User Credit Information Integration Method, Device, Electronic Device and Readable Medium

Abstract

Embodiments of the present disclosure disclose a user credit information integration method, apparatus, electronic device, and readable medium. A specific implementation of the method includes: obtaining a user information set of each user using a preset client; performing identity recognition and integration processing on the obtained user information sets of each user to obtain integrated user information of each user; generating respective same user identity codes corresponding to the integrated user information of each user; generating a coding mapping relationship table; generating unified user credit information corresponding to the integrated user information, and storing the unified user credit information in a preset storage device; performing tagging processing on the generated unified user credit information of each user to obtain respective tag information corresponding to the unified user credit information of each user; and clustering and sending the unified user credit information of each user and the respective tag information to each preset client based on the coding mapping relationship table. This implementation improves the data quality of the integrated data.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technologies, and particularly to methods, apparatuses, electronic devices, and computer-readable media for integrating user credit information. Background Art

[0002] With the continuous development of big data and cloud computing technologies, technical support is provided for data sharing and integration. Integrating user credit information is a technology for integrating and sharing the credit information of each user from various client terminals (such as various B terminals). Currently, when integrating and sharing the credit information of each user from various client terminals (such as various B terminals), the commonly adopted method is to integrate the credit information of each user from various client terminals (such as various B terminals) into a user credit information set for sharing.

[0003] However, when using the above method to integrate and share the credit information of each user, the following technical problems often exist:

[0004] Since a user can have different user identity identifiers and corresponding different user credit information on different client terminals (for example, B terminals). Integrating the credit information of each user from various client terminals (such as various B terminals) into a user credit information set for sharing will result in the same user having multiple different identity identifiers and different user credit information, leading to relatively serious fragmentation of the user identity. At the same time, when integrating the credit information of each user from various client terminals (such as various B terminals) into a user credit information set for sharing without performing tagging processing on the user credit information, the integrated data of the shared user credit information will be relatively chaotic, and the data quality of the integrated data of the shared user credit information will be poor.

[0005] The above information disclosed in this background art section is only used to enhance the understanding of the background of the inventive concept, and thus, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] This content part of the present disclosure is used to briefly introduce concepts, which will be described in detail in the following detailed implementation part. This content part of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure propose methods, apparatuses, electronic devices, and computer-readable media for integrating user credit information to solve one or more of the technical problems mentioned in the above background art section.

[0008] In a first aspect, some embodiments of the present disclosure provide a method for integrating user credit information. The method includes: for each preset client corresponding to each preset client identifier among the various preset client identifiers, obtaining a set of user information of each user using the above preset client, where each user information in the above set of user information includes: user identity information and user credit information, and the above user information corresponds to a user identifier and a preset client identifier; performing identity recognition and integration processing on the obtained sets of user information to obtain respective integrated user information, where each integrated user information in the above respective integrated user information includes at least one user information, and the integrated user information corresponds to at least one user identifier of the same user on at least one preset client; generating respective identical user identity codes corresponding to the above respective integrated user information, where each integrated user information in the above respective integrated user information corresponds one-to-one with each identical user identity code in the above respective identical user identity codes; generating a coding mapping relationship table based on the above respective integrated user information and the above respective identical user identity codes; for each integrated user information in the above respective integrated user information, generating unified user credit information corresponding to the integrated user information, and storing the unified user credit information in a preset storage device; performing tagging processing on the generated respective unified user credit information to obtain respective tag information corresponding to the above respective unified user credit information; and clustering and sending the above respective unified user credit information and respective tag information to each preset client based on the above coding mapping relationship table.

[0009] Second aspect, some embodiments of the present disclosure provide a user credit information integration device, the device comprising: an acquisition unit configured to acquire, for each preset client corresponding to each preset client identifier among the preset client identifiers, a set of user information of each user using the preset client, wherein each user information in the set of user information includes: user identity information and user credit information, and the user information corresponds to a user identifier and a preset client identifier; an identity recognition and integration processing unit configured to perform identity recognition and integration processing on the acquired sets of user information to obtain integrated user information, wherein each integrated user information in the integrated user information includes at least one user information, and the integrated user information corresponds to at least one user identifier of the same user on at least one preset client; a first generation unit configured to generate respective identical user identity codes corresponding to the integrated user information, wherein each integrated user information in the integrated user information corresponds one-to-one with each identical user identity code in the respective identical user identity codes; a second generation unit configured to generate a coding mapping relationship table based on the integrated user information and the respective identical user identity codes; a third generation unit configured to generate, for each integrated user information in the integrated user information, a unified user credit information corresponding to the integrated user information, and store the unified user credit information in a preset storage device; a tagging processing unit configured to perform tagging processing on the generated unified user credit information to obtain respective tag information corresponding to the unified user credit information; a clustering and sending unit configured to cluster and send the unified user credit information and the respective tag information to each preset client based on the coding mapping relationship table.

[0010] Third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having stored thereon one or more programs, which when executed by the one or more processors cause the one or more processors to implement the method described in any implementation manner of the first aspect above.

[0011] Fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having stored thereon a computer program, wherein when the program is executed by a processor, the method described in any implementation manner of the first aspect above is implemented.

