A data asset management method and system based on distributed ledger technology

Abstract

This invention discloses a data asset management method and system based on distributed ledger technology, relating to the field of distributed ledger technology. The method includes: triggering a dynamic evaluation smart contract based on an ownership certificate to generate a dynamic valuation matrix through a federated learning model and an off-chain network; inputting the dynamic valuation matrix into a cross-chain gateway to perform atomic binding operations, and deploying a dynamic token contract on the target chain to output a token certificate; driving a cross-chain transaction engine to process the token certificate through an authentication blockchain protocol to generate a transaction certificate containing a quantum-resistant aggregate signature; and using the transaction certificate to activate a monitoring smart contract to analyze asset state parameters in real time and output adaptive rebalancing instructions. This invention ensures the atomicity of cross-chain transactions in quantum-resistant signature verification, dynamic valuation validity consensus, and automatic fee allocation through an improved hash time-locking protocol and a three-dimensional time window state machine.

Description

Technical Field

[0001] This invention relates to the field of distributed ledger technology, and in particular to a data asset management method and system based on distributed ledger technology. Background Technology

[0002] With the widespread application of distributed ledger technology in data asset management, asset ownership confirmation, cross-chain transactions, and dynamic valuation mechanisms based on blockchain have become research hotspots. In existing technologies, traditional digital signature schemes rely on large integer factorization or the discrete logarithm problem, but Shor's algorithm in quantum computing can efficiently solve such mathematical problems, leading to long-term security risks. Meanwhile, dynamic asset valuation often depends on off-chain centralized data sources and static models, making it difficult to integrate real-time market data from multiple parties with physical state parameters. Furthermore, cross-chain transactions rely on mechanisms such as hash time locks, which suffer from insufficient atomicity guarantees and weak resistance to quantum attacks.

[0003] The main shortcomings of the current technology are twofold: First, traditional signature schemes lack post-quantum security and cannot resist quantum computing attacks, leading to the risk of key leakage; second, dynamic valuation models rely on a single data source and fixed parameters, making it difficult to achieve real-time collaborative computing involving multiple parties, and cross-chain operations suffer from high transaction latency and valuation lag due to the lack of quantum-resistant verification and federated learning fusion mechanisms. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a data asset management method based on distributed ledger technology to solve the problems of quantum attack vulnerability and insufficient real-time dynamic asset valuation in existing distributed ledger systems.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a data asset management method based on distributed ledger technology, which includes using a quantum-resistant signature algorithm to encrypt and authenticate asset metadata and generate an ownership certificate; Based on the ownership certificate, a dynamic evaluation smart contract is triggered to generate a dynamic valuation matrix through a federated learning model and an off-chain network. The dynamic valuation matrix is ​​input into the cross-chain gateway to perform an atomic binding operation, and a dynamic token contract is deployed on the target chain to output token certificates. The token is processed by a cross-chain transaction engine driven by a certified blockchain protocol to generate a transaction credential containing a quantum-resistant aggregate signature. The transaction certificate is used to activate the monitoring smart contract, which analyzes asset status parameters in real time and outputs adaptive rebalancing instructions. The adaptive rebalancing instruction is used to perform reverse cross-chain verification, destroy the target chain token, and generate an asset unstaking certificate.

[0007] As a preferred embodiment of the data asset management method based on distributed ledger technology described in this invention, the generation of ownership certificates includes the following steps: Configure security parameters based on ring dimension and modulus parameters to generate a uniform random matrix; A multinomial sampling algorithm is applied to the security parameters to independently sample from a discrete Gaussian distribution to generate a private key vector, and a statistically independent error term is generated by isolating the random seed. The uniform random matrix, private key vector, and error term are input into a polynomial ring operation, and a public key is generated through modular addition. The linear relationship of the key pair is verified to obtain a quantum-resistant key pair. Based on quantum-resistant key pairs, unique identifiers and asset type classification codes are generated. At the same time, the initial asset valuation value is obtained, converted into floating-point numbers, and combined to form structured metadata. Perform a SHA3-512 hash operation on the structured metadata to generate a digital fingerprint; The digital fingerprint is input into the quantum-resistant signature algorithm to generate a signed transaction record and attached with a Coordinated Universal Time (UTC) timestamp. After broadcasting and verification, a global index is built based on a Merkle tree structure to form an ownership certificate.

[0008] As a preferred embodiment of the data asset management method based on distributed ledger technology described in this invention, the generation of the dynamic valuation matrix includes the following steps: Read the reserved fields of the valuation trajectory layer of the ownership certificate, extract the asset type classification code and match it with the preset asset valuation parameter template to generate a valuation parameter weight matrix; Subscribe to off-chain real-time market data streams and generate real-time market feature vectors through feature extraction and normalization; Using the valuation parameter weight matrix as the initial weights of the federated learning model and the real-time market feature vector as the external feature, the local gradient is calculated to generate the encrypted gradient tensor. The encrypted gradient tensor is subjected to trusted weighted aggregation to obtain the aggregated global gradient, and the parameters of the federated learning model are updated based on the global gradient to generate a dynamic estimation correction factor. The SPDZ framework is used to perform secret sharing verification and threshold signing on the dynamic valuation correction factor. The signed dynamic valuation correction factor is written into the sidechain evaluation log partition and immutable data blocks are generated according to the time window. Adjustment instructions are written into the header of the immutable data block to form a dynamic valuation matrix.

[0009] As a preferred embodiment of the data asset management method based on distributed ledger technology described in this invention, the adjustment instruction is a token issuance adjustment instruction written into the header of the immutable data block by the dynamic evaluation smart contract when the threshold-signed dynamic valuation correction factor is written into the sidechain evaluation log partition and immutable data blocks are generated according to the time window.

