 8:00 q8 r7 2022 / 9 / 14 8:00 s9 2022 / 9 / 16 8:00 q9 s1 2022 / 9 / 1 8:00 r1 s2 q4 2022 / 9 / 6 8:00 r4 2022 / 9 / 4 8:00 s3 q3 2022 / 9 / 7 8:00 r3 2022 / 9 / 9 8:00 s5 2022 / 9 / 10 8:00 r5 s6 2022 / 9 / 12 8:00 r8 s7 q8 2022 / 9 / 15 8:00 r7 2022 / 9 / 14 8:00 s9 2022 / 9 / 17 8:00 r10

[0082] Step 3.2.4: Split and merge the conflicting relationships of the multi-relation group b to obtain the multi-relation group c.

[0083] Split and merge the conflicting relationships of the multi-relation group b to obtain the multi-relation group c, including:

[0084] If the associated binary relationship group 3 has only one user type or no user type is included in the initial multi-tuple relationship group, no conflict processing is required;

[0085] If all user types in the associated binary relationship group 3 are included in the initial multi-relationship group, determine the relationship strength weights between the two types of users in the binary relationship group 2 and the other types of users in the initial multi-relationship group. The user types with greater weights are grouped with the other user types in the initial multi-relationship group into group 1, and the user types with smaller weights are grouped separately into group 2.

[0086] In the multi-relationship group b, the duplicate records in group 1 are judged by the time when the user relationship is established with the user type with a smaller weight, and the records with a later user relationship establishment time are retained. The duplicate records in group 2 are handled in two cases. In the first case, if the user type of group 1 corresponding to group 2 is not duplicated, they are merged. In the second case, if the user type of group 1 corresponding to group 2 is duplicated, the user relationship establishment time is judged and compared with the user in group 1. The records with a later user relationship establishment time retain the relationship, and the records with an earlier user relationship establishment time disconnect the relationship between group 1 and group 2.

[0087] The multivariate relationship group c is shown in Table 7 below:

[0088] Table 7 Multivariate relationship groups Table 7

[0089] deviceid device_cust_conn_ts custid device_user_conn_ts userid cust_user_conn_ts s1 2022 / 9 / 2 8:00 q1 r2 2022 / 9 / 3 8:00 r1 q2 s2 q4 2022 / 9 / 6 8:00 r4 2022 / 9 / 4 8:00 q6 r6 2022 / 9 / 11 8:00 s6 2022 / 9 / 12 8:00 r8 s7 q8 2022 / 9 / 15 8:00 r7 2022 / 9 / 14 8:00 s9 2022 / 9 / 16 8:00 q9 2022 / 9 / 17 8:00 r10 s3 q3 2022 / 9 / 7 8:00 r3 s5 2022 / 9 / 10 8:00 r5

[0090] Step 3.3: Compare the user creation time of different types of users in the multivariate relationship group (Table 7), take the standardized user ID of the user with the earliest user creation time as oneID, and obtain IDMapping.

[0091] Table 8 IDMapping

[0092] oneid deviceid device_cust_conn_ts custid device_user_conn_ts userid cust_user_conn_ts 1 s1 2022 / 9 / 2 8:00 q1 r2 2022 / 9 / 3 8:00 9 r1 19 q2 2 s2 q4 2022 / 9 / 6 8:00 r4 2022 / 9 / 4 8:00 14 q6 r6 2022 / 9 / 11 8:00 5 s6 2022 / 9 / 12 8:00 r8 6 s7 q8 2022 / 9 / 15 8:00 r7 2022 / 9 / 14 8:00 8 s9 2022 / 9 / 16 8:00 q9 2022 / 9 / 17 8:00 r10 3 s3 q3 2022 / 9 / 7 8:00 r3 4 s5 2022 / 9 / 10 8:00 r5

[0093] Step 4: Synchronize the IDMapping data to the k,v in-memory database.

[0094] In this embodiment, the k,v memory database is Figure 1 The database server 101 in.

[0095] This embodiment provides an efficient normalization method with preset relationship strength weights, saving the time of calculating relationship weights from massive data and complex relationship networks; based on user behavior information and user business processing information, the two user identifiers with the most recent user relationship establishment time are extracted to establish a user binary relationship group, which can maximize the cleaning of weak relationships and eliminate invalid data, reduce the amount of data, and reduce the complexity of the relationship network.

[0096] This embodiment uses the efficient Impala computing engine and the k / v in-memory storage database Redis as the storage medium to provide ID Mapping query services. With hundreds of millions of users and tens of millions of daily user relationship flows, the Impala computing engine can normalize all user data for three types of user identifiers within 30 minutes. The calculation results are highly accurate, and the ID Mapping query service can reach a QPS of over 200,000.

