User profiling methods, devices, electronic equipment, and storage media

Abstract

This disclosure provides a method, apparatus, electronic device, and storage medium for determining user profiles, relating to the field of computer technology. The method includes: obtaining a first blockchain address corresponding to a target user and first behavioral data, the first behavioral data including transaction information between the first blockchain address and various second blockchain addresses; determining various second blockchain addresses based on the first behavioral data; constructing a knowledge graph using the first blockchain address and the various second blockchain addresses, the knowledge graph representing the relationships between the first blockchain address and the various second blockchain addresses; filtering from the knowledge graph to obtain various second blockchain addresses that meet preset aggregation conditions; and determining a user profile based on the first blockchain address and the various second blockchain addresses that meet the preset aggregation conditions. This disclosure determines user profiles using multiple blockchain addresses, improving the richness of user profile content and thus enhancing the accuracy and reliability of the user profile.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a method, apparatus, electronic device, and storage medium for determining user profiles. Background Technology

[0002] With the development of computer technology, user profiling is being used more and more widely. For example, every user who needs to access a blockchain application needs to be identified and authenticated through a user profile.

[0003] In related technologies, to obtain an accurate user profile, the user's transaction information can be obtained through the user's corresponding blockchain address. The user profile is then determined based on this transaction information.

[0004] However, because blockchain accounts can be replicated, users may not expose all core operations to a single blockchain address, but rather distribute them across multiple blockchain addresses. Therefore, the methods provided by related technologies may result in information obtained based on a single blockchain address being singular and incomplete, leading to lower accuracy and reliability of the resulting user profiles.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This disclosure provides a method, apparatus, electronic device, and storage medium for determining user profiles, which at least to some extent overcomes the problem in related technologies that the information obtained based on a single blockchain address is singular and one-sided, resulting in low accuracy and reliability of the obtained user profiles.

[0007] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0008] According to one aspect of the present disclosure, a user profile determination method is provided, comprising: obtaining a first blockchain address corresponding to a target user and first behavioral data corresponding to the first blockchain address, the first behavioral data including transaction information between the first blockchain address and various second blockchain addresses; determining various second blockchain addresses based on the first behavioral data; constructing a knowledge graph through the first blockchain address and various second blockchain addresses, the knowledge graph being used to represent the association relationship between the first blockchain address and various second blockchain addresses; filtering out various second blockchain addresses that meet preset aggregation conditions from the knowledge graph; and determining a user profile of the target user based on the first blockchain address and various second blockchain addresses that meet the preset aggregation conditions.

[0009] In some embodiments of this disclosure, the process of filtering out second blockchain addresses that meet preset aggregation conditions from the knowledge graph includes: calculating the aggregation degree of each second blockchain address, wherein the aggregation degree of a target second blockchain address is used to indicate the degree of similarity of behavioral data between the target second blockchain address and the first blockchain address, wherein the target second blockchain address is any second blockchain address; and filtering out second blockchain addresses with aggregation degrees not lower than a first threshold from the knowledge graph.

[0010] In some embodiments of this disclosure, determining the user profile of the target user based on the first blockchain address and various second blockchain addresses that meet the preset aggregation conditions includes: obtaining a target knowledge graph based on the first blockchain address and various second blockchain addresses that meet the preset aggregation conditions; and analyzing the target knowledge graph to obtain the user profile of the target user.

[0011] In some embodiments of this disclosure, calculating the aggregation degree of each second blockchain address includes: obtaining the total transaction amount, average transaction amount, transaction cycle, and transaction frequency between each second blockchain address and the first blockchain address; and calculating the aggregation degree of each second blockchain address based on the total transaction amount, average transaction amount, transaction cycle, and transaction frequency corresponding to each second blockchain address.

[0012] In some embodiments of this disclosure, the aggregation degree of the i-th second blockchain address is calculated using the following formula:

[0013]

[0014] Among them, X i A represents the aggregation degree of the i-th second blockchain address, where i is an integer greater than or equal to 1. i B represents the total transaction amount corresponding to the i-th second blockchain address. i Let C represent the average transaction amount corresponding to the i-th second blockchain address. i D represents the transaction period corresponding to the i-th second blockchain address. i w1 represents the transaction frequency corresponding to the i-th second blockchain address, and w1 represents A. i The corresponding weight information, w2 represents B i The corresponding weight information, w3 represents C i The corresponding weight information, w4 represents D i The corresponding weight information, where M and N both represent governance parameters.

[0015] In some embodiments of this disclosure, after constructing a knowledge graph using the first blockchain address and various second blockchain addresses, the user profile determination method provided in this disclosure further includes: when the knowledge graph does not meet preset target conditions, acquiring second behavioral data corresponding to each second blockchain address, wherein each second behavioral data includes transaction information between any second blockchain address and at least one third blockchain address; iteratively updating the knowledge graph based on each second behavioral data to obtain an updated knowledge graph, wherein the knowledge graph is used to represent the association between the first blockchain address, at least one second blockchain address, and at least one third blockchain address; and filtering out each third blockchain address that meets the preset aggregation condition from the updated knowledge graph.

[0016] Specifically, determining the user profile of the target user based on the first blockchain address and each second blockchain address that meets the preset aggregation conditions includes: determining the user profile of the target user based on the first blockchain address, each second blockchain address that meets the preset aggregation conditions, and each third blockchain address that meets the preset aggregation conditions.

