Digital asset tracing method based on block chain

Abstract

The invention discloses a digital asset tracing method based on a block chain, and the method comprises the steps: generating a unique identification code on each digital asset, and carrying out the uplink operation of the block chain through employing the identification code as the identity label of the digital asset; extracting key information of the digital assets in issuing and transaction links, and performing structured processing on the key information to obtain an issuing matrix and a transaction matrix; obtaining an issuing relevance index for the issuing matrix; obtaining a transaction association index for the transaction matrix; based on the issuing association index and the transaction association index, an ASE encryption algorithm is adopted to generate a key; the identity information of the user is verified based on a zero-knowledge proof algorithm, and the user decrypts the encrypted data in the block chain through the identification code of the digital asset, so that the tracing of the digital asset by the user is realized; according to the method, data in different stages are analyzed and written into the block chain, so that tampering prevention and traceability of issuing data and transaction data are realized.

Description

Technical Field

[0001] The present invention belongs to the field of data processing, and specifically refers to a digital asset tracing method based on blockchain. Background Art

[0002] Using blockchain for anti-counterfeiting and traceability is an innovative technological application that utilizes the immutable and decentralized characteristics of blockchain to ensure the authenticity and traceability of product information.

[0003] The Chinese invention patent with the patent publication number CN116936132A discloses a digital asset traceability method based on blockchain management, including: data processing of each digital asset transaction information to be traced, obtaining binary data and transaction time, and then imaging the binary data to obtain a grayscale image; using the transaction time to perform preliminary encryption processing on each bit layer of the grayscale image to obtain a preliminary encrypted image, and then obtain a first encrypted grayscale image; calculating the spatial complexity index of the first encrypted grayscale image, using the spatial complexity index to determine the second encrypted grayscale image, blockchain management of the second encrypted grayscale image, obtaining blocks, and realizing digital asset traceability based on blocks. The present invention solves the problems of poor accuracy of digital asset traceability and inability to achieve effective traceability, improves the accuracy of digital asset traceability, and can be applied to the field of digital asset traceability.

[0004] However, the traceability of digital assets is not limited to each digital asset transaction information. The digital asset process mainly includes issuance and trading. Therefore, the traceability of issuance and trading becomes extremely important. However, encryption in the issuance and trading process, and anti-counterfeiting traceability involving multiple parties may face challenges in the secure distribution of keys to all parties. Transmitting keys through non-secure channels may lead to key leakage, making encryption invalid and thus affecting the security of traceability. Summary of the invention

[0005] In order to solve the problems in the above-mentioned prior art that the traceability of digital assets is not limited to each digital asset transaction information, the encryption during the issuance and transaction process, the anti-counterfeiting traceability involving multiple parties, the secure distribution of keys to all parties may face challenges, the transmission of keys without a secure channel may lead to key leakage, making the encryption invalid, and then the security of image traceability, etc., the present invention proposes a digital asset traceability method based on blockchain to improve the above problems.

[0006] The specific application is as follows:

[0007] A digital asset tracing method based on blockchain, comprising:

[0008] S1: Generate a unique identification code for each digital asset, and use the identification code as the identity of the digital asset to perform blockchain operations;

[0009] S2: extracting key information of the digital asset in the issuance and transaction stages, and performing structured processing on the key information to obtain an issuance matrix and a transaction matrix;

[0010] S3: For the issuance matrix, evaluating the correlation of the digital asset issuance process by analyzing the correlation of parameters, and obtaining an issuance correlation index;

[0011] S4: For the transaction matrix, evaluating the correlation of the transaction process by analyzing the correlation of parameters of different issuance links, and obtaining a transaction correlation index;

[0012] S5: Based on the issuance correlation index and the transaction correlation index, an ASE encryption algorithm is used to generate a key;

[0013] S6: The identity information of the user is verified based on a zero-knowledge proof algorithm. After verifying that the identity information of the user is valid, the user decrypts the encrypted data in the blockchain through the identification code of the digital asset, thereby enabling the user to trace the source of the digital asset.