[0097] Example 2:

[0098] The application environment of the method for implementing IDMapping provided in this embodiment is as follows: Figure 2 As shown. Terminal 102 communicates with server 103 via a network, and database server 101 communicates with server 103 via a network. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablet computers, and portable wearable devices. Server 103 and database server 101 can be independent servers or multiple servers forming a server cluster.

[0099] like Figure 3 As shown, this embodiment performs incremental user relationship analysis on the IDMapping obtained in the first embodiment, including:

[0100] Get the original incremental user data;

[0101] Determine whether a standardized user ID exists for the incremental user based on the incremental user's user ID. If not, call a unified distributed auto-increment sequence to generate a standardized user ID for the incremental user.

[0102] Determine whether the incremental user relationship exists in the IDMapping data; if so, no reorganization is required;

[0103] If it does not exist, IDMapping associates the incremental user binary relationship group, splits and merges the conflicting relationships of multiple incremental users according to the user relationship establishment time and the preset relationship strength weight, and realizes the partial reorganization of the multi-relationship group; compares the user creation time of different types of users in the reorganized multi-relationship group, and takes the standardized user ID of the user with the earliest user creation time as oneID to obtain the IDMapping of the incremental user relationship.

[0104] The incremental user relationships are shown in Table 9 below:

[0105] Table 9 Incremental user relationships

[0106] deviceid device_cust_conn_ts custid s1 2022 / 11 / 2 8:00 q2

[0107] The IDMapping of the incremental user relationship is shown in Table 10 below:

[0108] Table 10 IDMapping of incremental user relationships

[0109] oneid deviceid device_cust_conn_ts custid device_user_conn_ts userid cust_user_conn_ts 1 q1 r2 2022 / 9 / 3 8:00 9 r1 19 s1 2022 / 11 / 2 8:00 q2 2 s2 q4 2022 / 9 / 6 8:00 r4 2022 / 9 / 4 8:00 14 q6 r6 2022 / 9 / 11 8:00 5 s6 2022 / 9 / 12 8:00 r8 6 s7 q8 2022 / 9 / 15 8:00 r7 2022 / 9 / 14 8:00 8 s9 2022 / 9 / 16 8:00 q9 2022 / 9 / 17 8:00 r10 3 s3 q3 2022 / 9 / 7 8:00 r3 4 s5 2022 / 9 / 10 8:00 r5

[0110] This embodiment uses the Flink real-time computing engine to update incremental user relationships in seconds.

[0111] Example 3:

[0112] An embodiment of the present invention provides a system for implementing IDMapping, including:

[0113] Acquisition module: used to acquire original user data; the original user data includes basic user information, which includes user ID, user creation time and user relationship establishment time;

[0114] Standardized user ID generation module: used to determine whether the user has a standardized user ID based on the user identifier. If not, a unified distributed auto-increment sequence is called to generate a standardized user ID for the user.

[0115] ID relationship normalization module: This module is used to normalize the different identifiers of multiple users based on time and relationship strength to obtain a normalized multi-relation group. It then compares the user creation time of different types of users in the multi-relation group and uses the standardized user ID of the user with the earliest user creation time as oneID to obtain IDMapping.

[0116] Data synchronization module: used to synchronize IDMapping data to the k,v in-memory database.

[0117] It also includes a k,v memory database for storing IDMapping data.

[0118] Example 4:

[0119] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for implementing IDMapping as described in the first and second embodiments is implemented.

[0120] Embodiment 5:

[0121] An embodiment of the present invention further provides a computer device, including:

[0122] a memory for storing instructions;

[0123] The processor is used to execute the instruction so that the device performs the operation of implementing the method for implementing IDMapping as described in Example 1 and Example 2.