[0010] As a preferred embodiment of the data asset management method based on distributed ledger technology described in this invention, the atomic binding operation includes the following steps: After sending a casting request message through a quantum-safe communication channel and verifying the quantum-resistant ring signature, the main chain ownership certificate is frozen, and a freeze proof is generated. Deploy a smart token contract on the target chain. The smart token contract uses the freeze proof as the initialization parameter and binds the dynamic valuation correction factor with the adjustment instruction to generate zero-knowledge proof public parameters. Based on the public parameters of zero-knowledge proof, a zk-STARK arithmetic circuit is constructed to generate a non-interactive zero-knowledge proof. This proof is then submitted to the verification contracts of the main chain and the target chain via the relay chain cross-chain verification bridge. This completes the atomic mapping between the frozen state of the main chain and the token issuance of the target chain, and outputs a token certificate with a nested smart contract structure.

[0011] As a preferred embodiment of the data asset management method based on distributed ledger technology described in this invention, the process of driving the cross-chain transaction engine refers to parsing the quantum-resistant threshold signature in the transaction offer, verifying the validity of the dynamic valuation matrix through the relay chain, generating a verification certificate carrying time window parameters, triggering the federated Byzantine consensus mechanism to perform four rounds of interactive verification, restoring the cross-chain address mapping relationship based on the hierarchical deterministic key derivation algorithm, releasing payment tokens and allocating transaction fees in stages through a three-dimensional time window state machine, and generating transaction certificates.

[0012] As a preferred embodiment of the data asset management method based on distributed ledger technology described in this invention, the output adaptive rebalancing instruction includes the following steps. The system monitors smart contracts, parses transaction credentials, and matches the freeze proof hash recorded in the main chain regulatory sandbox with the smart token contract address stored in the side chain liquidity pool. It also synchronously accesses the physical characteristic data stream of the basic attribute layer of the ownership certificate and obtains real-time market characteristic vectors. The system calculates the deviation of the asset's physical state, the volatility of the market valuation, and the deviation of the token liquidity. If any of these indicators exceeds the safety threshold, a cross-chain transfer freeze is triggered. The cross-chain transfer freeze triggers the dynamic evaluation smart contract to call the arithmetic circuit of the SPDZ framework, generate corresponding cryptographic gradient tensors from the latest market valuation volatility, asset physical state deviation and token liquidity deviation, update the parameters of the federated learning model, generate a new asset discount coefficient, and generate a supply adjustment proposal through the threshold signature mechanism. The supply adjustment proposal is submitted to the target chain via the relay chain cross-chain verification bridge. The target chain's smart token contract uses the zk-STARK algorithm to construct and verify a non-interactive zero-knowledge proof, updates the target chain's token issuance, and outputs an adaptive rebalancing instruction.

[0013] As a preferred embodiment of the data asset management method based on distributed ledger technology described in this invention, the reverse cross-chain verification includes the following steps: After the adaptive rebalancing instruction is synchronized to the target chain smart token contract via the relay chain cross-chain message bus, the asset holder initiates a redemption request. The target chain smart token contract parses the token destruction quantity in the redemption request, executes the token destruction, generates the target chain destruction record, and submits the destruction proof to the main chain verification contract through the relay chain cross-chain verification bridge using a quantum-resistant threshold signature. The main chain verification contract performs triple verification. Once the verification is successful, the main chain ownership certificate is unfrozen, and the three-dimensional time window indicators of the side chain liquidity pool are updated to generate asset unstaking certificates.

[0014] As a preferred embodiment of the data asset management method based on distributed ledger technology described in this invention, the triple verification includes quantum signature validity verification, dynamic evaluation log consistency verification, and IoT device status verification.

[0015] Secondly, the present invention provides a data asset management system based on distributed ledger technology, including an authentication module that uses a quantum-resistant signature algorithm to encrypt and authenticate asset metadata and generate an ownership certificate; The valuation module triggers a dynamic valuation smart contract based on the ownership certificate, and generates a dynamic valuation matrix through a federated learning model and an off-chain network. The cross-chain binding module inputs the dynamic valuation matrix into the cross-chain gateway to perform atomic binding operations, and deploys a dynamic token contract on the target chain to output token certificates. The transaction engine module drives the cross-chain transaction engine to process the token credentials through the authentication blockchain protocol, generating transaction credentials containing quantum-resistant aggregate signatures; The monitoring module uses the transaction certificate to activate the monitoring smart contract, analyzes asset status parameters in real time, and outputs adaptive rebalancing instructions. The unstaking module performs reverse cross-chain verification according to the adaptive rebalancing instruction, destroys the target chain token, and generates an asset unstaking certificate.

[0016] The beneficial effects of this invention are as follows: A public key is generated by superimposing error terms through polynomial ring operations, and the unforgeability of ownership certificates is ensured through triple verification; real-time off-chain market data and asset physical characteristics are fused into a real-time market feature vector, and multi-party secure computation verification is achieved using the SPDZ framework to generate a dynamic valuation correction factor, enabling real-time accurate valuation under multi-party data collaboration; a non-interactive zero-knowledge proof is constructed using the zk-STARK algorithm, combined with an improved hash time-locking protocol and a three-dimensional time window state machine, increasing the success rate of cross-chain transactions, and reducing the risk of malicious attacks through four rounds of verification using federated Byzantine consensus; based on real-time monitoring network analysis of asset state deviation and market volatility, the parameters of the federated learning model are updated and a supply adjustment proposal is generated, enabling dynamic token destruction and unfreezing, shortening the asset liquidity risk response time, and ensuring the non-repudiation of reverse cross-chain verification. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a data asset management method based on distributed ledger technology.

[0019] Figure 2 A schematic diagram of the generation of the dynamic valuation matrix.

[0020] Figure 3 This is a schematic diagram of cross-chain atomic binding.

[0021] Figure 4 This is a schematic diagram of a cross-chain transaction and monitoring system. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Example 1, referring to Figures 1-4 This embodiment provides a data asset management method based on distributed ledger technology, including the following steps: S1. All participants configure security parameters for a lattice-based quantum-resistant signature algorithm when starting a node on the distributed ledger main chain.

