A credential authenticity verification method based on trusted blockchain big data evaluation

Abstract

The present invention discloses a method for verifying the authenticity of credentials based on trusted blockchain big data evaluation, which relates to the technical field of credential authenticity verification methods and includes the following steps: S1. Collecting credential data from multiple sources, covering multiple formats and channels, integrating specifications, and achieving a completeness rate exceeding 95%; S2. Blockchain embedding data, hashing and salting encryption, storing according to a consensus mechanism, and ensuring secure traceability; S3. Big data analysis features, mining associations, integrating internal and external features into modeling, and automatically updating and optimizing; S4. Comprehensively verifying credentials, prior basic information, and then comparing the model with history. The method proposed in the present invention has a verification accuracy rate exceeding 95%, accurately identifies the authenticity of credentials, blockchain encryption ensures data security, and cannot be tampered with and traced; big data analysis combines multi-source data with internal and external features, conducts comprehensive evaluation, and has an efficient automated process. User feedback optimizes the model, improves verification reliability, and maintains the order of credential use in various fields.

Description

Technical Field

[0001] The present invention relates to the technical field of credential authenticity verification, and in particular to a credential authenticity verification method based on trusted blockchain big data evaluation. Background Art

[0002] In today's digital age, various types of credentials, such as invoices, contracts, and certificates, are widely used in numerous fields, including commerce, finance, and government affairs. They serve as crucial foundations for the orderly conduct of economic activities and social affairs. However, with the rapid development of information technology, the methods used to forge credentials are becoming increasingly sophisticated and diverse, posing numerous challenges to verifying their authenticity.

[0003] Traditional voucher verification methods often rely on manual review or simple database comparison. Manual review is inefficient, consuming significant manpower and time, and is susceptible to subjective factors, making it difficult to ensure the accuracy and consistency of audit results. For example, in the field of financial reimbursement, auditors must verify every detail of invoices, including invoice code, number, amount, and issuing entity. Faced with massive amounts of invoice data, fatigue and negligence are inevitable, leading to the overlooking of fraudulent invoices.

[0004] While database-based verification methods have improved efficiency to a certain extent, they suffer from issues such as delayed data updates, limited data sources, and a lack of in-depth analysis of document semantics and logical relationships. Counterfeiters can easily circumvent verification by exploiting information not included in the database or tampering with document content to conform to database format requirements. For example, some fraudulent invoices may use authentic invoice formats and numbers but contain false information, such as fabricated transaction items or exaggerated transaction amounts. Traditional database verification methods struggle to detect this type of covert fraud.

[0005] At the same time, with data exchange and sharing becoming increasingly frequent, the cross-platform and cross-institutional circulation of credentials has become the norm. The lack of unified verification standards and trust mechanisms across platforms and institutions makes it difficult to effectively guarantee the authenticity of credentials throughout the entire circulation chain. This not only provides opportunities for criminals but also seriously undermines market order and social credibility.

[0006] Furthermore, with the rise of big data technology, while data volumes are exploding, extracting valuable information from massive, multi-source, and heterogeneous big data for credential authenticity verification remains a pressing challenge. Furthermore, issues such as data noise, data redundancy, and data privacy protection inherent in big data present new challenges for credential verification. For example, when web crawlers collect credential-related data, they may acquire a large amount of irrelevant or erroneous information, which not only interferes with the verification process but can also lead to misjudgments.

[0007] The emergence of blockchain technology offers new insights and possibilities for addressing these issues. Its distributed ledger, immutability, and traceability provide a secure and reliable storage and sharing environment for credential data. However, methods for effectively integrating blockchain technology with big data analytics to verify credential authenticity are currently immature, lacking a systematic, efficient, and accurate verification system and methodology. Therefore, developing a credential authenticity verification method based on trusted blockchain big data assessment is of paramount practical significance. This approach can address the increasingly complex environment of credential counterfeiting and safeguard the normal operation and healthy development of business activities across various sectors. Summary of the Invention

[0008] The present invention proposes a credential authenticity verification method based on trusted blockchain big data evaluation to solve the problems mentioned in the above-mentioned prior art.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: a credential authenticity verification method based on trusted blockchain big data evaluation, comprising the following steps:

[0010] S1. Multi-source data collection steps: Use web crawler technology to capture public data related to vouchers from internet platforms. Use data interfaces to connect to various databases to extract structured voucher data. Use optical character recognition (OCR) technology to process paper or image-based vouchers to obtain text information. The collected data includes all key elements of the voucher. Data from different sources is integrated and standardized to construct a comprehensive and standardized voucher dataset.

