Digital copyright evidence storage method based on block chain, electronic equipment and readable storage medium

Abstract

The invention discloses a digital copyright evidence storage method based on a block chain, electronic equipment and a readable storage medium. The digital copyright evidence storage method comprises the following steps: collecting original content and environmental data of a digital work, generating a content hash value through a hash algorithm, combining the content hash value with the environmental data to form a to-be-signed meta-information packet, generating a data signature by using an elliptic curve digital signature algorithm, forming a signature copyright abstract data packet, and performing hash again to obtain a structured abstract hash value; the method comprises the following steps: analyzing environmental data of a digital work to obtain a data confidence coefficient, constructing a Merkel tree in combination with a structured hash value, and obtaining a root node value and path index information; a block chain main chain stores root node and key element information, generates a block chain auxiliary chain mapping identifier through a work ID and type hash, and stores a structured copyright data signature packet, a path index and a confidence coefficient in an under-chain database.

Description

Technical Field

[0001] This application belongs to the technical field of digital copyright, and specifically relates to a digital copyright deposit method, an electronic device, and a readable storage medium based on blockchain. Background Art

[0002] With the booming development of the digital content industry, the creation and dissemination frequencies of various digital works such as images, audios, and codes have increased significantly, and the resulting copyright infringement problems have become increasingly severe. The copyright registration method has problems such as cumbersome processes and low efficiency. Although the deposit technology based on blockchain has shown great potential in the field of digital copyright protection due to its characteristics such as decentralization and immutability, there is still room for optimization in aspects such as data generation, storage architecture, and collaborative verification mechanisms.

[0003] In related technologies, during the process of depositing digital works, a single on-chain storage mode is adopted, and a large amount of detailed copyright data is directly written into the blockchain, resulting in low storage efficiency and difficulty in meeting the requirements of efficient retrieval and rapid confirmation of rights for depositing a large number of digital works. Summary of the Invention

[0004] This application provides a digital copyright deposit method, an electronic device, and a readable storage medium based on blockchain, aiming to at least improve the efficient retrieval of depositing digital works to a certain extent and meet the requirements of rapid confirmation of rights.

[0005] In the first aspect of this application, a digital copyright deposit method based on blockchain is provided, including 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 through a hash algorithm to obtain the original digital content hash value; Step S2: Combine the obtained original digital content hash value and original content environment data to obtain a meta-information packet to be signed; based on the obtained meta-information packet to be signed, generate a private key by generating a random number, and based on the private key, perform digital signature on the meta-information packet to be signed through an elliptic curve digital signature algorithm to obtain a data signature; Step S3: Combine the obtained data signature and original content environment data to obtain a signed copyright summary data packet; and perform hash calculation on the obtained signed copyright summary data packet through a hash algorithm to obtain a structured summary hash value; Step S4: Based on the structured summary hash value and the data confidence coefficient of the original content environment data, construct a Merkle tree in a binary hash combination manner to obtain a constructed Merkle tree; and based on the constructed Merkle tree, obtain the root node value and path index information of the Merkle tree; Step S5: Based on the blockchain hierarchical chain writing strategy, by combining the root node value of the Merkle tree and the key meta-information, a main chain tuple structure is generated, where the key meta-information is the basic information of the blockchain; and by performing a hash calculation on the work ID and work type in the original content environment data, an auxiliary chain mapping identifier of the original work content is obtained; based on the obtained auxiliary chain mapping identifier, the signed copyright summary data packet, the corresponding path index information and structured summary 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; Step S6: Calculate the data confidence coefficient of the digital work to be verified, and calculate the verification evaluation index 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; Step S13: Compare the verification evaluation index with a preset threshold to implement the deposit query of the digital work to be verified.

[0006] In some embodiments, step S1 includes: using the hash algorithm SHA-256 to perform a hash calculation on the original digital content to generate an original digital content hash value: , where is the original digital content hash value, is the hash function, and Content is the original digital content.

[0007] In some embodiments, step S3 includes: Combining the original digital content hash value with the generated timestamp , device unique identifier DevID, author digital identity identifier AID, work ID, and work type to obtain the meta-information packet to be signed Meta, where Meta = { , , DevID, AID, ID, }; Generate a random integer within the range of the elliptic curve order as the private key d through the true random number generator TRNG built into the trusted execution environment TEE by a secure random number generation method, where 1 d < n, and n is the base order of the elliptic curve; Based on the generated private key, use the elliptic curve digital signature algorithm to sign the meta-information packet to be signed, and perform a hash calculation on the meta-information packet to be signed: , where is the hash value of the meta-information packet to be signed, is the hash function, is the meta - information 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; Calculate the point P on the elliptic curve through the temporary random number k and the base point G of the ellipse: , where P is a point on the elliptic curve obtained by multiplying the base point G of the elliptic curve by the temporary random number k; Take the x - coordinate of the point P on the elliptic curve. Through the x - coordinate, the hash value of the meta - information packet to be signed, and combined with the private key, calculate the signature component: , where s represents the signature component, k represents the temporary random number, e is the hash value of the meta - information 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 base point of the elliptic curve, and mod is the remainder symbol; Combine the signature component s and the x - coordinate r of the point P on the elliptic curve to finally obtain the data signature , =(r, s), where r is the x - coordinate of the elliptic curve point P and s is the signature component.

