Block chain copyright authentication method for non-perpetual culture elements

Abstract

The invention relates to the field of copyright authentication of non-perpetual culture elements, and discloses a block chain copyright authentication method for non-perpetual culture elements, which comprises the following steps: S1, performing multi-dimensional data acquisition through a distributed storage system, performing structured data acquisition and feature coding on the non-perpetual culture elements, and storing the structured data in a database; comprising the following steps: multi-modal data fusion acquisition, dynamic metadata standardization processing, and generation of unique digital identifiers and cultural feature codes; and S2, constructing a hybrid block chain architecture, constructing a hierarchical network architecture comprising a permission chain layer and an open chain layer, and realizing heterogeneous chain data synchronization through a cross-chain interoperation protocol. Through multi-modal data acquisition and dynamic metadata structured processing, the spatial form, the process time sequence and the oral context of non-residual elements are completely recorded, digital twin bodies with microscopic details and semantic association are formed, the problem of fragmentation of traditional archiving is solved, and a unique block chain identifier and two-way index mapping are combined, so that the method is simple and convenient to implement. And the originality of the cultural heritage in virtual restoration and cross-domain propagation is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of copyright authentication of intangible cultural heritage elements, and specifically to a blockchain copyright authentication method for intangible cultural heritage elements. Background Art

[0002] With the accelerated digitization of intangible cultural heritage and the rapid development of emerging industries such as the metaverse and digital collections, the protection of intangible cultural heritage must adapt to the needs of multimodal data integration, dynamic rights management, and global collaboration. The maturity of blockchain technology, artificial intelligence, and multi-source sensing devices provides the technical foundation for building a trusted digital cultural ecosystem. By integrating multi-dimensional data collection, smart contracts, and a hierarchical governance architecture, this approach supports the copyright authentication, value transfer, and innovative transformation of intangible cultural heritage elements in digital creation, virtual performances, and cross-border cultural exchanges, promoting the sustainable revitalization and inheritance of cultural heritage in the digital age.

[0003] Existing technologies lack an authenticity verification mechanism for multimodal data sources in the protection of intangible cultural heritage copyrights, making it difficult to effectively prevent data tampering or false information from being uploaded to the chain during the collection phase, resulting in doubts about the credibility of the underlying data stored in blockchain evidence. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a blockchain copyright authentication method for intangible cultural heritage elements, which solves the problem that the existing technology lacks an authenticity verification mechanism for multimodal data sources in intangible cultural heritage copyright protection, making it difficult to effectively prevent data tampering or false information from being uploaded to the chain during the collection stage, resulting in doubts about the credibility of the underlying data stored on the blockchain.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: The blockchain copyright authentication method for intangible cultural heritage elements includes the following steps: S1. Multi-dimensional data collection through a distributed storage system, structured data collection and feature coding of intangible cultural heritage elements, including: multi-modal data fusion collection, dynamic metadata standardization processing, generation of unique digital identifiers and cultural feature coding; S2. Build a hybrid blockchain architecture, constructing a layered network architecture that includes a permissioned chain layer and an open chain layer, and achieve heterogeneous chain data synchronization through a cross-chain interoperability protocol; S3. A verifiable ownership confirmation mechanism based on smart contracts completes copyright registration for a single entity or multiple entities, supporting trusted evidence storage and dynamic rights allocation on the inheritance relationship chain; S4. Implement full-lifecycle copyright management of intangible cultural heritage elements based on smart contracts, and use smart contracts to implement multimodal authorization strategies, fragmented digital asset circulation, and automatic allocation of cultural protection funds. S5. AI-assisted infringement tracking uses deep learning models to extract infringement features and generate a blockchain evidence chain that complies with judicial recognition.

[0006] Preferably, the multimodal data collection in step S1 includes: High-resolution image acquisition equipment, three-dimensional modeling equipment and environmental sensing equipment are used to collect multi-source data on the physical form, production process and oral inheritance of intangible cultural heritage elements, generating image data containing spatial coordinate information, three-dimensional topological models, process flow time series data and multilingual oral records.

