Activity information template generation method and device, storage medium and electronic equipment

Abstract

The invention discloses an activity information template generation method and device, a storage medium and electronic equipment, and relates to the field of financial science and tech, and the method comprises the steps: obtaining financial activity information of a bank outlet; determining user portrait features of the user bound with the bank outlet according to historical business handling data, financial transaction behavior data and financial business change indexes of the user at the bank outlet; acquiring data features of the encryption form from the privacy computing platform, wherein the data features at least comprise telecommunication behavior features, financial risk features and associated enterprise features corresponding to the user; the user portrait features and the data features are fused into a feature matrix, an activity information template is generated according to the feature matrix and the financial activity information, the activity information template is used for displaying the financial activity information, and the typesetting style of the activity information template is determined based on the feature matrix. The technical problem that in the prior art, a bank is difficult to generate a personalized activity information template is solved.

Description

Technical Field

[0001] This application relates to the field of financial technology, and more specifically, to a method, apparatus, storage medium, and electronic device for generating activity information templates. Background Technology

[0002] Commercial banks face numerous challenges in acquiring new users and pushing financial activity content. On the one hand, potential users are difficult to reach, making it hard for banks to effectively push financial activity content to them; on the other hand, for those users who can be reached, banks often cannot accurately push personalized financial activity information, and the generic activity information templates greatly limit effective participation in financial activities.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a method, apparatus, storage medium, and electronic device for generating activity information templates, so as to at least solve the technical problem in the prior art that banks have difficulty generating personalized activity information templates.

[0005] According to one aspect of the embodiments of this application, a method for generating an activity information template is provided, comprising: obtaining financial activity information of a bank branch; determining user profile features associated with the bank branch based on the user's historical business handling data, financial transaction behavior data, and financial business change indicators at the bank branch; obtaining encrypted data features from a privacy computing platform, wherein the data features include at least telecommunications behavior features, financial risk features, and associated enterprise features corresponding to the user; fusing the user profile features and data features into a feature matrix, and generating an activity information template based on the feature matrix and the financial activity information, wherein the activity information template is used to display financial activity information, and the layout style of the activity information template is determined based on the feature matrix.

[0006] Optionally, obtaining encrypted data features from a privacy computing platform includes: preprocessing the user identifier and encrypting it using a homomorphic encryption algorithm to obtain an encrypted user identifier; sending the encrypted user identifier to the privacy computing platform, wherein the privacy computing platform has pre-established a secure data interaction protocol with the data provider; interacting with the data provider through the privacy computing platform to determine the target data provider corresponding to the encrypted user identifier; sending a data request to the target data provider through the privacy computing platform and receiving the data features returned by the target data provider based on the data request, wherein the target data provider encrypts the data features in its database using a homomorphic encryption algorithm.

[0007] Optionally, user profile features and data features are fused into a feature matrix, including: establishing an encrypted data feature exchange channel with a privacy computing platform using a federated learning framework; synchronizing metadata information of the feature matrix through an encrypted communication protocol under the federated learning framework, wherein the metadata information includes the number of features, matrix dimensions, and encryption parameters; converting user profile features into locally encrypted high-dimensional sparse vectors based on the metadata information of the feature matrix, and converting data features into privacy feature vectors; concatenating the locally encrypted high-dimensional sparse vectors with the privacy feature vectors to obtain an encrypted feature matrix.

[0008] Optionally, generating an activity information template based on the feature matrix and financial activity information includes: parsing the feature matrix to obtain a set of user feature labels, wherein the user feature labels are converted into feature information through a secure protocol under the federated learning framework; selecting target activity content from the financial activity information based on the user feature labels, where the relevance of the user feature labels is greater than a preset threshold; and generating an activity information template based on the target activity content and the feature matrix.

[0009] Optionally, an activity information template is generated based on the target activity content and the feature matrix, including: extracting style feature labels related to user classification information, user preference information and user behavior pattern information from the feature matrix; and generating an activity information template based on the style feature labels and the target activity content.

