System and method for sharing transaction metadata information of financial assets based on blockchain

Abstract

The present invention discloses a system and method for sharing transaction metadata information of financial assets based on blockchain, and relates to the technical field of transaction data processing. The first identification unit, the second identification unit and the third identification unit integrated in the system can analyze the asset type, transaction amount and key metadata of transaction participants in real time by using text recognition, big data analysis and identity authentication technology. By calculating and evaluating the asset transaction matching index JPs, the transaction anomaly index YCz, the digital signature integrity index QZd and the participant reputation index XYs, an early warning signal is automatically generated. When an abnormal transaction or risk is detected, the system can issue an early warning instruction in time and take corresponding restrictive measures to avoid the expansion of risks, thereby enhancing the security and real-time response capability of the transaction. The system adopts blockchain technology, and each transaction in the blockchain has a digital signature and a timestamp, which ensures the immutability and transparency of the data.

Description

Technical Field

[0001] The present invention relates to the technical field of transaction data processing, and specifically to a system and method for sharing transaction metadata information of financial assets based on blockchain. Background Art

[0002] In modern financial transaction systems, the management and transaction process of financial assets is becoming increasingly complex, involving the participation of multiple parties and the processing of massive amounts of data. The transaction data of financial assets not only contains basic information such as transaction amount and transaction time, but also includes metadata such as the identity authentication, digital signature, and asset type of the participants. The accuracy and security of these data are crucial to the stable operation of the financial system. However, traditional financial asset management systems have many defects in processing these complex metadata information, which restricts the security, real-time nature, and transaction efficiency of the system.

[0003] Traditional financial transaction systems often rely on manual data verification and cleaning when processing complex transactions, which is prone to errors and inefficient. For high-frequency, cross-platform transaction scenarios, manual processing methods are stretched. Especially in the collection and analysis of financial asset metadata, it involves the processing of a large amount of participant data, transaction amounts, time, digital signatures and other information. Manual processing is difficult to meet real-time requirements, especially in the face of abnormal fluctuations in financial asset transactions, participant reputation analysis, and transaction matching. Complex tasks, manual operations are prone to miss risk warning signals, and also increase the risk of transaction errors and data tampering. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a system and method for sharing transaction metadata information of financial assets based on blockchain to solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a system for sharing transaction metadata information of financial assets based on blockchain, comprising a blockchain collection unit, a first identification unit, a second identification unit, a third identification unit, an early warning unit and a strategy unit;

[0006] The blockchain collection unit is used to collect metadata information of financial assets and related transaction data to form a financial asset metadata set, and clean and pre-process the financial asset metadata set to remove invalid information and duplicate data, and then convert the financial asset metadata set into a unified standard format; the financial asset metadata includes asset type data, transaction time data, transaction amount data, transaction participants and digital signature data;

[0007] The first identification unit is used to extract asset type data, identify and classify different asset type data through text recognition technology, quantify the number of asset transactions, and compare the asset transaction quantity with the account book record to obtain the asset transaction matching index JPs; when the asset transaction matching index JPs is lower than the matching threshold C, the early warning unit generates and issues a first early warning instruction; when the asset transaction matching index JPs is higher than or equal to the matching threshold C, a first qualified mark is generated;

[0008] The second identification unit is used to extract transaction amount data, compare historical transaction records with market benchmarks using big data analysis technology, and obtain transaction amount volatility index bdx and transaction abnormality index YCz; when the transaction abnormality index YCz exceeds the set abnormal threshold B, the warning unit generates and issues a second warning instruction; when the transaction amount fluctuation is within a reasonable range, a second qualified mark is generated;

[0009] The third identification unit is used for transaction participants and digital signature data, identifying and verifying the identity and digital signature of each participant, and obtaining the digital signature integrity index QZd and the participant reputation index XYs; when the digital signature integrity index QZd is lower than the integrity threshold E, the early warning unit generates and issues a third early warning instruction, otherwise, a third qualified mark is generated; when the participant reputation index XYs is lower than the reputation threshold R, the early warning unit generates and issues a fourth early warning instruction, otherwise, a fourth qualified mark is generated;

[0010] The strategy unit is used to generate and execute corresponding strategies according to the first warning instruction, the second warning instruction, the third warning instruction and the fourth warning instruction.

