A blockchain-based cross-platform data resource right confirmation method

Abstract

This invention discloses a cross-platform data resource ownership confirmation method based on blockchain, relating to the fields of data ownership confirmation and blockchain application technology. It includes: S1, at the source platform where the data resource is initially generated, performing multi-level feature extraction on the original data to generate a composite digital fingerprint containing a basic hash fingerprint, a structured metadata feature vector, and semantic tags. This composite digital fingerprint serves as the unique identity credential for the data resource. This invention, by generating a composite digital fingerprint containing a basic hash fingerprint, a structured metadata feature vector, and semantic tags on the source platform, combined with a cross-platform identity mapping mechanism using a unified identity management smart contract on a consortium blockchain and zero-knowledge proofs, avoids the problems of ambiguous ownership and lack of cross-platform identity mutual recognition caused by centralized management; it adopts a layered on-chain storage architecture, only putting the core fingerprint on the chain, while retaining the original data in a local trusted execution environment.

Description

Technical Field

[0001] This invention relates to the field of data ownership confirmation and blockchain application technology, specifically a cross-platform data resource ownership confirmation method based on blockchain. Background Technology

[0002] With the development of the digital economy, the value of data as a new production factor in cross-platform circulation and sharing is becoming increasingly prominent. However, the issue of data ownership has become a core bottleneck restricting its efficient utilization and secure transactions. Currently, various platforms generally use centralized databases or local log recording methods to manage data ownership, lacking a unified, reliable, and tamper-proof rights proof mechanism. This leads to blurred ownership boundaries and difficulties in tracing the source during data flow involving multiple entities, easily causing disputes and even infringements.

[0003] Among these technologies, blockchain-based digital rights confirmation is considered an important approach to resolving data ownership disputes due to its decentralized, traceable, and tamper-proof characteristics. This technology aims to build a transparent and trustworthy rights confirmation system by storing data fingerprints, operation records, and authorization information on the blockchain.

[0004] However, existing blockchain-based rights confirmation solutions mostly focus on data registration within a single platform, making it difficult to adapt to data interaction scenarios between heterogeneous systems. Different platforms exhibit significant differences in data formats, identity identifiers, and permission models, leading to issues such as lack of mutual identity recognition, inconsistent metadata standards, and conflicts in smart contract logic during cross-platform rights confirmation. Furthermore, traditional methods typically upload complete data or hash values ​​directly to the blockchain, resulting in redundant on-chain storage and the potential leakage of sensitive information, failing to achieve fine-grained rights definition and dynamic authorization while ensuring privacy. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a blockchain-based cross-platform data resource ownership confirmation method, which solves the problems of lack of mutual identity recognition, inconsistent metadata standards, storage redundancy, privacy leakage, and difficulty in tracing existing cross-platform data ownership confirmation methods.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a cross-platform data resource ownership confirmation method based on blockchain, comprising: S1. On the source platform where the data resource is first generated, multi-level feature extraction is performed on the original data to generate a composite digital fingerprint containing a basic hash fingerprint, a structured metadata feature vector, and a semantic label. The composite digital fingerprint serves as the unique identity credential of the data resource. S2. Construct a cross-platform identity mapping mechanism, deploy a unified identity management smart contract on the consortium blockchain, and register platform identity certificates issued by authoritative certification authorities for each participating platform. The smart contract maintains the public key infrastructure mapping table and permission level configuration of each platform entity. When data interaction occurs between different platforms, the legality of the operator's identity is verified through a zero-knowledge proof protocol without exposing the original identity information. S3. Design a layered on-chain storage architecture, upload the basic hash fingerprint and the encrypted structured metadata feature vector in the composite digital fingerprint to the consensus node of the consortium chain for distributed storage. The original data itself and its complete semantic tags are kept in the local trusted execution environment of the source platform. The encrypted data pointer and access authorization path are recorded on the chain, and fine-grained access control of sensitive fields is performed through attribute-based encryption algorithm. S4. Define an extensible rights expression language, build a dynamic rights model based on graph structure, and abstract data usage rights, copying rights, derivative rights and revenue distribution rights into composable rights nodes. Each rights node is associated with a validity period, usage limit, geographical scope and revenue sharing ratio constraints. The rights transfer process is automatically executed through a pre-set on-chain rights transfer smart contract. The contract logic supports multi-condition triggering and multi-party co-signing mechanisms. S5. Establish a full lifecycle operation audit mechanism. Each time a data resource is accessed, modified, authorized, or transferred, the platform generates an operation log containing timestamps, operation types, participant identifiers, and rights status before and after the change. The operation log is signed by a local trusted module and submitted to the consortium blockchain for batch on-chain storage. The continuous logs are aggregated into a single verification root through a Merkel accumulator structure.