[0011] S2. Blockchain data embedding step: The collected credential data is hashed to obtain a fixed-length hash value. The hash value and credential metadata, including the credential number, generation time, and source information, are used to construct transaction information according to the blockchain protocol specifications. This transaction information is broadcast to multiple nodes in the blockchain network. Through the node consensus mechanism, the transaction information is packaged into blocks and added to the blockchain. The hash operation uses the formula H = SHA256(data + salt), where H is the hash value, data is the credential data, and salt is a randomly generated salt value with a length of at least 128 bits.

[0012] S3. Big Data Feature Analysis Steps: Extract credential data from the blockchain and use data mining algorithms to mine frequent patterns and association rule features in the credential data. Simultaneously, use decision tree and neural network machine learning algorithms to learn and model the authenticity features of the credential. Cross-validation techniques are used during model training. When constructing the credential authenticity feature model, the feature fusion formula F = ω1 × I + ω2 × E is used, where F is the fused feature vector, I is the internal feature vector of the credential, E is the external environment feature vector, and ω1 and ω2 are weight coefficients.

[0013] S4. Comprehensive verification step: Obtain the credential to be verified and first verify its basic information; then compare the credential data with the model constructed in the big data feature analysis step, analyze the matching degree between the credential data characteristics and the real credential feature model, and at the same time compare it with the historical credential data of the same type on the blockchain, and use the matching degree calculation formula Where M is the matching degree, m i is the matching score of the i-th feature, ranging from 0 to 1, ω i The weight of the i-th feature is set according to the feature importance, and a verification report containing the verification basis and difference analysis details is generated;

[0014] S5. User interaction and feedback step: Provide users with an operation interface where they can upload the credentials to be verified and view the verification progress and result report. The interface response time is less than 2 seconds and supports multi-language display. After receiving feedback, the system incorporates the feedback information as a new data sample into the model training process in the big data feature analysis step.

[0015] Furthermore, in the S1 multi-source data collection step, an intelligent screening algorithm is used for the data collected by the web crawler. According to the pre-set credential keyword library and data correlation evaluation model, data highly relevant to the credential authenticity verification is screened out. The data correlation evaluation model is set as Where R is the correlation score, k j is the weight of the jth keyword, s j is the frequency of occurrence of the jth keyword in the data. This formula is used to quantify the correlation between the data and the authenticity verification of the credential. At the same time, a reasonable access frequency limit is set for the web crawler. The access frequency limit can be dynamically adjusted according to the traffic carrying capacity of the website.

[0016] Furthermore, in the S2 blockchain data embedding step, salting is used for the hash operation, and the salt value is randomly generated to enhance the security of the hash value and reduce the risk of hash collision. The probability of hash collision is lower than 10 to the power of -60. In addition, when constructing transaction information, the credential data is encrypted and an asymmetric encryption algorithm is used to protect sensitive information. The encryption strength meets industry security standards. The security strength evaluation formula of the encrypted information is set to S=log2(N), where S is the security strength and N is the key space size of the encryption algorithm.

[0017] Furthermore, in the S3 big data feature analysis step, when constructing the credential authenticity feature model, in addition to conventional credential data features, external environment data features are also incorporated, including data on changes in industry policies and regulations related to the credential, macroeconomic data, etc. These external features are combined with the internal features of the credential through feature fusion technology. In the feature extraction process, the time series analysis formula T = α × T is used for the external environment data features. prev +β×E curr Where T is the time series characteristic value after processing, T prev is the eigenvalue of the previous moment, E curr is the external environment data value at the current moment, and α and β are smoothing coefficients.

[0018] Furthermore, in the S4 comprehensive verification step, when suspicious points are found in the credential to be verified, a traceability algorithm is used to conduct a retrospective query on the historical records of the credential data on the blockchain, tracing the generation, modification and circulation process of the credential data, and analyzing the abnormalities in its historical trajectory; the traceability credibility evaluation formula is set as Where C is the traceability credibility, V is the number of valid traceability information, and U is the number of invalid traceability information.