[0011] Preferably, the blockchain collection unit includes a data collection module, a data preprocessing module and a data verification module;

[0012] The data acquisition module is used to obtain metadata information of financial assets and related transaction data in real time through API external system and financial platform interface interaction technology and blockchain technology by accessing blockchain nodes or using blockchain API to form a financial asset metadata set;

[0013] The data preprocessing module is used to clean and preprocess the financial asset metadata set, remove invalid information and duplicate data, convert the financial asset metadata set into a unified standard format, and convert the transaction data including time format and currency unit into a unified standard;

[0014] The data verification module is used to perform data format verification on the financial asset data set, including date format verification, amount format verification and digital signature format verification, and mark the data that does not conform to the format as abnormal data and issue a correction alarm, which requires manual confirmation and manual correction.

[0015] Preferably, the first identification unit includes a first data extraction module and a first statistics module;

[0016] The first data extraction module is used to extract asset type data from the financial asset metadata set, extract and classify different asset type data from the account book through text recognition technology, and obtain the number of account book asset transactions Tbx, the total amount of account book transactions Zje, the transaction time error index Jysj and the transaction participant matching index Jycyf through the first statistical module through the following formula;

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] In the formula, N represents the number of transactions of this asset type recorded in the ledger. represents the i-th transaction record, Indicates the amount data value of the i-th transaction, is the timestamp of the i-th transaction recorded in the system, is the timestamp of the i-th transaction recorded in the ledger, is the number of participants in the i-th transaction recorded by the system that matches the ledger record, The total number of participants in the i-th system record and ledger takes the maximum value.

[0022] Preferably, the first identification unit further includes a first association module and a first judgment module;

[0023] The first association module is used to extract the number of ledger asset transactions Tbx, the total amount of ledger transactions Zje, the transaction time error index Jysj and the transaction participant matching index Jycyf. After dimensionless processing, the asset transaction matching index JPs is calculated by the following formula:

[0024] ;

[0025] In the formula, , , and They represent the weight coefficients of the number of ledger asset transactions Tbx, the total amount of ledger transactions Zje, the transaction time error index Jysj, and the transaction participant matching index Jycyf, respectively. , , , ,and , represents the first correction constant;

[0026] The first judgment module is used to preset a matching threshold C, and compare and judge the asset transaction matching index JPs with the matching threshold C to obtain a first evaluation result, including:

[0027] When the asset transaction matching index JPs ≥ matching threshold C, it means that the transaction matching degree is qualified, and the first qualified mark is generated, and it continues or enters the next stage;

[0028] When the asset transaction matching index JPs is less than the matching threshold C, it indicates that the transaction matching degree is unqualified, triggering the first warning instruction to remind relevant personnel or the system to pay attention to abnormal transaction matching problems.

[0029] Preferably, the second identification unit includes a second data extraction module and a second evaluation module;

[0030] The second data extraction module is used to extract relevant transaction amount data from the transaction record, input the original transaction data and timestamp, and output the extracted transaction amount data set , N represents the number of transactions of this asset type recorded in the account book, and the transaction amount volatility index bdx and transaction anomaly index YCz are calculated by the following formula:

[0031] ;

[0032] ;

[0033] In the formula, Indicates the amount data value of the i-th transaction, Represents the mean of transaction amount data; Indicates the market benchmark volatility threshold;

[0034] The second evaluation module is used to preset an abnormality threshold B, and compare and evaluate the transaction abnormality index YCz with the abnormality threshold B to obtain a second evaluation result, including:

[0035] When the transaction abnormality index YCz ≥ abnormal threshold B, it means that the transaction amount is abnormal, and a second warning instruction is generated;

[0036] When the transaction anomaly index YCz is less than the anomaly threshold B, it indicates that the transaction amount fluctuation is within a reasonable range, and a second qualified mark is generated.

