Block chain multi-platform data trusted management method and system based on user portraits

Abstract

The invention relates to the technical field of block chains, in particular to a block chain multi-platform data credible management method and system based on a user portrait, and the method comprises the steps: carrying out the multi-platform data collection, obtaining a plurality of original user data sets, carrying out the encryption processing of the original user data sets, obtaining an encrypted user data set, carrying out the block packaging processing of the encrypted user data set, and carrying out the block packaging processing of the encrypted user data set. Performing consensus verification on the block to be subjected to consensus encryption to obtain an on-chain encryption block; performing feature extraction on the on-chain encryption block to obtain a user feature data set; performing feature division on the user feature data set to obtain a user feature type set; performing portrait construction based on the user feature type set to obtain a user portrait; and obtaining a portrait access standard, and according to the portrait access standard, performing authorization judgment by using the data access authorization unit. According to the method and the device, the problem of low data credibility in the current management process of the multi-platform data of the block chain can be solved.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, and in particular to a blockchain multi-platform data trust management method and system based on user profiles. Background Technology

[0002] With societal development, user profiles, as core data assets for characterizing user needs and supporting precise services, have been widely applied in areas such as product recommendation, service optimization, and risk management. However, leveraging the decentralized and immutable characteristics of blockchain to achieve secure transfer and reliable use of user profile data across multiple platforms has become a key requirement for enterprise digital transformation.

[0003] Traditional blockchain multi-platform data management methods mainly rely on centralized data storage and access control mechanisms. While these mechanisms can accomplish basic data management tasks, the lack of a unified mechanism for assessing and verifying the credibility of user data makes it difficult to guarantee the reliability of user data. Therefore, the current management of blockchain multi-platform data suffers from low data credibility. Summary of the Invention

[0004] This invention provides a blockchain multi-platform data trust management method and system based on user profiles, the main purpose of which is to solve the problem of low data trust in the current process of managing blockchain multi-platform data.

[0005] To achieve the above objectives, this invention provides a blockchain multi-platform data trust management method based on user profiles, comprising: A multi-platform data management environment is defined, wherein the multi-platform data management environment includes: a data acquisition unit, a blockchain network node set, a data processing unit, and a data access authorization unit, wherein the blockchain network node set includes: a master node and multiple participating nodes; The data acquisition unit is used to collect data from multiple platforms to obtain multiple raw user datasets. For each of the plurality of original user datasets, the following operation is performed: The original user dataset is encrypted using a data processing unit and a blockchain network node set to obtain an encrypted user dataset. Based on the master node and the preset block structure, the encrypted user dataset is packaged into blocks to obtain an encrypted block to be reached for consensus. Multiple participating nodes are used to verify the consensus of the encrypted block to be agreed upon, thus obtaining the on-chain encrypted block; Feature extraction is performed on the on-chain encrypted blocks to obtain a user feature dataset, and feature segmentation is performed on the user feature dataset to obtain a user feature type set; Based on the user feature type set, a user profile is constructed to obtain a user profile. The user profiles are then aggregated to obtain a user profile set. Based on the user profile set, the profile access standard is obtained. Based on the profile access standard, the data access authorization unit is used to make authorization judgment, thereby completing the trusted data management of the blockchain multi-platform.

[0006] Optionally, the step of encrypting the original user dataset using a data processing unit and a blockchain network node set to obtain an encrypted user dataset includes: The data processing unit identifies duplicate data from the original user dataset and removes the duplicate data to obtain a dataset without duplicates. Identify missing datasets in the non-duplicate dataset, wherein the missing datasets include one or more missing data points; For each missing data point in the missing dataset, perform the following operation: Identify missing fields in the missing data and determine whether the missing fields are preset important fields; If the missing field is an important field, the missing data will be marked as invalid data; Otherwise, mark the missing data as data to be filled; Summarize the invalid data and the data to be filled to obtain the invalid dataset and the dataset to be filled. Invalid datasets are removed from the non-duplicate dataset to obtain the valid dataset; Fill the unfilled data in the valid dataset to obtain the complete dataset; The complete dataset is then standardized to obtain a standard user dataset. The standard user dataset is encrypted using a set of blockchain network nodes to obtain an encrypted user dataset.

[0007] Optionally, the encryption of the standard user dataset based on the blockchain network node set to obtain an encrypted user dataset includes: An asymmetric encryption key pair is generated using the master node in the blockchain network node set, wherein the asymmetric encryption key pair includes a public key and a private key; The sensitive field set and data field set were identified from the standard user dataset; The sensitive field set is encrypted using the public key in the asymmetric encryption key pair to obtain the encrypted sensitive field set. Perform a hash operation on the data field set to obtain a data hash value set; Construct an encrypted user dataset based on the set of encrypted sensitive fields and the set of data hash values.

[0008] Optionally, the step of packaging the encrypted user dataset into blocks according to the master node and a preset block structure to obtain the encrypted blocks to be consensus includes: Based on the master node, the encrypted user dataset is sharded according to the preset number of data pieces to obtain multiple encrypted data shards; For each of the plurality of encrypted data fragments, the following operation is performed: Obtain the fragment identifier based on encrypted data fragmentation; Calculate the hash value of each encrypted data fragment; The shard identifier is combined with the shard hash value to obtain the identifier shard hash group; By summing the aforementioned identifier fragment hash groups, multiple identifier fragment hash groups are obtained, wherein each identifier fragment hash group corresponds one-to-one with each encrypted data fragment in the multiple encrypted data fragments; The shard hash values ​​of each of the multiple shard hash groups are sorted to obtain the shard hash group sequence. Extract the first identifier fragment hash group sequentially from the identifier fragment hash group sequence, and perform the following operation on each of the first identifier fragment hash groups: The second identifier fragment hash group is determined using the first identifier fragment hash group, wherein the second identifier fragment hash group is adjacent to and lags behind the first identifier fragment hash group; The fragment hash value of the first identifier fragment hash group is confirmed as the preceding hash value of the second identifier fragment hash group. When the first identifier fragment hash group is the preset first identifier fragment hash group in the identifier fragment hash group sequence, the preset initial hash value is used as the preceding hash value of the first identifier fragment hash group. By summing the second identifier fragment hash group and the preceding hash value, we obtain the second identifier fragment hash group set and the preceding hash value set; Construct a sharded hash chain based on the first identifier sharded hash group, the second identifier sharded hash group set, and the preceding hash value set; Calculate the root hash value of the fragmented hash chain; The preceding hash of the block is determined from the sharded hash chain; Confirm the number of shards based on multiple encrypted data shards; Get the current time, and according to the block structure, assemble the root hash value, the block preorder hash, the number of shards, and the current time to obtain the block header; The block body is constructed based on multiple encrypted data fragments and multiple identifier fragment hash groups; The block header and block body are integrated and concatenated to obtain the encrypted block to be consensused.

[0009] Optionally, the step of using multiple participating nodes to perform consensus verification on the encrypted block to obtain the on-chain encrypted block includes: Generate an integrity verification value and a node digital signature based on the encrypted block to be reached and the private key; Extract participating nodes sequentially from the plurality of participating nodes, and perform the following operations on the participating nodes: The validity of the node digital signature is verified using the participating nodes and the integrity verification value to obtain the digital signature verification result. When the digital signature verification result is a preset pass, a verification pass certificate is generated; The verified credentials are summarized to obtain a set of verified credentials; The number of verified credentials is obtained from the set of verified credentials. When the number of verified credentials is greater than or equal to a preset threshold, the encrypted block awaiting consensus is confirmed as an on-chain encrypted block.

[0010] Optionally, the step of extracting features from the on-chain encrypted blocks to obtain a user feature dataset includes: The user dataset to be decrypted is identified from the on-chain encrypted blocks, wherein the user dataset to be decrypted corresponds one-to-one with the encrypted user dataset; Identify the set of sensitive fields to be decrypted and the set of hash values ​​of the data to be decrypted from the user dataset to be decrypted; The set of sensitive fields to be decrypted is homomorphically decrypted using a private key and a preset decryption algorithm to obtain the decrypted sensitive field set; The hash value set of the data to be decrypted is hash-verified to obtain the decrypted dataset; The user feature dataset is obtained by parsing the fields of the decrypted dataset.

