Intelligent contract non-heritage art digital asset right confirmation and circulation tracking method and system

Abstract

The application discloses an intelligent contract non-heritage art digital asset right confirmation and circulation tracking method and system, and belongs to the field of blockchains and digital assets. The method comprises the following steps: acquiring physical attribute data set and process parameter set of a non-heritage work, and calculating a unique asset hash value; triggering a multi-role collaborative signature consensus protocol based on the unique asset hash value, comparing and verifying a non-heritage feature vector with a preset skill standard threshold matrix; when the comparison and verification is passed and the aggregated signature is passed through cryptographic verification, executing an asset casting intelligent contract and initializing a deterministic account distribution routing tree; listening to state transition events about the non-heritage digital asset in a blockchain network, and distributing circulation funds to a target address bound to the deterministic account distribution routing tree through an atomic transaction instruction. The application establishes a rigid binding relationship between a physical object and a digital certificate.

Description

Technical Field

[0001] This invention relates to the field of blockchain and digital assets, and in particular to a method and system for confirming ownership and tracking the circulation of smart contract-based digital assets of intangible cultural heritage art. Background Technology

[0002] Intangible cultural heritage (hereinafter referred to as "ICH") is an important part of human civilization. Its carriers are typically tangible artworks produced using traditional techniques, such as Shu embroidery, Zisha teapots, Jingdezhen porcelain, lacquerware, and Shu brocade. With the rapid development of the digital economy, utilizing blockchain technology to digitally establish ownership of ICH works and facilitate their circulation and trading in the secondary market has become a crucial direction for the protection and value transformation of ICH. Against this backdrop, non-fungible tokens (NFTs) are widely used in the creation and circulation of digital assets.

[0003] However, existing NFT minting schemes have the following prominent technical flaws when applied to the field of intangible cultural heritage artworks: First, there is a lack of rigid binding between physical objects and digital credentials. Existing schemes typically only hash the digital image files (such as photos or scans) of intangible cultural heritage works, using the resulting hash value as the unique identifier of the on-chain digital asset. However, digital images can be easily copied; anyone can take a photo or screenshot of the original work to obtain a digital image highly similar to the original, thereby generating the same or similar hash value. Second, the digital asset ownership mechanism suffers from single-point-of-trust risk. The ownership process of existing schemes usually relies on the review decisions of a single platform or a single authoritative entity. In this model, once the review account of this single entity is compromised or maliciously manipulated, attackers can arbitrarily mint non-compliant digital assets, and the system lacks effective defense mechanisms. Third, original creators cannot obtain continuous economic returns in the secondary market. The core value of intangible cultural heritage works comes from the skills and labor of the inheritors; however, in the existing digital asset circulation model, inheritors usually only receive a one-time income during the first minting and sale.

[0004] In summary, existing technologies have not yet formed a complete technical solution for the digital asset ownership confirmation and circulation tracking of intangible cultural heritage artworks, which can simultaneously achieve deterministic binding of physical objects and digital certificates, dual ownership confirmation through multi-party consensus and objective data verification, perpetual automatic economic returns for the original creators, and atomic consistency guarantee for asset circulation and fund allocation. Summary of the Invention

[0005] One of the objectives of this invention is to provide a smart contract-based method and system for confirming and tracking the ownership and circulation of digital assets related to intangible cultural heritage art. This addresses the problems in existing technologies, such as the lack of a rigid binding relationship between physical objects and digital certificates due to the reliance on digital image hashes for the creation of digital assets related to intangible cultural heritage, the lack of multi-party consensus and objective data verification capabilities due to the reliance on single-point trust in the ownership confirmation mechanism, the inability of original inheritors to obtain continuous and guaranteed automatic economic returns in the secondary market, and the lack of atomic consistency guarantees in asset circulation and fund allocation.

[0006] This invention is achieved through the following technical solution: a smart contract method for confirming the ownership and tracking the circulation of digital assets of intangible cultural heritage art, comprising the following steps: S100, obtaining a dataset of physical attributes and a set of process parameters of intangible cultural heritage works, fusing the physical attribute dataset and the process parameter set to generate an intangible cultural heritage feature vector, and applying a cryptographic one-way hash function to the intangible cultural heritage feature vector to calculate a unique asset hash value; S200, triggering a multi-role collaborative signature consensus protocol based on the unique asset hash value, broadcasting signature requests to multiple predefined nodes with different roles, collecting digital signature fragments returned by each node and aggregating them to generate an aggregate signature, and simultaneously combining the intangible cultural heritage feature vector with a pre-defined cryptographic one-way hash function to generate a unique asset hash value; The standard threshold matrix is ​​used for comparison and verification; S300, when the comparison and verification pass and the aggregated signature passes cryptographic verification, the asset casting smart contract is executed to encapsulate the unique asset hash value and the aggregated signature into a non-heritage digital asset, and a deterministic revenue sharing routing tree is initialized in the asset casting smart contract; S400, the state transition events of the non-heritage digital asset in the blockchain network are monitored, and when an asset transfer request is intercepted, the profit sharing logic executor in the asset casting smart contract is triggered to calculate the revenue distribution amount of each path according to the deterministic revenue sharing routing tree, and the transfer funds are distributed to the target address bound to the deterministic revenue sharing routing tree through atomic transaction instructions.

[0007] Furthermore, the step of applying a cryptographic one-way hash function to the intangible cultural heritage feature vector to calculate the unique asset hash value includes: applying a SHA-256 hash function to the intangible cultural heritage feature vector and outputting a fixed-length 256-bit hash value as the unique asset hash value.

[0008] Further, in S100, generating the intangible cultural heritage feature vector includes: extracting features from the physical attribute dataset to generate physical feature row vectors; performing time-series sampling and feature extraction on the process parameter set to generate process feature row vectors; assigning a first weight to the physical feature row vectors and a second weight to the process feature row vectors; performing a head-to-tail concatenation operation on the weighted physical feature row vectors and the process feature row vectors to form a composite data vector; and performing dimensionality reduction processing on the composite data vectors to generate the intangible cultural heritage feature vector.

[0009] Furthermore, the generation of physical feature row vectors includes: acquiring a micro-gradient image of the surface texture of the intangible cultural heritage work; dividing the micro-gradient image into pixel blocks of fixed size; applying a frequency domain transformation to each pixel block to extract frequency domain components; and flattening and stitching the frequency domain components of each pixel block to construct the physical feature row vector.

[0010] Further, the extraction of frequency domain components includes: applying a two-dimensional discrete cosine transform to each pixel block, decomposing the spatial domain pixel information within the pixel block into energy contributions at different spatial frequencies, and obtaining the frequency domain energy coefficients of the pixel block; and extracting a preset number of low-frequency and mid-frequency energy coefficients from the frequency domain energy coefficients as the frequency domain components.

[0011] Furthermore, the generation of the process feature row vector includes: sampling the temperature, humidity, and usage time of the creation tools in the intangible cultural heritage work's creation environment using sensors at fixed time intervals to generate a process time series matrix, where each row of the process time series matrix corresponds to the time series record of a process parameter throughout the entire creation cycle; performing normalization processing on the process time series matrix to eliminate dimensional differences between different process parameters; extracting statistical features from the normalized process time series matrix and reducing its dimensionality to generate the process feature row vector.

[0012] Further, the process timing matrix is ​​normalized, including: calculating the mean and standard deviation of each process parameter in the process timing matrix over the entire acquisition period; performing a zero-center shift on the measured values ​​of each process parameter at each time point using the mean, then scaling the measured values ​​using the standard deviation, and adding a very small positive constant to the standard deviation to prevent division by zero anomalies, thereby converting the measured values ​​at each time point into dimensionless standardized values.

[0013] Further, dimensionality reduction processing is performed on the composite data vector, including: based on a training sample set of similar intangible cultural heritage works, solving for the eigenvalues ​​of the covariance matrix of the composite data vector, taking the eigenvectors corresponding to the top N largest eigenvalues ​​to form an orthogonal projection matrix, where N is determined according to the cumulative variance contribution rate; projecting the composite data vector onto the low-dimensional subspace spanned by the orthogonal projection matrix to obtain the intangible cultural heritage feature vector.

[0014] Further, in S200, the intangible cultural heritage feature vector is simultaneously compared and verified with a preset craft standard threshold matrix, including: parsing multiple craft indicator scalars corresponding to the craft attribute dimension from the intangible cultural heritage feature vector; calling the pre-hard-coded and stored craft standard threshold matrix, which defines a valid high-dimensional bounding box composed of the upper and lower limits of each craft indicator; performing an interval inclusion test on each craft indicator scalar to determine whether it falls within the upper and lower limit value intervals of the corresponding dimension in the valid high-dimensional bounding box, and performing a logical AND operation on the test results of all dimensions; if all craft indicator scalars satisfy the interval conditions, a comparison pass signal is output; if any craft indicator scalar exceeds the limit, the comparison is determined to fail and a craft non-compliance exception is returned.

[0015] Furthermore, in S200, multiple nodes with different roles include: inheritor nodes, intangible cultural heritage protection organization nodes, and expert committee nodes. Each node holds a private key shard allocated through a key splitting scheme during system initialization and publishes the corresponding public key set on the blockchain.

[0016] Furthermore, before collecting the digital signature fragments returned by each node and aggregating them to generate an aggregate signature, the method further includes: performing an admission policy verification on the collected digital signature fragments. The admission policy verification requires that the digital signature fragments contain at least one signature from the inheritor node, one signature from the intangible cultural heritage protection organization node, and no less than a preset signature number threshold number of signatures from the expert committee nodes. The preset signature number threshold number is a positive integer greater than or equal to three.

