Blockchain and smart contract-based intellectual property transaction traceability method and system

Abstract

The application belongs to the technical field of intellectual property transaction, and discloses an intellectual property transaction traceability method and system based on a blockchain and a smart contract; the method comprises the following steps: acquiring and storing intellectual property information uploaded by a user; performing encryption processing on the stored intellectual property information, and performing security protection in combination with access information of an access subject; selecting an optimal contract template and deploying the contract template to a corresponding blockchain transaction end; acquiring demand information of both a buyer and a seller, generating a matching satisfaction degree, and selecting an optimal buying and selling combination for transaction; acquiring key data of an entire process of intellectual property transaction, generating a traceability index, and performing traceability on the intellectual property transaction based on the traceability index. The application unifies various process data by using a blockchain decentralized storage record, avoids the occurrence of an information island, is convenient for traceability, and reconstructs data credibility from a storage layer by using a distributed consensus mechanism and an unalterable nature of the blockchain, thereby providing strong trust support for authenticity of intellectual property transaction traceability.

Description

Technical Field

[0001] This invention relates to the field of intellectual property transaction technology, and more specifically, to a method and system for tracing the source of intellectual property transactions based on blockchain and smart contracts. Background Technology

[0002] Intellectual property (IP) is a core asset in the digital economy era, and its transactions and transfers are becoming increasingly frequent. IP transactions refer to commercial activities in which rights holders transfer or authorize the use of their intangible assets, such as patents, trademarks, copyrights, and trade secrets, to others through sale, licensing, or pledge. As an intangible asset, IP transactions involve multiple stages, including rights confirmation, authorization, revenue distribution, and rights protection; therefore, tracing the entire IP transaction process is crucial.

[0003] Existing intellectual property information is scattered across the private databases of various centralized institutions, which easily leads to data silos. The public and subsequent transacting parties find it difficult to obtain complete and continuous historical records, making effective and reliable traceability impossible. Moreover, when trading existing intellectual property, suitable transacting parties are often selected based on fixed contract templates and fixed requirements. Since different types of intellectual property and different transacting parties have different requirements, the selection of transacting parties using fixed contracts and fixed requirements is not accurate enough, resulting in low matching efficiency and long transaction cycles.

[0004] In view of this, the present invention proposes a method and system for tracing intellectual property transactions based on blockchain and smart contracts to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and achieve the above objectives, the present invention provides the following technical solution:

[0006] Methods for tracing intellectual property transactions based on blockchain and smart contracts include:

[0007] The system obtains intellectual property information uploaded by users, conducts compliance reviews of the uploaded intellectual property information, and uploads it to the blockchain for storage after the review is passed.

[0008] The stored intellectual property information is encrypted, and the access information of the accessing subject is obtained. The stored intellectual property information is then protected by combining the access information.

[0009] The uploaded intellectual property information is matched with a pre-built set of contract templates to obtain multiple contract templates. The optimal contract template is then selected and deployed to the corresponding blockchain transaction terminal.

[0010] Obtain the demand information of buyers and sellers on the blockchain trading platform, match them with each other, generate a matching satisfaction rate, and select the optimal buy and sell combination for trading based on the matching satisfaction rate;

[0011] Key data from the entire intellectual property transaction process is acquired, stored on the blockchain, and linked in a chain to generate a traceability index. The traceability index is then used to trace the intellectual property transactions.

[0012] Furthermore, the method for encrypting the stored intellectual property information is as follows:

[0013] The uploaded intellectual property information is divided into public information and sensitive information. The sensitive information is encrypted to obtain encrypted information, and an encrypted certificate and a verification key for verifying the authenticity of the encrypted certificate are generated. The public information, the encrypted certificate and the verification key are sent to the blockchain together.

[0014] Furthermore, the methods for protecting stored intellectual property information include:

[0015] A master key for decrypting encrypted information is constructed. The master key consists of Q key fragments, each of which is stored in secure hardware of a pre-determined authorized entity.

[0016] When accessing encrypted information, obtain the access information of the accessing subject. Based on the access information, calculate the security value of the accessing subject. When the security value is less than the preset security threshold, prevent the accessing subject from logging in and update the master key and key fragments.

[0017] When the security value is greater than or equal to the set security threshold, a call instruction is generated and sent to all authorized entities holding key fragments. The number of key fragments collected by the accessing entity is obtained. When the number of key fragments is greater than or equal to the preset access key fragment threshold, the master key is automatically reassembled based on the number of collected key fragments to enable secure access. When the number of key fragments is less than or equal to the access key fragment threshold, access is blocked.

[0018] Furthermore, the method for calculating the security value of the accessing subject is as follows:

[0019] Access information includes the login information, behavioral information, and login environment information of the accessing entity;

[0020] When the access subject is an old user, collect the access subject's historical access information, extract the indicator features from the historical access information, obtain the quantitative value of each indicator feature, and construct the access subject's login habit vector A, access subject's behavior habit vector B, and access subject's login environment habit vector C based on the quantitative value of each indicator feature.

[0021] Obtain the access information currently accessed by the access subject, and based on the access information currently accessed by the access subject, construct the real-time login vector A1, the real-time behavior vector B1, and the real-time login environment vector C1 of the access subject respectively.

[0022] Calculate the cosine similarity between login habit vector A and login real-time vector A1, behavior habit vector B and behavior real-time vector B1, and login environment habit vector C and login environment real-time vector C1; sum the cosine similarities to obtain the security value;

[0023] When the access subject is a new user, obtain the quantitative values ​​of each indicator feature of all access subjects, calculate the average quantitative value of each indicator feature after averaging, and construct the login standard vector A2, the behavior standard vector B2, and the login environment standard vector C2 of the access subject based on the average quantitative value of each indicator feature.

[0024] Calculate the cosine similarity between the login standard vector A2 and the login real-time vector A1, the behavior standard vector B2 and the behavior real-time vector B1, and the login environment standard vector C2 and the login environment real-time vector C1; add the cosine similarities to obtain the security value.