[0008] The step S3 includes: Combine the data signature and the original content environment data to obtain the signature copyright summary data packet PZ, PZ = { , , DevID, AID, ID, , }, where, is the timestamp, DevID is the device unique identifier, AID is the author digital identity identifier, ID is the work ID, is the work type; Combine multiple signature copyright summary data packets to obtain the summary data packet set PZ_N, PZ_N = { , ,..., }. Through the hash algorithm, calculate the hash value of each signature copyright summary data packet: , where, is the hash value of the i - th signature copyright summary data packet, is the i - th signature copyright summary data packet in the summary data packet set, represents the hash function; Take the structured digest hash values of n signature copyright summary data packets as the structured digest hash value : = , , ,... 。

[0009] In some embodiments, the data confidence coefficient of the original content environment data is obtained through the following steps: Calculate the confidence of the device unique identifier DevID: , where, is the confidence of the device unique identifier DevID, is the existence of the hardware security module. If the device has a hardware security module, it is 1; if not, it is 0.6. 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; Calculate the confidence of the author digital identity identifier AID: , where, is the confidence of the author digital identity identifier AID, is the author authentication level, represents the number of historical infringement times of the author, represents the number of historical deposit times of the author, represents the duration of the author's on-chain identity; Calculate the confidence of the work type : , where, 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, and the confidence of the work type , calculate the data confidence coefficient of the original content environment data: , where 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 work type 's confidence, , , is the device unique identifier DevID, the author digital identity identifier AID, and the work type​​​​ Confidence coefficient weight is a coordination effect function, representing the synergistic effect between the confidence of the device unique identifier DevID and the confidence of the author digital identity identifier AID

[0010] In some embodiments, the step S4 includes: Taking the structured summary hash values of n signature copyright summary data packets as the leaf node array : = , , ,... , where represents the nth structured summary hash value in the leaf node array; performing even number completion on the leaf node array by the completion strategy: , where is the structured summary hash value, represents the hash value of the nth signature copyright summary data packet in the structured summary hash value; For the data confidence coefficient of each leaf node of the signature copyright summary data packet and the data confidence coefficient of its adjacent leaf node, calculate the data confidence coefficient of the parent node in the corresponding Merkle tree: , where is the hash value of the parent node of the ith signature copyright summary data packet, is the data confidence coefficient of the ith signature copyright summary data packet, is the hash value of the ith signature copyright summary data packet, is the data confidence coefficient of the (i + 1)th signature copyright summary data packet, is the hash value of the (i + 1)th signature copyright summary data packet; Finally, obtain the father node Q of the signature copyright summary data packet of the bottom - layer leaf node = , , ,... ; According to the structured summary hash value and the data confidence coefficient, construct a Merkle tree by means of binary hash combination to obtain the constructed Merkle tree, and through the constructed Merkle tree, obtain the root node value and path index information of the Merkle tree: , where is the root node value of the Merkle tree, It is the path index information of the Merkle tree, and Merkle() is the Merkle tree construction function. It is the structured digest hash value, TC_N is the data confidence coefficient sequence, and SHA_256() is the hash function.

[0011] In some embodiments, step S5 specifically includes: By combining the root node value of the Merkle tree and the key meta-information, the main chain tuple structure is generated: , where is the main chain tuple structure, is the root node value of the Merkle tree, is the deposit time of the blockchain address, is the work type, is the network identifier of the blockchain; By performing a 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: , where is the work type, represents the work ID of the i-th signed copyright summary data packet, is the auxiliary chain mapping identifier of the i-th signed copyright summary data packet, is the hash function; Based on the auxiliary chain mapping identifier, the signed copyright summary data packet, the corresponding path index information of the Merkle tree, and the structured digest hash value are combined and stored off-chain to obtain the off-chain tuple structure in the database , =( , , , TC, ), is the auxiliary chain mapping identifier of the i-th signed copyright summary data packet, is the off-chain tuple structure of the i-th signed copyright summary data packet, is the path index information of the signed copyright summary data packet, is the structured digest hash value, represents the i-th signed copyright summary data packet, is the data confidence coefficient of the signed copyright summary data packet.

[0012] In some embodiments, step S6 specifically includes: For the digital work to be verified, calculate the data confidence coefficient of the digital work to be verified. Through the data confidence coefficient of the digital work to be verified and the corresponding off-chain tuple structure, perform re-fitting of the Merkle tree to obtain the root node value of the digital work to be verified in the Merkle tree For the digital work to be verified, perform digital signature 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 and the corresponding main-chain tuple structure and off-chain tuple structure, calculate the verification evaluation index: 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 is the root node value of the Merkle tree of the main-chain tuple structure is the digital signature of the digital work to be verified is the data signature in the structured summary data packet in the off-chain tuple structure, and I() is a judgment function. If the two parameters in I() are equal, it is 1; if they are not equal, it is 0

[0013] In the second aspect of the present application, the present application also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. The characteristic is that when the processor executes the program, it implements the blockchain-based digital copyright deposit method as described above.

[0014] In the third aspect of the present application, the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the blockchain-based digital copyright deposit method as described above.

[0015] The present application constructs a Merkle tree based on the structured summary hash value and the data confidence coefficient. The structured summary hash value is used as the leaf node, and each signature copyright summary data packet containing key information such as content hash, timestamp, and digital signature is converted into a unique identifier of a fixed length to ensure the immutability of individual data; the data confidence coefficient assigns a quantifiable attribute of environmental credibility to each leaf node, enabling the tree structure to not only record the content fingerprint but also implicitly contain the credibility characteristics of the data generation environment. Through binary hashing, the parent nodes are combined layer by layer until the root node. On the one hand, the large data set is compressed into a compact root hash value, significantly reducing the storage and transmission costs, and facilitating the blockchain main chain to only store the root node to anchor the global data integrity. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0017] Figure 1 It shows a flowchart of the steps of a digital copyright deposit method based on blockchain proposed by the present application; Figure 2 It shows a hierarchical diagram of the steps of a digital copyright deposit method based on blockchain proposed by the present application. Detailed implementation manners

[0018] To enable those skilled in the art in the technical field to which the present application belongs to better understand the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by the present application.

[0019] Please refer to Figure 1-2 , a digital copyright deposit method based on blockchain provided by the present application includes the following steps: Step S1: Collect the original digital content of the digital work and the original content environment data, and generate a hash of the original digital content through a hash algorithm to obtain the original digital content hash value.

[0020] The steps to generate 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 the TEE with hardware-level security isolation, which can prevent interference and monitoring by the operating system or third-party applications; then, the TEE performs a hash calculation on the original digital content through the SHA-256 hash algorithm to obtain the original digital content hash value , the original digital content hash value , which can accurately represent the integrity of the content.