[0010] Optionally, an activity information template is generated based on style feature tags and target activity content, including: generating a personalized template based on style feature tags; and using a homomorphic encryption algorithm to encrypt and fuse the target activity content with the personalized template to obtain the activity information template.

[0011] Optionally, after using a homomorphic encryption algorithm to encrypt and fuse the target activity content with a personalized template to obtain an activity information template, the method for generating the activity information template further includes: when a target user requests financial activity information, obtaining the unique decryption key corresponding to the target user; decrypting the activity information template corresponding to the target user based on the unique decryption key corresponding to the target user; and sending the decrypted activity information template corresponding to the target user to the target user's terminal device.

[0012] According to another aspect of the embodiments of this application, an apparatus for generating an activity information template is also provided, comprising: an activity information acquisition unit for acquiring financial activity information of bank branches; a profile feature determination unit for determining user profile features associated with bank branches based on the user's historical business handling data, financial transaction behavior data, and financial business change indicators at the bank branches; a data feature acquisition unit for acquiring encrypted data features from a privacy computing platform, wherein the data features include at least telecommunications behavior features, financial risk features, and associated enterprise features corresponding to the user; and an information template generation unit for fusing user profile features and data features into a feature matrix, and generating an activity information template based on the feature matrix and financial activity information, wherein the activity information template is used to display financial activity information, and the layout style of the activity information template is determined based on the feature matrix.

[0013] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, which stores a computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located executes the above-described method for generating the activity information template.

[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors cause the one or more processors to perform the above-described method for generating activity information templates.

[0015] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program or instructions, which, when executed by a processor, implement the above-described method for generating an activity information template.

[0016] In this application, the data processing system can obtain financial activity information from bank branches, and then determine user profile features linked to the bank branch based on the user's historical business transaction data, financial transaction behavior data, and financial business change indicators. Next, it obtains encrypted data features from a privacy computing platform, wherein the data features include at least telecommunications behavior features, financial risk features, and associated enterprise features corresponding to the user. Finally, the user profile features and data features are merged into a feature matrix, and an activity information template is generated based on the feature matrix and financial activity information. The activity information template is used to display financial activity information, and the layout style of the activity information template is determined based on the feature matrix.

[0017] As described above, the data processing system first acquires financial activity information from bank branches. Then, based on users' historical business transactions, financial transaction behavior data, and financial business change indicators at bank branches, it determines user profile characteristics. These user profile characteristics accurately reflect users' financial behavior patterns and preferences. The data processing system obtains encrypted data features from a privacy computing platform, including telecommunications behavior characteristics, financial risk characteristics, and related enterprise characteristics, further enriching the user profile. By integrating user profile characteristics and data features into a feature matrix, the data processing system can comprehensively understand users' multi-dimensional information. Based on the feature matrix and financial activity information, the data processing system generates personalized activity information templates. These templates not only contain financial activity information highly relevant to user characteristics but also have a layout style determined by the feature matrix, ensuring that the information display meets users' personalized needs. This allows banks to provide customized activity information for each user, increasing user attention and engagement, thus solving the technical problem of banks struggling to generate personalized activity information templates in existing technologies. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This is a schematic diagram of an optional method for generating an activity information template according to an embodiment of this application;

[0020] Figure 2 This is a schematic diagram of an optional activity information template generation apparatus according to an embodiment of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] According to an embodiment of this application, a method embodiment for generating an activity information template is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0024] According to the embodiments of this application, a data processing system can be used as the execution subject of the activity information template generation method of this application embodiment. The system can be a software system or an embedded system combining software and hardware. Of course, the method execution subject in the embodiments of this application can also be other forms of execution subject, such as devices, equipment, etc. It should be known by those skilled in the art that this application does not particularly limit the specific form of the method execution subject.

[0025] Figure 1 This is a method for generating activity information templates according to embodiments of this application, such as... Figure 1 As shown, the method includes the following steps:

[0026] Step S101: Obtain financial activity information of bank branches.

[0027] Optionally, obtaining financial activity information from bank branches can be used to identify and collect detailed information related to various financial-related activities held by bank branches. For example, financial activities may take the form of financial lectures, business promotions, etc. The information on these activities may include the theme, time, location, target audience, specific content, list of participants, and expected results. The specific content may include the theme of the lecture and promotional information for products.