[0007] Preferably, the dynamic metadata structuring processing in step S1 includes: Standard metadata fields include hash values of cultural elements, digital identity credentials of inheritors, geographic coordinate identifiers, and cultural classification codes, and comply with ISO / IEC11179 metadata registration specifications; The extended metadata field uses natural language processing to perform dialect semantic entity annotation on the audio transcription text of the intangible cultural heritage inheritors’ oral narration. The Skip-gram model is used to generate dialect word vectors and construct a dynamic knowledge graph containing the relationship between inheritors and technical processes. The objective function is: ; in, For text datasets, For the vocabulary, Indicates the central word, that is, the dialect vocabulary in the intangible cultural heritage oral text currently being processed, Represents context words, that is, related words that appear around the central word in the text.

[0008] Preferably, the layered blockchain architecture in step S2 includes: The permission chain layer is composed of cultural regulatory departments, certification agencies, and inheritor nodes authenticated by digital certificates. It adopts the Byzantine fault-tolerant consensus mechanism, which satisfies N≥3f+1, and is used for the trusted storage of core copyright data. Where N is the total number of cultural regulatory departments, certification agencies, and inheritor nodes, and f is the maximum number of abnormal nodes in the intangible cultural heritage collaboration network; The open chain layer is deployed on a public blockchain network that supports smart contracts and is used for digital asset issuance and cross-platform transactions.

[0009] Preferably, the smart contract-driven ownership confirmation in step S3 includes: When a single entity confirms ownership, the digital identity of the inheritor is verified through zero-knowledge proof, and after combining multi-node cross-verification of the creation process data, a verifiable non-fungible token ownership certificate containing smart contract code is generated; When multiple entities confirm ownership, a threshold signature mechanism is used to generate a combined equity certificate, and its secret sharing polynomial is: ; in, is the secret value, is the minimum number of collaborators, is a large prime modulus.

[0010] Preferably, the dynamic inheritance rights update in step S3 includes: Based on the inheritance network graph generated in step S1, an on-chain inheritance relationship graph is constructed, and the creation association is derived through multi-party signature authentication of trusted nodes, and the equity distribution function is triggered based on the preset rules of the smart contract.

[0011] Preferably, the multi-level authorization template in step S4 includes: The standardized authorization agreement template includes permanent transfer, time-limited license and revenue sharing models. The revenue sharing model supports triggering account splitting conditions based on external data from oracles and realizes automatic settlement through multi-signature wallets.

[0012] Preferably, the equity fragmentation and cultural feedback mechanism in step S4 includes: Split the intangible cultural heritage digital assets into control rights certificates and usage rights certificates. The control rights certificates serve as proof of asset ownership and are bound to the voting rights of decentralized autonomous organization governance. The usage rights certificates support liquidity mining and staking derivatives on cross-chain asset exchanges. Configurable profit distribution rules are embedded in the smart contract. Each transaction automatically withdraws a preset proportion of the cultural protection fund to the multi-signature supervision account. The fund usage record is verifiably recorded on the chain through the Merkle tree structure. The parent node hash calculation is: ; in, is the hash value of the left and right child nodes, Indicates data splicing.

[0013] Preferably, the convolutional neural network operation formula for infringement feature extraction in step S5 is: ; in, Represents the input feature map of intangible cultural heritage multimodal data, is the convolution kernel weight parameter, is a non-linear activation function.

[0014] Preferably, the infringement monitoring and judicial evidence storage in step S5 include: Using convolutional neural networks to extract pattern feature vectors of intangible cultural heritage derivative works , the action sequences in the skill demonstration video are coded by spatiotemporal action coding algorithm Encoding is performed to generate a judicial evidence package containing blockchain timestamps, data hash values, and information that complies with the Supreme People's Court's electronic evidence storage regulations. The infringement similarity determination formula is: ; in: is the feature vector of the original work and the derivative work, is the infringement action weight coefficient, For infringing action sequences.

[0015] This invention provides a blockchain copyright authentication method for intangible cultural heritage elements. It has the following beneficial effects: 1. This invention uses multimodal data collection and dynamic metadata structured processing to fully record the spatial form, process sequence and oral context of intangible cultural heritage elements, forming a digital twin with both microscopic details and semantic associations, solving the problem of traditional archiving fragmentation. Combined with blockchain unique identification and two-way index mapping, it ensures the authenticity of cultural heritage in virtual restoration and cross-domain dissemination, and provides a verifiable and interactive permanent digital archive for endangered skills.

