Data asset cross-region registration right confirmation method based on block chain

Abstract

The invention discloses a block chain-based data asset cross-regional registration right confirmation method, which comprises the following steps of S1, obtaining data asset information and an identity certificate, and constructing right confirmation request data; s2, screening effective request data, and submitting the effective request data to a block chain network; s3, constructing a sparse Merkle tree according to the effective request data, and constructing a right confirmation registration chain; s4, adopting a HotStuff consensus algorithm to perform joint judgment on the right confirmation registration chain to generate a right confirmation consensus result; s5, executing time sequence protection calculation on a right confirmation consensus result, and screening a registration result; and S6, writing the data fingerprint corresponding to the registration result into the data asset index table. According to the method, the verifiable identity credentials, the HotStuff consensus algorithm, the verifiable delay function and other technologies are fused, and the method has the advantages of being high in cross-regional consistency, high in right confirmation efficiency and high in conflict prevention and control capacity.

Description

Technical Field

[0001] This invention relates to the field of data asset management technology, and in particular to a method for cross-regional registration and confirmation of rights of data assets based on blockchain. Background Technology

[0002] With the widespread circulation and high-frequency interaction of data assets in finance, healthcare, energy, manufacturing, and other fields, effectively realizing the confirmation and reliable registration of data asset ownership has become one of the important directions for the construction of digital economy infrastructure. Existing data ownership confirmation methods mostly rely on centralized systems, using a single authoritative institution to register, approve, and archive data to confirm and manage data ownership. However, in cross-regional, cross-departmental, or multi-institutional collaborative scenarios, this approach often faces problems such as inconsistent data standards, complex permission verification processes, and difficulty in mutually recognizing ownership results, failing to meet the real-time, consistency, and security requirements of distributed data asset ownership confirmation.

[0003] In recent years, blockchain technology, due to its decentralized, immutable, and traceable characteristics, has been widely studied for the construction of data ownership confirmation and evidence preservation systems. Some solutions attempt to achieve "ownership confirmation as evidence preservation" by uploading data summary information to the blockchain, and combine this with the blockchain consensus mechanism to improve the credibility of the ownership confirmation process. However, most existing technologies focus on simple storage and basic verification of on-chain data, lacking a structured registration mechanism for cross-regional collaborative ownership confirmation. In particular, in key areas such as identity binding of data assets, time-sequencing of ownership confirmation requests, conflict detection, and status tracing, there is still a lack of unified processes and technical details, which can easily lead to risks such as duplicate registration, ownership disputes, and incomplete ownership confirmation chains.

[0004] Furthermore, existing blockchain-based data ownership confirmation schemes typically employ static key authentication in their identity verification mechanisms, making it difficult to adapt to the high-frequency ownership confirmation requests from dynamic subjects. In terms of data structure organization, most adopt ordinary Merkle trees or linear chain structures, which are difficult to efficiently support the parallel processing and state tracing of large-scale ownership confirmation requests. In terms of consensus mechanisms and signature confirmation, they also lack in-depth optimization for high-throughput, low-latency, and multi-node scenarios, and cannot guarantee the temporal security and rapid verification capabilities of ownership confirmation results.

[0005] Therefore, how to provide a blockchain-based method for cross-regional registration and ownership confirmation of data assets is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] One objective of this invention is to propose a blockchain-based method for cross-regional registration and ownership confirmation of data assets. This invention fully utilizes technologies such as data fingerprint extraction, verifiable identity credentials, sparse Merkle trees, HotStuff consensus algorithm, BLS aggregate signature, and verifiable delay function. It describes in detail the entire process algorithm for performing trusted ownership confirmation and registration of data assets in a multi-regional collaborative environment, achieving accurate identification, security verification, order protection, and tamper-proof registration of data assets. It has the advantages of strong cross-regional consistency, high ownership confirmation efficiency, and strong conflict prevention capabilities.

[0007] A method for cross-regional registration and confirmation of rights of data assets based on blockchain according to an embodiment of the present invention includes the following steps: S1. Obtain data asset information and the identity certificate of the registrant, perform information feature extraction operation on the data asset information, generate data fingerprint and bind it with the identity certificate, and construct the rights confirmation request data; S2. Perform identity verification operation based on verifiable credentials on the rights confirmation request data, mark the rights confirmation request data that passes the verification as valid request data, and submit it to each regional node in the blockchain network; S3. Assign time numbers to valid request data, construct a sparse Merkle tree based on the assignment results, and use an accumulator to perform layer-by-layer hash aggregation and state accumulation operations to construct the rights registration chain; S4. Using the HotStuff consensus algorithm, the rights registration chain is jointly judged in each regional node, and the BLS aggregation signature operation is performed on the judgment result to generate a consensus state containing a consensus timestamp as the rights confirmation consensus result. S5. Use a verifiable delay function to perform time-series protection calculations on the rights confirmation consensus results, generate verification credentials and perform time-series verification operations, and write the verified rights confirmation consensus results as registration results to each regional node. S6. Write the data fingerprint corresponding to the registration result into the data asset index table of the blockchain network, and perform a conflict matching operation on the newly submitted rights confirmation request data. If the match is successful, the rights confirmation registration will not be granted.

[0008] Optionally, the data asset information refers to the set of data content to be registered for ownership confirmation, including structured or unstructured data files, data summaries or data fragments; the blockchain network refers to a consortium blockchain structure composed of multiple regional nodes; the regional nodes refer to consensus participating nodes deployed in different geographical or management areas, used to jointly perform ownership confirmation data verification and status update operations; and the time number refers to the time sequence identifier for the allocation of ownership confirmation request data.