[0028] Optionally, the data processing system can extract relevant data on financial activities from the bank's internal management system. This financial activity information can be detailed information about various financial activities that the bank's branches are about to hold or are currently conducting.

[0029] By acquiring information on financial activities at bank branches, the data processing system can better understand the business priorities and activity objectives of the branches, thereby customizing activity information templates that meet the needs and interests of different user groups and increasing the attractiveness and participation of the activities.

[0030] Step S102: Determine the user profile characteristics that are associated with the bank branch based on the user's historical business transaction data, financial transaction behavior data, and financial business change indicators at the bank branch.

[0031] Optionally, user profile features are used to characterize a data model that reflects user characteristics and preferences, constructed by collecting and analyzing various user data. These user profile features may include user business preferences, consumption habits, risk preferences, etc. Financial transaction behavior data is used to characterize specific user behaviors in financial transactions, such as transaction amount, transaction frequency, and transaction time. Financial business change indicators are used to reflect changes in the user's financial business status, such as fluctuations in account balances and adjustments to loan limits.

[0032] By analyzing users' historical transaction data, financial behavior data, and indicators of changes in financial transactions at bank branches, banks can establish user profiles that allow their data processing systems to more accurately understand each user's needs and preferences. For example, if a user frequently engages in wealth management transactions with large amounts, their user profile might be characterized as a high-net-worth individual with a preference for wealth management. The data processing system can then recommend wealth management products that better align with the user's risk tolerance and return expectations. Furthermore, accurate user profiling helps banks optimize their promotional information delivery strategies, improving the targeting and effectiveness of financial activities, thereby increasing user satisfaction and the bank's business conversion rate.

[0033] Step S103: Obtain encrypted data features from the privacy computing platform. The data features include at least telecommunications behavior features, financial risk features, and related enterprise features corresponding to the user.

[0034] Optionally, a privacy-preserving computing platform is used to characterize systems capable of processing and analyzing data while ensuring that data remains encrypted during processing, thereby protecting data privacy and security. Encrypted data characteristics characterize that on the privacy-preserving computing platform, data exists in encrypted form, and only authorized users or systems can decrypt and use the encrypted data. Telecommunications behavior characteristics may include users' communication behavior patterns, such as call frequency and SMS volume, reflecting users' communication habits and social networks. Financial risk characteristics may involve users' credit ratings, default probabilities, etc., used to assess users' risk levels in financial transactions. Related enterprise characteristics characterize the relationship information between users and enterprises, such as whether the user is a shareholder or senior executive of an enterprise, and the enterprise's operating status, helping banks to more comprehensively understand the user's background and potential needs.

[0035] Optionally, the data processing system can encrypt the user identifier using homomorphic encryption technology before sending it to the privacy computing platform. The privacy computing platform establishes a secure data interaction protocol with the data provider, identifies the target data provider, and obtains its encrypted data features. Alternatively, the data processing system can utilize a federated learning framework to establish an encrypted data feature exchange channel. After synchronizing the metadata information of the feature matrix, the user profile features are converted into locally encrypted high-dimensional sparse vectors, and the data features are converted into privacy feature vectors. Finally, the locally encrypted high-dimensional sparse vectors are concatenated with the privacy feature vectors to obtain an encrypted feature matrix. Both methods effectively ensure the privacy and security of data during transmission and processing.

[0036] By acquiring encrypted data characteristics from privacy-preserving computing platforms, banks' data processing systems can obtain more multi-dimensional user information while protecting user privacy. This helps banks more accurately assess users' creditworthiness, risk preferences, and potential needs, thereby providing more personalized financial services. For example, by analyzing telecommunications behavior characteristics, the data processing system can understand a user's social activity and stability; through financial risk characteristics, the system can better assess a user's credit risk; and by associating with enterprise characteristics, the system can provide users with enterprise-related financial products and services. This multi-dimensional data analysis not only improves the quality of bank services but also enhances the bank's risk management capabilities, while facilitating data security and compliance.