[0021] It should be noted that the original content environment data includes: timestamp , device unique identifier DevID, author digital identity identifier AID, work ID, work type .

[0022] Perform a hash calculation on the original digital content using the SHA-256 hash algorithm to generate the original digital content hash value: , Among them, is the hash value of the original digital content, is a hash function, and Content is the original digital content.

[0023] It should be noted that the hash algorithm SHA-256 is used to perform hash calculation on the original digital content to generate the hash value of the original digital content. First, the original digital content is preprocessed to meet specific length requirements through padding. For example, the message length of the padded original digital content must meet the multiple of 512 bits minus 64 bits. The padding rule is: add a "1" bit at the end of the original content, and then pad several "0" bits so that the total length after padding (including the original content length and padding bits) meets the above conditions. Finally, a 64-bit binary number is appended to represent the length of the original content (encoded in big-endian order). After padding, the message is divided into multiple fixed-length data blocks of 512 bits. Each data block will go through message expansion processing to generate 64 32-bit words (referred to as the message schedule table). The first 16 words are directly obtained by decomposing the data block, and the subsequent 48 words are generated through a recurrence formula that combines circular right shift and exclusive OR operations to ensure that each new word has a non-linear association with the previous words. Next, the algorithm initializes a hash value (initial hash value) containing 8 32-bit constants, which are calculated based on the fractional part of the square root of mathematical constants. When processing each 512-bit data block, the hash value is updated through a 64-round compression function. In each round of operation, complex operations are performed on the current hash value and working variables using logical functions (such as the selection function Ch, the majority function Maj, and the circular right shift functions Σ0 and Σ1), and are mixed with the words in the message schedule table and predefined constants.

[0024] After processing all data blocks, the final hash value of the original digital content is formed by concatenating 8 32-bit intermediate values generated in the last iteration, forming a 256-bit binary sequence, which is usually converted into a 64-bit hexadecimal string as the final output. This process ensures that even if the input content changes slightly, the generated hash value will be significantly different through multiple rounds of non-linear transformation and diffusion mechanisms, thus meeting the strict requirements for data integrity and uniqueness in digital copyright deposit.

[0025] Step S2: Combine the obtained hash value of the original digital content and the original content environment data to obtain the meta-information packet to be signed. Generate a private key by generating a random number. Based on the private key, perform a digital signature on the meta-information packet to be signed through the elliptic curve digital signature algorithm to obtain the data signature.

[0026] The TEE takes the hash value of the original digital content Bind with the original content environment data to form a meta information packet Meta to be signed that includes content features, time, device, and ownership subject, and the hash value of the original digital content and the generated timestamp , device unique identifier DevID, author digital identity identifier AID, work ID, work type are combined to obtain the meta information packet Meta to be signed, Meta = { , , DevID, AID, ID, }.

[0027] The hardware-level security guarantee of the TEE ensures that the hash generation, meta information binding, and signature processes are completed in a closed environment, preventing the content from being tampered with or forged, and laying the foundation for data credibility from the source; the uniqueness of the hash value can accurately identify the integrity of the content, and any minor changes 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 copyright generation, forming a traceable ownership certificate; the digital signature ensures the authenticity of the data packet source through a cryptographic mechanism, preventing repudiation. Overall, this step transforms digital content into legally credible electronic evidence through technical means, providing a core trust anchor for subsequent off-chain storage, on-chain authentication, and rapid rights confirmation in copyright disputes, solving the key problem of "the data generation process is not credible", and is the security starting point of the entire digital copyright storage system. Combine the meta information packet to be signed: First, calculate the hash value (such as SHA-256) for the original digital content (such as documents, data files), and this hash value uniquely represents the characteristics of the original content; at the same time, collect the environmental data of the original content (such as context information such as timestamps, device identifiers, user IDs, etc.). The hash value and environmental data are encapsulated into a "meta information packet to be signed" in a specific format (such as JSON, ASN.1) to ensure that the signature object includes content characteristics and generation scenarios.

[0028] Generate a private key: Through a secure random number generation method, the true random number generator TRNG built into the trusted execution environment TEE generates a random integer that meets the range of the elliptic curve order as the private key d, that is, 1 d < n, where n is the base point order of the elliptic curve, ensuring the randomness and unpredictability of the private key.

[0029] Execute ECDSA signature: Based on the generated private key, use the elliptic curve digital signature algorithm to sign the meta information packet to be signed. The specific process is as follows: Perform a hash calculation on the meta information packet to be signed: , where is the hash value of the meta information packet to be signed, is Hash functions, The meta-information package to be signed.

[0030] Generate a temporary random number k, the random number k satisfies 1 k <n,且与n互质,通过临时随机数k和椭圆的基点G,计算椭圆曲线上的点P: , where P is a point on an elliptic curve, obtained by multiplying the base point G of the elliptic curve and the temporary random number k.

[0031] Take the x-coordinate of point P on the elliptic curve, and calculate the signature component using the x-coordinate and the hash value of the metadata package to be signed, combined with the private key: , where s represents the signature component, k represents a temporary random number, e is the hash value of the meta-information package to be signed, d is the private key, r is the x-coordinate of the elliptic curve point P, k is a temporary random number, n is the order of the elliptic curve base point, and mod is the modulo symbol.

[0032] Combine the signature component s with the x-coordinate r of the point P on the elliptic curve to obtain the data signature. , =(r,s), where r is the x-coordinate of the elliptic curve point P and s is the signature component.

[0033] It should be noted that the base point of the elliptic curve is a specific point selected on the elliptic curve, which is one of the core elements of building a cryptographic system. = The equation for ax + ax + b is defined (the specific parameters must satisfy the curve's nonsingularity condition), and the base point G is a verified valid point on the curve (i.e., its coordinates satisfy the curve equation). After selecting the base point G, point addition operations on the elliptic curve generate a subgroup consisting of all integer multiples of the base point G. This subgroup is a cyclic group, meaning that all points can be represented as kG (where k is an integer).