[0025] Furthermore, the method for selecting the optimal contract template is as follows:

[0026] Based on the uploaded intellectual property information, the fields of key features are extracted from the intellectual property information to obtain the demand feature values ​​of each key feature. The user demand matrix is ​​constructed using the demand feature values ​​of each key feature as elements.

[0027] Construct a contract template set, extract the fields of the corresponding key features of each contract template in the contract template set, obtain the template feature values ​​of each key feature of the contract template, and construct the template matrix of each contract template using the template feature values ​​of each key feature as elements.

[0028] The user demand matrix is ​​compared with the template matrix of each contract template to obtain the matching matrix of each contract template. The elements in the matching matrix are weighted and summed to obtain the matching value of each contract template.

[0029] The contract templates are sorted from smallest to largest according to their matching values. The top three contract templates are selected and sent to the user. The user then selects one of the three contract templates as the optimal contract template.

[0030] Furthermore, the method for selecting the optimal buy-sell combination for trading is as follows:

[0031] Obtain seller's demand information, and set multiple sets of seller demand question sequences S and multiple sets of buyer demand question response sequences RS based on the seller's demand information; obtain buyer's demand information, and set multiple sets of buyer demand question sequences R and seller demand question response sequences SR based on the buyer's demand information;

[0032] When either the buyer or the seller initiates a transaction access, the two parties are automatically matched for intellectual property needs;

[0033] Intellectual property demand matching involves the matching between the seller's demand question sequence and the seller's demand question response sequence, as well as the matching between the buyer's demand question sequence and the buyer's demand question response sequence.

[0034] Based on the matching results, the matching satisfaction is calculated, and the buy / sell combination with the highest matching satisfaction is selected as the optimal buy / sell combination for trading.

[0035] Furthermore, methods for selecting the optimal buy-sell combination for trading also include:

[0036] When the optimal buy / sell combination fails to complete the transaction, the matching satisfaction is sorted in descending order to generate a candidate buy / sell combination order list. Each buy / sell combination in the candidate buy / sell combination order list is iterated and traded until the transaction is successful.

[0037] Furthermore, the method for generating satisfaction ratings is as follows:

[0038] Based on the matching results, the number of successful sequence matches between the seller demand question sequence S and the seller demand question response sequence SR is counted and denoted as the first match number. Based on the matching results, a successful seller demand matching set is constructed, and the absolute value of the seller demand deviation between the sequence value of each seller demand question sequence in the successful seller demand matching set and the sequence value of the corresponding seller demand question response sequence is calculated and denoted as the first absolute deviation value. The first deviation coefficient is obtained by summing all the first absolute deviation values ​​in the successful seller demand matching set.

[0039] Based on the matching results, count the number of successfully matched sequences in the buyer demand question sequence R and the buyer demand question response sequence RS, and record it as the second matching number; construct a buyer demand successfully matched set based on the matching results; calculate the absolute value of the buyer demand deviation between the sequence value of each buyer demand question sequence in the buyer demand successfully matched set and the sequence value of the corresponding buyer demand question response sequence, and record it as the second deviation absolute value; sum all the second deviation absolute values ​​in the buyer demand successfully matched set to obtain the second deviation coefficient.

[0040] The satisfaction value is obtained by adding the ratio of the first number of matches to the first deviation coefficient and the ratio of the second number of matches to the second deviation coefficient. The satisfaction value is then normalized to obtain the matching satisfaction level.

[0041] Furthermore, the method for tracing intellectual property transactions based on the traceability index is as follows:

[0042] Acquire key data from each stage of the entire intellectual property transaction process, construct key datasets for each stage, and use cryptographic hash algorithms to calculate the hash value of each key dataset to generate independent data blocks for each stage.

[0043] Each data block and its corresponding timestamp are uploaded to the blockchain node for on-chain notarization. Using the unique intellectual property identifier as the link, all independent data blocks are linked in chronological order to generate a traceability index. The association rule for the chain is that the hash value of the subsequent data block contains the hash value of the previous data block.

[0044] The system receives the hash value of any data block provided by the user tracing the source, determines the starting point of the index for the user's operation, and sequentially reads each data block in the tracing index from the starting point. Based on the key data in each data block, the system recalculates the hash value of each data block and records it as the new hash value. The new hash value is then matched and verified against the hash values ​​of the preceding data blocks contained in the subsequent data blocks in the tracing index. If the new hash values ​​of all data blocks completely match the corresponding hash values ​​in the tracing index, the verification passes, and all key data is reassembled in chronological order to generate a tracing report, which is then pushed to the user tracing the source. If there is a mismatch between the new hash value and the corresponding hash value in the tracing index, the verification fails, and tracing is impossible.

[0045] The intellectual property transaction traceability system based on blockchain and smart contracts, implementing the aforementioned intellectual property transaction traceability method based on blockchain and smart contracts, includes:

[0046] The intellectual property registration module is used to obtain intellectual property information uploaded by users, conduct compliance review of the uploaded intellectual property information, and upload it to the blockchain for storage after the review is passed.

[0047] The intellectual property storage module is used to encrypt the stored intellectual property information, obtain the access information of the accessing subject, and combine the access information to protect the stored intellectual property information.

[0048] The smart contract generation module is used to match the uploaded intellectual property information with a pre-built set of contract templates to obtain multiple contract templates, and select the optimal contract template to deploy to the corresponding blockchain transaction terminal.

[0049] The intellectual property transaction module is used to obtain the demand information of buyers and sellers on the blockchain transaction platform, match them with each other, generate a matching satisfaction rate, and select the optimal buy and sell combination for transaction based on the matching satisfaction rate.

[0050] The intellectual property traceability module is used to acquire key data from the entire intellectual property transaction process, perform on-chain notarization and chain association, generate a traceability index, and trace the intellectual property transaction based on the traceability index.

[0051] The technical effects and advantages of the intellectual property transaction traceability method and system based on blockchain and smart contracts of this invention are as follows:

[0052] This invention uses blockchain decentralized storage to unify data from various processes, avoiding information silos, facilitating traceability, and utilizing blockchain's distributed consensus mechanism and immutability to reconstruct data credibility at the storage layer, providing strong trust support for the authenticity of intellectual property transaction traceability.

