Product image storage method, device, electronic device and computer-readable medium

Abstract

The embodiments of the present disclosure disclose a product portrait storage method, device, electronic device and computer-readable medium. A specific implementation of the method includes: obtaining user value flow data from a user database; determining at least one user group information and user value attribute information set based on the user value flow data; generating a value value; in response to monitoring that the value value is greater than the target value, encrypting the value value and at least one user group information to generate an initial encryption result; sending the initial encryption result to a multi-layer audit terminal for auditing the value value; in response to receiving audit-qualified information, generating a product value usage portrait corresponding to the target user; and encrypting and storing the product value usage portrait. This implementation effectively protects user information security through multiple encryptions. In addition, it is also possible to accurately generate a usage portrait related to product usage corresponding to the target user.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and specifically to a product portrait storage method, device, electronic device, and computer-readable medium. Background Art

[0002] With the ongoing development of virtual value products, accurately identifying their usage profiles is crucial to effectively identify eligible users. This process typically involves having technical personnel manually assess each user's historical data to determine their usage profile.

[0003] However, when using the above method to generate product usage portraits, the following technical problems often arise:

[0004] The manual process of determining product usage profiles carries the risk of leaking users' personal privacy information, and the uncertainty factor of human judgment is large, resulting in inaccurate product usage profiles. In addition, product usage profiles often waste a large amount of storage resources.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the Invention

[0006] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure propose product portrait storage methods, devices, electronic devices and computer-readable media to solve one or more of the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide a product portrait storage method, including: obtaining user value flow data corresponding to a target user from a user database; determining at least one user group information and a user value attribute information set corresponding to the target user based on the user value flow data; generating a value value corresponding to the target user that can be provided by a target virtual value product based on the at least one user group information and the user value attribute information set; in response to monitoring that the value value is greater than the target value, encoding and encrypting the value value and the at least one user group information to generate an initial encryption result; sending the initial encryption result to a multi-layer audit terminal to audit the value value; in response to receiving audit qualification information indicating that the audit has passed, generating a product value usage portrait corresponding to the target user based on the value value, the at least one user group information and the user value attribute information set; and encrypting and storing the product value usage portrait on the target blockchain.

[0009] In a second aspect, some embodiments of the present disclosure provide a product portrait storage device, including: an acquisition unit, configured to acquire user value flow data corresponding to a target user from a user database; a determination unit, configured to determine at least one user group information and a user value attribute information set corresponding to the target user based on the above-mentioned user value flow data; a first generation unit, configured to generate a value value corresponding to the target user that can be provided by a target virtual value product based on the above-mentioned at least one user group information and the above-mentioned user value attribute information set; an encryption unit, configured to, in response to monitoring that the above-mentioned value value is greater than the target value, encode and encrypt the above-mentioned value value and the above-mentioned at least one user group information to generate an initial encryption result; a sending unit, configured to send the above-mentioned initial encryption result to a multi-layer audit terminal for auditing the above-mentioned value value; a second generation unit, configured to, in response to receiving audit qualification information indicating that the audit has passed, generate a product value usage portrait corresponding to the target user based on the above-mentioned value value, the above-mentioned at least one user group information and the above-mentioned user value attribute information set; and a storage unit, configured to encrypt and store the above-mentioned product value usage portrait on the target blockchain.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

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

[0012] The above-described embodiments of the present disclosure have the following beneficial effects: Through the product profile storage methods of some embodiments of the present disclosure, multiple encryption steps are used to effectively safeguard user information security. Furthermore, a usage profile corresponding to the target user and related to product usage can be accurately generated. Specifically, the generation process of the relevant usage profile is subject to confidentiality and inaccuracy because the manual determination of the product usage profile carries the risk of leaking user privacy information, and the uncertainty of human judgment is significant, resulting in inaccurate generated product usage profiles. Furthermore, product usage profiles often waste a significant amount of storage resources. Based on this, the product profile storage methods of some embodiments of the present disclosure first obtain user value flow data corresponding to the target user from a user database. This user value flow data is then used to determine user group information and user value attribute information. Then, based on the user value flow data, at least one user group information and a user value attribute information set corresponding to the target user can be accurately determined. Determining the at least one user group information allows the subsequent determination of a value value from the perspective of the user group. Determining the user value attribute information set allows the subsequent determination of a value value from the perspective of the user value attributes. Next, based on the at least one user group information and the user value attribute information set, a value value representing the target virtual value product's availability for the target user can be accurately generated in various aspects. Next, in response to monitoring that the value value is greater than the target value, the value value and the at least one user group information are encrypted to generate an initial encryption result. Encoding and encrypting the value value and the at least one user group information mitigates the risk of information leakage during subsequent transmission to the review terminal. Furthermore, the encoding and encryption process reduces the data transmission volume, improves transmission efficiency, saves transmission time, and avoids excessive transmission resource usage. Next, the initial encryption result is transmitted to a multi-layer review terminal for review of the value value. Transmitting the initial encryption result to the multi-layer review terminal ensures the accuracy of the content without leaking information. Furthermore, in response to receiving a pass / qualification message indicating that the review has passed, a product value usage profile corresponding to the target user can be accurately generated based on the value value, the at least one user group information, and the user value attribute information set. The product value usage profile can be a combination of images and text. By adding a picture, it is possible to effectively identify whether the person requesting to use the virtual value product is the real person, while also preventing information leakage during subsequent requests. Finally, the above product value usage portrait is encrypted and stored on the target blockchain to prevent the leakage of the product usage portrait, facilitate subsequent use, and avoid wasting storage resources.In summary, through multiple encryption processes, generating product value usage portraits from the perspective of user groups and user value attributes, and encrypting and storing them on the target area chain, not only can product value usage portraits be accurately generated, the waste of data transmission resources can be reduced, and transmission efficiency can be improved, but also user information leakage during the generation and use processes can be avoided. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0014] Figure 1 is a flowchart of some embodiments of the product portrait storage method according to the present disclosure;

