A blockchain-based technology achievement transaction storage and traceability system

Abstract

This application relates to the field of technology achievement transaction management technology, and discloses a blockchain-based technology achievement transaction evidence storage and traceability system, including: a blockchain storage module, which serves as the core data hub of the system, used to store evidence storage data and traceability data during the technology achievement transaction process. The evidence storage data includes ownership certificates of the technology achievement, transaction contracts, and value assessment reports, while the traceability data includes the research and development information of the technology achievement. A first algorithm is used to perform preliminary semantic annotation on the traceability data, combined with a second algorithm to accurately correct fuzzy labels, solving the problems of "fuzzy labels and semantic ambiguity" in traditional traceability. Simultaneously, the traceability logic is intuitively displayed through a label classification view and a correction record view, making information such as the research and development stage and transaction model of the technology achievement clearly interpretable. This helps both parties to the transaction and regulatory authorities quickly understand the meaning of the data, significantly improving the transparency and credibility of the entire technology achievement transaction process.

Description

Technical Field

[0001] This invention relates to the field of technology achievement transaction management technology, specifically a blockchain-based technology achievement transaction evidence storage and traceability system. Background Technology

[0002] Technological achievements, as the core carrier of scientific and technological innovation, are traded throughout the entire chain of research and development, transformation, and industrialization, and are a key link in promoting the flow of technology and the realization of value.

[0003] Currently, the preservation and traceability of technology transfer transactions mainly rely on traditional centralized information systems or paper-based document management models. For preservation, transaction data is typically recorded through transaction platform databases and third-party archives, with some stages supplemented by physical carriers such as paper contracts and certificates. For traceability, it is necessary to manually trace the source, flow path, and related information of technology transfer achievements by querying transaction platform history records, connecting to third-party databases, or retrieving paper archives. The entire process relies on information exchange and manual verification among multiple entities.

[0004] However, the evidence data is mostly a direct record of the original information, lacking unified semantic annotation and standardized processing. Data from different stages often becomes semantically ambiguous due to differences in expression and inconsistent terminology. This can easily lead to ambiguity when the parties to the transaction or the regulators interpret the data. Furthermore, during the traceability process, due to the scattered storage of information and the lack of structured connections, it is difficult to quickly and accurately locate key node information, resulting in low traceability efficiency. The lack of transparency of core information such as the research and development stage of technological achievements and transaction models not only affects the efficiency of transaction decisions but also creates hidden dangers for ownership disputes. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a blockchain-based system for the storage, verification, and traceability of technological achievements, which solves the problems of ambiguous data semantics and insufficient interpretability.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a blockchain-based system for the storage, verification, and traceability of technological achievements, comprising:

[0007] The blockchain storage module serves as the core data hub of the system, used to store evidence and traceability data during the transaction of technological achievements. The evidence includes ownership certificates, transaction contracts, and value assessment reports of the technological achievements, while the traceability data includes R&D information, transaction flow information, and version iteration information of the technological achievements.

[0008] The evidence storage processing module has its output end directly connected to the input end of the blockchain storage module. After receiving the original data of the technology achievement transaction, it performs standardized processing on the original data to generate evidence storage data containing timestamps and digital signatures. The evidence storage data is then transmitted to the blockchain storage module for immutable storage. After the evidence storage data is uploaded, the blockchain storage module is triggered to automatically generate a unique traceability index. This traceability index is bound to the evidence storage data and forms a one-to-one mapping relationship.

[0009] The semantic processing module, whose data input end is connected to the output end of the blockchain storage module, is used to monitor newly stored evidence data with traceability indexes and associated traceability data in the blockchain storage module. It actively reads the data, performs semantic annotation processing on the traceability data through a built-in first algorithm, generates semantic traceability data containing technical tags and stage tags, and then verifies and corrects the classification results of the first algorithm through a second algorithm to generate accurate semantic traceability data and send it back to the blockchain storage module for binding and storage. When a query request is received, the optimized semantic traceability data is called to output the search results.

[0010] The interactive display module has an instruction input end connected to the user interaction interface and a data input end connected to the semantic processing module and the blockchain storage module output ends, respectively. It forwards user query instructions to the semantic processing module, receives the search results, retrieves the corresponding evidence storage data according to the index, and integrates and displays it visually.