[0009] Optionally, S1 specifically includes: S11. Obtain the data asset information to be registered and confirmed and the identity certificate data of the registrant. Call the TextCNN model to perform segmented structure recognition on the data asset information, split the data asset information into multiple data segments, and extract field labels, data structure features and content verification values ​​from each data segment to form a feature fragment sequence. S12. The feature segment sequence is subjected to multiple rounds of hash encoding using a preset hash function. Local features of each feature segment are extracted, and aggregation calculations are performed on all local features to generate global features. Specifically, this includes: A preset hash function is used to perform hash encoding operations on each field label, data structure feature, and content verification value in the feature fragment sequence to generate the corresponding local feature sequence; The local features are spliced ​​together in their original order, and multiple preset data segments are inserted at fixed positions in the splicing result to form a perturbation feature sequence. The data segments are generated based on fixed parameters. A global hash aggregation operation is performed on the perturbation feature sequence. In each round, intermediate summary nodes are generated according to the preset hash rotation rules. Based on the intermediate summary nodes, a multi-level nested structure is recursively aggregated to generate global features. S13. Perform a sequence perturbation operation on the global features, and use bit operation permutation and digest block rearrangement based on preset rules to re-encode the global features and generate data fingerprints. S14. Bind the data fingerprint with the identity credential data and record the request timestamp to construct the rights confirmation request data.

[0010] Optionally, S11 specifically includes: S111. Obtain the data asset information to be registered and confirmed, and perform a format standardization operation on the data asset information to convert it into a fixed-length character sequence. Then, divide the fixed-length character sequence into multiple text segments according to a preset sliding window to form a text segment set. S112. Call the TextCNN model, input the set of text segments into multiple convolutional layers with different convolutional kernel sizes, extract the corresponding field boundary features and structural arrangement features in each convolutional layer, and generate the corresponding text feature set. S113. Perform pooling and concatenation operations on the text feature set to integrate the convolution results from multiple windows into a set of structural representation vectors. S114. Based on the structural representation vector, locate the start and end positions of each field in each text segment, extract the field name, structural feature information and content verification information, and combine them into a feature segment sequence in a fixed order.

[0011] Optionally, S2 specifically includes: S21. Extract the identity credentials of the registrant from the rights confirmation request data, perform structural parsing on the identity credentials, extract public key information, permission identification information and signature content, and construct an identity dataset; S22. Invoke the trusted identity information database deployed in the blockchain network, perform consistency verification on the public key information and permission identification information in the identity dataset based on the preset identity mapping structure, and perform authenticity verification based on the signature content using the ECDSA algorithm. If all verifications pass, mark the corresponding rights confirmation request data as valid request data, specifically including: Extract public key information and permission identification information from the identity dataset to construct a set of identity fields for mapping verification; In the blockchain network, a deployed trusted identity information database is invoked. Based on a preset identity mapping structure, a one-to-one structure comparison operation is performed on the identity field set to determine whether a consistent mapping relationship exists. Under the premise that the consistency verification is passed, the ECDSA algorithm is used to verify the authenticity of the signature content in the identity dataset, and to verify whether the signature content is generated by the extracted public key information and has not been tampered with. When the structure comparison results are consistent and the signature authenticity verification is passed, the corresponding rights confirmation request data will be marked as valid request data. S23. Bind the valid request data with the verification timestamp and send it to all regional nodes in the blockchain network.

[0012] Optionally, S3 specifically includes: S31. In each regional node, receive valid request data and register and sort it. Assign a time number to each valid request data and record the mapping relationship between the time number and the corresponding data fingerprint to build a time number mapping table. S32. Organize the valid request data according to the time number mapping table, initialize the sparse Merkle tree with the time number as the index, write each data fingerprint into the leaf node of the sparse Merkle tree according to the time number, and insert a preset null value node at the missing number position to form a set of leaf nodes with continuous structure. S33. Iteratively perform hash aggregation operations based on the leaf node set. In the first round of hash aggregation, concatenate the data fingerprints of two adjacent leaf nodes and perform hash calculation to generate multiple intermediate nodes as input for the next round of hash aggregation, specifically including: The set of leaf nodes is traversed, and the data fingerprints of each pair of adjacent leaf nodes are concatenated in a preset order to form a hash aggregation sequence. Perform a hash calculation operation on each concatenation result in the hash aggregation sequence to generate the corresponding intermediate node hash value, and add the intermediate node hash value to the intermediate node set; Determine if the number of nodes in the intermediate node set is greater than 1. If so, use the intermediate node set as the leaf node set for the next round of hash aggregation operation. S34. Iteratively execute the hash aggregation operation until the number of intermediate nodes generated in the hash aggregation operation is one. Use this intermediate node as the root node to form a sparse Merkle tree structure from the leaf node to the root node. S35. Perform state accumulation operation on each level node in the sparse Merkle tree. Starting from each leaf node, traverse along the direction of the parent node to the root node to construct a set of state accumulation paths. S36. Input the set of state accumulation paths into the accumulator and perform parallel hash calculation operations. Perform number alignment and state compression operations on all nodes on the state accumulation path to form a rights registration chain.

[0013] Optionally, S36 specifically includes: S361. Input the set of state accumulation paths into the accumulator structure, arrange the hash values ​​of each node in each state accumulation path according to the path order, generate a set of node hash sequences, and mark the path number and level number of each node. The path number and time number correspond one-to-one. S362. Based on the set of node hash sequences, perform number alignment operation, classify the nodes in all state accumulation paths according to the level number, and construct a multi-level node hash matrix. Each level of the node hash matrix corresponds to a height level of the sparse Merkle tree. S363. Perform an aggregation operation on the hash matrix of each layer of nodes, concatenate the hash values ​​of all nodes in the hash matrix in the order of path number, and use a hash function to perform digest calculation to generate the aggregated hash representation of the layer and construct the aggregation result set. S364. Starting from the lowest level aggregation result, the aggregation results of adjacent two levels are progressively merged in order from bottom to top, and a hash compression operation is performed on the merged result to generate a compressed aggregation chain. S365. Perform path reconstruction and structure verification operations on all nodes in the compressed aggregation chain to ensure that the hash value of each node is not tampered with during the merging process and to maintain the integrity of the hash dependency relationship. S366. Take the root node hash value of the compressed aggregate chain that has passed the structure verification as the root node of the rights registration chain, and record the correspondence between the root node and the time number mapping table to construct the rights registration chain.

