CN111753154B - User data processing method, device, server and computer readable storage medium - Google Patents

Abstract

This application discloses a user data processing method, device, server and computer-readable storage medium, which belongs to the field of Internet technology. The method obtains characteristic data of at least one object to be identified, and the characteristic data includes at least one of environmental data, registration data, device data and historical behavior data of the object to be identified; and combines the characteristic data of at least one object to be identified, Obtain m feature combinations that meet the reference conditions; obtain m clusters to be identified based on the feature data corresponding to the m feature combinations, and the m clusters to be identified correspond to m feature combinations; cluster and filter the m clusters to be identified Target clusters that meet preset conditions. When screening target clusters that meet preset conditions, the above method takes into account the characteristic data of the objects to be identified in the cluster to be identified, making the determination of the target cluster more accurate, thus improving the accuracy and reliability of user data processing.

Description

User data processing method, device, server and computer-readable storage medium

Technical field

The embodiments of this application relate to the field of Internet technology, and in particular to a user data processing method, device, server and computer-readable storage medium.

Background technique

In recent years, with the rapid development of Internet technology, online businesses such as e-commerce and third-party payment have also experienced explosive growth, and Internet fraud and criminal activities have become more and more aggressive. Therefore, there is an urgent need for a user data processing method. Methods to identify target clusters in the Internet, such as fraud clusters in the Internet.

In the related technology, based on the determined target object, the objects to be identified related to the determined target object are mined; a relationship network is constructed based on the object to be identified and the target object, and the relationship network is clustered and discovered to obtain the relationship network At least one cluster included in each cluster includes multiple objects to be identified and target objects; according to the correlation between the objects to be identified in the cluster and the cluster, it is determined that the correlation in the cluster does not meet the reference correlation. Objects to be identified, the objects to be identified that do not satisfy the reference correlation are removed to obtain the target cluster.

However, the above user data processing method is to identify the cluster based on the determined target object, which makes the identification of the target cluster subject to certain limitations. When the determined target object does not exist in the server, the accuracy of the identification of the target cluster will be reduced. degree and reliability.

Contents of the invention

The embodiments of the present application provide a user data processing method, device, server and computer-readable storage medium, which can be used to solve problems in related technologies. The technical solution is as follows:

In the first aspect, embodiments of the present application provide a user data processing method, which method includes:

Obtain characteristic data of at least one object to be identified, the characteristic data including at least one of environmental data, registration data, device data and historical behavior data of the object to be identified;

Combining the feature data of at least one object to be identified to obtain m feature combinations that meet the reference conditions, where m is an integer greater than or equal to 1;

According to the feature data corresponding to the m feature combinations, m clusters to be identified are obtained, and the m clusters to be identified correspond to the m feature combinations;

The m clusters to be identified are clustered, and target clusters that meet the preset conditions are selected.

In a possible implementation, the feature data of at least one object to be identified is combined to obtain m feature combinations that meet the reference conditions, including:

Freely combine the feature data of at least one object to be identified to obtain n feature combinations, each feature combination includes k feature data, where n is an integer greater than m, and k is an integer greater than or equal to 1;

Calculate the scores of the n feature combinations based on the feature scores of the feature data included in the n feature combinations;

Sort according to the scores of the n feature combinations to obtain the sorted n feature combinations;

Among the sorted n feature combinations, m feature combinations that meet the reference conditions are determined.

In a possible implementation, the m clusters to be identified are clustered and target clusters that meet the preset conditions are screened, including:

Match a label for each of the m clusters to be identified, and the label is used to identify the cluster to be identified;

Update the label of the cluster to be identified according to the labels of the neighbor clusters adjacent to the cluster to be identified, and obtain the updated label of the cluster to be identified;

Cluster clusters with the same labels in the updated labels of the clusters to be identified to obtain candidate clusters, which include multiple clusters to be identified;

Screen the candidate clusters for target clusters that meet preset conditions.

In a possible implementation, the label of the cluster to be identified is updated according to the labels of neighbor clusters adjacent to the cluster to be identified, and the updated label of the cluster to be identified is obtained, including:

According to the labels of neighbor clusters adjacent to the cluster to be identified, update the label of the cluster to be identified according to the following formula to obtain the updated label of the cluster to be identified:

Among them, the argmax is the maximum independent variable function, the i represents the i-th cluster to be identified, the j represents the neighbor cluster j adjacent to the i-th cluster to be identified, and the Wi _,j is the i-th cluster to be identified. The weight between the cluster and the neighbor cluster j, the weight is the number of common objects to be identified included in the cluster to be identified and the neighbor cluster, N is the number of neighbor clusters, and A _N is the Nth neighbor cluster.

In a possible implementation, the candidate cluster is screened for target clusters that meet preset conditions, including:

Based on the label of the candidate cluster, determine the risk score corresponding to the candidate cluster;

In response to the risk score of the candidate cluster meeting the preset condition, the candidate cluster is determined as the target cluster.

In a possible implementation, the candidate cluster is screened for target clusters that meet preset conditions, including:

Calculate the relative entropy of the candidate cluster. The relative entropy includes discrete relative entropy and continuous relative entropy. The discrete relative entropy is used to represent the external difference of the candidate cluster. The continuous relative entropy is used to represent the external difference of the candidate cluster. Internal aggregation;

In response to the discrete relative entropy satisfying the first reference relative entropy and the continuous relative entropy satisfying the second reference relative entropy, the candidate cluster is determined as the target cluster.

In a possible implementation, the risk score corresponding to the candidate cluster is determined based on the label of the candidate cluster, including:

The label of the candidate cluster is input into the target risk calculation model, and the risk score of the candidate cluster is calculated through the target risk calculation model to obtain the risk score of the candidate cluster.

In a possible implementation, before inputting the label of the candidate cluster into the target risk calculation model, the method further includes:

Get the label of at least one historical cluster;

The initial risk calculation model is trained according to the label of the at least one historical cluster to obtain a target risk calculation model.

In a possible implementation, the environmental data includes at least one of the IP address and geographical location data of the object to be identified; the registration data includes the personal information filled in by the object to be identified when registering; the device The data includes the type of device used by the object to be identified, and the historical behavior data includes the historical browsing, purchasing, and commenting behaviors of the object to be identified.