[0016] 2. The permission chain layer of the present invention guarantees the judicial validity of core copyright data through authoritative node consensus, and the open chain layer uses smart contracts to achieve compliant circulation of digital assets. This architecture not only maintains the control of cultural elements by the inheritance group, but also activates the derivatives market through cross-chain transactions, promotes the transformation of intangible cultural heritage resources from closed protection to an open value ecosystem, and promotes a dynamic balance between cultural protection and commercial innovation.

[0017] 3. This invention is based on the gradient authorization rules automatically executed by smart contracts, the single-subject zero-knowledge verification and the multi-subject threshold signature mechanism take into account both privacy and collaboration needs, and the dynamic equity distribution function binds the inheritance network topology relationship to respond to changes in creation associations in real time, solving the problem of inefficient traditional equity division and providing a trusted technical framework for the intangible cultural heritage inheritance model with diversified participation and continuous innovation.

[0018] 4. The present invention realizes infringement monitoring through multimodal feature vector comparison and dynamic skill behavior coding algorithm, automatically triggers the blockchain evidence storage process, generates an evidence chain containing judicial identification, and transforms technical identification capabilities into legally effective evidence, significantly reducing the cost of intangible cultural heritage rights protection and the threshold for judicial recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] Please see the attached Figure 1 , an embodiment of the present invention provides a blockchain copyright authentication method for intangible cultural heritage elements, comprising the following steps: S1. Multi-dimensional data collection through a distributed storage system, structured data collection and feature coding of intangible cultural heritage elements, including: multi-modal data fusion collection, dynamic metadata standardization processing, generation of unique digital identifiers and cultural feature coding; S2. Build a hybrid blockchain architecture, constructing a layered network architecture that includes a permissioned chain layer and an open chain layer, and achieve heterogeneous chain data synchronization through a cross-chain interoperability protocol; S3. A verifiable ownership confirmation mechanism based on smart contracts completes copyright registration for a single entity or multiple entities, supporting trusted evidence storage and dynamic rights allocation on the inheritance relationship chain; S4. Implement full-lifecycle copyright management of intangible cultural heritage elements based on smart contracts, and use smart contracts to implement multimodal authorization strategies, fragmented digital asset circulation, and automatic allocation of cultural protection funds. S5. AI-assisted infringement tracking uses deep learning models to extract infringement features and generate a blockchain evidence chain that complies with judicial recognition.

[0022] Multimodal data collection in step S1 includes: High-resolution image acquisition equipment, three-dimensional modeling equipment and environmental sensing equipment are used to collect multi-source data on the physical form, production process and oral inheritance of intangible cultural heritage elements, generating image data containing spatial coordinate information, three-dimensional topological models, process flow time series data and multilingual oral records.

[0023] Specifically, through high-resolution imaging equipment, three-dimensional modeling equipment and environmental sensing equipment, multi-source data collection is carried out on the physical form, production process and oral inheritance of intangible cultural heritage elements, and finally image data containing spatial coordinate information, three-dimensional topological models, process flow time series data and multilingual oral records are generated, which completely cover the physical characteristics, production process and oral inheritance content of intangible cultural heritage elements.

[0024] The dynamic metadata structuring process in step S1 includes: Standard metadata fields include hash values of cultural elements, digital identity credentials of inheritors, geographic coordinate identifiers, and cultural classification codes, and comply with ISO / IEC11179 metadata registration specifications; The extended metadata field uses natural language processing to perform dialect semantic entity annotation on the audio transcription text of the intangible cultural heritage inheritors’ oral narration. The Skip-gram model is used to generate dialect word vectors and construct a dynamic knowledge graph containing the relationship between inheritors and technical processes. The objective function is: ; in, For text datasets, For the vocabulary, Indicates the central word, that is, the dialect vocabulary in the intangible cultural heritage oral text currently being processed, Represents context words, that is, related words that appear around the central word in the text.

[0025] Specifically, by constructing standard metadata fields, hash value identification of intangible cultural heritage elements, digital identity binding of inheritors and geographic coordinate calibration are realized to ensure that the data complies with the ISO / IEC11179 standard and form a standardized management basis; at the same time, natural language processing technology is used to annotate dialect semantic entities of the oral texts of inheritors, generate dialect word vectors based on the Skip-gram model, and construct a dynamic knowledge graph containing the relationship between inheritors and technical processes. Through the objective function, the semantic association between the central dialect vocabulary and the context vocabulary is mined to enhance the structured representation ability of intangible cultural heritage knowledge.