[0014] Optionally, S4 specifically includes: S41. Receive the registration chain in each regional node, and perform integrity verification operation based on the status information in the registration chain to select the registration chain that meets the structural consistency requirements. S42. Based on the verified ownership registration chain, the HotStuff consensus algorithm is invoked, and the preset master node broadcasts a proposal message to each regional node. Each regional node verifies the ownership registration chain in the proposal message and generates a verification vote result. S43. Collect the verification voting results of all regional nodes. After meeting the preset voting quantity requirement, enter the preparation stage. The master node broadcasts the preparation message to each regional node. After receiving the preparation message, the regional node completes the confirmation operation and generates the confirmation voting result. S44. After collecting a number of confirmed votes that meet the preset preparation conditions, the master node broadcasts a submission message to each regional node. The regional nodes perform a confirmation signature operation based on the confirmation registration chain in the submission message and mark it as a consensus completion status. S45. After marking the consensus completion status, call the BLS aggregate signature method to aggregate the confirmation signatures of all regional nodes and generate the aggregate signature result. S46. Add a consensus timestamp to the aggregated signature result and generate a consensus state as the ownership confirmation consensus result.

[0015] Optionally, S45 specifically includes: S451. Collect all confirmation signatures generated by all regional nodes during the submission phase, and bind each confirmation signature with the corresponding regional node identifier to construct a signature binding set; S452. Perform a classification operation on all confirmation signatures in the signature binding set, arrange them according to the preset order of node identity identifiers, and generate a signature sorting sequence; S453. Call the BLS aggregate signature method to perform aggregate calculation on all confirmed signatures in the signature sorting sequence and generate an aggregate signature value. S454. Perform verification preparation operation on the aggregated signature value and extract the common parameter set containing the public key information and signature digest index of each regional node. S455. Perform consistency verification on the aggregated signature value based on the common parameter set to determine whether the consistency conditions of all confirmed signatures in terms of time and structure are met. S456. If the aggregated signature value passes verification, the aggregated signature value is used as the binding signature of the corresponding rights registration chain to form an aggregated signature result.

[0016] Optionally, S5 specifically includes: S51. Based on the consensus result, a verifiable delay function is invoked to perform sequential constraint calculations on the consensus state and consensus timestamp, generating data results with a unique calculation order and recording the corresponding delay calculation identifiers to form a time-series calculation result, specifically including: Extract the consensus status and consensus timestamp from the consensus results of rights confirmation, and bind the two in chronological order to construct consensus binding data; The function calls a pre-defined verifiable delay function to perform sequential constraint calculations on the consensus-bound data. The calculation process is controlled according to the built-in delay parameters of the function to generate a function calculation result with time uniqueness. The binding relationship between the function calculation result and the corresponding rights confirmation consensus result is recorded as a delayed calculation identifier, and the delayed calculation identifier is structurally mapped according to the preset mapping rules to form a time-series calculation result; S52. Generate verification credentials based on the time-series calculation results, and bind the verification credentials with the consensus state to construct a verifiable data set; S53. Perform time-series verification operations on the verifiable data set in each regional node. By verifying the consistency of the correspondence between the verification credentials and the consensus timestamp, determine whether the rights confirmation consensus result meets the preset time sequence constraints. S55. If the time order constraint is met, the corresponding consensus result is marked as verified and written into the blockchain ledger of each regional node as the registration result, while recording the registration timestamp.

[0017] The beneficial effects of this invention are: First, this invention introduces an asset feature extraction method based on deep structural analysis and hash perturbation coding at the front end of the data ownership confirmation process. This method can accurately identify the key fields and structural features of data assets and, combined with verifiable credentials, complete the binding of data fingerprints and identity credentials, providing a technical foundation for the uniqueness and legality of ownership confirmation request data.

[0018] Secondly, by constructing sparse Merkle trees and state accumulation chains among nodes in each region, and combining them with the HotStuff consensus algorithm and BLS aggregation signature mechanism, this invention achieves rapid consensus on the confirmation status, structural consistency verification, and signature normalization processing among nodes in different regions, significantly improving the efficiency and reliability of confirmation in a multi-node environment.

[0019] Finally, this invention introduces a verifiable delay function to protect the execution order of the rights confirmation consensus results and verify time consistency. Combined with the blockchain ledger writing mechanism and the data asset index table construction operation, it realizes the anti-tampering registration of the rights confirmation results, rapid conflict identification, and full-chain backtracking query, ensuring the consistency, continuity, and security of the registration status of data assets in different regions. It has significant engineering feasibility and promotional application value. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a blockchain-based method for cross-regional registration and confirmation of rights for data assets proposed in this invention; Figure 2 This invention presents a flowchart of the time-series verification and blockchain registration process for a blockchain-based method for cross-regional registration and confirmation of data assets. Figure 3 This invention presents a flowchart of a multi-path driven rights registration chain generation process for a blockchain-based cross-regional data asset registration and rights confirmation method. Detailed Implementation

[0021] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0022] refer to Figure 1-3 A blockchain-based method for cross-regional registration and confirmation of data assets includes the following steps: S1. Obtain data asset information and the identity certificate of the registrant, perform information feature extraction operation on the data asset information, generate data fingerprint and bind it with the identity certificate, and construct the rights confirmation request data; S2. Perform identity verification operation based on verifiable credentials on the rights confirmation request data, mark the rights confirmation request data that passes the verification as valid request data, and submit it to each regional node in the blockchain network; S3. Assign time numbers to valid request data, construct a sparse Merkle tree based on the assignment results, and use an accumulator to perform layer-by-layer hash aggregation and state accumulation operations to construct the rights registration chain; S4. Using the HotStuff consensus algorithm, the rights registration chain is jointly judged in each regional node, and the BLS aggregation signature operation is performed on the judgment result to generate a consensus state containing a consensus timestamp as the rights confirmation consensus result. S5. Use a verifiable delay function to perform time-series protection calculations on the rights confirmation consensus results, generate verification credentials and perform time-series verification operations, and write the verified rights confirmation consensus results as registration results to each regional node. S6. Write the data fingerprint corresponding to the registration result into the data asset index table of the blockchain network, and perform a conflict matching operation on the newly submitted rights confirmation request data. If the match is successful, the rights confirmation registration will not be granted.