In a second aspect, embodiments of the present application provide a user data processing device. The method includes:

An acquisition module, configured to acquire characteristic data of at least one object to be identified, where the characteristic data includes at least one of environmental data, registration data, device data and historical behavior data of the object to be identified;

The combination module is used to combine the feature data of at least one object to be identified to obtain m feature combinations that meet the reference conditions, where m is an integer greater than or equal to 1;

A determination module, configured to obtain m clusters to be identified based on the feature data corresponding to the m feature combinations, and the m clusters to be identified correspond to the m feature combinations;

The screening module is used to cluster the m clusters to be identified and screen target clusters that meet the preset conditions.

In a possible implementation, the combination module is used to freely combine the feature data of at least one object to be identified to obtain n feature combinations, each feature combination including k feature data, where n is greater than m is an integer, and k is an integer greater than or equal to 1;

Calculate the scores of the n feature combinations based on the feature scores of the feature data included in the n feature combinations;

Sort according to the scores of the n feature combinations to obtain the sorted n feature combinations;

Among the sorted n feature combinations, m feature combinations that meet the reference conditions are determined.

In a possible implementation, the screening module is used to match a label for each of the m clusters to be identified, and the label is used to identify the cluster to be identified;

Update the label of the cluster to be identified according to the labels of the neighbor clusters adjacent to the cluster to be identified, and obtain the updated label of the cluster to be identified;

Cluster clusters with the same labels in the updated labels of the clusters to be identified to obtain candidate clusters, which include multiple clusters to be identified;

Screen the candidate clusters for target clusters that meet preset conditions.

In a possible implementation, the screening module is used to update the label of the cluster to be identified according to the following formula according to the labels of neighbor clusters adjacent to the cluster to be identified, and obtain the updated label of the cluster to be identified. :

Among them, the argmax is the maximum independent variable function, the i represents the i-th cluster to be identified, the j represents the neighbor cluster j adjacent to the i-th cluster to be identified, and the Wi _,j is the i-th cluster to be identified. The weight between the cluster and the neighbor cluster j, the weight is the number of common objects to be identified included in the cluster to be identified and the neighbor cluster, N is the number of neighbor clusters, and A _N is the Nth neighbor cluster.

In a possible implementation, the screening module is used to determine the risk score corresponding to the candidate cluster based on the label of the candidate cluster;

In response to the risk score of the candidate cluster meeting the preset condition, the candidate cluster is determined as the target cluster.

In a possible implementation, the screening module is used to calculate the relative entropy of the candidate cluster. The relative entropy includes discrete relative entropy and continuous relative entropy. The discrete relative entropy is used to represent the external environment of the candidate cluster. Difference, this continuous relative entropy is used to represent the internal aggregation of the candidate cluster;

In response to the discrete relative entropy satisfying the first reference relative entropy and the continuous relative entropy satisfying the second reference relative entropy, the candidate cluster is determined as the target cluster.

In a possible implementation, the screening module is used to input the label of the candidate cluster into a target risk calculation model, calculate the risk score of the candidate cluster through the target risk calculation model, and obtain the risk score of the candidate cluster. .

In a possible implementation, the acquisition module is also used to acquire the label of at least one historical cluster;

The device also includes:

The training module is used to train the initial risk calculation model according to the label of the at least one historical cluster to obtain the target risk calculation model.

In a possible implementation, the environmental data includes at least one of the IP address and geographical location data of the object to be identified; the registration data includes the personal information filled in by the object to be identified when registering; the device The data includes the type of device used by the object to be identified, and the historical behavior data includes the historical browsing, purchasing, and commenting behaviors of the object to be identified.

In a third aspect, embodiments of the present application provide a server. The server includes a processor and a memory. At least one program code is stored in the memory. The at least one program code is loaded and executed by the processor to implement any of the above. User Data Processing Methods.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium. The computer-readable storage medium stores at least one program code. The at least one program code is loaded and executed by a processor to implement any of the above-mentioned user functions. Data processing methods.

The technical solutions provided by the embodiments of this application at least bring the following beneficial effects:

When processing user data, the method provided by the embodiment of the present application takes into account the characteristic data of the object to be identified, determines the feature combination based on the characteristic data of the object to be identified, and obtains the cluster to be identified based on the feature combination, making the determination of the cluster to be identified more accurate. precise. The clusters to be identified are clustered and the target clusters that meet the preset conditions are selected to make the determination of the target clusters more accurate, thereby improving the accuracy and reliability of user data processing.

Description of the drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without exerting creative efforts.

Figure 1 is a schematic diagram of the implementation environment of a user data processing method provided by an embodiment of the present application;

Figure 2 is a flow chart of a user data processing method provided by an embodiment of the present application;

Figure 3 is a schematic diagram of a candidate cluster provided by an embodiment of the present application;

Figure 4 is a flow chart of a user data processing method provided by an embodiment of the present application;

Figure 5 is a schematic structural diagram of a user data processing device provided by an embodiment of the present application;

Figure 6 is a schematic structural diagram of a server provided by an embodiment of the present application;

FIG. 7 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

Figure 1 is a schematic diagram of an implementation environment of a user data processing method provided by an embodiment of the present application. As shown in Figure 1, the implementation environment includes: a server 101 and an electronic device 102.

The server 101 may be one server or a server cluster composed of multiple servers. The server 101 may be at least one of a cloud computing platform and a virtualization center, which is not limited in this embodiment of the present application. The server 101 is used to obtain the characteristic data of the object to be recognized, and determine the characteristic combination according to the characteristic data of the object to be recognized. Determine the clusters to be identified based on the combination of features, cluster the clusters to be identified, and screen the target clusters that meet the preset conditions. Of course, the server 101 may also include other functional servers to provide more comprehensive and diverse services.

The electronic device 102 may be a smartphone, a game console, a desktop computer, a tablet, an MP3 (Moving Picture Experts Group Audio Layer III, moving picture experts compression standard audio layer 3) player, an MP4 (Moving Picture Experts Group Audio Layer IV, moving picture expert) Expert compression standard audio layer 4) at least one of a player and a laptop. The electronic device 102 is connected to the server 101 through a wired network or a wireless network, and an application program for processing user data is installed and run in the electronic device 102 . The electronic device 102 may also send the identifier of the object to be recognized to the server 101, so that the server 101 can obtain the characteristic data of the object to be recognized based on the identifier of the object to be recognized.