[0015] Figure 2 is a schematic structural diagram of some embodiments of the product image storage device according to the present disclosure;

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

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0018] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

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

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

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

[0022] With regard to the collection, storage, and use of user personal information (such as user value flow data) involved in this disclosure, before performing the corresponding operations, the relevant organizations or individuals shall fulfill their obligations, including conducting personal information security impact assessments, fulfilling the obligation to inform the personal information subject, and obtaining the authorization and consent of the personal information subject in advance.

[0023] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0024] refer to Figure 1 , shows a process 100 of some embodiments of the product portrait storage method according to the present disclosure. The product portrait storage method includes the following steps:

[0025] Step 101: Obtain user value flow data corresponding to the target user from the user database.

[0026] In some embodiments, in response to receiving a session communication request message for a first user terminal and a second user terminal, the executing entity (e.g., an electronic device) of the above-described product profile storage method may obtain user value transfer data corresponding to a target user from a user database. The user database may be a database storing various user-related data. The various user-related data may be a value processing data set stored after the user performs various value processing operations. For example, a value processing operation may be one of the following: a value transfer operation, a value storage operation, or a value borrowing operation. In practice, in the credit reporting field, the corresponding value transfer operation may be a credit operation within a target virtual value product. The target virtual value product may be a pre-configured virtual product associated with a value operation. For example, in a credit reporting scenario, the target virtual value product may be a credit product. The target user may be the user for whom a product profile is to be determined. The product profile may represent various possible usage characteristics of the target user using the product profile. In practice, the product profile may include: at least one user group information related to the target user, a user value attribute information set, a value value, and user information. The user group information may be group information of the user group. For example, the group information may be a group identifier. The at least one user group may be a user group whose corresponding group characteristics are similar to those of the target user. That is, the target user belongs to at least one user group. For example, a user group can be characterized by the presence of high amounts of credit. The corresponding target user can also be a group with high amounts of credit. User value attribute information can be attribute information related to user value transfer operations. Attribute information can be attribute content. In practice, for user value transfer operations, the corresponding attribute can be one of the following: deposit amount, usage duration, loan amount, loan duration, repayment amount, and repayment duration. The value value can be the value information that the target virtual value product can provide to the target user. In practice, in the credit reporting field, the corresponding value value can be the loan amount, repayment amount, loan duration, or repayment duration. User information can be the target user's basic personal information. For example, user information can include, but is not limited to, at least one of the following: user name, user ID, user gender, user age, and user residence.

[0027] Step 102: Determine at least one user group information and user value attribute information set corresponding to the target user based on the user value flow data.

[0028] In some embodiments, the execution entity may determine at least one user group information and user value attribute information set corresponding to the target user based on the user value flow data.

[0029] As an example, the execution entity may first determine, based on the user value flow data, at least one user whose flow data is most similar to the target user, as at least one piece of similar user information. Next, the user group to which the at least one similar user belongs is determined, obtaining at least one piece of user group information. Next, a user group attribute set corresponding to the at least one piece of user group information is determined. Finally, a user value attribute information set corresponding to the user group attribute set is extracted from the user value flow data.

[0030] In some optional implementations of some embodiments, the execution entity may determine at least one user group information and user value attribute information set corresponding to the target user based on the user value flow data, including the following steps:

[0031] The first step is to obtain a user group characteristic information group corresponding to each user group identifier in the user group identifier set from a user group database to obtain a user group characteristic information group set. Each user group characteristic information in the user group characteristic information group has corresponding encrypted first user group value attribute information. The user group database may be a database storing group data related to user groups. The user group identifier may represent the identity information of the user group. For example, the user group identifier may be "0087." There is a one-to-one correspondence between the user group characteristic information in the user group characteristic information group and the user group value attributes in the user group value attribute group. The user group value attributes may represent the significant group characteristics of the user group. The user group value attributes may be attributes that highlight the value of the user group. For example, for user group A, the corresponding user group value attribute may be, but is not limited to, one of the following: high user consumption characteristics, high user loan amount characteristics, or high user income characteristics. The user group characteristic information group may include at least one user group characteristic information. The corresponding user group characteristic groups may be different for each user group. The user group characteristic information may be feature information in vector form, representing the various attributes of the corresponding user group. Here, by encrypting the first user group value attribute information, it is possible to effectively ensure that the user group value attribute information can be traced through the user group characteristic information, thereby avoiding the leakage of user group privacy.

[0032] The second step is to extract the user group value attribute information corresponding to each user group value attribute from the user value flow data. There is a one-to-one correspondence between the user group value attribute in each user group value attribute and the user group value attribute information in each user group value attribute information. The user group value attribute information may be the attribute content corresponding to the user group value attribute.