[0034] It should be noted that the order of the base point means that adding the base point G to itself n times will result in the point at infinity 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 the private key (the private key d must satisfy 1 d <n的整数),确保私钥对应的运算始终在该循环子群内进行,从而保证密码系统的安全性。

[0035] Step S3: Combine the data signature and the original content environment data to obtain a signed copyright summary data packet, and perform hash calculation on the signed copyright summary data packet using a hash algorithm to obtain a structured summary hash value.

[0036] Utilize TEE internal private key The meta-information package Meta to be signed is digitally signed, and the signature and meta-information are encapsulated into a signed copyright summary data package.

[0037] Combine the data signature and the original content environment data to obtain the signature copyright summary data packet PZ, PZ={ , ,DevID,AID,ID, , },in, is the timestamp, DevID is the device unique identifier, AID is the author's digital identity, ID is the work ID, For the type of work.

[0038] Combine multiple signature copyright summary data packets to obtain summary data packet set PZ_N, PZ_N={ , ,..., }, hash the signature copyright summary data packet using the hash algorithm to obtain the hash value of each signature copyright summary data packet: ,in, is the hash value of the i-th signed copyright summary data packet, is the i-th signature copyright summary data packet in the summary data packet set, express Hash function.

[0039] Take the structured digest hash value of n signed copyright summary packets as the structured digest hash value : =[ , , ,... ].

[0040] 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 packet containing complete copyright-related information. Subsequently, the signed copyright summary data packet is hashed using the SHA-256 hashing algorithm to generate a unique, fixed-length structured summary hash value. The core significance of this step lies in: on the one hand, by integrating the scattered copyright-related information (data signature and original content environment data) into a structured data packet, the standard encapsulation of copyright information is achieved, facilitating subsequent storage, verification, and management; on the other hand, by utilizing the one-way property and collision resistance of the hashing algorithm, the complex signed copyright summary data packet is converted into a concise hash value, which not only ensures the integrity of the content of the signed copyright summary data packet but also provides a basic unit for subsequent operations such as constructing a Merkle tree and blockchain evidence storage. Each hash value can be used as a leaf node to efficiently support the rapid verification and chained association of multi-level data structures, thereby achieving reliable recording, efficient traceability, and anti-tampering proof of content copyright information in the copyright protection scenario.

[0041] Step S4: Based on the data confidence coefficients of the structured summary hash value and the original content environment data, construct a Merkle tree in a binary hash combination manner to obtain the constructed Merkle tree. Through the constructed Merkle tree, obtain the root node value and path index information of the Merkle tree.

[0042] Among them, the data confidence coefficient of the original content environment data is obtained through the following steps: Calculate the confidence coefficient of the Merkle tree leaf node through the original content environment data. Its core value lies in transforming the deposit environment of digital works into a quantifiable trust index, building a core bridge from data generation credibility to verification decision intelligence, and realizing the standardized aggregation of multi-dimensional evidence. The original content environment data (timestamp, device ID, author identity, etc.) is collected in a hardware-level trusted manner by the TEE, carrying the key context information of the work generation. Through the calculation of the confidence coefficient, these heterogeneous evidences are transformed into a unified numerical index (in the range of 0-1), enabling abstract concepts such as the authority of time, the security of the device, and the credibility of the author to be efficiently processed by algorithms, and solving the problem of fragmented traditional deposit evidence. The confidence coefficient introduces a "continuous trust measurement" mechanism into the Merkle tree verification system, rather than the traditional "true or false" binary judgment. For example, in the judicial deposit scenario, strong trusted evidence can be quickly confirmed through a high confidence coefficient (such as ≥0.95), while in the ordinary rights confirmation scenario, the threshold can be adjusted (such as ≥0.8) to balance efficiency and security, adapting to the different credibility requirements of different businesses. Strengthen the traceability and anti-attack ability of the deposit data. The calculation logic of the confidence coefficient closely depends on the TEE trusted execution environment and the blockchain hierarchical storage architecture defined in this article. The score of each dimension corresponds to specific technical guarantees (such as the TEE ensures that the timestamp cannot be tampered with, and the on-chain identity authentication ensures that the author's credibility is traceable). This design makes the confidence coefficient a "trust bond" connecting technology implementation and business verification. Any defect in a single dimension of evidence will lead to a decrease in the coefficient, thus significantly increasing the difficulty of forgery and forming a security reinforcement for the entire link of data generation, storage, and verification.

[0043] The original content environment data includes: the device unique identifier DevID, the author digital identity identifier AID, and the work type . Calculate the confidence of the device unique identifier DevID: , where is the confidence of the device unique identifier DevID, is the existence of the hardware security module. If the device has a hardware security module, it is 1; if not, it is 0.6. TEE is the level of the executable environment, and DHE is the entropy value of the device fingerprint hash, which is obtained by calculating the Shannon entropy of the device unique identifier collected by the TEE device.

[0044] It should be noted that for the TEE executable environment level, TEE = 1.0: the highest level TEE (such as Intel SGX, AMD SEV, which support hardware-level memory encryption and code isolation, and are the judicial-level deposit configurations required by step S1 in this article). TEE = 0.9: the second highest level TEE (such as ARM TrustZone, which realizes trusted execution through system-level security partitioning and is suitable for enterprise-level deposits). TEE = 0.7: software-simulated TEE, such as a lightweight trusted environment implemented through Docker security containers, which is only used for test scenarios.

[0045] Calculate the confidence level of the author's digital identity identifier AID: , where is the confidence level of the author's digital identity identifier AID, is the author authentication level, represents the number of historical infringement times of the author, represents the number of historical evidence storage times of the author, represents the duration of the author's on-chain identity existence.

[0046] It should be noted that is the author authentication level, for example: = 1.5: Diamond-certified author (verified by the judicial chain identity and biometric binding. = 1.2: Gold-certified author, platform review passed and on-chain evidence storage for 1 year, applicable to enterprise-level copyright operations. = 1.0: Ordinary-certified author, verified by email and mobile phone number, the minimum authentication requirement for the basic evidence storage scenario of this solution.