[0017] Further, after the admission policy verification is passed, the aggregation step is entered; the aggregation step generates an aggregate signature, including: each participating node uses its own private key fragment to perform a signature operation on the unique asset hash value to generate the digital signature fragment, and each digital signature fragment is attached with the corresponding node's permission type label; the digital signature fragments that meet the admission policy requirements are combined by weighted product on the elliptic curve group using interpolation coefficients based on the Lagrange interpolation method to generate the aggregate signature.

[0018] Furthermore, the aggregate signature is cryptographically verified, including: determining the validity of the aggregate signature through bilinear pairing verification: under the bilinear mapping on the elliptic curve, calculating the first pairing value between the aggregate signature and the group generator, and calculating the second pairing value of the interpolated weighted product of the curve point after the unique asset hash value is mapped to the curve function and the public keys of each participating node; the aggregate signature is determined to be verified as passed if and only if the first pairing value is equal to the second pairing value.

[0019] Furthermore, in S200, triggering the multi-role collaborative signature consensus protocol also includes an exception handling step: while broadcasting the signature request to the expert committee node, a deterministic timing decrement timer is started; if the number of digital signature fragments collected from the expert committee node does not reach a preset threshold when the deterministic timing decrement timer reaches zero, the received valid digital signature fragments and their corresponding public key indexes are stored in a time lock cache pool, and the current consensus verification process is suspended; within the extended lifespan of the time lock cache pool, if the remaining nodes supplement the missing digital signature fragments, historical fragments are extracted from the time lock cache pool and newly submitted fragments are concatenated and verified a second time; if the required digital signature fragments are not supplemented after the extended lifespan, the time lock cache pool is cleared and the minting process of the asset is terminated.

[0020] Further, in S300, initializing the deterministic revenue-sharing routing tree includes: establishing an address routing table in the storage structure of the asset-casting smart contract, wherein the address routing table records the blockchain address of the original inheritor and the blockchain address of the protection fund; establishing a profit-sharing benchmark parameter table, wherein the profit-sharing benchmark parameter table records the initial maximum profit-sharing ratio, successive decay coefficient, bottom-line safety net threshold, and internal splitting weight between the inheritor's personal feedback and the protection institution's public feedback; initializing the circulation depth counter of the intangible cultural heritage digital asset to zero; and encapsulating the address routing table, the profit-sharing benchmark parameter table, and the circulation depth counter into the immutable state variables of the asset-casting smart contract.

[0021] Furthermore, the deterministic revenue sharing routing tree calculates the revenue distribution amount for each route, including: extracting the total transaction payment amount from the asset transfer request; reading the current value of the transfer depth counter, calculating the product of the initial maximum profit sharing ratio and the retention factor raised to the power of the current value of the transfer depth counter as the decay value, where the retention factor is the difference between one and the successive decay coefficient; taking the larger of the decay value and the bottom-line safety net threshold as the actual profit sharing ratio; and calculating the total return funds based on the product of the total transaction payment amount and the actual profit sharing ratio.

[0022] Further, calculating the revenue allocation amount for each route based on the deterministic revenue sharing routing tree also includes: splitting the total return amount into individual return funds and public return funds based on the internal partitioning weights in the deterministic revenue sharing routing tree, wherein the individual return funds are equal to the total return amount multiplied by the internal partitioning weights, and the public return funds are equal to the total return amount multiplied by the difference between the total return amount and the internal partitioning weights; calculating the amount received by the seller is the total transaction payment amount minus the total return amount.

[0023] Furthermore, in S400, after intercepting the asset transfer request and before triggering the profit-sharing logic executor, the following steps are also included: extracting the buyer's address and the seller's address that initiated the asset transfer request; performing a byte-by-byte collision comparison between the buyer's address and the seller's address and the blacklist address array pre-installed in the asset minting smart contract; if either the buyer's address or the seller's address matches the blacklist address array, the asset transfer request is forcibly intercepted, the transfer rights of the intangible cultural heritage digital asset are frozen, and the metadata hash of the illegal transfer path is recorded on the blockchain for evidence storage.

[0024] Furthermore, distributing the transferred funds to the target address bound to the deterministic ledger routing tree via atomic transaction instructions includes: constructing three independent fund transfer instructions within the same smart contract execution function, corresponding to the seller's proceeds, the personal reward, and the public reward, respectively; binding the three independent fund transfer instructions with the ownership status change instruction of the intangible cultural heritage digital asset in the same state tree transaction for execution; if any one of the three independent fund transfer instructions fails to execute, a transaction rollback mechanism is triggered to revoke the ownership status change instruction and reject the asset transfer request; when the same state tree transaction is executed successfully, the transfer depth counter is incremented by one.

[0025] Another aspect of the present invention provides a smart contract-based digital asset ownership confirmation and circulation tracking system for intangible cultural heritage art, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, it implements any of the smart contract-based digital asset ownership confirmation and circulation tracking methods for intangible cultural heritage art as described above.

[0026] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0027] 1. This invention integrates the physical attribute dataset and process parameter set of intangible cultural heritage works to generate intangible cultural heritage feature vectors that encode the unique physical microstructure and process information of the works. Since the generation of intangible cultural heritage feature vectors depends on the unreplicable physical characteristics of the works, any imitation of intangible cultural heritage objects will cause a significant shift in the intangible cultural heritage feature vectors. This shift, in turn, will generate completely different unique asset hash values ​​through the avalanche effect of cryptographic one-way hash functions. This mechanism ensures that even if counterfeiters obtain an image of the original work, they cannot generate a legitimate digital asset identifier without touching the physical original, thus establishing a rigid binding relationship between physical objects and digital credentials.

[0028] 2. This invention achieves automatic determination of process compliance by comparing the scalar of process indicators in the feature vector of intangible cultural heritage with the pre-hard-coded threshold matrix of skill standards in a dimension-by-dimensional interval. It can identify works with abnormal process data without human intervention, eliminating the possibility of high-quality counterfeits passing through subjective review. Furthermore, it adopts a threshold signature mechanism based on a multi-role collaborative signature consensus protocol, requiring inheritor nodes, intangible cultural heritage protection institution nodes, and expert committee nodes to jointly sign using their respective private key shards. The validity of the aggregated signature is confirmed through polynomial interpolation aggregation and bilinear pairing verification. If the generated aggregated signature cannot pass the bilinear pairing verification mathematically, the smart contract will reject the minting operation from the bottom layer. The above two verification levels are independent of each other and must be satisfied simultaneously, mathematically eliminating the possibility of any single node or single entity arbitrarily minting non-compliant digital assets.

[0029] 3. This invention initializes a deterministic revenue-sharing routing tree in the asset-mining smart contract, and writes the address routing table, revenue-sharing benchmark parameter table, and circulation depth counter into the immutable state variables of the smart contract. This ensures that the revenue-sharing rules are entirely determined by the smart contract code and the on-chain state. No external entity can bypass the contract code to unilaterally modify the revenue-sharing ratio or cancel the feedback mechanism. When the revenue-sharing logic executor intercepts an asset circulation request, it dynamically calculates the actual revenue-sharing ratio based on the circulation depth counter and the preset decay rules. This transforms the economic feedback of intangible cultural heritage from an uncertain promise based on the goodwill of a centralized platform into a deterministic rule enforced by the smart contract, thus achieving the perpetual tracking of the long-tail revenue of the original creators.

[0030] 4. This invention binds the three fund transfer instructions—the seller's proceeds, the inheritor's personal reward, and the protection fund's public reward—to the ownership status change instruction of the intangible cultural heritage digital asset within the same state tree transaction. This is executed atomically by the blockchain virtual machine. When any of the three fund transfers fails due to insufficient network balance or an abnormal target address, the blockchain virtual machine automatically triggers a transaction rollback mechanism, restoring all executed status changes to their state before the transaction began and rejecting the asset transfer request. This atomicity guarantee mechanism technically ensures that there is no forced binding without a reward, i.e., no transfer. There is no intermediate state where ownership has changed but the profit-sharing funds have not arrived, or funds have been deducted but ownership has not changed. This fundamentally eliminates the risk of asset disputes caused by non-atomic execution in a decentralized network environment.

[0031] 5. This invention synchronously starts a deterministic timing decrement timer when a signature request is broadcast. When the timer reaches zero, the collected valid signature fragments are temporarily stored in a time-locked cache pool, leaving an extension window for subsequent signature submissions. When the signature is completed within the extended lifespan, a secondary splicing verification is performed to restore the consensus process. If the signature is not completed by the end of the extended lifespan, the cache pool is automatically cleared and the forging process is terminated. This mechanism avoids the system from being suspended indefinitely due to some nodes not responding, and also avoids the unnecessary discarding of validly collected signature fragments due to temporary network interruptions, achieving a reasonable balance between system activity and security. Attached Figure Description

[0032] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0033] Figure 1 This is a flowchart of the method provided in Embodiment 1 of the present invention.

[0034] Figure 2 The image shows a representative needlework area and its two-dimensional discrete cosine transform spectrum obtained after processing the original Shu embroidery work, as provided in Embodiment 1 of the present invention.

[0035] Figure 3 The cumulative variance contribution rate curve provided in Embodiment 1 of the present invention.

[0036] Figure 4 This is a schematic diagram of the scatter distribution of genuine and counterfeit products in the low-dimensional feature space after dimensionality reduction, as provided in Embodiment 1 of the present invention.

[0037] Figure 5 This is a schematic diagram showing the relationship between the input perturbation and the hash Hamming distance for the avalanche effect of the SHA-256 hash function provided in Embodiment 1 of the present invention.

[0038] Figure 6 This is a schematic diagram of a two-dimensional bounding box for the verification of the technical standard threshold provided in Embodiment 1 of the present invention.