[0053] This invention can flexibly adjust key features according to different intellectual property information and different intellectual property needs, achieving accurate template matching in multiple scenarios and ensuring that the selected contract template is highly consistent with the intellectual property transaction needs. At the same time, through bidirectional matching between the seller's set seller demand question sequence and the buyer's set seller demand question response sequence, and the buyer's set buyer demand sequence and the seller's set buyer demand question response sequence, and based on the comprehensive analysis of the number of successful matches and the magnitude of the deviation coefficient, it can better achieve accurate quantitative matching, ensuring that the selected buy and sell combination is highly consistent with the transaction needs, and fundamentally reducing transaction disputes caused by matching deviations.

[0054] This invention uses a comprehensive analysis of the login information, behavior, and login environment of the accessing subject to determine whether there is any access anomaly, thus providing initial protection for the security of encrypted information. When it is determined that there is no anomaly, a fragmented storage method is used, and the master key can only be reassembled for secure access if a threshold condition is met, to further protect the encrypted information. This can significantly improve the security of encrypted information and reduce the risk of encrypted information leakage. Attached Figure Description

[0055] Figure 1 This is a flowchart of the intellectual property transaction traceability method based on blockchain and smart contracts according to Embodiment 1 of the present invention;

[0056] Figure 2 This is a block diagram of the intellectual property transaction traceability system based on blockchain and smart contracts according to Embodiment 2 of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1:

[0058] Please see Figure 1As shown, this embodiment discloses a method for tracing intellectual property transactions based on blockchain and smart contracts. The method includes:

[0059] The system obtains intellectual property information uploaded by users, conducts compliance reviews of the uploaded intellectual property information, and uploads it to the blockchain for storage after the review is passed.

[0060] The stored intellectual property information is encrypted, and the access information of the accessing subject is obtained. The stored intellectual property information is then protected by combining the access information.

[0061] The uploaded intellectual property information is matched with a pre-built set of contract templates to obtain multiple contract templates. The optimal contract template is then selected and deployed to the corresponding blockchain transaction terminal.

[0062] Obtain the demand information of buyers and sellers on the blockchain trading platform, match them with each other, generate a matching satisfaction rate, and select the optimal buy and sell combination for trading based on the matching satisfaction rate;

[0063] Key data from the entire intellectual property transaction process is acquired, stored on the blockchain, and linked in a chain to generate a traceability index. The traceability index is then used to trace the intellectual property transactions.

[0064] The aforementioned scheme discloses a specific method for tracing intellectual property transactions based on blockchain and smart contracts. First, the intellectual property information to be traded is registered. Specifically, this involves acquiring user-uploaded intellectual property information, including but not limited to core data such as the rights holder, type of rights (patent, copyright, trademark, etc.), scope of rights, creation time, and application time. Then, the uploaded intellectual property information undergoes compliance review to ensure its compliance and authenticity. This can be done by connecting to authoritative databases in the field (such as the State Intellectual Property Office or Copyright Protection Center) or third-party auditing institutions to verify the authenticity of the information and eliminate the risk of duplicate registration or infringement. After approval, the information is uploaded to the blockchain for storage. During uploading, sensitive information within the intellectual property information is encrypted. If an accessing entity needs to access the information... When accessing sensitive information, the system obtains the access information of the accessing entity and uses this information to protect the stored intellectual property information, preventing unauthorized access and potential information leakage. Then, based on the content of the intellectual property information, it automatically selects the most suitable contract template and deploys it to the corresponding blockchain transaction platform. This allows for automatic selection of the most appropriate contract based on the differences in each piece of intellectual property information and specific needs, improving adaptability. Next, based on the needs of both buyers and sellers on the blockchain transaction platform, a matching satisfaction rate is generated. Based on this satisfaction rate, the optimal buyer-seller combination is selected for the transaction. Finally, based on the key data obtained from the entire intellectual property transaction process, the data is stored on the blockchain and linked in a chain to generate a traceability index. This index is then used to trace the intellectual property transaction back to its source, thus achieving traceability of the intellectual property transaction.

[0065] The method for encrypting stored intellectual property information is as follows: the uploaded intellectual property information is divided into public information and sensitive information, the sensitive information is encrypted to obtain encrypted information, and an encryption certificate and a verification key for verifying the authenticity of the encryption certificate are generated. The public information, encryption certificate and verification key are sent to the blockchain together.

[0066] The method for protecting the stored intellectual property information is as follows: a master key for decrypting encrypted information is constructed. The master key consists of Q key fragments, and each key fragment is stored in the secure hardware of a pre-determined authorized entity.

[0067] When accessing encrypted information, obtain the access information of the accessing subject. Based on the access information, calculate the security value of the accessing subject. When the security value is less than the preset security threshold, prevent the accessing subject from logging in and update the master key and key fragments.

[0068] When the security value is greater than or equal to the set security threshold, a call command is generated and sent to all authorized entities holding key fragments. The number of key fragments collected by the accessing entity is obtained. When the number of key fragments is greater than or equal to the preset access key fragment threshold, the master key is automatically reassembled based on the number of collected key fragments to enable secure access. When the number of key fragments is less than or equal to the access key fragment threshold, access is blocked.

[0069] The method for calculating the security value of an access subject is as follows: access information includes the access subject's login information, behavioral information, and login environment information;

[0070] When the access subject is an old user, collect the access subject's historical access information, extract the indicator features from the historical access information, obtain the quantitative value of each indicator feature, and construct the access subject's login habit vector A, access subject's behavior habit vector B, and access subject's login environment habit vector C based on the quantitative value of each indicator feature.

[0071] Obtain the access information currently accessed by the access subject, and based on the access information currently accessed by the access subject, construct the real-time login vector A1, the real-time behavior vector B1, and the real-time login environment vector C1 of the access subject respectively.

[0072] Calculate the cosine similarity between login habit vector A and login real-time vector A1, behavior habit vector B and behavior real-time vector B1, and login environment habit vector C and login environment real-time vector C1; sum the cosine similarities to obtain the security value;

[0073] When the access subject is a new user, obtain the quantitative values ​​of each indicator feature of all access subjects, calculate the average quantitative value of each indicator feature after averaging, and construct the login standard vector A2, the behavior standard vector B2, and the login environment standard vector C2 of the access subject based on the average quantitative value of each indicator feature.