[0017] In some embodiments of this disclosure, the process of filtering out third blockchain addresses that meet the preset aggregation conditions from the updated knowledge graph includes: calculating the aggregation degree of each third blockchain address, wherein the aggregation degree of a target third blockchain address is used to indicate the degree of similarity of behavioral data between the target third blockchain address and a second blockchain address that is associated with the target third blockchain address, wherein the target third blockchain address is any third blockchain address; and filtering out third blockchain addresses with aggregation degrees not lower than a first threshold from the knowledge graph.

[0018] According to another aspect of this disclosure, a user profile determination device is provided, comprising: a first blockchain address acquisition module, configured to acquire a first blockchain address corresponding to a target user, and first behavioral data corresponding to the first blockchain address, the first behavioral data including transaction information between the first blockchain address and various second blockchain addresses; a second blockchain address determination module, configured to determine various second blockchain addresses based on the first behavioral data, and construct a knowledge graph through the first blockchain address and various second blockchain addresses, the knowledge graph representing the association relationship between the first blockchain address and various second blockchain addresses; a second blockchain address filtering module, configured to filter out various second blockchain addresses that meet preset aggregation conditions from the knowledge graph; and a user profile determination module, configured to determine the user profile of the target user based on the first blockchain address and various second blockchain addresses that meet the preset aggregation conditions.

[0019] In some embodiments of this disclosure, a second blockchain address filtering module is used to calculate the aggregation degree of each second blockchain address. The aggregation degree of a target second blockchain address is used to indicate the degree of similarity of behavioral data between the target second blockchain address and the first blockchain address, wherein the target second blockchain address is any second blockchain address; and second blockchain addresses with aggregation degrees not lower than a first threshold are filtered from the knowledge graph.

[0020] In some embodiments of this disclosure, the user profile determination module is used to obtain a target knowledge graph based on the first blockchain address and various second blockchain addresses that meet the preset aggregation conditions; and to analyze the target knowledge graph to obtain a user profile of the target user.

[0021] In some embodiments of this disclosure, the second blockchain address filtering module is used to obtain the total transaction amount, average transaction amount, transaction cycle, and transaction frequency between each second blockchain address and the first blockchain address; and to calculate the aggregation degree of each second blockchain address based on the total transaction amount, average transaction amount, transaction cycle, and transaction frequency corresponding to each second blockchain address.

[0022] In some embodiments of this disclosure, the second blockchain address filtering module is used to calculate the aggregation degree of the i-th second blockchain address using the following formula:

[0023]

[0024] Among them, X i A represents the aggregation degree of the i-th second blockchain address, where i is an integer greater than or equal to 1. i B represents the total transaction amount corresponding to the i-th second blockchain address. i Let C represent the average transaction amount corresponding to the i-th second blockchain address. i D represents the transaction period corresponding to the i-th second blockchain address. i w1 represents the transaction frequency corresponding to the i-th second blockchain address, and w1 represents A. i The corresponding weight information, w2 represents B i The corresponding weight information, w3 represents C i The corresponding weight information, w4 represents D i The corresponding weight information, where M and N both represent governance parameters.

[0025] In some embodiments of this disclosure, the user profile determination device provided in this disclosure further includes: a second behavior data acquisition module, used to acquire second behavior data corresponding to each second blockchain address when the knowledge graph does not meet preset target conditions, wherein each second behavior data includes transaction information between any second blockchain address and at least one third blockchain address; a knowledge graph updating module, used to iteratively update the knowledge graph according to each second behavior data to obtain an updated knowledge graph, wherein the knowledge graph is used to represent the association relationship between the first blockchain address, at least one second blockchain address, and at least one third blockchain address; and to filter out each third blockchain address that meets the preset aggregation condition from the updated knowledge graph; wherein the user profile determination module is used to determine the user profile of the target user according to the first blockchain address, each second blockchain address that meets the preset aggregation condition, and each third blockchain address that meets the preset aggregation condition.

[0026] In some embodiments of this disclosure, the knowledge graph update module is used to calculate the aggregation degree of each third blockchain address. The aggregation degree of the target third blockchain address is used to indicate the degree of similarity of behavioral data between the target third blockchain address and a second blockchain address that is associated with the target third blockchain address. The target third blockchain address is any third blockchain address. Third blockchain addresses with aggregation degrees not lower than a first threshold are selected from the knowledge graph.

[0027] According to another aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the above-described user profile determination method by executing the executable instructions.

[0028] According to another aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the above-described user profile determination method.

[0029] According to another aspect of this disclosure, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the user profile determination method provided in various alternative embodiments of this disclosure.

[0030] The technical solution provided in this disclosure can obtain a second blockchain address based on a first blockchain address, and can filter the blockchain addresses through preset aggregation conditions to obtain other blockchain addresses belonging to the same target user as the first blockchain address. Therefore, this disclosure can determine a user profile through multiple blockchain addresses, improving the richness of the user profile content and thus improving the accuracy and reliability of the user profile.

[0031] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0033] Figure 1 A schematic diagram of a system architecture according to an embodiment of this disclosure is shown;

[0034] Figure 2 This diagram illustrates a process for determining a user profile according to an embodiment of the present disclosure.

[0035] Figure 3 This diagram illustrates a user profile determination method according to an embodiment of the present disclosure.