[0039] Figure 7 This is a heatmap of the combined verification matrix of the multi-role admission strategy function provided in Embodiment 1 of the present invention.

[0040] Figure 8 This is a diagram illustrating the authorization recovery effect of the time lock caching mechanism in a node communication blocking scenario provided in Embodiment 1 of the present invention.

[0041] Figure 9 This is a curve showing the rate of decrease in the number of cells attenuated and the percentage of decrease in the number of cells attenuated, provided in Embodiment 1 of the present invention.

[0042] Figure 10This is a stacked area diagram showing the variation of the three-way fund allocation with the depth of circulation, as provided in Embodiment 1 of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0044] Example 1

[0045] This embodiment discloses a method for confirming ownership and tracking the circulation of digital assets of intangible cultural heritage art using smart contracts. Figure 1 The overall method flowchart of this embodiment is shown. As can be seen from the figure, this embodiment includes the following steps:

[0046] Step S100: Obtain the physical attribute dataset and process parameter set of intangible cultural heritage works, perform matrix concatenation and normalization processing on the physical attribute dataset and process parameter set through a preset structured mapping algorithm, generate the corresponding intangible cultural heritage feature vector, and calculate the unique asset hash value of the intangible cultural heritage feature vector.

[0047] Intangible cultural heritage works refer to tangible artworks produced by traditional techniques that are listed in the national or local representative list of intangible cultural heritage. For example, intangible cultural heritage works can include Shu embroidery pieces, Shu brocade, purple clay teapots, Jingdezhen porcelain, lacquerware, etc.

[0048] A physical property dataset refers to a set of raw data obtained through non-destructive or minimal-destructive testing of the physical entities of intangible cultural heritage works, used to describe the microstructural characteristics of the materials used in the works. Physical property datasets include, but are not limited to, gradient images of the surface microtexture of the works, spectral data of material elemental composition, and surface roughness parameters.

[0049] A process parameter set refers to a set of time-series data collected by sensors or recording devices during the creation of intangible cultural heritage works, used to describe the environmental conditions and tool usage during the creation process. The process parameter set includes, but is not limited to, temperature and humidity curves of the creation environment, the sequence of tool usage times, and the duration of specific procedures.

[0050] Structured mapping algorithm refers to an algorithmic process that transforms raw physical attribute data and process parameters from different acquisition sources with different dimensions and data structures into a unified and compact numerical vector expression according to predefined mathematical rules.

[0051] Matrix concatenation refers to the operation of splicing two data matrices or vectors, which represent physical property characteristics and process characteristics respectively, end to end in the direction of rows or columns to form a composite data array with a higher dimension.

[0052] Normalization refers to the process of standardizing the values ​​of each dimension in a composite data array to eliminate scale differences caused by different units and numerical ranges, so that the data of each dimension can be compared at the same scale.

[0053] The intangible cultural heritage feature vector is a low-dimensional numerical vector obtained after the above-mentioned structured mapping, matrix concatenation, normalization and dimensionality reduction processing. This vector encodes the unique physical microstructure information and process information of a specific intangible cultural heritage work in a compact mathematical form, and is the core data foundation of the work's digital identity.

[0054] A unique asset hash value is a fixed-length hexadecimal string output after applying a cryptographic one-way hash function to the feature vector of intangible cultural heritage. This string has collision resistance and avalanche effect, serving as a globally unique identifier for intangible cultural heritage digital assets on the blockchain.

[0055] Existing digital asset (NFT) minting schemes typically only hash the digital image files of the artwork. This approach results in a lack of rigid binding between the physical object and its digital certificate—anyone can generate the same or similar hash value simply by taking a photo or screenshot of the original, making forgery extremely easy. Therefore, the core idea of ​​this embodiment is to deterministically encode the irreproducible physical information of the intangible cultural heritage work and its creation process information as part of its digital identity, thereby establishing a rigid binding between the physical object and the digital certificate.

[0056] In this embodiment, the specific steps for obtaining the physical attribute dataset and process parameter set of intangible cultural heritage works and generating intangible cultural heritage feature vectors include:

[0057] Step S110: Collect micro-gradient images of the surface texture of intangible cultural heritage works, divide the micro-gradient images into pixel blocks of fixed size, and extract the frequency domain components of each pixel block to construct a physical feature matrix.

[0058] Among them, micro-gradient images refer to grayscale or multi-channel image data obtained by imaging the surface of intangible cultural heritage works at a microscale using sensing devices such as high-magnification optical microscopes, electron microscopes or high-resolution industrial cameras. The pixel values ​​in the image reflect the texture gradient changes of the surface material of the work at the micro level (such as the needlework direction of Shu embroidery, the particle distribution of ceramic glaze, the superimposed lacquer layer texture on the surface of lacquerware, etc.).

[0059] Pixel blocks refer to the arrangement of micro-gradient images according to a preset fixed pixel size (e.g., Each sub-image block is obtained by dividing pixels into a grid.

[0060] Frequency domain components refer to the energy coefficients extracted after applying a frequency domain transformation to each pixel block. These coefficients reflect the spatial frequency distribution characteristics of the material texture within the block, rather than the original pixel grayscale values.

[0061] Understandably, the core advantage of converting the original spatial domain pixel information into frequency domain energy distribution information lies in the fact that frequency domain features reflect the inherent texture structure of the material, rather than the accidental distribution of surface illumination, thus possessing high stability and anti-interference capabilities. Even when the same intangible cultural heritage work is repeatedly sampled under different lighting conditions and shooting angles, the core characteristics of its frequency domain energy distribution remain highly consistent.

[0062] In this embodiment, the specific method for extracting the frequency domain component for each pixel block is as follows: for the micro gradient image, the first... Two-dimensional discrete cosine transform (2D-DCT) is applied to each pixel block to obtain the frequency domain energy coefficient of that block.

[0063] For example, in this embodiment, the first Frequency domain energy coefficient of each pixel block It can be calculated using the following formula:

[0064]

[0065] in, For the first The spatial coordinates of a pixel block The pixel gradient value at that location reflects the intensity of the material texture at that spatial location; The spatial frequency coordinates correspond to the frequency components in the horizontal and vertical directions, respectively. The low-frequency components reflect the overall brightness and large-scale structure of the block, while the mid-to-high frequency components reflect the fine texture and edge details. and These represent the number of pixels in the horizontal and vertical directions of the pixel block, respectively. and These are the orthogonalization normalization coefficients, used to ensure the orthogonality of the transformation, so that the transformed coefficients have a clear physical meaning in terms of energy distribution.

[0066] Understandably, the two-dimensional discrete cosine transform (DCT) is a mature signal processing tool with wide applications in image compression and texture analysis. Its core function is to decompose pixel information in the spatial domain into energy contributions at different spatial frequencies. For intangible cultural heritage works, such as the needlework patterns of Shu embroidery, the unique stitch spacing and direction patterns generate stable energy peaks at specific spatial frequencies. This characteristic makes frequency domain coefficients an ideal feature for characterizing the physical microstructure of the work.

[0067] By performing the aforementioned two-dimensional discrete cosine transform on all pixel blocks in the micro-gradient image, the most representative low-frequency and mid-frequency energy coefficients in each block are extracted. These coefficients are then flattened and stitched together to form a feature row vector describing the physical properties of the intangible cultural heritage work. ,in The dimension of the physical feature row vector depends on the number of pixel blocks and the number of frequency domain coefficients extracted from each block.

[0068] The physical feature matrix is ​​the feature row vector constructed by the above process. In subsequent steps, it serves as input for the physical attribute dimension and participates in the fusion calculation.

[0069] Step S120: At fixed time intervals, use sensors to sample the temperature, humidity and usage time of the creation tools in the intangible cultural heritage creation environment to generate a process time sequence matrix.

[0070] The fixed time interval refers to the time step in which the sensor performs data acquisition operations throughout the entire creation cycle of the intangible cultural heritage work, such as recording once every 1 second, 5 seconds, or 1 minute.

[0071] Sensors refer to various physical quantity acquisition devices deployed in the creative environment of intangible cultural heritage works, including but not limited to temperature sensors, humidity sensors, tool usage timers, pressure sensors, etc.

[0072] Temporal sampling refers to sampling at fixed time intervals throughout the entire creation cycle. Internal The process of continuously recording various process parameters, among which The number of types of process parameters monitored. This represents the total number of sampling time points within the creation period.

[0073] The process timing matrix refers to organizing the raw data obtained from the above timing sampling into a single... Two-dimensional matrix Each row of the matrix corresponds to a time sequence record of a process parameter throughout the entire creation cycle, and each column corresponds to the sampled value of all process parameters at the same moment.

[0074] Understandably, because different types of process parameters have different dimensions and numerical ranges (for example, temperature is measured in degrees Celsius, and its value may range from several hundred to several thousand; while humidity is measured in percentages, and its value ranges from 0 to 100), directly using these raw data for subsequent fusion calculations will lead to bias in the feature space due to the differences in dimensions and numerical ranges—parameters with larger numerical ranges will unreasonably dominate the fusion results. Therefore, it is necessary to normalize the original process time series matrix.

[0075] In this embodiment, Z-score dynamic timing normalization is performed on the original process timing matrix to eliminate the dimensional differences between different process parameters.

[0076] For example, in this embodiment, the first in the normalized process timing matrix Such process parameters at time normalized value It can be calculated using the following formula:

[0077]

[0078] in, For the first Such process parameters at time The original measurement value; For the first The average value of a process parameter over the entire acquisition period is used to shift the numerical distribution of the parameter to a value centered at zero. For the first The standard deviation of a process parameter over the entire acquisition period serves to scale the numerical distribution of that parameter to a scale in units of standard deviation. For a very small positive constant (e.g.) Its function is to prevent a certain process parameter from fluctuating almost no throughout the entire acquisition period (i.e., the standard deviation is almost constant). A division-by-zero error occurs when the value approaches zero.