[0011] Preferably, the first algorithm is a basic semantic label classification algorithm, including the following steps:

[0012] S11: Obtain source data D, and extract keyword set K={k1,k2,...,k...} using a word segmentation tool. n The keyword set includes technical feature words, stage description words, and transaction type words;

[0013] S12: Call the preset basic tag library L for technological achievements. This tag library contains three basic tags: technology type, R&D stage, and transaction model, as well as corresponding feature words. The tag set is T={t1,t2,...,t...} n};

[0014] S13: Calculate keyword k using the keyword-tag matching formula. ᵢ With tag t n The matching degree of corresponding feature words is calculated using the formula: keyword k ᵢ With tag t n Feature word matching degree = (keyword k) ᵢ With tag t n (Number of overlapping features) / (Tag t) n(Total number of feature words); the label category is determined based on the maximum matching degree, i.e., t n =argmaxn(keyword k) ᵢ With tag t n (Feature word matching degree), forming preliminary semantic source data S1={D,t n};

[0015] S14: Temporarily store the initial semantic traceability data S1 to the semantic processing module, waiting for the second algorithm to process it.

[0016] Preferably, the preset basic tag library L for technological achievements in S12 includes:

[0017] The basic tags for technology types include "patent", "software copyright", and "trade secret", with corresponding keywords such as "patent number", "authorization announcement", "software", "code", "copyright registration", "unpublished", "confidential" and "proprietary technology".

[0018] The basic tags for the R&D stage include "laboratory R&D", "pilot production", and "industrialization", with corresponding keywords such as "experimental report", "sample", "small batch production", "trial production", "mass production" and "market application".

[0019] The basic tags for the transaction model include "transfer", "license" and "equity investment", with corresponding characteristic words such as "ownership transfer", "buyout", "right of use", "authorization period", "equity" and "capital contribution ratio".

[0020] Preferably, in the process of calculating the matching degree in S13, when a keyword matches the feature words of multiple basic tags at the same time, the tag category is determined by calculating the comprehensive matching degree using the formula.

[0021] The formula for calculating the overall matching degree is:

[0022] The overall matching degree M = w1・M1 + w2・M2 + w3・M3; where M is the overall matching degree, w1, w2, and w3 are weight coefficients with w1 > w2 > w3, and M1, M2, and M3 are the matching degrees of the tags of technology type, R&D stage, and transaction mode, respectively. The category is determined according to the tag with the highest overall matching degree.

[0023] Preferably, the second algorithm is a label optimization and calibration algorithm, used to promote and optimize the classification results of the first algorithm, including the following steps:

[0024] S21: Call the preset fuzzy label recognition rule base R, which contains easily confused feature word combinations and cross-category feature word associations;

[0025] S22: Extract the keyword K and corresponding label n1 from the preliminary semantic traceability data S1 generated by the first algorithm, and calculate the fuzziness using the fuzziness calculation formula: Fuzziness F = (number of keywords that match other label feature words at the same time) / (total number of keywords);

[0026] S23: When the ambiguity F ≥ the preset threshold θ, the initial label is corrected according to the rule base R, and a new label n1' is generated to form accurate semantic traceability data S2={D,n1'}.

[0027] S24: Store the accurate semantic traceability data S2 to the blockchain storage module.

[0028] Preferably, the fuzzy label recognition rule base R includes:

[0029] Technology type fuzzy rule: When F≥θ and contains both the features of "patent application" and "unpublished", the "patent" label is modified to the sub-label of "patent application pending (unpublished)".

[0030] Fuzzy rules for the R&D stage: When F≥θ and contains both the feature words "sample" and "mass production equipment", the "laboratory R&D" label will be modified to the sub-label "transition from pilot production to industrialization".

[0031] Transaction pattern ambiguity rule: When F≥θ and contains both the features of "right of use" and "equity dividend", the "license" label will be modified to the composite label of "license + equity investment".

[0032] Preferably, the blockchain storage module adopts a combined on-chain and off-chain storage mode: the hash value H is generated from the tag set in the precise semantic traceability data S2 using the tag hash value calculation formula: H=SHA-256(n1'||n2'||...||nn'); where SHA-256 is the hash function, and "||" is the concatenation symbol. The hash value H is stored on the main blockchain chain; the detailed description of the tag and the original traceability data are stored in the distributed file system. The on-chain hash value is correlated with the off-chain data for verification. The hash value H is bound to the precise semantic data S2 through the traceability index to ensure the consistency of on-chain and off-chain data.