[0074] Calculate the cosine similarity between the login standard vector A2 and the login real-time vector A1, the behavior standard vector B2 and the behavior real-time vector B1, and the login environment standard vector C2 and the login environment real-time vector C1; add the cosine similarities to obtain the security value.

[0075] The above solution provides a specific method for protecting stored intellectual property information. Specifically, the uploaded intellectual property information is first divided into public information and sensitive information. Public information, such as the title, creation time, and rights holder name, is used for on-chain public display and retrieval. Sensitive information, such as key technologies and algorithm code, is accessed only after authorization. The sensitive information is then encrypted using the ZK-SNARKs algorithm (an existing technology, which will not be discussed further here). Simultaneously, an encryption proof and a verification key are generated to verify the authenticity of the encryption proof. The key, or cryptographic proof, contains crucial information such as the hash value and creation time of the intellectual property information, but does not contain the intellectual property information itself. It only proves the validity of the intellectual property information without disclosing the original content. This ensures information security even if an unauthorized entity subsequently obtains the cryptographic proof and verification key, they cannot derive any information about the original intellectual property information. The public information, cryptographic proof, and verification key are sent together to the blockchain for immutable notarization. Nodes on the blockchain automatically verify the cryptographic proof using the verification key, confirming that the information has not been tampered with and that the rights holder's identity is legitimate. The public information provides the public or inquiring parties with basic information about the intellectual property.

[0076] For encrypted information, a master key for decryption is first constructed. For example, the Shamir secret sharing algorithm is used as the core, combined with the AES-256 algorithm to generate the master key. The master key consists of Q key fragments, which are distributed to pre-determined authorized entities and stored in the secure hardware of these entities. At the same time, a key fragment access threshold is preset. Only when the number of key fragments collected by the accessing entity is greater than or equal to the access key fragment access threshold can the master key be automatically reassembled to securely access the encrypted information. Otherwise, access is not allowed to ensure information security. For example, this implementation sets five key fragments, which are stored in different authorized entities. The key fragment access threshold is set to three. Secure access is only allowed when the number of key fragments collected by the accessing entity is greater than or equal to three. Here, the key fragment access threshold is set by the administrator.

[0077] Specifically, when an entity needs to access encrypted information, the system obtains the entity's access information, including login information, behavioral information, and login environment information. Login information includes authentication method, login method, and organizational identity level; behavioral information includes access time, location, frequency of sensitive operation requests, and login duration; and login environment information includes device security status, IP reputation value, and deviation of geographical location from commonly used locations on the blockchain. When the entity is a returning user (a historical user with a history of accessing the encrypted information), the system collects the entity's historical access information (including historical login information, behavioral information, and login environment information). It then extracts indicator features from this historical access information to obtain quantified values ​​for each indicator feature. These indicator features are specific to the login information, behavioral information, and login environment information. For example, login information indicators include authentication method, login method, and organizational identity level; behavioral information indicators include access time, access location, login duration, and frequency of sensitive operation requests; and login environment information indicators include device security status, IP reputation value, and deviation of geographical location from commonly used locations on the blockchain.

[0078] The quantified values ​​of each indicator feature indicate the security level of that feature; the higher the quantified value, the higher the security level. These values ​​can be derived from a pre-constructed indicator feature quantification mapping table, which can be set by professionals in the field based on business rules, risk levels, and experience. For example, in the authentication method indicator feature, different quantified values ​​are set according to the authentication method used. Since biometric authentication uses facial recognition, fingerprint recognition, and other biometric technologies, it has high security and therefore a large quantified value. Conversely, password verification using only username and password authentication has low security and therefore a small quantified value. (Example: Access time...) The indicator features are assigned different quantification values ​​based on different access time periods. Access during normal working hours results in a larger quantification value, while access during non-working hours such as early morning results in a smaller quantification value. The IP reputation value indicator feature is assigned different quantification values ​​based on the IP's reputation level. A higher reputation level indicates higher security and a larger quantification value, while a lower reputation level indicates lower security and a smaller quantification value. This process yields the quantification values ​​for each indicator feature. Based on the quantification values ​​of each indicator feature, the following vectors are constructed: login habit vector A, behavioral habit vector B, and login environment habit vector C for the accessing subject.

[0079] The system obtains the access information of the current user, compares it with a pre-built quantitative mapping table of indicator features to obtain the quantitative values ​​of each indicator feature, and thus constructs the user's real-time login vector A1, real-time behavior vector B1, and real-time login environment vector C1. It then calculates the cosine similarity between login habit vector A and real-time login vector A1, behavior habit vector B and real-time behavior vector B1, and login environment habit vector C and real-time login environment vector C1. A higher cosine similarity indicates a higher similarity between the two vectors, meaning a higher similarity between the current login and historical logins, and thus a higher level of security. For example, a higher cosine similarity between the user's login habit vector A and real-time login vector A1 indicates a higher degree of similarity between the current login information and historical login information, thus indicating higher security. Finally, the cosine similarities are summed to obtain a security value; a higher security value indicates higher security.

[0080] When the access subject is a new user, who is accessing the encrypted information for the first time, the quantitative values ​​of various indicator features of all historical access subjects are obtained, and the average quantitative value of each indicator feature is obtained after averaging. Based on the average quantitative value of each indicator feature, the login standard vector A2, the behavior standard vector B2, and the login environment standard vector C2 of the access subject are constructed respectively. The cosine similarity between the login standard vector A2 and the login real-time vector A1, the behavior standard vector B2 and the behavior real-time vector B1, and the login environment standard vector C2 and the login environment real-time vector C1 are calculated. The security value is obtained by adding the cosine similarity values. When the access subject is a new user, there is no historical access information. At this time, a standard access information is determined based on the access information of all access subjects who accessed the encrypted information. The security of the new user is judged by the standard access information. It can be seen that the higher the security value, the more secure the login is.