[0012] The above embodiments of the present disclosure have the following beneficial effects: Through the user credit information integration method of some embodiments of the present disclosure, the phenomenon of fragmented user identities is reduced and the data quality of the integrated data of user credit information is improved. Specifically, the reasons for the relatively serious fragmentation of user identities and the relatively poor data quality of the integrated data of user credit information are as follows: Since users can have different user identity identifiers on different clients (for example, the B side), corresponding to different user credit information. Integrating each user credit information from each client (for example, each B side) into a user credit information set for sharing will result in the same user having multiple different identity identifiers and different user credit information, leading to relatively serious fragmentation of user identities. At the same time, integrating each user credit information from each client (for example, each B side) into a user credit information set for sharing without performing tagging processing on the user credit information will result in the integrated data of the shared user credit information being relatively chaotic and the data quality of the integrated data of the shared user credit information being relatively poor. Based on this, in the user credit information integration method of some embodiments of the present disclosure, first, for each preset client corresponding to each preset client identifier among the various preset client identifiers, obtain the user information sets of each user using the above preset client, where each user information in the above user information set includes: user identity information, user credit information, and the above user information corresponds to a user identifier and a preset client identifier. Thus, the user information sets of each preset client can be obtained. Then, perform identity recognition and integration processing on the obtained user information sets to obtain each integrated user information, where each integrated user information in the above integrated user information includes at least one user information, and the above integrated user information corresponds to at least one user identifier of the same user on at least one preset client. Thus, identity recognition and integration processing can be performed on each user information set, and at least one user information of the same user on at least one preset client can be integrated into one integrated user information, reducing the phenomenon of fragmented user identities. Generate each same user identity code corresponding to the above each integrated user information, where each integrated user information in the above each integrated user information corresponds one-to-one to each same user identity code in the above each same user identity code. Thus, each integrated user information of each same user can be encoded to generate each same user identity code corresponding to each same user, and a unique same user identity code is assigned to the same user from different clients. Then, based on the above each integrated user information and the above each same user identity code, generate a coding mapping relationship table. Thus, a coding mapping relationship table can be generated to represent the corresponding relationship between the same user identity code and at least one user identifier of at least one preset client.Next, for each integrated user information among the above-mentioned integrated user information, a unified user credit information corresponding to the above-mentioned integrated user information is generated, and the above-mentioned unified user credit information is stored in a preset storage device. Thus, the unified user credit information corresponding to each integrated user information can be generated. Next, the generated unified user credit information is marked to obtain respective tag information corresponding to the above-mentioned unified user credit information. Thus, the unified user credit information can be marked, the meaning of each unified user credit information is clarified, the readability and usability of each unified user credit information are improved, data chaos is reduced, and data quality is improved. Based on the above-mentioned coding mapping relationship table, the above-mentioned unified user credit information and respective tag information are clustered and sent to respective preset clients. Thus, the above-mentioned unified user credit information and respective tag information can be clustered and sent to respective preset clients. Also, because in the process of integrating user credit information, through identity recognition and integration processing of each user information set, at least one user information of the same user on at least one preset client is integrated into one integrated user information, and a unique same user identity code corresponding to the integrated user information is generated, the phenomenon of fragmented user identities is reduced. At the same time, before sharing each unified user credit information, the above-mentioned unified user credit information is marked to clarify the meaning of each unified user credit information, improve the readability and usability of each unified user credit information, reduce data chaos, and improve data quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages and aspects of the various embodiments of the present disclosure will become more apparent. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the elements and elements are not necessarily drawn to scale.

[0014] Figure 1 is a flowchart of some embodiments of a method for integrating user credit information according to the present disclosure;

[0015] Figure 2 is a schematic structural diagram of some embodiments of a device for integrating user credit information according to the present disclosure;

[0016] Figure 3 is a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0018] In addition, it should be noted that for ease of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0019] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence relationship of the functions performed by these devices, modules or units.

[0020] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless clearly stated otherwise in the context, it should be understood as "one or more".

[0021] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0022] The present disclosure will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0023] Figure 1 Flow 100 of some embodiments of the user credit information integration method according to the present disclosure is shown. The user credit information integration method includes the following steps:

[0024] Step 101, for each preset client corresponding to each preset client identifier among the various preset client identifiers, obtain the user information sets of each user using the preset client.

[0025] In some embodiments, the execution subject of the user credit information integration method (such as a server) may obtain the user information sets of each user corresponding to each preset client identifier for each preset client identifier among the various preset client identifiers. Among them, each user information in the above user information set includes: user identity information and user credit information, and the above user information corresponds to a user identifier and a preset client identifier. The execution subject may obtain the user information sets of each user using the above preset client through a wired connection method or a wireless connection method. Among them, the above various preset clients may be various B - ends (for example, online banks, lending platforms, e - commerce platforms, etc.). Each of the above users may be each C - end user of the above preset client (for example, a preset B - end). The above user identity information includes encoded identity information and biometric data. The above encoded identity information may include at least one of the following: identity code, identity identifier, user account. The above biometric data may be data of the user's body, physiology, behavior, etc. The above biometric data may be, but is not limited to, one of the following: fingerprint (for example, fingerprint image), voiceprint, palmprint, iris, facial recognition feature (for example, face image). The above identity code may be the user's ID number. The above identity identifier may be the user's name. The above user account may be the personal account registered and used by the user on the preset client (for example, a mobile phone number). The above user credit information may be the credit information of the user on the preset client (for example, credit transaction information). The above user identifier may be a user account or a user name. The above preset client identifier may be the name of the preset client.

[0026] It should be noted that the above wireless connection method may include, but is not limited to, 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future - developed wireless connection methods.

[0027] Step 102: Perform identity recognition and integration processing on the obtained user information sets to obtain each integrated user information.

[0028] In some embodiments, the execution subject may perform identity recognition and integration processing on the obtained user information sets to obtain each integrated user information. Among them, each integrated user information in the above integrated user information sets includes at least one user information, and the above integrated user information corresponds to at least one user identifier of the same user on at least one preset client.

[0029] In some optional implementation manners of some embodiments, the execution subject may perform identity recognition and integration processing on the obtained user information sets through the following steps to obtain each integrated user information:

[0030] First, determine the encoded identity information included in each user information in the above-mentioned user information sets to obtain an encoded identity information set.

[0031] Second, perform format normalization processing on each encoded identity information in the above-mentioned encoded identity information set to obtain standardized encoded identity information after format normalization processing. In practice, first, the above-mentioned execution entity can extract the identity code, identity identifier, and user account from the above-mentioned encoded identity information in sequence through regular expressions. Then, the above-mentioned execution entity can arrange the extracted identity code, identity identifier, and user account in a preset arrangement order to obtain standardized encoded identity information. As an example, the above-mentioned encoded identity information can be "Identity code: A, Identity identifier: B", and the extracted identity code, identity identifier, and user account can be "A, B, Null". The above-mentioned preset arrangement order can be "Identity code - Identity identifier - User account". The standardized encoded identity information can be "A - B - Null".

[0032] Third, sort the obtained standardized encoded identity information to obtain a sequence of standardized encoded identity information. In practice, the above-mentioned execution entity can store the above-mentioned standardized encoded identity information in sequence to obtain a sequence of standardized encoded identity information.