Based on the above implementation environment, the embodiment of the present application provides a user data processing method. Taking the flow chart of a user data processing method provided by the embodiment of the present application shown in Figure 2 as an example, the method can be performed by the server in Figure 1 101 execution. As shown in Figure 2, the method includes the following steps:

In step 201, characteristic data of at least one object to be recognized is obtained. The characteristic data includes at least one of environmental data, registration data, device data and historical behavior data of the object to be recognized.

In this embodiment of the present application, the server and the electronic device communicate through a wired network or a wireless network. The electronic device can send an identification request of the object to be identified to the server. The identification request carries the object identifier of the object to be identified. The object identifier can It can be a number, or it can be the account number of the object to be identified, as long as the object identifier can correspond to an object to be identified, the embodiment of the present application does not limit the object identifier.

In one possible implementation, the storage space of the server stores the object identifiers of all objects to be identified and their corresponding user data. When the server receives the identification request sent by the electronic device, the server parses the identification request. Obtain the object identifier of the object to be identified carried in the identification request. Based on the object identifier of the object to be identified, the user data of the object to be identified is obtained from the storage space of the server.

In a possible implementation, the storage space of the server can store user data of objects to be identified in the following manner. The server divides its storage space into a target number of first storage spaces, and each first storage space is used for Store user data for an object to be identified. For example, the server divides its storage space into five first storage spaces. The first first storage space is used to store user data corresponding to object one to be identified, and the second first storage space is used to store the user data corresponding to object two to be identified. user data, the third first storage space is used to store user data corresponding to object three to be recognized, the fourth first storage space is used to store user data corresponding to object four to be recognized, and the fifth first storage space is used to store user data corresponding to object three to be recognized. To store user data corresponding to object 5 to be identified.

In a possible implementation manner, after the server obtains the user data of the object to be recognized, the server extracts the characteristic data of the object to be recognized from the user data of the object to be recognized. The characteristic data of the object to be recognized includes at least one of environmental data, registration data, device data and historical behavior data of the object to be recognized. Among them, the environmental data includes at least one of the IP address and geographical location data of the object to be identified, and the registration data includes the personal information filled in by the object to be identified when registering. The personal information includes but is not limited to the name and phone number of the object to be identified. number, ID number and other information. The device data includes the type of device used by the object to be identified. Historical behavior data includes historical browsing, purchasing, commenting and other behaviors of the object to be identified.

For example, the server receives an identification request for object one to be identified sent by the electronic device, and the identification request carries the object identifier 0001 of object one to be identified. The identification request is parsed to obtain the object identifier 0001 carried in it, and the first storage space corresponding to the object identifier 0001 is determined in its storage space, that is, the first first storage space, and the first first storage space is obtained. The user data stored in the space extracts the characteristic data of object one to be recognized from the user data stored in the first storage space, that is, the server obtains the characteristic data of object one to be recognized.

It should be noted that the process by which the server obtains the characteristic data of each object to be identified is consistent with the above-mentioned process of obtaining the characteristic data of object one to be identified, and will not be described again here.

In step 202, the feature data of at least one object to be identified is combined to obtain m feature combinations that meet the reference conditions, where m is an integer greater than or equal to 1.

In this embodiment of the present application, the process of combining the feature data of at least one object to be identified obtained in step 201 to obtain m feature combinations that meet the reference conditions includes the following steps 2021 to 2024.

Step 2021: Freely combine the feature data of at least one object to be identified to obtain n feature combinations, each feature combination including k feature data.

In a possible implementation, based on the characteristic data of at least one object to be identified obtained in the above step 201, the characteristic data is freely combined to obtain n characteristic combinations, each of which includes k characteristic data, Where n is an integer greater than m, and k is an integer greater than or equal to 1.

Illustratively, the above-mentioned step 201 obtains the characteristic data of object one to be recognized, object two to be recognized, object three to be recognized, object four to be recognized, and object five to be recognized. Based on the free combination of the feature data of these five objects to be identified, taking the number k of feature data included in each feature combination as 3 as an example, five feature combinations are obtained, namely feature combination one, feature combination two, feature combination Combination three, feature combination four and feature combination five. For example, the feature data included in feature combination one is name, ID number, and phone number; the feature data included in feature combination two is name, phone number, and geographical location data; the feature data included in feature combination three is phone number, Geographical location data and device type; the feature data included in feature combination four is name, location data, and device type; the feature data included in feature combination five is name, IP address, and device type.

Based on the feature data corresponding to the five objects to be identified, the five objects to be identified are added to the corresponding feature combinations to obtain a feature combination including the objects to be identified. Among them, feature combination one includes object one to be identified and object two to be identified; feature combination two includes object one to be identified, object three to be identified, and object five to be identified; feature combination three includes object two to be identified, object to be identified 3. Object 4 to be identified; feature combination 4 includes object 1 to be identified and object 5 to be identified; feature combination 5 includes object 2 to be identified, object 4 to be identified, and object 5 to be identified.

It should be noted that the above description only takes the number of objects to be recognized as 5, the number of feature data included in the feature combination as 3, and the number of feature combinations as 5 as an example, and is not intended to limit the present application. The number of objects to be recognized may be more or less, the number k of feature data included in the feature combination may be more or less, and the number of feature combinations may be more or less. This is not limited in the embodiments of the present application. .

In a possible implementation, feature combinations that do not meet the requirements can also be deleted. For example, the number of objects to be recognized included in each feature combination is determined. If the number of objects to be recognized included in the feature combination is less than the target number, the corresponding feature combination is deleted. For example, assuming that the number of targets is 2, since the number of objects to be recognized included in the feature combination is greater than or equal to 2, no feature combination is deleted. Taking the number of targets as 3 as an example, since the number of objects to be recognized included in feature combination two and feature combination four is 2, feature combination two and feature combination four can be deleted.

Step 2022: Calculate the scores of the n feature combinations based on the feature scores of the feature data included in the n feature combinations.

In a possible implementation, each feature data of the object to be recognized has a corresponding feature score. The feature score can be represented by 0 and 1, or other numbers. The embodiment of this application has The expression form of the score is not limited.