[0037] Preferably, the third identification unit includes a third data extraction module and a digital signature integrity index calculation module;

[0038] The third data extraction module is used to identify and verify the identity and digital signature of each transaction participant, extract and verify each transaction data of each transaction participant, and the transaction data includes: original information, digital signature, public key of the signer and signature timestamp ;

[0039] The original message hash value is calculated using the same hash function SHA-256 as the digital signature algorithm ;

[0040] Decrypt the digital signature using the sender's public key to obtain the hash value calculated when the digital signature was signed ;

[0041] and compare the original message hash Calculate the hash value after digital signature , and obtain the effectiveness index H:

[0042] ;

[0043] when =1, it means that the original information is consistent with the hash value obtained after decryption. When ≠1, the message is considered to be tampered;

[0044] And based on the signature timestamp and effective time threshold , calculate the effective time index using the following formula ;

[0045] ;

[0046] T represents the current time, the effective time threshold Set up for 3-7 days;

[0047] The digital signature integrity index calculation module is used to extract the validity index H and the validity time index , after dimensionless processing, the digital signature integrity index QZd is calculated by the following associated formula:

[0048] ;

[0049] In the formula, and Represents the effectiveness index H and the effective time index respectively The weight coefficient is set by the user. , ,and .

[0050] Preferably, the third identification unit further includes a reputation index calculation module and a third evaluation module; the reputation index calculation module is used to calculate and obtain the reputation index XYs of the transaction participant through the following formula:

[0051] ;

[0052] In the formula, Indicates the total number of transactions between transaction participants. Indicates the number of successful transactions of the transaction participants, Indicates the number of failed or defaulted transactions by the transaction participants, represents the credit score of the trading party, Indicates the maximum credit score of the transaction party, represents the transaction volume of the largest counterparty of the i-th trading party, j represents the number of the trading counterparties of the trading party, Indicates the total number of delayed transactions by the trading parties; , , and Represents the weight coefficient, which is adjusted by the user. , , , ,and ;

[0053] The third evaluation module is used to preset the integrity threshold E and the reputation threshold R;

[0054] The digital signature integrity index QZd is compared with the integrity threshold E to obtain a third evaluation result, including:

[0055] When the digital signature integrity index QZ is less than the integrity threshold E, indicating that the digital signature is unqualified, the warning unit generates and issues a third warning instruction;

[0056] When the digital signature integrity index QZ ≥ the integrity threshold E, it means that the digital signature is qualified and a third qualified mark is generated;

[0057] The participant's reputation index XYs is compared with the reputation threshold R to obtain a fourth evaluation result, including:

[0058] When the participant's reputation index XYs is less than the reputation threshold R, it means that the participant's reputation is unqualified, and the early warning unit generates and issues a fourth early warning instruction;

[0059] When the participant's reputation index XYs≥reputation threshold R, it indicates that the participant's reputation is qualified, and a fourth qualified mark is generated.

[0060] Preferably, the strategy unit is used to generate corresponding strategies according to the first warning instruction, the second warning instruction, the third warning instruction and the fourth warning instruction, including:

[0061] When the first qualified mark, the second qualified mark, the third qualified mark and the fourth qualified mark are obtained at the same time, the system automatically allocates 10%-15% more computing resources and 10%-15% more network bandwidth to the transaction processing to promote the transaction speed;

[0062] When the first warning instruction is identified, the first strategy is triggered, including: if the transaction match fails, the current transaction is automatically suspended, and the manual review or secondary verification process is entered. At the same time, the system supports the rollback function to safely restore the transaction status when the transaction match fails;

[0063] When the second warning instruction is identified, the second strategy is triggered, including: abnormal fluctuations in transaction amount, reducing the frequency of transaction request processing by 30%-50%, and limiting the number of daily transactions to 1-3 times;

[0064] When the third warning instruction is identified, the third strategy is triggered, including: digital signature anomaly, re-verification using multi-layer encryption and identity authentication mechanism, and freezing the transaction if the verification still fails;

[0065] When it is identified as the fourth warning instruction, the fourth strategy is triggered, including: abnormal credit, automatic partition isolation measures to prevent untrustworthy participants from affecting other normal transaction parties and restricting their operating permissions.