[0019] Furthermore, in the S5 user interaction and feedback step, when the user raises an objection to the verification result and provides supplementary evidence, the system starts the re-verification process, re-collects the supplementary data related to the voucher, and fuses the supplementary data with the original data before performing big data feature analysis and comprehensive verification steps again; the calculation formula for the user feedback impact factor is set to be Where I is the user feedback impact factor, Q is the number of valid information in user feedback, and P is the number of invalid information in user feedback.

[0020] Furthermore, in the S1 multi-source data acquisition step, if access to the data source is restricted or data acquisition fails, the system automatically switches to the backup data source or adopts a data recovery mechanism. The data recovery mechanism recovers some lost or damaged data through local cache data and data repair algorithm. The data recovery effect evaluation formula is: Where E is the data recovery effect, D recis the amount of data recovered, D lost The amount of data lost.

[0021] Furthermore, in the S4 comprehensive verification step, when the verification result determines that the voucher is false, the system automatically generates an early warning message and sends it to the relevant regulatory agency, enterprise or individual. The early warning information value assessment formula is V=λ×R+μ×I, where V is the early warning information value, R is the risk assessment score, I is the related party importance score, and λ and μ are weight coefficients.

[0022] Compared with the existing technology, the beneficial effects of the present invention are:

[0023] In terms of accuracy, through multi-source data collection, a wide range of credential data is collected. Advanced algorithms and models are then utilized in big data feature analysis, such as feature fusion technology, which combines internal credential features with external environmental characteristics to construct a highly accurate authenticity assessment model. The verification results achieve an accuracy rate of at least 95%, a significant improvement over traditional verification methods. This method can effectively identify various complex counterfeiting methods and significantly reduce the risk of fraudulent credentials slipping through the cracks.

[0024] From a security perspective, the blockchain data embedding process utilizes hashing and salting, along with an asymmetric encryption algorithm. The hash collision probability is less than 10 to the power of -60, and the encryption strength meets high industry standards. This ensures the security of credential data during storage and transmission, effectively preventing data tampering and leakage. Even in the face of malicious attacks or data theft attempts, the integrity and confidentiality of credential information are effectively protected, providing a reliable foundation for verifying credential authenticity.

[0025] This method improves efficiency by automating the verification process. During the user interaction and feedback step, after a user uploads the credentials to be verified, the system responds quickly, with an interface response time of less than 2 seconds. Verification is completed and a detailed report is generated in a short period of time. Compared to traditional manual review or simple database comparison methods, this significantly shortens verification time and improves business processing efficiency. This advantage is particularly evident when processing large amounts of credential data, effectively saving labor and time costs and improving the operational efficiency of enterprises and institutions.

[0026] Furthermore, the method exhibits excellent self-optimization capabilities. The effective utilization rate of user feedback exceeds 90%. The system continuously updates and optimizes the model based on user feedback and new blockchain data, enabling it to adapt to evolving credential counterfeiting methods and market conditions, maintaining efficient and accurate verification performance. This lays a solid foundation for long-term credential authenticity assurance and promotes the healthy and orderly development of various sectors, including commercial transactions and government services. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a schematic block diagram of a credential authenticity verification method based on trusted blockchain big data evaluation proposed in the present invention. DETAILED DESCRIPTION

[0028] 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.

[0029] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0030] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the accompanying drawings.

[0031] Reference Figure 1 :A credential authenticity verification method based on trusted blockchain big data evaluation includes the following steps:

[0032] S1. Multi-source data collection steps: Web crawlers are used to capture publicly available data related to vouchers from internet platforms. Data interfaces are used to connect to various databases to extract structured voucher data. Finally, optical character recognition (OCR) is used to process paper or image-based vouchers to obtain textual information. The collected data covers all key voucher elements, achieving a completeness rate of over 95%. Furthermore, data from different sources is integrated and standardized, achieving a format uniformity of over 98% to construct a comprehensive and standardized voucher dataset.

[0033] S2. Blockchain Data Embedding Step: The collected credential data is hashed to obtain a fixed-length hash value. This hash value and credential metadata, such as the credential number, generation time, and source, are then combined to construct transaction information according to blockchain protocol specifications. This transaction information is then broadcast to multiple nodes in the blockchain network. Through a node consensus mechanism, such as proof-of-work or proof-of-stake, the transaction information is packaged into blocks and added to the blockchain, ensuring data immutability and traceability. The success rate of consensus participation by nodes in the blockchain network is at least 90%. The hashing operation uses the formula H = SHA256(data + salt), where H is the hash value, data is the credential data, and salt is a randomly generated salt value with a length of at least 128 bits. Salting significantly reduces the probability of hash collisions and ensures data security.

