A blockchain-based digital copyright evidence storage method, electronic equipment and readable storage medium

Abstract

The application discloses a blockchain-based digital copyright evidence storage method, an electronic device and a readable storage medium. The digital copyright evidence storage method comprises the following steps: collecting original content and environment data of a digital work, generating a content hash value and environment data by a hash algorithm, combining the content hash value and the environment data into a to-be-signed meta-information package, generating a data signature by an elliptic curve digital signature algorithm, forming a signed copyright digest data package, and obtaining a structured digest hash value by hashing again; analyzing the environment data of the digital work to obtain a data confidence coefficient, constructing a Merkle tree in combination with the structured hash value, obtaining a root node value and path index information; storing the root node and key meta-information in a blockchain main chain, generating a blockchain auxiliary chain mapping identifier through a work ID and a type hash, and storing a structured copyright data signature package, path index and confidence coefficient in an off-chain database.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of digital copyright, and particularly relates to a digital copyright evidence storage method based on a block chain, an electronic device and a readable storage medium. BACKGROUND

[0002] With the vigorous development of the digital content industry, the creation and dissemination of various digital works such as images, audio, codes and the like have significantly increased in frequency, and the problem of copyright infringement has become increasingly serious. The copyright registration mode has problems such as complicated process and low efficiency, and although the evidence storage technology based on the block chain has great potential in the field of digital copyright protection due to its characteristics such as decentralization and non-tamperability, there is still room for optimization in terms of data generation, storage architecture and collaborative verification mechanism.

[0003] In the related art, in the digital work evidence storage process, a single on-chain storage mode is adopted, and a large amount of detailed copyright data is directly written into the block chain, resulting in low storage efficiency and difficulty in meeting the efficient retrieval and rapid right confirmation needs of massive digital work evidence storage SUMMARY

[0004] The application provides a digital copyright evidence storage method based on a block chain, an electronic device and a readable storage medium, aiming to at least improve the efficient retrieval of digital work evidence storage and meet the rapid right confirmation needs.

[0005] In the first aspect of the application, a digital copyright evidence storage method based on a block chain is provided, comprising the following steps:

[0006] Step S1: collecting original digital content and original content environment data of a digital work, performing hash calculation on the original digital content through a hash algorithm to obtain an original digital content hash value;

[0007] Step S2: combining the obtained original digital content hash value and the original content environment data to obtain a meta-information package to be signed; based on the obtained meta-information package to be signed, generating a private key through a random number generation method, and based on the private key, performing digital signature on the meta-information package to be signed through an elliptic curve digital signature algorithm to obtain a data signature;

[0008] Step S3: combining the obtained data signature and the original content environment data to obtain a signed copyright summary data package; and performing hash calculation on the obtained signed copyright summary data package through a hash algorithm to obtain a structured summary hash value;

[0009] Step S4: based on the structured abstract hash value and the data confidence coefficient of the original content environment data, a Merkle tree is constructed by means of binary hash combination, and a constructed Merkle tree is obtained; and based on the constructed Merkle tree, a root node value and path index information of the Merkle tree are obtained;

[0010] Step S5: based on a blockchain hierarchical chain writing strategy, a main chain tuple structure is generated by combining the root node value of the Merkle tree and key meta information, the key meta information being basic information of the blockchain; and a secondary chain mapping identifier of the original work content is obtained by performing hash calculation on the work ID and the work type in the original content environment data; based on the obtained secondary chain mapping identifier, the signed copyright abstract data packet and the corresponding path index information and structured abstract hash value of the Merkle tree, and the data confidence coefficient are combined and stored off-chain to obtain an off-chain tuple structure in the database;

[0011] Step S6: the data confidence coefficient of the digital work to be verified is calculated, and a verification evaluation index is calculated by combining the data confidence coefficient of the digital work to be verified and the corresponding main chain tuple structure and off-chain tuple structure;

[0012] Step S13: comparing the verification evaluation index with a preset threshold value to realize the notarization query of the digital work to be verified.

[0013] In some embodiments, the step S1 includes performing hash calculation on the original digital content using a hash algorithm SHA-256 to generate an original digital content hash value: , wherein, is the original digital content hash value, is a hash function, and Content is the original digital content.

[0014] In some embodiments, the step S3 includes:

[0015] combining the original digital content hash value with a generation timestamp , a device unique identifier DevID, an author digital identity identifier AID, a work ID, and a work type to obtain a meta information packet Meta to be signed, Meta={ , , DevID, AID, ID, };

[0016] A random integer conforming to the elliptic curve order range is generated as a private key d by a secure random number generation method through a trusted execution environment (TEE) built-in true random number generator (TRNG), wherein, 1 d < n, n is the base point order of the elliptic curve;

[0017] Based on the generated private key, the elliptic curve digital signature algorithm is used to sign the to-be-signed meta information package, and the to-be-signed meta information package is hashed:

[0018] ,

[0019] Wherein, is the hash value of the to-be-signed meta information package, is a hash function, is the to-be-signed meta information package; a temporary random number k is generated, and the random number k satisfies 1 k < n, and is relatively prime to n;

[0020] The point P on the elliptic curve is calculated by the temporary random number k and the base point G of the ellipse:

[0021] ,

[0022] Wherein, P is a point on the elliptic curve, which is obtained by point multiplication operation of the base point G of the elliptic curve and the temporary random number k;

[0023] The x coordinate of the point P on the elliptic curve is taken, and the signature component is calculated by the x coordinate and the hash value of the to-be-signed meta information package combined with the private key: , wherein s represents the signature component, k represents the temporary random number, e is the hash value of the to-be-signed meta information package, d is the private key, r is the x coordinate of the elliptic curve point P, k is the temporary random number, n is the order of the base point of the elliptic curve, and mod is the remainder symbol;

[0024] The signature component s and the x coordinate r of the point P on the elliptic curve are combined to obtain the data signature , =(r,s), wherein r is the x coordinate of the elliptic curve point P, and s is the signature component.