[0066] The method for sharing transaction metadata information of financial assets based on blockchain includes the following steps:

[0067] S1. Collect metadata information of financial assets and related transaction data to form a set of financial asset metadata, clean and pre-process the set of financial asset metadata, remove invalid information and duplicate data, and convert the set of financial asset metadata into a unified standard format; the financial asset metadata includes asset type data, transaction time data, transaction amount data, transaction participants and digital signature data;

[0068] S2. Extract asset type data, identify and classify different asset type data through text recognition technology, quantify the number of asset transactions, and compare the asset transaction quantity with the account book record to obtain the asset transaction matching index JPs; when the asset transaction matching index JPs is lower than the matching threshold C, the early warning unit generates and issues a first early warning instruction; when the asset transaction matching index JPs is higher than or equal to the matching threshold C, a first qualified mark is generated;

[0069] S3. Extract transaction amount data, use big data analysis technology to compare historical transaction records and market benchmarks, and obtain transaction amount volatility index bdx and transaction abnormality index YCz; when the transaction abnormality index YCz exceeds the set abnormal threshold B, the warning unit generates and issues a second warning instruction; when the transaction amount fluctuation is within a reasonable range, a second qualified mark is generated;

[0070] S4, transaction participants and digital signature data, identify and verify the identity and digital signature of each participant, obtain the digital signature integrity index QZd and the participant reputation index XYs; when the digital signature integrity index QZd is lower than the integrity threshold E, the early warning unit generates and issues a third early warning instruction, otherwise, generates a third qualified mark; when the participant reputation index XYs is lower than the reputation threshold R, the early warning unit generates and issues a fourth early warning instruction, otherwise, generates a fourth qualified mark;

[0071] S5. Generate and execute corresponding strategies according to the first warning instruction, the second warning instruction, the third warning instruction and the fourth warning instruction.

[0072] The present invention provides a system and method for sharing transaction metadata information of financial assets based on blockchain. It has the following beneficial effects:

[0073] (1) The first identification unit extracts asset type data from the metadata of financial assets, uses text recognition technology to classify data of different asset types, and quantifies the number of asset transactions. This unit effectively identifies the degree of match between asset transaction records in the ledger and actual transaction data by calculating the asset transaction matching index JPs. When the degree of match is unqualified, the system can issue an early warning in a timely manner to remind relevant personnel to review or conduct further verification to avoid transaction errors. Through the automated processing of this unit, the accuracy of transaction records is greatly improved, the risk of manual intervention and misjudgment is reduced, and the efficiency and accuracy of transaction processing are improved.

[0074] (2) The second identification unit extracts transaction amount data and compares historical transactions and market benchmarks with big data analysis technology to calculate the transaction amount volatility index bdx and transaction abnormality index YCz. This unit can effectively monitor abnormal fluctuations in transaction amounts and conduct risk assessment and early warning through set thresholds. When the transaction amount is abnormal, the system can respond in a timely manner and limit the transaction frequency, thereby reducing the potential losses caused by abnormal fluctuations. Through the real-time monitoring and early warning mechanism of this unit, the stability and security of the transaction process are significantly improved.

[0075] (3) The third identification unit extracts and verifies the identity information and digital signatures of the transaction participants, calculates the digital signature integrity index QZd and the participant reputation index XYs, and ensures the security and credibility of the transaction. This unit can effectively identify the risks of signature tampering or insufficient credibility that may exist during the transaction process, and ensure the integrity of transaction information and the reliability of the participants. When the digital signature or the credibility of the participant is unqualified, the system can issue an early warning and take corresponding restrictive measures to prevent bad transactions from occurring. Through the identification and verification of this unit, the transparency and security of the transaction are further enhanced, and the transaction risk is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a schematic diagram of the system block diagram of the present invention for sharing transaction metadata information of financial assets based on blockchain;

[0077] Figure 2 Schematic diagram of the method steps for sharing transaction metadata information of financial assets based on blockchain in the present invention. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] Example 1