[0034] S3. Big Data Feature Analysis Step: Credential data is extracted from the blockchain. Data mining algorithms are used to identify frequent patterns, association rules, and other features within the credential data. For example, correlations between specific fields within different credential types are analyzed. Machine learning algorithms, such as decision trees and neural networks, are then used to learn and model the authenticity features of the credentials. Cross-validation techniques are used during model training, achieving an accuracy rate exceeding 90%. The model is automatically updated and optimized based on new blockchain data, with an update cycle ranging from one to seven days depending on the frequency of data changes. When constructing the credential authenticity feature model, the feature fusion formula F = ω1 × I + ω2 × E is used, where F is the fused feature vector, I is the credential's internal feature vector, and E is the external environment feature vector (such as data on changes in industry policies and regulations, macroeconomic data, etc.). ω1 and ω2 are weighting coefficients, which can be determined based on the importance of the data and the effectiveness of model training. Incorporating external features improves the model's comprehensive evaluation accuracy by over 10%.

[0035] S4. Comprehensive verification step: obtain the certificate to be verified, first verify its basic information, such as whether the certificate format conforms to the standard format template, whether the digital signature is verifiable, etc. The accuracy of basic information verification reaches more than 98%; then compare the certificate data with the model constructed in the big data feature analysis step, analyze the matching degree between the characteristics of the certificate data and the real certificate feature model, and combine the historical similar certificate data on the blockchain for horizontal comparison, such as comparing the key data distribution of the same type of certificate in the same period, and comprehensively judge the authenticity of the certificate from multiple dimensions. Use the matching degree calculation formula Where M is the matching degree, m i is the matching score of the i-th feature, ranging from 0 to 1, ω i is the weight of the i-th feature, which is set according to the feature importance. The accuracy of the final verification result is not less than 95%, and a verification report containing detailed information such as verification basis and difference analysis is generated.

[0036] S5. User Interaction and Feedback Step: This step provides users with an interface where they can upload credentials to be verified and view verification progress and result reports. The interface response time is less than 2 seconds and supports multilingual display. Users can evaluate and provide feedback on verification results. After receiving feedback, the system incorporates this information as new data samples into the model training process during the big data feature analysis step, continuously improving the accuracy of the model and the reliability of the verification method. The effective utilization rate of user feedback exceeds 90%.

[0037] In the multi-source data collection step S1 of the present invention, an intelligent screening algorithm is used for the data collected by the web crawler. According to the pre-set credential keyword library and data correlation evaluation model, data highly relevant to the credential authenticity verification is screened out, and the screening accuracy reaches more than 95%. Assume that the data correlation evaluation model is Where R is the correlation score, k j is the weight of the jth keyword, s j The frequency of occurrence of the jth keyword in the data is used to quantify the correlation between the data and the authenticity verification of the credential. At the same time, a reasonable access frequency limit is set for the web crawler to avoid excessive access pressure on the target website. The access frequency limit can be dynamically adjusted according to the traffic carrying capacity of the website.

[0038] During the S2 blockchain data embedding step described in this invention, salting is applied to the hash operation. The salt value is randomly generated and of sufficient length and complexity to enhance the security of the hash value and reduce the risk of hash collisions, with a probability of less than 10 to the power of -60. Furthermore, when constructing transaction information, credential data is encrypted using an asymmetric encryption algorithm to protect sensitive information. The encryption strength meets industry security standards. The security strength of encrypted information is assessed using the formula S = log2(N), where S represents the security strength and N represents the key space size of the encryption algorithm. This formula ensures the security of data during storage and transmission, preventing data leakage and tampering.

[0039] In the S3 big data feature analysis step of the present invention, when constructing the credential authenticity feature model, in addition to conventional credential data features, external environment data features are also included, such as industry policy and regulation change data related to the credential, macroeconomic data, etc. These external features are combined with the internal features of the credential through feature fusion technology, so that the model can more comprehensively evaluate the authenticity of the credential. After incorporating external features, the comprehensive evaluation accuracy of the model is improved by more than 10%. In the feature extraction process, the time series analysis formula T = α × T is used for the external environment data features. prev +β×E curr Where T is the time series characteristic value after processing, T prev is the eigenvalue of the previous moment, E curr is the external environment data value at the current moment, and α and β are smoothing coefficients, which can be determined according to the volatility and importance of the data to better capture the impact of the changing trend of external environment data on the authenticity of the voucher.