[0026] Furthermore, the safety parameters include ring dimension parameters (e.g., n=256) and modulus parameters (e.g., q=8380417).

[0027] Based on the ring dimension parameter (e.g., n=256), the matrix is ​​determined to be an n×n square matrix. A random seed for each element is generated independently using a deterministic random number generator. Element-wise sampling is performed from a uniform integer distribution with modulus q. During the sampling process, an anti-predictive entropy source is used to fill the random pool, and cyclic redundancy check is used to ensure that there is no implicit correlation between matrix elements, thus generating a random matrix. The generated random matrix is ​​encoded into a polynomial ring format in row-major order, and modulus normalization is performed on the coefficients of each encoded matrix element. That is, modulo operation is performed on the coefficients of each matrix element according to the modulus parameter, and each matrix element coefficient is uniformly mapped to a normalized value in the interval [0, q−1], forming a uniform random matrix.

[0028] Based on the configured security parameters, a polynomial sampling algorithm is executed to generate a private key vector. The private key vector is a polynomial ring structure, and each coefficient in the private key vector is a polynomial coefficient in the polynomial ring structure. The standard deviation parameter of the discrete Gaussian distribution is determined from the security parameters, and the standard deviation parameter is used as the sampling parameter. Discrete Gaussian sampling is performed independently on each coefficient, and the rejection sampling method is used to screen the effective coefficients that satisfy the modulus constraint [0, q−1]. After the effective coefficient sampling is completed, the effective coefficient sequence is converted into a polynomial ring structure to generate an error term.

[0029] Furthermore, each coefficient of the error term is sampled independently and completely isolated from the sampling process of the private key vector. The random seed used for sampling is different from that of the private key vector, ensuring that the error term and the private key vector satisfy statistical independence, and the generated result serves as a noise interference term.

[0030] The uniform random matrix, private key vector, and error term are input into the polynomial ring operation process. The uniform random matrix and private key vector are multiplied by polynomials. The result and error term are added by modulo addition. Finally, the public key value is output.

[0031] Furthermore, the output public key value is processed through modulo normalization.

[0032] The multiplication operation between the uniform random matrix and the private key vector is re-executed, and the result of the multiplication operation and the error term are added modulo to obtain the recalculated public key value. The recalculated public key value is compared with the generated public key value. If the comparison is consistent, it is confirmed that the public key value and the private key vector satisfy a linear relationship on a polynomial ring, and the complete quantum resistance key pair is obtained.

[0033] The asset metadata is uniquely identified based on the complete quantum-resistant key pair, as follows: A secure random number generator produces byte-based random numbers, which are then converted into hexadecimal strings to generate unique identifiers. Based on the asset type field in the asset metadata, a classification code is generated according to the asset type matching classification standard. This standard is a pre-defined asset type-classification hierarchy mapping rule set during deployment initialization by the data asset management platform, used to map asset types to their corresponding classification levels. The classification code generation process determines the corresponding classification level by querying a code table. This code table is a pre-compiled and pre-stored hierarchy code lookup table based on the asset type matching classification standard. The code table provides coding rules by level, with each level including a two-digit first-level identifier representing the product category, a two-digit second-level identifier representing the product group, and a two-digit third-level identifier representing the product type.

[0034] The initial valuation of the asset is obtained and converted into a floating-point number denominated in USDT (Tether, used for value anchoring and payment settlement in blockchain transactions). The valuation conversion process converts the initial valuation value by calling the real-time exchange rate. The unique identifier, classification code and USDT valuation value are combined into structured metadata. The structured metadata is then subjected to SHA3-512 hash operation to generate a digital fingerprint.

[0035] Furthermore, the hash operation process converts the structured metadata string into a byte stream, inputs it into the SHA3-512 hash function for iterative compression calculation, and outputs a 512-bit hash value, from which the first 256 bits are truncated as a digital fingerprint.

[0036] The generated digital fingerprint is input into a quantum-resistant signature algorithm for signing. The signature generates a data packet containing a challenge polynomial and a signature vector. After the data packet is bound to the original metadata, a Coordinated Universal Time (UTC) timestamp accurate to milliseconds is appended, forming a complete signed transaction record. The signed transaction record is broadcast to the verification node queue of the distributed ledger main chain via the Gossip (peer-to-peer) network protocol. The main chain verification nodes perform triple verification on the received signed transaction record, as follows: The first round of verification checks whether the mathematical relationship between the public and private keys satisfies a linear equation on a polynomial ring. The second round of verification compares whether the hash values ​​of the digital fingerprint and metadata are completely consistent. The final round of verification checks whether the infinity norm of the signature vector is lower than a preset security threshold. Signature transaction records that pass all verifications are packaged into a new block in chronological order. The block height and Merkle root hash are written into the header of the new block, generating a draft ownership certificate containing timestamps and transaction hash values.

[0037] Furthermore, the preset security threshold refers to the maximum allowed value of the infinite norm of the signature vector (i.e., the element with the largest absolute value in the vector). It is an upper limit of the signature vector norm set through comprehensive analysis of cryptographic theory and standardized practices. Its core objective is to resist quantum computing attacks and ensure the unforgeability of the signature scheme.

[0038] The draft ownership certificate is structured and coded according to a hierarchical structure (basic attribute layer, valuation trajectory layer, and cross-chain interaction layer), as follows: The basic attribute layer is filled with asset physical characteristic data and compressed and stored using the Protobuf (protocol buffer) binary serialization format. The valuation trajectory layer reserves slots for dynamic evaluation fields and allocates storage space in byte alignment. The cross-chain interaction layer generates a multi-chain compatible sub-key tree based on a hierarchical deterministic key derivation algorithm, resulting in a formal ownership certificate containing a three-layer data structure.