[0026] The layered blockchain architecture in step S2 includes: The permission chain layer is composed of cultural regulatory departments, certification agencies, and inheritor nodes authenticated by digital certificates. It adopts the Byzantine fault-tolerant consensus mechanism, which satisfies N≥3f+1, and is used for the trusted storage of core copyright data. Where N is the total number of cultural regulatory departments, certification agencies, and inheritor nodes, and f is the maximum number of abnormal nodes in the intangible cultural heritage collaboration network; The open chain layer is deployed on a public blockchain network that supports smart contracts and is used for digital asset issuance and cross-platform transactions.

[0027] Specifically, a layered blockchain architecture achieves the dual functions of intangible cultural heritage copyright management: the permissioned chain layer relies on certified regulatory agencies and inheritor nodes, and adopts a Byzantine fault-tolerant consensus mechanism to ensure the reliable storage of core copyright data even in the presence of up to f abnormal nodes. The open chain layer leverages the smart contract support of the public blockchain to achieve standardized issuance and multi-platform circulation and trading of digital assets. This layered architecture balances the authority of cultural regulation with the openness of market circulation, ensuring that the storage of core data meets legal requirements while improving the market circulation efficiency of digital assets.

[0028] The smart contract-driven ownership confirmation in step S3 includes: When a single entity confirms ownership, the digital identity of the inheritor is verified through zero-knowledge proof, and after combining multi-node cross-verification of the creation process data, a verifiable non-fungible token ownership certificate containing smart contract code is generated; When multiple entities confirm ownership, a threshold signature mechanism is used to generate a combined equity certificate, and its secret sharing polynomial is: ; in, is the secret value, is the minimum number of collaborators, is a large prime modulus.

[0029] Specifically, through zero-knowledge proof and multi-node cross-verification mechanism, the dual trusted binding of the digital identity of the intangible cultural heritage inheritor and the creative process data is achieved, and a non-homogeneous token confirmation certificate containing smart contract code is generated to ensure the unforgeability of the creation rights of a single subject; in the multi-subject collaboration scenario, a threshold signature mechanism based on secret sharing polynomials is adopted, requiring at least The joint signature of the collaborating parties can generate a combined equity certificate to prevent the risk of single-point key leakage and ensure the security of title confirmation in the multi-heir collaboration scenario.

[0030] The dynamic inheritance rights update in step S3 includes: Based on the inheritance network graph generated in step S1, an on-chain inheritance relationship graph is constructed, and the creation association is derived through multi-party signature authentication of trusted nodes, and the equity distribution function is triggered based on the preset rules of the smart contract.

[0031] Specifically, by constructing an on-chain relationship graph based on the inheritance network graph, using multi-party signatures from trusted nodes to verify the relevance of derivative creations to the original intangible cultural heritage project, and automatically triggering the equity distribution function based on the preset rules of the smart contract, dynamic on-chain updates of inheritance rights are achieved. The effect is to ensure the credibility of the creative relationship through a multi-party authentication mechanism, while relying on the automated execution capabilities of smart contracts to complete equity distribution according to preset logic, ensuring that the legitimate rights and interests of intangible cultural heritage inheritors in derivative creations are reasonably and objectively distributed.

[0032] The multi-level authorization template in step S4 includes: The standardized authorization agreement template includes permanent transfer, time-limited license and revenue sharing models. The revenue sharing model supports triggering account splitting conditions based on external data from oracles and realizes automatic settlement through multi-signature wallets.

[0033] Specifically, a standardized management framework for intangible cultural heritage digital assets is provided through multi-tiered authorization templates (including perpetual transfers, time-limited licenses, and revenue-sharing models). The revenue-sharing model relies on off-chain oracles to obtain external data to trigger account-sharing conditions, and automated settlement is achieved through a multi-signature wallet, ensuring transparent enforcement of account-sharing rules. This will enhance the flexibility and efficiency of the commercial development of intangible cultural heritage copyrights, while ensuring the security and timeliness of revenue distribution for inheritors and related stakeholders through a multi-party verified automated settlement mechanism.