[0011] Optionally, the step of partitioning the user feature dataset to obtain a user feature type set includes: Extract a content feature set and a behavior feature set from the user feature dataset. The content feature set includes one or more content features, and the behavior feature set includes one or more behavior features. The content features and behavior features correspond one-to-one. The user feature dataset is classified according to the content feature set to obtain a user access type set, wherein the user access set contains one or more user access types. For each user access type in the set of user access types, the following operation is performed: Identify target behavioral features corresponding to user access types within the behavioral feature set; Based on the target behavioral characteristics, the frequency of the user access type behavior is counted; Determine the access duration for the user access type; Access types with a frequency greater than a preset frequency threshold and an access duration greater than a preset duration threshold are defined as user feature types. The user feature types are summarized to obtain a user feature type set, wherein the user feature type set contains one or more user feature types.

[0012] Optionally, the step of constructing a user profile based on the user feature type set includes: For each user feature type in the set of user feature types, the following operation is performed: Obtain the interaction depth of user characteristic types; Confirm the frequency of characteristic behaviors and the duration of characteristic accesses for user characteristic types; The feature preference degree is calculated based on the interaction depth, feature behavior frequency, and feature access duration, using the following formula: ; in, Indicates feature preference. Indicates the frequency of characteristic behaviors. Indicates the duration of feature access. Indicates the depth of interaction. , , These represent the adjustment coefficients for the preset frequency of feature behaviors, the preset duration of feature access, and the preset depth of interaction, respectively. Represents the natural constant; By summing up the aforementioned feature preference degrees, a feature preference degree set is obtained; Based on the feature preference set, the user feature type set is sorted in descending order to obtain the feature preference sequence; Based on a preset feature preference threshold, a set of key features is selected from the feature preference sequence. User profiles are constructed based on key feature sets and decrypted sensitive field sets.

[0013] Optionally, obtaining the profile access criteria based on the user profile set includes: Obtain security level standards and access requirements; For each user profile in the user profile set, perform the following operations: Based on the security level standards, the user profile is assessed for security level to obtain the profile security level; By summarizing the security levels of the images, a set of image security levels is obtained; Cluster analysis is performed on the user profile set to obtain a profile type set, wherein the profile type set includes one or more profile types, and each profile type includes one or more user profiles; For each image type in the image type set, the following operation is performed: Based on the access requirements, the image type is adapted to the access type to obtain the set of permitted access types; By summing the aforementioned sets of permitted access types, multiple sets of permitted access types are obtained; A profile access standard is constructed based on the user profile set, profile security level set, and multiple permitted access type sets.

[0014] To achieve the above objectives, the present invention also provides a blockchain multi-platform data trust management system based on user profiles, comprising: The data management environment module is used to determine a multi-platform data management environment, wherein the multi-platform data management environment includes: a data acquisition unit, a blockchain network node set, a data processing unit, and a data access authorization unit, wherein the blockchain network node set includes: a master node and multiple participating nodes, and the data acquisition unit is used to collect data from multiple platforms to obtain multiple original user datasets; The data encryption module is used to perform the following operations on each of the multiple original user datasets: encrypt the original user dataset using the data processing unit and the blockchain network node set to obtain an encrypted user dataset; and package the encrypted user dataset into blocks according to the master node and the preset block structure to obtain an encrypted block to be reached for consensus. The feature extraction module is used to perform consensus verification on the cryptographic block to be reached using multiple participating nodes to obtain the on-chain cryptographic block, extract features from the on-chain cryptographic block to obtain the user feature dataset, and perform feature segmentation on the user feature dataset to obtain the user feature type set. The profile building module is used to build profiles based on the user feature type set, obtain user profiles, summarize the user profiles to obtain a user profile set, obtain profile access standards based on the user profile set, and make authorization determination using the data access authorization unit based on the profile access standards to complete the trusted data management of blockchain multi-platform.

[0015] To address the above problems, the present invention also provides an electronic device, the electronic device comprising: A memory for storing at least one instruction; and a processor for executing the instructions stored in the memory to implement the aforementioned blockchain multi-platform data trust management method based on user profiles.

[0016] To address the aforementioned issues, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned blockchain multi-platform data trust management method based on user profiles.

[0017] To address the problems described in the background art, this invention establishes a multi-platform data management environment. This environment includes a data acquisition unit, a blockchain network node set, a data processing unit, and a data access authorization unit. The blockchain network node set includes a master node and multiple participating nodes. The data acquisition unit collects data from multiple platforms to obtain multiple raw user datasets. For each of these raw user datasets, the following operations are performed: the data processing unit and the blockchain network node set encrypt the raw user dataset to obtain an encrypted user dataset. This invention ensures the security of user data and prevents tampering during transmission by encrypting the raw user datasets. Based on the master node and a preset block structure, the encrypted user dataset is packaged into blocks to obtain encrypted blocks awaiting consensus. By determining the block structure, this invention ensures the consistency of subsequent steps and avoids verification failures caused by inconsistent block formats. This invention utilizes multiple participating nodes to perform consensus verification on a blockchain-based encrypted block, thereby ensuring the credibility of the block. Features are extracted from this encrypted block to obtain a user feature dataset. This dataset is then segmented to obtain a user feature type set. By segmenting these features, the invention determines user preference types, providing a data foundation for subsequent user profiling. Based on this user feature type set, user profiles are constructed. The invention calculates feature preference degrees, using user feature types with preference degrees exceeding a threshold as key feature sets to build user profiles. These user profiles are then aggregated to obtain a user profile set. Based on this set, profile access standards are obtained, and authorization is determined using a data access authorization unit, thus completing the trusted management of multi-platform blockchain data. Therefore, this invention solves the problem of low data credibility in current multi-platform blockchain data management. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a blockchain-based multi-platform data trust management method based on user profiles, provided in an embodiment of the present invention. Figure 2 This is a functional module diagram of a blockchain multi-platform data trust management system based on user profiles provided in an embodiment of the present invention; Figure 3This is a schematic diagram of the structure of an electronic device that implements the blockchain multi-platform data trust management method based on user profiles, according to an embodiment of the present invention.

[0019] Explanation of reference numerals in the attached figures: 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0022] This application provides a blockchain multi-platform data trust management method based on user profiles. The executing entity of this user profile-based blockchain multi-platform data trust management method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application embodiment: a server, a terminal, etc. In other words, the user profile-based blockchain multi-platform data trust management method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0023] Reference Figure 1 The diagram shown is a flowchart illustrating a blockchain multi-platform data trust management method based on user profiles, according to an embodiment of the present invention. In this embodiment, the blockchain multi-platform data trust management method based on user profiles includes: S1. Determine a multi-platform data management environment, wherein the multi-platform data management environment includes: a data acquisition unit, a blockchain network node set, a data processing unit, and a data access authorization unit, wherein the blockchain network node set includes: a master node and multiple participating nodes.

[0024] It should be explained that the data collection unit refers to a distributed data collection module deployed on multiple platforms (such as e-commerce platforms, social platforms, content platforms, etc.), which can collect basic user information (such as registration information, device information), behavioral data (such as browsing history, click history, transaction history), and interaction data (such as comments, shares, favorites) through SDK tools. The blockchain network node set refers to a collection of multiple blockchain network nodes responsible for executing blockchain-related functions (such as data encryption, consensus mechanism verification, etc.), specifically divided into master nodes and participating nodes. The data processing unit refers to a module responsible for data cleaning and feature extraction of the collected user data. It supports homomorphic computation or privacy computation in an encrypted state to ensure privacy and security during data processing. The data access authorization unit refers to a module that authorizes data access requests based on the profile access standard (which will be explained in detail in subsequent steps). When a data access request is received, it is responsible for verifying whether the requester meets the access conditions (such as whether it has obtained user authorization and whether it meets sensitive data access restrictions), and ultimately decides whether to grant data access permissions to ensure the secure and compliant use of user profiles. The master node refers to the node device in the blockchain network that has management and control authority. It is typically deployed by the initiator or core operator of multi-platform data management and is responsible for key operations such as generating asymmetric encryption key pairs and packaging encrypted user datasets into blocks, playing a leading role in the blockchain network. The participating nodes refer to the subordinate node devices in the blockchain network that have data storage, verification, and backup capabilities. They can be deployed by multi-platform partners, third-party regulatory agencies, or other authorized entities. Their main responsibility is to verify the encrypted blocks generated by the master node to ensure the integrity and consistency of data in the blockchain network.