[0033] Fourth, based on the sequence of standardized encoded identity information, perform the following clustering steps:

[0034] The first sub-step is to determine the similarity between the first standardized encoded identity information in the sequence of standardized encoded identity information and each of the standardized encoded identity information other than the first standardized encoded identity information in the sequence of standardized encoded identity information as each screening similarity. Among them, each standardized encoded identity information in each of the standardized encoded identity information corresponds to each screening similarity in each of the screening similarities. Among them, each screening similarity in the above-mentioned screening similarities can be represented by the Levenshtein distance or the Jaccard similarity.

[0035] The second sub-step is to determine each of the screening similarities that meet the preset screening conditions in each of the screening similarities as each target similarity. Among them, the above-mentioned preset screening condition can be that the screening similarity is greater than a preset value.

[0036] The third sub-step is to determine the standardized encoded identity information corresponding to the first standardized encoded identity information and each of the target similarities as a standardized encoded identity information clustering group.

[0037] The fourth sub-step is to delete each of the standardized encoded identity information in the standardized encoded identity information clustering group from the sequence of standardized encoded identity information to update the sequence of standardized encoded identity information.

[0038] The fifth sub-step, in response to determining that the updated standardized coding identity information sequence is not empty, based on the updated standardized coding identity information sequence, execute the above clustering step again.

[0039] The fifth step, in response to determining that the updated standardized coding identity information sequence is empty, determine each of the determined standardized coding identity information clustering groups as a set of standardized coding identity information clustering groups.

[0040] The sixth step, based on the above set of standardized coding identity information clustering groups, perform data correction and filling processing on each of the above user information sets to obtain each corrected user information set, where each user information in each of the above user information sets corresponds one-to-one with each corrected user information set in each of the above corrected user information sets.

[0041] The seventh step, based on each of the above corrected user information sets, generate each integrated user information.

[0042] Since the identity code (e.g., ID card number) is an important and indispensable piece of user identity information, the user identity information in each of the user information sets obtained from each preset client often has the situation of missing or incorrect identity codes, resulting in incomplete user identity information. When filling and correcting the missing or incorrect identity codes, the conventional solution is generally: from the biometric data of all users, match the biometric data that is most similar to the biometric data corresponding to the missing or incorrect identity code, and use the identity code corresponding to the most similar biometric data to fill and correct the missing or incorrect identity code.

[0043] The above conventional solution still has the following problems: Usually, the amount of biometric data of all users is huge. Matching the biometric data that is most similar to the biometric data corresponding to the missing or incorrect identity code from the biometric data of all users requires a large amount of data operations. At the same time, the identity code corresponding to the most similar biometric data that is matched may also be missing or incorrect, resulting in the filled and corrected identity code still having missing and incorrect situations, and the data correction and filling efficiency is low. It is necessary to perform data correction and filling processing again. The large amount of data operations and the low data correction and filling efficiency increase the waste of computer computing resources.

[0044] Facing the above technical problems, the inventor decides to adopt the following solution:

[0045] In some alternative implementations of some embodiments, the above-mentioned execution subject may perform data correction and filling processing on each of the above-mentioned user information sets based on the above-mentioned standardized encoded identity information clustering set to obtain each corrected user information set through the following steps:

[0046] First step, for each user identity information in each of the above-mentioned user information sets, perform the following steps:

[0047] First sub-step, determine the standardized encoded identity information corresponding to the above-mentioned user identity information as the target standardized encoded identity information.

[0048] Second sub-step, determine the standardized encoded identity information clustering group including the above-mentioned target standardized encoded identity information in the above-mentioned standardized encoded identity information clustering set as the target standardized encoded identity information clustering group.

[0049] Third sub-step, in response to determining that the above-mentioned user identity information includes an identity code, determine each identity code included in the target standardized encoded identity information clustering group as each target identity code.

[0050] Fourth sub-step, determine the number of target identity codes that are the same as the above-mentioned identity code among the above-mentioned each target identity code as the target number.

[0051] Fifth sub-step, in response to determining that the above-mentioned user identity information does not include an identity code or in response to determining that the target number is less than or equal to a preset value, determine each user information corresponding to the above-mentioned target standardized encoded identity information clustering group in the above-mentioned user information set as each reference user information. For example, the above-mentioned preset value may be 2.

[0052] Sixth sub-step, determine each biometric data included in the above-mentioned each reference user information.

[0053] Seventh sub-step, determine the biometric data corresponding to the user identity information in each of the above-mentioned user information sets as the biometric data to be compared.

[0054] The eighth sub-step is to perform a comparison process on each of the above biometric data and the above biometric data to be compared, and obtain each target biometric data. In practice, for each biometric data among the biometric data, the above execution entity can extract a first extraction feature vector representing the biometric data from the biometric data, and obtain each first extraction feature vector corresponding to each of the biometric data. Then, the above execution entity can extract a second extraction feature vector representing the biometric data to be compared from the biometric data to be compared. After that, the above execution entity can determine the similarity between each of the first extraction feature vectors and the second extraction feature vector as each comparison similarity. Then, the above execution entity can determine each comparison similarity with a comparison similarity value greater than or equal to the preset similarity among each of the comparison similarities as each target comparison similarity. After that, the above execution entity can determine each biometric data corresponding to each target comparison similarity pair as each target biometric data. Among them, the above execution entity can extract the first extraction feature vector from the biometric data through biometric recognition technology, and the above execution entity can extract the second extraction feature vector from the biometric data to be compared through biometric recognition technology. The above comparison similarity can be represented by cosine similarity.

[0055] The ninth sub-step is to determine each reference user information corresponding to each of the above target biometric data in each of the above reference user information as each target reference user information.

[0056] The tenth sub-step is to perform a filling and correction process on the above user identity information based on each of the above target reference user information, so as to update each user information set. In practice, the above execution entity can determine each identity code included in each of the above target reference user information as each filling identity code. After that, the above execution entity can divide the filling codes with the same filling code among each of the filling codes into a group, and obtain each filling code group. Then, the above execution entity can determine the filling code group with the largest number of filling codes as the filling code group to be filled. Finally, in response to determining that the above user identity information does not include an identity code, the above execution entity can add one of the filling codes in the filling code group to the above user identity information to update the user information where the user identity information is located. In response to determining that the above user identity information includes an identity code, the above execution entity can replace the identity code included in the above user identity information with the above filling code to update the user information where the user identity information is located.

[0057] The second step is to determine each updated user information set as each corrected user information set.