[0025] The step S3 comprises:

[0026] The data signature and the original content environment data are combined to obtain the signed copyright digest data package PZ,

[0027] PZ={ , , DevID, AID, ID, , },

[0028] Wherein, is the time stamp, DevID is the unique identifier of the device, AID is the author digital identity identifier, ID is the work ID, is a work type;

[0029] The plurality of signed copyright digest data packets are combined to obtain a digest data packet set PZ_N, PZ_N={ , ,..., }, the hash value of each signed copyright digest data packet is obtained by hash calculation on the signed copyright digest data packet through a hash algorithm:

[0030] ,

[0031] wherein, is the hash value of the i-th signed copyright digest data packet, is the i-th signed copyright digest data packet in the digest data packet set, represents a hash function;

[0032] The structured digest hash values of the n signed copyright digest data packets are taken as structured digest hash values : =[ , , ,... ]。

[0033] In some embodiments, the data confidence coefficient of the original content environment data is obtained by the following steps:

[0034] The confidence of the device unique identifier DevID is calculated:

[0035] ,

[0036] wherein, is the confidence of the device unique identifier DevID, is the hardware security module existence, 1 if the device has a hardware security module, 0.6 if not, TEE is the executable environment level, and DHE is the device fingerprint hash entropy value, which is obtained by calculating the Shannon entropy of the device unique identifier collected by the TEE device;

[0037] The confidence of the author digital identity AID is calculated: ,

[0038] wherein, is the confidence of the author digital identity AID, is the author authentication level, represents the number of historical infringements of the author, represents the number of historical evidence storage of the author, represents the on-chain identity survival time of the author;

[0039] Computing the confidence of the work type : wherein, is the confidence of the work type , is the work infringement risk level of the work type, is the work size; according to the confidence of the device unique identifier DevID, the confidence of the author digital identity identifier AID, the confidence of the work type , the original content environment data data confidence coefficient is calculated: ,

[0040] wherein, TC is the data confidence coefficient, is the confidence of the device unique identifier DevID, is the confidence of the author digital identity identifier AID, is the confidence of the work type , , , is the confidence coefficient weight of the device unique identifier DevID, the author digital identity identifier AID, and the work type , is the coordination effect function, indicating the synergistic effect of the confidence of the device unique identifier DevID and the confidence of the author digital identity identifier AID.

[0041] In some embodiments, the step S4 comprises:

[0042] The structured summary hash values of the n signature copyright summary data packets are taken as the leaf node array : [ , , ... ], wherein represents the nth structured summary hash value in the leaf node array; the leaf node array is even-completed by a completion strategy:

[0043] wherein, is the structured summary hash value, represents the hash value of the nth signature copyright summary data packet in the structured summary hash value;

[0044] For each leaf node data confidence coefficient of a signature copyright summary data packet, the data confidence coefficient of its adjacent leaf node is calculated to obtain the data confidence coefficient of the parent node in the corresponding Merkle tree:

[0045] ,

[0046] wherein, is the hash value of the parent node of the i-th signed copyright digest data packet, is the data confidence coefficient of the i-th signed copyright digest data packet, is the hash value of the i-th signed copyright digest data packet, is the data confidence coefficient of the i+1-th signed copyright digest data packet, is the hash value of the i+1-th signed copyright digest data packet;

[0047] Finally, the father node Q of the bottommost leaf node signed copyright digest data packet is obtained: , , ... ];

[0048] According to the structured digest hash value and the data confidence coefficient, a Merkle tree is constructed by means of binary hash combination, and a constructed Merkle tree is obtained, and through the constructed Merkle tree, a root node value and path index information of the Merkle tree are obtained:

[0049] ,

[0050] wherein, is the root node value of the Merkle tree, is the path index information of the Merkle tree, and Merkle() is a Merkle tree construction function, is the structured digest hash value, TC_N is the data confidence coefficient sequence, and SHA_256() is a hash function.

[0051] In some embodiments, the step S5 specifically comprises:

[0052] By combining the root node value of the Merkle tree and the key meta information, a main chain meta group structure is generated: wherein, is the main chain meta group structure, is the root node value of the Merkle tree, is the notarization time of the blockchain address, is the work type, is the network identifier of the blockchain;

[0053] By performing hash calculation on the work ID and the work type in the original content environment data, an auxiliary chain mapping identifier of the original work content is obtained: ,

[0054] wherein, is the work type, an ID of a work represented by the i-th signed copyright digest data packet, an auxiliary chain mapping identifier of the i-th signed copyright digest data packet, an auxiliary chain mapping identifier of the i-th signed copyright digest data packet, a hash function;

[0055] based on the auxiliary chain mapping identifier, the signed copyright digest data packet and the path index information and the structured digest hash value of the corresponding Merkle tree are combined off-chain to obtain an off-chain tuple structure in the database , ( , , , TC, ), an auxiliary chain mapping identifier of the i-th signed copyright digest data packet, an off-chain tuple structure of the i-th signed copyright digest data packet, path index information of the signed copyright digest data packet, a structured digest hash value, the i-th signed copyright digest data packet, a data confidence coefficient of the signed copyright digest data packet.

[0056] In some embodiments, the step S6 specifically comprises:

[0057] for the digital work to be verified, a data confidence coefficient of the digital work to be verified is calculated, and by the data confidence coefficient of the digital work to be verified and the corresponding off-chain tuple structure, the Merkle tree is refitted to obtain the root node value of the digital work to be verified in the Merkle tree , for the digital work to be verified, a digital signature is performed to obtain the digital signature of the digital work to be verified ;

[0058] by combining the data confidence coefficient of the digital work to be verified and the corresponding main chain tuple structure, the off-chain tuple structure, a verification evaluation index is calculated: wherein K is the verification evaluation index of the digital work to be verified, obtaining the root node value of the digital work to be verified in the Merkle tree, the root node value of the Merkle tree of the main chain tuple structure, the digital signature of the digital work to be verified, the data signature in the structured digest data packet in the off-chain tuple structure, I() is a judgment function, and if the two parameters in I() are equal, it is 1, and if they are not equal, it is 0.

[0059] In a second aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for storing digital copyright evidence based on a blockchain when executing the program.

[0060] In a third aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program is executable on a processor to implement the above-mentioned method for storing digital copyright evidence based on a blockchain.

[0061] The present application constructs a Merkle tree according to a structured abstract hash value and a data confidence coefficient. The structured abstract hash value is used as a leaf node, and converts key information such as content hash, timestamp, and digital signature of each signed copyright abstract data packet into a unique identifier of fixed length, ensuring the tamper resistance of a single data. The data confidence coefficient gives each leaf node a quantified attribute of environmental trust, so that the tree structure not only records the content fingerprint, but also implicitly records the trustworthiness of the data generation environment. Through binary hash layer-by-layer combination to generate parent nodes until the root node, on the one hand, the huge data set is compressed into a compact root hash value, greatly reducing the storage and transmission cost, and the main chain of the blockchain can only store the root node to anchor the global data integrity. BRIEF DESCRIPTION OF DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0063] Fig. 1 A method for storing digital copyright evidence based on a blockchain according to the present application is shown in the step flowchart;

[0064] Fig. 2 A method for storing digital copyright evidence based on a blockchain according to the present application is shown in the step flowchart. DETAILED DESCRIPTION

[0065] In order to make those skilled in the art of the present application more clearly understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0066] Please refer toFigs. 1-2 The application provides a blockchain-based digital copyright evidence storage method, which comprises the following steps:

[0067] Step S1: Collect the original digital content and original content environment data of the digital work, and generate a hash value of the original digital content by hashing algorithm.