[0079] Understandably, after Z-score normalization, the time series data of each process parameter is transformed into a dimensionless value centered at zero and scaled by the standard deviation. This means that no matter how different the dimensions and numerical ranges of the original parameters are, the normalized data are comparable on the same scale, thus providing a fair data foundation for subsequent fusion calculations.

[0080] After normalization, the temporal covariance or statistical moment characteristics of the normalized matrix can be further calculated, and its dimensionality reduced to a compact feature row vector. ,in This represents the dimension of the process feature row vector. This feature row vector encodes the statistical distribution patterns of various process parameters and the relationships between the parameters during the creation process.

[0081] Step S130: Assign the first weight to the physical feature matrix and the second weight to the process timing matrix, and perform row and column concatenation operation on the two weighted matrices to form a composite data array.

[0082] The first weight refers to the weight assigned to the physical feature row vector. Adjustable weight scalar This is used to control the relative importance of physical microstructure features in the final fusion result.

[0083] The second weight refers to assigning the process timing feature row vector. Adjustable weight scalar This is used to control the relative importance of process features in the final fusion result.

[0084] Row and column concatenation refers to connecting two weighted feature row vectors into a higher-dimensional composite data vector by concatenating their beginning and end.

[0085] Understandably, the design motivation for introducing adjustable weight scalars lies in the fact that the distinguishing power and importance of physical microstructural features and process characteristics may differ significantly for different types of intangible cultural heritage works. For example, for Shu embroidery, the micro-frequency domain coefficients of its needlework texture may be the most critical feature distinguishing genuine from imitations; therefore, the weight of physical features is crucial. The weighting should be set to a higher value; however, for ceramic works, the temperature profile and kiln transformation control during firing may be more discerning, therefore the weighting of process characteristics should be lower. It should be set to a relatively high value. The specific value of the weight can be determined based on the expert experience of the intangible cultural heritage category or statistical analysis of training data.

[0086] For example, in this embodiment, the composite data vector It can be constructed using the following formula:

[0087]

[0088] in, It is the first weighted scalar; It is the second weighted scalar; For physical feature row vectors; This represents the row vector of process timing features; This represents the concatenation operation of the first and last vectors. The total dimension of the composite data vector.

[0089] Step S140: Perform principal component analysis (PCA) on the composite data array to reduce dimensionality, and truncate the data before... Each principal component feature is used as an intangible cultural heritage feature vector, and a one-way hash operation is performed on the intangible cultural heritage feature vector using the SHA-256 hash function to output a unique asset hash value.

[0090] Principal Component Analysis (PCA) is a linear dimensionality reduction method based on orthogonal projection. Its core idea is to find the orthogonal directions (i.e., principal components) with the largest data variance by solving the eigenvalue decomposition of the data covariance matrix, and then project the high-dimensional data into the low-dimensional subspace spanned by these directions, thereby reducing the data dimensionality while preserving the discriminative information in the data to the greatest extent.

[0091] forward The principal component features refer to the features obtained by sorting the covariance matrix eigenvalues ​​from largest to smallest and taking the top ones. The projection component on the subspace spanned by the eigenvectors corresponding to the largest eigenvalues. The value can be determined based on the cumulative variance contribution rate (for example, the number of principal components corresponding to a cumulative variance contribution rate of 95% or higher).

[0092] The SHA-256 hash function is a standard cryptographic one-way hash function that outputs a fixed-length 256-bit (32-byte) hexadecimal string. This function has two key cryptographic properties: first, collision resistance, meaning it is computationally infeasible to find two different inputs that produce the same output; second, avalanche effect, meaning that any tiny change in the input data (even changing just one bit) will cause a drastic and unpredictable change in the output.

[0093] In this embodiment, the specific process of performing PCA dimensionality reduction includes: based on a training sample set of similar intangible cultural heritage works, solving the eigenvalue equation of the covariance matrix of the composite data vector, and taking the first... The eigenvectors corresponding to the largest eigenvalues ​​form an orthogonal projection matrix, which projects the high-dimensional composite data vector onto a low-dimensional orthogonal subspace.

[0094] It is understandable that the training sample set mentioned here refers to a composite data vector set of a batch of similar intangible cultural heritage works (such as multiple Shu embroidery works) collected in advance. The system needs to learn the data distribution characteristics of this type of intangible cultural heritage work based on this training set, so as to determine the optimal orthogonal projection direction. This means that before the system is put into use, it needs to go through a training phase to extract features and perform statistical analysis on a certain number of similar intangible cultural heritage works.

[0095] For example, in this embodiment, the covariance matrix of the composite data is solved based on the training sample set. eigenvalue equations Take the front The largest eigenvalues The corresponding eigenvector Form an orthogonal projection matrix The final intangible cultural heritage feature vector and a unique digital asset hash identifier generated based on this vector. It can be calculated using the following formula:

[0096]

[0097]

[0098] in, The feature vector of the intangible cultural heritage work is obtained after orthogonal dimensionality reduction. It encodes the unique physical microstructure information and craft process information of the work in a compact low-dimensional form. This refers to the high-dimensional composite data vector constructed in step S130. For the reason before An orthogonal projection matrix consisting of the eigenvectors corresponding to the largest eigenvalues; This is the SHA-256 cryptographic hash function.

[0099] Understandably, the model constructed in steps S110 to S140 above achieves a deterministic binding between physical objects and digital credentials, ensuring that any act of imitating intangible cultural heritage objects (such as using machines to imitate Shu embroidery, where the micro-frequency domain coefficients of the stitches are not explicitly stated) will be bound to the digital credentials. This will be significantly altered due to the fundamental difference between the regularity of machine stitching and the randomness of hand stitching, or by falsifying the process (time parameters such as temperature and humidity). (the deviation), after PCA orthogonal projection After mapping, the differences will be amplified rather than smoothed in the low-dimensional feature space, resulting in the feature vectors being... This results in a significant offset, and due to the avalanche effect of the SHA-256 hash function, ultimately generating completely different asset hashes. .

[0100] For example, Figure 2 The diagram shows a representative needlework area and its two-dimensional discrete cosine transform spectrum obtained after processing the original Shu embroidery work in this embodiment. Figure 3 The cumulative variance contribution rate curve in this embodiment is shown. Figure 3The X-axis represents the principal component number (1 to 30), and the Y-axis represents the cumulative variance contribution rate (0% to 100%). The curves in the figure show typical elbow inflection point characteristics. The first N principal components (e.g., the first 8) can explain more than 95% of the total variance. The marginal contribution of subsequent principal components quickly approaches zero. The horizontal dashed line marks the 95% threshold, and the vertical dashed line marks the corresponding cutoff point N. Figure 4 This diagram illustrates the scatter plot distribution of genuine and counterfeit products in the low-dimensional feature space after dimensionality reduction, as shown in this embodiment. Figure 4 The X-axis represents the first principal component PC1, and the Y-axis represents the second principal component PC2. Genuine samples (blue dots) cluster together in a compact cluster, while counterfeit samples (red triangles) are distributed in areas far from the genuine sample clusters. There is a clear inter-class distance between the two classes. The elliptical confidence region marks the 95% distribution range. Figure 5 This diagram illustrates the relationship between the input perturbation and the hash Hamming distance for the avalanche effect of the SHA-256 hash function in this embodiment. Figure 5 The X-axis represents the perturbation magnitude applied to the intangible cultural heritage feature vector (from 0 to 0.1), and the Y-axis represents the Hamming distance (number of bits difference) between the output hash value and the original hash value. Even with extremely small input perturbations, the hash Hamming distance quickly jumps to around 128 bits (the theoretical expected value of a 256-bit hash). This figure illustrates that the avalanche effect of SHA-256 causes any tiny tampering with the intangible cultural heritage feature vector to lead to a drastic change in the asset hash value, thus ensuring the unforgeability of the unique asset hash value.

[0101] Step S200: Trigger a multi-role collaborative signature consensus protocol based on the unique asset hash value, broadcast signature requests to predefined inheritor nodes, intangible cultural heritage protection institution nodes and expert committee nodes, collect digital signature fragments returned by each node, aggregate the collected digital signature fragments to generate an aggregate signature, and compare and verify the intangible cultural heritage feature vector with the pre-set skill standard threshold matrix.

[0102] After generating unique digital asset hash identifiers for intangible cultural heritage works through joint physical and technological modeling, a dual ownership confirmation mechanism is needed to prevent any individual or single node from arbitrarily creating non-compliant digital assets. This mechanism combines objective data verification with authoritative multi-party consensus. This dual ownership confirmation mechanism consists of two independent but simultaneously satisfying verification layers: the first layer is automatic algorithmic review based on a hard-coded skill standard threshold matrix; the second layer is authoritative collective ownership confirmation based on multi-party threshold signatures. Together, these constitute the entry barrier for digital asset creation.

[0103] Among them, the multi-role collaborative signature consensus protocol refers to a predefined collaborative rights confirmation procedure in the system. This procedure requires multiple blockchain nodes from different authoritative roles to recognize the same asset hash value by means of cryptographic signatures, and to combine the signature fragments of each party into a unified aggregate signature through a specific aggregation algorithm.

[0104] Inheritor nodes refer to nodes in the blockchain network that represent the identity of inheritors of intangible cultural heritage skills. These nodes hold private key shards allocated by the system during the initialization phase and have the right to sign and confirm asset ownership requests under the category of intangible cultural heritage skills they inherit.