[0036] Figure 4 A schematic diagram of a knowledge graph according to an embodiment of this disclosure is shown;

[0037] Figure 5 A schematic diagram of an updated knowledge graph according to an embodiment of this disclosure is shown;

[0038] Figure 6 This diagram illustrates a process for determining a user profile according to an embodiment of the present disclosure.

[0039] Figure 7 This invention discloses a flowchart of another user profile determination method in an embodiment of the present invention.

[0040] Figure 8 This diagram illustrates a user profile determination device according to an embodiment of the present disclosure;

[0041] Figure 9 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0042] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0043] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0044] To facilitate understanding, the following is an explanation of several terms used in this disclosure:

[0045] User personas, also known as user characters, are an effective tool for identifying target users and connecting user needs with design direction, and are widely used across various fields. In practice, user personas often use the simplest and most relatable language to connect user attributes, behaviors, and expectations with data. As virtual representatives of actual users, user personas are not constructed outside of the product and market; they need to be representative, representing the product's main audience and target group.

[0046] Figure 1 A schematic diagram of an exemplary system architecture that can be applied to the user profile determination method or user profile determination apparatus in the embodiments of this disclosure is shown.

[0047] like Figure 1 As shown, system architecture 100 may include terminal device 101 and server 102.

[0048] Specifically, terminal device 101 can obtain a first blockchain address corresponding to the target user, and first behavioral data corresponding to the first blockchain address. The first behavioral data includes transaction information between the first blockchain address and various second blockchain addresses. Then, terminal device 101 can determine each second blockchain address based on the first behavioral data, and construct a knowledge graph through the first blockchain address and each second blockchain address. This knowledge graph is used to represent the association relationship between the first blockchain address and each second blockchain address.

[0049] Next, terminal device 101 can filter from the knowledge graph to obtain various second blockchain addresses that meet the preset aggregation conditions. Terminal device 101 can then send the first blockchain address and each of the second blockchain addresses that meet the preset aggregation conditions to server 102. Finally, server 102 can determine the user profile of the target user based on the first blockchain address and each of the second blockchain addresses that meet the preset aggregation conditions.

[0050] Alternatively, terminal device 101 can obtain the first blockchain address corresponding to the target user, and the first behavioral data corresponding to that first blockchain address. Based on the first behavioral data, it determines various second blockchain addresses and constructs a knowledge graph using the first blockchain address and the various second blockchain addresses. Then, terminal device 101 can filter from the knowledge graph to obtain various second blockchain addresses that meet preset aggregation conditions. Finally, terminal device 101 can determine the user profile of the target user based on the first blockchain address and the various second blockchain addresses that meet the preset aggregation conditions.

[0051] Alternatively, server 102 can obtain the first blockchain address corresponding to the target user, and the first action data corresponding to that first blockchain address. Based on the first action data, it determines various second blockchain addresses and constructs a knowledge graph using these first and second blockchain addresses. Then, server 102 can filter from the knowledge graph to obtain various second blockchain addresses that meet preset aggregation conditions. Finally, server 102 can determine the user profile of the target user based on the first blockchain address and the various second blockchain addresses that meet the preset aggregation conditions.

[0052] For example, the network used to provide a communication link between terminal device 101 and server 102 can be a wired network or a wireless network.

[0053] Optionally, the aforementioned wireless or wired networks use standard communication technologies and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to Local Area Networks (LANs), Metropolitan Area Networks (MANs), Wide Area Networks (WANs), mobile, wired or wireless networks, private networks, or any combination of virtual private networks. In some embodiments, technologies and / or formats including Hyper Text Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network. Furthermore, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Networks (VPNs), and Internet Protocol Security (IPsec) can be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0054] Terminal device 101 can be various electronic devices, including but not limited to smartphones, tablets, laptops, desktop computers, wearable devices, augmented reality devices, virtual reality devices, etc.

[0055] Optionally, the client of the application installed on different terminal devices 101 may be the same, or the client of the same type of application based on different operating systems. Depending on the terminal platform, the specific form of the application client may also be different; for example, the application client may be a mobile client, a PC client, etc.

[0056] Server 102 can be a server that provides various services. For example, server 102 can be a backend management server that supports the device operated by the user using terminal device 101. The backend management server can analyze and process received requests and other data, and feed the processing results back to the terminal device.

[0057] Optionally, server 102 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0058] Those skilled in the art will know that Figure 1 The number of terminal devices 101 and servers 102 shown is merely illustrative; any number of terminal devices 101 and servers 102 can be used as needed. This disclosure does not limit the number of such devices.

[0059] For example, a schematic diagram of a process for determining a user profile can be as follows: Figure 2 As shown.

[0060] exist Figure 2 The process begins by obtaining the first blockchain address corresponding to the target user. Next, user behavior data from this first blockchain address is collected, yielding initial behavioral data. Finally, this initial behavioral data is analyzed to create a user profile for the target user.

[0061] because, Figure 2 The user profiles obtained are based solely on a single blockchain address. The information is therefore limited and incomplete. Therefore, through... Figure 2 The user profiles obtained by this method have low accuracy and reliability.

[0062] The following detailed description of this exemplary implementation method is provided in conjunction with the accompanying drawings and embodiments.

[0063] First, this disclosure provides a user profile determination method, which can be executed by any electronic device with computing power.

[0064] Figure 3 This diagram illustrates a user profile determination method according to an embodiment of the present disclosure, such as... Figure 3 As shown, the user profile determination method provided in this embodiment includes the following steps S302 to S308.