In a possible implementation, based on the n feature combinations obtained in the above step 2021, the feature score of the feature data of the object to be identified included in each feature combination is determined. According to the object to be identified included in each feature combination The feature score of the feature data is used to calculate the score of the corresponding feature combination. For example, the feature scores of the feature data of the object to be identified included in the feature combination can be added to obtain a score of the feature combination.

For example, feature combination one includes object one to be recognized and object two to be recognized. The feature scores corresponding to the feature data of object one to be recognized are as shown in Table 1 below. The feature scores corresponding to the feature data of object two to be recognized are as shown in Table 2. shown.

Table I

Feature data Name telephone number ID number geolocation data IP address Equipment type feature score 1 0 1 1 0 1

Table II

Feature data Name telephone number ID number geolocation data IP address Equipment type feature score 0 1 1 0 0 1

Based on the feature scores corresponding to the feature data in Table 1 and Table 2 above, the total feature value corresponding to the object to be recognized is calculated as 1+0+1+1+0+1=4, and the total feature value corresponding to the object to be recognized is calculated as 1+0+1+1+0+1=4. The value is 0+1+1+0+0+1=3. According to the total feature value corresponding to object one to be recognized and the total feature value corresponding to object two to be recognized, the score corresponding to feature combination one is calculated as 4+3=7, so that the score of feature combination one can be obtained.

It should be noted that the embodiment of the present application only uses the method of adding the feature scores of feature objects as an example to calculate the score of the feature combination. The score of the feature combination can also be calculated in other ways, and the embodiment of the present application is not limited to this.

It should also be noted that the above only takes feature combination one as an example to illustrate the calculation process of the score of the feature combination. The calculation process of the scores of other feature combinations is consistent with the calculation process of the score of the feature combination one, and will not be described again here.

In a possible implementation, if a certain feature data of the object to be identified does not have a corresponding feature score, a random number generator is used to generate a corresponding feature score for the feature data to prevent the feature data from affecting the The score for the combination of features in which it occurs.

In a possible implementation, if the object to be identified included in the feature combination contains a suspected object to be identified, the feature combination can be first determined as a suspected feature combination, the suspect score of the feature combination is calculated, and then the feature combination is performed. When calculating the feature score, the suspicion score will be taken into account. Among them, the calculation process of the suspicion score can be shown as the following formula (1):

For example, object one to be identified in feature combination one is a suspected object to be identified, When calculating the feature score of feature combination one, the suspicion score of feature combination one is taken into account. That is, the feature score of feature combination one is the total feature value of object one to be identified + the feature value of object two to be identified. Total feature value + suspicion score of object one to be identified = 4 + 3 + 0.5 = 7.5.

Step 2023: Sort the n feature combinations according to their scores to obtain the sorted n feature combinations.

In a possible implementation, the scores of the n feature combinations obtained in the above step 2022 are sorted. The sorting method may be from high to low, or may be from low to high. In this embodiment of the present application, Not limited.

For example, the score of feature combination one is 7, the score of feature combination two is 9, the score of feature combination three is 5, the score of feature combination four is 8, the score of feature combination five is 10, the score based on feature combination is in order of high Sort in order of lowest, and the sorted feature combinations obtained are feature combination five, feature combination two, feature combination four, feature combination one, and feature combination three.

Step 2024: Among the sorted n feature combinations, determine m feature combinations that meet the reference conditions.

In a possible implementation, based on the sorted n feature combinations, m feature combinations that meet the reference conditions are determined among the n feature combinations. Among them, the m feature combinations that satisfy the reference condition may be m feature combinations with scores greater than the reference score, or they may be the top m feature combinations ranked according to the score. The embodiment of the present application does not limit the reference condition.

For example, based on the scores of the n feature combinations, it is determined that the top three feature combinations, that is, feature combination five, feature combination two, and feature combination four are feature combinations that meet the reference conditions.

In step 203, m clusters to be identified are obtained based on the feature data corresponding to the m feature combinations, and the m clusters to be identified correspond to the m feature combinations.

In this embodiment of the present application, according to the feature data corresponding to the m feature combinations determined in step 202, the objects to be identified included in each feature combination are formed into a cluster to be identified, so that m clusters to be identified can be obtained, each of which is The cluster to be identified corresponds to a feature combination. For example, cluster 1 to be identified includes object 1 and object 2 to be identified; cluster 2 to be identified includes object 1, object 3, and object 5; cluster 3 to be identified includes object 2 and 2. Object three and object four to be identified; cluster four to be identified includes object one and object five to be identified; cluster five to be identified includes object two, object four and object five.

In a possible implementation, in order to make the objects to be recognized included in the cluster to be recognized more extensive, the objects to be recognized that are consistent with the feature data can be determined based on the feature data included in the feature combination, and the features included in the feature combination can be The objects to be identified and the objects to be identified that are consistent with the characteristic data together form the cluster to be identified corresponding to the feature combination, so that the number of objects to be identified included in the cluster to be identified is larger, so that the cluster to be identified includes more to be identified. The objects are wider.

In step 204, m clusters to be identified are clustered and target clusters that meet preset conditions are selected.

In this embodiment of the present application, clustering m clusters to be identified and selecting target clusters that meet preset conditions includes the following steps 2041 to 2044.

Step 2041: Match a label for each of the m clusters to be identified, and the label is used to identify the cluster to be identified.

In a possible implementation, the m clusters to be identified obtained in the above step 203 are all assigned a label. Each label identifies the corresponding cluster to be identified. The label can be expressed in a numerical manner, or can be It is represented by letters, and the embodiment of the present application does not limit the representation method of the label.

Step 2042: Update the label of the cluster to be identified based on the labels of neighbor clusters adjacent to the cluster to be identified, and obtain the updated label of the cluster to be identified.

In one possible implementation, the label of the cluster to be identified is updated according to the following formula (2) according to the labels of neighbor clusters adjacent to the cluster to be identified, thereby obtaining the updated label of the cluster to be identified.