[0014] Furthermore, the key information of the digital assets in the issuance and transaction links is extracted, and the key information is structured to obtain an issuance matrix and a transaction matrix, including:

[0015] Extracting key features of the digital asset in the issuance phase and key features of the digital asset in the transaction phase;

[0016] The key feature is converted into a plurality of binary features by a One-Hot encoding algorithm;

[0017] Use XML data structure to define the format structure of data and assign field name and data type to each information;

[0018] According to the predefined fields, the key information is converted into a data structure to obtain an issuance matrix and a transaction matrix.

[0019] Furthermore, the key features of the digital assets in the issuance phase are extracted, including:

[0020] Obtaining issuance information of a digital asset, the issuance information including a digitized image and product information, wherein the digital asset is an asset represented in a digital form, the product information is used to represent the asset information of the digital asset obtained by querying the blockchain, and the asset information includes the generation time, asset identifier, and asset name corresponding to the digital asset as the product information corresponding to the digital asset;

[0021] generating a first product image according to the product information of the digital asset, wherein the first product image is used to encrypt and store the product information in the form of an image;

[0022] generating a target product image by fusing the first product image and the digitized image, wherein the image information of the target product image at least includes the product information and the image information of the digitized image;

[0023] Input the target product image into a pre-trained ResNet model to extract key features of the issuance link, and obtain a key feature vector of the issuance link; wherein the ResNet model is a deep learning network composed of residual blocks;

[0024] The key features of the digital assets in the transaction process include:

[0025] Obtain the time of the digital asset transaction;

[0026] Obtain the process of the digital asset transaction;

[0027] Obtain information about both parties of the digital asset transaction;

[0028] Obtain the transaction time, process and transaction parties of each digital asset transaction.

[0029] Further, generating a target product image by fusing the first product image and the digitized image includes:

[0030] Inputting the first product image and the digitized image into a first model, wherein the first model is a Transfor network model based on an attention mechanism obtained by training N groups of training samples, each group of training samples in the N groups of training samples includes a first group of training images and a second group of training images, and the image information of the second group of training images at least includes the image information of the first group of training images, and N is a positive integer greater than 1;

[0031] The first product image is converted into a first product matrix by using the first model, and the digitized image is converted into a second product matrix, wherein the first product matrix is ​​used to represent the grayscale features, mean brightness features and local key features corresponding to each pixel point included in the first product image, and the second product matrix is ​​used to represent the grayscale features, mean brightness features and local key features corresponding to each pixel point included in the digitized image;

[0032] The mean brightness feature means that the brightness mean of the adjacent i*i pixels is taken as the mean brightness feature of each pixel, with each pixel as the center, where i represents the row value and column value of the pixel, and i is an integer greater than 1;

[0033] The target product image is generated by the first model according to the first product matrix and the second product matrix, wherein the similarity between the target product image and the digitized image is greater than a preset similarity.

[0034] Furthermore, the training step of the first model includes:

[0035] Inputting the first group of training images and the fingerprint code image included in each group of training samples in the N groups of training samples into the Transfor network model, and generating the first group of predicted images corresponding to the group of training samples through the Transfor network model according to the first group of training images and the fingerprint code image in each group of training samples, wherein the image information of the first group of predicted images corresponding to each group of training samples at least includes the image information of the first group of training images and the image information of the fingerprint code image in the group of training samples;

[0036] The Transfor network model is updated multiple times based on the similarity between the first group of predicted images corresponding to each group of training samples in the N groups of training samples and the second group of training images in the group of training samples, until the similarity between the first group of predicted images generated by the Transfor network model after the last update based on the first group of training images in each group of training samples and the fingerprint code image and the second group of training images in the group of training samples is greater than the preset similarity.

[0037] Furthermore, after obtaining the issuance matrix and the transaction matrix, the method further includes:

[0038] Using a Patricia tree on the issuance matrix and the transaction matrix, taking the generated data structure as input and SHA-512 as a key generation function, calculating a hash value of the digital asset, and then combining multiple hash values ​​into a larger second hash value;

[0039] Recording the second hash value and the data structure together in a corresponding block;

[0040] The unique identification code generated on the digital asset is stored in the identification blockchain;

[0041] The issuance matrix is ​​stored in an issuance block;

[0042] The transaction matrix is ​​stored in a transaction block;

[0043] The key is stored in a key block;

[0044] The issuance block, the transaction block, and the key block are all associated through a unique identification code.