[0033] Preferably, the visualization form of the interactive display module includes a tag classification view and a correction record view, wherein:

[0034] The tag classification view displays the tag distribution of traceability data according to technology type, R&D stage, and transaction mode, with sub-tags marked with special identifiers.

[0035] The correction record view shows the process by which the second algorithm corrects the classification results of the first algorithm, including the original label, ambiguity F, correction basis, and new label.

[0036] This invention provides a blockchain-based system for the storage, verification, and traceability of technological achievements in transactions. It offers the following advantages:

[0037] 1. This invention uses a first algorithm to perform preliminary semantic annotation on traceability data, combined with a second algorithm to accurately correct ambiguous labels, thus solving the problems of "fuzzy labels and semantic ambiguity" in traditional traceability. Simultaneously, it visually displays the traceability logic through label classification and correction record views, making information such as the R&D stage and transaction model of technological achievements clearly interpretable. This helps both parties in the transaction and regulatory authorities quickly understand the meaning of the data, significantly improving the transparency and credibility of the entire technological achievement transaction process.

[0038] 2. This invention achieves immutable storage of evidence and traceability data through a blockchain storage module, and ensures data integrity by combining the "on-chain hash storage + off-chain details storage" model. At the same time, the precise semantic tags generated by the semantic processing module are bound to the traceability index. By utilizing a collaborative mechanism, it not only solves the problems of easy data tampering and unreliable evidence in traditional technology transaction, but also breaks through the bottleneck of "data can be stored but not understood" through semantic processing, providing reliable technical support for the traceability of ownership of technological achievements and the resolution of transaction disputes. Attached Figure Description

[0039] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0040] The technical solutions in 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.

[0041] Example:

[0042] Please see the appendix Figure 1 This invention provides a blockchain-based system for the storage, preservation, and traceability of technological achievements in transactions. The system includes a blockchain storage module, a storage processing module, a semantic processing module, and an interactive display module, as specifically deployed as follows:

[0043] Blockchain storage module: As the core data hub, it adopts a consortium blockchain architecture (such as Hyperledger Fabric) and deploys multiple nodes (such as R&D institution nodes, trading platform nodes, and regulatory nodes) to achieve distributed storage, ensuring data immutability. Its stored content includes two categories: Evidence data: Proof of ownership of technological achievements (such as scanned copies of patent certificates and copyright registration numbers), transaction contracts (including signatures of both parties), and valuation reports (issued by third-party institutions); Traceability data: R&D information (such as experimental record time and iteration version number), transaction flow information (transferor, transferee, and transaction time), and version iteration information (technology update logs and optimization content).

[0044] The evidence storage module communicates directly with the blockchain storage module. After receiving the raw transaction data input by the user, it performs the following operations:

[0045] Data standardization: Convert unstructured data (such as handwritten experimental records) into structured formats (such as PDF text) and extract key information (such as time, subject, and content).

[0046] Generate evidence storage certificates: Add timestamps (accurate to milliseconds) and digital signatures (based on RSA asymmetric encryption algorithm) to standardized data to form tamper-proof evidence storage data;

[0047] Triggering the traceability index: After the evidence data is uploaded to the blockchain, a unique traceability index is automatically generated (generated by a hash algorithm, such as "Index_+SHA-256(evidence data)"), which is bound to the evidence data for storage, realizing "one data, one index".

[0048] Semantic processing module: By listening for data upload events from the blockchain storage module through a Webhook callback mechanism, when a blockchain node generates a new block, it automatically pushes an event notification to the RESTful API interface (port number: 8080) of the semantic processing module, actively reads the stored evidence data with traceability index and related traceability data, and performs two-level algorithm processing:

[0049] First algorithm (basic semantic label classification): performs preliminary semantic annotation on the source data;

[0050] The second algorithm (label optimization and calibration) corrects the initial labeling results, generates accurate semantic data, and sends it back to the blockchain storage module to be bound to the original index.

[0051] Interactive display module: Provides user operation entry points (such as web interface, client interface), receives query commands and forwards them to semantic processing module, obtains search results, and combines them with the evidence storage data of blockchain storage module to display them in a visual form.