[0034] The equity fragmentation and cultural feedback mechanism in step S4 includes: Split the intangible cultural heritage digital assets into control rights certificates and usage rights certificates. The control rights certificates serve as proof of asset ownership and are bound to the voting rights of decentralized autonomous organization governance. The usage rights certificates support liquidity mining and staking derivatives on cross-chain asset exchanges. Configurable profit distribution rules are embedded in the smart contract. Each transaction automatically withdraws a preset proportion of the cultural protection fund to the multi-signature supervision account. The fund usage record is verifiably recorded on the chain through the Merkle tree structure. The parent node hash calculation is: ; in, is the hash value of the left and right child nodes, Indicates data splicing.

[0035] The convolutional neural network operation formula for infringement feature extraction in step S5 is: ; in, Represents the input feature map of intangible cultural heritage multimodal data, is the convolution kernel weight parameter, is a non-linear activation function.

[0036] Specifically, by splitting the intangible cultural heritage digital assets into control certificates and usage certificates, the refined division and management of asset rights can be achieved; the smart contract automatically extracts transaction income to the cultural protection fund account according to the preset ratio, and realizes the on-chain verifiable evidence of fund usage records through the Merkle tree structure. The infringement detection part uses a convolutional neural network, which uses weight parameters to and the feature map of intangible cultural heritage multimodal data Convolution operations are performed to extract infringement feature vectors, supporting the automatic identification and verification of infringement behaviors.

[0037] Infringement monitoring and judicial evidence storage in step S5 include: Using convolutional neural networks to extract pattern feature vectors of intangible cultural heritage derivative works , the action sequences in the skill demonstration video are coded by spatiotemporal action coding algorithm Encoding is performed to generate a judicial evidence package containing blockchain timestamps, data hash values, and information that complies with the Supreme People's Court's electronic evidence storage regulations. The infringement similarity determination formula is: ; in: is the feature vector of the original work and the derivative work, is the infringement action weight coefficient, For infringing action sequences.

[0038] Specifically, the convolutional neural network is used to extract the pattern feature vectors of the intangible cultural heritage derivative works. , and uses spatiotemporal action coding algorithms to encode the action sequences in the skill demonstration videos Encoding is performed to generate a judicial evidence package containing blockchain timestamps, data hash values and electronic evidence storage standards that comply with the Supreme People's Court. The infringement similarity determination formula is used as the feature vectors of the original and derivative works to achieve automated monitoring and judicial storage of infringements of intangible cultural heritage derivative works, ensuring the objectivity of infringement determinations and the judicial acceptance of evidence data.

[0039] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The blockchain copyright authentication method for intangible cultural heritage elements is characterized by: The following steps are involved: S1. Multi-dimensional data collection through a distributed storage system, structured data collection and feature coding of intangible cultural heritage elements, including: multi-modal data fusion collection, dynamic metadata standardization processing, generation of unique digital identifiers and cultural feature coding; S2. Build a hybrid blockchain architecture, constructing a layered network architecture that includes a permissioned chain layer and an open chain layer, and achieve heterogeneous chain data synchronization through a cross-chain interoperability protocol; S3. A verifiable ownership confirmation mechanism based on smart contracts completes copyright registration for a single entity or multiple entities, supporting trusted evidence storage and dynamic rights allocation on the inheritance relationship chain; S4. Implement full-lifecycle copyright management of intangible cultural heritage elements based on smart contracts, and use smart contracts to implement multimodal authorization strategies, fragmented digital asset circulation, and automatic allocation of cultural protection funds. S5. AI-assisted infringement tracking uses deep learning models to extract infringement features and generate a blockchain evidence chain that complies with judicial recognition.

2. The blockchain copyright authentication method for intangible cultural heritage elements according to claim 1 is characterized in that: The multimodal data acquisition in step S1 includes: High-resolution image acquisition equipment, three-dimensional modeling equipment and environmental sensing equipment are used to collect multi-source data on the physical form, production process and oral inheritance of intangible cultural heritage elements, generating image data containing spatial coordinate information, three-dimensional topological models, process flow time series data and multilingual oral records.