[0045] Furthermore, the calculation formula of the issuance correlation index is:

[0046]

[0047] Among them, GL p Y is the issuance correlation index of the digital asset numbered p in this batch of digital assets. j and Y j+1 are the jth normalized issuance-related parameter and the j+1th normalized issuance-related parameter of the pth digital asset, respectively. is the mean of all normalized issuance-related parameter data, and k is the number of issuance-related parameters.

[0048] Furthermore, for the transaction matrix, the correlation of the transaction process is evaluated by analyzing the correlation of the parameters of different issuance links, and the transaction correlation indicators are obtained, including:

[0049] Extract the transaction-related data from the nth and n+1th issuance data, and construct two transaction-related matrices T n and T n+1 ;

[0050] Compare transaction correlation matrix T n and T n+1 The parameter types of both matrices are retained, and the parameter types contained in only one matrix are discarded to obtain the optimized transaction correlation matrix T' n and T' n+1 ;

[0051] Calculate the optimized transaction correlation matrix T' n and T' n+1 The standard deviation matrix Dev(T' n ) and Dev(T' n+1 );

[0052] Through the standard deviation matrix Dev(T' n ) and Dev(T' n+1 ), obtain the correlation coefficient matrix;

[0053] Based on the difference between the correlation coefficient matrix and the initial matrix, and combined with the optimized transaction correlation matrix T' n and T' n+1 The Spearman rank correlation coefficient between them was used to obtain the transaction correlation index;

[0054] The correlation coefficient matrix is: The standard deviation matrix Dev(T'n+1 ) and the standard deviation matrix Dev(T' n ) ratio.

[0055] Furthermore, based on the issuance correlation index and the transaction correlation index, an ASE encryption algorithm is used to generate a key, including:

[0056] Normalizing the values ​​of the issuance correlation index and the transaction correlation index to obtain a normalized issuance correlation index and a normalized transaction correlation index;

[0057] The ASE encryption algorithm is adopted, and the normalized issuance correlation index and the normalized transaction correlation index are used as input parameters to define a key generation function, and the generated key is:

[0058] Y=Hash(A'⊕B'),

[0059] Among them, Y represents the generated key, Hash represents the hash function, A' and B' represent the normalized issuance correlation index and the normalized transaction correlation index respectively, and ⊕ represents the XOR operator.

[0060] Furthermore, the user decrypts the encrypted data in the blockchain through the identification code of the digital asset to enable the user to trace the source of the digital asset, including:

[0061] The traceability results are sorted by matching degree and combined with the business system to generate a digital asset traceability report which is returned to the user; wherein the digital asset traceability report includes traceability image, evidence hash, blockchain address, evidence time, evidence location, evidence depositor, evidence number, digital asset issuance information and digital asset transaction information.

[0062] The beneficial effects of the blockchain-based digital asset tracing method of the present invention are as follows:

[0063] The present invention generates a unique identification code on each digital asset, and uses the identification code as the identity of the digital asset to perform a blockchain on-chain operation; extracts key information of the digital asset in the issuance and transaction links, performs structured processing on the key information, and obtains an issuance matrix and a transaction matrix; for the issuance matrix, evaluates the correlation of the digital asset issuance process by analyzing the correlation of parameters, and obtains an issuance correlation index; for the transaction matrix, evaluates the correlation of the transaction process by analyzing the correlation of parameters in different issuance links, and obtains a transaction correlation index; based on the issuance correlation index and the transaction correlation index, an ASE encryption algorithm is used to generate a key; based on a zero-knowledge proof algorithm, the user's identity information is verified, and after verifying that the user's identity information is valid, the user decrypts the encrypted data in the blockchain through the identification code of the digital asset, so that the user can trace the digital asset; the present invention writes data at different stages into the blockchain, realizes the tamper-proof and traceability of the issuance data and the transaction data, and realizes the secure traceability of the entire link of the digital asset issuance link and the transaction link. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 The present invention is a flowchart of a digital asset tracing method based on blockchain. DETAILED DESCRIPTION

[0065] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] Example 1

[0067] Figure 1 The present invention is a flowchart of a digital asset tracing method based on blockchain.