[0058] The above technical solution and its related content, as an inventive point of the embodiments of the present disclosure, solve the technical problem of "waste of computer computing power resources in the final problem". The factors leading to the waste of computer computing power resources are often as follows: Generally, the data volume of each biometric data of all users is huge. Matching the biometric data most similar to the biometric data corresponding to the missing or incorrect identity code from the biometric data of all users requires a large amount of data operations. At the same time, the identity code corresponding to the most similar biometric data may also be lost or incorrect, resulting in the filled and corrected identity code still having missing and incorrect situations, and the data correction and filling efficiency is low. It is necessary to perform data correction and filling processing again. The large amount of data operations and the low data correction and filling efficiency increase the waste of computer computing power resources. If the above factors are solved, the effect of reducing the waste of computer computing power resources can be achieved. To achieve this effect, in the first step, for each user identity information in the above user information sets, the following steps are performed: The first sub-step is to determine the standardized coding identity information corresponding to the above user identity information as the target standardized coding identity information. Thus, the target standardized coding identity information for determining the clustering group of the target standardized coding identity information can be obtained. The second sub-step is to determine the standardized coding identity information clustering group including the above target standardized coding identity information in the above standardized coding identity information clustering group set as the target standardized coding identity information clustering group. Thus, a target standardized coding identity information clustering group including the target standardized coding identity information and having relatively similar information to the target standardized coding identity information can be obtained. The third sub-step is to, in response to determining that the above user identity information includes an identity code, determine each identity code included in the target standardized coding identity information clustering group as each target identity code. Thus, each target identity code for determining the target quantity can be obtained. The fourth sub-step is to determine the quantity of the target identity codes that are the same as the above identity code among the above each target identity code as the target quantity. Thus, the quantity of each target identity code that is the same as the above identity code in the target standardized coding identity information clustering group can be determined. The above target standardized coding identity information clustering group is a group of information with relatively similar information to the target standardized coding identity information. The fifth sub-step is to, in response to determining that the above user identity information does not include an identity code or in response to determining that the target quantity is less than or equal to a preset value, determine each user information corresponding to the above target standardized coding identity information clustering group in the above user information set as each reference user information. Thus, when there is a possibility of missing or incorrect identity code in the user identity information, each reference user information with relatively similar coding identity information to the target standardized coding identity information can be determined within a smaller clustering range. The sixth sub-step is to determine each biometric data included in the above each reference user information.Thus, when the identity code in the user identity information is missing or there is a possibility of an error in the identity code, the respective biometric data included in each reference user information is determined within a relatively small clustering range. In the seventh sub-step, the biometric data corresponding to the user identity information in each of the above user information sets is determined as the biometric data to be compared. Thus, the biometric data to be compared for filling and correcting the user identity information with a missing identity code or a possible error in the identity code can be obtained. In the eighth sub-step, the above respective biometric data is compared with the biometric data to be compared, and respective target biometric data is obtained. Thus, the respective biometric data within a relatively small clustering range (i.e., within the range of the clustered similar user groups) can be compared with the biometric data to be compared in a targeted manner, and respective target biometric data that represents being relatively similar to the biometric data to be compared is matched. Among the respective biometric data of the clustered similar user groups, respective target biometric data that is relatively similar to the biometric data to be compared with missing or incorrect data is matched, reducing the amount of data calculation in the comparison and matching process. In the ninth sub-step, the respective reference user information corresponding to the above respective target biometric data in each of the above reference user information is determined as each target reference user information. Thus, respective target reference user information that is relatively similar to the identity information of the user identity information with a missing identity code or a possible error in the identity code can be obtained. In the tenth sub-step, based on the above respective target reference user information, the above user identity information is filled and corrected to update each user information set. Thus, the user identity information can be filled and corrected through respective target reference user information that is relatively similar to the identity information of the user identity information with a missing identity code or a possible error in the identity code, reducing the impact of the loss or error of individual single identity codes (i.e., the identity codes corresponding to the most similar biometric data) on filling and correction, and improving the data correction and filling efficiency. In the second step, each updated user information set is determined as each corrected user information set. Also, because during the data correction and filling process of each user information set, by comparing the respective biometric data of the clustered similar user groups, the amount of data calculation in the comparison and matching process is reduced. By generating respective target reference user information and filling and correcting the user identity information based on the respective target reference user information, the impact of the loss or error of individual single identity codes on filling and correction is reduced, the data correction and filling efficiency is improved, the number of times of re-filling and correction is reduced, and thus the waste of computer computing resources is reduced.

[0059] In some alternative implementation manners of some embodiments, the above execution subject may generate each integrated user information based on each of the above corrected user information sets through the following steps:

[0060] First step, determine the identity code included in each corrected user information in the above-mentioned respective corrected user information sets as the initial clustering code, and obtain an initial clustering code set.

[0061] Second step, perform a deduplication process on the above-mentioned initial clustering code set to obtain a clustering code set.

[0062] Third step, for each clustering code in the above-mentioned clustering code set, perform the following integration steps:

[0063] First sub-step, determine at least one corrected user information including the above-mentioned clustering code in the above-mentioned respective corrected user information sets as a corrected user information cluster.

[0064] Second sub-step, determine at least one user information corresponding to the above-mentioned corrected user information cluster in the above-mentioned respective user information sets as integrated user information.

[0065] Step 103, generate respective identical user identity codes corresponding to the respective integrated user information.

[0066] In some embodiments, the above-mentioned execution entity may generate respective identical user identity codes corresponding to the above-mentioned respective integrated user information. Among them, each integrated user information in the above-mentioned respective integrated user information corresponds one-to-one with each identical user identity code in the above-mentioned respective identical user identity codes.

[0067] In some optional implementation manners of some embodiments, after generating the respective identical user identity codes corresponding to the above-mentioned respective integrated user information, the above-mentioned execution entity may further perform the following steps:

[0068] First step, for each integrated user information in the respective integrated user information, perform the following encrypted storage steps:

[0069] First sub-step, determine the identical user identity code corresponding to the above-mentioned integrated user information in the above-mentioned respective identical user identity codes as the target user identity code.

[0070] Second sub-step, obtain the pre-stored encryption algorithm information from the above-mentioned preset database. Among them, the above-mentioned preset database may be a MySQL database for storing encryption algorithm information. The above-mentioned encryption algorithm information may represent a preset encryption algorithm. For example, the above-mentioned preset encryption algorithm may be the RSA (Rivest-Shamir-Adleman) encryption algorithm.