[0007] Furthermore, the multi-level feature extraction includes: the basic hash fingerprint is generated by slicing and aggregating the data content using a block hash algorithm; the structured metadata feature vector covers the data type, generation time, ownership entity identifier, access control policy, and data lifecycle status; and the semantic tags are generated by extracting keywords and classifying the data description text using a pre-trained natural language understanding model.

[0008] Furthermore, the encryption key for the encrypted structured metadata feature vector is generated by the distributed key management center based on a Lagrange interpolation polynomial, and the number of key shares is assumed to be... The reconstruction threshold is ,in Then the complete key The reconstruction formula is: ; in, For the first The value of each key share, For the first The non-zero x-coordinates corresponding to each key share, and all parameters are dimensionless values.

[0009] Furthermore, in the dynamic rights model, any rights node Comprehensive constraint strength It is determined by multiple associated constraints, and the calculation formula is as follows: ; in, As a normalization factor for the effective period, To limit the number of times the normalization factor is used, As a geographical normalization factor, This is a normalization factor for the profit-sharing ratio. These are the weight coefficients for the corresponding constraints.

[0010] Furthermore, the continuous logs are aggregated into a single validation root using a Merkle accumulator structure, and the validation root of the Merkle accumulator... In the The calculation formula for the next update is: ; in, For cryptographic hash functions, This is the verification root from the previous time step. For the first The Merkel root of the operation log batch for the next package, symbol This indicates string concatenation.

[0011] Furthermore, the method also includes: before data resources are transmitted across platforms, the source platform and the target platform negotiate to establish a temporary secure channel, the channel key is dynamically generated through the elliptic curve Diffie-Hellman protocol, the transmission process is encrypted and protected by the TLS protocol, and the integrity of the data packets is verified by the HMAC checksum.

[0012] Furthermore, the method also includes: setting up off-chain index service nodes, which maintain a metadata feature vector retrieval database based on an inverted index structure, supporting fuzzy queries based on data type, generation time range, ownership subject, and semantic tag combination conditions, and ensuring that the index data and on-chain evidence storage data maintain eventual consistency.

[0013] Furthermore, the method also includes: introducing data value assessment, using a weighted scoring model to dynamically calculate the valuation of data assets based on the historical call frequency, authorized scope, derivative usage depth, and market supply and demand relationship of data resources, and selectively anchoring the valuation results on the blockchain.

[0014] Furthermore, in the weighted scoring model, the data asset valuation... The calculation formula is: ; in, The score is normalized based on the frequency of historical calls. To normalize the score for the breadth of authorization scope, To derive depth-normalized scores, To normalize the market supply and demand relationship score, These are the weight coefficients for the corresponding dimensions.

[0015] Furthermore, the local trusted execution environment meets the preset security level standards, the memory isolation granularity reaches the page level, the consortium blockchain adopts the Byzantine fault-tolerant consensus algorithm, and the consensus nodes are distributed in physical clusters of multiple independent operating entities.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention generates a composite digital fingerprint containing a basic hash fingerprint, a structured metadata feature vector, and semantic tags on the source platform. Combined with a unified identity management smart contract on the consortium blockchain and a cross-platform identity mapping mechanism using zero-knowledge proofs, it avoids the problems of ambiguous ownership and lack of cross-platform identity recognition caused by centralized management. It adopts a layered on-chain storage architecture, only putting the core fingerprint on-chain while retaining the original data in a local trusted execution environment. Coupled with attribute-based encryption and distributed key management, it reduces on-chain storage redundancy and protects sensitive information, resolving privacy risks. A graph-based dynamic rights model and on-chain rights transfer contracts enable fine-grained rights definition and automatic transfer. A full lifecycle operation audit mechanism combined with a Merkel accumulator ensures traceability, resolving issues of unclear rights definition and difficulty in tracing. Data value assessment and off-chain indexing services further improve data utilization efficiency and operability. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart of the composite digital fingerprint generation process of the present invention; Figure 3 This is a diagram of the hierarchical on-chain storage architecture of the present invention; Figure 4 This is a diagram illustrating the dynamic rights model and transfer mechanism of the present invention; Figure 5 This is a diagram of the full lifecycle operation audit mechanism of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1