3. The blockchain copyright authentication method for intangible cultural heritage elements according to claim 1 is characterized in that: The dynamic metadata structuring process in step S1 includes: Standard metadata fields include hash values of cultural elements, digital identity credentials of inheritors, geographic coordinate identifiers, and cultural classification codes, and comply with ISO / IEC11179 metadata registration specifications; The extended metadata field uses natural language processing to perform dialect semantic entity annotation on the audio transcription text of the intangible cultural heritage inheritors’ oral narration. The Skip-gram model is used to generate dialect word vectors and construct a dynamic knowledge graph containing the relationship between inheritors and technical processes. The objective function is: ； in, For text datasets, For the vocabulary, Indicates the central word, that is, the dialect vocabulary in the intangible cultural heritage oral text currently being processed, Represents context words, that is, related words that appear around the central word in the text.

4. The blockchain copyright authentication method for intangible cultural heritage elements according to claim 1 is characterized in that: The layered blockchain architecture in step S2 includes: The permission chain layer is composed of cultural regulatory departments, certification agencies, and inheritor nodes authenticated by digital certificates. It adopts the Byzantine fault-tolerant consensus mechanism, which satisfies N≥3f+1, and is used for the trusted storage of core copyright data. Where N is the total number of cultural regulatory departments, certification agencies, and inheritor nodes, and f is the maximum number of abnormal nodes in the intangible cultural heritage collaboration network; The open chain layer is deployed on a public blockchain network that supports smart contracts and is used for digital asset issuance and cross-platform transactions.

5. The blockchain copyright authentication method for intangible cultural heritage elements according to claim 1 is characterized in that: The smart contract-driven ownership confirmation in step S3 includes: When a single entity confirms ownership, the digital identity of the inheritor is verified through zero-knowledge proof, and after combining multi-node cross-verification of the creation process data, a verifiable non-fungible token ownership certificate containing smart contract code is generated; When multiple entities confirm ownership, a threshold signature mechanism is used to generate a combined equity certificate, and its secret sharing polynomial is: ； in, is the secret value, is the minimum number of collaborators, is a large prime modulus.

6. The blockchain copyright authentication method for intangible cultural heritage elements according to claim 1 is characterized in that: The dynamic inheritance rights update in step S3 includes: Based on the inheritance network graph generated in step S1, an on-chain inheritance relationship graph is constructed, and the creation association is derived through multi-party signature authentication of trusted nodes, and the equity distribution function is triggered based on the preset rules of the smart contract.

7. The blockchain copyright authentication method for intangible cultural heritage elements according to claim 1 is characterized in that: The multi-level authorization template in step S4 includes: The standardized authorization agreement template includes permanent transfer, time-limited license and revenue sharing models. The revenue sharing model supports triggering account splitting conditions based on external data from oracles and realizes automatic settlement through multi-signature wallets.

8. The blockchain copyright authentication method for intangible cultural heritage elements according to claim 1 is characterized in that: The equity fragmentation and cultural feedback mechanism in step S4 includes: Split the intangible cultural heritage digital assets into control rights certificates and usage rights certificates. The control rights certificates serve as proof of asset ownership and are bound to the voting rights of decentralized autonomous organization governance. The usage rights certificates support liquidity mining and staking derivatives on cross-chain asset exchanges. Configurable profit distribution rules are embedded in the smart contract. Each transaction automatically withdraws a preset proportion of the cultural protection fund to the multi-signature supervision account. The fund usage record is verifiably recorded on the chain through the Merkle tree structure. The parent node hash calculation is: ； in, is the hash value of the left and right child nodes, Indicates data splicing.

9. The blockchain copyright authentication method for intangible cultural heritage elements according to claim 1 is characterized in that: The convolutional neural network operation formula for infringement feature extraction in step S5 is: ； in, Represents the input feature map of intangible cultural heritage multimodal data, is the convolution kernel weight parameter, is a non-linear activation function.

10. The blockchain copyright authentication method for intangible cultural heritage elements according to claim 1 is characterized in that: Infringement monitoring and judicial evidence storage in step S5 include: Using convolutional neural networks to extract pattern feature vectors of intangible cultural heritage derivative works , the action sequences in the skill demonstration video are coded by spatiotemporal action coding algorithm Encoding is performed to generate a judicial evidence package containing blockchain timestamps, data hash values, and information that complies with the Supreme People's Court's electronic evidence storage regulations. The infringement similarity determination formula is: ； in: is the feature vector of the original work and the derivative work, is the infringement action weight coefficient, For infringing action sequences.

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