[0071] Third sub-step, perform an encryption operation on the above-mentioned integrated user information according to the above-mentioned encryption algorithm information to obtain encrypted integrated user information. Among them, the above-mentioned encrypted integrated user information may be the information after encrypting the integrated user information.

[0072] Fourth sub-step, storing the above-mentioned encrypted integrated user information in a preset storage device. Among them, the storage label of the above-mentioned encrypted integrated user information is the above-mentioned target user identity code. Among them, the above-mentioned preset storage device can be a storage device for storing encrypted integrated user information and unified user credit information (for example, a database or a hard disk for storing data).

[0073] In some alternative implementation manners of some embodiments, the above-mentioned execution entity can generate respective same user identity codes corresponding to the above-mentioned respective integrated user information through the following steps:

[0074] First step, for each integrated user information among the above-mentioned respective integrated user information, performing encoding processing on the above-mentioned integrated user information to obtain a code corresponding to the above-mentioned integrated user information. In practice, the above-mentioned execution entity can perform encoding processing on the above-mentioned integrated user information through a hash function to obtain a code corresponding to the above-mentioned integrated user information. Optionally, the above-mentioned execution entity can perform encoding processing on the above-mentioned integrated user information through a UUID generation algorithm to obtain a code corresponding to the above-mentioned integrated user information.

[0075] Second step, determining the generated respective codes as respective same user identity codes corresponding to the above-mentioned respective integrated user information.

[0076] Step 104, generating a code mapping relationship table based on the respective integrated user information and the respective same user identity codes.

[0077] In some embodiments, the above-mentioned execution entity can generate a code mapping relationship table based on the above-mentioned respective integrated user information and the above-mentioned respective same user identity codes.

[0078] In some alternative implementation manners of some embodiments, the above-mentioned execution entity can generate a code mapping relationship table based on the above-mentioned respective integrated user information and the above-mentioned respective same user identity codes through the following steps:

[0079] First step, for each integrated user information among the above-mentioned respective integrated user information, performing the following steps for generating a mapping relationship table:

[0080] First sub-step, determining the same user identity code corresponding to the above-mentioned integrated user information among the above-mentioned respective same user identity codes as the target same user identity code.

[0081] The second sub-step is to determine at least one user identifier corresponding to at least one user information included in the above integrated user information as at least one identical user identifier. Among them, the above at least one identical user identifier is at least one user identifier of the same user on at least one preset client, and the above target identical user identity code corresponds to the above at least one identical user identifier.

[0082] The third sub-step is to determine at least one preset client identifier corresponding to at least one user information included in the above integrated user information. Among them, the above target identical user identity code corresponds to the above at least one preset client identifier, and each identical user identifier in the above at least one identical user identifier corresponds one-to-one with each preset client identifier in the above at least one preset client identifier.

[0083] The fourth sub-step is to store the above target identical user identity code, the above at least one identical user identifier, the above at least one preset client identifier, and their corresponding relationship among the three in a pre-created data table to update the data table. Among them, the corresponding relationship among the three can be the corresponding relationship among the target identical user identity code, at least one identical user identifier, and at least one preset client identifier. As an example, the corresponding relationship among the three can be that the target identical user identity code corresponds to at least one identical user identifier, the target identical user identity code corresponds to at least one preset client identifier, and each identical user identifier in at least one identical user identifier corresponds one-to-one with each preset client identifier in at least one preset client identifier.

[0084] The second step is to determine the updated data table as the coding mapping relationship table.

[0085] Step 105: For each integrated user information in each integrated user information, generate unified user credit information corresponding to the integrated user information, and store the unified user credit information in a preset storage device.

[0086] In some embodiments, the above execution subject can, for each integrated user information in each integrated user information, generate unified user credit information corresponding to the integrated user information, and store the unified user credit information in a preset storage device. In practice, the above execution subject can determine at least one user credit information included in the integrated user information as the unified user credit information.

[0087] Step 106: Perform tagging processing on each generated unified user credit information to obtain each tag information corresponding to each unified user credit information.

[0088] In some embodiments, the above-mentioned execution entity may perform tagging processing on each generated unified user credit information to obtain respective tag information corresponding to each of the above-mentioned unified user credit information. In practice, the above-mentioned execution entity may perform tagging processing on each generated unified user credit information through a pre-trained tag model to obtain respective tag information corresponding to each of the above-mentioned unified user credit information. For example, the above-mentioned tag model may be a credit scoring large model. Each tag information in the above-mentioned respective tag information may characterize the tag of the unified user credit information. For example, the above-mentioned tag information may be "good credit", "higher credit risk", etc.

[0089] Step 107, based on the coding mapping relationship table, cluster and send each unified user credit information and each tag information to each preset client.

[0090] In some embodiments, the above-mentioned execution entity may, based on the above-mentioned coding mapping relationship table, cluster and send each of the above-mentioned unified user credit information and each tag information to each preset client.

[0091] After generating each unified user credit information and each tag information, it is necessary to share each unified user credit information and each tag information with each preset client. However, directly sharing all the generated unified user credit information and all the tag information with each preset client does not take into account the actual needs of each client for the unified user credit information and its corresponding tag information. Since some of the unified user credit information and its corresponding tag information in all the unified user credit information and all the tag information do not have corresponding users on some preset clients, when sending the user credit information and its corresponding tag information that do not have corresponding users on the preset client to the above-mentioned client, it will cause waste of network resources.

[0092] In some optional implementation manners of some embodiments, the above-mentioned execution entity may, based on the above-mentioned coding mapping relationship table, cluster and send each of the above-mentioned unified user credit information and each tag information to each preset client through the following steps:

[0093] First step, determine each preset client identifier included in the above-mentioned coding mapping relationship table as each target preset client identifier.

[0094] Second step, perform duplicate removal processing on each of the above-mentioned target preset client identifiers to obtain each de-duplicated preset client identifier.

[0095] Third step, for each de-duplicated preset client identifier among each of the above-mentioned de-duplicated preset client identifiers, perform the following cluster sending processing:

[0096] The first sub-step is to determine at least one filtered same-user identity code by using the above encoding mapping table and at least one target same-user identity code corresponding to the above deduplicated preset client identifier as at least one filtered same-user identity code.