[0124] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0125] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxesFigure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0128] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for implementing IDMapping, characterized in that: include: Obtain original user data; the original user data includes basic user information, which includes user ID, user creation time, and user relationship establishment time; Determine whether the user has a standardized user ID based on the user ID. If not, call the unified distributed auto-increment sequence to generate a standardized user ID for the user. Normalizing different identifiers of multiple users according to time and relationship strength to obtain a normalized multi-relationship group, comparing user creation times of different types of users in the multi-relationship group, and using the standardized user ID of the user with the earliest user creation time as oneID to obtain IDMapping; wherein, normalizing different identifiers of multiple users according to time and relationship strength to obtain a normalized multi-relationship group includes: Preset the weight of the relationship between two user identities; Based on user behavior information and user business processing information, extract the two user identifiers with the most recent user relationship establishment time to establish a user binary relationship group; Number the user binary relationship groups from 1 to n according to the preset relationship strength weights; Start traversing the associations from binary relationship group 1 and splicing it with the next binary relationship group 2 to form a multi-relation group a. If the user type in binary relationship group 1 does not overlap with the user type in binary relationship group 2, multi-relation group a is a four-relation group. If the user type in binary relationship group 1 overlaps with the user type in binary relationship group 2, multi-relation group a is a three-relation group. Take the multi-tuple relationship group a as the initial multi-tuple relationship group for traversal association, and form the multi-tuple relationship group b with the binary relationship group 3; Split and merge the conflicting relationships of the multi-relation group b to obtain the multi-relation group c; Repeat the splitting and merging of traversal associations and conflicting relationships until the traversal of the binary relationship group n is completed, and a normalized multi-relation group is obtained; The multi-relation group b is split and merged based on conflicting relationships to obtain the multi-relation group c, including: If the associated binary relationship group 3 has only one user type or no user type is included in the initial multi-tuple relationship group, no conflict processing is required; If all user types in the associated binary relationship group 3 are included in the initial multi-relationship group, determine the relationship strength weights between the two types of users in the binary relationship group 2 and the other types of users in the initial multi-relationship group. The user types with greater weights are grouped with the other user types in the initial multi-relationship group into group 1, and the user types with smaller weights are grouped separately into group 2. In multi-relationship group b, for duplicate records in group 1, the user relationship establishment time with the user type with smaller weight is judged, and the record with later user relationship establishment time is retained. For duplicate records in group 2, two situations are handled: the first situation is that the user type of group 1 corresponding to group 2 is not duplicated, and they are merged; the second situation is that the user type of group 1 corresponding to group 2 is duplicated, and the user relationship establishment time with the user in group 1 is judged and compared. The record with later user relationship establishment time is retained, and the record with earlier user relationship establishment time is disconnected from group 1 and group 2; Synchronize IDMapping data to the k,v in-memory database.

2. The method for implementing IDMapping according to claim 1, wherein: The original user data also includes user behavior information and user business processing information used to establish a relationship between two users. The user behavior information includes: device identification code, user registration information, customer account opening information, and user login information.

3. The method for implementing IDMapping according to claim 1, wherein: It also includes incremental user relationships for IDMapping, specifically including: Get the original incremental user data; Determine whether a standardized user ID exists for the incremental user based on the incremental user's user ID. If not, call a unified distributed auto-increment sequence to generate a standardized user ID for the incremental user. Determine whether the incremental user relationship exists in the IDMapping data; if so, no reorganization is required; If it does not exist, IDMapping associates the incremental user binary relationship group, splits and merges the conflicting relationships of multiple incremental users according to the user relationship establishment time and the preset relationship strength weight, and realizes the partial reorganization of the multi-relationship group; compares the user creation time of different types of users in the reorganized multi-relationship group, and takes the standardized user ID of the user with the earliest user creation time as oneID to obtain the IDMapping of the incremental user relationship.

4. The method for implementing IDMapping according to claim 1, wherein: The user identification includes a device user ID, a registered user ID and a brokerage client ID, and the user identification is associated with the user through login behavior and business handling behavior.

5. A system for implementing IDMapping based on the method for implementing IDMapping according to any one of claims 1 to 4, characterized in that: include, Acquisition module: used to acquire original user data; the original user data includes basic user information, which includes user ID, user creation time and user relationship establishment time; Standardized user ID generation module: used to determine whether the user has a standardized user ID based on the user identifier. If not, a unified distributed auto-increment sequence is called to generate a standardized user ID for the user. ID relationship normalization module: This module is used to normalize the different identifiers of multiple users based on time and relationship strength to obtain a normalized multi-relation group. It then compares the user creation time of different types of users in the multi-relation group and uses the standardized user ID of the user with the earliest user creation time as oneID to obtain IDMapping. Data synchronization module: used to synchronize IDMapping data to the k,v in-memory database.

6. The system for implementing IDMapping according to claim 5, characterized in that: It also includes a k,v memory database for storing IDMapping data.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for implementing IDMapping as claimed in any one of claims 1 to 4 is implemented.

8. A computer device, characterized in that: include: a memory for storing instructions; The processor is configured to execute the instruction so that the device performs operations of the method for implementing IDMapping as described in any one of claims 1 to 4.

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