[0020] Please see Figure 1-5 This embodiment provides a blockchain-based method for confirming ownership of cross-platform data resources and details it in conjunction with cross-platform data flow scenarios in the digital content ecosystem. The specific implementation includes the following steps: First, a composite digital fingerprint generation step is performed. On the source platform where the data resource is initially generated, multi-level feature extraction is performed on the original data to generate a composite digital fingerprint containing a basic hash fingerprint, a structured metadata feature vector, and semantic tags. This composite digital fingerprint serves as the unique identity credential of the data resource. Specifically, the basic hash fingerprint is generated by slicing and aggregating the data content using a block hash algorithm. Considering the balance between data integrity and computational efficiency, SHA-256 is selected as the block hash algorithm. The original data is sliced ​​into 1MB chunks, and after calculating the hash value for each slice, the final basic hash fingerprint is generated by aggregating the slices using a Merkle tree structure. This ensures that any change in the content of any slice will result in a change in the basic hash fingerprint, thereby achieving accurate verification of the data content. The structured metadata feature vector covers data type, generation time, ownership entity identifier, access control policy, and data lifecycle status. Data type is labeled using the MIME standard, such as short video files labeled as "video / mp4" and office documents labeled as "application / vnd.openxmlformats-officedocument.wordprocessingml.document". Generation time is recorded in UTC timestamp format, accurate to the millisecond level. Ownership entity identifier is generated by combining a platform-assigned unique identifier with a public key digest to ensure the uniqueness of the ownership entity. Access control policy is represented in binary bit string form, with each bit corresponding to a permission access, such as read, modify, copy, etc., 1 indicates allowed, and 0 indicates prohibited. Data lifecycle status is divided into four categories: "generating", "valid", "expired", and "archived", stored in the form of enumeration values. Semantic tags are generated by a pre-trained natural language understanding model that extracts keywords and classifies the data description text. A BERT pre-trained model is used, and through fine-tuning to adapt to the domain characteristics of the data description text, keywords such as core themes, uses, and related fields are extracted. These are then labeled according to industry standard classification systems. For example, for a document with the theme "Application of Artificial Intelligence Technology," semantic tags such as "Artificial Intelligence," "Technology Application," and "Machine Learning" are generated, enabling the semantic information of the data resources to be identified and retrieved across platforms. The composite digital fingerprint generated through this step ensures both the unique identification of the data resource and encompasses the core features and semantic information of the data, laying the foundation for cross-platform ownership confirmation and effectively solving the ownership confirmation obstacles caused by inconsistent metadata standards across different platforms.

[0021] Next, a cross-platform identity mapping mechanism is constructed. A unified identity management smart contract is deployed on the consortium blockchain. Each participating platform registers an identity certificate issued by an authoritative certification authority. This smart contract maintains the public key infrastructure mapping table and permission level configuration for each platform entity. When data interaction occurs between different platforms, the legitimacy of the operating entity's identity is verified through a zero-knowledge proof protocol without exposing the original identity information. The consortium blockchain selects Hyperledger Fabric as its underlying framework, which supports flexible smart contract deployment and permission management, adapting to cross-platform collaboration scenarios. The authoritative certification authority is a third-party institution that complies with relevant national cryptographic management standards. The platform identity certificate includes fields such as platform name, institution code, public key information, and certificate validity period, and is generated using the X.509 standard format to ensure the universality and security of the certificate. The unified identity management smart contract is written in Solidity and deployed in the consortium blockchain's channel, with only authorized nodes able to call the contract's functions. The public key infrastructure mapping table is stored in key-value pairs. The key is a unique identifier for the platform, and the value is the platform's public key and the corresponding permission level. Permission levels are divided into three categories: "core nodes," "ordinary nodes," and "access nodes." Different levels correspond to different on-chain operation permissions. For example, core nodes can participate in consensus decisions, ordinary nodes can upload and query data on the chain, and access nodes can only query authorized data. When data interaction occurs between the source and target platforms, platform administrators, data owners, and other operating entities initiate identity verification requests. Using the zk-SNARKs algorithm in the zero-knowledge proof protocol, they prove to the verifier that they possess a valid platform identity certificate and corresponding permissions without exposing the complete certificate information or original identity data. Specifically, the operating entity first obtains a proof key from an authoritative certification authority, generates a proof document based on its own identity information, and sends the proof document to the target platform. The target platform calls the unified identity management smart contract on the consortium blockchain to verify the validity of the proof document. If the verification is successful, data interaction is allowed; otherwise, it is rejected. This mechanism achieves trusted mutual recognition of identities across platforms while protecting the identity privacy of operating entities, solving the problem of the lack of cross-platform identity mutual recognition in existing technologies.