[0039] Furthermore, the formal ownership certificate is constructed using a Merkle tree structure for global indexing. The digital fingerprint of each certificate serves as the leaf node of the Merkle tree. The Keccak-256 hash algorithm is used to calculate the node hash value layer by layer from bottom to top, constructing a complete binary tree structure. Finally, the Merkle root hash value is generated and broadcast and synchronized among all nodes in the network via the Gossip protocol. Based on the Paxos consensus algorithm (a distributed consensus protocol based on majority voting, used to achieve state consensus in asynchronous networks), a majority of nodes confirm the Merkle root, forming the initial consensus state.

[0040] S2. After the formal ownership certificate completes the global index construction through the Merkle tree structure, the dynamic evaluation smart contract triggers initialization based on the reserved field slots of the valuation trajectory layer of the ownership certificate, as follows: The system dynamically evaluates the physical characteristics data of the basic attribute layer of the smart contract's ownership certificate, extracts the asset type classification code, and matches the preset asset valuation parameter template (e.g., the manufacturing equipment asset template includes depreciation rate parameters, residual value calculation rules, and useful life indicators) to generate a valuation parameter weight matrix.

[0041] Among them, the asset valuation parameter template is a set of valuation parameters (such as depreciation rate parameters, residual value calculation rules and useful life indicators) that are pre-configured and built into the system during the deployment / initialization phase and correspond one-to-one with the asset type classification code. The dynamic evaluation smart contract matches and calls the parameters according to the classification code.

[0042] The dynamic evaluation smart contract subscribes to real-time market data streams through off-chain networks and performs feature extraction and normalization to form a real-time market feature vector.

[0043] Furthermore, the real-time data stream includes the Refinitiv commodity price index, the Bank for International Settlements exchange rate midpoint, and the S&P Global Industry Sentiment Index.

[0044] Feature extraction and normalization processes include unifying data of different frequencies into minute-by-minute time slices, calculating volatility for commodity price indices, and mapping PMI indicators to sub-sector impact weights based on classification codes.

[0045] The valuation parameter weight matrix is ​​used as the initial weights of the federated learning model, and the real-time market feature vector is used as the external features of the federated learning model. The federated learning model adopts a horizontal federated architecture, and each participant conducts distributed model training based on the initial weights and external features, calculating the local gradient through a chain-like backpropagation algorithm. During the training process, the spatiotemporal features of the real-time market data are simultaneously fused, and an encrypted gradient tensor is generated considering both the physical state of the equipment and the economic environment. Each participant uploads the encrypted gradient tensor to each coordination node for trusted weighted aggregation, obtaining the aggregated global gradient. The aggregated global gradient is used to perform tensor operations with the real-time market feature vector to update the global federated learning model parameters. The weights in the updated global federated learning model parameters that correspond one-to-one with each feature dimension of the real-time market feature vector are extracted as a multiple linear regression coefficient vector, and the intercept term is also extracted. The real-time market data stream is sliced ​​into minute-by-minute time segments to extract and normalize features, resulting in a real-time market feature vector. Linear regression calculation is performed on the multiple linear regression coefficient vector, the intercept term, and the real-time market feature vector, with the expression as follows: ; in, This represents the regression output value, i.e., the prediction result of the multiple linear regression model for the current input features, and serves as a dynamic estimation correction factor. Represents the intercept term. Indicates feature index, Indicates the number of feature dimensions. Indicates the first The regression coefficients corresponding to each feature Indicates the first Each feature input value.

[0046] The dynamic valuation correction factor is input into a multi-party secure computation protocol. The protocol uses the SPDZ framework (Secure Multi-Party Computation Framework) to perform secret sharing verification. Each participating party divides its locally held historical asset valuation data (e.g., valuation series over the past 12 months) into arithmetic secret shares, performs multiplication verification using Beaver triples (random number triples), and outputs the dynamic valuation correction factor verification result. A verification flag of "pass" indicates that all Beaver triple multiplication verifications are valid and the verification value meets the consistency condition; a verification flag of "fail" indicates that any multiplication verification failed or the verification value does not meet the consistency condition. A dynamic valuation correction factor that passes verification triggers a threshold signature mechanism, indicating... Each node generates a signature fragment, which is then aggregated to form a dynamic valuation correction factor for the complete digital signature carrying a timestamp and a federated learning model version identifier. The dynamic valuation correction factor for the complete digital signature is written to the evaluation log partition of the sidechain. The log partition is stored using an LSM tree (Log Structure Merging Tree) structure, and immutable data blocks are generated according to time windows. An adjustment instruction is written at the beginning of each data block. The adjustment instruction generates a quantum signature verification path through the subkey tree of the ownership certificate cross-chain interaction layer. The dynamic valuation correction factor and the adjustment instruction are combined to form a dynamic evaluation report, which is appended with the digital fingerprint of the ownership certificate, the Coordinated Universal Time encoding of the timestamp, and the quantum signature verification path to form a dynamic valuation matrix.

[0047] S3. The asset holder sends a minting request message containing dynamic valuation matrix metadata through the quantum-secure communication channel of the cross-chain gateway. The minting request message carries the digital fingerprint of the ownership certificate, the verification result of the dynamic valuation correction factor, and the federated learning model version identifier. After verifying the validity of the quantum-resistant ring signature in the minting request message, the cross-chain gateway initiates a three-step atomic operation sequence: The first step is to freeze the main chain ownership certificate: The main chain smart contract parses the dynamic valuation matrix in the minting request message, extracts the digital fingerprint and timestamp Coordinated Universal Time (UTC) code of the ownership certificate, and retrieves the Merkle tree global index to match the corresponding draft ownership certificate; the cross-chain interaction layer sub-key tree of the draft ownership certificate generates a freeze instruction signature, which is verified by the federated Byzantine consensus nodes. The physical feature data field of the basic attribute layer of the ownership certificate is marked as frozen, and the freeze status change record is broadcast to all network nodes through the Gossip protocol to generate a freeze certificate with a timestamp.

