Book copyright protection and information tracing method based on block chain technology

Abstract

The invention discloses a book copyright protection and information traceability method based on a block chain technology, and particularly relates to the field of book copyright protection, comprising multi-source data acquisition, core index calculation, network stability evaluation, copyright credibility correction, infringement risk detection and comprehensive traceability rating. According to the method, block chain activeness, node distribution and copyright transaction data are fused, network stability and credibility correction coefficients are dynamically calculated, and traditional single-dimension limitation is broken through; a weight adjustment strategy is triggered by monitoring network fluctuation in real time, so that the fault-tolerant capability of the system is enhanced, and misjudgment caused by node abnormity is avoided; a hierarchical early warning system based on multi-dimensional quantitative indexes is constructed, and automatic three-level credibility rating is generated in combination with content similarity dynamic calibration and contract execution frequency analysis, so that the timeliness and judgment precision of copyright traceability are remarkably improved, full-process dynamic risk early warning is realized, and manual intervention dependence is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of book copyright protection, and more specifically, to a method for book copyright protection and information traceability based on blockchain technology. Background Art

[0002] The application of blockchain technology in the field of book copyright protection is gradually emerging. Traditional methods mainly rely on a single hash value or timestamp to record copyright information to achieve tamper-proof functions. Existing technologies usually store book copyright registration data based on blockchain networks, solidify copyright ownership information through smart contracts, and compare the hash values ​​of content stored on the chain during infringement detection. If there is inconsistency, it is determined that there is a risk of tampering.

[0003] However, existing technologies suffer from the problem of insufficient correlation between multi-dimensional data. It is difficult to dynamically evaluate network stability and node consensus credibility during the copyright period by relying solely on a single hash value or timestamp. At the same time, there is a lack of quantitative analysis mechanism for copyright transaction links, content similarity changes and contract execution status, resulting in delayed infringement risk judgment and reliance on manual review, making it impossible to achieve automated, multi-level copyright credibility traceability and dynamic risk warning. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a book copyright protection and information traceability method based on blockchain technology. Through the following scheme, it solves the problems raised in the above-mentioned background technology, such as reliance on a single hash value or timestamp, lack of multi-dimensional data correlation analysis, inability to dynamically evaluate network stability and node credibility, and delayed infringement judgment and reliance on manual review.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for book copyright protection and information traceability based on blockchain technology, comprising: S1: Multi-source data collection: Collect the copyright transaction records, node geographic location information, block generation timestamps, copyright status change history, content block hash values, and contract call logs of the target book from the blockchain network, smart contracts, and external systems to provide input for subsequent analysis; S2: Core Indicator Calculation: Based on the collected data, we calculate quantitative indicators reflecting transaction volatility, node dispersion, timestamp credibility, copyright operation relevance, content similarity, and contract activity, and establish a mathematical model foundation for copyright analysis. S3: Network Stability Assessment: The blockchain network stability score is evaluated by the ratio of the blockchain activity index to the node distribution entropy. When the result exceeds the preset threshold, the high volatility warning branch is triggered. Otherwise, the decentralized compensation algorithm is used to correct the score. S4: Copyright credibility correction: The network stability score is weighted by combining the timestamp consistency factor and the influence of the copyright change correlation coefficient to generate a correction score that integrates time credibility and operation relevance. S5: Infringement risk detection: Dynamically adjust the infringement risk score based on the comparison between the content similarity score and the plagiarism threshold. When the score is lower than the plagiarism threshold, the author's historical behavior data is introduced to compensate for the credibility. S6: Comprehensive traceability rating: The infringement risk score is normalized and weighted by the contract execution frequency, and the final traceability credibility level is output, which is divided into three levels: red warning, yellow prompt and green certification.

[0006] Preferably, the copyright transaction records obtain on-chain transaction records related to the copyright of the target work through a blockchain browser or a node API interface, count the total number of daily transaction behaviors at the natural day granularity, filter out non-copyright operations, retain copyright registration, transfer, and authorization operation types, and continuously collect at least 30 days of transaction data to form a time series.