[0047] Calculate the work type confidence level: , where is the confidence level of the work type , is the work infringement risk level of the work type, is the work size.

[0048] It should be noted that the work infringement risk level of the work type, for example: code = 0.9, image = 0.7, text = 0.5.

[0049] It should be noted that the work size is the digital capacity size of the work, with the unit of MB.

[0050] According to the confidence level of the device unique identifier DevID, the confidence level of the author's digital identity identifier AID, and the confidence level of the work type confidence level, calculate the data confidence coefficient of the original content environment data: , where TC is the data confidence coefficient, is the confidence level of the device unique identifier DevID, is the confidence level of the author's digital identity identifier AID, is the work type confidence level, , , is the device unique identifier DevID, the author's digital identity identifier AID, and the work type Confidence coefficient weight Is a coordination effect function, representing the synergistic effect between the confidence of the device unique identifier DevID and the confidence of the author digital identity identifier AID.

[0051] It should be noted that Is a coordination effect function, representing the synergistic effect between 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 > 0.8 and the confidence of the author digital identity identifier AID > 0.9, Is 0.1, otherwise it is 0.

[0052] Data confidence coefficient , = , , ,.., , Represents the path index information of the hash value of the nth signed copyright summary data packet.

[0053] The specific steps to construct a Merkle tree based on the structured summary hash value are as follows: First, take the hash value of each signed copyright summary data packet as the leaf node of the Merkle tree, and arrange them in order to form an initial node list; if the number of nodes is odd, copy the last node to make it an even number to ensure that each layer of nodes can be grouped in pairs. Subsequently, starting from the leaf layer, hash the concatenated hash values of two adjacent nodes to generate the parent node of the upper layer, and so on, combining layer by layer upward until a unique root node value is generated. During the construction process, synchronously record the path index information from each leaf node to the root node, that is, the position of each node in each layer and the hash value of its sibling node, to form a complete verification path from the leaf to the root.

[0054] Construct a Merkle tree through binary hash combination, organize a large number of scattered structured digest hash values into an efficient tree structure, and achieve the unity of global fast verification and local precise positioning. The root node value serves as the overall fingerprint of all data packets. By storing or transmitting only the root hash of a fixed length (such as 256 bits), the integrity of the entire data set can be quickly verified. Tampering with any single data packet will cause a drastic change in the root hash. At the same time, the path index information supports the authentication of each structured digest hash value, that is, without obtaining all the data, only through the path branches (the set of sibling node hashes) of the target node, it is possible to efficiently verify whether the data packet exists in the set, greatly reducing the storage cost and transmission burden. In the scenario of copyright protection, this structure can condense a large amount of signature copyright data into a trustworthy root hash, and combine with the path index to achieve fast traceability and anti-tampering proof of specific copyright information, providing underlying technical support for applications such as blockchain evidence storage and distributed copyright registration, and ensuring the integrity, verifiability, and efficient management of the data set.

[0055] Take the structured digest hash values of n signature copyright summary data packets as the leaf node array : = , , ,... , where represents the nth structured digest hash value in the leaf node array.

[0056] Perform even-number complementation on the leaf node array through a complementation strategy: , where, is the structured digest hash value, represents the hash value of the nth signature copyright summary data packet in the structured digest hash value.

[0057] It should be noted that when performing even-number complementation on the structured digest hash value , if n is odd, duplicate the last node to make it even. For example: if = , , , after complementation, it becomes , , , .

[0058] Take the hash value of the completed structured abstract as the initial layer, and generate the parent node layer layer by layer starting from the initial layer. Specifically, the nodes of each layer need to be grouped in pairs in order. The hash values of two adjacent nodes in each group are byte-concatenated in the order of left front and right back, and then the concatenated result is calculated through a hash function to obtain a new parent node hash value. All parent nodes form the node array of the upper layer in the grouping order. This process is repeated: group the nodes of the current layer in pairs, concatenate and then hash to generate the nodes of the next layer until there is only one node left in this layer, which is the root node of the Merkle tree.

[0059] For the data confidence coefficient of each leaf node of the signed copyright abstract data packet, calculate the data confidence coefficient of the parent node in the corresponding Merkle tree with the data confidence coefficient of its adjacent leaf node: , where 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.

[0060] Finally, obtain the father node Q of the signed copyright abstract data packet of the bottom-layer leaf node = , , ,... .

[0061] According to the structured abstract hash value and the data confidence coefficient, construct a Merkle tree by means of binary hash combination to obtain the constructed Merkle tree. Through the constructed Merkle tree, obtain the root node value and path index information of the Merkle tree: , where is the root node value of the Merkle tree, is the path index information of the Merkle tree, Merkle() is the Merkle tree construction function, is the structured abstract hash value, TC_N is the data confidence coefficient sequence, and SHA_256() is the hash function.

[0062] Finally, obtain the root node value and the path index information , = , , ,.., . Path index information representing the hash value of the nth signed copyright summary data packet.

[0063] Construct a Merkle tree based on the structured summary hash value and the data confidence coefficient, and realize the efficient organization, lightweight storage and fast integrity verification of massive digital copyright data through the tree-like hash structure. The structured summary hash value serves as the leaf node, converting the copyright key information of each digital work into a unique fingerprint of a fixed length to ensure the immutability of a single data unit; the data confidence coefficient assigns a quantitative attribute of environmental trust to each leaf node, enabling the Merkle tree to not only record content features but also implicitly contain the credibility features of the data generation environment. In the process of constructing the Merkle tree, the core role of the data confidence coefficient is to inject a quantitative attribute of "environmental credibility" into the leaf nodes of the Merkle tree, upgrading the traditional tree structure only used for content integrity verification to a dual verification system integrating "content features" and "generation environment credibility". Specifically, each leaf node corresponds to a structured summary hash value (representing the content fingerprint of the digital work), and the data confidence coefficient, as an additional attribute of the node, records multi-dimensional credible information such as the device security (such as TEE level) at the time of work generation, the reliability of the author's identity (such as authentication level and historical records), and the risk of work type (such as infringement probability). When constructing the parent nodes layer by layer, the data confidence coefficient participates in the combined calculation of the hash values (such as combining the confidence coefficients and hash values of adjacent nodes through a weighted formula), so that the upper-layer nodes not only inherit the aggregated features of the content hash but also implicitly contain the comprehensive credibility of the underlying data generation environment.