[0065] S302, obtain the first blockchain address corresponding to the target user, and the first line data corresponding to the first blockchain address, the first line data including transaction information between the first blockchain address and each second blockchain address.

[0066] This disclosure does not limit the application scenarios. For example, in an online shopping scenario, a user of an e-commerce platform can store their purchase records on nodes in a blockchain. Alternatively, in an instant messaging scenario, a user of an instant messaging application can store their communication records on nodes in a blockchain.

[0067] This disclosure does not limit the type of the first blockchain address. For example, the first blockchain address can be an ETH (Ether) address, or it can be any other type of blockchain address.

[0068] In some embodiments, when it is necessary to determine the user profile of a target user, the first blockchain address corresponding to the target user can be obtained first. For example, the first behavioral data corresponding to the first blockchain address can be stored in a data warehouse. Therefore, the first behavioral data can be obtained from the data warehouse through the first blockchain address.

[0069] For example, the transaction information included in the first action data could be, for instance, a record of a transfer from a first blockchain address to a second blockchain address, or a record of the purchase of virtual resources.

[0070] S304, determine each second blockchain address based on the first line of data, and construct a knowledge graph through the first blockchain address and each second blockchain address. The knowledge graph is used to represent the association between the first blockchain address and each second blockchain address.

[0071] In an exemplary embodiment, the first row of data corresponding to the first blockchain address includes transaction information between the first blockchain address and each of the second blockchain addresses. Therefore, each of the second blockchain addresses that have transacted with the first blockchain address can be obtained through the first row of data.

[0072] For example, each second blockchain address can be represented by a set {Ca}.

[0073] In one possible implementation, a schematic diagram of the knowledge graph constructed through the first blockchain address and various second blockchain addresses can be shown as follows: Figure 4 As shown.

[0074] exist Figure 4 In this context, blockchain address A is the first blockchain address. The first line of data corresponding to blockchain address A includes transferring funds with blockchain address B, transferring funds with blockchain address C, transferring funds with blockchain address D, purchasing virtual resource A, investing in virtual resource B, and purchasing virtual resource C.

[0075] Among them, blockchain addresses B, C, and D are all second blockchain addresses. Therefore, Figure 4 The knowledge graph shown can represent the association between blockchain address A and various second blockchain addresses.

[0076] Alternatively, the knowledge graph can also be used to represent investment, purchase, and other transaction activities corresponding to the first blockchain address. Therefore, Figure 4 The knowledge graph shown also represents the transaction behavior of blockchain address A purchasing virtual resource A, investing in virtual resource B, and purchasing virtual resource C.

[0077] S306, Select the second blockchain addresses that meet the preset aggregation conditions from the knowledge graph.

[0078] In an exemplary embodiment, the preset aggregation condition is used to represent a threshold indicating the probability that two blockchain addresses belong to the same target user. For example, if the probability that any second blockchain address and the first blockchain address belong to the same target user is higher than the preset aggregation condition, then the second blockchain address can be considered to meet the preset aggregation condition.

[0079] In some embodiments, selecting second blockchain addresses that meet preset aggregation conditions from the knowledge graph may include: calculating the aggregation degree of each second blockchain address, wherein the aggregation degree of a target second blockchain address is used to indicate the degree of similarity of behavioral data between the target second blockchain address and the first blockchain address, wherein the target second blockchain address is any second blockchain address; and selecting second blockchain addresses with aggregation degrees not lower than a first threshold from the knowledge graph.

[0080] The embodiments disclosed herein do not limit the size of the first threshold, which can be determined based on the application scenario or experience.

[0081] In some embodiments, calculating the aggregation degree of each second blockchain address includes: obtaining the total transaction amount, average transaction amount, transaction cycle, and transaction frequency between each second blockchain address and the first blockchain address; and calculating the aggregation degree of each second blockchain address based on the total transaction amount, average transaction amount, transaction cycle, and transaction frequency corresponding to each second blockchain address.

[0082] For example, embodiments of this disclosure can determine the degree of similarity in behavioral data between different blockchain addresses using four data points: total transaction amount, average transaction amount, transaction cycle, and transaction frequency. Furthermore, embodiments of this disclosure can assign a weight to each of these four data points.

[0083] For example, the definitions of the four data points and the corresponding weight information for each of the four data points can be shown in Table 1.

[0084] Table 1

[0085]

[0086] Wherein, the sum of w1, w2, w3, and w4 must be equal to 100%. In addition, this embodiment of the disclosure does not limit the values ​​of w1, w2, w3, and w4 respectively. The values ​​of w1, w2, w3, and w4 can be determined according to the application scenario or experience.

[0087] In some embodiments, the aggregation degree of the i-th second blockchain address can be calculated using the following formula (1):

[0088]

[0089] Among them, X i A can represent the aggregation degree of the i-th second blockchain address, where i is an integer greater than or equal to 1. i B represents the total transaction amount corresponding to the i-th second blockchain address. i Let C represent the average transaction amount corresponding to the i-th second blockchain address. i D represents the transaction period corresponding to the i-th second blockchain address. i w1 represents the transaction frequency corresponding to the i-th second blockchain address, and w1 represents A. i The corresponding weight information, w2 represents B i The corresponding weight information, w3 represents C i The corresponding weight information, w4 represents D i The corresponding weight information, where M and N both represent governance parameters.

[0090] The embodiments disclosed herein do not limit the values ​​of M and N; the values ​​of M and N can be determined based on the application scenario or experience.