[0007] Preferably, the node geographic location information is obtained by calling the node discovery protocol or management tool of the blockchain network to obtain the IP address list of active nodes in the entire network, using the IP geolocation service to map the nodes to designated geographical area classifications, counting the number of nodes in each area, and excluding private network nodes and anonymous proxy nodes.

[0008] Preferably, the block generation timestamp is obtained by synchronizing block header data from the blockchain full node, extracting the generation timestamps of 300 consecutive blocks to form a time series, recording the correspondence between the block height and the actual generation time, and synchronously obtaining the theoretical block interval parameters defined by the blockchain protocol.

[0009] Preferably, the copyright status change history is obtained by parsing the event log of the copyright management smart contract, extracting the copyright status change records of the target work in the last 36 months, and simultaneously retrieving the on-chain change records of other works with the same creator, copyright holder or IP association chain, and aligning them at a monthly granularity to form two sets of time series data.

[0010] Preferably, the content block hash value divides the e-book text content into blocks of a fixed size of 1024KB and calculates the SHA3-256 hash value of each block, calls the batch query interface of the blockchain content evidence contract, retrieves the hash library of all evidence content on the chain, and uses a multi-threaded comparison algorithm to count the number of completely consistent hash blocks.

[0011] Preferably, the contract call log deploys blockchain event monitoring middleware to capture the FunctionCalled event log of the target smart contract in real time, record the call timestamp, transaction initiation address and input parameters, count the total number of calls within the monitoring period, and use the HyperLogLog algorithm to deduplicate and count the number of unique call addresses.

[0012] Preferably, the quantitative indicators include blockchain activity index, node distribution entropy, timestamp consistency factor, copyright correlation coefficient, content similarity score and contract execution frequency.

[0013] Preferably, the blockchain activity index is obtained by calculating the ratio of the standard deviation of the number of daily transactions to the average value, which is used to measure transaction volatility and is specifically expressed as: , T: number of copyright transactions per day, σ(T): standard deviation of the number of transactions, μ(T): mean number of transactions.

[0014] Preferably, the node distribution entropy is calculated by counting the proportion of nodes in each region and substituting it into the information entropy formula to quantify the degree of decentralization, which is specifically expressed as: , : the node proportion of the kth geographical area, m: the total number of geographical areas.

[0015] Preferably, the timestamp consistency factor is obtained by calculating the inverse of the mean absolute deviation between the actual block time and the theoretical expected time, which is specifically expressed as: , B i : The actual generation timestamp of the i-th block, E i =B i-1 +Δ: expected generation time, Δ: theoretical block interval defined by the blockchain protocol.

[0016] Preferably, the copyright correlation coefficient is calculated by analyzing the ratio of the covariance and standard deviation product of the copyright change sequences of the current work and the related works, reflecting the operational synergy, which is specifically expressed as: , : The number of copyright changes of the current book in t time periods, : The number of changes in related works during the same period, Cov(X,Y): The covariance of X and Y, σ X ,σ Y : standard deviation of X and Y.

[0017] Preferably, the content similarity score is obtained by dividing the book into blocks and generating hash values, comparing them with the hash library of all the content on the chain, and counting the percentage of matching blocks in the total number of blocks, specifically expressed as: , N total : The total number of book content blocks, N match: The number of blocks that match the hash of other content on the chain.

[0018] Preferably, the contract execution frequency is calculated by calculating the average daily number of contract calls and multiplying it by the logarithmic weight of the number of participating accounts to reflect the contract activity and participation scale, specifically expressed as: , C call : Number of smart contract calls during the monitoring period, D: Number of monitoring days, A: Number of unique calling accounts.