[0025] S2. Use the data acquisition unit to collect data from multiple platforms to obtain multiple raw user datasets.

[0026] It should be noted that the multi-platform data collection refers to the process of collecting user data from multiple different data sources (such as e-commerce platforms, social platforms, IoT devices, etc.) using a data collection unit. The original user dataset refers to the collection of all user data collected through the data collection unit, including: basic user information data (such as gender, age, region, device model, etc.), user behavior data (such as product browsing time, click path, search keywords, transaction amount, etc.), and user interaction data (such as comments, sharing, favorites, etc.).

[0027] S3. Perform the following operation on each of the plurality of original user datasets: encrypt the original user dataset using the data processing unit and the blockchain network node set to obtain an encrypted user dataset.

[0028] In detail, the encryption of the original user dataset using the data processing unit and the blockchain network node set to obtain the encrypted user dataset includes: The data processing unit identifies duplicate data from the original user dataset and removes the duplicate data to obtain a dataset without duplicates. Identify missing datasets in the non-duplicate dataset, wherein the missing datasets include one or more missing data points; For each missing data point in the missing dataset, perform the following operation: Identify missing fields in the missing data and determine whether the missing fields are preset important fields; If the missing field is an important field, the missing data will be marked as invalid data; Otherwise, mark the missing data as data to be filled; Summarize the invalid data and the data to be filled to obtain the invalid dataset and the dataset to be filled. Invalid datasets are removed from the non-duplicate dataset to obtain the valid dataset; Fill the unfilled data in the valid dataset to obtain the complete dataset; The complete dataset is then standardized to obtain a standard user dataset. The standard user dataset is encrypted using a set of blockchain network nodes to obtain an encrypted user dataset.

[0029] It is understood that the preprocessing operation refers to a series of operations that standardize the original user dataset, including: removing duplicate data, filling missing values, and unifying data format. Preprocessing operations can improve the quality of the original user dataset and ensure data consistency and usability. The standard user dataset refers to the original user dataset after preprocessing. Duplicate data refers to the same data appearing multiple times in the original user dataset. The duplicate-free dataset refers to the dataset obtained after removing duplicate data. The missing dataset refers to the set of all missing data identified in the duplicate-free dataset. Missing data refers to a situation where one or more fields of a certain data in the original user dataset are empty or not recorded, such as missing access time or user ID. Missing fields refer to the specific fields missing in the missing data. Important fields refer to data fields that have a critical impact on subsequent steps such as feature extraction and profile construction; their absence will render the data unusable or seriously affect the accuracy of the analysis results, such as user identifier or access time. Invalid data refers to data records that cannot be used due to missing important fields. Data to be filled refers to data records where the missing field is not an important field (i.e., the missing field does not have a critical impact on subsequent processing steps). The invalid dataset refers to a set consisting of all invalid data, and the dataset to be filled refers to a set of all data to be filled. Data filling refers to using a filling method to complete missing fields in the data to be filled. The filled data refers to the data to be filled after data filling, where missing fields have been filled. The filling method refers to a pre-defined method used to complete unfilled data (such as statistical filling or multiple imputation). The valid dataset refers to the dataset obtained after removing invalid data from the unique dataset. The complete dataset refers to the dataset formed after all data to be filled in the valid dataset has been updated with filled data. Data filling has been performed on the missing dataset to obtain filled data; at this point, the data to be filled in the valid dataset has not yet been filled and needs to be updated. Format standardization refers to converting data of different formats in the complete dataset into a standardized structure (e.g., unifying user identifiers to string types, unifying behavior frequencies to integer types, etc.). The method of converting data of different formats into a standardized structure is existing technology and will not be elaborated here.

[0030] Specifically, the encryption of the standard user dataset based on the blockchain network node set to obtain the encrypted user dataset includes: An asymmetric encryption key pair is generated using the master node in the blockchain network node set, wherein the asymmetric encryption key pair includes a public key and a private key; The sensitive field set and data field set were identified from the standard user dataset; The sensitive field set is encrypted using the public key in the asymmetric encryption key pair to obtain the encrypted sensitive field set. Perform a hash operation on the data field set to obtain a data hash value set; Construct an encrypted user dataset based on the set of encrypted sensitive fields and the set of data hash values.

[0031] It should be understood that the encryption process refers to using encryption algorithms, hash operations, and digital signatures to differentiate different types of fields (sensitive and non-sensitive fields) in the standard user dataset, ultimately generating an encrypted user dataset with confidentiality (sensitive data is not visible), integrity (data has not been tampered with), and verifiability (trustworthy source). The encrypted user dataset refers to the collection of encrypted user data obtained after encrypting the standard user dataset; this encrypted user data is structured data containing encrypted sensitive fields, data hash values, and digital signatures. The asymmetric encryption key pair refers to a pair of keys generated by the master node based on an asymmetric encryption algorithm (such as RSA or ECC elliptic curve cryptography), namely a public key and a private key. The public key is the key used in the asymmetric encryption key pair to encrypt the sensitive field set in the standard user dataset and can be publicly distributed. The private key is a key exclusively stored by the master node and cannot be publicly disclosed; it is the unique key for decrypting data encrypted with the corresponding public key. Only the master node can hold the private key, which is used for compliant decryption or homomorphic computation operations on the encrypted sensitive field set in subsequent feature extraction and other stages. The sensitive field set refers to a set of fields selected from the standard user dataset that contain user privacy information, identity information, or high-value attributes. Leakage of these fields could lead to user privacy violations or data security risks. Examples include basic user identity fields (such as name, mobile phone number, and fragments of ID card number) and associated account fields (such as third-party platform bound IDs). The data field set refers to a set of data fields in the standard user dataset that do not directly involve user privacy, such as user browsing content categories, browsing duration, and access content types. The encryption process refers to encrypting the sensitive data field set using a preset encryption algorithm (such as the Paillier algorithm or the ElGamal algorithm). The encrypted sensitive field set refers to the sensitive data field set after encryption. The hash operation refers to the process of using a hash function (such as SHA-256) to perform one-way mathematical calculations on the data field set to generate a fixed-length hash value. The data hash value set refers to the set of all hash values ​​obtained after hashing the data field set. Constructing an encrypted user dataset based on the encrypted sensitive field set and the data hash value set means aggregating the encrypted sensitive field set and the data hash value set into a single set, which is the encrypted user dataset.

[0032] S4. Based on the master node and the preset block structure, the encrypted user dataset is packaged into blocks to obtain the encrypted block to be consensused. Multiple participating nodes are used to verify the consensus of the encrypted block to be consensused to obtain the on-chain encrypted block.