[0091] In an exemplary embodiment, after obtaining the aggregation degree of each second blockchain address, the second blockchain addresses can be filtered using a first threshold to obtain second blockchain addresses with an aggregation degree not lower than the first threshold. This disclosure does not limit the number of second blockchain addresses with an aggregation degree not lower than the first threshold; for example, there can be three second blockchain addresses with an aggregation degree not lower than the first threshold. Alternatively, there can be zero second blockchain addresses with an aggregation degree not lower than the first threshold.

[0092] S308. Based on the first blockchain address and each of the second blockchain addresses that meet the preset aggregation conditions, determine the user profile of the target user.

[0093] In some embodiments, determining the user profile of the target user based on the first blockchain address and various second blockchain addresses that meet the preset aggregation conditions includes: obtaining a target knowledge graph based on the first blockchain address and various second blockchain addresses that meet the preset aggregation conditions; and analyzing the target knowledge graph to obtain the user profile of the target user.

[0094] For example, the first blockchain address and all second blockchain addresses that meet the preset aggregation conditions can be grouped into an entity class. Then, the target knowledge graph can be obtained through this entity class. It should be noted that the target knowledge graph only includes the first blockchain address and all second blockchain addresses that meet the preset aggregation conditions. Subsequently, the target knowledge graph can be analyzed to obtain a user profile of the target user.

[0095] This disclosure does not limit the method of analyzing the target knowledge graph to obtain the user profile of the target user. It should be noted that the method of calculating the aggregation degree through formula (1) and finally obtaining the entity class through the aggregation degree can be called the weighted aggregation algorithm.

[0096] This embodiment calculates the aggregation degree using four data points: total transaction amount, average transaction amount, transaction cycle, and transaction frequency, and allows for flexible setting of weight information for each of these four data points. Therefore, this embodiment improves the practicality and flexibility of aggregation degree calculation, thereby enhancing the accuracy and reliability of user profiling.

[0097] In some embodiments, after constructing a knowledge graph using the first blockchain address and various second blockchain addresses in step S304, the user profile determination method provided in this disclosure may further include:

[0098] When the knowledge graph does not meet the preset target conditions, the second line data corresponding to each second blockchain address is obtained respectively. Each second line data includes transaction information between any second blockchain address and at least one third blockchain address. The knowledge graph is iteratively updated according to each second line data to obtain an updated knowledge graph. This knowledge graph is used to represent the relationship between the first blockchain address, at least one second blockchain address, and at least one third blockchain address. Each third blockchain address that meets the preset aggregation conditions is selected from the updated knowledge graph.

[0099] Furthermore, in this case, determining the user profile of the target user based on the first blockchain address and each of the second blockchain addresses that meet the preset aggregation conditions may include: determining the user profile of the target user based on the first blockchain address, each of the second blockchain addresses that meet the preset aggregation conditions, and each of the third blockchain addresses that meet the preset aggregation conditions.

[0100] For example, since blockchain addresses belonging to the same target user may have direct or indirect relationships, after obtaining the various second blockchain addresses through the first row of data, the third blockchain address can be obtained through the second row of data corresponding to the second blockchain address. Similarly, the fourth blockchain address can be obtained through the third row of data corresponding to the third blockchain address, and so on, thereby continuously iterating and updating the knowledge graph.

[0101] For example, a data acquisition depth can be preset, which can be an integer greater than or equal to 1. For instance, a data acquisition depth of 3 indicates that the knowledge graph can be updated 3 times. In one possible implementation, the preset target condition can be whether the current number of iterations is not less than the data acquisition depth. If so, the knowledge graph update can be terminated; otherwise, the knowledge graph update can continue.

[0102] This disclosure allows for flexible control over knowledge graph updates by setting the data collection depth, thereby enabling control over the richness of user profile content. Therefore, this disclosure further improves the practicality and flexibility of the user profile determination method.

[0103] In some embodiments, selecting third blockchain addresses that meet the preset aggregation conditions from the updated knowledge graph may include: calculating the aggregation degree of each third blockchain address, where the aggregation degree of a target third blockchain address is used to indicate the degree of similarity of behavioral data between the target third blockchain address and a second blockchain address that is associated with the target third blockchain address, wherein the target third blockchain address is any third blockchain address; and selecting third blockchain addresses with aggregation degrees not lower than a first threshold from the knowledge graph.

[0104] It should be noted that the formula used to calculate the aggregation degree of the third blockchain address can be the same as the formula used to calculate the aggregation degree of the second blockchain address, and will not be elaborated here.

[0105] Furthermore, following this logic, if a fourth blockchain address is obtained by using the third behavioral data corresponding to the third blockchain address, the aggregation degree of each fourth blockchain address can be calculated subsequently. The aggregation degree of the target fourth blockchain address indicates the degree of similarity of behavioral data between the target fourth blockchain address and any third blockchain address associated with it. The target fourth blockchain address can be any fourth blockchain address.

[0106] It should be noted that this embodiment of the disclosure continuously acquires more blockchain addresses by iteratively updating the knowledge graph. This allows for the further mining of more blockchain addresses that may belong to the target user, thereby further enriching the user profile content and improving the accuracy and reliability of the user profile.

[0107] The method provided in this disclosure can obtain a second blockchain address based on a first blockchain address, and can filter the blockchain addresses through preset aggregation conditions to obtain other blockchain addresses belonging to the same target user as the first blockchain address. Therefore, this disclosure can determine a user profile through multiple blockchain addresses, improving the richness of the user profile content and thus improving the accuracy and reliability of the user profile.