[0081] After obtaining the security value, a security threshold is pre-set based on the experience and expertise of those in the field. When the security value is less than the pre-set threshold, it indicates that the access subject's login security is poor. To ensure the security of encrypted information, the access subject is prevented from logging in, and the master key and key fragments are updated. A new master key is then generated and split into Q new key fragments, which are distributed to the pre-determined authorized subjects. This replaces the original key fragments with new ones, updating the master key and key fragments and ensuring data security. When the security value is greater than or equal to the set security threshold, further verification is required. At this point, a call command is generated and sent to all authorized subjects holding key fragments. The accessing entity begins collecting key fragments. When the number of key fragments is greater than or equal to a preset access key fragment threshold, the master key is automatically reassembled based on the collected key fragments for secure access. When the number of key fragments is less than or equal to the access key fragment threshold, access is blocked. This method allows for comprehensive analysis of the accessing entity's login information, behavior, and login environment to determine if there is any abnormal access, thus providing initial protection for the security of encrypted information. If no abnormal access is determined, a fragmented storage method is used, and the master key can only be reassembled for secure access if the threshold condition is met, to further protect the encrypted information. This significantly improves the security of encrypted information and reduces the risk of encrypted information leakage.

[0082] The method for selecting the optimal contract template is as follows: Based on the uploaded intellectual property information, extract the fields of key features of the intellectual property information to obtain the demand feature values ​​of each key feature, and construct a user demand matrix using the demand feature values ​​of each key feature as elements.

[0083] Construct a contract template set, extract the fields of the corresponding key features of each contract template in the contract template set, obtain the template feature values ​​of each key feature of the contract template, and construct the template matrix of each contract template using the template feature values ​​of each key feature as elements.

[0084] The user demand matrix is ​​compared with the template matrix of each contract template to obtain the matching matrix of each contract template. The elements in the matching matrix are weighted and summed to obtain the matching value of each contract template.

[0085] The contract templates are sorted from smallest to largest according to their matching values. The top three contract templates are selected and sent to the user. The user then selects one of the three contract templates as the optimal contract template.

[0086] The above technical solution provides a specific method for selecting the optimal contract template. Based on the uploaded intellectual property information, key feature fields are extracted from the intellectual property information. Key features include authorization type, payment method, authorization period, and technical field, etc., to obtain the demand feature values ​​of each key feature. A user demand matrix is ​​constructed using the demand feature values ​​of each key feature as elements. For example, a demand feature value comparison table containing the fields of each key feature is pre-built. The extracted fields of each key feature are compared with the demand feature value comparison table to obtain the demand feature values ​​of each key feature. A user demand matrix is ​​constructed using the demand feature values ​​of each key feature as elements. Then, a large number of contract templates are extracted from the blockchain to construct a contract template set. The fields of the corresponding key features of each contract template in the contract template set are extracted and compared with the pre-built demand feature value comparison table to obtain the template feature values ​​of each key feature of the contract template. The template feature value is used as an element to construct a template matrix for each contract template. The user demand matrix is ​​compared with the template matrices of each contract template by taking the absolute value of the difference between the user demand matrix and the template matrices of each contract template. This yields a matching matrix for each contract template. The smaller the value of each element in the matching matrix, the better the contract template matches the intellectual property information requirements. The elements in the matching matrix are then weighted and summed to obtain the matching value for each contract template. The smaller the matching value, the higher the matching degree. Finally, the contract templates are sorted from smallest to largest according to their matching values, and the top three contract templates are sent to the user. The user selects one contract template from the three as the optimal contract template. In this way, key features can be flexibly adjusted according to different intellectual property information and different intellectual property requirements to achieve accurate template matching in multiple scenarios and ensure that the selected contract template is highly consistent with the intellectual property transaction requirements.

[0087] The method for selecting the optimal buy-sell combination for trading is as follows: obtain the seller's demand information, and set multiple sets of seller demand question sequences S and multiple sets of buyer demand question response sequences RS based on the seller's demand information; obtain the buyer's demand information, and set multiple sets of buyer demand question sequences R and multiple sets of seller demand question response sequences SR based on the buyer's demand information.

[0088] When either the buyer or the seller initiates a transaction access, the two parties are automatically matched for intellectual property needs;

[0089] Intellectual property demand matching involves the matching between the seller's demand question sequence and the seller's demand question response sequence, as well as the matching between the buyer's demand question sequence and the buyer's demand question response sequence.

[0090] Based on the matching results, the matching satisfaction is calculated, and the buy / sell combination with the highest matching satisfaction is selected as the optimal buy / sell combination for trading.

[0091] The method for generating satisfaction is as follows: Based on the matching results, count the number of successfully matched sequences in the matching of the seller's demand question sequence S and the seller's demand question response sequence SR, and record this as the first matching number; construct a set of successfully matched seller's demands based on the matching results, calculate the absolute value of the seller's demand deviation between the sequence value of each seller's demand question sequence in the set of successfully matched seller's demands and the sequence value of the corresponding seller's demand question response sequence, and record this as the first absolute deviation value; sum all the first absolute deviation values ​​in the set of successfully matched seller's demands to obtain the first deviation coefficient;

[0092] Based on the matching results, the number of successfully matched sequences in the buyer demand question sequence R and the buyer demand question response sequence RS is counted and denoted as the second matching number; a buyer demand successfully matched set is constructed based on the matching results; the absolute value of the buyer demand deviation between the sequence value of each buyer demand question sequence in the buyer demand successfully matched set and the sequence value of the corresponding buyer demand question response sequence is calculated and denoted as the second deviation absolute value; the second deviation coefficient is obtained by summing all the second deviation absolute values ​​in the buyer demand successfully matched set.

[0093] The satisfaction value is obtained by adding the ratio of the first number of matches to the first deviation coefficient and the ratio of the second number of matches to the second deviation coefficient. The satisfaction value is then normalized to obtain the matching satisfaction.

[0094] The method for selecting the optimal buy-sell combination for trading also includes: when the result of trading the optimal buy-sell combination is a failure, sorting the matching satisfaction in descending order to generate a candidate buy-sell combination order list, and iteratively trading each buy-sell combination in the candidate buy-sell combination order list according to the sorting order until the trade is successful.