[0023] In this embodiment, the data asset information refers to the set of data content to be registered for ownership confirmation, including structured or unstructured data files, data summaries or data fragments. The blockchain network refers to a consortium blockchain structure composed of multiple regional nodes. The regional nodes refer to consensus participating nodes deployed in different geographical or management areas, used to jointly perform ownership confirmation data verification and status update operations. The time number represents the time sequence identifier for the allocation of ownership confirmation request data.

[0024] In this embodiment, S1 specifically includes: S11. Obtain the data asset information to be registered and confirmed and the identity certificate data of the registrant. Call the TextCNN model to perform segmented structure recognition on the data asset information, split the data asset information into multiple data segments, and extract field labels, data structure features and content verification values ​​from each data segment to form a feature fragment sequence. S12. The feature segment sequence is subjected to multiple rounds of hash encoding using a preset hash function. Local features of each feature segment are extracted, and aggregation calculations are performed on all local features to generate global features. Specifically, this includes: A preset hash function is used to perform hash encoding operations on each field label, data structure feature, and content verification value in the feature fragment sequence to generate the corresponding local feature sequence; The local features are spliced ​​together in their original order, and multiple preset data segments are inserted at fixed positions in the splicing result to form a perturbation feature sequence. The data segments are generated based on fixed parameters. A global hash aggregation operation is performed on the perturbation feature sequence. In each round, intermediate summary nodes are generated according to the preset hash rotation rules. Based on the intermediate summary nodes, a multi-level nested structure is recursively aggregated to generate global features. S13. Perform a sequence perturbation operation on the global features, and use bit operation permutation and digest block rearrangement based on preset rules to re-encode the global features and generate data fingerprints. S14. Bind the data fingerprint with the identity credential data and record the request timestamp to construct the rights confirmation request data.

[0025] In this embodiment, S11 specifically includes: S111. Obtain the data asset information to be registered and confirmed, and perform a format standardization operation on the data asset information to convert it into a fixed-length character sequence. Then, divide the fixed-length character sequence into multiple text segments according to a preset sliding window to form a text segment set. S112. Call the TextCNN model, input the set of text segments into multiple convolutional layers with different convolutional kernel sizes, extract the corresponding field boundary features and structural arrangement features in each convolutional layer, and generate the corresponding text feature set. S113. Perform pooling and concatenation operations on the text feature set to integrate the convolution results from multiple windows into a set of structural representation vectors. S114. Based on the structural representation vector, locate the start and end positions of each field in each text segment, extract the field name, structural feature information and content verification information, and combine them into a feature segment sequence in a fixed order.

[0026] In this embodiment, S2 specifically includes: S21. Extract the identity credentials of the registrant from the rights confirmation request data, perform structural parsing on the identity credentials, extract public key information, permission identification information and signature content, and construct an identity dataset; S22. Invoke the trusted identity information database deployed in the blockchain network, perform consistency verification on the public key information and permission identification information in the identity dataset based on the preset identity mapping structure, and perform authenticity verification based on the signature content using the ECDSA algorithm. If all verifications pass, mark the corresponding rights confirmation request data as valid request data, specifically including: Extract public key information and permission identification information from the identity dataset to construct a set of identity fields for mapping verification; In the blockchain network, a deployed trusted identity information database is invoked. Based on a preset identity mapping structure, a one-to-one structure comparison operation is performed on the identity field set to determine whether a consistent mapping relationship exists. Under the premise that the consistency verification is passed, the ECDSA algorithm is used to verify the authenticity of the signature content in the identity dataset, and to verify whether the signature content is generated by the extracted public key information and has not been tampered with. When the structure comparison results are consistent and the signature authenticity verification is passed, the corresponding rights confirmation request data will be marked as valid request data. S23. Bind the valid request data with the verification timestamp and send it to all regional nodes in the blockchain network.

[0027] In this embodiment, the authenticity verification specifically includes: Extract the signature content from the identity dataset, the signature content being composed of horizontal and vertical coordinate components; Perform a hash function operation on the data fingerprint to generate the corresponding digest value; Perform modular inverse calculation on the ordinate components to obtain the multiplicative inverse; The hash weight value and the horizontal axis component are multiplied by the multiplicative inverse respectively, and the modulo operation is performed on the product results to generate the hash weight value and the signature weight value. The hash weight value is used as a scalar, and an elliptic curve scalar multiplication operation is performed with the preset elliptic curve base point. At the same time, the signature weight value is used as a scalar, and the public key information is used as a point on the elliptic curve, and an elliptic curve scalar multiplication operation is performed. Perform elliptic curve addition on the two multiplication results, generate verification points, and extract the x-coordinate values ​​of the verification points; The horizontal coordinate value is compared with the horizontal coordinate component in the signature content. If the two are equal, the signature content is confirmed to be authentic and valid, and the data fingerprint has not been tampered with.