[0048] Furthermore, the freeze proof includes the freeze operation hash, Merkel path verification data, and a quantum-resistant ring signature fragment.

[0049] The second step is to deploy the smart token contract on the target chain: The contract deployment interface on the target chain receives the freeze proof, parses the dynamic valuation correction factor verification result in the freeze proof, and inputs the federated learning model version identifier and real-time market feature vector as initialization parameters into the smart token contract. The token supply module of the smart token contract calls the adjustment instructions in the dynamic valuation matrix to calculate the initial token issuance based on the dynamic valuation correction factor. The expression is: ; in, Indicates the initial token issuance. This indicates the baseline issuance quantity given by the adjustment instruction. This represents the dynamic valuation adjustment factor. This indicates a floor operation, used to round the result down to the integer token quantity.

[0050] The value revaluation module is bound to the secret sharing verification rules of the SPDZ framework, and the cross-chain liquidation module is pre-set with three-dimensional time window parameters; the deployed smart token contract address and the Merkel path verification data in the freeze proof are combined to generate public parameters for zero-knowledge proof.

[0051] The third step involves cross-chain value mapping: The zero-knowledge proof generator receives public parameters and classification codes from the dynamic valuation matrix. It constructs an arithmetic circuit using the zk-STARK algorithm (a zero-knowledge concise and transparent knowledge proof algorithm). The main chain's frozen state, the target chain's token issuance, and the federated learning model version identifier are used as private inputs. Constraint calculations are performed on the private inputs according to the gate-level order of the arithmetic circuit, gradually obtaining intermediate variable values ​​and the final output value. The intermediate variable values ​​and corresponding constraint verification results of each step are sequentially written into the execution trajectory table. A hash commitment is generated from the execution trajectory table to form a challenge-response sequence. The execution trajectory table, hash commitment, and challenge-response sequence together constitute the interactive verification proof trajectory. The proof trajectory is then transformed using the Fiat-Shamir heuristic (converting interactive zero-knowledge proofs into non-interactive cryptographic methods) to generate a non-interactive zero-knowledge proof. This non-interactive zero-knowledge proof is submitted to the main chain and target chain for verification via the relay chain's cross-chain verification bridge. The contract verifies that it extracts the circuit constraint commitments and verification primitives for verification from the public parameters and parses the commitment, challenge, and response values ​​from the non-interactive zero-knowledge proof. Then, it calls the verification interface of the Bulletproofs library, using the public parameters as verification input, to replay the Fiat-Shamir transcription to recalculate the challenge, and performs consistency checks on the commitment and response relationships, obtaining a Boolean verification result. A True verification result indicates that the verification contract performs consistency checks on the commitment, challenge, and response values ​​from the non-interactive zero-knowledge proof based on the public parameters, and all checks are successful, thus proving that the arithmetic circuit constraint is satisfied. A False verification result indicates that any consistency check is unsuccessful, proving that the arithmetic circuit constraint is not satisfied. When the verification result is True, the verification is considered successful, triggering an improved hash time locking protocol. The main chain freezes the timestamp in the proof, coordinates with UTC, starts the minting window countdown, and generates a SHA3-256 hash lock.

[0052] After completing the three atomic operations, the nested smart contract structure of the dynamic token is written into the block data of the target chain. The token supply module and the adjustment instructions in the dynamic valuation matrix establish an automated data pipeline. The value revaluation module subscribes to the gradient tensor update events of the federated learning model. The cross-chain token certificate is frozen by the main chain regulatory sandbox, which records the proof hash and zero-knowledge proof verification status. The side chain liquidity pool synchronously stores the smart token contract address and the three-dimensional time window state machine. After the dispute window T3 ends, the state machine destroys the temporarily generated quantum-resistant ring signature fragment and generates the final atomic binding record.

[0053] S4. After the cross-chain token certificate generates an atomic binding record, the cross-chain transaction engine based on the Federated Byzantine consensus protocol receives a transaction request containing dynamic valuation matrix metadata; the buyer, holding a quantum-resistant threshold signature device, generates a transaction offer carrying multiple conditions; the cross-chain transaction engine parses the quantum-resistant threshold signature in the transaction offer, verifies the signature validity through the Federated Byzantine consensus node, and then initiates a four-stage atomic swap: The first phase executes source chain payment token locking: The source chain's smart contract parses the dynamic valuation matrix metadata in the transaction offer, matches it with Merkel path verification data in the main chain's freeze proof, and calls an improved hash time locking contract to generate a quantum-safe commitment; the quantum-safe commitment has a two-layer structure: the first layer generates a 256-bit random number as the hash lock preimage using a quantum-resistant ring signature, and the second layer generates a cross-chain address mapping relationship through a hierarchical deterministic key derivation algorithm; the hash lock preimage generates a lock hash value through SHA3-512 hash operation, and the lock hash value is combined with the current block height and written into the source chain's lock record. The lock record is appended with the buyer node's threshold signature fragment and a timestamp Coordinated Universal Time (UTC) encoding to form a payment token freeze proof containing a three-dimensional time window.

[0054] The second stage verifies the validity of the dynamic valuation through the relay chain: The verification nodes of the relay chain retrieve the dynamic valuation matrix metadata from the latest block header of the target chain in the payment token freeze proof, and extract the federated learning model version identifier and real-time market feature vector corresponding to the smart token contract address; The verification nodes call the arithmetic circuit verification program of the SPDZ framework, input the classification code and federated learning model version identifier from the dynamic valuation matrix metadata into the multi-party secure computation protocol, execute the secret sharing verification process, and generate a validity proof.

[0055] Furthermore, the verification process compares the Merkel path verification data of the ownership certificate with the target chain token issuance record to check whether the dynamic valuation correction factor verification result is within the validity period of the timestamp Coordinated Universal Time encoding; the verified dynamic valuation matrix metadata generates a verification certificate carrying a time window parameter, and after the verification certificate is partially signed by the threshold signature participants, it is aggregated to form a validity proof against quantum aggregation signature.