[0095] The above scheme provides a specific method for selecting the optimal buyer-seller combination in intellectual property transactions. First, it obtains the seller's demand information, which includes their own desired transfer price, scope of authorization requirements, payment cycle requirements, and a series of responses to the buyer's demand information. Then, based on the seller's demand information, it sets multiple sets of seller demand question sequences S and multiple sets of buyer demand question response sequences RS. Next, it obtains the buyer's demand information, which includes their own desired transfer price, scope of authorization requirements, payment cycle requirements, and a series of responses to the seller's demand information. Based on the buyer's demand information, it sets multiple sets of buyer demand question sequences R and multiple sets of seller demand question response sequences SR. When either the buyer or seller initiates a transaction access, the buyer and seller automatically perform intellectual property demand matching. This intellectual property demand matching involves the mutual matching between the seller's demand question sequence S and the seller's demand question response sequence SR, and the mutual matching between the buyer's demand question sequence R and the buyer's demand question response sequence RS.

[0096] Based on the matching results, the number of successful matches between the seller's demand question sequence S and the seller's demand question response sequence SR is recorded as the first match count. A set of successfully matched seller demands is constructed based on the matching results. A higher first match count indicates a higher degree of matching accuracy. Each set of sequences corresponds to a set of sequence values, which are uniform quantitative indicators set for each sequence. The purpose is to convert multi-dimensional demand characteristics into uniform and comparable quantitative indicators, with values ​​ranging from 1 to 10. For example, based on different transfer price ranges, a rule base for transfer price sequence values ​​is set. Based on the sequence values ​​and the transfer price ranges corresponding to each sequence value in the rule base, corresponding sequence values ​​are set. Depending on the authorization scope... A sequence value rule base for setting authorization ranges is established, and corresponding sequence values ​​are set based on the authorization range intervals corresponding to each sequence value in the sequence value rule base. A sequence value rule base for setting payment cycles is also established, and corresponding sequence values ​​are set based on the payment cycles corresponding to each sequence value in the sequence value rule base. The construction of each sequence value rule base can be pre-set based on the experience of those skilled in the art. The absolute value of the seller demand deviation between the sequence values ​​of each seller demand question sequence in the successfully matched seller demand set and the sequence values ​​of the corresponding seller demand question response sequence is calculated and denoted as the first absolute deviation value. The first deviation coefficient is obtained by summing all the first absolute deviation values ​​in the successfully matched seller demand set.

[0097] Based on the matching results, the number of successfully matched sequences in the buyer demand question sequence R and the buyer demand question response sequence RS is counted and denoted as the second matching number. A successful buyer demand matching set is constructed based on the matching results. The absolute value of the buyer demand deviation between the sequence value of each buyer demand question sequence and the sequence value of the corresponding buyer demand question response sequence in the successful buyer demand matching set is calculated and denoted as the second deviation absolute value. All second deviation absolute values ​​in the successful buyer demand matching set are summed to obtain the second deviation coefficient. It can be seen that the smaller the first deviation coefficient in the successful buyer demand matching set, the higher the matching degree. The smaller the second deviation coefficient in the successful buyer demand matching set, the higher the matching degree.

[0098] Finally, the ratio of the first number of matches to the first deviation coefficient and the ratio of the second number of matches to the second deviation coefficient are added together to obtain the satisfaction value. This satisfaction value is then normalized to obtain the matching satisfaction level. A higher number of matches, a smaller deviation coefficient, and a larger ratio indicate a higher matching satisfaction between the buyer and seller. Therefore, a higher satisfaction value indicates a higher degree of matching accuracy. The satisfaction value is then normalized to obtain the matching satisfaction level, and the buy-sell combination with the highest matching satisfaction is selected as the optimal buy-sell combination for trading. This is achieved through bidirectional matching between the seller's demand question sequence and the buyer's demand question response sequence, as well as between the buyer's demand sequence and the seller's demand question response sequence. Based on the matching results... By comprehensively analyzing the number of successful transactions and the magnitude of the deviation coefficient, more accurate quantitative matching can be achieved, ensuring that the selected buy-sell combinations are highly compatible with transaction needs and reducing transaction disputes caused by matching deviations at the root. Simultaneously, when the optimal buy-sell combination fails to complete the transaction, the matching satisfaction is sorted in descending order to generate a candidate buy-sell combination sequence list. Each buy-sell combination in the candidate list is iteratively traded according to the sorting order until a successful transaction is achieved. The buy-sell combination is a pairing between intellectual property providers and intellectual property demanders. This method improves the transaction's fault tolerance rate, quickly selecting suitable alternatives when a transaction fails, increasing the transaction success rate, and effectively shortening the transaction cycle.

[0099] The method for tracing intellectual property transactions based on the traceability index is as follows: obtain key data of each process in the entire intellectual property transaction process, construct key datasets for each process, use cryptographic hash algorithms to calculate the hash value of each key dataset, and generate independent data blocks for each process.

[0100] Each data block and its corresponding timestamp are uploaded to the blockchain node for on-chain notarization. Using the unique intellectual property identifier as the link, all independent data blocks are linked in chronological order to generate a traceability index. The association rule for the chain is that the hash value of the subsequent data block contains the hash value of the previous data block.

[0101] The system receives the hash value of any data block provided by the user tracing the source, determines the starting point of the index for the user's operation, and sequentially reads each data block in the tracing index from the starting point. Based on the key data in each data block, the system recalculates the hash value of each data block and records it as the new hash value. The new hash value is then matched and verified against the hash values ​​of the preceding data blocks contained in the subsequent data blocks in the tracing index. If the new hash values ​​of all data blocks completely match the corresponding hash values ​​in the tracing index, the verification passes, and all key data is reassembled in chronological order to generate a tracing report, which is then pushed to the user tracing the source. If there is a mismatch between the new hash value and the corresponding hash value in the tracing index, the verification fails, and tracing is impossible.