[0033] In the third step, the aforementioned user group value attribute information is sent to an attribute information encryption terminal to generate encrypted second user group value attribute information. The attribute information encryption terminal may be a terminal that encrypts attribute information. The encrypted second user group value attribute information in each second user group value attribute information corresponds one-to-one to the user group value attribute information in each user group value attribute information. In practice, the attribute information encryption terminal may be a terminal that deploys a pre-set encryption algorithm.

[0034] Here, by using an independent encryption terminal (i.e., attribute information encryption terminal) to encrypt the user group value attribute information, the information security of the user group value attribute information can be effectively guaranteed, avoiding the single-end attribute information generation and single-end attribute information encryption, which leads to information leakage caused by the single-end execution of various steps.

[0035] The fourth step is to vectorize the encrypted value attribute information of each second user group to generate a target user group characteristic information group.

[0036] As an example, the execution entity may input the encrypted value attribute information of each second user group into a vector conversion model to generate a target user group characteristic information group. The target user group characteristic information may be characteristic information in vector form.

[0037] Step 5: Determine the value attribute similarity between each user group characteristic information group in the user group characteristic information group set and the target user group characteristic information group, thereby obtaining a value attribute similarity information set. The value attribute similarity information may be a numerical value between 0 and 1. A greater value attribute similarity indicates a higher degree of similarity between the corresponding user group characteristic information and the target user group characteristic information. The value attribute similarity information may be a cosine similarity.

[0038] Step 6: Filter out user group characteristic information groups from the aforementioned user group characteristic information groups whose value attribute similarity information is higher than the target similarity information, thereby obtaining at least one user group characteristic information group. The target similarity information may be pre-set similarity information. For example, the target similarity information may be configured by relevant experts and technicians based on their experience. The target similarity information may be a value between 0 and 1.

[0039] In the seventh step, each user group identifier corresponding to the at least one user group characteristic information group is determined as at least one piece of user group information.

[0040] Step 8: Obtain a user value attribute set corresponding to the aforementioned user value-risk information. Each user value attribute in the user value attribute set may be a value attribute used to subsequently generate user value-risk information. User value-risk information may be the magnitude of risk associated with various value operations performed by the user. For example, if the user value operation is a loan, the corresponding user value-risk information may be loan risk assessment information. Each user value attribute in the corresponding user value attribute set may be a value attribute relevant to generating loan risk assessment information.

[0041] In the ninth step, a user value attribute information set corresponding to the user value attribute set is extracted from the user value flow data. There is a one-to-one correspondence between the user value attributes in the user value attribute set and the user value attribute information in the user value attribute information set. The user value attribute information may be the attribute content corresponding to the user value attribute.

[0042] Step 103 : generating a value value representing the value that the target virtual value product can provide corresponding to the target user based on the at least one user group information and the user value attribute information set.

[0043] In some embodiments, the execution entity may generate a value value representing the value that the target virtual value product can provide for the target user based on the at least one user group information and the user value attribute information set.

[0044] In some optional implementations of some embodiments, the execution entity may generate a value value representing the value that the target virtual value product can provide for the target user based on the at least one user group information and the user value attribute information set, including the following steps:

[0045] The first step is to determine at least one first value adjustment information corresponding to the at least one user group information. There is a one-to-one correspondence between the user group information in the at least one user group information and the first value adjustment information in the at least one first value adjustment information. The first value adjustment information can be adjustment information for numerically adjusting the initial value value corresponding to the user. The initial value value can be the basic value value that the target virtual value product can provide to each user. The initial value value can be the basic value size of the value information that the target virtual value product can provide to the target user. In practice, in the field of credit reporting, the corresponding initial value value can be the basic loan amount, the basic repayment amount, the basic loan period, or the basic repayment period.

[0046] The second step is to generate user value risk information corresponding to the target user based on the user value attribute information set. The user value risk information may represent the risk level associated with the user's value transfer operations. The user value risk information may be in numerical form. A higher numerical value indicates a greater risk associated with the user's value transfer operations.

[0047] As an example, the execution entity may directly input each user value attribute information in the user value attribute information set into a pre-trained user value risk information generation model to generate user value risk information. In practice, the user value risk information generation model may be a time series neural network model. For example, the user value risk information generation model may be a recurrent neural network model.

[0048] The third step is to generate second value adjustment information corresponding to the user value risk information, wherein the second value adjustment information may be adjustment information for adjusting the initial value corresponding to the user.

[0049] As an example, the execution entity may determine the second value adjustment information corresponding to the user value risk information by querying an association table.

[0050] The fourth step is to determine the initial value corresponding to the above target users.

[0051] As an example, the execution entity may determine the basic value corresponding to each user as the initial value, that is, each user has the same corresponding value.

[0052] In a fifth step, the initial value value is adjusted according to the at least one first value adjustment information and the second value adjustment information to generate the value value.

[0053] As an example, the execution entity may add at least one first value adjustment information, the second value adjustment information, and the initial value value to generate a value value.

[0054] In some optional implementations of some embodiments, the first value adjustment information in the at least one first value adjustment information is generated by the following steps:

[0055] The first step is to obtain the user group information corresponding to the first value adjustment information as the target user group information.

[0056] The second step is to determine the user group characteristic information group corresponding to the target user group information as the target user group characteristic information group.