[0028] In this embodiment, S3 specifically includes: S31. In each regional node, receive valid request data and register and sort it. Assign a time number to each valid request data and record the mapping relationship between the time number and the corresponding data fingerprint to build a time number mapping table. S32. Organize the valid request data according to the time number mapping table, initialize the sparse Merkle tree with the time number as the index, write each data fingerprint into the leaf node of the sparse Merkle tree according to the time number, and insert a preset null value node at the missing number position to form a set of leaf nodes with continuous structure. S33. Iteratively perform hash aggregation operations based on the leaf node set. In the first round of hash aggregation, concatenate the data fingerprints of two adjacent leaf nodes and perform hash calculation to generate multiple intermediate nodes as input for the next round of hash aggregation, specifically including: The set of leaf nodes is traversed, and the data fingerprints of each pair of adjacent leaf nodes are concatenated in a preset order to form a hash aggregation sequence. Perform a hash calculation operation on each concatenation result in the hash aggregation sequence to generate the corresponding intermediate node hash value, and add the intermediate node hash value to the intermediate node set; Determine if the number of nodes in the intermediate node set is greater than 1. If so, use the intermediate node set as the leaf node set for the next round of hash aggregation operation. S34. Iteratively execute the hash aggregation operation until the number of intermediate nodes generated in the hash aggregation operation is one. Use this intermediate node as the root node to form a sparse Merkle tree structure from the leaf node to the root node. S35. Perform state accumulation operation on each level node in the sparse Merkle tree. Starting from each leaf node, traverse along the direction of the parent node to the root node to construct a set of state accumulation paths. S36. Input the set of state accumulation paths into the accumulator and perform parallel hash calculation operations. Perform number alignment and state compression operations on all nodes on the state accumulation path to form a rights registration chain.

[0029] In this embodiment, S36 specifically includes: S361. Input the set of state accumulation paths into the accumulator structure, arrange the hash values ​​of each node in each state accumulation path according to the path order, generate a set of node hash sequences, and mark the path number and level number of each node. The path number and time number correspond one-to-one. S362. Based on the set of node hash sequences, perform number alignment operation, classify the nodes in all state accumulation paths according to the level number, and construct a multi-level node hash matrix. Each level of the node hash matrix corresponds to a height level of the sparse Merkle tree. S363. Perform an aggregation operation on the hash matrix of each layer of nodes, concatenate the hash values ​​of all nodes in the hash matrix in the order of path number, and use a hash function to perform digest calculation to generate the aggregated hash representation of the layer and construct the aggregation result set. S364. Starting from the lowest level aggregation result, the aggregation results of adjacent two levels are progressively merged in order from bottom to top, and a hash compression operation is performed on the merged result to generate a compressed aggregation chain. S365. Perform path reconstruction and structure verification operations on all nodes in the compressed aggregation chain to ensure that the hash value of each node is not tampered with during the merging process and to maintain the integrity of the hash dependency relationship. S366. Take the root node hash value of the compressed aggregate chain that has passed the structure verification as the root node of the rights registration chain, and record the correspondence between the root node and the time number mapping table to construct the rights registration chain.

[0030] In this embodiment, S4 specifically includes: S41. Receive the registration chain in each regional node, and perform integrity verification operation based on the status information in the registration chain to select the registration chain that meets the structural consistency requirements. S42. Based on the verified ownership registration chain, the HotStuff consensus algorithm is invoked, and the preset master node broadcasts a proposal message to each regional node. Each regional node verifies the ownership registration chain in the proposal message and generates a verification vote result. S43. Collect the verification voting results of all regional nodes. After meeting the preset voting quantity requirement, enter the preparation stage. The master node broadcasts the preparation message to each regional node. After receiving the preparation message, the regional node completes the confirmation operation and generates the confirmation voting result. S44. After collecting a number of confirmed votes that meet the preset preparation conditions, the master node broadcasts a submission message to each regional node. The regional nodes perform a confirmation signature operation based on the confirmation registration chain in the submission message and mark it as a consensus completion status. S45. After marking the consensus completion status, call the BLS aggregate signature method to aggregate the confirmation signatures of all regional nodes and generate the aggregate signature result. S46. Add a consensus timestamp to the aggregated signature result and generate a consensus state as the ownership confirmation consensus result.

[0031] In this embodiment, S45 specifically includes: S451. Collect all confirmation signatures generated by all regional nodes during the submission phase, and bind each confirmation signature with the corresponding regional node identifier to construct a signature binding set; S452. Perform a classification operation on all confirmation signatures in the signature binding set, arrange them according to the preset order of node identity identifiers, and generate a signature sorting sequence; S453. Call the BLS aggregate signature method to perform aggregate calculation on all confirmed signatures in the signature sorting sequence and generate an aggregate signature value. S454. Perform verification preparation operation on the aggregated signature value and extract the common parameter set containing the public key information and signature digest index of each regional node. S455. Perform consistency verification on the aggregated signature value based on the common parameter set to determine whether the consistency conditions of all confirmed signatures in terms of time and structure are met. S456. If the aggregated signature value passes verification, the aggregated signature value is used as the binding signature of the corresponding rights registration chain to form an aggregated signature result.

[0032] In this embodiment, S453 specifically includes: S4531. Traverse each confirmed signature in the signature sorting sequence, perform a hash mapping operation based on the confirmation and registration chain corresponding to the signature sorting sequence, generate a signature digest value, and pair the signature digest value with the corresponding regional node to construct a signature pairing set. S4532. Perform structured encoding on all signature pairing data in the signature pairing set to generate a signature encoding sequence; S4533. Using a preset BLS signature aggregation function, perform elliptic curve pairing calculation on the signature encoding sequence to generate aggregated signature points; S4534. Perform a formatting operation on the aggregate signature point according to the preset template to generate an aggregate signature value.