[0108] In one possible implementation, to Figure 4 Taking the knowledge graph shown as an example, for Figure 4 The knowledge graph shown is iteratively updated, and the resulting diagram of the updated knowledge graph can be seen as follows. Figure 5 As shown.

[0109] exist Figure 5 In this process, the second-line data corresponding to each of the third blockchain addresses, namely blockchain address B, blockchain address C, and blockchain address D, can be obtained. For example, the second-line data corresponding to blockchain address B includes transferring funds with blockchain address G and investing in virtual resource B. The second-line data corresponding to blockchain address C includes transferring funds with blockchain addresses A, B, F, and G and purchasing virtual resource C. The second-line data corresponding to blockchain address D includes transferring funds with blockchain address E and purchasing virtual resource D. Therefore, the third blockchain addresses include blockchain addresses E, F, and G.

[0110] For example, since the second blockchain address associated with blockchain address E is blockchain address D, the aggregation degree corresponding to blockchain address E can be used to indicate the degree of similarity of behavioral data between blockchain address E and blockchain address D. Similarly, the aggregation degree corresponding to blockchain address F can be used to indicate the degree of similarity of behavioral data between blockchain address F and blockchain address C.

[0111] Furthermore, since the second blockchain addresses associated with blockchain address G are blockchain address B and blockchain address C, the similarity of behavioral data between blockchain address G and blockchain address C, and the similarity of behavioral data between blockchain address G and blockchain address B, can be calculated separately. In an exemplary embodiment, if either the similarity of behavioral data between blockchain address G and blockchain address C, or the similarity of behavioral data between blockchain address G and blockchain address B, is not lower than a first threshold, then blockchain address G can be considered a third blockchain address that satisfies the preset aggregation conditions.

[0112] Alternatively, blockchain address G can be considered a third blockchain address that meets the preset aggregation conditions only when the similarity of behavioral data between blockchain address G and blockchain address C, and the similarity of behavioral data between blockchain address G and blockchain address B, are both not lower than the first threshold.

[0113] In some embodiments, if the updated knowledge graph meets preset target conditions, then the blockchain addresses that meet the preset aggregation conditions can be filtered. Alternatively, in other embodiments, after each iteration update, the blockchain addresses in the current knowledge graph that meet the preset aggregation conditions can be filtered, and the blockchain addresses in the current knowledge graph that do not meet the preset aggregation conditions can be deleted. Thus, in the next iteration update, only the behavioral data corresponding to each blockchain address that meets the preset aggregation conditions can be obtained.

[0114] Therefore, the embodiments of this disclosure can further reduce the computational cost of updating the knowledge graph and reduce the number of times the aggregation degree is calculated.

[0115] For example, a possible schematic diagram of the process for determining a user profile can be as follows: Figure 6 As shown.

[0116] exist Figure 6 In this process, a first blockchain address can be obtained, and the first line of data corresponding to that first blockchain address can be collected. Then, a knowledge graph can be constructed. Furthermore, when the knowledge graph does not meet the preset aggregation conditions, each second blockchain address can be determined using the first line of data, and the second line of data corresponding to each second blockchain address can be collected.

[0117] Then, the knowledge graph can be iteratively updated using data from each second row. After the iterative update is complete, a weighted aggregation algorithm can be used to filter out second blockchain addresses, third blockchain addresses, etc., that meet the preset aggregation conditions from the updated knowledge graph.

[0118] Next, all blockchain addresses that meet the preset aggregation conditions can be aggregated together with the first blockchain address into a single entity class. Finally, this entity class can be analyzed to obtain a user profile of the target user.

[0119] For example, a flowchart of a user profile determination method can be as follows: Figure 7 As shown. Figure 7 As shown, the user profile determination method includes the following steps S702 to S712.

[0120] S702, obtain the first blockchain address and the first line of data.

[0121] S704, Data Processing, Building Knowledge Graphs.

[0122] For example, the first blockchain address and the first line of data can be processed to construct a knowledge graph.

[0123] S706, Update the knowledge graph.

[0124] For example, second-behavior data corresponding to each second blockchain address can be collected, and the knowledge graph can be updated using each second-behavior data.

[0125] S708: Set the data collection depth and iteratively update the knowledge graph.

[0126] S710 uses a weighted aggregation algorithm to obtain entity classes.

[0127] S712 analyzes the entity class to obtain the user profile of the target user.

[0128] It should be noted that the implementation methods of each step in steps S702 to S712 can be found in the relevant descriptions in steps S302 to S308 above, and will not be repeated here.

[0129] Furthermore, the algorithms for iteratively updating the knowledge graph and the weight aggregation algorithms can be continuously improved through deep learning to further enhance the accuracy and reliability of entity classes, thereby improving the accuracy and reliability of user profiles.

[0130] Based on the same inventive concept, this disclosure also provides a user profile determination device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the method embodiments described above, the implementation of this device embodiment can refer to the implementation of the method embodiments described above, and repeated details will not be repeated.