[0057] As an example, the execution entity may determine the user group characteristic information group corresponding to the target user group information by querying characteristic information, as the target user group characteristic information group.

[0058] The third step is to determine the attribute importance information corresponding to each user group attribute in the user group attribute set. There is a one-to-one correspondence between the user group attributes in the user group attribute set and the target user group characteristic information in the target user group characteristic information set. Specifically, the target user group characteristic information may be the attribute content corresponding to the user group attribute. The attribute importance information may represent the importance of the attribute content corresponding to the user group attribute. The attribute importance information may be a numerical value between 0 and 1. A higher numerical value indicates a higher importance of the corresponding attribute content.

[0059] In the fourth step, based on the attribute importance information corresponding to the user group attributes, each user group attribute in the user group attribute set is divided into attribute intervals to obtain a user group attribute group set. The user group attribute group set includes: a first user group attribute group, a second user group attribute group, and a third user group attribute group. The importance of each user group attribute corresponding to the first user group attribute group is higher than the importance of each user group attribute corresponding to the second user group attribute group. The importance of each user group attribute corresponding to the second user group attribute group is higher than the importance of each user group attribute corresponding to the third user group attribute group. Each attribute interval may be a pre-set attribute value interval.

[0060] Step 5: Determine the currently available computing resources corresponding to the value adjustment information generating terminal. The value adjustment information generating terminal may be a terminal that generates the value adjustment information. That is, the first value adjustment information corresponding to the user group information may be updated and generated by the value adjustment information generating terminal. The currently available computing resources may be computing resources currently available to the value adjustment information generating terminal.

[0061] As an example, the execution entity may determine the currently available computing resources corresponding to the value adjustment information generating terminal through a computing resource query instruction.

[0062] Step 6: Based on the currently available computing resources, determine the user group attribute extraction ratios corresponding to the first user group attribute group, the second user group attribute group, and the third user group attribute group. The currently available computing resources are associated with the user group attribute extraction ratios. The user group attribute extraction ratios may be the ratio of the number of attributes extracted from the first user group attribute group, the second user group attribute group, and the third user group attribute group. For example, the user group attribute extraction ratio may be {5:4:2}. In this case, five first user group attributes are extracted from the first user group attribute group, four second user group attributes are extracted from the second user group attribute group, and two third user group attributes are extracted from the third user group attribute group.

[0063] As an example, the execution entity may extract the user group attribute extraction ratio corresponding to the currently available computing resources from a resource-extraction ratio association table. The resource-extraction ratio association table may represent the correspondence between the available computing resources corresponding to the value adjustment information generating terminal and the user group attribute extraction ratio.

[0064] In the seventh step, group attributes are extracted from the first user group attribute group, the second user group attribute group, and the third user group attribute group according to the user group attribute extraction ratio to generate an extracted group attribute group.

[0065] In the eighth step, a user group characteristic information subgroup corresponding to the extracted group attribute group is extracted from the target user group characteristic information group, wherein the extracted group attributes in the extracted group attribute group and the user group characteristic information in the user group characteristic information subgroup have a one-to-one correspondence.

[0066] In a ninth step, the user group characteristic information subset is input into a pre-trained value adjustment information generation model to generate the first value adjustment information. The value adjustment information generation model may be a neural network model that generates value adjustment information. In practice, the value adjustment information generation model may be a multi-input, multi-layer convolutional network. In practice, the multi-layer convolutional network may be an 11-layer convolutional network. The value adjustment information generation model may be obtained through conventional model training methods.

[0067] Step 104 : In response to monitoring that the value value is greater than the target value, encrypt the value value and the at least one user group information to generate an initial encryption result.

[0068] In some embodiments, in response to monitoring that the value value is greater than a target value, the execution entity may encrypt the value value and the at least one user group information to generate an initial encryption result, wherein the target value may be a preset number.

[0069] As an example, the execution entity may first obtain an encryption algorithm sent by the target terminal, and then encrypt the value and the at least one user group information using the encryption algorithm to generate an initial encryption result.

[0070] Step 105: Send the initial encryption result to the multi-layer audit terminal to audit the value.

[0071] In some embodiments, the execution entity may send the initial encryption result to a multi-level review terminal for review of the value, wherein the multi-level review terminal may be a terminal that reviews the content corresponding to the initial encryption result at multiple levels.

[0072] Step 106 , in response to receiving the audit qualification information indicating that the audit has passed, generating a product value usage portrait corresponding to the target user based on the value value, the at least one user group information and the user value attribute information set.

[0073] In some embodiments, in response to receiving the audit qualification information indicating that the audit has passed, the above-mentioned execution entity can generate the product value usage portrait corresponding to the above-mentioned target user based on the above-mentioned value value, the above-mentioned at least one user group information and the above-mentioned user value attribute information set.

[0074] Step 107: Encrypt and store the above-mentioned product value using the portrait.

[0075] In some embodiments, the execution entity may encrypt and store the product value usage portrait.

[0076] In some optional implementations of some embodiments, after step 107, the steps further include:

[0077] The first step is to, in response to receiving information indicating that the target user requests value provision from the target virtual value product, obtain the encrypted portrait corresponding to the target user based on the user information corresponding to the target user and decrypt it to generate the product value usage portrait.

[0078] The second step is to use the user image included in the product value usage image to determine the user authenticity information corresponding to the target user. The user image can be a pre-stored real image of the target user. For example, the user image can be an ID card image.