[0033] In this embodiment, S5 specifically includes: S51. Based on the consensus result, a verifiable delay function is invoked to perform sequential constraint calculations on the consensus state and consensus timestamp, generating data results with a unique calculation order and recording the corresponding delay calculation identifiers to form a time-series calculation result, specifically including: Extract the consensus status and consensus timestamp from the consensus results of rights confirmation, and bind the two in chronological order to construct consensus binding data; The function calls a pre-defined verifiable delay function to perform sequential constraint calculations on the consensus-bound data. The calculation process is controlled according to the built-in delay parameters of the function to generate a function calculation result with time uniqueness. The binding relationship between the function calculation result and the corresponding rights confirmation consensus result is recorded as a delayed calculation identifier, and the delayed calculation identifier is structurally mapped according to the preset mapping rules to form a time-series calculation result; S52. Generate verification credentials based on the time-series calculation results, and bind the verification credentials with the consensus state to construct a verifiable data set; S53. Perform time-series verification operations on the verifiable data set in each regional node. By verifying the consistency of the correspondence between the verification credentials and the consensus timestamp, determine whether the rights confirmation consensus result meets the preset time sequence constraints. S55. If the time order constraint is met, the corresponding consensus result is marked as verified and written into the blockchain ledger of each regional node as the registration result, while recording the registration timestamp.

[0034] In this embodiment, S6 specifically includes: S61. Extract data fingerprints from the registration results and write the data fingerprints into the data asset index table in the blockchain network based on a preset index structure. S62. Bind each data fingerprint in the data asset index table with the corresponding registration timestamp, ownership consensus status, and verification identifier, and update the index table structure according to the binding results; S63. When a new rights confirmation request data is received, extract the data fingerprint from the rights confirmation request data, and perform consistency matching operation on the extracted data fingerprint and all data fingerprints in the index field set in sequence according to the preset matching rules. S64. If a registered record consistent with the current data fingerprint is found in the data asset index table, the requested data will be marked as a duplicate confirmation request, and the registration process will be terminated without confirmation of rights. S65. If no duplicate record is found, the corresponding rights confirmation request data will be submitted to the next processing stage.

[0035] Example 1: To verify the feasibility of the present invention in practice, the present invention was applied to the data asset ownership confirmation process of a cross-regional data governance platform. In view of the data interaction needs between multiple administrative regions, a unified data registration and ownership confirmation process was constructed, which focused on solving problems such as difficulty in confirming data ownership across regions, lack of consistent standards in the ownership confirmation process, and inability to trace registration results.

[0036] In practical applications, each data provider, based on its own original data assets, performs format standardization and field extraction operations on the data assets through the data asset processing module. It then calls a deep learning model to perform structured parsing of the data content, extracting field labels, data structure features, and content verification values, ultimately generating a complete set of asset feature fragments. Subsequently, the system performs hash perturbation and multi-level aggregation operations on the feature fragment sequence to generate a unique and conflict-resistant data fingerprint, which is then bound to the registrant's verifiable identity certificate to construct the rights confirmation request data.

[0037] After the rights confirmation request data is generated, the platform access service submits it to blockchain nodes deployed in different regions. Each node first performs an identity credential verification operation, verifying the consistency of the registrant's public key, permission tag, and signature information, and confirming the signature's legality using the ECDSA algorithm. Verified data is marked as valid request data and assigned a timestamp on the chain for subsequent sorting. At this point, each regional node constructs a sparse Merkle tree and performs a state accumulation operation to generate the rights confirmation registration chain. The HotStuff consensus algorithm is then used to achieve consensus on the rights confirmation chain, followed by the BLS aggregate signature algorithm to generate a unified signature result, forming a rights confirmation consensus state.

[0038] To prevent malicious tampering of the rights confirmation order, this invention also introduces a verifiable delay function to protect the consensus results in terms of timing. The system performs order constraint calculations on the consensus state and timestamp, generates a timing verification credential, and after successful verification, formally writes the rights confirmation result into the blockchain ledger of each regional node and constructs a corresponding asset index table. This asset index table supports real-time conflict matching and detection for subsequent rights confirmation requests, ensuring that the same data asset is not registered repeatedly.

[0039] Through deployment testing in a multi-regional data collaboration scenario, five typical data assets were selected for rights confirmation and verification experiments: government records, structured form data, medical image reports, enterprise operation logs, and industrial equipment sensor data, covering unstructured, semi-structured, and structured data types. During the experiments, the system statistically analyzed key performance indicators such as rights confirmation request processing time, identity verification success rate, consensus completion latency, and conflict identification accuracy. The table below shows the key performance indicators of various data assets in a real deployment environment: Table 1. Statistical Table of Performance Indicators for the Confirmation of Ownership of Various Data Assets

[0040] As can be seen from the data in Table 1, the method described in this invention exhibits good versatility and performance stability when processing different types of data assets. Even in medical imaging scenarios with large data volumes, the system can still complete the entire rights confirmation process within 100ms, demonstrating high processing efficiency.

[0041] Regarding identity verification, the success rate of this invention generally remains above 99%, thanks to the dual verification mechanism based on verifiable credentials and the ECDSA algorithm, which effectively prevents forged registration. The average consensus completion latency remains between 80ms and 90ms, indicating that the HotStuff algorithm and BLS signature mechanism have good throughput performance and response speed in a multi-node environment.

[0042] Most importantly, the accuracy rate of conflict detection for all types of data exceeded 99.8%, which fully verified that the present invention has excellent deduplication judgment ability and ownership anti-conflict mechanism in multi-regional data registration scenarios, and can effectively improve the security and reliability of data asset ownership registration.