[0102] The aforementioned technical solution provides a specific method for tracing the origin of intellectual property transactions. First, it acquires key data from each stage of the entire intellectual property transaction process, constructing a key dataset for each stage. For example, key data includes digital certificates, compliance review information, and confirmation timestamps for the intellectual property rights confirmation stage; transaction order information, fund flow information, and contract execution information for the transaction stage; and authorized playback information and dissemination scope information for the usage stage. This is used to construct key datasets for each stage. Then, a cryptographic hash algorithm is used to calculate the hash value of each key dataset, generating independent data blocks for each stage. Each data block, along with its corresponding timestamp, is uploaded to a blockchain node for on-chain storage. Using the unique identifier of intellectual property rights as a link, all independent data blocks are linked in chronological order, generating a traceability index. The association rule for the chain is: the hash value of a later data block contains the hash value of a previous data block; the unique identifier of intellectual property rights... The identifier can be pre-selected and determined manually based on the type of intellectual property. For example, patents and trademarks use an official number plus a confirmation hash value as the unique identifier, while digital copyrights use an NFT number to ensure uniqueness and immutability. The system receives the hash value of any data block provided by the traceability user, determines the starting point of the traceability user's operation, and sequentially reads each data block in the traceability index. Based on the key data in each data block, the hash value of each data block is recalculated and recorded as the new hash value. The new hash value is then matched and verified against the hash values ​​of the preceding data blocks contained in the subsequent data blocks in the traceability index. If all the new hash values ​​of all data blocks completely match the corresponding hash values ​​in the traceability index, the verification passes, and all key data is reassembled in chronological order to generate a traceability report, which is then pushed to the traceability user. If there is a mismatch between the new hash value and the corresponding hash value in the traceability index, the verification fails, and traceability is impossible. In this way, the decentralized storage of blockchain data unifies the data of each process, avoids the occurrence of information silos, facilitates traceability, and utilizes the distributed consensus mechanism and immutability of blockchain to reconstruct the credibility of data at the storage layer, providing strong trust support for the authenticity of intellectual property transaction traceability. Example 2:

[0103] Please see Figure 2 As shown, this embodiment discloses an intellectual property transaction traceability system based on blockchain and smart contracts. The system includes:

[0104] The intellectual property registration module is used to obtain intellectual property information uploaded by users, conduct compliance review of the uploaded intellectual property information, and upload it to the blockchain for storage after the review is passed.

[0105] The intellectual property storage module is used to encrypt the stored intellectual property information, obtain the access information of the accessing subject, and combine the access information to protect the stored intellectual property information.

[0106] The smart contract generation module is used to match the uploaded intellectual property information with a pre-built set of contract templates to obtain multiple contract templates, and select the optimal contract template to deploy to the corresponding blockchain transaction terminal.

[0107] The intellectual property transaction module is used to obtain the demand information of buyers and sellers on the blockchain transaction platform, match them with each other, generate a matching satisfaction rate, and select the optimal buy and sell combination for transaction based on the matching satisfaction rate.

[0108] The intellectual property traceability module is used to acquire key data from the entire intellectual property transaction process, perform on-chain notarization and chain association, generate a traceability index, and trace the intellectual property transaction based on the traceability index.

[0109] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0110] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0111] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for tracing the source of intellectual property transactions based on blockchain and smart contracts, characterized in that, include: The system obtains intellectual property information uploaded by users, conducts compliance reviews of the uploaded intellectual property information, and uploads it to the blockchain for storage after the review is passed. The stored intellectual property information is encrypted, and the access information of the accessing subject is obtained. The stored intellectual property information is then protected by combining the access information. The uploaded intellectual property information is matched with a pre-built set of contract templates to obtain multiple contract templates. The optimal contract template is then selected and deployed to the corresponding blockchain transaction terminal. Obtain the demand information of buyers and sellers on the blockchain trading platform, match them with each other, generate a matching satisfaction rate, and select the optimal buy and sell combination for trading based on the matching satisfaction rate; Acquire key data from the entire intellectual property transaction process, store it on the blockchain and link it in a chain, generate a traceability index, and trace the intellectual property transaction based on the traceability index; The method for selecting the optimal buy-sell combination for trading is as follows: obtain the seller's demand information, and set multiple sets of seller demand question sequences S and multiple sets of buyer demand question response sequences RS based on the seller's demand information; The system acquires buyer demand information and sets multiple buyer demand question sequences R and multiple seller demand question response sequences SR based on this information. When either the buyer or seller initiates a transaction access, the two parties automatically perform intellectual property demand matching. Intellectual property demand matching involves matching the seller demand question sequences with the seller demand question response sequences, as well as matching the buyer demand question sequences with the buyer demand question response sequences. Based on the matching results, the matching satisfaction is calculated, and the buyer-seller combination with the highest matching satisfaction is selected as the optimal buyer-seller combination for the transaction. The method for generating satisfaction is as follows: Based on the matching results, count the number of successfully matched sequences in the matching of the seller's demand question sequence S and the seller's demand question response sequence SR, and record this as the first matching number; construct a set of successfully matched seller's demands based on the matching results, calculate the absolute value of the seller's demand deviation between the sequence value of each seller's demand question sequence in the set of successfully matched seller's demands and the sequence value of the corresponding seller's demand question response sequence, and record this as the first absolute deviation value; sum all the first absolute deviation values ​​in the set of successfully matched seller's demands to obtain the first deviation coefficient; Based on the matching results, count the number of successfully matched sequences in the buyer demand question sequence R and the buyer demand question response sequence RS, and record it as the second matching number; construct a buyer demand successfully matched set based on the matching results; calculate the absolute value of the buyer demand deviation between the sequence value of each buyer demand question sequence in the buyer demand successfully matched set and the sequence value of the corresponding buyer demand question response sequence, and record it as the second deviation absolute value; sum all the second deviation absolute values ​​in the buyer demand successfully matched set to obtain the second deviation coefficient. The satisfaction value is obtained by adding the ratio of the first number of matches to the first deviation coefficient and the ratio of the second number of matches to the second deviation coefficient. The satisfaction value is then normalized to obtain the matching satisfaction. The method for selecting the optimal buy-sell combination for trading also includes: when the result of trading the optimal buy-sell combination is a failure, sorting the matching satisfaction in descending order to generate a candidate buy-sell combination order list, and iteratively trading each buy-sell combination in the candidate buy-sell combination order list according to the sorting order until the trade is successful.