[0068] The steps of generating a signed copyright summary data packet through a trusted execution environment (TEE) are as follows: load the original digital content (such as images, audio, code, etc.) into a TEE with hardware-level security isolation, which can prevent interference and monitoring by the operating system or third-party applications; then, the TEE performs hash calculation on the original digital content through a hash algorithm SHA-256 to obtain a hash value of the original digital content , which can accurately represent the integrity of the content.

[0069] It should be noted that the original content environment data includes: a timestamp , a device unique identifier DevID, an author digital identity identifier AID, a work ID, and a work type .

[0070] The original digital content is calculated by using a hash algorithm SHA-256 to generate a hash value of the original digital content:

[0071] ,

[0072] wherein is the hash value of the original digital content, is a hash function, and Content is the original digital content.

[0073] ​It should be noted that the original digital content is hashed using the hash algorithm SHA-256 to generate the original digital content hash value. The original digital content is preprocessed by padding to meet the specific length requirement. For example, the message length of the padded original digital content must meet the condition of being a multiple of 512 bits minus 64 bits. The padding rule is: add a "1" bit at the end of the original content, then fill in several "0" bits, so that the total length after padding (including the length of the original content and the padding bits) meets the above condition, and finally append a 64-bit binary number representing the length of the original content (encoded in big-endian order). After padding is completed, the message is divided into multiple 512-bit fixed-length data blocks. Each data block is processed by message expansion to generate 64 32-bit words (called message schedule table). The first 16 words are directly obtained from the data block decomposition, and the subsequent 48 words are generated by a recursive formula that combines cyclic right shift and XOR operation to ensure that each new word is nonlinearly related to the previous word. Next, the algorithm initializes a hash value (initial hash value) containing 8 32-bit constants, which are calculated based on the decimal part of the mathematical constant square root. When processing each 512-bit data block, the hash value is updated by 64 rounds of compression function. In each round of operation, the current hash value and the working variable are operated by using logical functions (such as selection function Ch, majority function Maj, and cyclic right shift function Σ0 and Σ1), and mixed with the words in the message schedule table and the predefined constant.

[0074] After processing all data blocks, the final original digital content hash value is obtained by concatenating the 8 32-bit intermediate values generated by the last iteration to form a 256-bit binary sequence, which is usually converted into a 64-bit hexadecimal string as the final output. This process uses multiple rounds of nonlinear transformation and diffusion mechanism to ensure that even if the input content changes slightly, the generated hash value will also change significantly, thereby meeting the strict requirements for data integrity and uniqueness in digital copyright evidence.

[0075] Step S2: Combine the obtained original digital content hash value and the original content environment data to obtain a meta-information package to be signed, generate a private key by generating a random number, and based on the private key, digitally sign the meta-information package to be signed by using an elliptic curve digital signature algorithm to obtain a data signature.

[0076] The TEE binds the original digital content hash value with the original content environment data to form a meta-information package to be signed Meta containing content features, time, device, and ownership subject, and the original digital content hash value is bound with the generation timestamp , device unique identifier DevID, author digital identity identifier AID, work ID, work type The combination is performed to obtain the meta-information package to be signed Meta, Meta={ , , DevID, AID, ID, }.

[0077] The hardware-level security guarantee of the TEE ensures that the hash generation, meta-information binding, and signature process are completed in a closed environment, avoiding tampering or forgery of the content, and laying a foundation for data credibility from the source; the uniqueness of the hash value can accurately identify the integrity of the content, and any slight modification will cause the hash value to change, providing a reliable basis for subsequent verification; the binding of the timestamp, device ID, and author ID clearly records the key elements (time, subject, environment) of the copyright generation, forming a traceable proof of ownership; the digital signature ensures the authenticity of the data package source through a cryptography mechanism, preventing repudiation. Overall, this step converts digital content into electronic evidence with legal credibility through technical means, providing a core trust anchor for subsequent off-chain notarization, on-chain authentication, and quick right confirmation in copyright disputes, solving the key problem of "untrusted data generation process", and serving as the security starting point of the entire digital copyright notarization system. The combination of the meta-information package to be signed: first, calculate the hash value (such as SHA-256) of the original digital content (such as a document or data file), which uniquely represents the characteristics of the original content; at the same time, collect the environmental data (such as timestamp, device identification, user ID, and other context information) of the original content. Encapsulate the hash value and environmental data into a "meta-information package to be signed" in a specific format (such as JSON or ASN.1), ensuring that the signature object contains content characteristics and generation scenarios.

[0078] Generate private key: through a secure random number generation method, the built-in true random number generator TRNG in the trusted execution environment TEE generates a random integer that meets the elliptic curve order range as the private key d, i.e. 1 d < n, n is the base point order of the elliptic curve, ensuring the randomness and unpredictability of the private key.

[0079] Perform ECDSA signature: based on the generated private key, use the elliptic curve digital signature algorithm to sign the meta-information package to be signed. The specific process is as follows:

[0080] Hash calculation on the meta-information package to be signed: , where is the hash value of the meta-information package to be signed, is the hash function, is the meta-information package to be signed.

[0081] Generate a temporary random number k, which satisfies 1 k < n and coprime with n, a point P on the elliptic curve is calculated by a temporary random number k and a base point G of the elliptic curve: , wherein P is a point on an elliptic curve, obtained by point multiplication operation of a base point G of the elliptic curve and a temporary random number k.

[0082] The x-coordinate of the point P on the elliptic curve is taken, and a signature component is calculated by combining the x-coordinate and the hash value of the meta-information package to be signed with a private key. , wherein s represents a signature component, k represents a temporary random number, e is a hash value of a meta-information package to be signed, d is a private key, r is an x-coordinate of an elliptic curve point P, k is a temporary random number, n is an order of an elliptic curve base point, and mod is a remainder symbol.

[0083] The signature component s and the x-coordinate r of the point P on the elliptic curve are combined to obtain a data signature , = (r, s), wherein r is an x-coordinate of an elliptic curve point P, and s is a signature component.

[0084] It should be noted that the base point of the elliptic curve is a selected specific point on the elliptic curve, which is one of the core elements of building a cryptographic system. The elliptic curve is defined by an equation of the form = + ax + b (the specific parameters need to satisfy the non-singular condition of the curve), and the base point G is a valid point on the curve that has been verified (i.e. the coordinates satisfy the curve equation). After selecting the base point G, a sub-group consisting of all integer multiples of the base point G is generated by point addition operation on the elliptic curve, and this sub-group is a cyclic group, which means that all points can be represented as kG (k is an integer).