[0131] Figure 8 This diagram illustrates a user profile determination device according to an embodiment of the present disclosure, such as... Figure 8 As shown, the device includes:

[0132] The first blockchain address acquisition module 801 is used to acquire the first blockchain address corresponding to the target user, and the first behavior data corresponding to the first blockchain address, the first behavior data including transaction information between the first blockchain address and each second blockchain address;

[0133] The second blockchain address determination module 802 is used to determine each second blockchain address based on the first line of data, and to construct a knowledge graph through the first blockchain address and each second blockchain address. The knowledge graph is used to represent the association relationship between the first blockchain address and each second blockchain address.

[0134] The second blockchain address filtering module 803 is used to filter out each second blockchain address that meets the preset aggregation conditions from the knowledge graph.

[0135] The user profile determination module 804 is used to determine the user profile of the target user based on the first blockchain address and each of the second blockchain addresses that meet the preset aggregation conditions.

[0136] In some embodiments of this disclosure, the second blockchain address filtering module 803 is used to calculate the aggregation degree of each second blockchain address. The aggregation degree of the target second blockchain address is used to indicate the degree of similarity of behavioral data between the target second blockchain address and the first blockchain address, wherein the target second blockchain address is any second blockchain address; and second blockchain addresses with aggregation degrees not lower than a first threshold are filtered from the knowledge graph.

[0137] In some embodiments of this disclosure, the user profile determination module 804 is used to obtain a target knowledge graph based on the first blockchain address and various second blockchain addresses that meet the preset aggregation conditions; and to analyze the target knowledge graph to obtain a user profile of the target user.

[0138] In some embodiments of this disclosure, the second blockchain address filtering module 803 is used to obtain the total transaction amount, average transaction amount, transaction cycle, and transaction frequency between each second blockchain address and the first blockchain address; and to calculate the aggregation degree of each second blockchain address based on the total transaction amount, average transaction amount, transaction cycle, and transaction frequency corresponding to each second blockchain address.

[0139] In some embodiments of this disclosure, the second blockchain address filtering module 803 is used to calculate the aggregation degree of the i-th second blockchain address using the following formula:

[0140]

[0141] Among them, Xi A represents the aggregation degree of the i-th second blockchain address, where i is an integer greater than or equal to 1. i B represents the total transaction amount corresponding to the i-th second blockchain address. i Let C represent the average transaction amount corresponding to the i-th second blockchain address. i D represents the transaction period corresponding to the i-th second blockchain address. i w1 represents the transaction frequency corresponding to the i-th second blockchain address, and w1 represents A. i The corresponding weight information, w2 represents B i The corresponding weight information, w3 represents C i The corresponding weight information, w4 represents D i The corresponding weight information, where M and N both represent governance parameters.

[0142] In some embodiments of this disclosure, the user profile determination device provided in this disclosure further includes:

[0143] The second behavior data acquisition module is used to acquire the second behavior data corresponding to each second blockchain address when the knowledge graph does not meet the preset target conditions. Each second behavior data includes transaction information between any second blockchain address and at least one third blockchain address.

[0144] The knowledge graph update module is used to iteratively update the knowledge graph based on the data of each second row to obtain an updated knowledge graph. This knowledge graph is used to represent the relationship between the first blockchain address, at least one second blockchain address, and at least one third blockchain address. The module also filters out each third blockchain address that meets the preset aggregation condition from the updated knowledge graph.

[0145] The user profile determination module 804 is used to determine the user profile of the target user based on the first blockchain address, each second blockchain address that meets the preset aggregation condition, and each third blockchain address that meets the preset aggregation condition.

[0146] In some embodiments of this disclosure, the knowledge graph update module is used to calculate the aggregation degree of each third blockchain address. The aggregation degree of the target third blockchain address is used to indicate the degree of similarity of behavioral data between the target third blockchain address and a second blockchain address that is associated with the target third blockchain address. The target third blockchain address is any third blockchain address. Third blockchain addresses with aggregation degrees not lower than a first threshold are selected from the knowledge graph.

[0147] The apparatus provided in this disclosure can obtain a second blockchain address based on a first blockchain address, and can filter the blockchain addresses through preset aggregation conditions to obtain other blockchain addresses belonging to the same target user as the first blockchain address. Therefore, this disclosure can determine a user profile through multiple blockchain addresses, improving the richness of the user profile content and thus improving the accuracy and reliability of the user profile.

[0148] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0149] The following reference Figure 9 To describe an electronic device 900 according to such an embodiment of the present disclosure. Figure 9 The electronic device 900 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0150] like Figure 9 As shown, the electronic device 900 is manifested in the form of a general-purpose computing device. The components of the electronic device 900 may include, but are not limited to: at least one processing unit 910, at least one storage unit 920, and a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910).

[0151] The storage unit stores program code that can be executed by the processing unit 910, causing the processing unit 910 to perform the steps described in the "Detailed Description" section of this specification according to various exemplary embodiments of this disclosure.

[0152] Storage unit 920 may include readable media in the form of volatile storage units, such as random access memory (RAM) 9201 and / or cache memory 9202, and may further include read-only memory (ROM) 9203.

[0153] Storage unit 920 may also include a program / utility 9204 having a set (at least one) program module 9205, such program module 9205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0154] Bus 930 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0155] Electronic device 900 can also communicate with one or more external devices 940 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 900, and / or with any device that enables electronic device 900 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 950. Furthermore, electronic device 900 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 960. As shown, network adapter 960 communicates with other modules of electronic device 900 via bus 930. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0156] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0157] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. A program product capable of implementing the methods described above is stored thereon. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code, which, when run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this disclosure described in the "Detailed Description" section of this specification.