[0079] In the third step, in response to determining that the user authenticity information indicates that the target user is a real individual user, a value value is extracted from the product value usage portrait.

[0080] The fourth step is to generate the value value reasons corresponding to the target users based on the above product value usage portrait. The value value reasons can be the reasons for generating the value values.

[0081] As an example, the above-mentioned execution entity can use the Bert model to generate the value numerical reasons corresponding to the target users based on the above-mentioned product value usage portrait.

[0082] Step 5: Determine the value processing operation information corresponding to the value value. Different value values ​​correspond to different value processing operations. The value processing operation information can be an operation identifier corresponding to the value processing operation.

[0083] In the sixth step, the value processing operation information and the value numerical reason are sent to the request terminal corresponding to the target user, so that the request terminal can perform the corresponding value processing operation and display the corresponding value processing value numerical reason.

[0084] In some optional implementations of some embodiments, the execution entity may generate user value risk information corresponding to the target user based on the user value attribute information set, including the following steps:

[0085] In the first step, each piece of user value attribute information in the user value attribute information set is encrypted to generate a user value attribute encrypted information set. The encrypted user value attribute information in the user value attribute encrypted information set corresponds one-to-one with the user value attribute information in the user value attribute encrypted information set. The encryption method may be based on a predetermined encryption algorithm.

[0086] The second step is to send the encrypted user value attribute information set to a user value risk information generation terminal, which uses the graphics processor set to generate user value risk information. The user value risk information generation terminal may be a terminal that generates user value risk information. User value risk information may represent the risk associated with a user's value operation. In practice, user value risk information may be numerical. A larger numerical value indicates a greater risk associated with the user's value operation.

[0087] And using the above-mentioned graphics processor group set, the above-mentioned user value risk information is generated through the following steps:

[0088] Sub-step 1: Divide each user value attribute information in the above-mentioned user value attribute information set to generate a first user value attribute information subset for generating user credit information, a second user value attribute information subset for generating user value risk information, a third user value attribute information subset for generating product usage credit information, and a fourth user value attribute information subset for generating product usage risk information. The first user value attribute information subset is the attribute information set used to subsequently generate a user credit value. The user credit value can represent the user's credit level. Product usage credit information can be the user's credit level for using the target product. Product usage risk information can be the risk level for using the target product.

[0089] Sub-step 2: Input the first user value attribute information subset into a pre-trained user credit generation model deployed by the first image processor group to generate first user credit information. In practice, the user credit generation model can be a neural network model for generating user credit. For example, the user credit generation model can be a time series neural network model.

[0090] Sub-step 3, input the above-mentioned first user value attribute information subset and the above-mentioned second user value attribute information subset into the first dual-tower model deployed by the pre-trained second image processor group to generate the second user credit information and the first user value risk information. The first dual-tower model can be a dual-tower model used to output user credit and user value risk. For example, the first dual-tower model can be a dual-tower model obtained by splicing multiple time series neural network models. For example, the first dual-tower model includes: a first time series neural network model, a second time series neural network model, a fusion model, a first output layer and a second output layer. The first time series neural network model is a model for extracting user credit features. The second time series neural network model is a model for extracting user value risk features. The fusion model is a model that fuses the corresponding outputs of the first time series neural network model and the second time series neural network model. The first output layer and the second output layer can be fully connected layers that output user credit and user value risk features, respectively.

[0091] Sub-step 4: Input the second user value attribute information subset into a pre-trained user value risk information generation model deployed by the third image processor group to generate second user value risk information. The user value risk information generation model can be a neural network model that generates user value risk information. In practice, the user value risk information generation model can be a regression model. For example, the user value risk information generation model can be a regression model based on a Transformer model.

[0092] Sub-step 5: Input the third user value attribute information subset into a pre-trained product usage credit information generation model deployed by the fourth image processor group to generate first product usage credit information. The product usage credit information generation model can be a neural network model that generates product usage credit information. In practice, the product usage credit information generation model can be a regression model based on the BERT model.

[0093] Sub-step 6: Input the third user value attribute information subset and the fourth user value attribute information subset into a second dual-tower model deployed by the pre-trained fifth image processor group to generate second product usage credit information and first product usage risk information. The second dual-tower model can refer to the model structure corresponding to the first dual-tower model.

[0094] Sub-step 7: Input the fourth user value attribute information subset into the pre-trained product usage risk information generation model deployed by the sixth image processor group to generate second product usage risk information. The product usage risk information generation model may be a neural network model that generates product usage risk information. In practice, the product usage risk information generation model may be a classification model. For example, the product usage risk information generation model may comprise multiple convolutional layers connected in series.

[0095] Sub-step 8: generating first initial user value-risk information based on the first user credit information, the second user credit information, the first user value-risk information, and the second user value-risk information.

[0096] As an example, the execution entity may perform a weighted summation of the first user's credit information, the second user's credit information, the first user's value-risk information, and the second user's value-risk information to generate the first initial user value-risk information. The weights corresponding to the first user's credit information and the second user's credit information are negative. A larger credit value indicates a smaller corresponding value-risk information.

[0097] Sub-step 9: generating second initial user value risk information based on the first product usage credit information, the second product usage credit information, the first product usage risk information, and the second product usage risk information.