[0085] It should be noted that the order of the base point indicates that the base point G itself will be added n times to obtain the infinity point of the elliptic curve (i.e. the additive identity), and n is the smallest positive integer that satisfies this condition. The "order" here describes the size of the cyclic subgroup generated by the base point - the subgroup contains n different points. In cryptography, the order of the base point is crucial: it determines the range of values for the private key (the private key d must be an integer satisfying 1 d < n), ensures that the operation corresponding to the private key is always performed within the cyclic subgroup, thereby ensuring the security of the cryptographic system.

[0086] Step S3: Combine the data signature and the original content environment data to obtain a signed copyright digest data package, and perform hash calculation on the signed copyright digest data package by a hash algorithm to obtain a structured digest hash value.

[0087] The TEE internal private key The meta information package Meta to be signed is digitally signed, and the signature and the meta information are encapsulated as a signed copyright digest data package.

[0088] The data signature and the original content environment data are combined to obtain a signed copyright digest data package PZ, PZ={ , , DevID, AID, ID, , }, wherein, is a timestamp, DevID is a device unique identifier, AID is an author digital identity identifier, ID is a work ID, is a work type.

[0089] A plurality of signed copyright digest data packages are combined to obtain a digest data package set PZ_N, PZ_N={ , ,..., }, a hash algorithm is used to calculate a hash value of each signed copyright digest data package: , wherein, is a hash value of the i-th signed copyright digest data package, is the i-th signed copyright digest data package in the digest data package set, denotes a hash function.

[0090] The structured digest hash values of the n signed copyright digest data packages are taken as structured digest hash values : =[ , , ... ].

[0091] It should be noted that the data signature is combined with the original content environment data (such as content metadata, timestamp, user identification, device information, etc.) to form a signed copyright summary data package containing complete copyright related information, and then the signed copyright summary data package is calculated by a hash algorithm SHA-256 to generate a unique, fixed-length structured summary hash value. The core significance of this step is: on the one hand, by integrating the dispersed copyright related information (data signature and original content environment data) into a structured data package, the standardization of copyright information is realized, which is convenient for subsequent storage, verification and management; on the other hand, by using the one-wayness and collision resistance of the hash algorithm, the complex signed copyright summary data package is converted into a simple hash value, which not only ensures the integrity of the content of the signed copyright summary data package, but also provides a basic unit for subsequent operations such as building a Merkle tree and a block chain evidence, each hash value can be used as a leaf node, efficiently supporting the rapid verification and chain association of multi-level data structure, so as to realize the reliable recording, efficient tracing and tamper-proofing of content copyright information in the copyright protection scene.

[0092] Step S4: Based on the structured summary hash value and the data confidence coefficient of the original content environment data, a Merkle tree is constructed by means of binary hash combination, and a constructed Merkle tree is obtained, and through the constructed Merkle tree, a root node value and path index information of the Merkle tree are obtained.

[0093] Among them, the data confidence coefficient of the original content environment data is obtained by the following steps:

[0094] The core value of calculating the Merkel tree leaf node confidence coefficient through the original content environment data is to convert the digital work's notarization environment into a quantifiable trust index, and to build a core bridge from data generation to trusted decision-making intelligence. The standardized aggregation of multi-dimensional evidence is achieved. The original content environment data (timestamp, device ID, author identity, etc.) is collected by TEE hardware level trust, carrying the key context information of the work generation. Through the confidence coefficient calculation, these heterogeneous evidence is converted into a unified numerical index (0-1 interval), so that the abstract concepts such as the authority of time, the security of device, and the reputation of author can be efficiently processed by the algorithm, solving the problem of traditional notarization evidence fragmentation. The confidence coefficient introduces the "continuous trust measurement" mechanism for the Merkel tree verification system, rather than the traditional "false or true" binary judgment. For example, in the judicial notarization scene, strong trusted evidence can be quickly confirmed by a high confidence coefficient (such as ≥0.95), while in the ordinary right protection scene, the threshold can be adjusted (such as ≥0.8) to balance efficiency and security, and to adapt to the differentiated needs of trust in different businesses. The traceability and attack resistance of notarization data are strengthened. The calculation logic of the confidence coefficient is closely related to the TEE trusted execution environment and the block chain hierarchical storage architecture defined in this paper. The score of each dimension corresponds to a specific technical guarantee (such as TEE ensuring that the timestamp is not tamperable, and on-chain identity authentication ensuring that the author's reputation is traceable). This design makes the confidence coefficient a "trust link" connecting technology implementation and business verification. Any single dimension evidence flaw will cause the coefficient to drop, thereby significantly increasing the difficulty of forgery and forming a security reinforcement of the whole chain of data generation, storage, and verification.

[0095] The original content environment data includes: device unique identifier DevID, author digital identity identifier AID, work type The confidence of the device unique identifier DevID is calculated as: wherein, is the confidence of the device unique identifier DevID, is the hardware security module existence, the device has a hardware security module, then it is 1, and it is 0.6 if it does not exist. TEE is the executable environment level, and DHE is the device fingerprint hash entropy value, which is obtained by calculating the Shannon entropy of the device unique identifier collected by TEE device.

[0096] It should be noted that the TEE executable environment level, TEE=1.0: the highest level TEE (such as Intel SGX, AMD SEV, supporting hardware-level memory encryption and code isolation, which is the judicial-level notarization configuration required in step S1 of this paper). TEE=0.9: the next level TEE (such as ARM TrustZone, which realizes trusted execution through system-level security partitioning, suitable for enterprise-level notarization). TEE=0.7: software simulation TEE such as lightweight trusted environment realized through Docker security container, only used in test scenarios.

[0097] The confidence of the author digital identity AID is calculated as follows: wherein, is the confidence of the author digital identity AID, is the author authentication level, represents the number of historical infringements of the author, represents the number of historical evidence storage of the author, represents the on-chain identity survival time of the author.

[0098] It should be noted that, is the author authentication level, for example: = 1.5: Diamond certified author (judicial chain identity verification and biological feature binding. = 1.2: Gold certified author platform review passed and on-chain evidence storage for more than 1 year, suitable for enterprise-level copyright operation. = 1.0: Ordinary certified author, verified by email and mobile phone, the minimum authentication requirement for the basic evidence storage scenario of the present solution.

[0099] The confidence of the work type is calculated as follows: wherein, is the confidence of the work type, is the work infringement risk level of the work type, is the work size. It should be noted that the work infringement risk level of the work type is, for example: code = 0.9, image = 0.7, text = 0.5.

[0100] It should be noted that, is the work size, which is the digital capacity size of the work, in MB.

[0101] According to the confidence of the device unique identifier DevID, the confidence of the author digital identity AID, and the confidence of the work type , the data confidence coefficient of the original content environment data is calculated as follows:

[0102] wherein, TC is the data confidence coefficient,

[0103] is the confidence of the device unique identifier DevID, is the confidence of the author digital identity AID, is the confidence of the work type, , , , , , DevID, AID, type of work a confidence coefficient weight, is a coordination effect function, representing the synergistic effect of the confidence of the device unique identifier DevID and the confidence of the author digital identity identifier AID.