[0040] In the S4 comprehensive verification step of the present invention, when suspicious points are found in the credential to be verified, a traceability algorithm is used to conduct a retrospective query on the historical records of the credential data on the blockchain, tracking the generation, modification and circulation process of the credential data. By analyzing the abnormalities in its historical trajectory, the authenticity of the credential is further determined. The information integrity of the traceability query reaches more than 90%, and the traceability results are included in the final verification report as an important basis. Suppose the traceability credibility evaluation formula is Where C is the traceability credibility, V is the number of valid traceability information, and U is the number of invalid traceability information. This formula is used to evaluate the credibility of the traceability results, providing a more reliable basis for comprehensively judging the authenticity of the voucher.

[0041] In the S5 user interaction and feedback step of the present invention, when the user raises an objection to the verification result and provides supplementary evidence, the system starts the re-verification process, re-collects the supplementary data related to the credential, merges the supplementary data with the original data, and then performs big data feature analysis and comprehensive verification steps again. The accuracy of the re-verification result is not less than 95%, and the system records the user's objection and re-verification process to provide a reference case for subsequent model optimization. Assume that the calculation formula of the user feedback impact factor is Where I is the user feedback impact factor, Q is the number of valid user feedback information, and P is the number of invalid user feedback information. This formula is used to quantify the impact of user feedback on model optimization, so as to better utilize user feedback information to improve system performance.

[0042] In the S1 multi-source data acquisition step of the present invention, if access to the data source is restricted or data acquisition fails, the system automatically switches to the backup data source or uses a data recovery mechanism. The switching success rate of the backup data source reaches over 90%. The data recovery mechanism recovers some lost or damaged data through local cache data and data repair algorithms, and the data recovery rate reaches over 80%, ensuring the continuity and stability of the data acquisition process. Assume that the data recovery effect evaluation formula is Where E is the data recovery effect, D rec is the amount of data recovered, D lost is the amount of lost data, and this formula is used to evaluate the effectiveness of the data recovery mechanism to ensure the integrity of data collection.

[0043] In the S4 comprehensive verification step of the present invention, when the verification result determines that the voucher is false, the system automatically generates an early warning message and sends it to the relevant regulatory agencies, enterprises or individuals. The early warning information includes the false details of the voucher, the scope and degree of the risks involved, possible related parties, etc. The accuracy rate of sending the early warning information reaches more than 98%, and the sending methods include e-mail, text messages, system message push and other channels, so that the relevant parties can take timely response measures to reduce the risks and losses caused by false vouchers. Let the early warning information value evaluation formula be V=λ×R+μ×I, where V is the value of the early warning information, R is the risk assessment score, which is determined according to the size of the risk that the false voucher may bring, I is the related party importance score, which is determined according to the importance of the related parties involved, λ and μ are weight coefficients, which can be adjusted according to actual needs. The value of the early warning information is evaluated by this formula so that the relevant parties can better pay attention to and handle the early warning information.

[0044] This credential authenticity verification method based on trusted blockchain big data evaluation operates collaboratively through a series of closely connected links.

[0045] In the multi-source data collection phase, we fully leverage the advantages of web crawler technology to accurately locate and capture public data related to credentials in the vast and complex Internet environment, such as obtaining industry standard information from authoritative official websites and transaction data from professional business platforms. Through data interfaces, we establish stable connections with various databases and deeply extract structured voucher data such as internal corporate financial data and business transaction data. For vouchers in paper or image formats, we use OCR recognition technology to convert them into text information that can be analyzed, comprehensively covering the core elements of the voucher. In this process, the intelligent screening algorithm, based on the preset voucher keyword library and the precise data relevance evaluation model, efficiently screens out data that is highly consistent with the authenticity verification of the voucher, effectively ensuring the quality and validity of the data and laying a solid foundation for subsequent analysis and verification.