[0098] As an example, the execution entity may perform a weighted summation of the first product usage credit information, the second product usage credit information, the first product usage risk information, and the second product usage risk information to generate the second initial user value-risk information. The weights corresponding to the first product usage credit information and the second product usage credit information are negative. A larger credit value indicates a smaller corresponding value-risk information.

[0099] Sub-step 10: generating the user value risk information according to the first initial user value risk information and the second initial user value risk information.

[0100] As an example, the execution entity may determine an average value between the first initial user value risk information and the second initial user value risk information as the user value risk information.

[0101] Optionally, the execution entity may input the first user value attribute information subset and the second user value attribute information subset into a first dual-tower model deployed by a pre-trained second image processor group to generate second user credit information and first user value risk information, including the following steps:

[0102] In the first step, the first user value attribute information subset is input into the first user value attribute extraction layer included in the first dual-tower model to generate first user value attribute feature information. The first user value attribute extraction layer may be a network layer that extracts user value attribute feature information. For example, the first user value attribute extraction layer may be an encoding layer in a Transformer model.

[0103] The second step is to input the second user value attribute information subset into the second user value attribute extraction layer included in the first dual-tower model to generate second user value attribute feature information. The network structure corresponding to the second user value attribute extraction layer can be the same as the network structure of the first user value attribute extraction layer.

[0104] In the third step, the first user value attribute feature information and the second user value attribute feature information are input into the first difference information extraction layer included in the first dual-tower model to generate first feature difference information. The first difference information extraction layer may be a network layer for extracting the difference in feature semantic content between the first user value attribute feature information and the second user value attribute feature information. For example, the first difference information extraction layer may be an attention layer based on a convolutional network. The first feature difference information may represent the difference in feature semantic content between the first user value attribute feature information and the second user value attribute feature information.

[0105] In the fourth step, the first user value attribute characteristic information and the second user value attribute characteristic information are input into the second difference information extraction layer included in the first dual-tower model to generate second characteristic difference information.

[0106] Step 5: Set first feature weight information between the first feature difference information and the first user value attribute feature information. The first feature weight information may represent the feature importance of the first feature difference information and the first user value attribute feature information.

[0107] Step 6: Set the second feature weight information between the second feature difference information and the second user value attribute feature information. The first feature weight information can represent the feature importance of the second feature difference information and the second user value attribute feature information.

[0108] In the seventh step, the first feature weight information, the first user value attribute feature information, and the first feature difference information are input into a user credit information generation layer to generate second user credit information. The user credit information generation layer may be a fully connected layer for generating user credit information.

[0109] In step 8, the second feature weight information, the second user value attribute feature information, and the second feature difference information are input into a user value risk generation layer to generate first user value risk information. The user value risk generation layer may be a fully connected layer for generating user value risk information.

[0110] Optionally, as one of the invention points, the problem of insufficiently comprehensive and accurate generation of user credit information and user value risk information is solved. Based on this, through the various network layers included in the first dual-tower model, more feature content can be considered to accurately generate the second user credit information and the first user value risk information.

[0111] The aforementioned embodiments of the present disclosure have the following beneficial effects: Through the product profile storage methods of some embodiments of the present disclosure, multiple encryption steps are used to effectively safeguard user information security. Furthermore, a usage profile corresponding to the target user and related to product usage can be accurately generated. Specifically, the generation process of the relevant usage profile is subject to confidentiality and inaccuracy because the manual determination of the product usage profile carries the risk of leaking user personal privacy information, and the uncertainty factor of human judgment is significant, resulting in inaccurate product usage profiles. Based on this, the product profile storage methods of some embodiments of the present disclosure first obtain user value flow data corresponding to the target user from a user database. This user value flow data is then used to subsequently determine user group information and user value attribute information. Then, based on this user value flow data, at least one user group information and a user value attribute information set corresponding to the target user can be accurately determined. By determining the at least one user group information, a value value can be subsequently determined from the perspective of the user group. By determining the user value attribute information set, a value value can be subsequently determined from the perspective of the user value attributes. Next, based on the at least one user group information and the user value attribute information set, a value value representing the target virtual value product's availability for the target user can be accurately generated in various aspects. Next, in response to monitoring that the value value is greater than the target value, the value value and the at least one user group information are encrypted to generate an initial encryption result. Encoding and encrypting the value value and the at least one user group information mitigates the risk of information leakage during subsequent transmission to the review terminal. Furthermore, the encoding and encryption process reduces the data transmission volume, improves transmission efficiency, saves transmission time, and avoids excessive transmission resource usage. Next, the initial encryption result is transmitted to a multi-layer review terminal for review of the value value. Transmitting the initial encryption result to the multi-layer review terminal ensures the accuracy of the content without leaking information. Furthermore, in response to receiving a pass / qualification message indicating that the review has passed, a product value usage profile corresponding to the target user can be accurately generated based on the value value, the at least one user group information, and the user value attribute information set. The product value usage profile can be a combination of images and text. By adding a picture, it is possible to effectively identify whether the person who subsequently requests to use the virtual value product is the real person, while also preventing information leakage during subsequent requests. Finally, the above product value usage portrait is encrypted and stored on the target blockchain to prevent the leakage of the product usage portrait and facilitate subsequent use.In summary, through multiple encryption processes, generating product value usage portraits from the perspective of user groups and user value attributes, and encrypting and storing them on the target area chain, not only can product value usage portraits be accurately generated, the waste of data transmission resources can be reduced, and transmission efficiency can be improved, but also user information leakage during the generation and use processes can be avoided.