[0105] Intangible cultural heritage protection organization nodes refer to nodes in the blockchain network that represent the identity of intangible cultural heritage protection organizations authorized by the government or recognized by the industry. These nodes also hold private key shards allocated by the system and have the right to sign and confirm asset ownership requests on behalf of the official body.

[0106] Expert committee nodes refer to nodes in the blockchain network that represent independent experts in the relevant intangible cultural heritage field. There are usually multiple such nodes, and each node holds its own independent private key shard.

[0107] A digital signature fragment refers to the signature data generated by each participating node using its private key fragment to perform a cryptographic signature operation on the hash value of a unique asset. Each signature fragment represents only the unilateral approval of that node and is insufficient to pass the verification of a smart contract on its own.

[0108] Aggregate signatures refer to combining multiple digital signature fragments that meet predefined participation structure requirements into a single, fixed-length signature value using a cryptographic aggregation algorithm. Only this aggregate signature can pass the bilinear pairing verification function deployed in the smart contract.

[0109] The craft standard threshold matrix refers to a set of legal range data of craft indicators that are pre-formulated and hard-coded by experts or standardization institutions in the field of intangible cultural heritage and stored locally or in smart contracts. This matrix defines the legal upper and lower limits of each craft dimension for a specific intangible cultural heritage category, which together constitute a legal bounding box in a high-dimensional space.

[0110] In this embodiment, the specific steps for comparing and verifying the intangible cultural heritage feature vector with the preset skill standard threshold matrix may include:

[0111] Step S210: Extract multiple process index scalars representing the physical process dimension from the intangible cultural heritage feature vector.

[0112] Among them, the scalar quantity of the process index refers to the value derived from the intangible cultural heritage feature vector. These are scalar values ​​extracted that directly correspond to specific physical or technological attributes. For example, in Shu embroidery, certain dimensions might correspond to the characteristic expression of embroidery stitch density; in ceramic works, certain dimensions might correspond to firing temperature characteristics or elemental composition ratio characteristics.

[0113] It is understandable that the intangible cultural heritage feature vector As a low-dimensional vector after PCA dimensionality reduction, its dimensions do not directly correspond to a specific physical quantity, but rather are projections of the original high-dimensional feature space onto the principal component directions. Therefore, the process of parsing out the scalar of the process indicators refers to extracting the subset components related to the verification of process standards from this vector.

[0114] In this embodiment, let the intangible cultural heritage feature vector The subset related to process parameters is ,in The number of dimensions for the subset of process indicators. .

[0115] Step S220: Call the locally stored hard-coded skill standard threshold matrix. This matrix defines a legal high-dimensional bounding box for a specific intangible cultural heritage category. The bounding box consists of the optimal upper and lower limit value ranges of each skill indicator.

[0116] Hard-coding refers to the fact that the values ​​of the threshold matrix of the technical standard are fixedly written into the local storage or the immutable state variables of the smart contract when the system is deployed, and cannot be dynamically modified during runtime, so as to prevent malicious tampering with the standard.

[0117] A valid high-dimensional bounding box refers to... In the dimensional process index space, there are upper bound vectors for each dimension. and lower bound vector A super-rectangular region is defined by the collective definition of the work. Only when all the process indicators of the work to be verified fall within this super-rectangular region can it pass the algorithmic review.

[0118] Understandably, the design philosophy behind this verification mechanism is that for specific categories of intangible cultural heritage techniques, their core process parameters exist within a reasonably recognized range within the industry. For example, the firing temperature of Zisha teapots should typically fall within a defined range (e.g., 1100°C to 1200°C); exceeding this range indicates an abnormality in the process. Similarly, the needlework density of Shu embroidery should fall within a specific frequency domain energy coefficient range. By digitizing these industry standards and hard-coding them into a threshold matrix, the system can automatically complete the initial screening of process compliance without human intervention. Figure 6 This diagram illustrates a two-dimensional bounding box for the craft standard threshold verification in this embodiment. Figure 6The two representative process indicators are the X-axis and Y-axis (such as needle density characteristic value and firing temperature characteristic value). A rectangular legal bounding box is drawn by the upper limit vector and the lower limit vector. Qualified samples (blue dots) fall inside the box, and unqualified samples (red crosses) fall outside the box, thus realizing zero tolerance for full-dimensional compliance verification.

[0119] Step S230: Perform Boolean logic judgment to verify whether each extracted process indicator scalar strictly falls within the upper and lower limit value range corresponding to the high-dimensional bounding box; if all indicator scalars meet the range conditions, output a comparison pass signal; if any scalar exceeds the limit, determine the comparison failure and return a process non-compliance exception to the trigger source.

[0120] Boolean logic judgment refers to performing an interval inclusion test on each process indicator dimension and performing a logical AND operation on the test results of all dimensions.

[0121] The pass signal is a permission signal sent by the system to the subsequent asset creation process when the final result of the Boolean logic judgment is true (i.e., all dimensions meet the interval conditions).

[0122] The process non-compliance anomaly refers to an error report generated by the system when Boolean logic judgment finds that any dimension of the process indicator scalar exceeds its corresponding legal range. This report will interrupt the current authority confirmation process and notify the triggering source.

[0123] For example, in this embodiment, the verification function of the technology standard It can be represented as follows:

[0124]

[0125] in, This is a Boolean indicator function that outputs the value 1 when the condition inside the parentheses is true and the value 0 when the condition inside the parentheses is false. The number of dimensions for the subset of process indicators; A subset of process parameters The Middle The values ​​of each component; For the first The lower limit threshold of each process indicator; For the first The upper limit threshold of each process indicator.

[0126] Understandably, since the above formula uses a series of multiplication operations, it only applies when all... When the indicator function outputs 1 for all dimensions, The final value is 1, indicating that the physical and technological parameters of the work fall completely within the legal boundaries of this type of intangible cultural heritage technique; as long as any dimension exists... Make or The corresponding indicator function outputs 0, the product result becomes 0, and the overall verification is judged to have failed.

[0127] In this embodiment, the multi-role collaborative signature consensus protocol in this step adopts an aggregation threshold signature mechanism based on BLS (Boneh-Lynn-Shacham), which may specifically include:

[0128] Step S240: When initializing the system, private key shards are allocated to the inheritor nodes, intangible cultural heritage protection institution nodes, and expert committee nodes respectively, and the corresponding public key sets are made public on the blockchain.

[0129] Private key sharding refers to the distribution of the signing authority required to mint assets among multiple participants using the Shamir secret sharing scheme or other key splitting schemes, resulting in each participant holding a separate piece of key data. No single party can hold a sufficient private key shard to generate a valid signature independently.

[0130] A public key set refers to the set of public key data corresponding to the private key shards of each participating node. This set is publicly recorded on the blockchain during system initialization for subsequent aggregate signature verification.

[0131] Step S250: When broadcasting a signature request, each returned digital signature fragment must be accompanied by the permission type tag of the corresponding node.

[0132] The permission type tag is a metadata marker that identifies the role category to which the signing node belongs, such as successor, conservation organization, or expert. The purpose of this tag is to determine, during subsequent access control policy verification, whether the currently collected set of signature fragments meets the predefined requirements of a multi-role participation structure.

[0133] Step S260: When collecting the digital signature fragments returned by each node, perform weight counter verification: This requires collecting at least one successor signature, one protection organization signature, and... The expert committee signed, among which It is a preset positive integer greater than or equal to 3.

[0134] The weight counter verification is essentially an admission policy function that performs a logical-level eligibility check before cryptographic aggregation, determining whether the currently collected set of signature fragments meets the predefined participation structure requirements.

[0135] Understandably, the admission policy function does not directly participate in the cryptographic operations themselves, but rather performs a logical review of the participants' qualifications upstream in the cryptographic process. Only after the qualification review is passed will the system send the qualified signature fragment into the cryptographic aggregation process.

[0136] For example, in this embodiment, the signature fragment set is defined as follows: ,in A collection of signatures for the inheritors. For a subset of institutional signatures, This is a subset of expert signatures. Admission policy function. It can be represented as follows:

[0137]

[0138] in, For Boolean indicator functions; the first indicator function verifies a subset of the successor's signature. Does it contain at least one valid signature? The second indicator function verifies a subset of the authority's signatures. The function checks whether the signature contains at least one valid signature; the third indicator function verifies the subset of expert signatures. Does it contain at least One valid signature, The default positive integer is greater than or equal to 3. Since the results of the three indicator functions are multiplied, the condition is only met when all three conditions are satisfied (i.e., ...). Only when this condition is met can the subsequent cryptographic aggregation steps be performed. Figure 7 The combined verification matrix heatmap of the multi-role admission policy function in this embodiment is shown. Figure 7 The X-axis represents the number of expert signatures (0-6), and the Y-axis represents different character combination scenarios. Green squares in the heatmap indicate... (Passed), red square indicates (Rejected). Only when there are ≥1 inheritors, ≥1 institutions, and ≥K experts is it green.

[0139] Step S270: After the weight counter passes the verification, multiply the digital signature fragments that meet the conditions and perform polynomial interpolation aggregation to generate an aggregated signature.

[0140] Polynomial interpolation aggregation refers to a cryptographic signature synthesis technique based on Lagrange interpolation, which performs weighted multiplication operations on multiple signature fragments over an elliptic curve group to recover an aggregated result equivalent to the original complete signature. This aggregation process is based on the mathematical principles of Shamir secret sharing, and its core characteristic is that the complete signature can be mathematically recovered from the signature fragments submitted by each party without exposing the private key fragments of any participant.