[0097] The second sub-step is to determine at least one unified user credit information to be sent as at least one unified user credit information corresponding to the above at least one filtered same-user identity code in each of the above unified user credit information.

[0098] The third sub-step is to determine the label information corresponding to each unified user credit information in the above at least one unified user credit information as the label information to be sent, obtaining at least one label information to be sent.

[0099] The fourth sub-step is to send the above at least one unified user credit information to be sent and the above at least one label information to be sent to the preset client corresponding to the above deduplicated preset client identifier.

[0100] The above technical solution and its related content, as an inventive point of the embodiments of the present disclosure, solve the technical problem of "the ultimate waste of network bandwidth resources". The factors leading to the waste of network bandwidth resources are often as follows: directly sharing all the generated unified user credit information and all the label information with each preset client without considering the actual needs of each client for the unified user credit information and its corresponding label information. Since some of the unified user credit information and its corresponding label information in all the unified user credit information and all the label information do not have corresponding users on some preset clients, when the user credit information and its corresponding label information that do not have corresponding users on the preset client are sent to the above-mentioned client, it will cause waste of network resources. If the above factors are solved, the effect of reducing the waste of network bandwidth resources can be achieved. To achieve this effect, first, each preset client identifier included in the above encoding mapping relationship table is determined as each target preset client identifier. Then, duplicate removal processing is performed on the above-mentioned each target preset client identifier to obtain each duplicate-removed preset client identifier. Thus, each duplicate-removed preset client identifier of each preset client that needs to share the unified user credit information can be obtained. After that, for each duplicate-removed preset client identifier in the above-mentioned each duplicate-removed preset client identifier, the following clustering and sending processing is performed: The first step is to determine at least one target same user identity code corresponding to the above encoding mapping relationship table and the above duplicate-removed preset client identifier as at least one filtered same user identity code. Thus, at least one target same user identity code of at least one user of the preset client corresponding to the duplicate-removed preset client identifier can be determined through the encoding mapping relationship table. The second step is to determine at least one unified user credit information corresponding to the above at least one filtered same user identity code among the above-mentioned each unified user credit information as at least one unified user credit information to be sent. Thus, considering the actual needs of the preset client for the unified user credit information, at least one unified user credit information to be sent can be screened out from each unified user credit information. The third step is to determine the label information corresponding to each unified user credit information in the above at least one unified user credit information as the label information to be sent, and obtain at least one label information to be sent. Thus, at least one label information to be sent required by the preset client can be obtained. The fourth step is to send the above at least one unified user credit information to be sent and the above at least one label information to be sent to the preset client corresponding to the above duplicate-removed preset client identifier. Thus, at least one unified user credit information to be sent and the above at least one label information to be sent can be sent to the preset client.Also, when sharing the unified user credit information and each tag information with each preset client, the actual needs of the preset client for the unified user credit information and its corresponding tag information are considered, reducing the possibility of sending user credit information and its corresponding tag information outside the actual needs of the preset client to the preset client, and reducing the waste of network resources.

[0101] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: Through the user credit information integration method of some embodiments of the present disclosure, the phenomenon of fragmented user identities is reduced, and the data quality of the integrated user credit data is improved. Specifically, the reasons for the relatively serious fragmentation of user identities and the relatively poor data quality of the integrated user credit data are as follows: Since users can have different user identity identifiers on different clients (for example, the B side), corresponding to different user credit information. Integrating the respective user credit information from each client (for example, each B side) into a user credit information set for sharing will result in the same user having multiple different identity identifiers and different user credit information, leading to relatively serious fragmentation of user identities. At the same time, integrating the respective user credit information from each client (for example, each B side) into a user credit information set for sharing without performing marking processing on the user credit information will result in the integrated data of the shared user credit information being relatively chaotic, and the data quality of the integrated data of the shared user credit information being relatively poor. Based on this, in the user credit information integration method of some embodiments of the present disclosure, first, for each preset client corresponding to each preset client identifier among the various preset client identifiers, obtain the user information sets of each user applying the above-mentioned preset client, where each user information in the above-mentioned user information set includes: user identity information, user credit information, and the above-mentioned user information corresponds to a user identifier and a preset client identifier. Thus, the user information sets of each user of each preset client can be obtained. Then, perform identity recognition and integration processing on the obtained user information sets to obtain each integrated user information, where each integrated user information in the above-mentioned integrated user information includes at least one user information, and the above-mentioned integrated user information corresponds to at least one user identifier of the same user on at least one preset client. Thus, identity recognition and integration processing can be performed on each user information set, and at least one user information of the same user on at least one preset client can be integrated into one integrated user information, reducing the phenomenon of fragmented user identities. Generate respective same user identity codes corresponding to the above-mentioned respective integrated user information, where each integrated user information in the above-mentioned integrated user information corresponds one-to-one with each same user identity code in the above-mentioned respective same user identity codes. Thus, each integrated user information of each same user can be encoded to generate respective same user identity codes corresponding to each same user, and a unique same user identity code is assigned to the same user from different clients. Then, based on the above-mentioned respective integrated user information and the above-mentioned respective same user identity codes, generate a coding mapping relationship table. Thus, a coding mapping relationship table can be generated to represent the corresponding relationship between the same user identity code and at least one user identifier of at least one preset client.Next, for each of the above integrated user information in the integrated user information, a unified user credit information corresponding to the above integrated user information is generated, and the above unified user credit information is stored in a preset storage device. Thus, the unified user credit information corresponding to each integrated user information can be generated. Next, the generated unified user credit information is marked to obtain the respective tag information corresponding to the above unified user credit information. Thus, the unified user credit information can be marked, the meaning of each unified user credit information is clarified, the readability and usability of each unified user credit information are improved, data chaos is reduced, and data quality is improved. Based on the above coding mapping relationship table, the above unified user credit information and each tag information are clustered and sent to each preset client. Thus, the above unified user credit information and each tag information can be clustered and sent to each preset client. Also, because in the process of integrating user credit information, by performing identity recognition and integration processing on each user information set, at least one user information of the same user on at least one preset client is integrated into one integrated user information, and a unique same user identity code corresponding to the integrated user information is generated, the phenomenon of fragmented user identities is reduced. At the same time, before sharing each unified user credit information, the above unified user credit information is marked to clarify the meaning of each unified user credit information, improve the readability and usability of each unified user credit information, reduce data chaos, and improve data quality.