[0068] The method comprises steps S1-S6:

[0069] S1: Generate a unique identification code for each digital asset, and use the identification code as the identity of the digital asset to perform blockchain operations;

[0070] S2: extracting key information of the digital asset in the issuance and transaction stages, and performing structured processing on the key information to obtain an issuance matrix and a transaction matrix;

[0071] S3: For the issuance matrix, evaluating the correlation of the digital asset issuance process by analyzing the correlation of parameters, and obtaining an issuance correlation index;

[0072] S4: For the said transaction matrix, evaluate the relevance of the transaction process by analyzing the correlation of parameters in different issuance links, and obtain a transaction correlation index;

[0073] S5: Based on the said issuance relevance index and the transaction correlation index, use the ASE encryption algorithm to generate a key;

[0074] S6: Verify the identity information of the user based on the zero - knowledge proof algorithm. After verifying that the identity information of the user is valid, the user decrypts the encrypted data in the blockchain through the identification code of the digital asset, realizing the traceability of the user to the digital asset;

[0075] Among them, the zero - knowledge proof algorithm means that the prover can make the verifier believe that a certain assertion is correct without providing any useful information to the verifier.

[0076] Specifically, the specific process of the zero - knowledge proof algorithm is as follows:

[0077] (1) Key initialization GenKey(p,q,i)

[0078] Step1 The algorithm is executed by blockchain node i. The selection of large prime numbers p and q should ensure that

[0079] gcd(pq,(p - 1)(q - 1)) = 1, and n = pq;

[0080] Step2 Select a random positive integer g, and there exists μ=(L(g λ mod n 2 )) -1 where the definition of the function L(x) is

[0081] Step3 Output the private key SK i =(λ,μ) and the public key PK i , and make the public key public.

[0082] (2) Proof generation Prove(c,C,PK i )

[0083] Step1 This algorithm is executed by blockchain node i. The unique identification code c of the digital asset, the identity information value C of the user, and the public key PK of blockchain node i i are used as inputs.

[0084] Step2 Split c = I1 / J1, C = I2 / J2.

[0085] Step3 Select a random number r1, satisfying 0 < r1 < n and (r1,n) is a relatively prime relationship, and calculate

[0086]

[0087] Step 4 Select a random number r2 such that 0 < r2 < n and (r2, n) are relatively prime, and calculate

[0088]

[0089] Step 5 Generate a zero - knowledge proof E using the above parameters and calculate:

[0090]

[0091] where I1, I2, J1, J2 are integers and are intermediate values in the splitting process, is the data encryption result of I1, is the data encryption result of n - J1, PK i is the public key of blockchain node i;

[0092] (3) Proof verification Verify(E, SK i , m)

[0093] Step 1 This algorithm is executed by blockchain node i, taking the zero - knowledge proof E, the private key SK i and the compliance information m of the user's identity information as inputs for zero - knowledge proof verification;

[0094] Step 2 Using the private key SK i , calculate:

[0095]

[0096] where X is the verification result of the zero - knowledge proof, is the data decryption result of E, is the private key of blockchain node i;

[0097] Step 3 Determine the validity of the user's identity information based on the verification result X.

[0098] Furthermore, extract the key information in the issuance and trading links of the digital asset, and perform structured processing on the key information to obtain an issuance matrix and a trading matrix, including:

[0099] Extract the key features in the issuance link and the key features in the trading link of the digital asset;

[0100] Convert the key features into multiple binary features through the One - Hot encoding algorithm;

[0101] Use the XML data structure to define the format structure of the data, and assign the field name and data type for each piece of information;

[0102] According to the predefined fields, the key information is converted into a data structure to obtain an issuance matrix and a transaction matrix.