[0037] Step S104: The user profile features and data features are fused into a feature matrix, and an activity information template is generated based on the feature matrix and financial activity information. The activity information template is used to display financial activity information, and the layout style of the activity information template is determined based on the feature matrix.

[0038] Optionally, data features are used to characterize encrypted data obtained from the privacy computing platform, including telecommunications behavior characteristics, financial risk characteristics, and related enterprise characteristics, to enrich user profiles. The feature matrix is ​​used to characterize the multi-dimensional data structure formed by fusing user profile features and data features, integrating various user characteristic information for easier subsequent personalized processing. The activity information template is used to characterize a display template generated based on the feature matrix and financial activity information, used to display information related to financial activities to users. The layout style of this activity information template is determined based on the feature matrix, and the visual presentation of the activity information template can be personalized according to user characteristics to better attract user attention and improve information delivery.

[0039] By fusing user profile features and data features into a feature matrix, and then generating activity information templates based on this feature matrix and financial activity information, personalized display of financial activity information can be achieved. The data processing system can customize suitable activity information templates according to each user's unique characteristics and preferences. For example, for users who prefer wealth management, the activity information template can highlight information about wealth management products; for users who frequently participate in social activities, the activity information template can adopt a more vivid and interactive layout style. This personalized display not only increases user attention to financial activity information but also enhances their willingness to participate.

[0040] In one optional embodiment, obtaining encrypted data features from a privacy computing platform includes: a data processing system preprocessing a user identifier and encrypting it using a homomorphic encryption algorithm to obtain an encrypted user identifier. The encrypted user identifier is then sent to the privacy computing platform, wherein the privacy computing platform has pre-established a secure data interaction protocol with the data provider, then interacts with the data provider to determine the target data provider corresponding to the encrypted user identifier, and finally sends a data request to the target data provider through the privacy computing platform and receives data features returned by the target data provider based on the data request, wherein the target data provider encrypts the data features in its database using a homomorphic encryption algorithm.

[0041] Optionally, a user identifier is information used to uniquely identify a user, such as a user ID or account. Preprocessing refers to performing necessary formatting or transformation operations on the user identifier before encryption to ensure data consistency and compatibility.

[0042] Optionally, a homomorphic encryption algorithm is used to characterize a specific encryption method that allows direct computation on encrypted data without prior decryption, thereby protecting data privacy and security. A data provider is used to characterize the entity that owns and provides the data, such as a telecommunications operator or financial institution. A secure data exchange protocol is used to characterize a pre-established agreement between the privacy computing platform and the data provider, ensuring the security and compliance of data exchange. A data request is used to characterize a request sent by the privacy computing platform to the data provider to obtain data characteristics of a specific user. The data characteristics returned by the target data provider based on the data request characterize various attributes and behavioral data related to the user; these data characteristics are returned in encrypted form to facilitate privacy protection.

[0043] By encrypting user identifiers using homomorphic encryption algorithms and interacting with data providers through a privacy computing platform, the privacy and security of user data can be ensured during transmission and processing. This allows banks' data processing systems to acquire and use user data characteristics from different data providers without exposing sensitive user information. For example, a bank's data processing system can use this encryption method to obtain a user's telecommunications behavior characteristics or financial risk characteristics without directly accessing the user's raw data. This not only improves data security and complies with data protection regulations but also provides banks with more comprehensive user information, enabling them to create more accurate user profiles and provide personalized services.

[0044] In one optional embodiment, user profile features and data features are fused into a feature matrix, including: the data processing system establishing an encrypted data feature exchange channel with a privacy computing platform using a federated learning framework; within the federated learning framework, synchronizing metadata information of the feature matrix via an encrypted communication protocol, wherein the metadata information includes the number of features, matrix dimensions, and encryption parameters; then, based on the metadata information of the feature matrix, the user profile features are converted into locally encrypted high-dimensional sparse vectors, and the data features are converted into privacy feature vectors; finally, the locally encrypted high-dimensional sparse vectors and the privacy feature vectors are concatenated to obtain an encrypted feature matrix.