[0158] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0159] In this disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0160] Optionally, the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0161] In practical implementation, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0162] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0163] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0164] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0165] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope of this disclosure is indicated by the appended claims.

Claims

1. A method for determining user profiles, characterized in that, include: Obtain the first blockchain address corresponding to the target user, and the first behavioral data corresponding to the first blockchain address, wherein the first behavioral data includes transaction information between the first blockchain address and each second blockchain address; Based on the first behavioral data, each second blockchain address is determined, and a knowledge graph is constructed using the first blockchain address and each second blockchain address. The knowledge graph is used to represent the association between the first blockchain address and each second blockchain address. Second blockchain addresses that meet preset aggregation conditions are obtained by filtering from the knowledge graph. If the probability that any second blockchain address and the first blockchain address belong to the same target user is higher than a threshold, then the second blockchain address meets the preset aggregation conditions. The user profile of the target user is determined based on the first blockchain address and each of the second blockchain addresses that meet the preset aggregation conditions.

2. The user profile determination method according to claim 1, characterized in that, The step of filtering out each second blockchain address that meets the preset aggregation conditions from the knowledge graph includes: The aggregation degree of each second blockchain address is calculated. The aggregation degree of the target second blockchain address is used to indicate the degree of similarity of behavioral data between the target second blockchain address and the first blockchain address, wherein the target second blockchain address is any second blockchain address. Second blockchain addresses with a aggregation degree of not less than the first threshold are obtained from the knowledge graph.

3. The user profile determination method according to claim 1, characterized in that, The step of determining the user profile of the target user based on the first blockchain address and various second blockchain addresses that meet the preset aggregation conditions includes: Based on the first blockchain address and each of the second blockchain addresses that meet the preset aggregation conditions, a target knowledge graph is obtained; The target knowledge graph is analyzed to obtain a user profile of the target user.

4. The user profile determination method according to claim 2, characterized in that, The calculation of the aggregation degree of each second blockchain address includes: Obtain the total transaction amount, average transaction amount, transaction cycle, and transaction frequency between each second blockchain address and the first blockchain address; The aggregation degree of each second blockchain address is calculated based on the total transaction amount, average transaction amount, transaction cycle, and transaction frequency corresponding to each second blockchain address.

5. The user profile determination method according to claim 4, characterized in that, The aggregation degree of the i-th second blockchain address is calculated using the following formula: Among them, X i A represents the aggregation degree of the i-th second blockchain address, where i is an integer greater than or equal to 1. i B represents the total transaction amount corresponding to the i-th second blockchain address. i Let C represent the average transaction amount corresponding to the i-th second blockchain address. i D represents the transaction period corresponding to the i-th second blockchain address. i w1 represents the transaction frequency corresponding to the i-th second blockchain address, and w1 represents A. i The corresponding weight information, w2 represents B i The corresponding weight information, w3 represents C i The corresponding weight information, w4 represents D i The corresponding weight information, where M and N both represent governance parameters.

6. The user profile determination method according to any one of claims 1 to 5, characterized in that, After constructing the knowledge graph using the first blockchain address and various second blockchain addresses, the method further includes: When the knowledge graph does not meet the preset target conditions, the second action data corresponding to each second blockchain address is obtained respectively, and each second action data includes transaction information between any second blockchain address and at least one third blockchain address. The knowledge graph is iteratively updated based on the data of each second line to obtain an updated knowledge graph. The knowledge graph is used to represent the association between the first blockchain address, at least one second blockchain address, and at least one third blockchain address. The third blockchain addresses that satisfy the preset aggregation conditions are obtained from the updated knowledge graph; The step of determining the user profile of the target user based on the first blockchain address and each of the second blockchain addresses that meet the preset aggregation conditions includes: The user profile of the target user is determined based on the first blockchain address, each of the second blockchain addresses that meet the preset aggregation conditions, and each of the third blockchain addresses that meet the preset aggregation conditions.

7. The user profile determination method according to claim 6, characterized in that, The step of filtering out third blockchain addresses that satisfy the preset aggregation conditions from the updated knowledge graph includes: The aggregation degree of each third blockchain address is calculated. The aggregation degree of the target third blockchain address is used to indicate the degree of similarity of behavioral data between the target third blockchain address and the second blockchain address that is associated with the target third blockchain address, wherein the target third blockchain address is any third blockchain address. Third blockchain addresses with a aggregation degree of not less than the first threshold are selected from the knowledge graph.

8. A user profile determination device, characterized in that, include: The first blockchain address acquisition module is used to acquire the first blockchain address corresponding to the target user, and the first behavior data corresponding to the first blockchain address. The first behavior data includes transaction information between the first blockchain address and each second blockchain address. The second blockchain address determination module is used to determine each second blockchain address based on the first behavioral data, and to construct a knowledge graph through the first blockchain address and each second blockchain address. The knowledge graph is used to represent the association relationship between the first blockchain address and each second blockchain address. The second blockchain address filtering module is used to filter out each second blockchain address that meets the preset aggregation conditions from the knowledge graph. The second blockchain address meets the preset aggregation conditions when the probability that any second blockchain address belongs to the same target user as the first blockchain address is higher than a threshold. The user profile determination module is used to determine the user profile of the target user based on the first blockchain address and each of the second blockchain addresses that meet the preset aggregation conditions.

9. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the user profile determination method according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the user profile determination method according to any one of claims 1 to 7.

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