In the above formula (2), argmax is the maximum independent variable function, i represents the i-th cluster to be identified, j represents the neighbor cluster j adjacent to the i-th cluster to be identified, and W _{i, j} are the i-th cluster to be identified. The weight between the cluster and neighbor cluster j, the weight is the number of common objects to be identified included in the cluster to be identified and the neighbor cluster, N is the number of neighbor clusters, and A _N is the Nth neighbor cluster.

For example, taking the label of the cluster to be identified as a letter, for example, the label of cluster 1 to be identified is A, and the clusters adjacent to cluster 1 to be identified include neighbor cluster 1, neighbor cluster 2, neighbor cluster 3, and neighbor cluster 4. Neighbor cluster 1 is labeled B, neighbor cluster 2 is labeled C, neighbor cluster 3 is labeled D, and neighbor cluster 4 is labeled B. Since the label B appears the most among the clusters adjacent to the cluster 1 to be identified, the label of the cluster 1 to be identified is updated, and the updated label of the cluster 1 to be identified is obtained as B.

It should be noted that the update process of labels of other clusters to be identified is consistent with the above-mentioned update process of labels of cluster 1 to be identified, and will not be described again here.

Step 2043: Cluster clusters with the same label in the updated labels of the clusters to be identified to obtain candidate clusters, which include multiple clusters to be identified.

In this embodiment of the present application, based on the updated labels of the clusters to be identified, clusters to be identified with consistent labels are clustered to obtain candidate clusters. The candidate clusters include multiple clusters to be identified with consistent labels.

In a possible implementation, for objects to be identified that belong to multiple clusters to be identified, they are classified into a cluster containing the largest number of objects to be identified. The number of objects to be identified included in each cluster to be identified can also be determined. If the number of objects to be identified included in the cluster to be identified is less than the reference number, the cluster to be identified can be deleted, that is, the number of objects to be identified is filtered out if the number does not meet the requirements. Reference number of clusters to be identified. The remaining clusters to be identified are clustered to obtain candidate clusters. This method can make the obtained candidate clusters more accurate. Figure 3 is a schematic diagram of a candidate cluster provided by the embodiment of the present application. In Figure 3, the black circle represents the cluster to be identified, the white circle represents the neighbor cluster adjacent to the cluster to be identified, and the clusters framed by dotted lines for candidate clusters.

Step 2044: Screen the candidate clusters for target clusters that meet preset conditions.

In this embodiment of the present application, screening target clusters that meet preset conditions among candidate clusters can be implemented in the following two ways.

Implementation method 1: Based on the risk score corresponding to the candidate cluster, determine the candidate cluster whose risk score meets the preset conditions as the target cluster.

In a possible implementation manner, a label of at least one historical cluster is obtained, and an initial risk calculation model is trained based on the label of at least one historical cluster, thereby obtaining a target risk calculation model.

In one possible implementation, the labels of the candidate clusters are input into the target risk calculation model, and the risk scores of the candidate clusters are calculated through the target risk calculation model to obtain the risk scores of the candidate clusters. In response to the risk score of the candidate cluster meeting the preset conditions, the candidate cluster is determined to be the target cluster; in response to the risk score of the candidate cluster not meeting the preset conditions, the candidate cluster is determined to be an ordinary cluster. The preset conditions can be set based on experience, or can be adjusted based on different application scenarios. The embodiments of this application do not limit the content and setting timing of the preset conditions.

For example, if the risk score corresponding to the preset condition is 0.80 and the risk score of the candidate cluster is 0.85, then the candidate cluster is determined as the target cluster. If the risk score of the candidate cluster is 0.75, the candidate cluster is determined as an ordinary cluster.

Implementation method 2: Determine whether the candidate cluster is the target cluster based on the relative entropy of the candidate cluster.

In a possible implementation, the relative entropy includes discrete relative entropy and continuous relative entropy. The discrete relative entropy is used to represent the external difference of the candidate cluster, and the continuous relative entropy is used to represent the internal aggregation of the candidate cluster. Ordinary clusters will have high internal clustering and low external diversity. When the discrete relative entropy of the candidate cluster satisfies the first reference relative entropy and the continuous relative entropy satisfies the second reference relative entropy, the candidate cluster is determined as the target cluster.

In a possible implementation, after the candidate cluster is determined as the target cluster, a reasonable decision can be made for the candidate cluster for the target cluster based on the similarity between the characteristic data of the objects to be identified included in the candidate cluster. explain. For example, the IP address of 100% of the objects to be identified in the candidate cluster is "222.32.60.147", the device type of 100% of the objects to be identified is "43", and the name of 100% of the objects to be identified is "**", so It can reflect the high consistency and strong correlation of all objects to be identified in the candidate cluster, so that a reasonable explanation can be made for the candidate cluster as the target cluster.

Figure 4 shows a flow chart of a user data processing method provided by an embodiment of the present application. Figure 4 includes a feature data module, a first-level clustering module, a second-level clustering module, a filtering module and a decision interpretation module. . Perform one-level clustering based on the characteristic data of the object to be identified to obtain at least one cluster to be identified. Perform two-level clustering based on at least one cluster to be identified. The second-level clustering includes weighted label propagation clustering and small cluster filtering to obtain candidates. Clusters are screened based on candidate clusters. The cluster characteristics of candidate clusters can be determined based on KL (Kullback-Leibler) divergence, that is, the discrete relative entropy and continuous relative entropy of candidate clusters. Based on discrete relative entropy and continuous relative entropy, Entropy,screens target clusters among candidate clusters. The risk score of the candidate cluster can also be calculated based on a supervised learning model, so that the target cluster included in the candidate cluster can be determined. For example, for fraud cluster identification, the target cluster included in the candidate cluster is regarded as the identified fraud cluster.

When processing user data, the above method takes into account the characteristic data of the object to be identified, determines a feature combination based on the characteristic data of the object to be identified, and obtains the cluster to be identified based on the feature combination, making the determination of the cluster to be identified more accurate. The clusters to be identified are clustered and the target clusters that meet the preset conditions are selected to make the determination of the target clusters more accurate, thereby improving the accuracy and reliability of user data processing.