[0033] Furthermore, the step of packaging the encrypted user dataset into blocks based on the master node and a preset block structure to obtain the encrypted blocks to be consensused includes: Based on the master node, the encrypted user dataset is sharded according to the preset number of data pieces to obtain multiple encrypted data shards; For each of the plurality of encrypted data fragments, the following operation is performed: Obtain the fragment identifier based on encrypted data fragmentation; Calculate the hash value of each encrypted data fragment; The shard identifier is combined with the shard hash value to obtain the identifier shard hash group; By summing the aforementioned identifier fragment hash groups, multiple identifier fragment hash groups are obtained, wherein each identifier fragment hash group corresponds one-to-one with each encrypted data fragment in the multiple encrypted data fragments; The shard hash values ​​of each of the multiple shard hash groups are sorted to obtain the shard hash group sequence. Extract the first identifier fragment hash group sequentially from the identifier fragment hash group sequence, and perform the following operation on each of the first identifier fragment hash groups: The second identifier fragment hash group is determined using the first identifier fragment hash group, wherein the second identifier fragment hash group is adjacent to and lags behind the first identifier fragment hash group; The fragment hash value of the first identifier fragment hash group is confirmed as the preceding hash value of the second identifier fragment hash group. When the first identifier fragment hash group is the preset first identifier fragment hash group in the identifier fragment hash group sequence, the preset initial hash value is used as the preceding hash value of the first identifier fragment hash group. By summing the second identifier fragment hash group and the preceding hash value, we obtain the second identifier fragment hash group set and the preceding hash value set; Construct a sharded hash chain based on the first identifier sharded hash group, the second identifier sharded hash group set, and the preceding hash value set; Calculate the root hash value of the fragmented hash chain; The preceding hash of the block is determined from the sharded hash chain; Confirm the number of shards based on multiple encrypted data shards; Get the current time, and according to the block structure, assemble the root hash value, the block preorder hash, the number of shards, and the current time to obtain the block header; The block body is constructed based on multiple encrypted data fragments and multiple identifier fragment hash groups; The block header and block body are integrated and concatenated to obtain the encrypted block to be consensused.

[0034] It should be explained that the block structure refers to a pre-selected, artificially set structured template used to standardize the composition and format of blocks, including a block header and a block body. The block packaging process refers to a series of operations by which the master node converts the encrypted user dataset into encrypted blocks awaiting consensus, including: sharding, constructing a shard hash chain, constructing the block body, and merging and concatenating. The encrypted block awaiting consensus refers to the data block generated after the encrypted user dataset has undergone block packaging processing. It contains a complete block header and block body, but has not yet been confirmed by participating nodes and requires subsequent consensus verification steps. The data quantity refers to a pre-set numerical value for the amount of data in each shard, for example, each shard contains 100 encrypted user data records. Sharding the encrypted user dataset according to the preset data quantity to obtain multiple encrypted data shards means that the master node divides the encrypted user dataset into several uniformly sized subsets based on the data quantity; each subset is an encrypted data shard. For example, if an encrypted user dataset contains 500 records, with 100 records per record, after sharding, it can be divided into 5 encrypted data shards. The purpose of sharding is to improve consensus verification efficiency and facilitate distributed data storage and retrieval. The shard identifier is a unique identifier assigned to each encrypted data shard to distinguish different shards. The shard hash value is a fixed-length hash value calculated using a hash function on the encrypted data shard. The identifier shard hash group is a data structure composed of shard identifiers and shard hash values, with the structure {shard identifier, shard hash value}. The identifier shard hash group sequence is an ordered list formed by sorting multiple identifier shard hash groups in ascending order of their shard hash values. The first identifier shard hash group refers to the identifier shard hash group extracted sequentially from the identifier shard hash group sequence and used as the current processing object. The second identifier shard hash group refers to the identifier shard hash group that is adjacent to and follows the first identifier shard hash group in the identifier shard hash group sequence.

[0035] It should be noted that the preceding hash value refers to the hash value of the preceding hash group (i.e., the first hash group) referenced by the second identifier shard hash group. This preceding hash value is used to establish the logical order and dependency relationship between shards, ensuring that the order of shards cannot be tampered with (if a shard is modified, the preceding hash values ​​of its subsequent shards will not match). The first identifier shard hash group refers to the first identifier shard hash group in the identifier shard hash group sequence (i.e., the first element of the sequence), which is the genesis block. Since it has no preceding shards, its initial hash value needs to be used as its preceding hash value to construct a complete chain structure. The initial hash value refers to a pre-set fixed hash value used as the preceding hash value of the first identifier shard hash group. The second identifier shard hash group set refers to the set composed of all second identifier shard hash groups. The preceding hash value set refers to the set composed of all preceding hash values. The construction of a sharded hash chain based on the first identifier sharded hash group, the second identifier sharded hash group set, and the preceding hash value set refers to: starting with the first identifier sharded hash group, associating its sharded hash value with the initial hash value, and then sequentially associating each second identifier sharded hash group in the second identifier sharded hash group set with its corresponding preceding hash value, forming a chain structure. For example: G1 is the first identifier sharded hash group (the preceding hash value is the initial hash value H0), G2 is the second identifier sharded hash group (the preceding hash value is the sharded hash value H1 of G1), and G3 is the third identifier sharded hash group (the preceding hash value is the sharded hash value H2 of G2). The resulting sharded hash chain is H0 (initial value) → H1 (sharded hash value of G1) → H2 (sharded hash value of G2). The root hash value refers to the single hash value obtained by calculating all hash values ​​in the entire sharded hash chain using the Merkle tree algorithm. The preceding hash of the block refers to the root hash value of the previous block (i.e., the previous encrypted block in the blockchain) referenced by the encrypted block to be consensused, used to establish the chain structure of the blockchain. The number of shards refers to the total number of encrypted data shards contained in the encrypted block to be consensused. The current time refers to the time when the master node completes the block packaging process and generates the encrypted block to be consensused. Assembling the root hash value, preceding hash of the block, number of shards, and current time according to the block structure to obtain the block header means: according to the block structure, the root hash value, preceding hash of the block, number of shards, and current time are integrated into a data unit, which is the block header. Constructing the block body based on multiple encrypted data shards and multiple identifier shard hash groups means: summarizing and integrating all encrypted data shards and their corresponding identifier shard hash groups into a set, which is the block body. The process of integrating and concatenating the block header and block body to obtain the encrypted block to be reached through consensus refers to: concatenating the block header and block body according to a pre-defined blockchain protocol specification to form the final encrypted block to be reached through consensus. Optionally, JSON format may be used as the blockchain protocol specification.

[0036] In detail, the process of using multiple participating nodes to perform consensus verification on the encrypted block to obtain the on-chain encrypted block includes: Generate an integrity verification value and a node digital signature based on the encrypted block to be reached and the private key; Extract participating nodes sequentially from the plurality of participating nodes, and perform the following operations on the participating nodes: The validity of the node digital signature is verified using the participating nodes and the integrity verification value to obtain the digital signature verification result. When the digital signature verification result is a preset pass, a verification pass certificate is generated; The verified credentials are summarized to obtain a set of verified credentials; The number of verified credentials is obtained from the set of verified credentials. When the number of verified credentials is greater than or equal to a preset threshold, the encrypted block awaiting consensus is confirmed as an on-chain encrypted block.

[0037] It is understood that the consensus verification refers to the process by which multiple participating nodes collectively verify and confirm the encrypted block to be agreed upon generated by the master node. The on-chain encrypted block refers to the encrypted block to be agreed upon that has been officially recorded in the blockchain after passing consensus verification. The integrity check value refers to a fixed-length hash value generated by calculating the encrypted block to be agreed upon using a hash function (such as SHA-256), used to verify whether the encrypted block to be agreed upon has been tampered with, lost, or damaged during transmission or storage. The node digital signature refers to the signature information generated by the master node after encrypting the integrity check value of the encrypted block to be agreed upon using its private key, which can be used to prove the trustworthiness of the encrypted block to be agreed upon. The validity verification refers to participating nodes decrypting the node digital signature using their public key and comparing the decrypted result with the encrypted block to be agreed upon. The decryption of the node digital signature using a public key is existing technology and will not be elaborated upon here. The digital signature verification result refers to the conclusion reached by participating nodes after decrypting the node's digital signature using their public key and comparing the decryption result with the integrity check value in the encrypted block to be reached for consensus. This includes: pass (the decryption result matches the integrity check value, proving the encrypted block to be reached for consensus has not been tampered with) and fail (the decryption result does not match the integrity check value, indicating the encrypted block to be reached for consensus may have been forged or tampered with). The verification pass credential refers to the credential generated when a participating node completes validity verification and the digital signature verification result is pass, indicating that the participating node has confirmed the integrity and trustworthiness of the encrypted block to be reached for consensus. The verification pass credential set refers to the set of all verification pass credentials. The number of verification pass credentials refers to the number of verification pass credentials. The number threshold refers to a pre-set critical number used to determine the validity of the encrypted block to be reached for consensus. Only when the number of verification pass credentials exceeds the number threshold is the encrypted block to be reached for consensus considered valid and can be determined as an on-chain encrypted block. When the number of verified credentials is less than or equal to the threshold, the block to be consensused is considered invalid. This indicates that the original user dataset has low credibility and should be discarded. In other words, no further steps should be performed on the invalid block to be consensused.