[0080] See also Figure 1 , the present invention provides a system for sharing transaction metadata information of financial assets based on blockchain, including a blockchain collection unit, a first identification unit, a second identification unit, a third identification unit, an early warning unit and a strategy unit;

[0081] The blockchain collection unit is used to collect metadata information of financial assets and related transaction data to form a financial asset metadata set, and clean and pre-process the financial asset metadata set to remove invalid information and duplicate data, and then convert the financial asset metadata set into a unified standard format; the financial asset metadata includes asset type data, transaction time data, transaction amount data, transaction participants and digital signature data;

[0082] The first identification unit is used to extract asset type data, identify and classify different asset type data through text recognition technology, quantify the number of asset transactions, and compare the asset transaction quantity with the account book record to obtain the asset transaction matching index JPs; when the asset transaction matching index JPs is lower than the matching threshold C, the early warning unit generates and issues a first early warning instruction; when the asset transaction matching index JPs is higher than or equal to the matching threshold C, a first qualified mark is generated;

[0083] The second identification unit is used to extract transaction amount data, compare historical transaction records with market benchmarks using big data analysis technology, and obtain transaction amount volatility index bdx and transaction abnormality index YCz; when the transaction abnormality index YCz exceeds the set abnormal threshold B, the warning unit generates and issues a second warning instruction; when the transaction amount fluctuation is within a reasonable range, a second qualified mark is generated;

[0084] The third identification unit is used for transaction participants and digital signature data, identifying and verifying the identity and digital signature of each participant, and obtaining the digital signature integrity index QZd and the participant reputation index XYs; when the digital signature integrity index QZd is lower than the integrity threshold E, the early warning unit generates and issues a third early warning instruction, otherwise, a third qualified mark is generated; when the participant reputation index XYs is lower than the reputation threshold R, the early warning unit generates and issues a fourth early warning instruction, otherwise, a fourth qualified mark is generated;

[0085] The strategy unit is used to generate and execute corresponding strategies according to the first warning instruction, the second warning instruction, the third warning instruction and the fourth warning instruction.

[0086] In this embodiment, in cross-platform and cross-system transaction scenarios, the format of financial asset metadata is often not uniform, making it difficult to achieve standardized processing. This system standardizes data from different sources into a unified format through data cleaning and format conversion, ensuring the compatibility and consistency of financial asset metadata and reducing the error rate in the data processing process. The first identification unit, the second identification unit, and the third identification unit integrated in the system can analyze key metadata such as asset type, transaction amount, and transaction participants in real time by using text recognition, big data analysis, and identity authentication technology. The system automatically generates an early warning signal by calculating the asset transaction matching index JPs, the transaction anomaly index YCz, the digital signature integrity index QZd, and the participant reputation index XYs according to the preset matching threshold C, abnormality threshold B, integrity threshold E, and reputation threshold R. When abnormal transactions or risks are detected, the system can issue early warning instructions in a timely manner and take corresponding restrictive measures to avoid the expansion of risks, thereby enhancing the security and real-time response capabilities of transactions. This system uses blockchain technology to ensure the immutability and transparency of data. Every transaction in the blockchain has a digital signature and a timestamp. The third identification unit can ensure the legitimacy of the transaction and the credibility of the participants by verifying the digital signature integrity index QZd and the credibility index XYs of the participants.

[0087] Example 2

[0088] See also Figure 1 , the blockchain acquisition unit includes a data acquisition module, a data preprocessing module and a data verification module;

[0089] The data acquisition module is used to obtain metadata information of financial assets and related transaction data in real time through API external system and financial platform interface interaction technology and blockchain technology by accessing blockchain nodes or using blockchain API to form a financial asset metadata set;

[0090] The data preprocessing module is used to clean and preprocess the financial asset metadata set, remove invalid information and duplicate data, convert the financial asset metadata set into a unified standard format, and convert the transaction data including time format and currency unit into a unified standard;

[0091] The data verification module is used to perform data format verification on the financial asset data set, including date format verification, amount format verification and digital signature format verification, and mark the data that does not conform to the format as abnormal data and issue a correction alarm, which requires manual confirmation and manual correction.