[0104] It should be noted that, is a coordination effect function, representing the synergistic effect of the confidence of the device unique identifier DevID and the confidence of the author digital identity identifier AID. For example: when the confidence of the device unique identifier DevID is > 0.8, and the confidence of the author digital identity identifier AID is > 0.9, is 0.1, otherwise it is 0.

[0105] Data confidence coefficient , [ , , ,.., ], represent the path index information of the hash value of the nth signed copyright digest data packet.

[0106] The specific steps of constructing a Merkle tree according to the structured digest hash value are as follows:

[0107] First, the hash value of each signed copyright digest data packet is taken as a leaf node of the Merkle tree, and arranged in order to form an initial node list; if the number of nodes is odd, the last node is copied to make it even, ensuring that each layer of nodes can be grouped two by two. Then, starting from the leaf layer, the hash values of adjacent two nodes are concatenated and hashed again to generate the parent node of the upper layer, and so on, combining layer by layer upwards until a unique root node value is generated. In the construction process, the path index information from each leaf node to the root node is recorded synchronously, i.e. the position of each node at each layer and the hash value of its sibling node, forming a complete verification path from the leaf to the root.

[0108] By constructing a Merkle tree through binary hash combinations, a large number of scattered structured digest hash values ​​are organized into an efficient tree structure, achieving a unified approach of rapid global verification and precise local location. The root node value serves as the overall fingerprint of all data packets. Only a fixed-length root hash (e.g., 256 bits) needs to be stored or transmitted to quickly verify the integrity of the entire dataset; tampering with any single data packet will cause a drastic change in the root hash. Simultaneously, path index information supports the authentication of each structured digest hash value. That is, without obtaining all the data, it is only necessary to efficiently verify the existence of a data packet in the set through the path branches of the target node (the set of hashes of sibling nodes), significantly reducing storage costs and transmission burden. In copyright protection scenarios, this structure can condense massive amounts of signed copyright data into a trustworthy root hash. Combined with the path index, it enables rapid tracing and tamper-proof proof of specific copyright information, providing underlying technical support for applications such as blockchain notarization and distributed copyright registration, ensuring the integrity, verifiability, and efficient management of the data set.

[0109] Use the structured digest hash values ​​of the n signed copyright digest data packets as the leaf node array. : =[ , , ,... ],in This represents the nth structured digest hash value in the leaf node array.

[0110] The leaf node array is processed using a completion strategy. Perform even number completion:

[0111] ,in, For structured digest hash value, This represents the hash value of the nth signed copyright digest data packet in the structured digest hash value.

[0112] It should be noted that the structured digest hash value is processed using a completion strategy. Perform even-number padding. If n is odd, copy the last node to make it even. For example: if =[ , , ], after completion it becomes [ , , , ].

[0113] The completed structured abstract hash value is taken as an initial layer, and parent node layers are generated layer by layer upwards from the initial layer. Specifically, the nodes of each layer are grouped two by two in order, the hash values of two adjacent nodes in each group are spliced in the order of left front and right back, and then a new parent node hash value is obtained by calculating the spliced result through a hash function, and all parent nodes form a node array of the previous layer in the grouping order. This process is repeated: the nodes of the current layer are grouped two by two, spliced, and the next layer of nodes is generated through hashing, until there is only one node left in the layer, which is the root node of the Merkle tree.

[0114] For each leaf node data confidence coefficient of the signed copyright abstract data packet, the data confidence coefficient of its adjacent leaf node is calculated to obtain the data confidence coefficient of the parent node in the corresponding Merkle tree:

[0115] , wherein is the hash value of the parent node of the i th signed copyright abstract data packet, is the data confidence coefficient of the i th signed copyright abstract data packet, is the hash value of the i th signed copyright abstract data packet, is the data confidence coefficient of the i+1 th signed copyright abstract data packet, is the hash value of the i+1 th signed copyright abstract data packet.

[0116] Finally, the parent node Q of the leaf node signed copyright abstract data packet at the bottom layer is obtained as , , ... .

[0117] According to the structured abstract hash value and the data confidence coefficient, a Merkle tree is constructed by means of binary hash combination, and a constructed Merkle tree is obtained, and the root node value and path index information of the Merkle tree are obtained through the constructed Merkle tree: , wherein is the root node value of the Merkle tree, is the path index information of the Merkle tree, and Merkle() is a Merkle tree construction function, is the structured abstract hash value, TC_N is the data confidence coefficient sequence, and SHA_256() is a hash function.

[0118] Finally, the root node value of the Merkle tree is obtained as and the path index information of the Merkle tree is obtained as , [ , , ,.., ], This represents the path index information for the hash value of the nth signed copyright digest data packet.

[0119] Merkle trees are constructed based on structured digest hash values ​​and data confidence coefficients. This tree-like hash structure enables efficient organization, lightweight storage, and rapid integrity verification of massive amounts of digital copyright data. The structured digest hash value, acting as a leaf node, transforms the key copyright information of each digital work into a fixed-length unique fingerprint, ensuring the immutability of individual data units. The data confidence coefficient, on the other hand, assigns a quantitative attribute of environmental credibility to each leaf node, allowing the Merkle tree to not only record content characteristics but also implicitly contain the credibility characteristics of the data generation environment. The core role of the data confidence coefficient in constructing the Merkle tree is to inject the quantitative attribute of "environmental credibility" into the leaf nodes, upgrading the traditional tree structure used only for content integrity verification into a dual verification system integrating "content characteristics" and "generation environment credibility." Specifically, each leaf node corresponds to a structured digest hash value (representing the content fingerprint of the digital work), while the data confidence coefficient, as an additional attribute of that node, records multi-dimensional credible information such as device security during work generation (e.g., TEE level), author identity reliability (e.g., authentication level and historical records), and work type risk (e.g., probability of infringement). When constructing parent nodes layer by layer, the data confidence coefficient participates in the combined calculation of hash values ​​(such as combining the confidence coefficients of adjacent nodes with hash values ​​through a weighted formula), so that the upper-level nodes not only inherit the aggregation characteristics of content hash, but also implicitly contain the comprehensive credibility of the underlying data generation environment.

[0120] Step S5: Based on the blockchain layered chain writing strategy, the main chain tuple structure is generated by combining the root node value of the Merkle tree and key element information, where the key element information is the basic information of the blockchain; the auxiliary chain mapping identifier of the original work content is obtained by hashing the work ID and work type in the original content environment data; based on the auxiliary chain mapping identifier, the signed copyright digest data packet, the corresponding Merkle tree path index information, the structured digest hash value, and the data confidence coefficient are combined and stored off-chain to obtain the off-chain tuple structure in the database.