[0019] Preferably, the network stability score is constructed by the ratio of the blockchain activity index to the node distribution entropy. First, the blockchain activity index is divided by the value of the node distribution entropy plus 1 to obtain an initial score. When the initial score exceeds a preset threshold, it is determined to be a high-volatility network state and an alarm branch is triggered. When it does not exceed the threshold, the algorithm of directly dividing the blockchain activity index by the node distribution entropy is used to compensate the stability score of the decentralized network. Finally, a quantitative result reflecting the network reliability is output. The threshold is dynamically determined according to the statistical distribution quantile of the historical normal network state score, which is specifically expressed as: .

[0020] Preferably, when the high fluctuation warning branch is triggered, the weight coefficient of NSS in S4 is reduced from a default value of 1.0 to 0.5.

[0021] Preferably, the modified score first establishes a basic credibility score by multiplying the timestamp consistency factor and the network stability score, then superimposes the impact value of the copyright change correlation coefficient, and uses a logarithmic function based on the number of days the copyright lasts to generate a dynamic adjustment coefficient to constrain the impact range of the correlation coefficient. When the number of days the work lasts exceeds the threshold, the maximum adjustment intensity is automatically locked, which is specifically expressed as: , D: the number of days the current book copyright lasts, λ: dynamic weight coefficient.

[0022] Preferably, the infringement risk score is based on the copyright credibility score. When the content similarity score exceeds the preset plagiarism threshold, the corresponding proportion of points is directly deducted to generate the risk score. If the content similarity is lower than the threshold, the behavior reference value is obtained by searching the on-chain similarity database of the author's historical works for positive compensation. The final output result is mapped into three levels of infringement risk identification: low, medium and high according to the risk level classification rules, which are specifically expressed as follows: , A author : Average similarity of the author's historical works.

[0023] Preferably, IRS < 60 indicates low risk, 60 ≤ IRS < 80 indicates medium risk, and IRS ≥ 80 indicates high risk.

[0024] Preferably, the comprehensive traceability rating is achieved by integrating the infringement risk score and the contract execution frequency. First, the infringement risk score is multiplied by the contract execution frequency to strengthen the positive impact of high-frequency compliance operations. Then, the contract execution frequency is normalized using a denominator maximum function to eliminate the risk of numerical distortion in low-frequency call scenarios. Finally, the calculation result is mapped to a preset credibility level interval to form a quantitative rating reflecting the credibility of the entire copyright life cycle, which is specifically expressed as: .

[0025] Preferably, when TR is less than 50, it indicates insufficient reliability and a red warning is output; when 50≤TR<75, it indicates basic credibility and a yellow prompt is output; when TR≥75, it indicates high credibility and a green certification is output.

[0026] Technical effects and advantages of the present invention: 1. This invention dynamically calculates the network stability score and copyright credibility correction coefficient by integrating and analyzing multi-dimensional data such as blockchain network activity, node topology distribution, copyright change history, and contract execution logs. This effectively overcomes the evaluation bias caused by the single data dimension of traditional methods and significantly improves the comprehensiveness and timeliness of copyright status judgment. 2. Compared to the static hash verification mechanism of existing technologies, this solution introduces a real-time monitoring model of blockchain activity index and node distribution entropy. It can automatically identify network fluctuation risks and trigger credibility weight adjustment strategies. While maintaining decentralization, it also enhances the system's fault tolerance to abnormal conditions and avoids misjudgments caused by local node instability. 3. To address the problem of delayed infringement risk detection, this solution combines a dynamic content similarity score with a normalized processing mechanism for contract execution frequency to build a hierarchical early warning system. By automatically comparing historical copyright-related data with real-time risk thresholds, it directly generates a three-level credibility rating result of red, yellow, and green. This reduces manual intervention while significantly improving the response speed and accuracy of copyright tracing decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0028] Figure 2 It is a schematic structural diagram of a complete embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] refer to Figure 1-Figure 2 The book copyright protection and information traceability method based on blockchain technology shown includes: S1: Multi-source data collection: Collect the copyright transaction records, node geographic location information, block generation timestamps, copyright status change history, content block hash values, and original data of contract call logs of the target book from the blockchain network, smart contracts, and external systems to provide input for subsequent analysis.