[0141] In this embodiment, the BLS signature scheme is based on a bilinear mapping on an elliptic curve. Its core advantage lies in supporting efficient signature aggregation. Multiple signature fragments can be merged into a fixed-length aggregate signature, and this aggregate signature can be verified by a single bilinear pairing operation. Its verification overhead is independent of the number of participants.

[0142] For example, in this embodiment, the bilinear mapping is defined as ,in and These are two cyclic groups on the elliptic curve. For the target group, the signature fragments submitted by each participating node are as follows: Aggregated signature It can be calculated using the following formula:

[0143]

[0144]

[0145] in, For the first Each participating node uses its private key to shard the asset hash. The signature fragment generated after BLS signing; For the Lagrange polynomial at the evaluation point Interpolation coefficients at the location, and The index number assigned to the participating node in the secret sharing scheme; This is the set of signature fragments that have passed the admission policy verification.

[0146] Ultimately, the smart contract determines the validity of the aggregate signature by performing bilinear pairing verification, as shown in the following equation:

[0147]

[0148] in, Elliptic curve group Generators; To hash assets Mapping to Elliptic Curve Group The hash-to-curve function on the curve; For the first The public keys published by each participating node on the blockchain. Verification passes if and only if the results of the bilinear pairing operation on both sides of the equation are equal, proving the aggregate signature. It is indeed generated collectively by legitimate participants who meet the access policy requirements, and the smart contract allows for the execution of subsequent asset minting operations. (Symbol:) The expression "Is it equal to?" refers to an equation that needs to be verified, rather than an objective fact that has already been established. The above expression describes a checking process. The program calculates the results on both sides of the equal sign separately and then compares whether they are equal. If the results on both sides are consistent, the verification passes; otherwise, it fails.

[0149] Understandably, the aforementioned dual-rights confirmation mechanism organically combines objective algorithmic review with authoritative multi-party consensus. At the algorithmic level, the verification function... This eliminates the possibility of falsified process data passing automated verification. At the authority level, the mathematical properties of cryptographic threshold signatures guarantee that, as long as the admission policy function... (i.e., lacking any one of the following: a successor, an official institution, or a sufficient number of experts), aggregate signatures. If bilinear pairing verification fails, the smart contract will reject the minting operation at the underlying state machine level. This model mathematically eliminates the possibility of a single point of malicious activity.

[0150] In this embodiment, to address the extreme scenario of node communication disruption in a distributed network, this step may further include the following exception handling mechanism:

[0151] While broadcasting the signature request to the expert committee nodes, a deterministic timing decrement timer is started.

[0152] The deterministic timing decrement timer is a countdown timer that is synchronously activated when a signature request is broadcast, with its initial value being a preset timeout threshold. The purpose of this timer is to set a finite waiting window for the signature collection process, preventing the system from hanging indefinitely due to some nodes being unresponsive for extended periods.

[0153] If the number of digital signature fragments collected from the expert committee has not reached the preset threshold when the timer resets to zero... The system will then perform the following operations: store the received valid signature fragment and its corresponding public key index into the smart contract's time lock cache pool.

[0154] The time-locked cache pool is a temporary storage area within a smart contract that has a time limit. It is used to temporarily store valid signature fragments that have not yet met the aggregation conditions. This cache pool has a preset extended lifespan, during which subsequently submitted signature fragments can be merged with historical fragments in the cache pool.

[0155] Simultaneously, the system triggers an asynchronous alarm event and suspends the current consensus verification process. When the network recovers and the remaining nodes submit the missing signature fragments within the extended lifecycle set by the time lock, the system retrieves historical fragments from the cache pool and performs a secondary concatenation verification with the newly submitted fragments, i.e., re-executes the admission policy function. And subsequent cryptographic aggregation processes.

[0156] If the required signature fragment is not completed within the extended lifecycle period, the system automatically clears all temporary data in the cache pool, destroys the context of this ownership confirmation task, and terminates the asset's creation process. This mechanism ensures that the system will not leave any unresolved intermediate states due to the permanent loss of some nodes. Figure 8 The diagram illustrates the authorization recovery effect of the time lock caching mechanism in the node communication blocking scenario of this embodiment.

[0157] Step S300: If and only if the intangible cultural heritage feature vector satisfies the comparison condition of the skill standard threshold matrix and the aggregate signature is cryptographically verified, execute the asset minting smart contract, encapsulate the unique asset hash value and the aggregate signature into an intangible cultural heritage digital asset, and initialize the pre-set deterministic ledger routing tree in the smart contract.

[0158] Among them, the asset casting smart contract refers to a piece of automatically executed program code deployed on the blockchain network. Its function is to permanently write the digital identity information of the intangible cultural heritage work (including the unique asset hash value and aggregate signature) into the immutable ledger of the blockchain after receiving the instruction of double confirmation, thereby generating an on-chain digital asset (i.e., intangible cultural heritage digital asset) with uniqueness and verifiability.

[0159] Intangible cultural heritage digital assets refer to on-chain tokens that represent the digital identity of specific intangible cultural heritage physical works, minted on the blockchain after physical characteristic verification and multi-party consensus confirmation. These tokens encapsulate a unique asset hash value and a aggregate signature as their core metadata, and anyone can independently verify the aggregate signature using the publicly available public key set on the blockchain.

[0160] A deterministic revenue routing tree refers to a set of state variables pre-initialized in the storage structure of a smart contract. These state variables define how transaction funds should be automatically allocated to various predefined target addresses when ownership of the intangible cultural heritage digital asset is transferred in subsequent transactions. "Deterministic" means that the allocation rules are entirely determined by the smart contract code and state variables, without any human intervention.

[0161] In this embodiment, the step of initializing a pre-defined deterministic revenue-sharing routing tree in a smart contract may include:

[0162] Step S310: Establish two sets of mapping tables in the storage structure of the smart contract. The first set of mapping tables is an address routing table, which records the blockchain address of the original successor and the blockchain address of the protection fund. The second set of mapping tables is a profit-sharing benchmark parameter table, which records the initial transfer profit-sharing ratio.

[0163] The address routing table is a key-value pair storage structure within a smart contract, where the key is a role identifier (such as a successor or protection fund), and the value is the corresponding blockchain wallet address. This routing table determines the specific flow of return funds during subsequent profit sharing.

[0164] The revenue sharing benchmark parameter table refers to another key-value pair storage structure in the smart contract, used to record benchmark parameters related to revenue sharing calculation, including but not limited to the initial maximum revenue sharing ratio. Successive decay coefficient Bottom-line safety net threshold And the internal weighting of feedback between the individual feedback of the inheritor and the public feedback of the institution. .

[0165] Initial turnover profit sharing ratio This refers to the maximum profit-sharing ratio applicable when the digital asset of intangible cultural heritage first circulates in the secondary market.

[0166] Step S320: Set the current circulation depth counter of intangible cultural heritage digital assets. Initialize to 0.

[0167] Among them, the flow depth counter This refers to a non-negative integer recorded in the smart contract's state variables, used to track the cumulative number of ownership transfers of this intangible cultural heritage digital asset on the secondary market since its creation. Each time the asset completes a valid ownership transfer transaction, The value increments by 1. The initial value is set to 0, indicating that the asset has not yet undergone any transfer.

[0168] Step S330: Combine the two mapping tables and the flow depth counter. It is encapsulated in the immutable state variables of the smart contract to form the root node of the revenue sharing routing tree.

[0169] Immutable state variables refer to data variables stored on the blockchain and exclusively managed by the smart contract code logic. These variables can only be read and written by executing predefined functions in the smart contract. No external entity can bypass the smart contract code to directly modify the values ​​of these variables, thus ensuring the immutability and auditability of the revenue sharing routing rules.

[0170] The root node is a combination of the two mapping tables and the flow depth counter mentioned above. This logical encapsulation forms the basic data structure of the revenue sharing routing tree. In each subsequent asset transfer, the revenue sharing logic executor will read the routing address, baseline parameters, and current transfer depth from the root node, and calculate the allocation scheme for this transfer accordingly.

[0171] Step S400: Listen for state transfer events related to intangible cultural heritage digital assets in the blockchain network. When an asset transfer request is intercepted, trigger the profit-sharing logic executor in the smart contract, calculate the profit distribution amount according to the proportion array defined by the revenue sharing routing tree, and automatically distribute the transferred funds to the original inheritor address and intangible cultural heritage protection fund address bound to the proportion array through atomic transaction instructions.

[0172] After the on-chain creation and ownership confirmation of intangible cultural heritage digital assets are completed, a deterministic, automated profit-sharing and tracking mechanism needs to be established when these assets are traded on the secondary market. The core objective of this mechanism is to ensure that the original inheritors of intangible cultural heritage skills and related protection institutions can automatically receive continuous economic returns in every asset transfer, thereby breaking the predicament of intermediaries intercepting profits or creators being unable to obtain secondary market gains in the traditional model.

[0173] Among them, the state transition event refers to the ownership change request event related to the intangible cultural heritage digital asset in the blockchain network. When a user initiates a transaction to buy or transfer the asset, the blockchain network will broadcast the corresponding event notification.

[0174] Listening refers to smart contracts or on-chain monitoring services continuously subscribing to and checking event logs related to specific intangible cultural heritage digital assets in the blockchain network in order to detect any requests for state changes to that asset in real time.

[0175] An asset transfer request is an on-chain transaction request initiated by the buyer to transfer ownership of a specific intangible cultural heritage digital asset from the current holder (seller) to the buyer. The request contains key information such as the buyer's address, the seller's address, and the total transaction amount.

[0176] The profit-sharing logic executor refers to a piece of automatically executed program logic within a smart contract. Its function is to dynamically calculate the profit-sharing ratio and the amount of funds allocated to each channel for this transaction based on the baseline parameters stored in the profit-sharing routing tree and the current transaction depth after intercepting an asset transfer request.