[0052] Furthermore, the basic semantic label classification algorithm includes the following steps:

[0053] S11: Keyword Extraction

[0054] The source data is processed using word segmentation tools (such as Jieba segmentation) to extract a set of keywords K. The word segmentation tool lexicon can be expanded by using a dictionary specifically for the field of technological achievements, and a general stop word list can be filtered. The semantics of ambiguous words (such as 'mass production') are determined through context analysis.

[0055] S12: Call the base tag library L

[0056] Tag library L pre-sets three types of tags and corresponding feature words

[0057] Technology type tags: "Patent" (characteristics: "patent application", "patent number"), "Software copyright" (characteristics: "code", "copyright registration"), etc.; R&D stage tags: "Laboratory R&D" (characteristics: "experimental records", "samples"), "Industrialization" (characteristics: "mass production", "market application"), etc.; Transaction model tags: "Transfer" (characteristics: "transfer to", "ownership transfer"), "License" (characteristics: "license period", "right of use"), etc.

[0058] S13: Matching Degree Calculation and Tag Classification. The matching degree between each keyword and tag feature word is calculated using the "Keyword-Tag Matching Degree Calculation Formula". For example, the keyword "patent application" overlaps with the feature word "patent application" of the tag "patent", so the matching degree = 1 (number of overlaps) / 2 (total number of feature words in the "patent" tag) = 0.5; the keyword "transfer" overlaps with the feature word "transfer to" of the tag "transfer", so the matching degree = 1 / 2 = 0.5.

[0059] If a keyword matches multiple tags simultaneously (e.g., "trial production" matches both "pilot production" and "industrialization"), the final tag is determined using the "comprehensive matching degree calculation formula":

[0060] The overall matching degree M = w1・M1 + w2・M2 + w3・M3 (w1 = 0.5, w2 = 0.3, w3 = 0.2, set according to the priority of technology type > R&D stage > transaction mode). The label corresponding to the maximum value of M is taken as the preliminary classification result, and the preliminary semantic data S1 = {D, tn} (tn is the label set) is generated.

[0061] Specifically: The weighting coefficients are set based on the tag priority rules: the technology type has the highest weight (w1=0.5) due to legal rights confirmation, followed by the R&D stage (w2=0.3), and the transaction model has the lowest weight (w3=0.2).

[0062] S14: Temporarily store the data from S1 in the cache of the semantic processing module, waiting for the second algorithm to process it.

[0063] Furthermore, a label optimization and calibration algorithm is used to correct the classification bias of the first algorithm and improve label accuracy, including the following steps:

[0064] S21: Call the fuzzy label recognition rule base R

[0065] The rule base R contains correction logic for easily confused tags, for example:

[0066] Ambiguous rules for technology types: If the keyword contains "patent application" and "authorization announcement" does not appear, then the "patent" label should be corrected to "patent application in progress";

[0067] Transaction model ambiguity rule: If the terms "right of use" and "equity" are present at the same time, the "license" label is revised to "license + equity investment".

[0068] The fuzzy label recognition rule base R is generated by machine learning from historical mislabeling cases. It currently contains 20 core rules and supports dynamic addition of rules (e.g., 'including clinical trial approvals + sales revenue' can be corrected to 'industrialization stage').

[0069] Furthermore: Rule base R automatically generates rules from historical mislabeling cases using the C4.5 decision tree algorithm. The specific steps include:

[0070] A1: Input dataset: 5,000 source data samples with incorrect labels. Each sample contains {keyword set K, incorrect label t, correct label t'}.

[0071] A2: Feature Engineering: Extracting keyword combinations, word frequency, and contextual part-of-speech tags as feature vectors;

[0072] A3: Model training: Using information gain ratio as the splitting criterion, generate a decision tree and prune it;

[0073] A4: Rule Extraction: Deriving core rules from the decision tree path;

[0074] A5: Dynamic addition: Users submit new rules via JSON configuration files, and the rule library is incrementally updated after administrator approval.

[0075] S22: Calculate the ambiguity F

[0076] The fuzziness level of the initial label is assessed using the "fuzziness calculation formula": F = (number of keywords that simultaneously match other label feature words) / (total number of keywords). For example, if the keyword "trial production" matches both the labels "pilot production" and "industrialization," and the total number of keywords is 5, then F = 1 / 5 = 0.2.