[0092] In this embodiment, the data acquisition module interacts with external financial platforms and blockchain nodes in real time through APIs to automatically obtain metadata and transaction information of financial assets. This method greatly improves the efficiency of data collection, eliminates the complexity and errors of manual collection, and can cope with high-frequency and large-scale transaction environments. The data preprocessing module cleans, converts and standardizes the collected financial asset metadata set, unifies the time format and currency unit, and ensures the consistency and compatibility of the data. This standardization process not only improves the accuracy of subsequent data processing and analysis, but also reduces the problems caused by differences in data formats of different financial platforms, ensuring seamless docking of cross-platform data. The data verification module automatically verifies data formats such as date, amount and digital signature, and timely identifies and marks abnormal data. When it is found that the data format does not meet the requirements, the system can issue a correction alarm to remind manual intervention. This mechanism reduces human errors in the data processing process, ensures the compliance and reliability of data, and greatly reduces the risk of data tampering or errors.

[0093] Example 3

[0094] See also Figure 1 , the first identification unit includes a first data extraction module and a first statistical module;

[0095] The first data extraction module is used to extract asset type data from the financial asset metadata set, extract and classify different asset type data from the account book through text recognition technology, and obtain the number of account book asset transactions Tbx, the total amount of account book transactions Zje, the transaction time error index Jysj and the transaction participant matching index Jycyf through the first statistical module through the following formula;

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] In the formula, N represents the number of transactions of this asset type recorded in the ledger. represents the i-th transaction record, Indicates the amount data value of the i-th transaction, is the timestamp of the i-th transaction recorded in the system, is the timestamp of the i-th transaction recorded in the ledger, is the number of participants in the i-th transaction recorded by the system that matches the ledger record, The total number of participants in the i-th system record and ledger takes the maximum value.

[0101] The first identification unit further includes a first association module and a first judgment module;

[0102] The first association module is used to extract the number of ledger asset transactions Tbx, the total amount of ledger transactions Zje, the transaction time error index Jysj and the transaction participant matching index Jycyf. After dimensionless processing, the asset transaction matching index JPs is calculated by the following formula:

[0103] ;

[0104] In the formula, , , and They represent the weight coefficients of the number of ledger asset transactions Tbx, the total amount of ledger transactions Zje, the transaction time error index Jysj, and the transaction participant matching index Jycyf, respectively. , , , ,and , represents the first correction constant;

[0105] The first judgment module is used to preset a matching threshold C, and compare and judge the asset transaction matching index JPs with the matching threshold C to obtain a first evaluation result, including:

[0106] When the asset transaction matching index JPs ≥ matching threshold C, it means that the transaction matching degree is qualified, and the first qualified mark is generated, and it continues or enters the next stage;

[0107] When the asset transaction matching index JPs is less than the matching threshold C, it indicates that the transaction matching degree is unqualified, triggering the first warning instruction to remind relevant personnel or the system to pay attention to abnormal transaction matching problems.

[0108] In this embodiment, the first data extraction module uses text recognition technology to extract asset type data from the financial asset metadata set and identify transaction information of different asset types in the account book. This automated extraction method not only reduces the tediousness of manual operation, but also can accurately and quickly identify multiple asset types, ensuring the accuracy and integrity of the data. Through the first statistical module, the system can accurately count the number of account book asset transactions Tbx and the total amount of account book transactions Zje. This module can realize the automatic calculation and quantitative processing of a large amount of transaction data, avoid errors in the manual statistical process, and improve the efficiency of the system in processing big data. Through the transaction time error index Jysj calculated by the formula, the system can accurately analyze the error between the transaction time recorded by the system and the time recorded in the account book. This function ensures that the system can detect potential time difference problems, discover abnormal transaction behavior in time, and avoid risks caused by transaction delays or data tampering. By calculating the participant matching index Jycyf, the system can quantify the matching of the participants recorded by the system and the participants recorded in the account book in each transaction, ensuring the legality and integrity of the transaction. This function can improve the accuracy of data matching, timely discover the identity discrepancies or fraudulent behaviors of the participants in the transaction, and ensure the credibility of the transaction. The first related module performs dimensionless processing on the number of book asset transactions Tbx, the total amount of book transactions Zje, the transaction time error index Jysj and the participant matching index Jycyf, and combines the weight coefficients of various indicators to calculate the asset transaction matching index JPs. This multi-dimensional data evaluation method not only improves the system's ability to analyze complex transactions, but also comprehensively considers various factors in the transaction to ensure the accuracy and scientificity of the overall evaluation. The first judgment module sets the matching threshold C. When the asset transaction matching index JPs is lower than the matching threshold C, the system will automatically trigger the first warning instruction to remind relevant personnel or the system to pay attention to potential transaction abnormalities. The threshold setting function of this module is flexible and can adjust the warning sensitivity according to the actual transaction scenario, which helps to detect risks in advance and improve the security and response speed of the system.