[0102] Further referring to Figure 2 , as an implementation of the methods shown in the respective figures, the present disclosure provides some embodiments of a user credit information integration device, and these device embodiments correspond to Figure 1 the method embodiments shown, and the device can be specifically applied to various electronic devices.

[0103] As Figure 2As shown in the figure, the user credit information integration device 200 of some embodiments includes: an acquisition unit 201, an identity recognition and integration process 202, a first generation unit 203, a second generation unit 204, a third generation unit 205, a tagging process unit 206, and a clustering and sending unit 207. Among them, the acquisition unit 201 is configured to, for each preset client corresponding to each preset client identifier among the various preset client identifiers, acquire the user information sets of each user of the application of the preset client. Among them, each user information in the user information set includes: user identity information and user credit information, and the user information corresponds to a user identifier and a preset client identifier; the identity recognition and integration process unit 202 is configured to perform identity recognition and integration processing on the acquired user information sets to obtain each integrated user information. Among them, each integrated user information in the various integrated user information includes at least one user information, and the integrated user information corresponds to at least one user identifier of the same user on at least one preset client; the first generation unit 203 is configured to generate each same user identity code corresponding to the various integrated user information. Among them, each integrated user information in the various integrated user information corresponds one-to-one with each same user identity code in the various same user identity codes; the second generation unit 204 is configured to generate a coding mapping relationship table based on the various integrated user information and the various same user identity codes; the third generation unit 205 is configured to, for each integrated user information in the various integrated user information, generate a unified user credit information corresponding to the integrated user information, and store the unified user credit information in a preset storage device; the tagging process unit 206 is configured to perform tagging processing on the generated various unified user credit information to obtain each tag information corresponding to the various unified user credit information; the clustering and sending unit 207 is configured to cluster and send the various unified user credit information and the various tag information to each preset client based on the coding mapping relationship table.

[0104] It can be understood that the various units described in the device 200 correspond to the various steps in the method described in the reference Figure 1 Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units included therein, and will not be repeated here.

[0105] Next, refer to Figure 3 , which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitations to the functions and usage scopes of the embodiments of the present disclosure.

[0106] As Figure 3As shown, the electronic device 300 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 301, which may perform various appropriate actions and processes according to a program stored in the read-only memory (ROM) 302 or a program loaded from the storage device 308 into the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0107] Generally, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 3 the electronic device 300 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices may be implemented or had alternatively. Figure 3 Each block shown in may represent one device or, as needed, multiple devices.

[0108] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for performing the methods shown in the flowcharts. In such some embodiments, the computer program may be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the functions defined in the methods of some embodiments of the present disclosure are executed.

[0109] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In some embodiments of the present disclosure, the computer-readable storage medium may 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 some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable signal medium may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0110] In some embodiments, the client and the server may communicate using any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0111] A computer-readable medium can be included in an electronic device; it can also exist separately without being assembled into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device is caused to: for each preset client corresponding to each preset client identifier among the preset client identifiers, obtain the user information sets of each user using the preset client, where each user information in the user information set includes: user identity information, user credit information, and the user information corresponds to a user identifier and a preset client identifier; perform identity recognition and integration processing on the obtained user information sets to obtain integrated user information, where each integrated user information in the integrated user information includes at least one user information, and the integrated user information corresponds to at least one user identifier of the same user on at least one preset client; generate respective same user identity codes corresponding to the integrated user information, where each integrated user information in the integrated user information corresponds one-to-one with each same user identity code in the respective same user identity codes; generate a code mapping relationship table based on the integrated user information and the same user identity codes; for each integrated user information in the integrated user information, generate unified user credit information corresponding to the integrated user information, and store the unified user credit information in a preset storage device; perform tagging processing on the generated unified user credit information to obtain respective tag information corresponding to the unified user credit information; and cluster and send the unified user credit information and the tag information to each preset client based on the code mapping relationship table.

[0112] Computer program code for performing the operations of some embodiments of the present disclosure can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0114] The units described in some embodiments of the present disclosure can be implemented in software or in hardware. The described units can also be provided in a processor. For example, it can be described as: a processor includes an acquisition unit, an identity recognition integration process, a first generation unit, a second generation unit, a third generation unit, a tagging process unit, and a clustering and sending unit. Among them, the names of these units do not constitute a limitation to the unit itself in some cases. For example, the acquisition unit can also be described as "the unit that acquires the user information sets of each user applying the above-mentioned preset client for each preset client corresponding to each preset client identifier among various preset client identifiers".

[0115] The functions described above can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), Systems on Chip (SOCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0116] The above description is only some preferred embodiments of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of technical features, but should also cover other technical solutions formed by any combination of technical features or their equivalent features without departing from the inventive concept. For example, technical solutions formed by mutually replacing features with similar functions (but not limited to) disclosed in the embodiments of the present disclosure.

Claims

1. A method for integrating user credit information, comprising: For each preset client corresponding to each preset client identifier among the various preset client identifiers, obtaining the user information sets of each user using the preset client, wherein each user information in the user information set includes: user identity information and user credit information, and the user information corresponds to a user identifier and a preset client identifier; Performing identity recognition and integration processing on the obtained user information sets of each user to obtain integrated user information of each user, wherein each integrated user information in the integrated user information of each user includes at least one user information, and the integrated user information corresponds to at least one user identifier of the same user on at least one preset client; Generating respective same user identity codes corresponding to the integrated user information of each user, wherein each integrated user information in the integrated user information of each user corresponds one-to-one with each same user identity code in the respective same user identity codes; Generating a coding mapping relationship table based on the integrated user information of each user and the respective same user identity codes, wherein generating the coding mapping relationship table based on the integrated user information of each user and the respective same user identity codes includes: For each integrated user information in the integrated user information of each user, performing the following steps for generating the mapping relationship table: Determining the same user identity code corresponding to the integrated user information among the respective same user identity codes as the target same user identity code; Determining at least one same user identifier corresponding to at least one user information included in the integrated user information, wherein the at least one same user identifier is at least one user identifier of the same user on at least one preset client, and the target same user identity code corresponds to the at least one same user identifier; Determining at least one preset client identifier corresponding to at least one user information included in the integrated user information, wherein the target same user identity code corresponds to the at least one preset client identifier, and each same user identifier in the at least one same user identifier corresponds one-to-one with each preset client identifier in the at least one preset client identifier; Storing the target same user identity code, the at least one same user identifier, the at least one preset client identifier, and their corresponding relationships in a pre-created data table to update the data table; Determining the updated data table as the coding mapping relationship table; For each integrated user information in the integrated user information of each user, generating unified user credit information corresponding to the integrated user information and storing the unified user credit information in a preset storage device; Performing tagging processing on the generated unified user credit information of each user to obtain respective tag information corresponding to the unified user credit information of each user; Based on the coding mapping relationship table, clustering and sending the unified user credit information of each user and the respective tag information to each preset client.