[0077] S23: Label Correction

[0078] The preset threshold θ = 0.2 (determined based on historical data statistics). When F ≥ θ, the label is corrected according to the rule base R. For example, if F = 0.2 ≥ θ, and the keywords contain "trial production" and "mass production equipment", then the "pilot production" label is corrected to "pilot production to industrialization transition", generating accurate semantic data S2 = {D, n1'}. The preset threshold θ ranges from 0.1 to 0.5, determined based on historical data statistics: when F ≥ 0.3, the correction accuracy improves.

[0079] S24: Store precise data

[0080] The S2 data is sent back to the blockchain storage module and bound to the original traceability index, covering the initial data.

[0081] Furthermore, the blockchain storage module adopts a combined "on-chain + off-chain" storage method.

[0082] On-chain storage: The hash value H=SHA-256(n1'||n2'||...||nn') is generated from the tag set of the precise semantic data S2 using the SHA-256 algorithm and stored on the main blockchain to ensure that the tags cannot be tampered with;

[0083] Off-chain storage: Detailed descriptions of tags (such as "patent pending" meaning "application submitted but not yet authorized") and original traceability data (such as experimental record PDFs) are stored in a distributed file system (such as IPFS). Consistency verification is achieved by associating the on-chain hash value H with the off-chain data.

[0084] Furthermore, the interactive display module provides two core views to support users in querying and understanding source data:

[0085] Tag Category View: Displays tag distribution categorized by technology type, R&D stage, and transaction model, for example:

[0086] Technology type: "Patent pending" "Software copyright";

[0087] Research and development stage: "Transition from pilot production to industrialization"; Transaction model: "Licensing + Equity investment";

[0088] The sub-labels (such as "transition from pilot production to industrialization") are marked with special symbols (such as orange font).

[0089] Correction Record View: Shows the correction process of the second algorithm, including: original labels (such as "pilot test");

[0090] Ambiguity F (e.g., 0.2);

[0091] Correction basis (e.g., "containing the characteristic words 'trial production' and 'mass production equipment'");

[0092] New labels (such as "transition from pilot production to industrialization").

[0093] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art 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 appended claims and their equivalents.

Claims

1. A blockchain-based system for the storage, verification, and traceability of technological achievements in transactions, characterized in that: include: The blockchain storage module serves as the core data hub of the system, used to store evidence and traceability data during the transaction of technological achievements. The evidence includes ownership certificates, transaction contracts, and value assessment reports of the technological achievements, while the traceability data includes R&D information, transaction flow information, and version iteration information of the technological achievements. The evidence storage processing module has its output end directly connected to the input end of the blockchain storage module. After receiving the original data of the technology achievement transaction, it performs standardized processing on the original data to generate evidence storage data containing timestamps and digital signatures. The evidence storage data is then transmitted to the blockchain storage module for immutable storage. After the evidence storage data is uploaded, the blockchain storage module is triggered to automatically generate a unique traceability index. This traceability index is bound to the evidence storage data and forms a one-to-one mapping relationship. The semantic processing module, whose data input end is connected to the output end of the blockchain storage module, is used to monitor newly stored evidence data with traceability index and associated traceability data in the blockchain storage module, actively read the data, perform semantic annotation processing on the traceability data through the built-in first algorithm, generate semantic traceability data containing technical tags and stage tags, and then verify and correct the classification results of the first algorithm through the second algorithm to generate accurate semantic traceability data and send it back to the blockchain storage module for binding and storage. When a query request is received, the optimized semantic source data is used to output the search results; The interactive display module has an instruction input end connected to the user interaction interface and a data input end connected to the semantic processing module and the blockchain storage module output ends, respectively. It forwards user query instructions to the semantic processing module, receives the search results, retrieves the corresponding evidence storage data according to the index, and integrates and displays it visually.

2. The blockchain-based technology achievement transaction storage and traceability system according to claim 1, characterized in that, The first algorithm is a basic semantic label classification algorithm, including the following steps: S11: Obtain source data D, and extract keyword set K={k1,k2,...,k...} using a word segmentation tool. n The keyword set includes technical feature words, stage description words, and transaction type words; S12: Call the preset basic tag library L for technological achievements. The basic tag library L contains three categories of basic tags: technology type, R&D stage, and transaction mode, as well as corresponding feature words. The tag set is T={t1,t2,...,t...} n }; S13: Calculate keyword k using the keyword-tag matching formula. ᵢ With tag t n The matching degree of corresponding feature words is calculated using the formula: keyword k ᵢ With tag t n Feature word matching degree = (keyword k) ᵢ With tag t n (Number of overlapping features) / (Tag t) n (Total number of feature words); the label category is determined based on the maximum matching degree, i.e., t n =argmaxn(keyword k) ᵢ With tag t n (Feature word matching degree), forming preliminary semantic source data S1={D,t n }; S14: Temporarily store the initial semantic traceability data S1 to the semantic processing module, waiting for the second algorithm to process it.