[0043] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for cross-regional registration and confirmation of rights for data assets based on blockchain, characterized in that, Includes the following steps: S1. Obtain data asset information and the identity certificate of the registrant, perform information feature extraction operation on the data asset information, generate data fingerprint and bind it with the identity certificate, and construct the rights confirmation request data; S2. Perform identity verification operation based on verifiable credentials on the rights confirmation request data, mark the rights confirmation request data that passes the verification as valid request data, and submit it to each regional node in the blockchain network; S3. Assign time numbers to valid request data, construct a sparse Merkle tree based on the assignment results, and use an accumulator to perform layer-by-layer hash aggregation and state accumulation operations to construct the rights registration chain; S4. Using the HotStuff consensus algorithm, the rights registration chain is jointly judged in each regional node, and the BLS aggregation signature operation is performed on the judgment result to generate a consensus state containing a consensus timestamp as the rights confirmation consensus result. S5. Use a verifiable delay function to perform time-series protection calculations on the rights confirmation consensus results, generate verification credentials and perform time-series verification operations, and write the verified rights confirmation consensus results as registration results to each regional node. S6. Write the data fingerprint corresponding to the registration result into the data asset index table of the blockchain network, and perform a conflict matching operation on the newly submitted rights confirmation request data. If the match is successful, the rights confirmation registration will not be granted.

2. The method for cross-regional registration and confirmation of rights of data assets based on blockchain according to claim 1, characterized in that, The data asset information refers to the set of data content to be registered for ownership confirmation, including structured or unstructured data files, data summaries or data fragments. The blockchain network refers to a consortium blockchain structure composed of multiple regional nodes. The regional nodes refer to consensus participating nodes deployed in different geographical or management areas, used to jointly perform ownership confirmation data verification and status update operations. The time number refers to the time sequence identifier for the allocation of ownership confirmation request data.

3. The method for cross-regional registration and confirmation of rights of data assets based on blockchain according to claim 1, characterized in that, S1 specifically includes: S11. Obtain the data asset information to be registered and confirmed and the identity certificate data of the registrant. Call the TextCNN model to perform segmented structure recognition on the data asset information, split the data asset information into multiple data segments, and extract field labels, data structure features and content verification values ​​from each data segment to form a feature fragment sequence. S12. The feature segment sequence is subjected to multiple rounds of hash encoding using a preset hash function. Local features of each feature segment are extracted, and aggregation calculations are performed on all local features to generate global features. Specifically, this includes: A preset hash function is used to perform hash encoding operations on each field label, data structure feature, and content verification value in the feature fragment sequence to generate the corresponding local feature sequence; The local features are spliced ​​together in their original order, and multiple preset data segments are inserted at fixed positions in the splicing result to form a perturbation feature sequence. The data segments are generated based on fixed parameters. A global hash aggregation operation is performed on the perturbation feature sequence. In each round, intermediate summary nodes are generated according to the preset hash rotation rules. Based on the intermediate summary nodes, a multi-level nested structure is recursively aggregated to generate global features. S13. Perform a sequence perturbation operation on the global features, and use bit operation permutation and digest block rearrangement based on preset rules to re-encode the global features and generate data fingerprints. S14. Bind the data fingerprint with the identity credential data and record the request timestamp to construct the rights confirmation request data.

4. The method for cross-regional registration and confirmation of rights of data assets based on blockchain according to claim 3, characterized in that, S11 specifically includes: S111. Obtain the data asset information to be registered and confirmed, and perform a format standardization operation on the data asset information to convert it into a fixed-length character sequence. Then, divide the fixed-length character sequence into multiple text segments according to a preset sliding window to form a text segment set. S112. Call the TextCNN model, input the set of text segments into multiple convolutional layers with different kernel sizes, extract the corresponding field boundary features and structural arrangement features in each convolutional layer, and generate the corresponding text feature set. S113. Perform pooling and concatenation operations on the text feature set to integrate the convolution results from multiple windows into a set of structural representation vectors. S114. Based on the structural representation vector, locate the start and end positions of each field in each text segment, extract the field name, structural feature information and content verification information, and combine them into a feature segment sequence in a fixed order.

5. A method for cross-regional registration and confirmation of rights of data assets based on blockchain according to claim 1, characterized in that, S2 specifically includes: S21. Extract the identity credentials of the registrant from the rights confirmation request data, perform structural parsing on the identity credentials, extract public key information, permission identification information and signature content, and construct an identity dataset; S22. Invoke the trusted identity information database deployed in the blockchain network, perform consistency verification on the public key information and permission identification information in the identity dataset based on the preset identity mapping structure, and perform authenticity verification based on the signature content using the ECDSA algorithm. If all verifications pass, mark the corresponding rights confirmation request data as valid request data, specifically including: Extract public key information and permission identification information from the identity dataset to construct a set of identity fields for mapping verification; In the blockchain network, a deployed trusted identity information database is invoked. Based on a preset identity mapping structure, a one-to-one structure comparison operation is performed on the identity field set to determine whether a consistent mapping relationship exists. Under the premise that the consistency verification is passed, the ECDSA algorithm is used to verify the authenticity of the signature content in the identity dataset, and to verify whether the signature content is generated by the extracted public key information and has not been tampered with. When the structure comparison results are consistent and the signature authenticity verification is passed, the corresponding rights confirmation request data will be marked as valid request data. S23. Bind the valid request data with the verification timestamp and send it to all regional nodes in the blockchain network.

6. The method for cross-regional registration and confirmation of rights of data assets based on blockchain according to claim 1, characterized in that, S3 specifically includes: S31. In each regional node, receive valid request data and register and sort it. Assign a time number to each valid request data and record the mapping relationship between the time number and the corresponding data fingerprint to build a time number mapping table. S32. Organize the valid request data according to the time number mapping table, initialize the sparse Merkle tree with the time number as the index, write each data fingerprint into the leaf node of the sparse Merkle tree according to the time number, and insert a preset null value node at the missing number position to form a set of leaf nodes with continuous structure. S33. Iteratively perform hash aggregation operations based on the leaf node set. In the first round of hash aggregation, concatenate the data fingerprints of two adjacent leaf nodes and perform hash calculation to generate multiple intermediate nodes as input for the next round of hash aggregation, specifically including: The set of leaf nodes is traversed, and the data fingerprints of each pair of adjacent leaf nodes are concatenated in a preset order to form a hash aggregation sequence. Perform a hash calculation operation on each concatenation result in the hash aggregation sequence to generate the corresponding intermediate node hash value, and add the intermediate node hash value to the intermediate node set; Determine if the number of nodes in the intermediate node set is greater than 1. If so, use the intermediate node set as the leaf node set for the next round of hash aggregation operation. S34. Iteratively execute the hash aggregation operation until the number of intermediate nodes generated in the hash aggregation operation is one. Use this intermediate node as the root node to form a sparse Merkle tree structure from the leaf node to the root node. S35. Perform state accumulation operation on each level node in the sparse Merkle tree. Starting from each leaf node, traverse along the direction of the parent node to the root node to construct a set of state accumulation paths. S36. Input the set of state accumulation paths into the accumulator and perform parallel hash calculation operations. Perform number alignment and state compression operations on all nodes on the state accumulation path to form a rights registration chain.