[0056] It should be noted that the validity period is determined by the timestamp of the dynamic valuation matrix metadata as the starting time, and the validity duration is limited by the time window parameter (e.g., 30 minutes). The relay chain verification node reads the timestamp of the current block header and compares it with the starting time and the validity duration. If it falls within the range, it is determined to be within the validity period; if it exceeds the range, it is determined to be outside the validity period.

[0057] The third phase triggers the Federal Byzantine consensus mechanism: the validity proof is broadcast to the Federal Byzantine consensus node group via the Gossip network of the Federal Byzantine consensus protocol, and the consensus node group performs four rounds of interactive verification: The first round of verification checks whether the time window parameter in the validity proof matches the three-dimensional time window of the source chain's locking record; the second round of verification compares the Merkle hash of the dynamic valuation matrix metadata with the state commitment of the target chain's block header; the third round of verification checks whether the number of threshold signature fragments for the quantum-resistant aggregate signature is not less than the threshold value t of the threshold signature mechanism (e.g., t=5 in the 5 / 9 threshold rule); the fourth round of verification checks whether the federated learning model version identifier is consistent with the smart token contract initialization parameters; the consensus result from the four rounds of verification generates a cross-chain state proof carrying a timestamp and Coordinated Universal Time (UTC) encoding; the cross-chain state proof is written to the relay chain's verification log partition through an LSM tree structure; the log partition header records the threshold signature fragments of the consensus node and the Merkle tree path verification data.

[0058] The fourth phase implements inter-chain value transfer and fee allocation: Cross-chain state proof triggers the atomic swap contract of the target chain. The atomic swap contract parses the hash lock preimage in the source chain's lock record and restores the cross-chain address mapping relationship through a hierarchical deterministic key derivation algorithm. The atomic swap contract calls the path verification data to generate time lock parameters, inputs the hash lock preimage and the target chain address into a hash operation to generate an unlock key. The unlock key is encrypted with a quantum-resistant ring signature and then divided into multiple key fragments. The key fragments are released in stages through a three-dimensional time window state machine: within the release window T1, the payment token is released to the target chain address; within the verification window T2, the validity of the dynamic valuation snapshot is verified; and within the dispute window T3, fees are automatically allocated to the federated Byzantine consensus nodes. After the value transfer is completed, a cross-chain transaction hash is generated. The cross-chain transaction hash, dynamic valuation matrix metadata, and quantum-resistant aggregate signature are combined to form a transaction certificate containing a quantum-resistant aggregate signature.

[0059] S5. Monitor the smart contract to parse the cross-chain transaction hash in the transaction certificate, retrieve the freeze proof hash recorded in the main chain regulatory sandbox, and match it with the smart token contract address stored in the sidechain liquidity pool. Obtain real-time market feature vectors through the gradient tensor update event subscription interface of the federated learning model, and synchronously access the physical feature data stream of the ownership certificate's basic attribute layer; monitor the smart contract to calculate core indicators including asset physical state deviation, market valuation volatility, and token liquidity deviation; when any core indicator exceeds its respective safety threshold, trigger the cross-chain transfer function freeze. The safety threshold is a preset threshold parameter for "asset physical state deviation, market valuation volatility, and token liquidity deviation" in the monitoring smart contract. Its value is determined during the system deployment / parameter configuration phase based on historical baseline data and risk strategies (e.g., obtaining the baseline mean and volatility by statistically analyzing historical windows and selecting the 95th percentile or "mean + 3σ" as an example threshold). Exceeding this threshold triggers a freeze; otherwise, it does not.

[0060] The monitoring smart contract calls the freeze proof hash of the main chain regulatory sandbox and generates a freeze instruction through a quantum-resistant ring signature. The freeze instruction triggers the dynamic evaluation smart contract to call the arithmetic circuit of the SPDZ framework, inputting the latest market valuation volatility, asset physical state deviation, and token liquidity deviation into the multi-party secure computation protocol. The cryptographic gradient tensor is generated through Beaver triple verification, and after updating the parameters of the federated learning model, multiple linear regression is performed to generate a new asset discount coefficient. The updated discount coefficient is input into the threshold signature mechanism to generate the supply adjustment proposal text containing the discount coefficient and the timestamp Coordinated Universal Time (UTC) code. Participating nodes in the federated Byzantine consensus node group that meet the threshold value of the threshold signature mechanism generate partial signature fragments of the supply adjustment proposal text. After collecting the partial signature fragments that reach the threshold value, they are aggregated to obtain a complete digital signature. The complete digital signature is combined with the supply adjustment proposal text to form a supply adjustment proposal carrying the timestamp Coordinated Universal Time (UTC) code. The supply adjustment proposal is submitted to the target chain through the cross-chain verification bridge of the relay chain, triggering the token supply module upgrade of the smart token contract and generating an adaptive rebalancing instruction.

[0061] Furthermore, the token supply module upgrade process uses the zk-STARK algorithm to construct an arithmetic circuit, taking the old contract state (e.g., issuance of 1 million tokens) and the new proposal parameters (e.g., discount factor of 0.85) as private inputs to generate a non-interactive zero-knowledge proof; after the verification contract calls the Bulletproofs library to verify the non-interactive zero-knowledge proof, the token supply module updates the issuance (e.g., adjusts it to 850,000 tokens) and writes it into the target chain block header.

[0062] Adaptive rebalancing instructions include proof of frozen state, record of discount factor update, zero-knowledge proof of supply adjustment, and quantum timestamp proof.

[0063] S6. The adaptive rebalancing instruction is synchronized to the target chain's smart token contract via the relay chain's cross-chain message bus, and the asset holder initiates a redemption request through a quantum-resistant threshold signature device.

[0064] Furthermore, the redemption request carries the target chain smart token contract address, the number of tokens destroyed, and the digital fingerprint of the ownership certificate.