[0038] S5. Extract features from the encrypted blocks on the chain to obtain a user feature dataset, and divide the user feature dataset into user feature type sets.

[0039] Specifically, the step of extracting features from the on-chain encrypted blocks to obtain a user feature dataset includes: The user dataset to be decrypted is identified from the on-chain encrypted blocks, wherein the user dataset to be decrypted corresponds one-to-one with the encrypted user dataset; Identify the set of sensitive fields to be decrypted and the set of hash values ​​of the data to be decrypted from the user dataset to be decrypted; The set of sensitive fields to be decrypted is homomorphically decrypted using a private key and a preset decryption algorithm to obtain the decrypted sensitive field set; The hash value set of the data to be decrypted is hash-verified to obtain the decrypted dataset; The user feature dataset is obtained by parsing the fields of the decrypted dataset.

[0040] It should be understood that feature extraction refers to the process of extracting the user dataset to be decrypted from the on-chain encrypted blocks and decrypting the user dataset. The user feature dataset refers to the set of data reflecting user behavior and preferences obtained after parsing the fields of the decrypted dataset. The user dataset to be decrypted refers to the encrypted user dataset extracted from the on-chain encrypted blocks that needs to be decrypted, including a set of sensitive fields to be decrypted and a set of hash values ​​of the data to be decrypted. The set of sensitive fields to be decrypted is the same as the set of encrypted sensitive fields in the encrypted user dataset. The set of hash values ​​of the data to be decrypted is the same as the set of hash values ​​of the data. The decryption algorithm refers to the algorithm used to restore the encrypted user dataset to the original user dataset. It must correspond to the encryption algorithm; for example, if the encryption process uses the RSA algorithm for asymmetric encryption, then the decryption algorithm must be the corresponding RSA decryption algorithm. Homomorphic decryption refers to the decryption method based on homomorphic encryption, decrypting the encrypted user dataset under the authorization of the private key, and the result obtained after decryption is consistent with the result of directly performing the same calculation on the user dataset. The decrypted sensitive field set refers to the set of sensitive fields to be decrypted after homomorphic decryption, which contains the user's privacy information. The hash verification refers to the process of converting the set of hash values ​​of the data to be decrypted into a corresponding decrypted dataset using a hash mapping standard. The hash mapping standard is a pre-defined standard used to convert the set of hash values ​​of the data to be decrypted into fixed-length hash values ​​using a hash function. During the encryption phase, the data field set obtains a corresponding set of data hash values ​​through a hash function. Based on this mapping relationship, the corresponding data hash values ​​can be matched with the decrypted data using the hash mapping standard during the hash verification process. The decrypted dataset refers to the set of behavioral data obtained through hash verification that does not involve user privacy information, including: user browsing content, access content types, etc. The field parsing refers to using a pre-defined keyword extraction algorithm (such as the TF-IDF algorithm) to filter data information from the decrypted dataset that reflects user behavioral characteristics (such as access duration, access content, etc.).

[0041] Furthermore, the step of segmenting the user feature dataset to obtain a user feature type set includes: Extract a content feature set and a behavior feature set from the user feature dataset. The content feature set includes one or more content features, and the behavior feature set includes one or more behavior features. The content features and behavior features correspond one-to-one. The user feature dataset is classified according to the content feature set to obtain a user access type set, wherein the user access set contains one or more user access types. For each user access type in the set of user access types, the following operation is performed: Identify target behavioral features corresponding to user access types within the behavioral feature set; Based on the target behavioral characteristics, the frequency of the user access type behavior is counted; Determine the access duration for the user access type; Access types with a frequency greater than a preset frequency threshold and an access duration greater than a preset duration threshold are defined as user feature types. The user feature types are summarized to obtain a user feature type set, wherein the user feature type set contains one or more user feature types.

[0042] It should be explained that the feature segmentation refers to a series of steps to convert the user feature dataset into a user feature type set, including: content classification, statistical behavior frequency, and determination of access duration. The user feature type set refers to the set composed of all user feature types. The content feature set refers to the set composed of all content features. Content features refer to the attribute characteristics used to describe the content that a user encounters or interacts with, such as: content category (e.g., electronic products, clothing), content theme (e.g., sports, technology), content format (e.g., video, image), etc., used to distinguish different types of accessed content. Behavioral features refer to the attribute characteristics describing the user's interactive behavior towards the accessed content, such as: number of clicks, dwell time, number of shares, etc. The behavioral feature set refers to the set composed of all behavioral features. Content classification refers to classifying the user feature set into different categories based on content features. Optionally, in this embodiment, content category is used as the content feature for classification. The user access type set refers to the set composed of all user access types. The target behavioral feature refers to the behavioral feature corresponding to the user access type in the behavioral feature set. The user access type refers to the different access types of user access obtained through content classification. The behavior frequency refers to the number of times a user accesses a certain access type within a preset time range (such as one day or one week). The access duration refers to the cumulative time a user spends on a certain access type. The frequency threshold refers to a pre-set threshold value for behavior frequency, used to determine whether the access type is a user's preferred type. The duration threshold refers to a pre-set threshold value for access duration, used to determine whether the access type is a user's preferred type. The user characteristic type refers to an access type that simultaneously satisfies both a behavior frequency greater than the frequency threshold and an access duration greater than the duration threshold.

[0043] S6. Construct user profiles based on the user feature type set to obtain user profiles, summarize the user profiles to obtain a user profile set, and obtain profile access standards based on the user profile set.

[0044] In detail, the process of constructing a user profile based on the user feature type set includes: For each user feature type in the set of user feature types, the following operation is performed: Obtain the interaction depth of user characteristic types; Confirm the frequency of characteristic behaviors and the duration of characteristic accesses for user characteristic types; The feature preference degree is calculated based on the interaction depth, feature behavior frequency, and feature access duration, using the following formula: ; in, Indicates feature preference. Indicates the frequency of characteristic behaviors. Indicates the duration of feature access. Indicates the depth of interaction. , , These represent the adjustment coefficients for the preset frequency of feature behaviors, the preset duration of feature access, and the preset depth of interaction, respectively. Represents the natural constant; By summing up the aforementioned feature preference degrees, a feature preference degree set is obtained; Based on the feature preference set, the user feature type set is sorted in descending order to obtain the feature preference sequence; Based on a preset feature preference threshold, a set of key features is selected from the feature preference sequence. User profiles are constructed based on key feature sets and decrypted sensitive field sets.