[0045] Optionally, the federated learning framework represents a distributed machine learning method that allows multiple participants to collaboratively train a model without sharing the original data, facilitating data privacy and security while enabling efficient data utilization. An encrypted data feature exchange channel represents the secure communication link established between the data processing system and the privacy computing platform within the federated learning framework, used for exchanging encrypted data features. Metadata information describes the basic attributes of the feature matrix, including the number of features, matrix dimensions, and encryption parameters, facilitating data processing and synchronization. Locally encrypted high-dimensional sparse vectors represent the high-dimensional vectors formed after encryption of user profile features, where most elements can be zero; sparsity improves data processing efficiency. Privacy feature vectors represent the encrypted vector form of data features obtained from the privacy computing platform.

[0046] This application's embodiments utilize a federated learning framework to establish an encrypted data feature exchange channel, ensuring data privacy and security during transmission and processing. By synchronizing metadata information through an encrypted communication protocol, the data processing system can accurately convert user profile features and data features into encrypted high-dimensional sparse vectors and privacy feature vectors. This conversion not only protects user data privacy but also improves data processing efficiency. Finally, the locally encrypted high-dimensional sparse vectors are concatenated with the privacy feature vectors to form an encrypted feature matrix, which facilitates the subsequent generation of personalized activity information templates. This not only enhances data security but also improves data usability, enabling banks to provide more accurate personalized financial services to users while protecting user privacy.

[0047] In one optional embodiment, generating an activity information template based on a feature matrix and financial activity information includes: a data processing system parses the feature matrix to obtain a set of user feature tags, wherein the user feature tags are converted into feature information through a secure protocol under a federated learning framework. Then, based on the user feature tags, target activity content with a relevance greater than a preset threshold to the user feature tags is selected from the financial activity information. Finally, an activity information template is generated based on the target activity content and the feature matrix.

[0048] Optionally, the feature matrix is ​​parsed to characterize the data processing system's analysis and processing of the feature matrix, which integrates user profile features and data features, to extract key information that describes user characteristics, i.e., user feature labels. Under the federated learning framework, user feature labels are transformed through a security protocol to ensure data privacy and security during transmission and processing.

[0049] Optionally, user feature tags are used to represent the parsed and transformed user feature information, which can be used for subsequent activity content filtering. The preset threshold can be a preset numerical standard used to measure the relevance between user feature tags and financial activity information. Only when the relevance between financial activity information and user feature tags exceeds the preset threshold will the corresponding activity content in the financial activity information be selected. Target activity content is used to represent financial activity information with a high relevance to user feature tags, and the target activity content can be used to generate activity information templates.

[0050] By analyzing the feature matrix and extracting a set of user feature tags, the data processing system can more accurately identify users' interests and needs. Based on these user feature tags, the system filters out activity content highly relevant to user characteristics from a large amount of financial activity information, ensuring the personalization and targeting of activity information templates. For example, if a user's feature tags indicate an interest in high-yield financial products, the system will filter out relevant high-yield financial product activities. Finally, the activity information template generated by combining the target activity content and the feature matrix not only displays financial activity information that matches user interests but also allows for personalized layout and display based on user characteristics, thereby increasing user attention and participation in activity information.

[0051] In one optional embodiment, generating an activity information template based on the target activity content and the feature matrix includes: the data processing system extracting style feature tags related to user classification information, user preference information and user behavior pattern information from the feature matrix, and then generating an activity information template based on the style feature tags and the target activity content.

[0052] Optionally, style feature labels are used to characterize information extracted from the feature matrix related to user classification, preferences, and behavioral patterns, and can be used to describe specific user characteristics and behavioral habits. For example, user classification information may include the group to which the user belongs, such as high-net-worth users, young users, etc.; user preference information may involve the user's preference for financial products, such as a preference for savings products or investment products; user behavioral pattern information may reflect the user's transaction habits, such as regular investment or occasional large-scale consumption.