Figure 5 shows a schematic structural diagram of a user data processing device provided by an embodiment of the present application. As shown in Figure 5, the device includes:

The acquisition module 501 is used to acquire characteristic data of at least one object to be identified, where the characteristic data includes at least one of environmental data, registration data, device data and historical behavior data of the object to be identified;

The combination module 502 is used to combine the feature data of at least one object to be identified to obtain m feature combinations that meet the reference conditions, where m is an integer greater than or equal to 1;

The determination module 503 is used to obtain m clusters to be identified based on the feature data corresponding to the m feature combinations, and the m clusters to be identified correspond to the m feature combinations;

The screening module 504 is used to cluster the m clusters to be identified and screen target clusters that meet preset conditions.

In a possible implementation, the combination module 502 is used to freely combine the feature data of at least one object to be identified to obtain n feature combinations, each feature combination includes k feature data, where n is An integer greater than m, where k is an integer greater than or equal to 1;

Calculate the scores of the n feature combinations based on the feature scores of the feature data included in the n feature combinations;

Sort according to the scores of the n feature combinations to obtain the sorted n feature combinations;

Among the sorted n feature combinations, m feature combinations that meet the reference conditions are determined.

In a possible implementation, the screening module 504 is used to match a label for each of the m clusters to be identified, and the label is used to identify the cluster to be identified;

Update the label of the cluster to be identified according to the labels of the neighbor clusters adjacent to the cluster to be identified, and obtain the updated label of the cluster to be identified;

Cluster clusters with the same labels in the updated labels of the clusters to be identified to obtain candidate clusters, which include multiple clusters to be identified;

Screen the candidate clusters for target clusters that meet preset conditions.

In a possible implementation, the filtering module 504 is configured to update the label of the cluster to be identified according to the following formula according to the labels of neighbor clusters adjacent to the cluster to be identified, and obtain the updated label of the cluster to be identified. Label:

Among them, the argmax is the maximum independent variable function, the i represents the i-th cluster to be identified, the j represents the neighbor cluster j adjacent to the i-th cluster to be identified, and the Wi _,j is the i-th cluster to be identified. The weight between the cluster and the neighbor cluster j, the weight is the number of common objects to be identified included in the cluster to be identified and the neighbor cluster, N is the number of neighbor clusters, and A _N is the Nth neighbor cluster.

In a possible implementation, the screening module 504 is used to determine the risk score corresponding to the candidate cluster based on the label of the candidate cluster;

In response to the risk score of the candidate cluster meeting the preset condition, the candidate cluster is determined as the target cluster.

In a possible implementation, the screening module 504 is used to calculate the relative entropy of the candidate cluster. The relative entropy includes discrete relative entropy and continuous relative entropy. The discrete relative entropy is used to represent the candidate cluster. External dissimilarity, this continuous relative entropy is used to represent the internal aggregation of the candidate cluster;

In response to the discrete relative entropy satisfying the first reference relative entropy and the continuous relative entropy satisfying the second reference relative entropy, the candidate cluster is determined as the target cluster.

In a possible implementation, the screening module 504 is used to input the label of the candidate cluster into a target risk calculation model, calculate the risk score of the candidate cluster through the target risk calculation model, and obtain the risk score of the candidate cluster. value.

In a possible implementation, the acquisition module 501 is also used to acquire the label of at least one historical cluster;

The device also includes:

The training module is used to train the initial risk calculation model according to the label of the at least one historical cluster to obtain the target risk calculation model.

In a possible implementation, the environmental data includes at least one of the IP address and geographical location data of the object to be identified; the registration data includes the personal information filled in by the object to be identified when registering; the device The data includes the type of device used by the object to be identified, and the historical behavior data includes the historical browsing, purchasing, and commenting behaviors of the object to be identified.

When processing user data, the above device takes into account the characteristic data of the object to be identified, determines a feature combination based on the characteristic data of the object to be identified, and obtains the cluster to be identified based on the feature combination, making the determination of the cluster to be identified more accurate. The clusters to be identified are clustered and the target clusters that meet the preset conditions are selected to make the determination of the target clusters more accurate, thereby improving the accuracy and reliability of user data processing.

It should be noted that when the user data processing device provided in the above embodiment performs user data processing, only the division of the above functional modules is used as an example. In actual applications, the above functions can be allocated to different functional modules as needed. Completion means dividing the internal structure of the user data processing device into different functional modules to complete all or part of the functions described above. In addition, the user data processing apparatus provided by the above embodiments and the user data processing method embodiments belong to the same concept. Please refer to the method embodiments for the specific implementation process, which will not be described again here.

Figure 6 shows a schematic structural diagram of a server provided by an embodiment of the present application. The server 600 may vary greatly due to different configurations or performance, and may include one or more processors (Central Processing Units, CPUs) 601 and one or more memories 602, wherein the one or more memories 602 store There is at least one instruction, which is loaded and executed by the one or more processors 601 to implement the user data processing method provided by the above method embodiment. Of course, the server 600 may also have components such as wired or wireless network interfaces, keyboards, and input and output interfaces for input and output. The server 600 may also include other components for implementing device functions, which will not be described again here.

FIG. 7 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device 700 can be: a smart phone, a tablet computer, an MP3 (Moving Picture Experts Group Audio Layer III, moving picture experts compression standard audio layer 3) player, an MP4 (Moving Picture Experts Group Audio Layer IV, moving picture experts compression standard) Audio level 4) player, laptop or desktop computer. Electronic device 700 may also be referred to as user equipment, portable electronic device, laptop electronic device, desktop electronic device, and other names.

Generally, the electronic device 700 includes one or more processors 701 and one or more memories 702 .

The processor 701 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 701 can adopt at least one hardware form among DSP (Digital Signal Processing, digital signal processing), FPGA (Field-Programmable Gate Array, field programmable gate array), and PLA (Programmable Logic Array, programmable logic array). accomplish. The processor 701 may also include a main processor and a co-processor. The main processor is a processor used to process data in the wake-up state, also called CPU (Central Processing Unit, central processing unit); the co-processor is A low-power processor used to process data in standby mode. In some embodiments, the processor 701 may be integrated with a GPU (Graphics Processing Unit, image processor), and the GPU is responsible for rendering and drawing content to be displayed on the display screen. In some embodiments, the processor 701 may also include an AI (Artificial Intelligence, artificial intelligence) processor, which is used to process computing operations related to machine learning.