[0022] Subsequently, a layered on-chain storage architecture was designed. The basic hash fingerprint and the encrypted structured metadata feature vector from the composite digital fingerprint were uploaded to the consensus nodes of the consortium blockchain for distributed storage. The original data itself and its complete semantic tags were retained in the local trusted execution environment of the source platform. The encrypted data pointer and access authorization path were recorded on the chain, and fine-grained access control for sensitive fields was performed through attribute-based encryption algorithms. The local trusted execution environment uses Intel SGX technology, which provides hardware-level memory isolation with a granularity down to the page level, meeting the preset security level standards and effectively resisting malware attacks to protect the secure storage of the original data and complete semantic tags. The encryption key for the encrypted structured metadata feature vector was generated by the distributed key management center based on a Lagrange interpolation polynomial. Let the number of key shares be n, and the reconstruction threshold be t, where t≤n. Considering the number of participants in cross-platform collaboration and security requirements, n=5, meaning the key is divided into 5 shares, and t=3, meaning at least 3 key shares are needed to reconstruct the complete key. The reconstruction formula for the complete key K is: ; in, The value of the i-th key share is a random integer within the range of [100000, 999999]. Let be the non-zero x-coordinate corresponding to the i-th key share. We select 1, 2, 3, 4, and 5 as the x-coordinates of the five key shares respectively; all parameters are dimensionless values. The distributed key management center is deployed on each participating platform of the consortium blockchain. Each platform holds one key share, and the distribution of key shares is carried out through a secure channel to ensure the security of the distribution process. The attribute-based encryption algorithm uses the KP-ABE key policy-based attribute-based encryption algorithm, using user attributes such as platform type, permission level, and industry as the basis for encryption and decryption. Sensitive fields such as the ownership entity identifier and core information of access control policies in the structured metadata feature vector are encrypted. Specifically, first, an attribute set is defined, such as {short video platform, ordinary node, internet industry}, and a public key and master key are generated. The master key is jointly protected by the key shares of the distributed key management center. When encrypting data, an access structure is set according to the protection requirements of sensitive fields, such as "short video platform AND ordinary node". Sensitive fields are encrypted based on the public key and the access structure. When decrypting, users need to provide attribute credentials that conform to the access structure, generate a decryption key using their own key shares and attribute credentials, and decrypt the encrypted fields. The on-chain stored encrypted data pointers are the hash values ​​of the storage paths of the original data in the local trusted execution environment. The access authorization path records the data access permission verification process and node information. The consensus nodes adopt the Byzantine Fault Tolerance (PBFT) consensus algorithm and are distributed across multiple independent operating entities in physical clusters, ensuring the immutability and high availability of on-chain data. This layered storage architecture reduces on-chain storage redundancy while protecting sensitive information through encryption technology and access control mechanisms, achieving a balance between privacy protection and data sharing.