2. The method according to claim 1, wherein, After generating the respective same user identity codes corresponding to the integrated user information of each user, the method further includes: For each integrated user information in the various integrated user information, perform the following encrypted storage steps: Determine the same user identity code corresponding to the integrated user information in the various same user identity codes as the target user identity code; Obtain the pre-stored encryption algorithm information from the preset database; Perform an encryption operation on the integrated user information according to the encryption algorithm information to obtain encrypted integrated user information; Store the encrypted integrated user information in the preset storage device, where the storage label of the encrypted integrated user information is the target user identity code.

3. The method according to claim 1, wherein The generation of the various same user identity codes corresponding to the various integrated user information includes: For each integrated user information in the various integrated user information, perform a coding process on the integrated user information to obtain a code corresponding to the integrated user information; Determine the various generated codes as the various same user identity codes corresponding to the various integrated user information.

4. The method according to claim 1, wherein Each user identity information in the various user information sets includes: coded identity information, biometric data, and the identity recognition and integration processing of the obtained various user information sets to obtain various integrated user information, including: Determine the coded identity information included in each user information in the various user information sets to obtain a coded identity information set; Perform a format unification process on each coded identity information in the coded identity information set to obtain a standardized coded identity information after format unification processing; Sort the obtained various standardized coded identity information to obtain a standardized coded identity information sequence; Based on the standardized coded identity information sequence, perform the following clustering steps: Determine the similarity between the first standardized coded identity information in the standardized coded identity information sequence and each of the standardized coded identity information other than the first standardized coded identity information in the standardized coded identity information sequence as the various screening similarities, where each of the standardized coded identity information in the various standardized coded identity information corresponds to each of the various screening similarities in the various screening similarities; Determine the various screening similarities that meet the preset screening conditions in the various screening similarities as the various target similarities; Determine the first standardized coded identity information and the various standardized coded identity information corresponding to the various target similarities as a standardized coded identity information clustering group; Delete the various standardized coded identity information in the standardized coded identity information clustering group from the standardized coded identity information sequence to update the standardized coded identity information sequence; In response to determining that the updated standardized coded identity information sequence is not empty, based on the updated standardized coded identity information sequence, perform the clustering step again; In response to determining that the updated standardized coded identity information sequence is empty, determine the various determined standardized coded identity information clustering groups as a standardized coded identity information clustering group set; Based on the standardized encoded identity information clustering set, perform data correction and filling processing on each of the user information sets to obtain each corrected user information set, where each user information in each of the user information sets corresponds one-to-one with each corrected user information set in each of the corrected user information sets; Based on each of the corrected user information sets, generate each integrated user information.

5. The method according to claim 4, wherein, Each corrected user information in each of the corrected user information sets includes user identity information and user credit information. The user identity information includes encoded identity information and biometric data. The encoded identity information includes an identity code, and generating each integrated user information based on each of the corrected user information sets includes: Determine the identity code included in each corrected user information in each of the corrected user information sets as an initial clustering code to obtain an initial clustering code set; Perform a duplicate removal process on the initial clustering code set to obtain a clustering code set; For each clustering code in the clustering code set, perform the following integration steps: Determine at least one corrected user information in each of the corrected user information sets that includes the clustering code as a corrected user information cluster; Determine at least one user information in each of the user information sets corresponding to the corrected user information cluster as integrated user information.

6. A user credit information integration device, comprising: An acquisition unit configured to, for each preset client corresponding to each preset client identifier among each preset client identifier, acquire the user information sets of each user applying the preset client, where each user information in the user information set includes: user identity information and user credit information, and the user information corresponds to a user identifier and a preset client identifier; An identity recognition and integration processing unit configured to perform identity recognition and integration processing on each of the acquired user information sets to obtain each integrated user information, where each integrated user information in each of the integrated user information sets includes at least one user information, and the integrated user information corresponds to at least one user identifier of the same user on at least one preset client; A first generation unit configured to generate each same user identity code corresponding to each of the integrated user information, where each integrated user information in each of the integrated user information sets corresponds one-to-one with each same user identity code in each of the same user identity codes; A second generation unit, configured to generate a coding mapping relationship table based on the respective integrated user information and the respective same user identity codes, wherein generating the coding mapping relationship table based on the respective integrated user information and the respective same user identity codes includes: for each integrated user information in the respective integrated user information, performing the following mapping relationship table generation steps: determining the same user identity code corresponding to the integrated user information among the respective same user identity codes as the target same user identity code; determining at least one user identifier corresponding to at least one user information included in the integrated user information as at least one same user identifier, wherein the at least one same user identifier is at least one user identifier of the same user on at least one preset client, and the target same user identity code corresponds to the at least one same user identifier; determining at least one preset client identifier corresponding to at least one user information included in the integrated user information, wherein the target same user identity code corresponds to the at least one preset client identifier, and each same user identifier in the at least one same user identifier corresponds one-to-one to each preset client identifier in the at least one preset client identifier; storing the target same user identity code, the at least one same user identifier, the at least one preset client identifier, and their corresponding relationships in a pre-created data table to update the data table; determining the updated data table as the coding mapping relationship table; A third generation unit, configured to generate, for each integrated user information in the respective integrated user information, a unified user credit information corresponding to the integrated user information, and store the unified user credit information in a preset storage device; A tagging processing unit, configured to perform tagging processing on the generated respective unified user credit information to obtain respective tag information corresponding to the respective unified user credit information; A clustering and sending unit, configured to cluster and send the respective unified user credit information and respective tag information to respective preset clients based on the coding mapping relationship table.

7. An electronic device, comprising: One or more processors; A storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, enabling the one or more processors to implement the method according to any one of claims 1 to 5.

8. A computer-readable medium having a computer program stored thereon, wherein, The program, when executed by the processor, implements the method according to any one of claims 1 to 5.

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