Memory 702 may include one or more computer-readable storage media, which may be non-transitory. Memory 702 may also include high-speed random access memory, and non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 702 is used to store at least one instruction, and the at least one instruction is used to be executed by the processor 701 to implement the user data provided by the method embodiments in this application. Approach.

In some embodiments, the electronic device 700 optionally further includes: a peripheral device interface 703 and at least one peripheral device. The processor 701, the memory 702 and the peripheral device interface 703 may be connected through a bus or a signal line. Each peripheral device can be connected to the peripheral device interface 703 through a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 704, a display screen 705, a camera component 706, an audio circuit 707, a positioning component 708 and a power supply 709.

The peripheral device interface 703 may be used to connect at least one I/O (Input/Output, input/output) related peripheral device to the processor 701 and the memory 702 . In some embodiments, the processor 701, the memory 702, and the peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one of the processor 701, the memory 702, and the peripheral device interface 703 or Both of them can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

The radio frequency circuit 704 is used to receive and transmit RF (Radio Frequency, radio frequency) signals, also called electromagnetic signals. Radio frequency circuit 704 communicates with communication networks and other communication devices through electromagnetic signals. The radio frequency circuit 704 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 704 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. Radio frequency circuitry 704 can communicate with other electronic devices through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: metropolitan area network, various generations of mobile communication networks (2G, 3G, 4G and 5G), wireless local area network and/or WiFi (Wireless Fidelity, wireless fidelity) network. In some embodiments, the radio frequency circuit 704 may also include NFC (Near Field Communication) related circuits, which is not limited in this application.

The display screen 705 is used to display UI (User Interface, user interface). The UI can include graphics, text, icons, videos, and any combination thereof. When display screen 705 is a touch display screen, display screen 705 also has the ability to collect touch signals on or above the surface of display screen 705 . The touch signal can be input to the processor 701 as a control signal for processing. At this time, the display screen 705 can also be used to provide virtual buttons and/or virtual keyboards, also called soft buttons and/or soft keyboards. In some embodiments, there may be one display screen 705, which is provided on the front panel of the electronic device 700; in other embodiments, there may be at least two display screens 705, which are respectively provided on different surfaces of the electronic device 700 or have a folding design. ; In some embodiments, the display screen 705 may be a flexible display screen disposed on a curved or folded surface of the electronic device 700 . Even, the display screen 705 can also be set in a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 705 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

Camera assembly 706 is used to capture images or videos. Optionally, camera assembly 706 includes a front camera and a rear camera. Usually, the front camera is set on the front panel of the electronic device, and the rear camera is set on the back of the electronic device. In some embodiments, there are at least two rear cameras, one of which is a main camera, a depth-of-field camera, a wide-angle camera, and a telephoto camera, so as to realize the integration of the main camera and the depth-of-field camera to realize the background blur function. Integrated with a wide-angle camera to achieve panoramic shooting and VR (Virtual Reality, virtual reality) shooting functions or other fusion shooting functions. In some embodiments, camera assembly 706 may also include a flash. The flash can be a single color temperature flash or a dual color temperature flash. Dual color temperature flash refers to a combination of warm light flash and cold light flash, which can be used for light compensation under different color temperatures.

Audio circuitry 707 may include a microphone and speakers. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals that are input to the processor 701 for processing, or to the radio frequency circuit 704 to implement voice communication. For the purpose of stereo collection or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the electronic device 700 . The microphone can also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert electrical signals from the processor 701 or the radio frequency circuit 704 into sound waves. The loudspeaker can be a traditional membrane loudspeaker or a piezoelectric ceramic loudspeaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert electrical signals into sound waves that are audible to humans, but also convert electrical signals into sound waves that are inaudible to humans for purposes such as ranging. In some embodiments, audio circuitry 707 may also include a headphone jack.

The positioning component 708 is used to locate the current geographical location of the electronic device 700 to implement navigation or LBS (LocationBased Service). The positioning component 708 may be a positioning component based on the American GPS (Global Positioning System), China's Beidou system, Russia's Galileo system, or the European Union's Galileo system.

The power supply 709 is used to power various components in the electronic device 700 . Power source 709 may be AC, DC, disposable batteries, or rechargeable batteries. When the power source 709 includes a rechargeable battery, the rechargeable battery may support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

In some embodiments, electronic device 700 also includes one or more sensors 170 . The one or more sensors 170 include, but are not limited to: an acceleration sensor 711 , a gyroscope sensor 712 , a pressure sensor 711 , a fingerprint sensor 714 , an optical sensor 715 and a proximity sensor 716 .

The acceleration sensor 711 can detect the acceleration on the three coordinate axes of the coordinate system established by the electronic device 700 . For example, the acceleration sensor 711 can be used to detect the components of gravity acceleration on three coordinate axes. The processor 701 can control the display screen 705 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 711 . The acceleration sensor 711 can also be used to collect game or user motion data.

The gyro sensor 712 can detect the body direction and rotation angle of the electronic device 700 , and the gyro sensor 712 can cooperate with the acceleration sensor 711 to collect the user's 3D movements on the electronic device 700 . Based on the data collected by the gyro sensor 712, the processor 701 can implement the following functions: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

The pressure sensor 711 may be disposed on the side frame of the electronic device 700 and/or on the lower layer of the display screen 705 . When the pressure sensor 711 is disposed on the side frame of the electronic device 700, it can detect the user's holding signal of the electronic device 700, and the processor 701 performs left and right hand identification or quick operation based on the holding signal collected by the pressure sensor 711. When the pressure sensor 711 is provided on the lower layer of the display screen 705, the processor 701 controls the operability controls on the UI interface according to the user's pressure operation on the display screen 705. The operability control includes at least one of a button control, a scroll bar control, an icon control, and a menu control.

The fingerprint sensor 714 is used to collect the user's fingerprint. The processor 701 identifies the user's identity based on the fingerprint collected by the fingerprint sensor 714, or the fingerprint sensor 714 identifies the user's identity based on the collected fingerprint. When the user's identity is recognized as a trusted identity, the processor 701 authorizes the user to perform relevant sensitive operations. The sensitive operations include unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 714 may be disposed on the front, back, or side of the electronic device 700 . When the electronic device 700 is provided with a physical button or a manufacturer's logo, the fingerprint sensor 714 can be integrated with the physical button or the manufacturer's logo.