[0065] The target chain smart token contract parses the token destruction quantity in the redemption request, executes the token destruction using the token supply module, and generates a destruction record containing the target chain address, the destruction quantity, the timestamp Coordinated Universal Time (UTC) code, and the current block height. The destruction record is input into a quantum-resistant threshold signature mechanism, and a designated node in the federated Byzantine consensus nodes generates a partial signature fragment (e.g., a 5 / 9 threshold rule) to form a complete digital signature destruction proof. The destruction proof is submitted to the main chain verification contract through the cross-chain verification bridge of the relay chain, triggering triple verification.

[0066] Triple verification includes quantum signature validity verification, dynamic evaluation log consistency verification, and IoT device status hash value and ownership certificate digital fingerprint verification.

[0067] After passing triple verification, the ownership certificate is unfrozen via the main chain smart contract execution, as follows: The cross-chain interaction layer sub-key tree generates a defreezing instruction signature. The defreezing instruction signature contains a 256-bit random number, the current block height, and the timestamp of the verified block, encoded in Coordinated Universal Time. After the defreezing instruction signature is confirmed by the Federal Byzantine consensus node, the physical characteristic data field of the ownership certificate's basic attribute layer is unfrozen. The defreezing status change record is broadcast to the main chain and side chain nodes via the Gossip protocol. At the same time, the three-dimensional time window state machine of the side chain liquidity pool updates the token liquidity index and adjusts the reserve data field (e.g., the corresponding proportion of the number of tokens burned to increase the liquidity depth), generating an asset unstaking certificate.

[0068] Furthermore, the asset release certificate includes the destruction certificate hash, the unfreezing operation record, and the liquidity update certificate.

[0069] This embodiment also provides a data asset management system based on distributed ledger technology, including: The authentication module uses a quantum-resistant signature algorithm to encrypt and authenticate asset metadata, generating an ownership certificate. The valuation module triggers a dynamic valuation smart contract based on the ownership certificate, and generates a dynamic valuation matrix through a federated learning model and an off-chain network. The cross-chain binding module inputs the dynamic valuation matrix into the cross-chain gateway to perform atomic binding operations, and deploys a dynamic token contract on the target chain to output token certificates. The transaction engine module drives the cross-chain transaction engine to process the token credentials through the authentication blockchain protocol, generating transaction credentials containing quantum-resistant aggregate signatures; The monitoring module uses the transaction certificate to activate the monitoring smart contract, analyzes asset status parameters in real time, and outputs adaptive rebalancing instructions. The unstaking module performs reverse cross-chain verification according to the adaptive rebalancing instruction, destroys the target chain token, and generates an asset unstaking certificate.

[0070] This embodiment also provides a computer device applicable to the data asset management method based on distributed ledger technology, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the data asset management method based on distributed ledger technology as proposed in the above embodiment.

[0071] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0072] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the data asset management method based on distributed ledger technology as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0073] In summary, this invention generates a public key by superimposing error terms through polynomial ring operations and ensures the unforgeability of ownership certificates through triple verification. It integrates off-chain real-time market data with asset physical characteristics into a real-time market feature vector, utilizes the SPDZ framework for multi-party secure computation verification, generates dynamic valuation correction factors, and achieves real-time accurate valuation through multi-party data collaboration. It employs the zk-STARK algorithm to construct non-interactive zero-knowledge proofs, combined with an improved hash time-locking protocol and a three-dimensional time window state machine, increasing the success rate of cross-chain transactions and reducing the risk of malicious attacks through four rounds of federated Byzantine consensus verification. Based on real-time monitoring network analysis of asset state deviation and market volatility, it triggers federated learning model parameter updates and generates supply adjustment proposals, enabling dynamic token destruction and unfreezing, shortening the asset liquidity risk response time, and ensuring the non-repudiation of reverse cross-chain verification.

[0074] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A data asset management method based on distributed ledger technology, characterized in that: include, The asset metadata is encrypted and authenticated using a quantum-resistant signature algorithm to generate an ownership certificate; Based on the ownership certificate, a dynamic evaluation smart contract is triggered to generate a dynamic valuation matrix through a federated learning model and an off-chain network. The dynamic valuation matrix is ​​input into the cross-chain gateway to perform an atomic binding operation, and a dynamic token contract is deployed on the target chain to output token certificates. The token is processed by a cross-chain transaction engine driven by a certified blockchain protocol to generate a transaction credential containing a quantum-resistant aggregate signature. The transaction certificate is used to activate the monitoring smart contract, which analyzes asset status parameters in real time and outputs adaptive rebalancing instructions. The adaptive rebalancing instruction is used to perform reverse cross-chain verification, destroy the target chain token, and generate an asset unstaking certificate.

2. The data asset management method based on distributed ledger technology as described in claim 1, characterized in that: The generation of the ownership certificate includes the following steps. Configure security parameters based on ring dimension and modulus parameters to generate a uniform random matrix; A multinomial sampling algorithm is applied to the security parameters to independently sample from a discrete Gaussian distribution to generate a private key vector, and a statistically independent error term is generated by isolating the random seed. The uniform random matrix, private key vector, and error term are input into a polynomial ring operation, and a public key is generated through modular addition. The linear relationship of the key pair is verified to obtain a quantum-resistant key pair. Based on quantum-resistant key pairs, unique identifiers and asset type classification codes are generated. At the same time, the initial asset valuation value is obtained, converted into floating-point numbers, and combined to form structured metadata. Perform a SHA3-512 hash operation on the structured metadata to generate a digital fingerprint; The digital fingerprint is input into the quantum-resistant signature algorithm to generate a signed transaction record and attached with a Coordinated Universal Time (UTC) timestamp. After broadcasting and verification, a global index is built based on a Merkle tree structure to form an ownership certificate.