[0053] The data processing system in this application extracts style feature tags and can generate personalized activity information templates for target activities based on user classification, preferences, and behavioral patterns. For example, if a user prefers conservative investment products, the data processing system can generate an activity information template themed around conservative investment products and adopt a layout style that matches the user's preferences. This personalized template not only better attracts the user's attention but also increases the user's acceptance of the activity information and their willingness to participate. This allows the data processing system to more effectively convey financial activity information to users, improving the accuracy and effectiveness of financial activities.

[0054] In one optional embodiment, generating an activity information template based on style feature tags and target activity content includes: the data processing system can generate a personalized template based on style feature tags, and then use a homomorphic encryption algorithm to encrypt and fuse the target activity content with the personalized template to obtain the activity information template.

[0055] Optionally, personalized templates represent targeted activity information display templates generated based on users' style characteristic tags. These templates reflect users' classification, preference, and behavioral patterns, ensuring that the display of activity information matches users' personalized needs. Encrypted fusion represents the process of combining target activity content with personalized templates, using homomorphic encryption algorithms to ensure data security during the fusion process.

[0056] The data processing system in this application generates personalized templates based on style feature tags, providing each user with an activity information display method that matches their individual characteristics and preferences. Personalized templates not only increase user attention to activity information but also enhance user participation. Homomorphic encryption algorithms are used to encrypt and fuse the target activity content with the personalized templates, facilitating privacy protection during data processing and transmission. This encryption fusion not only protects users' sensitive information but also ensures the integrity and security of the activity information template, enabling banks to provide personalized financial services while protecting user privacy.

[0057] For example, in the process of encrypting and merging target activity content and personalized templates, the target activity content and personalized template can first be converted into a data format suitable for encryption. The target activity content includes financial activity information related to user characteristics, such as the activity theme, time, and location, while the personalized template can include a layout style and display method designed according to the user's style characteristic tags. The target activity content and personalized template can be encoded into a data structure, such as a text string or binary data. Next, a homomorphic encryption algorithm is used to encrypt this data structure. Homomorphic encryption allows direct computation operations on encrypted data without first decrypting it, thus ensuring data privacy and security during processing. During the encryption fusion process, the encrypted target activity content and personalized template can be merged into a single encrypted data structure. For example, the encrypted target activity content can be embedded into the encrypted personalized template, or the data structures of the target activity content and personalized template can be logically combined. The final encrypted data structure can serve as an activity information template, containing all necessary activity information and displayed in a user-personalized style. Because the entire process is completed in an encrypted state, even if the data is intercepted during transmission or storage, third parties cannot obtain the original data content, thus ensuring the privacy and security of user data.

[0058] In an optional embodiment, after using a homomorphic encryption algorithm to encrypt and fuse the target activity content with a personalized template to obtain an activity information template, the method for generating the activity information template further includes: when a target user requests financial activity information, obtaining a unique decryption key corresponding to the target user, the data processing system can decrypt the activity information template corresponding to the target user according to the unique decryption key corresponding to the target user, and then sending the decrypted activity information template corresponding to the target user to the target user's terminal device.

[0059] Optionally, the decryption key is used to represent a personalized activity information template generated specifically for each user, ensuring that only the target user can access their personalized activity information template, thus guaranteeing data confidentiality and personal privacy. Even if the activity information template is intercepted during transmission, a third party cannot read the content without the correct decryption key.

[0060] Optionally, during the information acquisition process, the data processing system uses the unique decryption key corresponding to the target user to decrypt the target user's activity information template. The decrypted activity information template contains financial activity information that matches the user's personal characteristics and preferences, presented in a way that is easy for the user to understand and accept. Subsequently, the data processing system sends the decrypted activity information template to the target user's terminal device, such as a mobile phone or computer. This not only ensures that the user can obtain personalized financial activity information in a timely manner, but also guarantees the security and integrity of the information during transmission, enhancing the user's trust in financial services.

[0061] See Figure 2 According to another aspect of the embodiments of this application, an apparatus for generating activity information templates is also provided, including: an activity information acquisition unit 201, a portrait feature determination unit 202, a data feature acquisition unit 203, and an information template generation unit 204.