[0177] An atomic transaction is an instruction mechanism that binds multiple fund transfer operations and asset ownership change operations into a single, indivisible transaction. In an atomic transaction, either all operations are completed successfully, or if any operation fails, all operations are rolled back to their state before execution; there is no intermediate state of partial success or partial failure.

[0178] In this embodiment, the smart contract also includes a pre-defined blacklist address array filter. After intercepting an asset transfer request in this step, the buyer's address and seller's address that initiated the transfer request are first extracted.

[0179] The blacklist address array refers to a list of blockchain addresses that are pre-entered and stored in the state variables of the smart contract. These addresses have been marked as having a history of malicious behavior (such as money laundering, fraud, intellectual property infringement, etc.) and are prohibited from participating in the circulation and trading of intangible cultural heritage digital assets.

[0180] The system uses a Boolean matching algorithm to perform a byte-by-byte collision comparison between the addresses of the buyer and seller and a pre-set blacklist address array. If either address matches the blacklist, the system forcibly intercepts the transfer request, triggers a state machine lock-in instruction, freezes the transfer rights of the intangible cultural heritage digital assets, and uploads the metadata hash of the illegal transfer path to the blockchain for evidence storage, so as to facilitate subsequent auditing and tracing.

[0181] In this embodiment, the specific steps of triggering the revenue-sharing logic executor within the smart contract to calculate the revenue distribution amount based on the proportion array defined by the revenue-sharing routing tree include:

[0182] Step S410: Extract the total transaction payment amount from the asset transfer request. The total transaction payment amount is as follows. This refers to the total amount of funds paid by the buyer to purchase the intangible cultural heritage digital asset, denominated in the native token of the blockchain network or the agreed stablecoin.

[0183] Step S420: Read the flow depth counter in the state variables. The actual profit-sharing ratio that should be implemented at the current time is dynamically calculated based on the determined attenuation formula. Dynamic calculation means that the actual profit-sharing ratio is not a fixed constant, but rather varies based on the current turnover depth counter. The value is calculated in real time using a deterministic mathematical formula. The economic rationale behind this design lies in the fact that in the early stages of asset circulation, the premium is relatively large, and the original creator should rightfully receive a higher return. However, as the number of transactions increases, the profit margin for subsequent holders gradually narrows. Maintaining an excessively high commission rate would suppress liquidity in the secondary market. Therefore, a profit-sharing function that decays exponentially with the depth of circulation is used to balance this contradiction. Simultaneously, to ensure sustainable economic compensation for the transmission of intangible cultural heritage skills, even with numerous transactions, the profit-sharing ratio should not drop to zero; instead, a minimum threshold should be set as a safety net.

[0184] For example, in this embodiment, the actual triggered profit-sharing ratio It can be calculated using the following formula:

[0185]

[0186] in, The initial maximum profit-sharing ratio (e.g., 10%) indicates the first turnover ( The applicable upper limit of the profit-sharing ratio is when the formula degenerates into the following: This aligns with the design intent of taking the highest profit share during the initial transfer; This is a hard-coded decay coefficient in the smart contract, controlling the rate at which the profit-sharing ratio decreases with each transaction. The larger the value, the faster the decay. Establish a safety net threshold (e.g., 1%) to ensure that original creators and conservation organizations always receive a minimum economic return, regardless of how many times the content is transferred. The current transfer depth counter value is used for each ownership state transfer. Increment by 1.

[0187] It is understandable that in the above formula, It is an exponentially decaying function, increasing with the depth of circulation. As the value of increases, it gradually approaches zero. The function's purpose is to: when the exponential decay value falls below the baseline threshold. At that time, the actual profit-sharing ratio will be locked at It will not continue to decline. This mechanism achieves the perpetual tracking of long-tail returns—even if the intangible cultural heritage digital asset undergoes hundreds or thousands of transfers, the original inheritors and protection organizations can still obtain at least [amount missing] in each transaction. Proportional economic returns. Figure 9 The graph showing the number decay distribution ratio in this embodiment is illustrated. Figure 9 The X-axis represents the flow depth, the Y-axis represents the actual profit sharing ratio, and the three curves correspond to different successive decay coefficients. All curves are eventually truncated and locked by the bottom line threshold on their respective decay paths.

[0188] Step S430: Based on the calculated actual profit-sharing ratio Execute the multiplication instruction to calculate the total return amount. .

[0189] Of which, the total amount of return funds This refers to the total amount paid by the buyer in this transaction. According to the actual profit-sharing ratio The total amount of funds extracted to give back to the original inheritors and protection organizations.

[0190] Step S440: Based on the internal and external allocation weights set in the address routing table, allocate the total feedback funds. It is split into personal rewards transferred to the address of the successor and public rewards transferred to the address of the conservation fund.

[0191] Among them, the internal and external allocation weight refers to the ratio parameter between the personal feedback of the inheritor and the public feedback of the institution. .For example, This means that 70% of the total return funds will go to the inheritor personally, and 30% will go to the public fund of the protection organization.

[0192] Personal reward refers to the reward based on the weighting of the distribution. The calculated amount of funds directly paid to the original inheritor's personal blockchain address.

[0193] Public return funds refer to those allocated according to weighting. The calculated amount of funds paid to the blockchain address of the public fund for intangible cultural heritage protection organizations.

[0194] For example, in this embodiment, the three-way fund allocation executed by the smart contract within a single atomic transaction block can be represented by the following formula:

[0195]

[0196] in, The amount transferred to the current holder (seller) is equal to the total amount paid by the buyer minus the total return amount, i.e. ; The reward amount is paid directly to the original inheritor's personal address, i.e. ; The amount of feedback paid to the public fund address of the intangible cultural heritage protection organization, namely ; The internal weighting for the feedback from individual inheritors and the public feedback from institutions.

[0197] It is understandable that the sum of the amounts allocated from the three sources is always equal to the total funds paid by the buyer. ,Right now: This ensures the conservation of funds, with every unit of funds paid by the buyer having a clear destination, and there is no omission or over-allocation of funds. Figure 10 This diagram illustrates the stacked area of ​​the three-way fund allocation as the flow depth changes in this embodiment. Figure 10 The X-axis represents the circulation depth (0~30 times), and the Y-axis represents the percentage of each type of fund in the total transaction amount (0%~100%). The three stacked areas are the seller's proceeds (blue), the successor's return (orange), and the protection fund's return (green). As the circulation depth increases, the orange and green areas gradually narrow but never disappear, while the blue area gradually expands. The sum of the three areas is always 100%.

[0198] In this embodiment, the step of automatically distributing circulating funds through atomic transaction instructions includes:

[0199] Step S450: Within the same smart contract execution function, construct three independent fund transfer instructions: an instruction to transfer the remaining funds to the asset seller, an instruction to transfer the personal return funds to the heir, and an instruction to transfer the public return funds to the protection fund.

[0200] Among them, the fund transfer instruction refers to the operation command in the smart contract that calls the underlying blockchain transfer interface. Each instruction contains two parameters: the target address and the transfer amount. The three independent fund transfer instructions respectively transfer the funds calculated in step S440... , and Send to their respective target addresses.

[0201] Step S460: Bind the above three fund transfer instructions and the ownership status change instructions of intangible cultural heritage digital assets into the same state tree transaction.

[0202] Among them, the ownership status change instruction refers to the operation of updating the on-chain owner record of intangible cultural heritage digital assets from the current seller address to the buyer address.

[0203] A single-state-tree transaction refers to a mechanism in the blockchain virtual machine that packages multiple state change operations into a single, indivisible atomic transaction. In this transaction, fund allocation and ownership change are submitted to the blockchain as a whole.

[0204] Step S470: Execute transaction commit. If any fund transfer instruction fails to execute due to insufficient network balance or abnormal target address, the transaction rollback mechanism is triggered to revoke the change of ownership status and reject the asset transfer request, ensuring the atomic consistency of asset transfer and profit sharing logic.

[0205] Among them, the transaction rollback mechanism refers to the security mechanism in which the blockchain virtual machine automatically restores all executed state changes to the state before the transaction started when any operation in a transaction fails.

[0206] For example, in this embodiment, the global state transition function is defined as follows: The state update rules for smart contracts can be expressed as follows:

[0207]

[0208] in, To ensure the global state before the transaction is executed, including the owner record of intangible cultural heritage digital assets and the circulation depth counter. The current value, and the balance information of each account; Transaction instructions for the transfer of ownership of digital assets of intangible cultural heritage; , , The amounts are allocated to the three funding sources respectively; This is a blacklist checking function based on Boolean collision detection, when the buyer's address... or seller's address Output the value 1 when the blacklist address array is hit, and output the value 0 when the blacklist address array is not hit; For logical AND operator.

[0209] It is understandable that in the above formula, the state is updated to... The condition is a strict double AND condition: First, the blacklist check passes ( First, neither the buyer's nor the seller's addresses were blacklisted; second, all three fund transfers were successfully executed. This means that every sum of money has successfully reached its target address. The state transition function only functions when both of these conditions are met. Only after execution is the global state updated. (Including ownership change and transfer depth counters) Increment by 1). Otherwise, the system will perform a full transaction rollback, restoring the global state to its previous state. This ensures that there will be no inconsistencies such as funds being transferred out but ownership not changing, or ownership changing but profit-sharing funds not arriving, where some transactions are successful and others fail.