[0023] Next, an extensible rights expression language is defined, and a dynamic rights model is constructed based on a graph structure. Data usage rights, replication rights, derivative rights, and revenue distribution rights are abstracted into composable rights nodes. Each rights node is associated with constraints such as validity period, usage frequency limits, geographical scope, and revenue sharing ratio. The rights transfer process is automatically executed through a pre-built on-chain rights transfer smart contract. The contract logic supports multi-condition triggering and multi-party co-signing mechanisms. The rights expression language is designed using the JSON-LD format, possessing good scalability and cross-platform compatibility, and can be parsed and recognized by systems on different platforms. In the graph structure of the dynamic rights model, each rights node is a vertex in the graph, and the relationships between rights nodes, such as rights dependencies and conflicts, are edges. A graph traversal algorithm can quickly query all relevant rights and constraints of a given data resource. Any rights node... Comprehensive constraint strength It is determined by multiple associated constraints, and the calculation formula is as follows: ; in, This is a normalization factor for the validity period, with a value range of [0,1]. The longer the validity period, the better. The closer it is to 1, the better, according to the formula calculate, Set to 365 days, which can be adjusted flexibly according to data type; This is a normalization factor for the number of uses limit, with a value range of [0,1]. The more uses limit, the higher the normalization factor. The closer it is to 1, the better, according to the formula calculate, Set to 100 times, which can be adjusted based on the value of the data; This is a geographic range normalization factor, with values ​​ranging from [0,1]. The wider the geographic range, the better. The closer to 1, the more regions are classified using the ISO 3166-1 country code, according to the formula. calculate, This represents the total number of countries and regions worldwide. This is a normalization factor for the profit-sharing ratio, with a value range of [0,1]. A higher profit-sharing ratio results in a more balanced distribution. The closer it is to 1, the better, according to the formula The profit-sharing ratio is calculated and expressed as a decimal. Weighting coefficient. The Analytic Hierarchy Process (AHP) was used to determine the weighting coefficients for general scenarios, taking into account the type of data resources and application scenarios. , , , This ensures that the impact of various constraints on the rights nodes aligns with actual needs. The on-chain rights transfer smart contract, also written in Solidity, includes modules for rights creation, transfer, revocation, and revenue settlement. Rights transfer requires multiple triggering mechanisms, such as reaching the agreed-upon validity period and not exceeding usage limits. It also requires multi-party confirmation through the transferor, transferee, and consortium blockchain monitoring node. This confirmation process is implemented using digital signature technology to ensure the legality and non-repudiation of the rights transfer. For example, when a short video creator transfers data copying rights to a content distribution platform, the smart contract first verifies whether the validity period and usage limits of the copying rights are met, then verifies the legality of the creator's and platform's digital signatures. Once all conditions are met, it automatically updates the on-chain rights ownership information and records the rights transfer log. This dynamic rights model achieves fine-grained definition and flexible transfer of data rights, solving the problems of ambiguous rights definition and cumbersome transfer processes in traditional methods.

[0024] Finally, a full lifecycle operation audit mechanism is established. Each time data resources are accessed, modified, authorized, or transferred, the platform generates an operation log containing a timestamp, operation type, participant identifier, and the rights status before and after the change. This operation log is signed by the local trusted module and submitted to the consortium blockchain for batch on-chain storage. A Merkle accumulator structure aggregates continuous logs into a single verification root. The operation log fields are designed as follows: the timestamp uses UTC timestamp format, accurate to milliseconds; the operation type is divided into five categories: "Access," "Modification," "Authorization," "Transfer," and "Revocation," stored as enumerated values; the participant identifier is a combination of the platform's unique identifier and the public key digest of the operation subject; the rights status before and after the change records the core information and constraints of the rights node in JSON format. The local trusted module uses the TPM2.0 chip, which has secure storage and digital signature functions. After the operation log is generated, the TPM2.0 chip performs hash calculations on the log content and generates a digital signature, ensuring the integrity and immutability of the log content. The batch uploading of operation logs to the blockchain employs a timed batch submission mechanism, setting each batch to 10 minutes. All operation logs within a batch are packaged and submitted to the consortium blockchain, reducing the number of on-chain transactions and improving storage efficiency. (Merkel accumulator verification root) The calculation formula for the t-th update is: ; in, For cryptographic hash functions, the SHA-384 algorithm is selected to ensure the security of hash calculations; This is the verification root from the previous time step, and its initial value is the initial vector of the hash function; The Merkle tree root for the t-th batch of operation logs is generated by constructing a Merkle tree from the hash values ​​of all operation logs within that batch; (symbol) This indicates string concatenation. The verification root stored on the consortium blockchain serves as a core credential for the integrity of the operation log. When tracing and verifying a specific operation is required, the verification root can be compared with the corresponding operation log hash value to quickly confirm whether the log content has been tampered with. For example, in the event of a data ownership dispute, relevant parties can query data access records, rights transfer processes, and other information through the on-chain operation log, providing a credible basis for dispute resolution. This auditing mechanism achieves full traceability and verifiability of data operations, solving the problems of difficulty in tracing and defining responsibility during cross-platform data flow.