The optical sensor 715 is used to collect ambient light intensity. In one embodiment, the processor 701 can control the display brightness of the display screen 705 according to the ambient light intensity collected by the optical sensor 715 . Specifically, when the ambient light intensity is high, the display brightness of the display screen 705 is increased; when the ambient light intensity is low, the display brightness of the display screen 705 is decreased. In another embodiment, the processor 701 can also dynamically adjust the shooting parameters of the camera assembly 706 according to the ambient light intensity collected by the optical sensor 715 .

The proximity sensor 716 , also called a distance sensor, is usually provided on the front panel of the electronic device 700 . The proximity sensor 716 is used to collect the distance between the user and the front of the electronic device 700 . In one embodiment, when the proximity sensor 716 detects that the distance between the user and the front of the electronic device 700 gradually becomes smaller, the processor 701 controls the display screen 705 to switch from the bright screen state to the closed screen state; when the proximity sensor 716 detects When the distance between the user and the front of the electronic device 700 gradually increases, the processor 701 controls the display screen 705 to switch from the screen-off state to the screen-on state.

Those skilled in the art can understand that the structure shown in FIG. 7 does not constitute a limitation on the electronic device 700, and may include more or fewer components than shown, or combine certain components, or adopt different component arrangements.

In an exemplary embodiment, a computer-readable storage medium is also provided. At least one program code is stored in the storage medium. The at least one program code is loaded and executed by the processor to implement any of the above user data processing. method.

Optionally, the computer-readable storage medium may be a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), or a read-only compact disc (Compact Disc Read-Only Memory, CD-ROM). , tapes, floppy disks and optical data storage devices, etc.

It should be understood that "plurality" mentioned in this article means two or more. "And/or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and/or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the related objects are in an "or" relationship.

The above serial numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

The above are only exemplary embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application. Inside.

Claims (9)

1. A method of user data processing, the method comprising:

acquiring characteristic data of at least one object to be identified, wherein the characteristic data comprises at least one of environment data, registration data, equipment data and historical behavior data of the object to be identified;

combining the feature data of the at least one object to be identified to obtain m feature combinations meeting a reference condition, wherein m is an integer greater than or equal to 1;

obtaining m clusters to be identified according to the feature data corresponding to the m feature combinations, wherein the m clusters to be identified correspond to the m feature combinations;

clustering the m clusters to be identified, and screening target clusters meeting preset conditions;

the clustering of the m clusters to be identified, and screening of target clusters meeting preset conditions comprise:

respectively matching a label for the m clusters to be identified, wherein the label is used for identifying the clusters to be identified;

updating the label of the cluster to be identified according to the label of the neighbor cluster adjacent to the cluster to be identified, so as to obtain the label after the updating of the cluster to be identified;

clustering the clusters to be identified with the same labels in the labels after updating the clusters to be identified to obtain candidate clusters, wherein the candidate clusters comprise a plurality of clusters to be identified;

Screening target clusters meeting preset conditions from the candidate clusters;

updating the label of the cluster to be identified according to the label of the neighbor cluster adjacent to the cluster to be identified, to obtain the label after the update of the cluster to be identified, comprising:

updating the label of the cluster to be identified according to the label of the neighbor cluster adjacent to the cluster to be identified according to the following formula, and obtaining the label after updating the cluster to be identified:

wherein argmax is a maximum argument function, i represents an ith cluster to be identified, j represents a neighbor cluster j adjacent to the ith cluster to be identified, and W _i,j The weight between the ith cluster to be identified and the neighbor cluster j is the number of common objects to be identified, which are included in the clusters to be identified and the neighbor clusters, N is the number of neighbor clusters, A is _N Is the nth neighbor cluster.

2. The method according to claim 1, wherein combining the feature data of the at least one object to be identified to obtain m feature combinations satisfying a reference condition comprises:

freely combining the feature data of the at least one object to be identified to obtain n feature combinations, wherein each feature combination comprises k feature data, n is an integer greater than m, and k is an integer greater than or equal to 1;

Calculating scores of the n feature combinations based on feature scores of feature data included in the n feature combinations;

sorting according to the scores of the n feature combinations to obtain n sorted feature combinations;

and determining m feature combinations meeting a reference condition from the n feature combinations after sequencing.

3. The method of claim 1, wherein the screening target clusters that meet a preset condition from the candidate clusters includes:

determining a risk score corresponding to the candidate cluster based on the label of the candidate cluster;

and determining the candidate cluster as a target cluster in response to the risk score of the candidate cluster accords with a preset condition.

4. The method of claim 1, wherein the screening target clusters that meet a preset condition from the candidate clusters includes:

calculating the relative entropy of the candidate cluster, wherein the relative entropy comprises discrete relative entropy and continuous relative entropy, the discrete relative entropy is used for representing the external difference of the candidate cluster, and the continuous relative entropy is used for representing the internal aggregation of the candidate cluster;

and determining the candidate cluster as a target cluster in response to the discrete relative entropy satisfying a first reference relative entropy and the continuous relative entropy satisfying a second reference relative entropy.

5. The method of claim 3, wherein the determining the risk score corresponding to the candidate cluster based on the label of the candidate cluster comprises:

and inputting the label of the candidate cluster into a target risk calculation model, and calculating the risk score of the candidate cluster through the target risk calculation model to obtain the risk score of the candidate cluster.

6. The method of claim 5, wherein prior to entering the label of the candidate cluster into a target risk calculation model, the method further comprises:

acquiring a label of at least one history cluster;

and training the initial risk calculation model according to the label of the at least one history cluster to obtain a target risk calculation model.

7. The method according to any one of claims 1-6, wherein the environmental data comprises at least one of an IP address and geographic location data where the object to be identified is located; the registration data comprises personal information filled in by the object to be identified during registration; the device data comprises a device type used by the object to be identified, and the historical behavior data comprises historical browsing, purchasing and comment behaviors of the object to be identified.

8. A server comprising a processor and a memory, wherein the memory has stored therein at least one program code that is loaded and executed by the processor to implement the user data processing method of any of claims 1 to 7.

9. A computer readable storage medium having stored therein at least one program code, the at least one program code being loaded and executed by a processor to implement the user data processing method of any of claims 1 to 7.