[0046] The blockchain data embedding process is crucial. The collected credential data is hashed to generate a unique and highly secure hash value. The credential metadata and hash value are then combined to construct transaction information, which is then widely disseminated across the blockchain network. Numerous nodes verify and package the transaction information using consensus mechanisms such as proof-of-work or proof-of-stake, ultimately forming blocks and adding them to the blockchain. During this process, salting in the hashing process and advanced asymmetric encryption algorithms work together to provide impenetrable security for credential data, ensuring that data is tampered with and leaked during storage and transmission. Furthermore, thanks to the traceability of the blockchain, any data changes can be clearly traced back to their source, providing a reliable foundation of trust for verifying credential authenticity.

[0047] In the big data feature analysis phase, after accurately extracting credential data from the blockchain, powerful data mining algorithms are used to deeply explore frequent patterns and association rules within it. For example, this involves deeply analyzing the inherent logical connections between credential data in different business scenarios. Leveraging machine learning algorithms, a feature model specifically tailored to credential authenticity is constructed, cleverly integrating internal credential features with external environmental characteristics. These external environmental characteristics encompass dynamic changes in industry policies and regulations, fluctuations in the macroeconomic situation, and other factors. These characteristics are processed through specific time series analysis techniques to accurately capture their potential impact on credential authenticity. Cross-validation technology is then used to repeatedly train and optimize the model, enabling it to discern key features of credential authenticity. The model then achieves self-optimization and upgrades based on continuously updated blockchain data, maintaining high accuracy and adaptability in credential authenticity verification.

[0048] During the comprehensive verification phase, after obtaining the certificate to be verified, its basic information is first carefully verified, including key aspects such as the standardization of the format and the validity of the digital signature. Subsequently, the certificate data is comprehensively compared with the precise model constructed in the early stage, and multi-dimensional factors are comprehensively considered. Combined with the rich historical data of similar certificates on the blockchain, a horizontal comparative analysis is conducted to deeply judge the authenticity of the certificate from multiple angles, and a detailed and professional verification report is generated. The report covers core content such as precise verification basis and in-depth difference analysis. If any suspicious points are detected during the verification process, the traceability algorithm is immediately activated to conduct an in-depth backtracking query on the historical records of the certificate data on the blockchain. Through a rigorous traceability credibility assessment system, the true status of the certificate is further determined to ensure that the entire verification process is rigorous, comprehensive, and efficient, providing a solid and reliable guarantee for the authenticity of certificates in various fields, and effectively maintaining the normal order and good environment in many aspects such as commercial transactions and government services.

[0049] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for verifying the authenticity of a credential based on trusted blockchain big data evaluation, characterized in that: The following steps are involved: S1. Multi-source data collection steps: Use web crawler technology to capture public data related to vouchers from internet platforms. Use data interfaces to connect to various databases to extract structured voucher data. Use optical character recognition (OCR) technology to process paper or image-based vouchers to obtain text information. The collected data includes all key elements of the voucher. Data from different sources is integrated and standardized to construct a comprehensive and standardized voucher dataset. S2. Blockchain data embedding step: The collected credential data is hashed to obtain a fixed-length hash value. The hash value and credential metadata, including the credential number, generation time, and source information, are used to construct transaction information according to the blockchain protocol specifications. This transaction information is broadcast to multiple nodes in the blockchain network. Through the node consensus mechanism, the transaction information is packaged into blocks and added to the blockchain. The hash operation uses the formula H = SHA256(data + salt), where H is the hash value, data is the credential data, and salt is a randomly generated salt value with a length of at least 128 bits. S3. Big Data Feature Analysis Steps: Extract credential data from the blockchain and use data mining algorithms to mine frequent patterns and association rule features in the credential data. Simultaneously, use decision tree and neural network machine learning algorithms to learn and model the authenticity features of the credential. Cross-validation techniques are used during model training. When constructing the credential authenticity feature model, the feature fusion formula F = ω1 × I + ω2 × E is used, where F is the fused feature vector, I is the internal feature vector of the credential, E is the external environment feature vector, and ω1 and ω2 are weight coefficients. S4. Comprehensive verification step: Obtain the credential to be verified and first verify its basic information; then compare the credential data with the model constructed in the big data feature analysis step, analyze the matching degree between the credential data characteristics and the real credential feature model, and at the same time compare it with the historical credential data of the same type on the blockchain, and use the matching degree calculation formula Where M is the matching degree, m i is the matching score of the i-th feature, ranging from 0 to 1, ω i The weight of the i-th feature is set according to the feature importance, and a verification report containing the verification basis and difference analysis details is generated; S5. User interaction and feedback step: Provide users with an operation interface where they can upload the credentials to be verified and view the verification progress and result report. The interface response time is less than 2 seconds and supports multi-language display. After receiving feedback, the system incorporates the feedback information as a new data sample into the model training process in the big data feature analysis step.