[0025] Furthermore, before cross-platform data resource transmission, the source platform and the target platform negotiate to establish a temporary secure channel. The channel key is dynamically generated using the elliptic curve Diffie-Hellman protocol, selecting the secp256r1 elliptic curve parameter to ensure the security of the key negotiation process. The transmission process uses the TLS 1.3 protocol for encryption protection, which has higher encryption strength and transmission efficiency, and can effectively resist network attacks such as eavesdropping and tampering. Data packet integrity is verified through HMAC checksum verification, using the HMAC-SHA256 algorithm. The source platform calculates the HMAC checksum for each data packet and sends it along with the data packet. After receiving it, the target platform recalculates the checksum and compares it with the received checksum to ensure that the data packet has not been tampered with during transmission.

[0026] Simultaneously, off-chain index service nodes are set up. These nodes maintain a metadata feature vector retrieval database based on an inverted index structure. The keywords for the inverted index are selected from fields such as data type, generation time range, subject, and semantic tags in the structured metadata feature vector, supporting fuzzy searches based on single or combined conditions. For example, a user can query "short video data generated from January to June 2024 belonging to the internet industry." The index service node quickly locates data resources that meet the conditions using the inverted index and returns the corresponding on-chain basic hash fingerprint and encrypted data pointer. The index data maintains eventual consistency with the on-chain evidence data, and is scheduled to synchronize every 30 minutes to ensure the accuracy of the index data while avoiding resource consumption caused by frequent synchronization.

[0027] In addition, a data value assessment mechanism is introduced. Based on the historical frequency of data resource access, the breadth of authorized scope, the depth of derivative use, and market supply and demand, a weighted scoring model is used to dynamically calculate the data asset valuation. The valuation results are selectively anchored to the blockchain. In the weighted scoring model, the formula for calculating the data asset valuation V is: ; in, The normalized score for historical call frequency is calculated using the formula [0,1]. calculate, Set to 1000 times, which can be adjusted according to industry characteristics; The score is normalized to reflect the breadth of authorized platforms, with values ​​ranging from [0,1]. A higher number of authorized platforms results in a higher score, determined by the formula... calculate, The total number of platforms participating in the consortium blockchain; The deep normalized score is used for the derivative data, with a value range of [0,1]. The higher the quantity and value of the derivative data, the higher the score. The score is calculated by combining the expert scoring method with the frequency of the derivative data. This is a normalized score for market supply and demand, ranging from [0,1]. A score close to 1 indicates supply shortage, while a score close to 0 indicates supply surplus. The score is calculated based on industry market research data and price fluctuations. Weighting coefficients are also included. The Analytic Hierarchy Process (AHP) was used to determine, in conjunction with the assessment requirements of data assets, the following settings were established. , , , This ensures that the impact of each assessment dimension is reasonable. Data asset valuation results are selectively uploaded to the blockchain based on user needs. For high-value data resources, the valuation results and relevant assessment parameters are uploaded to the blockchain for evidence storage, providing a valuable reference for data transactions, staking, and other scenarios. For ordinary data resources, the valuation results are stored locally only, reducing on-chain storage pressure.