[0103] Furthermore, the key features of the digital assets in the issuance phase are extracted, including:

[0104] Obtaining issuance information of a digital asset, the issuance information including a digitized image and product information, wherein the digital asset is an asset represented in a digital form, the product information is used to represent the asset information of the digital asset obtained by querying the blockchain, and the asset information includes the generation time, asset identifier, and asset name corresponding to the digital asset as the product information corresponding to the digital asset;

[0105] generating a first product image according to the product information of the digital asset, wherein the first product image is used to encrypt and store the product information in the form of an image;

[0106] generating a target product image by fusing the first product image and the digitized image, wherein the image information of the target product image at least includes the product information and the image information of the digitized image;

[0107] Input the target product image into a pre-trained ResNet model to extract key features of the issuance link, and obtain a key feature vector of the issuance link; wherein the ResNet model is a deep learning network composed of residual blocks;

[0108] The key features of the digital assets in the transaction process include:

[0109] Obtain the time of the digital asset transaction;

[0110] Obtain the process of the digital asset transaction;

[0111] Obtain information about both parties of the digital asset transaction;

[0112] Obtain the transaction time, process and transaction parties of each digital asset transaction.

[0113] Further, generating a target product image by fusing the first product image and the digitized image includes:

[0114] Inputting the first product image and the digitized image into a first model, wherein the first model is a Transfor network model based on an attention mechanism obtained by training N groups of training samples, each group of training samples in the N groups of training samples includes a first group of training images and a second group of training images, and the image information of the second group of training images at least includes the image information of the first group of training images, and N is a positive integer greater than 1;

[0115] The first product image is converted into a first product matrix by using the first model, and the digitized image is converted into a second product matrix, wherein the first product matrix is ​​used to represent the grayscale features, mean brightness features and local key features corresponding to each pixel point included in the first product image, and the second product matrix is ​​used to represent the grayscale features, mean brightness features and local key features corresponding to each pixel point included in the digitized image;

[0116] The mean brightness feature means that the brightness mean of the adjacent i*i pixels is taken as the mean brightness feature of each pixel, with each pixel as the center, where i represents the row value and column value of the pixel, and i is an integer greater than 1;

[0117] The target product image is generated by the first model according to the first product matrix and the second product matrix, wherein the similarity between the target product image and the digitized image is greater than a preset similarity.

[0118] Furthermore, the training step of the first model includes:

[0119] Inputting the first group of training images and the fingerprint code image included in each group of training samples in the N groups of training samples into the Transfor network model, and generating the first group of predicted images corresponding to the group of training samples through the Transfor network model according to the first group of training images and the fingerprint code image in each group of training samples, wherein the image information of the first group of predicted images corresponding to each group of training samples at least includes the image information of the first group of training images and the image information of the fingerprint code image in the group of training samples;

[0120] The Transfor network model is updated multiple times based on the similarity between the first group of predicted images corresponding to each group of training samples in the N groups of training samples and the second group of training images in the group of training samples, until the similarity between the first group of predicted images generated by the Transfor network model after the last update based on the first group of training images in each group of training samples and the fingerprint code image and the second group of training images in the group of training samples is greater than the preset similarity.

[0121] Furthermore, after obtaining the issuance matrix and the transaction matrix, the method further includes:

[0122] Using a Patricia tree on the issuance matrix and the transaction matrix, taking the generated data structure as input and SHA-512 as a key generation function, calculating a hash value of the digital asset, and then combining multiple hash values ​​into a larger second hash value;

[0123] Recording the second hash value and the data structure together in a corresponding block;

[0124] The unique identification code generated on the digital asset is stored in the identification blockchain;

[0125] The issuance matrix is ​​stored in an issuance block;

[0126] The transaction matrix is ​​stored in a transaction block;

[0127] The key is stored in a key block;

[0128] The issuance block, the transaction block, and the key block are all associated through a unique identification code.

[0129] Furthermore, the calculation formula of the issuance correlation index is:

[0130]

[0131] Among them, GL p Y is the issuance correlation index of the digital asset numbered p in this batch of digital assets. j and Y j+1 are the jth normalized issuance-related parameter and the j+1th normalized issuance-related parameter of the pth digital asset, respectively. is the mean of all normalized issuance-related parameter data, and k is the number of issuance-related parameters.