[0121] Based on the blockchain hierarchical chain writing strategy, the hierarchical chain writing strategy is divided into "main chain + auxiliary chain", the main chain only records the root hash value of the Merkle tree and the key meta information; the auxiliary chain establishes the on-chain index through the hash mapping such as generating a retrieval key, directly associates the specific location (such as database index, file path) stored off-chain with the on-chain business query condition, realizes millisecond-level fast data positioning, and solves the problem of inefficient retrieval of the blockchain; the off-chain storage carries the complete copyright summary data including TEE signature, Merkle tree intermediate structure and path index, supports efficient local verification such as signature verification and root hash reconstruction, and realizes flexible access control and copyright management through a dynamic authorization table.

[0122] A large number of signature data packets are compressed into a single root hash (about 32 bytes) through a Merkle tree , which is written into the main chain and bound with key meta information. The key meta information is the basic information of the blockchain, including: work type, blockchain address, storage time, and blockchain network identifier.

[0123] By combining the root node value of the Merkle tree and the key meta information, the main chain tuple structure is generated:

[0124] , wherein, is the main chain tuple structure, is the root node value of the Merkle tree, is the storage time of the blockchain address, is the work type, is the network identifier of the blockchain.

[0125] By performing hash calculation on the work ID and work type in the original content environment data, the auxiliary chain mapping identifier of the original work content is obtained: , wherein, is the work type, represents the work ID of the i-th signature copyright summary data packet, is the auxiliary chain mapping identifier of the i-th signature copyright summary data packet, is a hash function.

[0126] Based on the auxiliary chain mapping identifier, the signature copyright summary data packet and the corresponding path index information and structured summary hash value of the Merkle tree are stored off-chain to obtain the off-chain tuple structure in the database , ( , , , TC, ), is the auxiliary chain mapping identifier of the i-th signature copyright summary data packet, The off-chain tuple structure of the i-th signed copyright digest data packet, The path index information of the signed copyright digest data packet, The structured digest hash value, Indicates the i-th signed copyright digest data packet, The data confidence coefficient of the signed copyright digest data packet.

[0127] It should be noted that the auxiliary chain mapping identifier is one-to-one corresponding to the off-chain tuple structure of the i-th digital work stored in the database through the auxiliary chain mapping identifier of the i-th digital work, for example: the database establishes a mapping relationship through an index table, and the auxiliary chain mapping identifier As a query key, the corresponding value is the off-chain tuple structure. When the user queries through the auxiliary identifier, the system first matches the corresponding in the index table, and then quickly locates the off-chain tuple structure =( , , , , ).

[0128] It should be noted that The structured digest hash value saves all the hash values of the child nodes of the i-th signed copyright digest data packet under the Merkle tree.

[0129] Step S6: For the digital work to be verified, calculate the data confidence coefficient of the digital work to be verified, and by combining the data confidence coefficient of the digital work to be verified and the corresponding main chain tuple structure, the off-chain tuple structure, the verification evaluation index is calculated, and by comparing the verification evaluation index with the preset threshold, the evidence query of the digital work to be verified is realized.

[0130] For the digital work to be verified, the data confidence coefficient of the digital work to be verified is calculated, and the process of step S3 is obtained. The data confidence coefficient of the digital work to be verified is obtained. By combining the data confidence coefficient of the digital work to be verified and the corresponding off-chain tuple structure, the Merkle tree is refitted to obtain the root node value of the digital work to be verified in the Merkle tree. For the digital work to be verified, digital signature is performed to obtain the digital signature of the digital work to be verified.

[0131] For the digital work to be verified, the main chain tuple structure and the off-chain tuple structure are combined to realize the evidence query of the digital work to be verified.

[0132] By combining the data confidence coefficient of the digital work to be verified and the corresponding main chain tuple structure, the off-chain tuple structure, the verification evaluation index is calculated: Wherein, K is the verification evaluation index of the digital work to be verified, The root node value of the Merkel tree of the digital work to be verified is obtained, The root node value of the Merkel tree of the main chain tuple structure is obtained, The digital signature of the digital work to be verified is obtained, The data signature in the structured abstract data packet in the off-chain tuple structure is obtained, and I() is a judgment function. If the two parameters in I() are equal, it is 1, and if they are not equal, it is 0.

[0133] Step S7: By comparing the verification evaluation index with the preset threshold value, if it is greater than the preset threshold value, it is considered to be a stored evidence work. By dynamically adjusting the preset threshold value (such as judicial evidence set to 0.95, enterprise right confirmation set to 0.8, and personal record set to 0.7), the verification evaluation index can seamlessly adapt to the requirements of different businesses for credibility. For example, financial institutions can use high threshold to ensure the safety of core data, and short video platforms can realize high-frequency and rapid right confirmation through low threshold, forming the universality and flexibility of the technical solution, and promoting the digital copyright evidence from the technical prototype to the industrial application.

[0134] The present application proposes a digital copyright evidence storage method based on blockchain, which realizes the credible storage and efficient verification of digital works through multi-technology fusion. The method first generates the hash value of the original digital content using the trusted execution environment TEE, combines the time stamp, device identifier, author identity and other environmental data to form the signed meta-information packet, and completes the digital signature through the elliptic curve digital signature algorithm; then the signature and environmental data are combined into a signed copyright abstract data packet, the structured abstract hash value is generated through hash calculation, and the Merkel tree is constructed based on the hash value, and the root node value and path index information are obtained; finally, the blockchain hierarchical chain writing strategy is adopted to generate the main chain tuple structure to store the Merkel tree root node and key meta-information, and the off-chain tuple structure to store the path index, structured hash value and off-chain unique identifier, and the storage and query of digital works are realized through the cooperative verification of the two.

[0135] The meta-information package is digitally signed based on a private key through an elliptic curve digital signature algorithm, and the authenticity and non-repudiation of the data source are ensured through a cryptography mechanism. The private key is generated by a true random number generator built in the TEE, which guarantees the randomness and security of the private key and avoids human intervention or malicious tampering. The elliptic curve digital signature algorithm binds the private key with key elements such as meta-information content hash, time, device, author, etc. through elliptic curve point multiplication operation and signature component calculation, to form a unique and unforgeable digital signature. This process not only provides a legally credible proof for copyright ownership, but also ensures that any tampering with the meta-information package will result in signature verification failure through mathematical algorithms, thereby strengthening the reliability of the copyright evidence chain from the source.

[0136] A Merkle tree is constructed according to the structured abstract hash value and the data confidence coefficient. The structured abstract hash value is used as a leaf node to convert key information such as content hash, timestamp, digital signature, etc. in each signed copyright abstract data package into a fixed-length unique identifier, ensuring the non-tamperability of individual data. The data confidence coefficient gives each leaf node a quantifiable attribute of environmental credibility, so that the tree structure not only records the content fingerprint, but also implicitly records the credibility characteristics of the data generation environment. The parent nodes are generated through binary hashing layer by layer until the root node, which on the one hand compresses the huge data set into a compact root hash value, greatly reducing the storage and transmission cost, and on the other hand enables the main chain to anchor the global data integrity only by storing the root node.