[0064] Step S5: Based on the blockchain hierarchical chain writing strategy, generate the main chain tuple structure by combining the root node value of the Merkle tree and the key meta-information, where the key meta-information is the basic information of the blockchain; calculate the hash of the work ID and work type in the original content environment data to obtain the auxiliary chain mapping identifier of the original work content; based on the auxiliary chain mapping identifier, store the signed copyright summary data packet, the corresponding path index information and structured summary hash value of the Merkle tree, and the data confidence coefficient in a combined manner off-chain to obtain the off-chain tuple structure in the database.

[0065] 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 an on-chain index through hash mapping such as generating a retrieval key, directly associating the specific off-chain storage location (such as database index, file path) with the on-chain business query conditions, realizing millisecond-level fast data positioning and solving the problem of inefficient blockchain retrieval; off-chain storage carries the complete copyright summary data including TEE signatures, the intermediate structure of the Merkle tree and path indexes, supports efficient local verification such as signature verification and root hash reconstruction, and at the same time realizes flexible access control and copyright management through a dynamic authorization table.

[0066] Compress a large number of signature data packets into a single root hash through a Merkle tree (about 32 bytes), write it to the main chain, and bind key meta-information, which is the basic information of the blockchain, including: work type, blockchain address, deposit time, blockchain network identifier.

[0067] Generate the main chain tuple structure by combining the root node value of the Merkle tree and the key meta-information: , where is the main chain tuple structure, is the root node value of the Merkle tree, is the deposit time of the blockchain address, is the work type, is the network identifier of the blockchain.

[0068] Calculate the auxiliary chain mapping identifier of the original work content by hashing the work ID and work type in the original content environment data: , where 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 the hash function.

[0069] Based on the auxiliary chain mapping identifier, store the signature copyright summary data packet, the corresponding path index information and structured summary hash value of the Merkle tree off-chain in combination 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, is the off-chain tuple structure of the i-th signature copyright summary data packet, is the path index information of the signature copyright summary data packet, is the structured summary hash value, represents the i-th signature copyright summary data packet, is the data confidence coefficient of the signature copyright summary data packet.

[0070] It should be noted that the auxiliary chain mapping identifier corresponds one-to-one with the off-chain tuple structure stored in the database through the auxiliary chain mapping identifier of the i-th digital work. For example, in the database, a mapping relationship is established through an index table, and the auxiliary chain mapping identifier is used as the query key, and the corresponding value is the off-chain tuple structure. When the user queries through this auxiliary identifier, the system first matches the corresponding in the index table, and then quickly locates the off-chain tuple structure through =( , , , , ,).

[0071] It should be noted that is the structured abstract hash value, which stores the hash values of all child nodes of the i-th signature copyright abstract data packet under the Merkle tree.

[0072] Step S6: For the digital work to be verified, 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 and the corresponding main chain tuple structure and off-chain tuple structure, calculate the verification evaluation index. Compare the verification evaluation index with the preset threshold to implement the deposit query of the digital work to be verified.

[0073] For the digital work to be verified, calculate the data confidence coefficient of the digital work to be verified. In the same process as step S3, obtain the data confidence coefficient of the digital work to be verified. Through the data confidence coefficient of the digital work to be verified and the corresponding off-chain tuple structure, perform re-fitting of the Merkle tree to obtain the root node value of the digital work to be verified in the Merkle tree . For the digital work to be verified, perform digital signature to obtain the digital signature of the digital work to be verified .

[0074] For the digital work to be verified, perform combined verification by combining the main chain tuple structure and the off-chain tuple structure to implement the deposit query of the digital work to be verified.

[0075] 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, calculate the verification evaluation index: , 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 is the root node value of the Merkle tree of the main chain tuple structure is the digital signature of the digital work to be verified It is the digital signature of the data in the structured summary data packet in the off-chain tuple structure. I() is a judgment function. If the two parameters in I() are equal, it is 1; if they are not equal, it is 0.

[0076] Step S7: Compare the verification evaluation index with a preset threshold. If it is greater than the preset threshold, it is considered a deposited work. By dynamically adjusting the preset threshold (such as setting it to 0.95 for judicial deposit, 0.8 for enterprise rights confirmation, and 0.7 for personal filing), the verification evaluation index can seamlessly adapt to the credibility requirements of different businesses. For example, financial institutions can use a high threshold to ensure the security of core data, and short video platforms can achieve high-frequency and fast rights confirmation through a low threshold, forming the universality and flexibility of the technical solution and promoting the leap of digital copyright deposit from a technical prototype to industrial application.

[0077] This application proposes a blockchain-based digital copyright deposit method to achieve the trustworthy deposit and efficient verification of digital works through multi-technology integration. This method first uses a trusted execution environment TEE to generate the hash value of the original digital content, combines environmental data such as time stamps, device identifiers, and author identities to form a meta-information packet to be signed, and completes the digital signature through the elliptic curve digital signature algorithm. Then, the signature and environmental data are combined into a signed copyright summary data packet, and the structured summary hash value is generated through hash calculation, and a Merkle tree is constructed based on this, obtaining the root node value and path index information. Finally, the blockchain hierarchical chain writing strategy is adopted to generate the main chain tuple structure to store the Merkle 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 deposit query of digital works is realized through the collaborative verification of the two.

[0078] Based on the private key, the meta-information packet is digitally signed through the elliptic curve digital signature algorithm, and the authenticity and non-repudiation of the data source are ensured through the cryptographic mechanism. The private key is generated by using the true random number generator built into 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 the key elements such as the content hash, time, device, and author in the meta-information packet through elliptic curve point multiplication operation and signature component calculation, forming a unique and unforgeable digital signature. This process not only provides a legally credible proof of copyright ownership, but also ensures through mathematical algorithms that any tampering with the meta-information packet will result in signature verification failure, strengthening the reliability of the copyright evidence chain from the source.