[0031] The copyright transaction records are obtained through a blockchain browser or node API interface to obtain on-chain transaction records related to the copyright of the target work, and the total number of daily transaction behaviors is counted at the natural day granularity, non-copyright operations are filtered out, copyright registration, transfer, and authorization operation types are retained, and transaction data for at least 30 days are continuously collected to form a time series.

[0032] The node geographic location information is obtained by calling the node discovery protocol or management tool of the blockchain network to obtain the IP address list of active nodes in the entire network, using the IP geolocation service to map the nodes to designated geographical area classifications, and counting the number of nodes in each area, excluding private network nodes and anonymous proxy nodes.

[0033] The block generation timestamp is generated by synchronizing block header data from the blockchain full node, extracting the generation timestamps of 300 consecutive blocks to form a time series, recording the corresponding relationship between the block height and the actual generation time, and synchronously obtaining the theoretical block interval parameters defined by the blockchain protocol.

[0034] The copyright status change history is extracted by parsing the event log of the copyright management smart contract to extract the copyright status change records of the target work in the past 36 months, and simultaneously retrieves the on-chain change records of other works with the same creator, copyright holder or IP association chain, and aligns them at a monthly granularity to form two sets of time series data.

[0035] The content block hash value divides the e-book text content into blocks of a fixed size of 1024KB and calculates the SHA3-256 hash value of each block, calls the batch query interface of the blockchain content evidence contract, retrieves the hash library of all evidence content on the chain, and uses a multi-threaded comparison algorithm to count the number of completely consistent hash blocks.

[0036] The contract call log deploys blockchain event monitoring middleware to capture the FunctionCalled event log of the target smart contract in real time, record the call timestamp, transaction initiation address and input parameters, count the total number of calls within the monitoring period, and use the HyperLogLog algorithm to deduplicate and count the number of unique call addresses.

[0037] S2: Calculation of core indicators: Based on the collected data, quantitative indicators reflecting transaction volatility, node dispersion, timestamp credibility, copyright operation relevance, content similarity and contract activity are calculated to establish the mathematical model foundation for copyright analysis.

[0038] The quantitative indicators include blockchain activity index, node distribution entropy, timestamp consistency factor, copyright correlation coefficient, content similarity score and contract execution frequency.

[0039] The blockchain activity index is obtained by calculating the ratio of the standard deviation of daily transaction times to the average value, and is used to measure transaction volatility. It is specifically expressed as: , T: number of copyright transactions per day, σ(T): standard deviation of the number of transactions, μ(T): mean number of transactions.

[0040] The node distribution entropy is calculated by counting the proportion of nodes in each region and substituting it into the information entropy formula to quantify the degree of decentralization, which is specifically expressed as: , : the node proportion of the kth geographical area, m: the total number of geographical areas.

[0041] The timestamp consistency factor is obtained by calculating the inverse of the mean absolute deviation between the actual block time and the theoretical expected time, which is specifically expressed as: , B i : The actual generation timestamp of the i-th block, E i =B i-1 +Δ: expected generation time, Δ: theoretical block interval defined by the blockchain protocol.

[0042] The copyright correlation coefficient is calculated by analyzing the ratio of the covariance and standard deviation product of the copyright change sequence of the current work and the related works, reflecting the operation synergy, which is specifically expressed as: , : The number of copyright changes of the current book in t time periods, : The number of changes in related works during the same period, Cov(X,Y): The covariance of X and Y, σ X ,σ Y : standard deviation of X and Y.

[0043] The content similarity score is achieved by dividing the book into blocks and generating hash values, which are then compared with the hash library of all the content on the chain. The percentage of matching blocks in the total number of blocks is calculated, which is specifically expressed as: , N total : The total number of book content blocks, N match : The number of blocks that match the hash of other content on the chain.

[0044] The contract execution frequency is calculated by counting the average number of daily contract calls and multiplying it by the logarithmic weight of the number of participating accounts to reflect the contract activity and participation scale, specifically expressed as: , C call : Number of smart contract calls during the monitoring period, D: Number of monitoring days, A: Number of unique calling accounts.