[0132] Furthermore, for the transaction matrix, the correlation of the transaction process is evaluated by analyzing the correlation of the parameters of different issuance links, and the transaction correlation indicators are obtained, including:

[0133] Extract the transaction-related data from the nth and n+1th issuance data, and construct two transaction-related matrices T n and T n+1 ;

[0134] Compare transaction correlation matrix T n and T n+1 The parameter types of both matrices are retained, and the parameter types contained in only one matrix are discarded to obtain the optimized transaction correlation matrix T' n and T' n+1;

[0135] Calculate the optimized transaction correlation matrix T' n and T' n+1 The standard deviation matrix Dev(T' n ) and Dev(T' n+1 );

[0136] Through the standard deviation matrix Dev(T' n ) and Dev(T' n+1 ), obtain the correlation coefficient matrix;

[0137] Based on the difference between the correlation coefficient matrix and the initial matrix, and combined with the optimized transaction correlation matrix T' n and T' n+1 The Spearman rank correlation coefficient between them was used to obtain the transaction correlation index;

[0138] The correlation coefficient matrix is: The standard deviation matrix Dev(T' n+1 ) and the standard deviation matrix Dev(T' n ) ratio.

[0139] Furthermore, based on the issuance correlation index and the transaction correlation index, an ASE encryption algorithm is used to generate a key, including:

[0140] Normalizing the values ​​of the issuance correlation index and the transaction correlation index to obtain a normalized issuance correlation index and a normalized transaction correlation index;

[0141] The ASE encryption algorithm is adopted, and the normalized issuance correlation index and the normalized transaction correlation index are used as input parameters to define a key generation function, and the generated key is:

[0142] Y=Hash(A'⊕B'),

[0143] Among them, Y represents the generated key, Hash represents the hash function, A' and B' represent the normalized issuance correlation index and the normalized transaction correlation index respectively, and ⊕ represents the XOR operator.

[0144] Furthermore, the user decrypts the encrypted data in the blockchain through the identification code of the digital asset to enable the user to trace the source of the digital asset, including:

[0145] The traceability results are sorted by matching degree and combined with the business system to generate a digital asset traceability report which is returned to the user; wherein the digital asset traceability report includes traceability image, evidence hash, blockchain address, evidence time, evidence location, evidence depositor, evidence number, digital asset issuance information and digital asset transaction information.

[0146] Example 2, establish a traceability and anti-counterfeiting solution for the issuance and transaction process of digital assets. Each digital asset is assigned a unique identification code to connect it to the blockchain network. The issuance process information and transaction data are encrypted and stored on the blockchain. The issuance and transaction information is encrypted and stored on the blockchain, and the data record generates blocks according to the timestamp. For each digital asset, the issuance correlation index and the transaction correlation index are calculated. The final key of the current block is generated by combining the final key of the previous block through the HKDF key derivation function, specifically the generated issuance block, and the HKDF key derivation function can be used to link the transaction block. Users can obtain blockchain information by generating a unique identification code on the digital asset, use ASE to decrypt the issuance information, and analyze the consistency of the transaction data. Finally, the scheme uses blockchain technology and indicator analysis to achieve traceability and anti-counterfeiting of digital assets.

[0147] The beneficial effects of the blockchain-based digital asset tracing method of the present invention are as follows:

[0148] The present invention generates a unique identification code on each digital asset, and uses the identification code as the identity of the digital asset to perform a blockchain on-chain operation; extracts key information of the digital asset in the issuance and transaction links, performs structured processing on the key information, and obtains an issuance matrix and a transaction matrix; for the issuance matrix, evaluates the correlation of the digital asset issuance process by analyzing the correlation of parameters, and obtains an issuance correlation index; for the transaction matrix, evaluates the correlation of the transaction process by analyzing the correlation of parameters in different issuance links, and obtains a transaction correlation index; based on the issuance correlation index and the transaction correlation index, an ASE encryption algorithm is used to generate a key; based on a zero-knowledge proof algorithm, the user's identity information is verified, and after verifying that the user's identity information is valid, the user decrypts the encrypted data in the blockchain through the identification code of the digital asset, so that the user can trace the digital asset; the present invention writes data at different stages into the blockchain, realizes the tamper-proof and traceability of the issuance data and the transaction data, and realizes the secure traceability of the entire link of the digital asset issuance link and the transaction link.