[0079] Construct a Merkle tree based on the structured summary hash value and the data confidence coefficient. The structured summary hash value serves as the leaf node. Each signature copyright summary data packet containing key information such as content hash, timestamp, and digital signature is transformed into a unique identifier of a fixed length to ensure the immutability of individual data. The data confidence coefficient assigns a quantifiable attribute of environmental trust to each leaf node, enabling the tree structure to not only record the content fingerprint but also implicitly contain the credibility characteristics of the data generation environment. By combining binary hashes layer by layer to generate parent nodes up to the root node, on the one hand, a large dataset is compressed into a compact root hash value, significantly reducing storage and transmission costs, facilitating the blockchain main chain to anchor the global data integrity with only the root node.

[0080] For the digital work to be verified, calculate its data confidence coefficient and generate a verification evaluation index in combination with the main chain and the off-chain tuple structure, transforming the complex trusted environment generated by the digital work into a quantifiable numerical index in the range of 0 - 1, breaking the simple true / false judgment of traditional digital preservation and achieving a refined evaluation of the data source and generation process. For example, it can identify potential risks such as whether the device has a hardware-level security module and whether the author has a history of infringement records.

[0081] On the other hand, the present application also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the blockchain-based digital copyright preservation method provided by each of the above methods.

[0082] On yet another hand, the present application also provides a non-transitory computer-readable storage medium with a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the blockchain-based digital copyright preservation method provided by each of the above methods.

[0083] The computer device can be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used 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 the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a carrier 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 covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0084] When a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0085] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a predefined sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0086] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROMs). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because, for example, the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0087] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logic functions on data signals, application specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0088] The titanium alloy VAR melting bath edge control method and its control program provided by the present application can accurately perceive the evolution characteristics of the bath boundary disturbance in a multi-dimensional space by constructing a disturbance identification process based on the angular dynamic parameter field; further combined with the modeling of the disturbance direction increasing potential vector field, so as to realize the dynamic prediction of the disturbance development trend. On this basis, the disturbance steepness function is applied to quantitatively evaluate local abnormal changes, ensuring that the system has a fine discrimination ability for the strength and mutation degree of the disturbance; at the same time, through the disturbance causal chain identification mechanism, the system can identify potential diffusion patterns and possible affected areas in the early stage of the disturbance occurrence, significantly improving the timeliness of pre-warning and response decision-making. Compared with the existing control technologies that only rely on global parameters or static feature analysis, this method can actively generate local directional control strategies according to the disturbance propagation direction, realize the dynamic adaptive correction of the bath morphology, and greatly improve the system's ability to maintain the stability of the bath boundary and the control accuracy in a complex disturbance environment. The method of the present application can effectively reduce the occurrence frequency of boundary distortion, improve the consistency of the bath morphology, and shorten the control response time in actual VAR melting production, and is applicable to the titanium alloy metallurgical preparation scenario with extremely high requirements for composition uniformity and tissue continuity, and has good industrial applicability, scalability and popularization and application value.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill 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 they should all be covered within the scope of the claims of the present application.

Claims

1. A digital copyright deposit method based on blockchain, characterized in that It includes the following steps: Step S1: Collect the original digital content of the digital work and the original content environment data, perform hash calculation on the original digital content through a hash algorithm to obtain the original digital content hash value; Step S2: Combine the obtained original digital content hash value and the original content environment data to obtain the meta-information packet to be signed; Based on the obtained meta-information packet to be signed, generate a private key by generating a random number, and based on the private key, perform a digital signature on the meta-information packet to be signed through the elliptic curve digital signature algorithm to obtain a data signature; Step S3: Combine the obtained data signature and the original content environment data to obtain a signed copyright summary data packet; and perform hash calculation on the obtained signed copyright summary data packet through a hash algorithm to obtain a structured summary hash value; Step S4: Based on the structured summary hash value and the data confidence coefficient of the original content environment data, construct a Merkle tree in a binary hash combination manner to obtain a constructed Merkle tree; and based on the constructed Merkle tree, obtain the root node value and path index information of the Merkle tree; Step S5: Based on the blockchain hierarchical chain writing strategy, generate a main chain tuple structure by combining the root node value of the Merkle tree and the key meta-information, where the key meta-information is the basic information of the blockchain; And perform hash calculation on the work ID and work type in the original content environment data to obtain an auxiliary chain mapping identifier of the original work content; based on the obtained auxiliary chain mapping identifier, store the signed copyright summary data packet, the corresponding path index information and structured summary hash value of the Merkle tree, and the data confidence coefficient in a combined manner off-chain to obtain an off-chain tuple structure in the database; Step S6: Calculate the data confidence coefficient of the digital work to be verified, and calculate a verification evaluation index 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; Step S7: Compare the verification evaluation index with a preset threshold to implement the deposit query of the digital work to be verified.

2. The digital copyright deposit method based on blockchain according to claim 1, wherein The step S1 includes: Perform hash calculation on the original digital content using the hash algorithm SHA-256 to generate the original digital content hash value: ， Among them, is the hash value of the original digital content, is a hash function, and Content is the original digital content.

3. The digital copyright deposit method based on blockchain according to claim 2, wherein, The step S3 includes: Hash value of the original digital content and the generated timestamp , device unique identifier DevID, author digital identity identifier AID, work ID, work type are combined to obtain the meta-information 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, use the elliptic curve digital signature algorithm to sign the meta-information packet to be signed and perform hash calculation on the meta-information packet to be signed: ， Among them, is the hash value of the meta-information packet to be signed, is a hash function, is the meta-information 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: ， Wherein, P is a point on an elliptic curve, obtained by multiplying the base point G of the elliptic curve by a temporary random number k; Take the x coordinate of the point P on the elliptic curve, and calculate the signature component through the x coordinate, the hash value of the meta-information packet to be signed, and in combination with the private key: ， Wherein, s represents the signature component, k represents the temporary random number, e is the hash value of the meta-information 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 remainder symbol; Combine the signature component s and the x - coordinate r of the point P on the elliptic curve to finally obtain the digital signature , =(r, s), where r is the x - coordinate of the elliptic curve point P and s is the signature component.