2. The method for tracing intellectual property transactions based on blockchain and smart contracts according to claim 1, characterized in that, The method for encrypting stored intellectual property information is as follows: The uploaded intellectual property information is divided into public information and sensitive information. The sensitive information is encrypted to obtain encrypted information, and an encrypted certificate and a verification key for verifying the authenticity of the encrypted certificate are generated. The public information, the encrypted certificate and the verification key are sent to the blockchain together.

3. The method for tracing intellectual property transactions based on blockchain and smart contracts according to claim 2, characterized in that, The methods for protecting stored intellectual property information are as follows: A master key for decrypting encrypted information is constructed. The master key consists of Q key fragments, each of which is stored in secure hardware of a pre-determined authorized entity. When accessing encrypted information, obtain the access information of the accessing subject. Based on the access information, calculate the security value of the accessing subject. When the security value is less than the preset security threshold, prevent the accessing subject from logging in and update the master key and key fragments. When the security value is greater than or equal to the set security threshold, a call command is generated and sent to all authorized entities holding key fragments to obtain the number of key fragments collected by the accessing entity. When the number of key fragments is greater than or equal to the preset access key fragment threshold, the master key is automatically reassembled based on the number of collected key fragments to enable secure access. Access is blocked when the number of key fragments is less than or equal to the key fragment access threshold.

4. The method for tracing intellectual property transactions based on blockchain and smart contracts according to claim 3, characterized in that, The method for calculating the security value of the access subject is as follows: Access information includes the login information, behavioral information, and login environment information of the accessing entity; When the access subject is an old user, collect the access subject's historical access information, extract the indicator features from the historical access information, obtain the quantitative value of each indicator feature, and construct the access subject's login habit vector A, access subject's behavior habit vector B, and access subject's login environment habit vector C based on the quantitative value of each indicator feature. Obtain the access information currently accessed by the access subject, and based on the access information currently accessed by the access subject, construct the real-time login vector A1, the real-time behavior vector B1, and the real-time login environment vector C1 of the access subject respectively. Calculate the cosine similarity between login habit vector A and login real-time vector A1, behavior habit vector B and behavior real-time vector B1, and login environment habit vector C and login environment real-time vector C1; sum the cosine similarities to obtain the security value; When the access subject is a new user, obtain the quantitative values ​​of each indicator feature of all access subjects, calculate the average quantitative value of each indicator feature after averaging, and construct the login standard vector A2, the behavior standard vector B2, and the login environment standard vector C2 of the access subject based on the average quantitative value of each indicator feature. Calculate the cosine similarity between the login standard vector A2 and the login real-time vector A1, the behavior standard vector B2 and the behavior real-time vector B1, and the login environment standard vector C2 and the login environment real-time vector C1; add the cosine similarities to obtain the security value.

5. The method for tracing intellectual property transactions based on blockchain and smart contracts according to claim 1, characterized in that, The method for selecting the optimal contract template is as follows: Based on the uploaded intellectual property information, the fields of key features are extracted from the intellectual property information to obtain the demand feature values ​​of each key feature. The user demand matrix is ​​constructed using the demand feature values ​​of each key feature as elements. Construct a contract template set, extract the fields of the corresponding key features of each contract template in the contract template set, obtain the template feature values ​​of each key feature of the contract template, and construct the template matrix of each contract template using the template feature values ​​of each key feature as elements. The user demand matrix is ​​compared with the template matrix of each contract template to obtain the matching matrix of each contract template. The elements in the matching matrix are weighted and summed to obtain the matching value of each contract template. The contract templates are sorted from smallest to largest according to their matching values. The top three contract templates are selected and sent to the user. The user then selects one of the three contract templates as the optimal contract template.

6. The method for tracing intellectual property transactions based on blockchain and smart contracts according to claim 1, characterized in that, The method for tracing intellectual property transactions based on the traceability index is as follows: Acquire key data from each stage of the entire intellectual property transaction process, construct key datasets for each stage, and use cryptographic hash algorithms to calculate the hash value of each key dataset to generate independent data blocks for each stage. Each data block and its corresponding timestamp are uploaded to the blockchain node for on-chain notarization. Using the unique intellectual property identifier as the link, all independent data blocks are linked in chronological order to generate a traceability index. The association rule for the chain is that the hash value of the subsequent data block contains the hash value of the previous data block. The system receives the hash value of any data block provided by the user tracing the source, determines the starting point of the index for the user's operation, and sequentially reads each data block in the tracing index from the starting point. Based on the key data in each data block, the system recalculates the hash value of each data block and records it as the new hash value. The new hash value is then matched and verified against the hash values ​​of the preceding data blocks contained in the subsequent data blocks in the tracing index. If the new hash values ​​of all data blocks completely match the corresponding hash values ​​in the tracing index, the verification passes, and all key data is reassembled in chronological order to generate a tracing report, which is then pushed to the user tracing the source. If there is a mismatch between the new hash value and the corresponding hash value in the tracing index, the verification fails, and tracing is impossible.

7. A blockchain- and smart contract-based intellectual property transaction traceability system, implementing the blockchain- and smart contract-based intellectual property transaction traceability method as described in any one of claims 1-6, characterized in that, include: The intellectual property registration module is used to obtain intellectual property information uploaded by users, conduct compliance review of the uploaded intellectual property information, and upload it to the blockchain for storage after the review is passed. The intellectual property storage module is used to encrypt the stored intellectual property information, obtain the access information of the accessing subject, and combine the access information to protect the stored intellectual property information. The smart contract generation module is used to match the uploaded intellectual property information with a pre-built set of contract templates to obtain multiple contract templates, and select the optimal contract template to deploy to the corresponding blockchain transaction terminal. The intellectual property transaction module is used to obtain the demand information of buyers and sellers on the blockchain transaction platform, match them with each other, generate a matching satisfaction rate, and select the optimal buy and sell combination for transaction based on the matching satisfaction rate. The intellectual property traceability module is used to acquire key data from the entire intellectual property transaction process, perform on-chain notarization and chain association, generate a traceability index, and trace the intellectual property transaction based on the traceability index.

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