[0045] It should be noted that the aforementioned user profile construction refers to the process of obtaining a user profile through quantitative analysis based on user behavioral data (such as interaction depth, frequency of characteristic behaviors, and duration of characteristic visits) and user characteristic types. The user profile refers to a comprehensive set of information that describes a user's behavioral preferences, habits, and identity characteristics. A user profile typically includes the user's behavioral habits (such as user characteristic types) and personal information (such as user ID, device type, IP address, etc.). The interaction depth refers to the degree of interaction a user has with a particular user characteristic type. Its specific value is obtained based on the number of interaction actions (such as liking, commenting, saving, purchasing, etc.) and pre-defined quantitative values ​​for these actions. For example, a liking action is quantified as 1, a comment as 2, a saving action as 1.5, and a purchasing action as 3. If a user likes a particular user characteristic type twice, comments once, and purchases once, then the user's interaction depth with that characteristic type is 2. 1 1 2 1 3 7. The frequency of characteristic behavior refers to the frequency of user characteristic type behavior. The duration of characteristic access refers to the duration of user characteristic type access. The characteristic preference degree refers to a quantitative indicator reflecting the degree of user's liking for a certain user characteristic type; the higher the characteristic preference degree, the deeper the user's liking for that user characteristic type. The adjustment coefficient of characteristic behavior frequency refers to a pre-set coefficient representing the degree of influence of characteristic behavior frequency on characteristic preference degree. The adjustment coefficient of characteristic access duration refers to a pre-set coefficient representing the degree of influence of characteristic access duration on characteristic preference degree. The adjustment coefficient of interaction depth refers to a pre-set coefficient representing the degree of influence of interaction depth on characteristic preference degree. The characteristic preference degree set refers to the set composed of all characteristic preferences. The step of sorting the user characteristic type set in descending order according to the characteristic preference degree set to obtain the characteristic preference sequence means: using the characteristic preference degree in the characteristic preference degree set as the sorting basis, arranging the corresponding user characteristic types in the user characteristic type set in descending order of characteristic preference degree to form a sequence, the formed sequence is the characteristic preference sequence, and each element of the characteristic preference sequence is {characteristic preference degree, user characteristic type}. The feature preference threshold refers to a pre-set, manually determined critical value used to distinguish whether a user feature type is a key user feature. When the feature preference corresponding to a user feature type is greater than the feature preference threshold, the user feature type can be identified as a key feature type. The key feature set refers to the set of user feature types selected from the feature preference sequence whose feature preference is greater than the feature preference threshold. The key feature set represents the user's most important behavioral preferences and interests, and is the basic element for building a user profile. Building a user profile based on the key feature set and the decrypted sensitive field set means summarizing the key feature set and the decrypted sensitive field set to obtain a set, which is the user profile. The user profile reflects the user's behavioral preferences and identity information, and needs to be managed to prevent the leakage of user information.

[0046] Specifically, the step of obtaining the profile access criteria based on the user profile set includes: Obtain security level standards and access requirements; For each user profile in the user profile set, perform the following operations: Based on the security level standards, the user profile is assessed for security level to obtain the profile security level; By summarizing the security levels of the images, a set of image security levels is obtained; Cluster analysis is performed on the user profile set to obtain a profile type set, wherein the profile type set includes one or more profile types, and each profile type includes one or more user profiles; For each image type in the image type set, the following operation is performed: Based on the access requirements, the image type is adapted to the access type to obtain the set of permitted access types; By summing the aforementioned sets of permitted access types, multiple sets of permitted access types are obtained; A profile access standard is constructed based on the user profile set, profile security level set, and multiple permitted access type sets.

[0047] It is understood that the aforementioned profile access standard refers to the standard used to determine the access permissions for different types of user profiles, which clarifies the permitted access types and security levels corresponding to each user profile. The aforementioned security level standard refers to a pre-set standard for assessing the privacy of user profiles. The aforementioned security level assessment refers to evaluating the user security level of a user profile using the security level standard. The aforementioned profile security level refers to the risk level identifier obtained by a user profile after undergoing security level assessment. Specifically, the aforementioned security level standards are as follows: These standards categorize user profile security into three levels: low, medium, and high. A low profile security level is defined as one where the user profile contains only basic personal information (such as user ID, gender, and name). A medium profile security level is defined as one where, in addition to basic personal information, the user profile also contains the user's identity privacy information (such as mobile phone number, fragments of ID card number, and home address area), and the leakage of the user profile may lead to related risks for the user (such as receiving fraudulent calls). A high profile security level is defined as one where, in addition to basic personal information and identity privacy information, the user profile also contains the user's core privacy information (such as accurate consumption records and financial account association identifiers), and the leakage of the user profile will directly threaten the user's property security, personal privacy, or social rights. The profile security level set refers to the collection of profile security levels for all user profiles. The clustering analysis refers to a data analysis operation that uses a preset clustering algorithm (such as K-Means clustering or hierarchical clustering) to divide the user profile set into multiple profile types based on the feature similarity of the user profiles (such as behavioral feature types and user personal information). For example, user profiles aged 25-35 who include maternity and baby products are grouped into the "Young Maternity and Baby User" category, and user profiles aged 18-24 who include sporting goods are grouped into the "Youth Sports User" category, thus achieving structured classification of user profiles. The profile type set refers to the collection composed of all profile types. The access requirements refer to pre-defined constraints on access behavior to user profiles, including access subject constraints and access level constraints. The access subject constraints clarify the scope of entities allowed to access the user profile; for example, if a user profile is classified as a "Young Maternity and Baby User," then the maternity and baby product development team and marketing team can access it. The access level constraints clarify the access restrictions corresponding to different profile security levels; for example, a high profile security level only allows access from relevant internal compliance departments and core business teams, prohibiting access from third-party partners; a medium profile security level allows access from relevant internal business teams and approved third-party partners; a low profile security level allows access from internal teams and third-party partners. Access type adaptation refers to determining, based on the access requirements, which institutions or units can access a particular user profile. The permitted access type set refers to the set of institutions or units that can be accessed after access type adaptation.The construction of the user profile access standard based on the user profile set, profile security level set, and multiple permitted access type sets refers to associating the user profile set, profile security level, and multiple permitted access type sets to obtain an access permission mapping table. This access permission mapping table is the user profile access standard, which consists of multiple rows, where each row contains information of {user profile, profile security level, permitted access type}. Specifically, this access permission mapping table clarifies the content of each user profile and its corresponding profile security level and permitted access type.

[0048] S7. Based on the profile access standard, use the data access authorization unit to make authorization determination and complete the trusted management of blockchain multi-platform data.

[0049] It should be explained that when a data access request is received, the data access authorization unit will perform an authorization determination. The data access request refers to a request initiated by an accessing entity (such as an internal team or a third-party partner) to obtain user profile data of a specific user or user group. The authorization determination refers to the process of evaluating and deciding on the data access request based on the profile access standards to determine whether the data access request has the necessary access permissions. Specifically, it verifies whether the identity of the accessing entity and the type of access requested are within the permitted scope of the profile access standards. If they are within the permitted scope, the accessing entity is determined to have access permissions, and access authorization can be granted; if they are not within the permitted scope, the accessing entity is determined to not have access permissions, and access authorization is refused.

[0050] To address the problems described in the background art, this invention establishes a multi-platform data management environment. This environment includes a data acquisition unit, a blockchain network node set, a data processing unit, and a data access authorization unit. The blockchain network node set includes a master node and multiple participating nodes. The data acquisition unit collects data from multiple platforms to obtain multiple raw user datasets. For each of these raw user datasets, the following operations are performed: the data processing unit and the blockchain network node set encrypt the raw user dataset to obtain an encrypted user dataset. This invention ensures the security of user data and prevents tampering during transmission by encrypting the raw user datasets. Based on the master node and a preset block structure, the encrypted user dataset is packaged into blocks to obtain encrypted blocks awaiting consensus. By determining the block structure, this invention ensures the consistency of subsequent steps and avoids verification failures caused by inconsistent block formats. This invention utilizes multiple participating nodes to perform consensus verification on a blockchain-based encrypted block, thereby ensuring the credibility of the block. Features are extracted from this encrypted block to obtain a user feature dataset. This dataset is then segmented to obtain a user feature type set. By segmenting these features, the invention determines user preference types, providing a data foundation for subsequent user profiling. Based on this user feature type set, user profiles are constructed. The invention calculates feature preference degrees, using user feature types with preference degrees exceeding a threshold as key feature sets to build user profiles. These user profiles are then aggregated to obtain a user profile set. Based on this set, profile access standards are obtained, and authorization is determined using a data access authorization unit, thus completing the trusted management of multi-platform blockchain data. Therefore, this invention solves the problem of low data credibility in current multi-platform blockchain data management.