[0045] S3: Network stability assessment: The blockchain network stability score is evaluated by the ratio of the blockchain activity index to the node distribution entropy. When the result exceeds the preset threshold, the high volatility warning branch is triggered. Otherwise, the decentralized compensation algorithm is used to correct the score.

[0046] The network stability score is constructed by the ratio of the blockchain activity index to the node distribution entropy. First, the blockchain activity index is divided by the value of the node distribution entropy plus 1 to obtain an initial score. When the initial score exceeds the preset threshold, it is determined to be a high-volatility network state and the alarm branch is triggered. If it does not exceed the threshold, the algorithm of directly dividing the blockchain activity index by the node distribution entropy is used to compensate the stability score of the decentralized network. Finally, a quantitative result reflecting the network reliability is output. The threshold is dynamically determined based on the statistical distribution quantile of the historical normal network state score, which is specifically expressed as: .

[0047] The high volatility warning branch is triggered when the network stability score exceeds the preset threshold. The system will automatically generate a high volatility alarm event, reduce the credibility weight of the blockchain network in subsequent analysis, and simultaneously record the characteristic transaction patterns and node distribution change data during the abnormal fluctuation period. It will also send a verification request to the copyright management platform through a preset interface, and freeze copyright transaction operations involving abnormal network status until manual review is completed.

[0048] When the high fluctuation warning branch is triggered, the weight coefficient of S4 on NSS is reduced from the default value of 1.0 to 0.5.

[0049] S4: Copyright credibility correction: The network stability score is weighted in combination with the timestamp consistency factor, and the influence of the copyright change correlation coefficient is superimposed to generate a correction score that integrates time credibility and operation relevance.

[0050] The modified score first establishes a basic credibility score by multiplying the timestamp consistency factor and the network stability score. It then adds the impact value of the copyright change correlation coefficient and uses a logarithmic function based on the number of days the copyright has existed to generate a dynamic adjustment coefficient to constrain the impact of the correlation coefficient. When the number of days the work has existed exceeds the threshold, the maximum adjustment intensity is automatically locked. Specifically, it is expressed as: , D: the number of days the current book copyright lasts, λ: dynamic weight coefficient.

[0051] S5: Infringement risk detection: Dynamically adjust the infringement risk score based on the comparison results between the content similarity score and the plagiarism threshold. When it is lower than the plagiarism threshold, the author's historical behavior data is introduced to compensate for credibility.

[0052] The infringement risk score is based on the copyright credibility score. When the content similarity score exceeds the preset plagiarism threshold, the corresponding proportion of points is directly deducted to generate the risk score. If the content similarity is lower than the threshold, the on-chain similarity database of the author's historical works is retrieved to obtain the behavioral reference value for positive compensation. The final output result is mapped into three levels of infringement risk identification: low, medium and high according to the risk level classification rules, which are specifically expressed as follows: , A author : average similarity of the author's historical works; When IRS is less than 60, it indicates low risk; when 60≤IRS<80, it indicates medium risk; when IRS≥80, it indicates high risk.

[0053] S6: Comprehensive traceability rating: The infringement risk score is normalized and weighted by the contract execution frequency, and the final traceability credibility level is output, which is divided into three levels: red warning, yellow prompt and green certification.

[0054] The comprehensive traceability rating is achieved by integrating the infringement risk score and the contract execution frequency. First, the infringement risk score is multiplied by the contract execution frequency to strengthen the positive impact of high-frequency compliance operations. Then, the contract execution frequency is normalized using the denominator maximum function to eliminate the risk of numerical distortion in low-frequency call scenarios. Finally, the calculation result is mapped to a preset credibility level range to form a quantitative rating reflecting the credibility of the entire copyright life cycle. Specifically expressed as: ; When TR is less than 50, it indicates that the reliability is insufficient and a red warning is output. When 50≤TR<75, it indicates that the reliability is basically reliable and a yellow prompt is output. When TR≥75, it indicates that the reliability is high and a green certification is output.