[0062] The system includes the following components: an activity information acquisition unit 201, used to acquire financial activity information from bank branches; a profile feature determination unit 202, used to determine user profile features associated with bank branches based on historical business transaction data, financial transaction behavior data, and financial business change indicators; a data feature acquisition unit 203, used to acquire encrypted data features from a privacy computing platform, wherein the data features include at least telecommunications behavior features, financial risk features, and associated enterprise features corresponding to the user; and an information template generation unit 204, used to fuse user profile features and data features into a feature matrix, and generate an activity information template based on the feature matrix and financial activity information, wherein the activity information template is used to display financial activity information, and the layout style of the activity information template is determined based on the feature matrix. The activity information template generation apparatus of this embodiment can be used to implement the above-described activity information template generation method.

[0063] Optionally, the data feature acquisition unit 203 includes: an encrypted identifier determination subunit, used to preprocess the user identifier and encrypt it using a homomorphic encryption algorithm to obtain the encrypted user identifier; an encrypted identifier sending subunit, used to send the encrypted user identifier to a privacy computing platform, wherein the privacy computing platform has pre-established a secure data interaction protocol with the data provider; a target data provider determination subunit, used to interact with the data provider through the privacy computing platform to determine the target data provider corresponding to the encrypted user identifier; and a data interaction subunit, used to send a data request to the target data provider through the privacy computing platform and receive the data features returned by the target data provider according to the data request, wherein the target data provider encrypts the data features in its database using a homomorphic encryption algorithm.

[0064] Optionally, the information template generation unit 204 includes: an exchange channel establishment subunit, used to establish an encrypted data feature exchange channel with the privacy computing platform using a federated learning framework; a data synchronization subunit, used to synchronize the metadata information of the feature matrix through an encrypted communication protocol under the federated learning framework, wherein the metadata information includes the number of features, matrix dimension, and encryption parameters; a vector transformation subunit, used to convert user profile features into locally encrypted high-dimensional sparse vectors based on the metadata information of the feature matrix, and to convert data features into privacy feature vectors; and an encryption matrix determination subunit, used to concatenate the locally encrypted high-dimensional sparse vectors with the privacy feature vectors to obtain an encrypted feature matrix.

[0065] Optionally, the information template generation unit 204 includes: a feature label determination subunit, used to parse the feature matrix to obtain a set of user feature labels, wherein the user feature labels are converted into feature information through a secure protocol under the federated learning framework; a target content filtering subunit, used to filter target activity content from financial activity information based on the user feature labels, where the relevance of the user feature labels is greater than a preset threshold; and an activity template generation subunit, used to generate an activity information template based on the target activity content and the feature matrix.

[0066] Optionally, the activity template generation subunit includes: a style feature extraction module, used to extract style feature labels related to user classification information, user preference information and user behavior pattern information from the feature matrix; and an activity template generation module, used to generate an activity information template based on the style feature labels and the target activity content.

[0067] Optionally, the activity template generation module includes: a personalized template generation submodule, used to generate personalized templates based on style feature tags; and an encrypted fusion submodule, used to use a homomorphic encryption algorithm to encrypt and fuse the target activity content with the personalized template to obtain the activity information template.

[0068] Optionally, the activity information template generation device further includes: a decryption key acquisition unit, used to acquire a unique decryption key corresponding to the target user when a target user requests to obtain financial activity information; a template decryption unit, used to decrypt the activity information template corresponding to the target user according to the unique decryption key corresponding to the target user; and a template sending unit, used to send the decrypted activity information template corresponding to the target user to the target user's terminal device.

[0069] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, which stores a computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located executes the above-described method for generating the activity information template.

[0070] According to another aspect of the embodiments of this application, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors cause the one or more processors to perform the above-described method for generating activity information templates.

[0071] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program or instructions, which, when executed by a processor, implement the above-described method for generating an activity information template.

[0072] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0073] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0078] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method of generating an active information template, characterized by, The method comprises the following steps: obtaining financial activity information of a bank outlet; determining a user portrait feature of a user bound to the bank outlet according to historical business handling data, financial transaction behavior data, and financial business change indicators of the user at the bank outlet; obtaining encrypted data features from a privacy computing platform, wherein the data features at least include telecommunication behavior features, financial risk features, and associated enterprise features corresponding to the user; fusing the user portrait feature and the data features into a feature matrix, and generating an activity information template according to the feature matrix and the financial activity information, wherein the activity information template is used to display the financial activity information, and the layout style of the activity information template is determined based on the feature matrix.