[0112] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a product image storage device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the product portrait storage device can be specifically applied to various electronic devices.

[0113] like Figure 2 As shown, a product portrait storage device 200 includes: an acquisition unit 201, a determination unit 202, a first generation unit 203, an encryption unit 204, a sending unit 205, a second generation unit 206 and a storage unit 207. The acquisition unit 201 is configured to acquire user value flow data corresponding to a target user from a user database; the determination unit 202 is configured to determine at least one user group information and a user value attribute information set corresponding to the target user based on the user value flow data; the first generation unit 203 is configured to generate a value value representing the value that can be provided by the target virtual value product corresponding to the target user based on the at least one user group information and the user value attribute information set; the encryption unit 204 is configured to, in response to monitoring that the value value is greater than the target value, encode and encrypt the value value and the at least one user group information to generate an initial encryption result; the sending unit 205 is configured to send the initial encryption result to a multi-layer audit terminal for audit of the value value; the second generation unit 206 is configured to, in response to receiving audit-qualified information indicating that the audit has passed, generate a product value usage profile corresponding to the target user based on the value value, the at least one user group information, and the user value attribute information set; and the storage unit 207 is configured to encrypt and store the product value usage profile on the target blockchain.

[0114] It is understandable that the units recorded in the product image storage device 200 and the reference Figure 1 The steps in the described method correspond to each other. Therefore, the operations, features and beneficial effects described above for the method are also applicable to the product image storage device 200 and the units contained therein, and will not be repeated here.

[0115] Reference below Figure 3, which shows a structural schematic diagram of an electronic device (eg, an electronic device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0116] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. Various programs and data required for the operation of the electronic device 300 are also stored in the RAM 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0117] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0118] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are performed.

[0119] It should be noted that in some embodiments of the present disclosure, the computer-readable medium mentioned above may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0120] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0121] The computer-readable medium may be included in the electronic device, or may exist separately and not incorporated into the electronic device. The computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: obtain user value flow data corresponding to a target user from a user database; determine at least one user group information and a user value attribute information set corresponding to the target user based on the user value flow data; generate a value value representing the target virtual value product that can be provided by the target user based on the at least one user group information and the user value attribute information set; in response to monitoring that the value value is greater than the target value, encode and encrypt the value value and the at least one user group information to generate an initial encryption result; send the initial encryption result to a multi-layer audit terminal for audit of the value value; in response to receiving audit-qualified information indicating that the audit has passed, generate a product value usage profile corresponding to the target user based on the value value, the at least one user group information, and the user value attribute information set; and store the product value usage profile in an encrypted manner on the target blockchain.

[0122] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0124] The units described in some embodiments of the present disclosure may be implemented by software or hardware. The units described may also be provided in a processor. For example, they may be described as follows: a processor including an acquisition unit, a determination unit, a first generation unit, an encryption unit, a sending unit, a second generation unit, and a storage unit. The names of these units do not, in some cases, constitute limitations on the units themselves. For example, the acquisition unit may also be described as a "unit for acquiring user value flow data corresponding to a target user from a user database."

[0125] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0126] The above description is only an illustration of some preferred embodiments of the present disclosure and the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A product portrait storage method, comprising: Obtain user value flow data corresponding to the target user from the user database; Determining at least one user group information and user value attribute information set corresponding to the target user based on the user value flow data; Generating a value value that can be provided by the target virtual value product corresponding to the target user based on the at least one user group information and the user value attribute information set, wherein generating a value value that can be provided by the target virtual value product corresponding to the target user based on the at least one user group information and the user value attribute information set includes: determining at least one first value adjustment information corresponding to the at least one user group information; generating user value risk information corresponding to the target user based on the user value attribute information set; generating second value adjustment information corresponding to the user value risk information; determining an initial value value corresponding to the target user; and adjusting the initial value value based on the at least one first value adjustment information and the second value adjustment information to generate the value value; In response to monitoring that the value value is greater than the target value, encoding and encrypting the value value and the at least one user group information to generate an initial encryption result; Sending the initial encryption result to a multi-layer audit terminal for auditing the value value; In response to receiving the audit qualification information indicating that the audit has passed, generating a product value usage profile corresponding to the target user based on the value value, the at least one user group information and the user value attribute information set; The product value usage portrait is encrypted and stored on the target blockchain.

2. The method according to claim 1, wherein The method further comprises: In response to receiving information indicating that the target user has requested value provision from the target virtual value product, obtaining an encrypted profile corresponding to the target user based on the user information corresponding to the target user and decrypting the profile to generate a product value usage profile; Determine user authenticity information corresponding to the target user using the user image included in the product value usage portrait; In response to determining that the user authenticity information indicates that the target user is a real individual user, extracting a value value from the product value usage portrait; Generate value numerical reasons corresponding to target users based on the product value usage portrait; Determining value processing operation information corresponding to the value value; The value processing operation information and the value numerical reason are sent to the request terminal corresponding to the target user, so that the request terminal can perform the corresponding value processing operation and display the corresponding value processing value numerical reason.