7. A method for cross-regional registration and confirmation of rights of data assets based on blockchain according to claim 6, characterized in that, Specifically, S36 includes: S361. Input the set of state accumulation paths into the accumulator structure, arrange the hash values ​​of each node in each state accumulation path according to the path order, generate a set of node hash sequences, and mark the path number and level number of each node. The path number and time number correspond one-to-one. S362. Based on the set of node hash sequences, perform number alignment operation, classify the nodes in all state accumulation paths according to the level number, and construct a multi-level node hash matrix. Each level of the node hash matrix corresponds to a height level of the sparse Merkle tree. S363. Perform an aggregation operation on the hash matrix of each layer of nodes, concatenate the hash values ​​of all nodes in the hash matrix in the order of path number, and use a hash function to perform digest calculation to generate the aggregated hash representation of the layer and construct the aggregation result set. S364. Starting from the lowest level aggregation result, the aggregation results of adjacent two levels are progressively merged in order from bottom to top, and a hash compression operation is performed on the merged result to generate a compressed aggregation chain. S365. Perform path reconstruction and structure verification operations on all nodes in the compressed aggregation chain to ensure that the hash values ​​of each node are not tampered with during the merging process and to maintain the integrity of the hash dependency relationship. S366. Take the root node hash value of the compressed aggregate chain that has passed the structure verification as the root node of the rights registration chain, and record the correspondence between the root node and the time number mapping table to construct the rights registration chain.

8. A method for cross-regional registration and confirmation of rights of data assets based on blockchain according to claim 1, characterized in that, S4 specifically includes: S41. Receive the registration chain in each regional node, and perform integrity verification operation based on the status information in the registration chain to select the registration chain that meets the structural consistency requirements. S42. Based on the verified ownership registration chain, the HotStuff consensus algorithm is invoked, and the preset master node broadcasts a proposal message to each regional node. Each regional node verifies the ownership registration chain in the proposal message and generates a verification vote result. S43. Collect the verification voting results of all regional nodes. After meeting the preset voting quantity requirement, enter the preparation stage. The master node broadcasts the preparation message to each regional node. After receiving the preparation message, the regional node completes the confirmation operation and generates the confirmation voting result. S44. After collecting a number of confirmed votes that meet the preset preparation conditions, the master node broadcasts a submission message to each regional node. The regional nodes perform a confirmation signature operation based on the confirmation registration chain in the submission message and mark it as a consensus completion status. S45. After marking the consensus completion status, call the BLS aggregate signature method to aggregate the confirmation signatures of all regional nodes and generate the aggregate signature result. S46. Add a consensus timestamp to the aggregated signature result and generate a consensus state as the ownership confirmation consensus result.

9. A method for cross-regional registration and confirmation of rights of data assets based on blockchain according to claim 8, characterized in that, Specifically, S45 includes: S451. Collect all confirmation signatures generated by all regional nodes during the submission phase, and bind each confirmation signature with the corresponding regional node identifier to construct a signature binding set; S452. Perform a classification operation on all confirmation signatures in the signature binding set, arrange them according to the preset order of node identity identifiers, and generate a signature sorting sequence; S453. Call the BLS aggregate signature method to perform aggregate calculation on all confirmed signatures in the signature sorting sequence and generate an aggregate signature value. S454. Perform verification preparation operation on the aggregated signature value and extract the common parameter set containing the public key information and signature digest index of each regional node. S455. Perform consistency verification on the aggregated signature value based on the common parameter set to determine whether the consistency conditions of all confirmed signatures in terms of time and structure are met. S456. If the aggregated signature value passes verification, the aggregated signature value is used as the binding signature of the corresponding rights registration chain to form an aggregated signature result.

10. A method for cross-regional registration and confirmation of rights of data assets based on blockchain according to claim 1, characterized in that, S5 specifically includes: S51. Based on the consensus result, a verifiable delay function is invoked to perform sequential constraint calculations on the consensus state and consensus timestamp, generating data results with a unique calculation order and recording the corresponding delay calculation identifiers to form a time-series calculation result, specifically including: Extract the consensus status and consensus timestamp from the consensus results of rights confirmation, and bind the two in chronological order to construct consensus binding data; The function calls a pre-defined verifiable delay function to perform sequential constraint calculations on the consensus-bound data. The calculation process is controlled according to the built-in delay parameters of the function to generate a function calculation result with time uniqueness. The binding relationship between the function calculation result and the corresponding rights confirmation consensus result is recorded as a delayed calculation identifier, and the delayed calculation identifier is structurally mapped according to the preset mapping rules to form a time-series calculation result; S52. Generate verification credentials based on the time-series calculation results, and bind the verification credentials with the consensus state to construct a verifiable data set; S53. Perform time-series verification operations on the verifiable data set in each regional node. By verifying the consistency of the correspondence between the verification credentials and the consensus timestamp, determine whether the rights confirmation consensus result meets the preset time sequence constraints. S55. If the time order constraint is met, the corresponding consensus result is marked as verified and written into the blockchain ledger of each regional node as the registration result, while recording the registration timestamp.

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