[0051] like Figure 2 The diagram shown is a functional block diagram of a blockchain multi-platform data trust management system based on user profiles provided in an embodiment of the present invention.

[0052] The blockchain multi-platform data trust management system 100 based on user profiles described in this invention can be installed in an electronic device. Depending on the functions implemented, the blockchain multi-platform data trust management system 100 may include a data management environment module 101, a data encryption module 102, a feature extraction module 103, and a profile construction module 104. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0053] The data management environment module 101 is used to determine a multi-platform data management environment, wherein the multi-platform data management environment includes: a data acquisition unit, a blockchain network node set, a data processing unit, and a data access authorization unit, wherein the blockchain network node set includes: a master node and multiple participating nodes, and the data acquisition unit is used to collect data from multiple platforms to obtain multiple original user datasets; The data encryption module 102 is used to perform the following operations on each of the multiple original user datasets: encrypt the original user dataset using the data processing unit and the blockchain network node set to obtain an encrypted user dataset; and package the encrypted user dataset into blocks according to the master node and the preset block structure to obtain an encrypted block to be reached through consensus. The feature extraction module 103 is used to perform consensus verification on the encrypted block to be consensus using multiple participating nodes to obtain an on-chain encrypted block, extract features from the on-chain encrypted block to obtain a user feature dataset, and perform feature segmentation on the user feature dataset to obtain a user feature type set. The profile construction module 104 is used to construct profiles based on the user feature type set to obtain user profiles, summarize the user profiles to obtain a user profile set, obtain profile access standards based on the user profile set, and use the data access authorization unit to make authorization determination based on the profile access standards, thereby completing the trusted data management of blockchain multi-platform.

[0054] In detail, the modules in the blockchain multi-platform data trust management system 100 based on user profiles described in this embodiment of the invention adopt the same approach as described above. Figure 1 The method uses the same technical means as the blockchain multi-platform data trust management method based on user profiles described in the article, and can produce the same technical effect, so it will not be repeated here.

[0055] like Figure 3 The diagram shown is a structural schematic of an electronic device that implements a blockchain multi-platform data trust management method based on user profiles, according to an embodiment of the present invention.

[0056] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a blockchain multi-platform data trust management method program based on user profiles.

[0057] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as the portable hard drive of the electronic device 1. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a blockchain multi-platform data trust management method program based on user profiles, but also to temporarily store data that has been output or will be output.

[0058] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a blockchain multi-platform data trust management method program based on user profiles) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0059] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0060] Figure 3 Only electronic devices with components are shown; those skilled in the art will understand that... Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0061] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0062] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0063] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0064] The blockchain multi-platform data trust management method program based on user profiles, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following: A multi-platform data management environment is defined, wherein the multi-platform data management environment includes: a data acquisition unit, a blockchain network node set, a data processing unit, and a data access authorization unit, wherein the blockchain network node set includes: a master node and multiple participating nodes; The data acquisition unit is used to collect data from multiple platforms to obtain multiple raw user datasets. For each of the plurality of original user datasets, the following operation is performed: The original user dataset is encrypted using a data processing unit and a blockchain network node set to obtain an encrypted user dataset. Based on the master node and the preset block structure, the encrypted user dataset is packaged into blocks to obtain an encrypted block to be reached for consensus. Multiple participating nodes are used to verify the consensus of the encrypted block to be agreed upon, thus obtaining the on-chain encrypted block; Feature extraction is performed on the on-chain encrypted blocks to obtain a user feature dataset, and feature segmentation is performed on the user feature dataset to obtain a user feature type set; Based on the user feature type set, a user profile is constructed to obtain a user profile. The user profiles are then aggregated to obtain a user profile set. Based on the user profile set, the profile access standard is obtained. Based on the profile access standard, the data access authorization unit is used to make authorization judgment, thereby completing the trusted data management of the blockchain multi-platform.

[0065] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0066] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0067] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following: A multi-platform data management environment is defined, wherein the multi-platform data management environment includes: a data acquisition unit, a blockchain network node set, a data processing unit, and a data access authorization unit, wherein the blockchain network node set includes: a master node and multiple participating nodes; The data acquisition unit is used to collect data from multiple platforms to obtain multiple raw user datasets. For each of the plurality of original user datasets, the following operation is performed: The original user dataset is encrypted using a data processing unit and a blockchain network node set to obtain an encrypted user dataset. Based on the master node and the preset block structure, the encrypted user dataset is packaged into blocks to obtain an encrypted block to be reached for consensus. Multiple participating nodes are used to verify the consensus of the encrypted block to be agreed upon, thus obtaining the on-chain encrypted block; Feature extraction is performed on the on-chain encrypted blocks to obtain a user feature dataset, and feature segmentation is performed on the user feature dataset to obtain a user feature type set; Based on the user feature type set, a user profile is constructed to obtain a user profile. The user profiles are then aggregated to obtain a user profile set. Based on the user profile set, the profile access standard is obtained. Based on the profile access standard, the data access authorization unit is used to make authorization judgment, thereby completing the trusted data management of the blockchain multi-platform.

[0068] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0069] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] Furthermore, the functional modules in the various embodiments of the present invention 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 in the form of hardware plus software functional modules.

[0071] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A blockchain-based multi-platform data trust management method based on user profiles, characterized in that, The method includes: A multi-platform data management environment is defined, wherein the multi-platform data management environment includes: a data acquisition unit, a blockchain network node set, a data processing unit, and a data access authorization unit, wherein the blockchain network node set includes: a master node and multiple participating nodes; The data acquisition unit is used to collect data from multiple platforms to obtain multiple raw user datasets. For each of the plurality of original user datasets, the following operation is performed: The original user dataset is encrypted using a data processing unit and a blockchain network node set to obtain an encrypted user dataset. Based on the master node and the preset block structure, the encrypted user dataset is packaged into blocks to obtain an encrypted block to be reached for consensus. Multiple participating nodes are used to verify the consensus of the encrypted block to be agreed upon, thus obtaining the on-chain encrypted block; Feature extraction is performed on the on-chain encrypted blocks to obtain a user feature dataset, and feature segmentation is performed on the user feature dataset to obtain a user feature type set; Based on the user feature type set, a user profile is constructed to obtain a user profile. The user profiles are then aggregated to obtain a user profile set. Based on the user profile set, the profile access standard is obtained. Based on the profile access standard, the data access authorization unit is used to make authorization judgment, thereby completing the trusted data management of the blockchain multi-platform.

2. The blockchain multi-platform data trust management method based on user profiles as described in claim 1, characterized in that, The process of encrypting the original user dataset using a data processing unit and a blockchain network node set to obtain an encrypted user dataset includes: The data processing unit identifies duplicate data from the original user dataset and removes the duplicate data to obtain a dataset without duplicates. Identify missing datasets in the non-duplicate dataset, wherein the missing datasets include one or more missing data points; For each missing data point in the missing dataset, perform the following operation: Identify missing fields in the missing data and determine whether the missing fields are preset important fields; If the missing field is an important field, the missing data will be marked as invalid data; Otherwise, mark the missing data as data to be filled; Summarize the invalid data and the data to be filled to obtain the invalid dataset and the dataset to be filled. Invalid datasets are removed from the non-duplicate dataset to obtain the valid dataset; Fill the unfilled data in the valid dataset to obtain the complete dataset; The complete dataset is then standardized to obtain a standard user dataset. The standard user dataset is encrypted using a set of blockchain network nodes to obtain an encrypted user dataset.

3. The blockchain multi-platform data trust management method based on user profiles as described in claim 2, characterized in that, The encryption of the standard user dataset based on the blockchain network node set yields an encrypted user dataset, including: An asymmetric encryption key pair is generated using the master node in the blockchain network node set, wherein the asymmetric encryption key pair includes a public key and a private key; The sensitive field set and data field set were identified from the standard user dataset; The sensitive field set is encrypted using the public key in the asymmetric encryption key pair to obtain the encrypted sensitive field set. Perform a hash operation on the data field set to obtain a data hash value set; Construct an encrypted user dataset based on the set of encrypted sensitive fields and the set of data hash values.