[0055] The present invention first collects multi-source data such as copyright transaction records, node geographic locations, block timestamps, copyright change history, content hash values, and contract call logs of target books from blockchain networks, smart contracts, and external systems. A quantitative analysis model is established by calculating core indicators such as blockchain activity index, node distribution entropy, timestamp consistency factor, copyright correlation coefficient, content similarity score, and contract execution frequency. The network stability is then evaluated, and the network fluctuation state is dynamically judged using the ratio of the blockchain activity index to the node distribution entropy. If the threshold is exceeded, a high fluctuation warning is triggered and the credibility weight is reduced. If the threshold is not exceeded, a decentralized compensation algorithm is used to correct the evaluation. points; then the network stability score is weighted in combination with the timestamp consistency factor, the impact value of the copyright change correlation coefficient is superimposed, and a dynamic adjustment mechanism of the number of days of existence is introduced to generate a copyright credibility correction score; the infringement risk level is adjusted according to the comparison result of the content similarity score and the preset plagiarism threshold. If it is lower than the threshold, the similarity data of the author's historical works on the chain is integrated for credibility compensation. Finally, the infringement risk score and the contract execution frequency are mapped into a comprehensive traceability credibility level through normalized weighted processing, and a three-level rating result of red warning, yellow prompt or green certification is output to achieve full-process automation and quantifiable book copyright protection and information traceability.

[0056] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict. Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The book copyright protection and information traceability method based on blockchain technology is characterized by: include: S1: Multi-source data collection: Collect the copyright transaction records, node geographic location information, block generation timestamps, copyright status change history, content block hash values, and contract call logs of the target book from the blockchain network, smart contracts, and external systems to provide input for subsequent analysis; S2: Core Indicator Calculation: Based on the collected data, we calculate quantitative indicators reflecting transaction volatility, node dispersion, timestamp credibility, copyright operation relevance, content similarity, and contract activity, and establish a mathematical model foundation for copyright analysis. S3: Network Stability Assessment: The blockchain network stability score is evaluated by the ratio of the blockchain activity index to the node distribution entropy. When the result exceeds the preset threshold, the high volatility warning branch is triggered. Otherwise, the decentralized compensation algorithm is used to correct the score. S4: Copyright credibility correction: The network stability score is weighted by combining the timestamp consistency factor and the influence of the copyright change correlation coefficient to generate a correction score that integrates time credibility and operation relevance. S5: Infringement risk detection: Dynamically adjust the infringement risk score based on the comparison between the content similarity score and the plagiarism threshold. When the score is lower than the plagiarism threshold, the author's historical behavior data is introduced to compensate for the credibility. S6: Comprehensive traceability rating: The infringement risk score is normalized and weighted by the contract execution frequency, and the final traceability credibility level is output, which is divided into three levels: red warning, yellow prompt and green certification.

2. The blockchain-based book copyright protection and information traceability method according to claim 1 is characterized by: The quantitative indicators include blockchain activity index, node distribution entropy, timestamp consistency factor, copyright correlation coefficient, content similarity score and contract execution frequency.

3. The blockchain-based book copyright protection and information traceability method according to claim 2 is characterized by: The blockchain activity index is obtained by calculating the ratio of the standard deviation of daily transaction times to the average value, and is used to measure transaction volatility. It is specifically expressed as: , T: daily copyright transaction times, σ(T): standard deviation of transaction times, μ(T): mean transaction times; The node distribution entropy is calculated by counting the proportion of nodes in each region and substituting it into the information entropy formula to quantify the degree of decentralization, which is specifically expressed as: , : the proportion of nodes in the kth geographical area, m: the total number of geographical areas; The timestamp consistency factor is obtained by calculating the inverse of the mean absolute deviation between the actual block time and the theoretical expected time, which is specifically expressed as: , B i : The actual generation timestamp of the i-th block, E i =B i-1 +Δ: expected generation time, Δ: theoretical block interval defined by the blockchain protocol.