[0137] For a digital work to be verified, the data confidence coefficient is calculated and combined with the main chain and off-chain meta-tuple structure to generate a verification evaluation index, which converts the complex and credible environment of digital work generation into a quantifiable numerical index in the interval 0-1, breaking the simple judgment of true or false in traditional evidence storage, and realizing fine evaluation of data source and generation process, such as identifying whether the device has a hardware-level security module and whether the author has a history of infringement records.

[0138] On the other hand, the application also provides a computer program product, which comprises a computer program that can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, so that the computer can execute the blockchain-based digital copyright evidence storage method provided by the above-mentioned methods.

[0139] In another aspect, the application also provides a non-transitory computer readable storage medium having a computer program stored thereon, which is executed by a processor to implement the blockchain-based digital copyright evidence storage method provided by the above-mentioned methods.

[0140] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved through WIFI, an operator network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or can be a key, a trackball or a touchpad arranged on the shell of the computer device, or can be an external keyboard, a touchpad or a mouse, etc.

[0141] If the functions are implemented in the form of software function units and sold or used as independent products, the functions can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0142] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer readable medium for use by an instruction execution system, device or apparatus, such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from the instruction execution system, device or apparatus, or in conjunction with these instructions. For the purpose of the present specification, the "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by an instruction execution system, device or apparatus, or in conjunction with these instructions.

[0143] More specific examples (a non-exhaustive list) of the computer readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for instance via an optical scanner, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, to generate an electronically readable version of the program, which can then be stored in the computer memory.

[0144] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In embodiments described above, various steps or methods can be implemented, individually and / or collectively, by software programs or firmware applications, which are stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the techniques described herein can be implemented with one or more of the following technologies, which are well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals; an application specific integrated circuit having appropriate combinational logic gates; a programmable gate array (PGA), such as a field programmable gate array (FPGA), having a fixed, programmable logic function; a programmable array logic (PAL); a generic array logic (GAL); or other suitable technology.

[0145] The VAR melting pool to edge control method and control program of the titanium alloy provided by the application can accurately perceive the disturbance evolution characteristics of the pool edge boundary in a multi-dimensional space by constructing a disturbance identification process based on an angle dynamic parameter field; further, the disturbance development trend is dynamically predicted by combining with the modeling of the disturbance direction increasing potential vector field. On this basis, the local abnormal change is quantitatively evaluated by using the disturbance steepness function, so as to ensure that the system has fine discrimination ability for the disturbance strength and mutation degree; at the same time, through the disturbance causal chain identification mechanism, the system can identify the potential diffusion mode and possible influence area in the early stage of the disturbance, thereby significantly improving the timeliness of the pre-warning and response decision. Compared with the existing control technology which only relies on global parameters or static feature analysis, the method can automatically generate a local directional regulation strategy according to the disturbance propagation direction, realize the dynamic self-adaptive correction of the pool shape, and greatly improve the maintenance ability and regulation accuracy of the pool edge boundary stability in a complex disturbance environment. The method of the application can effectively reduce the frequency of boundary distortion, improve the consistency of the pool shape, shorten the regulation response time, and is suitable for the titanium alloy metallurgical preparation scene with extremely high requirements for composition uniformity and organizational continuity, and has good industrial applicability, scalability and popularization and application value.

[0146] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, and all the modifications and equivalent replacements should be included in the scope of the claims of the present application.

Claims

1. A blockchain-based digital copyright notarization method, characterized in that, Includes the following steps: Step S1: Collect the original digital content and original content environment data of the digital work, and perform hash calculation on the original digital content using a hash algorithm to obtain the hash value of the original digital content; Step S2: Combine the obtained original digital content hash value and the original content environment data to obtain the metadata packet to be signed; Based on the obtained metadata packet to be signed, a private key is generated by generating random numbers, and based on the private key, the metadata packet to be signed is digitally signed using the elliptic curve digital signature algorithm to obtain the data signature. Step S3: Combine the obtained data signature and the original content environment data to obtain a signed copyright digest data packet; and perform hash calculation on the obtained signed copyright digest data packet using a hash algorithm to obtain a structured digest hash value; Step S4: Based on the structured digest hash value and the data confidence coefficient of the original content environment data, construct a Merkle tree using a binary hash combination method to obtain the constructed Merkle tree; and based on the constructed Merkle tree, obtain the root node value and path index information of the Merkle tree; the data confidence coefficient of the original content environment data is obtained through the following steps: Confidence level of the DevID (device unique identifier): ， in, The confidence level of the device's unique identifier, DevID. The value is 1 if the device has a hardware security module and 0.6 if it does not. TEE is the executable environment level and DHE is the device fingerprint hash entropy value, which is obtained by calculating the Shannon entropy of the device's unique identifier collected from the TEE device. Calculate the confidence level of the author's digital identity identifier (AID): ， in, The confidence level of the author's digital identity identifier (AID). Author certification level, This indicates the author's historical number of copyright infringements. Indicates the number of times the author's historical records have been preserved. Indicates the duration of the author's on-chain identity; Calculate the type of work Confidence level: ， in, For the type of work confidence level The copyright infringement risk level is determined by the type of work. Size of the artwork; Based on the confidence level of the device's unique identifier DevID, the confidence level of the author's digital identity identifier AID, and the type of work... The confidence level is used to calculate the confidence coefficient of the original content environment data: ， Where TC is the data confidence coefficient. The confidence level of the device's unique identifier, DevID. The confidence level of the author's digital identity identifier (AID). For the type of work confidence level , , The device's unique identifier is DevID, the author's digital identity is AID, and the work type is... The confidence coefficient weights, Let be the coordination effect function, representing the coordination effect between the confidence level of the device unique identifier DevID and the confidence level of the author's digital identity identifier AID; Step S5: Based on the blockchain layered chain writing strategy, the main chain tuple structure is generated by combining the root node value of the Merkle tree and key element information, where the key element information is the basic information of the blockchain; and the auxiliary chain mapping identifier of the original work content is obtained by hashing the work ID and work type in the original content environment data; based on the obtained auxiliary chain mapping identifier, the signed copyright digest data packet, the corresponding Merkle tree path index information, the structured digest hash value, and the data confidence coefficient are combined and stored off-chain to obtain the off-chain tuple structure in the database; Step S6: Calculate the data confidence coefficient of the digital work to be verified. By combining the data confidence coefficient of the digital work to be verified with the corresponding main chain tuple structure and off-chain tuple structure, the verification evaluation index is calculated. Step S7: Compare and verify the evaluation index with the preset threshold to realize the evidence query of the digital works to be verified.