3. The method according to claim 1, wherein The determining, based on the user value flow data, at least one user group information and user value attribute information set corresponding to the target user includes: Obtaining from the user group database a user group characteristic information group corresponding to each user group identifier in the user group identifier set to obtain a user group characteristic information group set, wherein each user group characteristic information in the user group characteristic information group has corresponding encrypted first user group value attribute information; Extracting each user group value attribute information corresponding to each user group value attribute from the user value flow data; Sending the value attribute information of each user group to the attribute information encryption terminal to generate encrypted value attribute information of each second user group; Vectorizing the encrypted value attribute information of each second user group to generate a target user group characteristic information group; Determining value attribute similarity information between each user group characteristic information group in the user group characteristic information group set and the target user group characteristic information group to obtain a value attribute similarity information set; Filtering out user group characteristic information groups whose value attribute similarity information is higher than target similarity information from the user group characteristic information groups to obtain at least one user group characteristic information group; Determining each user group identifier corresponding to the at least one user group characteristic information group as at least one piece of user group information; Obtaining a user value attribute set corresponding to the user value risk information; A user value attribute information set corresponding to the user value attribute set is extracted from the user value flow data.

4. The method according to claim 1, wherein The first value adjustment information in the at least one first value adjustment information is generated by the following steps: Obtaining user group information corresponding to the first value adjustment information as target user group information; Determining a user group characteristic information group corresponding to the target user group information as the target user group characteristic information group; Determine attribute importance information corresponding to each user group attribute in the user group attribute set; Dividing each user group attribute in the user group attribute set into attribute intervals according to attribute importance information corresponding to the user group attributes to obtain a user group attribute group set, wherein the user group attribute group set includes: a first user group attribute group, a second user group attribute group, and a third user group attribute group; Determining currently available computing resources corresponding to the value adjustment information generating terminal; determining, based on the currently available computing resources, user group attribute extraction ratios corresponding to the first user group attribute group, the second user group attribute group, and the third user group attribute group; extracting group attributes from the first user group attribute group, the second user group attribute group, and the third user group attribute group according to the user group attribute extraction ratio to generate an extracted group attribute group; Extracting a user group characteristic information subgroup corresponding to the extracted group attribute group from the target user group characteristic information group; The user group characteristic information subgroup is input into a pre-trained value adjustment information generation model to generate the first value adjustment information.

5. The method according to claim 1, wherein Generating user value risk information corresponding to the target user based on the user value attribute information set includes: Encrypting each user value attribute information in the user value attribute information set to generate a user value attribute encrypted information set; sending the user value attribute encrypted information set to a user value risk information generating terminal, so that the user value risk information generating terminal generates user value risk information using a graphics processor set; and Using the graphics processor group, the user value risk information is generated by the following steps: Dividing each user value attribute information in the user value attribute information set into a first user value attribute information subset for generating user credit information, a second user value attribute information subset for generating user value risk information, a third user value attribute information subset for generating product usage credit information, and a fourth user value attribute information subset for generating product usage risk information; inputting the first user value attribute information subset into a user credit generation model deployed by a pre-trained first image processor group to generate first user credit information; Inputting the first user value attribute information subset and the second user value attribute information subset into a first dual-tower model deployed by a pre-trained second image processor group to generate second user credit information and first user value risk information; inputting the second user value attribute information subset into a pre-trained user value risk information generation model deployed by a third image processor group to generate second user value risk information; inputting the third user value attribute information subset into a pre-trained product usage credit information generation model deployed by a fourth image processor group to generate first product usage credit information; Inputting the third user value attribute information subset and the fourth user value attribute information subset into a second dual-tower model deployed by a pre-trained fifth image processor group to generate second product usage credit information and first product usage risk information; inputting the fourth user value attribute information subset into a pre-trained product usage risk information generation model deployed by a sixth image processor group to generate second product usage risk information; generating first initial user value-risk information based on the first user credit information, the second user credit information, the first user value-risk information, and the second user value-risk information; generating second initial user value risk information based on the first product usage credit information, the second product usage credit information, the first product usage risk information, and the second product usage risk information; The user value risk information is generated according to the first initial user value risk information and the second initial user value risk information.

6. A product image storage device, comprising: an acquisition unit, configured to acquire user value flow data corresponding to a target user from a user database; a determining unit configured to determine at least one user group information and user value attribute information set corresponding to the target user based on the user value flow data; The first generating unit is configured to generate a value value representing the target virtual value product that can be provided by the target user based on the at least one user group information and the user value attribute information set, wherein the generating of the value value representing the target virtual value product that can be provided by the target user based on the at least one user group information and the user value attribute information set includes: determining at least one first value adjustment information corresponding to the at least one user group information; generating user value risk information corresponding to the target user based on the user value attribute information set; generating second value adjustment information corresponding to the user value risk information; determining an initial value value corresponding to the target user; and adjusting the initial value value based on the at least one first value adjustment information and the second value adjustment information to generate the value value; an encryption unit configured to, in response to monitoring that the value value is greater than a target value, perform encoding and encryption processing on the value value and the at least one user group information to generate an initial encryption result; a sending unit configured to send the initial encryption result to a multi-layer audit terminal for auditing the value value; The second generating unit is configured to generate, in response to receiving the audit passing information indicating that the audit has passed, a product value usage profile of the target user corresponding to the product value based on the value value, the at least one user group information, and the user value attribute information set; The storage unit is configured to encrypt and store the product value usage portrait on the target blockchain.

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

8. A computer-readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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