4. The blockchain-based book copyright protection and information traceability method according to claim 2 is characterized by: The copyright correlation coefficient is calculated by analyzing the ratio of the covariance and standard deviation product of the copyright change sequence of the current work and the related works, reflecting the operation synergy, which is specifically expressed as: , : The number of copyright changes of the current book in t time periods, : The number of changes in related works during the same period, Cov(X,Y): The covariance of X and Y, σ X ,σ Y : standard deviation of X and Y; The content similarity score is achieved by dividing the book into blocks and generating hash values, which are then compared with the hash library of all the content on the chain. The percentage of matching blocks in the total number of blocks is calculated, which is specifically expressed as: , N total : The total number of book content blocks, N match : The number of blocks that match the hash of other content on the chain; The contract execution frequency is calculated by counting the average number of daily contract calls and multiplying it by the logarithmic weight of the number of participating accounts to reflect the contract activity and participation scale, specifically expressed as: , C call : Number of smart contract calls during the monitoring period, D: Number of monitoring days, A: Number of unique calling accounts.

5. The blockchain-based book copyright protection and information traceability method according to claim 1 is characterized by: The network stability score is constructed by the ratio of the blockchain activity index to the node distribution entropy. First, the blockchain activity index is divided by the value of the node distribution entropy plus 1 to obtain an initial score. When the initial score exceeds the preset threshold, it is determined to be a high-volatility network state and the alarm branch is triggered. If it does not exceed the threshold, the algorithm of directly dividing the blockchain activity index by the node distribution entropy is used to compensate the stability score of the decentralized network. Finally, a quantitative result reflecting the network reliability is output. The threshold is dynamically determined based on the statistical distribution quantile of the historical normal network state score, which is specifically expressed as: .

6. The blockchain-based book copyright protection and information traceability method according to claim 1 is characterized by: When the high fluctuation warning branch is triggered, the weight coefficient of S4 on NSS is reduced from the default value of 1.0 to 0.

5.

7. The blockchain-based book copyright protection and information traceability method according to claim 1 is characterized by: The modified score first establishes a basic credibility score by multiplying the timestamp consistency factor and the network stability score. It then adds the impact value of the copyright change correlation coefficient and uses a logarithmic function based on the number of days the copyright has existed to generate a dynamic adjustment coefficient to constrain the impact of the correlation coefficient. When the number of days the work has existed exceeds the threshold, the maximum adjustment intensity is automatically locked. Specifically, it is expressed as: , D: the number of days the current book copyright lasts, λ: dynamic weight coefficient.

8. The blockchain-based book copyright protection and information traceability method according to claim 1 is characterized by: The infringement risk score is based on the copyright credibility score. When the content similarity score exceeds the preset plagiarism threshold, the corresponding proportion of points is directly deducted to generate the risk score. If the content similarity is lower than the threshold, the on-chain similarity database of the author's historical works is retrieved to obtain the behavioral reference value for positive compensation. The final output result is mapped into three levels of infringement risk identification: low, medium and high according to the risk level classification rules, which are specifically expressed as follows: , A author : average similarity of the author's historical works; When IRS is less than 60, it indicates low risk; when 60≤IRS<80, it indicates medium risk; when IRS≥80, it indicates high risk.

9. The blockchain-based book copyright protection and information traceability method according to claim 1 is characterized by: The comprehensive traceability rating is achieved by integrating the infringement risk score and the contract execution frequency. First, the infringement risk score is multiplied by the contract execution frequency to strengthen the positive impact of high-frequency compliance operations. Then, the contract execution frequency is normalized using the denominator maximum function to eliminate the risk of numerical distortion in low-frequency call scenarios. Finally, the calculation result is mapped to a preset credibility level range to form a quantitative rating reflecting the credibility of the entire copyright life cycle. Specifically expressed as: ; When TR is less than 50, it indicates that the reliability is insufficient and a red warning is output. When 50≤TR<75, it indicates that the reliability is basically reliable and a yellow prompt is output. When TR≥75, it indicates that the reliability is high and a green certification is output.

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