2. The blockchain-based digital copyright notarization method according to claim 1, characterized in that, Step S1 includes: The original digital content is hashed using the SHA-256 hash algorithm to generate a hash value for the original digital content: ， in, The hash value of the original digital content. for Hash function, where Content is the original numeric content.

3. The method for digital copyright registration based on blockchain according to claim 2, characterized in that, Step S3 includes: hash value of the original digital content With the generation timestamp Device unique identifier (DevID), author digital identity identifier (AID), work ID, work type By combining the data, we obtain the metadata packet Meta to be signed, Meta = { , DevID, AID, ID }; Using the true random number generator (TRNG) built into the trusted execution environment (TEE) through a secure random number generation method, a random integer within the range of the elliptic curve order is generated as the private key d, where 1 d < n, where n is the order of the base point of the elliptic curve; Based on the generated private key, the elliptic curve digital signature algorithm is used to sign the metadata packet to be signed, and a hash calculation is performed on the metadata packet to be signed: ， in, The hash value of the metadata packet to be signed. for Hash function This is the metadata packet to be signed; Generate a temporary random number k, where the random number k satisfies 1 k < n and is relatively prime to n. Through the temporary random number k and the base point G of the ellipse, calculate the point P on the elliptic curve: ， Where P is a point on an elliptic curve, obtained by multiplying the base point G of the elliptic curve with a temporary random number k. Take the x-coordinate of point P on the elliptic curve, and calculate the signature component using the x-coordinate, the hash value of the metadata packet to be signed, and the private key: ， Where s represents the signature component, k represents the temporary random number, e is the hash value of the metadata packet to be signed, d is the private key, r is the x-coordinate of the elliptic curve point P, k is the temporary random number, n is the order of the elliptic curve base point, and mod is the modulo operator. The signature component s is combined with the x-coordinate r of the point P on the elliptic curve to obtain the final data signature. , =(r,s), where r is the x-coordinate of the elliptic curve point P, and s is the signature component.

4. The method for digital copyright registration based on blockchain according to claim 3, characterized in that, Step S3 includes: The data signature and the original content context data are combined to obtain the signed copyright digest data packet PZ, where PZ = { , DevID, AID, ID , },in, For timestamps, DevID is the device's unique identifier, AID is the author's digital identity identifier, and ID is the work's ID. Type of work; Multiple signed copyright digest data packets are combined to obtain a digest data packet set PZ_N, where PZ_N = { , ,..., The hash value of each signed copyright digest data packet is obtained by performing a hash calculation on the signed copyright digest data packet using a hash algorithm. ， in, Let be the hash value of the i-th signed copyright digest data packet. For the i-th signed copyright digest data packet in the digest data packet set, express Hash function; Use the structured digest hash values ​​of the n signed copyright digest data packets as the structured digest hash value. : =[ , , ,... ].

5. A blockchain-based digital copyright notarization method according to claim 1, characterized in that, Step S4 includes: Use the structured digest hash values ​​of the n signed copyright digest data packets as the leaf node array. : =[ , , ,... ],in This represents the nth structured digest hash value in the leaf node array; The leaf node array is processed using a completion strategy. Perform even number completion: ， in, For structured digest hash value, This represents the hash value of the nth signed copyright digest data packet in the structured digest hash value; For each signed copyright digest data packet, calculate the data confidence coefficient of its leaf node, the data confidence coefficient of its adjacent leaf nodes, and the data confidence coefficient of its corresponding parent node in the Merkle tree: ， in, Let be the hash value of the parent node of the i-th signed copyright digest data packet. Let be the data confidence coefficient for the i-th signed copyright digest data packet. Let be the hash value of the i-th signed copyright digest data packet. Let be the data confidence coefficient for the (i+1)th signed copyright digest data packet. Let be the hash value of the (i+1)th signed copyright digest data packet; Finally, the parent node Q of the lowest-level leaf node's signed copyright digest data packet is obtained. , , ,... ]; Based on the structured digest hash value and data confidence coefficient, a Merkle tree is constructed using a binary hash combination method to obtain the constructed Merkle tree. From the constructed Merkle tree, the root node value and path index information are obtained: ， in, The value of the root node of the Merkle tree. The Merkle() function provides the path index information for the Merkle tree and constructs the Merkle tree. is the structured digest hash value, TC_N is the data confidence coefficient sequence, and SHA_256() is the hash function.

6. The method for digital copyright registration based on blockchain according to claim 1, characterized in that, Step S5 specifically includes: By combining the root node value and key element information of the Merkle tree, the main chain tuple structure is generated: ， in, Main chain tuple structure The value of the root node of the Merkle tree. The storage time for the blockchain address. As for the type of work, For blockchain network identification; By hashing the work ID and work type in the original content environment data, the auxiliary chain mapping identifier of the original work content is obtained: ， in, As for the type of work, This represents the work ID of the i-th signed copyright digest data packet. This is the auxiliary chain mapping identifier for the i-th signed copyright digest data packet. for Hash function; Based on the auxiliary chain mapping identifier, the signed copyright digest data packet, along with the corresponding Merkle tree path index information and structured digest hash value, are combined and stored off-chain to obtain the off-chain tuple structure in the database. , =( , , ,TC, ), This is the auxiliary chain mapping identifier for the i-th signed copyright digest data packet. This is the off-chain tuple structure of the i-th signed copyright digest data packet. Path index information for the signed copyright digest data packet. For structured digest hash value, This represents the i-th signed copyright digest data packet. The confidence coefficient for the signed copyright digest data packet.

7. A blockchain-based digital copyright notarization method according to claim 1, characterized in that, Step S6 specifically includes: For the digital work to be verified, the data confidence coefficient of the digital work is calculated. Then, a Merkle tree is refitted using the data confidence coefficient of the digital work and the corresponding off-chain tuple structure to obtain the root node value of the digital work in the Merkle tree. For the digital work to be verified, a digital signature is generated to obtain the digital signature of the digital work to be verified. ; By combining the data confidence coefficient of the digital work to be verified with the corresponding main chain tuple structure and off chain tuple structure, the verification evaluation index is calculated: ， Where K is the verification evaluation index of the digital work to be verified. Obtain the root node value of the digital work to be verified in the Merkle tree. The root node value of the Merkle tree with the main chain tuple structure. A digital signature for a digital work to be verified. I() is the data signature in the structured digest data packet of the off-chain tuple structure. I() is a judgment function. If the two parameters in I() are equal, it is 1; otherwise, it is 0.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the blockchain-based digital copyright certificate storage method as described in any one of claims 1 to 7.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the blockchain-based digital copyright certificate storage method as described in any one of claims 1 to 7.

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