[0149] The present invention and its embodiments are described above, which is not restrictive. What is shown in the accompanying drawings is only one of the embodiments of the present invention, and the actual content is not limited to this. In short, if ordinary technicians in this field are inspired by it and do not deviate from the purpose of the invention, they can creatively design structural methods and embodiments similar to the technical scheme, which should all fall within the scope of protection of the present invention.

Claims

1. A digital asset tracing method based on blockchain, characterized in that: include: S1: Generate a unique identification code for each digital asset, and use the identification code as the identity of the digital asset to perform blockchain operations; S2: extracting key information of the digital asset in the issuance and transaction stages, and performing structured processing on the key information to obtain an issuance matrix and a transaction matrix; S3: For the issuance matrix, evaluating the relevance of the digital asset issuance process by analyzing the correlation of parameters to obtain an issuance relevance index; S4: For the transaction matrix, evaluating the correlation of the transaction process by analyzing the correlation of parameters of different issuance links, and obtaining a transaction correlation index; S5: Based on the issuance correlation index and the transaction correlation index, an ASE encryption algorithm is used to generate a key; S6: The identity information of the user is verified based on a zero-knowledge proof algorithm. After verifying that the identity information of the user is valid, the user decrypts the encrypted data in the blockchain through the identification code of the digital asset, thereby enabling the user to trace the source of the digital asset.

2. A blockchain-based digital asset tracing method according to claim 1, characterized in that: Extract key information of the digital asset in the issuance and transaction stages, perform structured processing on the key information, and obtain an issuance matrix and a transaction matrix, including: Extracting the key features of the digital assets in the issuance phase and the key features in the transaction phase; The key feature is converted into a plurality of binary features by a One-Hot encoding algorithm; Use XML data structure to define the format structure of data and assign field name and data type to each information; According to the predefined fields, the key information is converted into a data structure to obtain an issuance matrix and a transaction matrix.

3. A blockchain-based digital asset tracing method according to claim 2, characterized in that: The key features of the digital assets in the issuance process include: Obtaining issuance information of a digital asset, the issuance information including a digitized image and product information, wherein the digital asset is an asset represented in a digital form, the product information is used to represent the asset information of the digital asset obtained by querying the blockchain, and the asset information includes the generation time, asset identifier, and asset name corresponding to the digital asset as the product information corresponding to the digital asset; generating a first product image according to the product information of the digital asset, wherein the first product image is used to encrypt and store the product information in the form of an image; generating a target product image by fusing the first product image and the digitized image, wherein the image information of the target product image at least includes the product information and the image information of the digitized image; Input the target product image into a pre-trained ResNet model to extract key features of the issuance link, and obtain a key feature vector of the issuance link; wherein the ResNet model is a deep learning network composed of residual blocks; The key features of the digital assets in the transaction process include: Obtain the time of the digital asset transaction; Obtain the process of the digital asset transaction; Obtain information about both parties of the digital asset transaction; Obtain the transaction time, process and transaction parties of each digital asset transaction.

4. A blockchain-based digital asset tracing method according to claim 3, characterized in that: Generating a target product image by fusing the first product image and the digitized image includes: Inputting the first product image and the digitized image into a first model, wherein the first model is a Transfor network model based on an attention mechanism obtained by training N groups of training samples, each group of training samples in the N groups of training samples includes a first group of training images and a second group of training images, and the image information of the second group of training images at least includes the image information of the first group of training images, and N is a positive integer greater than 1; The first product image is converted into a first product matrix by using the first model, and the digitized image is converted into a second product matrix, wherein the first product matrix is ​​used to represent the grayscale features, mean brightness features and local key features corresponding to each pixel point included in the first product image, and the second product matrix is ​​used to represent the grayscale features, mean brightness features and local key features corresponding to each pixel point included in the digitized image; The mean brightness feature means that the brightness mean of the adjacent i*i pixels is taken as the mean brightness feature of each pixel, with each pixel as the center, where i represents the row value and column value of the pixel, and i is an integer greater than 1; The target product image is generated by the first model according to the first product matrix and the second product matrix, wherein the similarity between the target product image and the digitized image is greater than a preset similarity.