[0028] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0029] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1.A blockchain-based cross-platform data resource right confirmation method, characterized in that, include: S1. On the source platform where the data resource is first generated, multi-level feature extraction is performed on the original data to generate a composite digital fingerprint containing a basic hash fingerprint, a structured metadata feature vector, and a semantic label. The composite digital fingerprint serves as the unique identity credential of the data resource. S2. Construct a cross-platform identity mapping mechanism, deploy a unified identity management smart contract on the consortium blockchain, and register platform identity certificates issued by authoritative certification authorities for each participating platform. The smart contract maintains the public key infrastructure mapping table and permission level configuration of each platform entity. When data interaction occurs between different platforms, the legality of the operator's identity is verified through a zero-knowledge proof protocol without exposing the original identity information. S3. Design a layered on-chain storage architecture, upload the basic hash fingerprint and the encrypted structured metadata feature vector in the composite digital fingerprint to the consensus node of the consortium chain for distributed storage. The original data itself and its complete semantic tags are kept in the local trusted execution environment of the source platform. The encrypted data pointer and access authorization path are recorded on the chain, and fine-grained access control of sensitive fields is performed through attribute-based encryption algorithm. S4. Define an extensible rights expression language, build a dynamic rights model based on graph structure, and abstract data usage rights, copying rights, derivative rights and revenue distribution rights into composable rights nodes. Each rights node is associated with a validity period, usage limit, geographical scope and revenue sharing ratio constraints. The rights transfer process is automatically executed through a pre-set on-chain rights transfer smart contract. The contract logic supports multi-condition triggering and multi-party co-signing mechanisms. S5. Establish a full lifecycle operation audit mechanism. Each time a data resource is accessed, modified, authorized, or transferred, the platform generates an operation log containing timestamps, operation types, participant identifiers, and rights status before and after the change. The operation log is signed by a local trusted module and submitted to the consortium blockchain for batch on-chain storage. The continuous logs are aggregated into a single verification root through a Merkel accumulator structure. 2.The blockchain-based cross-platform data resource right confirmation method according to claim 1, characterized in that, The multi-level feature extraction includes: basic hash fingerprints are generated by slicing and aggregating data content using a block hash algorithm; structured metadata feature vectors cover data type, generation time, ownership entity identifier, access control policy, and data lifecycle status; and semantic tags are generated by extracting keywords and classifying the data description text using a pre-trained natural language understanding model. 3.The blockchain-based cross-platform data resource right confirmation method of claim 1, wherein, The encryption key of the encrypted structured metadata feature vector is generated by a distributed key management center based on a Lagrange interpolation polynomial, assuming that the number of key shares is , and the reconstruction threshold is , wherein , the reconstruction formula of the complete key is: ； wherein, is the value of the th key share, is the non-zero abscissa corresponding to the th key share, all parameters being dimensionless values. 4.The blockchain-based cross-platform data resource right confirmation method of claim 1, wherein, In the dynamic right model, any right node The comprehensive constraint strength Is determined by the associated multiple constraint conditions together, and the calculation formula is: ； wherein, is a validity period normalization factor, is a usage limit normalization factor, is a geographical range normalization factor, is a revenue sharing ratio normalization factor, are weight coefficients corresponding to the respective constraints. 5.The blockchain-based cross-platform data resource right confirmation method according to claim 1, characterized in that, the single verification root of the continuous log by the Merkel accumulator structure, the verification root of the Merkel accumulator At the first The calculation formula at the second update is: ； wherein, is a cryptographic hash function, is the verification root of the previous time, is the Merkle tree root of the th packed operation log batch, and the notation denotes string concatenation. 6.The blockchain-based cross-platform data resource right confirmation method according to claim 1, characterized in that, The method further includes: before data resources are transmitted across platforms, the source platform and the target platform negotiate to establish a temporary secure channel, the channel key is dynamically generated through the elliptic curve Diffie-Hellman protocol, the transmission process is protected by encryption using the TLS protocol, and the integrity of the data packets is verified by HMAC checksum. 7.The blockchain-based cross-platform data resource right confirmation method according to claim 1, characterized in that, The method further includes: setting up off-chain index service nodes, which maintain a metadata feature vector retrieval database based on an inverted index structure, supporting fuzzy queries based on data type, generation time range, ownership subject, and semantic tag combination conditions, and ensuring that the index data and on-chain evidence storage data maintain eventual consistency. 8.The blockchain-based cross-platform data resource right confirmation method according to claim 1, characterized in that, The method also includes: introducing data value assessment, using a weighted scoring model to dynamically calculate the valuation of data assets based on the historical call frequency, authorized scope, derivative use depth and market supply and demand relationship of data resources, and selectively anchoring the valuation results on the blockchain. 9.The blockchain-based cross-platform data resource right confirmation method of claim 8, wherein, In the weighted scoring model, the data asset valuation is calculated by the following formula: ； wherein, is a historical call frequency normalized score, is an authorized scope breadth normalized score, is a derived use depth normalized score, is a market supply-demand relationship normalized score, are weight coefficients for the corresponding dimensions, respectively. 10.The blockchain-based cross-platform data resource right confirmation method according to claim 1, wherein, The local trusted execution environment meets the preset security level standards, and the memory isolation granularity reaches the page level. The consortium blockchain adopts the Byzantine fault-tolerant consensus algorithm, and the consensus nodes are distributed in physical clusters of multiple independent operating entities.

Images