2. The method of claim 1, wherein, Obtaining encrypted data features from a privacy computing platform comprises: preprocessing a user identifier and encrypting it using a homomorphic encryption algorithm to obtain an encrypted user identifier; sending the encrypted user identifier to the privacy computing platform, wherein the privacy computing platform has previously established a secure data interaction protocol with a data provider; determining a target data provider corresponding to the encrypted user identifier through interaction between the privacy computing platform and the data provider; sending a data request to the target data provider through the privacy computing platform, and receiving data features returned by the target data provider according to the data request, wherein the target data provider encrypts data features in its database using the homomorphic encryption algorithm.

3. The method of claim 1, wherein, Fusing the user portrait feature and the data features into a feature matrix comprises: establishing an encrypted data feature exchange channel with the privacy computing platform using a federated learning framework; synchronizing metadata information of the feature matrix through an encrypted communication protocol under the federated learning framework, wherein the metadata information includes feature quantity, matrix dimension, and encryption parameters; converting the user portrait feature into a locally encrypted high-dimensional sparse vector and the data features into a privacy feature vector according to the metadata information of the feature matrix; splicing the locally encrypted high-dimensional sparse vector and the privacy feature vector to obtain an encrypted feature matrix.

4. The method of claim 1, wherein, Generating an activity information template according to the feature matrix and the financial activity information comprises: parsing the feature matrix to obtain a set of user feature labels, wherein the user feature labels are converted into feature information through a secure protocol under the federated learning framework; filtering target activity content with a correlation degree greater than a preset threshold from the financial activity information according to the user feature labels; generating the activity information template according to the target activity content and the feature matrix.

5. The method of claim 4, wherein, Generating the activity information template according to the target activity content and the feature matrix comprises: extracting style feature labels related to user classification information, user preference information, and user behavior pattern information from the feature matrix; generating the activity information template according to the style feature labels and the target activity content.

6. The method of claim 5, wherein, Generating the activity information template according to the style feature labels and the target activity content comprises: Generate a personalized template according to the style feature label; Adopt a homomorphic encryption algorithm to encrypt and fuse the target activity content and the personalized template, and obtain the activity information template.

7. The method of claim 6, wherein, After adopting the homomorphic encryption algorithm to encrypt and fuse the target activity content and the personalized template, and obtaining the activity information template, the activity information template generation method further comprises: In the case of detecting that a target user requests to obtain the financial activity information, obtaining a unique decryption key corresponding to the target user; According to the unique decryption key corresponding to the target user, decrypting the activity information template corresponding to the target user; Sending the decrypted activity information template corresponding to the target user to the terminal device of the target user.

8. An apparatus for generating an active information template, characterized by Comprise: An activity information acquisition unit configured to acquire financial activity information of a bank outlet; A portrait feature determination unit configured to determine a user portrait feature bound to the user and the bank outlet according to historical business handling data, financial transaction behavior data, and financial business change indicators of the user at the bank outlet; A data feature acquisition unit configured to acquire encrypted data features from a privacy computing platform, wherein the data features at least include telecommunication behavior features, financial risk features, and associated enterprise features corresponding to the user; An information template generation unit configured to fuse the user portrait features and the data features into a feature matrix, and generate an activity information template according to the feature matrix and the financial activity information, wherein the activity information template is used to display the financial activity information, and the layout style of the activity information template is determined based on the feature matrix.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, wherein when the computer program runs, the device where the computer readable storage medium is located executes the activity information template generation method in any one of claims 1 to 7.

10. An electronic device, comprising: Comprise one or more processors and memories, the memories are used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the activity information template generation method in any one of claims 1 to 7.

11. A computer program product, characterised in that, Comprise computer programs or instructions, which when executed by a processor, implement the activity information template generation method in any one of claims 1 to 7.

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