[0210] Understandably, the above profit-sharing routing model design implements a forced binding process with "no feedback, i.e., no flow." The profit-sharing ratio exhibits an exponential decay function. This approach aligns with the economic principle of diminishing marginal returns, protecting liquidity in the secondary market in the later stages, and also meets the bottom-line threshold. This ensures the right of intangible cultural heritage inheritors to receive perpetual economic compensation. The atomic state transition mechanism mathematically guarantees the immutability of fund allocation and the integrity of transactions, making the commercialization of intangible cultural heritage a deterministic rule enforced by smart contracts, rather than relying on the goodwill or promises of any centralized platform.

[0211] Example 2

[0212] This embodiment discloses a smart contract-based digital asset ownership confirmation and circulation tracking system for intangible cultural heritage art. Specifically, this system can be integrated into an electronic device, such as a terminal or server.

[0213] The terminal can be a mobile phone, tablet computer, smart Bluetooth device, laptop computer, or personal computer; the server can be a single server or a server cluster composed of multiple servers. The server or server cluster runs blockchain node client software and can communicate and reach consensus with other peer nodes in the blockchain network. When the electronic device is running, it can realize the smart contract intangible cultural heritage art digital asset confirmation and circulation tracking method as in Example 1.

[0214] In some embodiments, the smart contract intangible cultural heritage art digital asset ownership confirmation and circulation tracking system can also be integrated into multiple electronic devices. For example, the system can be distributed and deployed in multiple server nodes, and the smart contract intangible cultural heritage art digital asset ownership confirmation and circulation tracking method of embodiment 1 of this application can be implemented by multiple server nodes in collaboration.

[0215] In some embodiments, the server may also be implemented as a terminal.

[0216] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for confirming ownership and tracking the transfer of digital assets of intangible cultural heritage art using smart contracts, characterized in that... The methods for asset ownership confirmation and transfer tracking include: S100. Obtain the physical attribute dataset and process parameter set of intangible cultural heritage works, fuse the physical attribute dataset and process parameter set to generate intangible cultural heritage feature vector, and apply a cryptographic one-way hash function to the intangible cultural heritage feature vector to calculate the unique asset hash value. S200. Trigger a multi-role collaborative signature consensus protocol based on the unique asset hash value, broadcast signature requests to multiple predefined nodes with different roles, collect the digital signature fragments returned by each node and aggregate them to generate an aggregate signature, and at the same time compare and verify the intangible cultural heritage feature vector with the preset skill standard threshold matrix. S300. When the comparison verification is passed and the aggregate signature is cryptographically verified, the asset casting smart contract is executed to encapsulate the unique asset hash value and the aggregate signature into a non-heritage digital asset, and a deterministic revenue sharing routing tree is initialized in the asset casting smart contract. S400: Monitor state transition events related to the intangible cultural heritage digital assets in the blockchain network. When an asset transfer request is intercepted, trigger the profit-sharing logic executor in the asset minting smart contract, calculate the profit distribution amount for each path according to the deterministic revenue routing tree, and distribute the transferred funds to the target address bound to the deterministic revenue routing tree through atomic transaction instructions.

2. The method for confirming ownership and tracking the circulation of digital assets of intangible cultural heritage art using smart contracts according to claim 1, characterized in that, In step S100, generating the intangible cultural heritage feature vector includes: Feature extraction is performed on the physical attribute dataset to generate physical feature row vectors; The process parameter set is subjected to time-series sampling and feature extraction to generate process feature row vectors; The physical feature row vector is assigned a first weight, and the process feature row vector is assigned a second weight. The weighted physical feature row vector and the process feature row vector are then concatenated and spliced ​​together to form a composite data vector. The composite data vector is subjected to dimensionality reduction processing to generate the intangible cultural heritage feature vector.

3. The method for confirming ownership and tracking the circulation of digital assets of intangible cultural heritage art using smart contracts according to claim 2, characterized in that, The dimensionality reduction process performed on the composite data vector includes: Based on a training sample set of similar intangible cultural heritage works, the eigenvalues ​​of the covariance matrix of the composite data vector are solved, and the eigenvectors corresponding to the top N largest eigenvalues ​​are used to form an orthogonal projection matrix, where N is determined according to the cumulative variance contribution rate. The composite data vector is projected onto the low-dimensional subspace spanned by the orthogonal projection matrix to obtain the intangible cultural heritage feature vector.

4. The method for confirming ownership and tracking the circulation of digital assets of intangible cultural heritage art using smart contracts according to claim 1, characterized in that, In step S200, the intangible cultural heritage feature vector is simultaneously compared and verified with a preset skill standard threshold matrix, including: Multiple process indicator scalars corresponding to the process attribute dimension are parsed from the intangible cultural heritage feature vector; The technical standard threshold matrix, which is pre-hard-coded and stored, is invoked. The technical standard threshold matrix defines a legal high-dimensional bounding box composed of the upper and lower limits of each technical indicator. Perform an interval inclusion test on each of the process index scalars to determine whether it falls within the upper and lower limit value range of the corresponding dimension of the legal high-dimensional bounding box, and perform a logical AND operation on the test results of all dimensions. If all the process index scalars meet the interval conditions, a comparison pass signal is output; if any of the process index scalars exceeds the limit, the comparison is determined to fail and a process non-compliance exception is returned.

5. The method for confirming ownership and tracking the circulation of digital assets of intangible cultural heritage art using smart contracts according to claim 1, characterized in that, In S200, multiple nodes with different roles include: The inheritor nodes, intangible cultural heritage protection organization nodes, and expert committee nodes each hold a private key fragment allocated through a key splitting scheme during system initialization, and publicly disclose the corresponding public key set on the blockchain; Before collecting the digital signature fragments returned by each node and aggregating them to generate the aggregate signature, the process also includes: An admission policy verification is performed on the collected digital signature fragments. The admission policy verification requires that the digital signature fragments contain at least one signature from the inheritor node, one signature from the intangible cultural heritage protection organization node, and no less than a preset signature number threshold number of signatures from the expert committee nodes. The preset signature number threshold number is a positive integer greater than or equal to three. Once the admission strategy is verified, the aggregation step begins. The aggregation step generates an aggregation signature, including: Each participating node uses its own private key fragment to perform a signature operation on the unique asset hash value to generate the digital signature fragment, and each digital signature fragment is attached with the corresponding node's permission type label; The digital signature fragments that meet the admission policy requirements are synthesized by weighted multiplication on an elliptic curve group using interpolation coefficients based on the Lagrange interpolation method, thereby generating the aggregate signature.

6. The method for confirming ownership and tracking the circulation of digital assets of intangible cultural heritage art using smart contracts according to claim 5, characterized in that, In step S200, triggering the multi-role collaborative signature consensus protocol also includes an exception handling step: While broadcasting the signature request to the expert committee node, a deterministic timing decrement timer is started; If the number of digital signature fragments collected from the expert committee node does not reach a preset threshold when the deterministic timing decrement timer reaches zero, the received valid digital signature fragments and their corresponding public key indexes are stored in the time lock cache pool, and the current consensus verification process is suspended. During the extended lifecycle of the time lock cache pool, if the remaining nodes supplement the missing digital signature fragments, then historical fragments are extracted from the time lock cache pool and the newly submitted fragments are spliced ​​together for secondary verification. If the required digital signature fragment is not completed within the extended lifecycle period, the time lock cache pool is cleared and the minting process for the asset is terminated.

7. The method for confirming ownership and tracking the circulation of digital assets of intangible cultural heritage art using smart contracts according to claim 1, characterized in that, In S300, initializing the deterministic accounting routing tree includes: An address routing table is established in the storage structure of the asset casting smart contract. The address routing table records the blockchain address of the original successor and the blockchain address of the protection fund. Establish a profit-sharing benchmark parameter table, which records the initial maximum profit-sharing ratio, successive attenuation coefficient, bottom-line safety net threshold, and the internal division weight between the personal feedback of inheritors and the public feedback of protection institutions. Initialize the circulation depth counter of the intangible cultural heritage digital assets to zero; The address routing table, the profit-sharing benchmark parameter table, and the circulation depth counter are encapsulated into the immutable state variables of the asset casting smart contract.

8. The method for confirming ownership and tracking the circulation of digital assets of intangible cultural heritage art using smart contracts according to claim 7, characterized in that, The revenue allocation for each route is calculated based on the deterministic revenue sharing routing tree, including: Extract the total transaction payment amount from the asset transfer request; Read the current value of the flow depth counter, calculate the product of the initial maximum profit sharing ratio and the retention factor raised to the power of the current value of the flow depth counter as the attenuation value, where the retention factor is the difference between the retention factor and the successive attenuation coefficient. The larger of the attenuation value and the bottom-line safety net threshold shall be taken as the actual profit sharing ratio; The total return amount is calculated based on the product of the total transaction payment amount and the actual profit-sharing ratio.

9. The method for confirming ownership and tracking the circulation of digital assets of intangible cultural heritage art using smart contracts according to claim 1, characterized in that, In S400, after intercepting the asset transfer request and before triggering the profit-sharing logic executor, the following is also included: Extract the buyer's address and seller's address that initiated the asset transfer request; The buyer's address and the seller's address are compared byte by byte with the blacklist address array pre-set in the asset casting smart contract; If either the buyer's address or the seller's address matches the blacklist address array, the asset transfer request will be forcibly intercepted, the transfer rights of the intangible cultural heritage digital asset will be frozen, and the metadata hash of the illegal transfer path will be recorded on the blockchain for evidence storage.

10. A smart contract-based system for confirming and tracking the ownership and circulation of digital assets related to intangible cultural heritage art, characterized in that: The asset ownership confirmation and transfer tracking system includes: processor; The memory stores a computer program, which, when executed by a processor, implements the smart contract method for confirming ownership and tracking the circulation of digital assets of intangible cultural heritage art as described in any one of claims 1 to 9.

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