5. A blockchain-based digital asset tracing method according to claim 4, characterized in that: The training steps of the first model include: Inputting the first group of training images and the fingerprint code image included in each group of training samples in the N groups of training samples into the Transfor network model, and generating the first group of predicted images corresponding to the group of training samples through the Transfor network model according to the first group of training images and the fingerprint code image in each group of training samples, wherein the image information of the first group of predicted images corresponding to each group of training samples at least includes the image information of the first group of training images and the image information of the fingerprint code image in the group of training samples; The Transfor network model is updated multiple times based on the similarity between the first group of predicted images corresponding to each group of training samples in the N groups of training samples and the second group of training images in the group of training samples, until the similarity between the first group of predicted images generated by the Transfor network model after the last update based on the first group of training images in each group of training samples and the fingerprint code image and the second group of training images in the group of training samples is greater than the preset similarity.

6. A blockchain-based digital asset tracing method according to claim 5, characterized in that: After obtaining the issuance matrix and the transaction matrix, the method further includes: Using a Patricia tree on the issuance matrix and the transaction matrix, taking the generated data structure as input and SHA-512 as a key generation function, calculating a hash value of the digital asset, and then combining multiple hash values ​​into a larger second hash value; Recording the second hash value and the data structure together in a corresponding block; The unique identification code generated on the digital asset is stored in the identification blockchain; The issuance matrix is ​​stored in an issuance block; The transaction matrix is ​​stored in a transaction block; The key is stored in a key block; The issuance block, the transaction block, and the key block are all associated through a unique identification code.

7. A blockchain-based digital asset tracing method according to claim 6, characterized in that: The calculation formula of the issuance correlation index is: Among them, GL p Y is the issuance correlation index of the digital asset numbered p in this batch of digital assets. j and Y j+1 are the jth normalized issuance-related parameter and the j+1th normalized issuance-related parameter of the pth digital asset, respectively. is the mean of all normalized issuance-related parameter data, and k is the number of issuance-related parameters.

8. A blockchain-based digital asset tracing method according to claim 7, characterized in that: For the transaction matrix, the correlation of the transaction process is evaluated by analyzing the correlation of the parameters of different issuance links, and the transaction correlation indicators are obtained, including: Extract the transaction-related data from the nth and n+1th issuance data, and construct two transaction-related matrices T n and T n+1 ; Compare transaction correlation matrix T n and T n+1 The parameter types of both matrices are retained, and the parameter types contained in only one matrix are discarded to obtain the optimized transaction correlation matrix T′ n and T′ n+1 ; Calculate the optimized transaction correlation matrix T′ n and T′ n+1 The standard deviation matrix Dev(T′ n ) and Dev(T′ n+1 ); Through the standard deviation matrix Dev(T′ n ) and Dev(T′ n+1 ), obtain the correlation coefficient matrix; Based on the difference between the correlation coefficient matrix and the initial matrix, and combined with the optimized transaction correlation matrix T' n and T′ n+1 The Spearman rank correlation coefficient between them was used to obtain the transaction correlation index; The correlation coefficient matrix is: The standard deviation matrix Dev(T′ n+1 ) and the standard deviation matrix Dev(T′ n ) ratio.

9. A blockchain-based digital asset tracing method according to claim 8, characterized in that: Based on the issuance correlation index and the transaction correlation index, the ASE encryption algorithm is used to generate a key, including: Normalizing the values ​​of the issuance correlation index and the transaction correlation index to obtain a normalized issuance correlation index and a normalized transaction correlation index; The ASE encryption algorithm is adopted, and the normalized issuance correlation index and the normalized transaction correlation index are used as input parameters to define a key generation function, and the generated key is: Where Y represents the generated key, Hash represents the hash function, A′ and B′ represent the normalized issuance correlation index and the normalized transaction correlation index, respectively. Represents the exclusive OR operator.

10. A blockchain-based digital asset tracing method according to claim 9, characterized in that: The user decrypts the encrypted data in the blockchain through the identification code of the digital asset to enable the user to trace the source of the digital asset, including: The traceability results are sorted by matching degree and combined with the business system to generate a digital asset traceability report which is returned to the user; wherein the digital asset traceability report includes traceability image, evidence hash, blockchain address, evidence time, evidence location, evidence depositor, evidence number, digital asset issuance information and digital asset transaction information.

Images