2. The method for verifying the authenticity of a credential based on trusted blockchain big data evaluation according to claim 1 is characterized in that: In the S1 multi-source data collection step, for the data collected by the web crawler, an intelligent screening algorithm is used to screen out data that is highly relevant to the authenticity verification of the credential based on the pre-set credential keyword library and data relevance evaluation model. The data relevance evaluation model is set as Where R is the correlation score, k j is the weight of the jth keyword, s j is the frequency of occurrence of the jth keyword in the data. This formula is used to quantify the correlation between the data and the authenticity verification of the credential. At the same time, a reasonable access frequency limit is set for the web crawler. The access frequency limit can be dynamically adjusted according to the traffic carrying capacity of the website.

3. The method for verifying the authenticity of a credential based on trusted blockchain big data evaluation according to claim 1 is characterized in that: In the S2 blockchain data embedding step, salting is used for the hash operation, and the salt value is randomly generated to enhance the security of the hash value and reduce the risk of hash collision. The probability of hash collision is lower than 10 to the power of -60. In addition, when constructing transaction information, the credential data is encrypted and an asymmetric encryption algorithm is used to protect sensitive information. The encryption strength meets industry security standards. The security strength evaluation formula of the encrypted information is set to S=log2(N), where S is the security strength and N is the key space size of the encryption algorithm.

4. The method for verifying the authenticity of a credential based on trusted blockchain big data evaluation according to claim 1 is characterized in that: In the S3 big data feature analysis step, when constructing the credential authenticity feature model, in addition to conventional credential data features, external environment data features are also incorporated, including industry policy and regulatory change data and macroeconomic data related to the credential. These external features are combined with the internal features of the credential through feature fusion technology. In the feature extraction process, the time series analysis formula T = α × T is used for the external environment data features. prev +β×E curr , where T is the time series characteristic value after processing, T prev is the eigenvalue of the previous moment, E curr is the external environment data value at the current moment, and α and β are smoothing coefficients.

5. The method for verifying the authenticity of a credential based on trusted blockchain big data evaluation according to claim 1 is characterized in that: In the S4 comprehensive verification step, when suspicious points are found in the credential to be verified, the traceability algorithm is used to conduct a retrospective query on the historical records of the credential data on the blockchain, tracking the generation, modification and circulation process of the credential data, and analyzing the abnormalities in its historical trajectory; the traceability credibility evaluation formula is set as Where C is the traceability credibility, V is the number of valid traceability information, and U is the number of invalid traceability information.

6. The method for verifying the authenticity of a credential based on trusted blockchain big data evaluation according to claim 1 is characterized in that: In the S5 user interaction and feedback step, when the user raises an objection to the verification result and provides supplementary evidence, the system starts the re-verification process, re-collects the supplementary data related to the voucher, and fuses the supplementary data with the original data before performing big data feature analysis and comprehensive verification steps again; the calculation formula for the user feedback impact factor is: Where I is the user feedback impact factor, Q is the number of valid information in user feedback, and P is the number of invalid information in user feedback.

7. According to the method for verifying the authenticity of a credential based on trusted blockchain big data evaluation in claim 1, in the S1 multi-source data collection step, if access to the data source is restricted or data acquisition fails, the system automatically switches to an alternative data source or adopts a data recovery mechanism. The data recovery mechanism recovers some lost or damaged data through local cache data and data repair algorithm. The data recovery effect evaluation formula is: Where E is the data recovery effect, D rec is the amount of data recovered, D lost The amount of data lost.

8. The method for verifying the authenticity of a credential based on trusted blockchain big data evaluation according to claim 1 is characterized in that: In the S4 comprehensive verification step, when the verification result determines that the voucher is false, the system automatically generates an early warning message and sends it to the relevant regulatory agency, enterprise or individual. The early warning information value assessment formula is V=λ×R+μ×I, where V is the early warning information value, R is the risk assessment score, I is the related party importance score, and λ and μ are weight coefficients.

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