3. The blockchain-based technology achievement transaction storage and traceability system according to claim 2, characterized in that, The pre-set basic tag library L for technological achievements in S12 includes: The basic tags for technology types include "patent", "software copyright", and "trade secret", with corresponding keywords such as "patent number", "authorization announcement", "software", "code", "copyright registration", "unpublished", "confidential", and "proprietary technology". The basic tags for the R&D stage include "laboratory R&D", "pilot production", and "industrialization", with corresponding keywords such as "experimental report", "sample", "small batch production", "trial production", "mass production" and "market application". The basic tags for transaction models include "transfer", "license" and "equity investment", with corresponding keywords such as "ownership transfer", "buyout", "right of use", "authorization period", "equity" and "capital contribution ratio".

4. A blockchain-based technology achievement transaction storage and traceability system according to claim 2, characterized in that, In the process of calculating the matching degree in S13, when a keyword matches the feature words of multiple basic tags at the same time, the tag category is determined by calculating the comprehensive matching degree using the formula. The formula for calculating the overall matching degree is: The overall matching degree M = w1・M1 + w2・M2 + w3・M3; where M is the overall matching degree, w1, w2, and w3 are weight coefficients with w1 > w2 > w3, and M1, M2, and M3 are the matching degrees of the tags of technology type, R&D stage, and transaction mode, respectively. The category is determined according to the tag with the highest overall matching degree.

5. A blockchain-based technology achievement transaction storage and traceability system according to claim 1, characterized in that, The second algorithm is a tag optimization calibration algorithm, which includes the following steps: S21: Call the preset fuzzy label recognition rule base R, which contains easily confused feature word combinations and cross-category feature word associations; S22: Extract the keyword K and corresponding label n1 from the preliminary semantic traceability data S1 generated by the first algorithm, and calculate the fuzziness using the fuzziness calculation formula: Fuzziness F = (number of keywords that match other label feature words at the same time) / (total number of keywords); S23: When the ambiguity F ≥ the preset threshold θ, the initial label is corrected according to the rule base R, and a new label n1' is generated to form accurate semantic traceability data S2={D,n1'}. S24: Store the accurate semantic traceability data S2 into the blockchain storage module.

6. A blockchain-based technology achievement transaction storage and traceability system according to claim 5, characterized in that, The fuzzy label recognition rule base R includes: Fuzzy rule for technology type: When F≥θ and contains both "patent application" and "unpublished" features, the "patent" label is modified to the sub-label "patent application pending (unpublished)"; Fuzzy rules for the R&D stage: When F≥θ and contains both the feature words "sample" and "mass production equipment", the "laboratory R&D" label will be modified to the sub-label "transition from pilot production to industrialization". Transaction pattern fuzzy rule: When F≥θ and contains both the feature words "right of use" and "equity dividend", the "license" label will be modified to the composite label "license + equity investment".

7. A blockchain-based technology achievement transaction storage and traceability system according to claim 1, characterized in that, The blockchain storage module adopts a combined on-chain and off-chain storage mode: The hash value H is generated from the set of tags in the precise semantic traceability data S2 using the tag hash value calculation formula: H=SHA-256(n1'||n2'||...||nn'); In this context, SHA-256 is the hash function, "||" is the concatenation symbol, and the hash value H is stored on the main blockchain chain. The detailed description of the tag and the original traceability data are stored in the distributed file system. The hash value is verified by associating it with the off-chain data through the on-chain hash value. The hash value H is bound to the precise semantic data S2 through the traceability index to ensure the consistency of on-chain and off-chain data.

8. A blockchain-based technology achievement transaction storage and traceability system according to claim 1, characterized in that, The interactive display module includes a tag category view and a correction record view, wherein: The tag classification view displays the tag distribution of traceability data according to technology type, R&D stage, and transaction mode, with sub-tags marked with special identifiers. The correction record view shows the process by which the second algorithm corrects the classification results of the first algorithm, including the original label, ambiguity F, correction basis, and new label.

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