3. The data asset management method based on distributed ledger technology as described in claim 1, characterized in that: The generation of the dynamic valuation matrix includes the following steps. Read the reserved fields of the valuation trajectory layer of the ownership certificate, extract the asset type classification code and match it with the preset asset valuation parameter template to generate a valuation parameter weight matrix; Subscribe to off-chain real-time market data streams and generate real-time market feature vectors through feature extraction and normalization; Using the valuation parameter weight matrix as the initial weights of the federated learning model and the real-time market feature vector as the external feature, the local gradient is calculated to generate the encrypted gradient tensor. The encrypted gradient tensor is subjected to trusted weighted aggregation to obtain the aggregated global gradient, and the parameters of the federated learning model are updated based on the global gradient to generate a dynamic estimation correction factor. The SPDZ framework is used to perform secret sharing verification and threshold signing on the dynamic valuation correction factor. The signed dynamic valuation correction factor is written to the sidechain evaluation log partition and immutable data blocks are generated according to the time window. Adjustment instructions are written to the header of the immutable data block to form a dynamic valuation matrix.

4. The data asset management method based on distributed ledger technology as described in claim 3, characterized in that: The adjustment instruction is a token issuance adjustment instruction written into the header of an immutable data block by the dynamic evaluation smart contract when it writes the threshold-signed dynamic valuation correction factor into the sidechain evaluation log partition and generates an immutable data block according to the time window.

5. The data asset management method based on distributed ledger technology as described in claim 1, characterized in that: The atomic binding operation includes the following steps. After sending a casting request message through a quantum-safe communication channel and verifying the quantum-resistant ring signature, the main chain ownership certificate is frozen, and a freeze proof is generated. Deploy a smart token contract on the target chain. The smart token contract uses the freeze proof as the initialization parameter and binds the dynamic valuation correction factor with the adjustment instruction to generate zero-knowledge proof public parameters. Based on the public parameters of zero-knowledge proof, a zk-STARK arithmetic circuit is constructed to generate a non-interactive zero-knowledge proof. This proof is then submitted to the verification contracts of the main chain and the target chain via the relay chain cross-chain verification bridge. This completes the atomic mapping between the frozen state of the main chain and the token issuance of the target chain, and outputs a token certificate with a nested smart contract structure.

6. The data asset management method based on distributed ledger technology as described in claim 1, characterized in that: The aforementioned cross-chain transaction engine processing refers to parsing the quantum-resistant threshold signature in the transaction offer, verifying the validity of the dynamic valuation matrix through the relay chain, generating a verification certificate carrying time window parameters, triggering the federated Byzantine consensus mechanism to perform four rounds of interactive verification, restoring the cross-chain address mapping relationship based on the hierarchical deterministic key derivation algorithm, releasing payment tokens and allocating transaction fees in stages through a three-dimensional time window state machine, and generating transaction certificates.

7. The data asset management method based on distributed ledger technology as described in claim 1, characterized in that: The output adaptive rebalancing instruction includes the following steps. The system monitors smart contracts, parses transaction credentials, and matches the freeze proof hash recorded in the main chain regulatory sandbox with the smart token contract address stored in the side chain liquidity pool. It also synchronously accesses the physical characteristic data stream of the basic attribute layer of the ownership certificate and obtains real-time market characteristic vectors. The system calculates the deviation of the asset's physical state, the volatility of the market valuation, and the deviation of the token liquidity. If any of these indicators exceeds the safety threshold, a cross-chain transfer freeze is triggered. The cross-chain transfer freeze triggers the dynamic evaluation smart contract to call the arithmetic circuit of the SPDZ framework, generate corresponding cryptographic gradient tensors from the latest market valuation volatility, asset physical state deviation and token liquidity deviation, update the parameters of the federated learning model, generate a new asset discount coefficient, and generate a supply adjustment proposal through the threshold signature mechanism. The supply adjustment proposal is submitted to the target chain via the relay chain cross-chain verification bridge. The target chain's smart token contract uses the zk-STARK algorithm to construct and verify a non-interactive zero-knowledge proof, updates the target chain's token issuance, and outputs an adaptive rebalancing instruction.

8. The data asset management method based on distributed ledger technology as described in claim 1, characterized in that: The reverse cross-chain verification process includes the following steps: After the adaptive rebalancing instruction is synchronized to the target chain's smart token contract via the relay chain cross-chain message bus, the asset holder initiates a redemption request. The target chain smart token contract parses the token destruction quantity in the redemption request, executes the token destruction, generates the target chain destruction record, and submits the destruction proof to the main chain verification contract through the relay chain cross-chain verification bridge using a quantum-resistant threshold signature. The main chain verification contract performs triple verification. Once the verification is successful, the main chain ownership certificate is unfrozen, and the three-dimensional time window indicators of the side chain liquidity pool are updated to generate asset unstaking certificates.

9. The data asset management method based on distributed ledger technology as described in claim 8, characterized in that: The triple verification includes quantum signature validity verification, dynamic evaluation log consistency verification, and IoT device status verification.

10. A data asset management system based on distributed ledger technology, comprising the data asset management method based on distributed ledger technology as described in any one of claims 1 to 9, characterized in that: include, The authentication module uses a quantum-resistant signature algorithm to encrypt and authenticate asset metadata, generating an ownership certificate. The valuation module triggers a dynamic valuation smart contract based on the ownership certificate, and generates a dynamic valuation matrix through a federated learning model and an off-chain network. The cross-chain binding module inputs the dynamic valuation matrix into the cross-chain gateway to perform atomic binding operations, and deploys a dynamic token contract on the target chain to output token certificates. The transaction engine module drives the cross-chain transaction engine to process the token certificate through the authentication blockchain protocol, generating a transaction certificate containing a quantum-resistant aggregate signature; The monitoring module uses the transaction certificate to activate the monitoring smart contract, analyzes asset status parameters in real time, and outputs adaptive rebalancing instructions. The unstaking module performs reverse cross-chain verification according to the adaptive rebalancing instruction, destroys the target chain token, and generates an asset unstaking certificate.

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