4. The blockchain multi-platform data trust management method based on user profiles as described in claim 3, characterized in that, The process of packaging the encrypted user dataset into blocks based on the master node and a preset block structure to obtain the encrypted blocks to be reached through consensus includes: Based on the master node, the encrypted user dataset is sharded according to the preset number of data pieces to obtain multiple encrypted data shards; For each of the plurality of encrypted data fragments, the following operation is performed: Obtain the fragment identifier based on encrypted data fragmentation; Calculate the hash value of each encrypted data fragment; The shard identifier is combined with the shard hash value to obtain the identifier shard hash group; By summing the aforementioned identifier fragment hash groups, multiple identifier fragment hash groups are obtained, wherein each identifier fragment hash group corresponds one-to-one with each encrypted data fragment in the multiple encrypted data fragments; The shard hash values ​​of each of the multiple shard hash groups are sorted to obtain the shard hash group sequence. Extract the first identifier fragment hash group sequentially from the identifier fragment hash group sequence, and perform the following operation on each of the first identifier fragment hash groups: The second identifier fragment hash group is determined using the first identifier fragment hash group, wherein the second identifier fragment hash group is adjacent to and lags behind the first identifier fragment hash group; The fragment hash value of the first identifier fragment hash group is confirmed as the preceding hash value of the second identifier fragment hash group. When the first identifier fragment hash group is the preset first identifier fragment hash group in the identifier fragment hash group sequence, the preset initial hash value is used as the preceding hash value of the first identifier fragment hash group. By summing the second identifier fragment hash group and the preceding hash value, we obtain the second identifier fragment hash group set and the preceding hash value set; Construct a sharded hash chain based on the first identifier sharded hash group, the second identifier sharded hash group set, and the preceding hash value set; Calculate the root hash value of the fragmented hash chain; The preceding hash of the block is determined from the sharded hash chain; Confirm the number of shards based on multiple encrypted data shards; Get the current time, and according to the block structure, assemble the root hash value, the block preorder hash, the number of shards, and the current time to obtain the block header; The block body is constructed based on multiple encrypted data fragments and multiple identifier fragment hash groups; The block header and block body are integrated and concatenated to obtain the encrypted block to be consensused.

5. The blockchain multi-platform data trust management method based on user profiles as described in claim 4, characterized in that, The process of using multiple participating nodes to perform consensus verification on the encrypted block to obtain the on-chain encrypted block includes: Generate an integrity verification value and a node digital signature based on the encrypted block to be reached and the private key; Extract participating nodes sequentially from the plurality of participating nodes, and perform the following operations on the participating nodes: The validity of the node digital signature is verified using the participating nodes and the integrity verification value to obtain the digital signature verification result. When the digital signature verification result is a preset pass, a verification pass certificate is generated; The verified credentials are summarized to obtain a set of verified credentials; The number of verified credentials is obtained from the set of verified credentials. When the number of verified credentials is greater than or equal to a preset threshold, the encrypted block awaiting consensus is confirmed as an on-chain encrypted block.

6. The blockchain multi-platform data trust management method based on user profiles as described in claim 5, characterized in that, The step of extracting features from the on-chain encrypted blocks to obtain a user feature dataset includes: The user dataset to be decrypted is identified from the on-chain encrypted blocks, wherein the user dataset to be decrypted corresponds one-to-one with the encrypted user dataset; Identify the set of sensitive fields to be decrypted and the set of hash values ​​of the data to be decrypted from the user dataset to be decrypted; The set of sensitive fields to be decrypted is homomorphically decrypted using a private key and a preset decryption algorithm to obtain the decrypted sensitive field set; The hash value set of the data to be decrypted is hash-verified to obtain the decrypted dataset; The user feature dataset is obtained by parsing the fields of the decrypted dataset.

7. The blockchain multi-platform data trust management method based on user profiles as described in claim 6, characterized in that, The step of partitioning the user feature dataset to obtain a set of user feature types includes: Extract a content feature set and a behavior feature set from the user feature dataset. The content feature set includes one or more content features, and the behavior feature set includes one or more behavior features. The content features and behavior features correspond one-to-one. The user feature dataset is classified according to the content feature set to obtain a user access type set, wherein the user access set contains one or more user access types. For each user access type in the set of user access types, the following operation is performed: Identify target behavioral features corresponding to user access types within the behavioral feature set; Based on the target behavioral characteristics, the frequency of the user access type behavior is counted; Determine the access duration for the user access type; Access types with a frequency greater than a preset frequency threshold and an access duration greater than a preset duration threshold are defined as user feature types. The user feature types are summarized to obtain a user feature type set, wherein the user feature type set contains one or more user feature types.

8. The blockchain multi-platform data trust management method based on user profiles as described in claim 7, characterized in that, The process of constructing a user profile based on the user feature type set, to obtain a user profile, includes: For each user feature type in the set of user feature types, the following operation is performed: Obtain the interaction depth of user characteristic types; Confirm the frequency of characteristic behaviors and the duration of characteristic access for each user's characteristic type; The feature preference degree is calculated based on the interaction depth, feature behavior frequency, and feature access duration, using the following formula: ； in, Indicates feature preference. Indicates the frequency of characteristic behaviors. Indicates the duration of feature access. Indicates the depth of interaction. , , These represent the adjustment coefficients for the preset frequency of feature behaviors, the preset duration of feature access, and the preset depth of interaction, respectively. Represents the natural constant; By summing up the aforementioned feature preference degrees, a feature preference degree set is obtained; Based on the feature preference set, the user feature type set is sorted in descending order to obtain the feature preference sequence; Based on a preset feature preference threshold, a set of key features is selected from the feature preference sequence. User profiles are constructed based on key feature sets and decrypted sensitive field sets.

9. The blockchain multi-platform data trust management method based on user profiles as described in claim 8, characterized in that, The process of obtaining user profile access criteria based on user profile sets includes: Obtain security level standards and access requirements; For each user profile in the user profile set, perform the following operations: Based on the security level standards, the user profile is assessed for security level to obtain the profile security level; By summarizing the security levels of the images, a set of image security levels is obtained; Cluster analysis is performed on the user profile set to obtain a profile type set, wherein the profile type set includes one or more profile types, and each profile type includes one or more user profiles; For each image type in the image type set, the following operation is performed: Based on the access requirements, the image type is adapted to the access type to obtain the set of permitted access types; By summing the aforementioned sets of permitted access types, multiple sets of permitted access types are obtained; A profile access standard is constructed based on the user profile set, profile security level set, and multiple permitted access type sets.

10. A blockchain-based multi-platform data trust management system based on user profiles, characterized in that, The system includes: The data management environment module is used to determine a multi-platform data management environment, wherein the multi-platform data management environment includes: a data acquisition unit, a blockchain network node set, a data processing unit, and a data access authorization unit, wherein the blockchain network node set includes: a master node and multiple participating nodes, and the data acquisition unit is used to collect data from multiple platforms to obtain multiple original user datasets; The data encryption module is used to perform the following operations on each of the multiple original user datasets: encrypt the original user dataset using the data processing unit and the blockchain network node set to obtain an encrypted user dataset; and package the encrypted user dataset into blocks according to the master node and the preset block structure to obtain an encrypted block to be reached for consensus. The feature extraction module is used to perform consensus verification on the cryptographic block to be reached using multiple participating nodes to obtain the on-chain cryptographic block, extract features from the on-chain cryptographic block to obtain the user feature dataset, and perform feature segmentation on the user feature dataset to obtain the user feature type set. The profile building module is used to build profiles based on the user feature type set, obtain user profiles, summarize the user profiles to obtain a user profile set, obtain profile access standards based on the user profile set, and make authorization determination using the data access authorization unit based on the profile access standards to complete the trusted data management of blockchain multi-platform.

Images