4. A digital copyright deposit method based on blockchain according to claim 3, characterized in that, The step S3 includes: Combine the data signature and the original content environment data to obtain the signed copyright summary data packet PZ, PZ = { , , DevID, AID, ID, , }, where is the timestamp, DevID is the device unique identifier, AID is the author digital identity identifier, ID is the work ID, is the work type; Combine multiple signed copyright summary data packets to obtain a summary data packet set PZ_N, PZ_N = { , ,..., }, and perform hash calculation on the signed copyright summary data packets through the hash algorithm to obtain the hash value of each signed copyright summary data packet: ， Wherein, is the hash value of the i-th signature copyright summary data packet, is the i-th signature copyright summary data packet in the set of summary data packets, represents a hash function; Take the structured digest hash values of n signature copyright summary data packets as the structured digest hash value : = , , ,... .

5. A digital copyright deposit method based on blockchain according to claim 1, characterized in that The data confidence coefficient of the original content environment data is obtained through the following steps: Calculate the confidence of the device unique identifier DevID: ， Among them, is the confidence level of the device unique identifier DevID, is the existence of the hardware security module. If the device has a hardware security module, it is 1; if not, it is 0.

6. TEE is the level of the executable environment, and DHE is the entropy value of the device fingerprint hash, which is obtained by calculating the Shannon entropy of the device unique identifier collected by the TEE device; Calculate the confidence of the author digital identity identifier AID: ， Among them, is the confidence level of the author's digital identity identifier AID, is the author authentication level, represents the number of historical infringement times of the author, represents the number of historical evidence storage times of the author, represents the duration of the author's on-chain identity; Calculate the confidence of the work type : ， Among them, is the work type confidence level of is the work infringement risk level of the work type is the work size; According to the confidence level of the device unique identifier DevID, the confidence level of the author digital identity identifier AID, and the confidence level of the work type , the original content environment data confidence coefficient is calculated as follows: ， Among them, TC is the data confidence coefficient, is the confidence level of the device unique identifier DevID, is the confidence level of the author digital identity identifier AID, is the work type 's confidence level, , , is the confidence coefficient weight of the device unique identifier DevID, author digital identity identifier AID, and work type , is the coordination effect function, representing the synergistic effect between the confidence level of the device unique identifier DevID and the confidence level of the author digital identity identifier AID.

6. The digital copyright deposit method based on blockchain according to claim 1, wherein The step S4 includes: Take the structured digest hash values of n signature copyright summary data packets as an array of leaf nodes : = , , ,... , where represents the nth structured digest hash value in the array of leaf nodes; Complete the leaf node array through a completion strategy Perform even number completion: ， Among them, is the structured abstract hash value, indicating the hash value of the nth signature copyright abstract data packet in the structured abstract hash value; For the data confidence coefficient of each leaf node of the signature copyright summary data packet, calculate the data confidence coefficient of the parent node in the corresponding Merkle tree with the data confidence coefficient of its adjacent leaf node: ， 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; Finally, obtain the father node Q of the signature copyright summary data packet of the bottommost leaf node = , , ,... ; According to the structured summary hash value and the data confidence coefficient, construct a Merkle tree in the way of binary hash combination to obtain the constructed Merkle tree, and through the constructed Merkle tree, obtain the root node value and path index information of the Merkle tree: ， Among them, is the root node value of the Merkle tree, is the path index information of the Merkle tree, and Merkle() is the Merkle tree construction function, is the structured digest hash value, TC_N is the data confidence coefficient sequence, and SHA_256() is the hash function.

7. A digital copyright deposit method based on blockchain according to claim 1, characterized in that The step S5 specifically includes: Generate the main chain tuple structure by combining the root node value of the Merkle tree and the key meta-information: ， Among them, is the main chain tuple structure, is the root node value of the Merkle tree, is the deposit time of the blockchain address, is the work type, is the network identifier of the blockchain; Obtain the auxiliary chain mapping identifier of the original work content by performing hash calculation on the work ID and work type in the original content environment data: ， Among them, is the work type, represents the work ID of the i-th signed copyright abstract data packet, is the auxiliary chain mapping identifier of the i-th signed copyright abstract data packet, is a hash function; Based on the auxiliary chain mapping identifier, the signed copyright summary data packet, along with the path index information and structured summary hash value of the corresponding Merkle tree, are combined and stored off-chain to obtain the off-chain tuple structure in the database , =( , , , TC, ), is the auxiliary chain mapping identifier of the i-th signed copyright summary data packet, is the off-chain tuple structure of the i-th signed copyright summary data packet, is the path index information of the signed copyright summary data packet, is the structured summary hash value, represents the i-th signed copyright summary data packet, is the data confidence coefficient of the signed copyright summary data packet.

8. A digital copyright deposit method based on blockchain according to claim 1, characterized in that The step S6 specifically includes: For the digital work to be verified, calculate the data confidence coefficient of the digital work to be verified, and through the data confidence coefficient of the digital work to be verified and the corresponding off-chain tuple structure, perform re-fitting of the Merkle tree to obtain the root node value of the digital work to be verified in the Merkle tree , sign the digital work to be verified to obtain the digital signature of the digital work to be verified ; Calculate the verification evaluation index 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: ， Among them, K is the verification and evaluation index of the digital work to be verified. Obtain the root node value of the Merkle tree of the digital work to be verified. Is the root node value of the Merkle tree of the main chain tuple structure. Is the digital signature of the digital work to be verified. Is the data signature in the structured summary data packet in the off-chain tuple structure. I() is a judgment function. If the two parameters in I() are equal, it is 1; if they are not equal, it is 0.

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

10. A non-transitory computer-readable storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by the processor, it implements the blockchain-based digital copyright deposit method according to any one of claims 1 to 8.

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