[0109] Example 4

[0110] See also Figure 1 , the second recognition unit includes a second data extraction module and a second evaluation module;

[0111] The second data extraction module is used to extract relevant transaction amount data from the transaction record, input the original transaction data and timestamp, and output the extracted transaction amount data set , N represents the number of transactions of this asset type recorded in the account book, and the transaction amount volatility index bdx and transaction anomaly index YCz are calculated by the following formula:

[0112] ;

[0113] ;

[0114] In the formula, Indicates the amount data value of the i-th transaction, Represents the mean of transaction amount data; Indicates the market benchmark volatility threshold;

[0115] The second evaluation module is used to preset an abnormality threshold B, and compare and evaluate the transaction abnormality index YCz with the abnormality threshold B to obtain a second evaluation result, including:

[0116] When the transaction abnormality index YCz ≥ abnormal threshold B, it means that the transaction amount is abnormal, and a second warning instruction is generated;

[0117] When the transaction anomaly index YCz is less than the anomaly threshold B, it indicates that the transaction amount fluctuation is within a reasonable range, and a second qualified mark is generated.

[0118] In this embodiment, through the calculation formula, the system can accurately obtain the volatility index bdx of each transaction amount. The index can reflect the fluctuation of each transaction amount compared to the average value, which helps to detect abnormal fluctuations in transactions in advance. This quantitative analysis method allows the system to carefully monitor abnormal fluctuations in transactions and reduce the potential risks of market fluctuations to transaction security. Through the calculation of the transaction anomaly index YCz, the system can accurately evaluate whether the amount of each transaction is in an abnormal state. This intelligent evaluation mechanism enables the system to automatically detect potential abnormal transactions, especially when the market fluctuates violently or certain special asset types are traded, and can capture abnormal changes in transaction amounts in a timely manner to prevent major risks.

[0119] Example 5

[0120] See also Figure 1 , the third identification unit includes a third data extraction module and a digital signature integrity index calculation module;

[0121] The third identification unit includes a third data extraction module and a digital signature integrity index calculation module;

[0122] The third data extraction module is used to identify and verify the identity and digital signature of each transaction participant, extract and verify each transaction data of each transaction participant, and the transaction data includes: original information, digital signature, public key of the signer and signature timestamp ;

[0123] The original message hash value is calculated using the same hash function SHA-256 as the digital signature algorithm ;

[0124] Decrypt the digital signature using the sender's public key to obtain the hash value calculated when the digital signature was signed ;

[0125] and compare the original message hash Calculate the hash value after digital signature , and obtain the effectiveness index H:

[0126] ;

[0127] when =1, it means that the original information is consistent with the hash value obtained after decryption. ≠1, the message is considered to have been tampered with; the third data extraction module uses the SHA-256 hash function to hash the original information, and uses the sender's public key to decrypt the digital signature to obtain the hash value calculated when signing. By comparing these two hash values, it is possible to accurately verify whether the information has been tampered with. This method ensures the integrity and credibility of the information, prevents the data from being illegally modified or forged during transmission, and improves the security of transactions.

[0128] And based on the signature timestamp and effective time threshold , calculate the effective time index using the following formula ;

[0129] ;

[0130] T represents the current time, the effective time threshold Set 3-7 days; effective time index It ensures the time validity of the signature, prevents the risk of expired signatures, and improves the system's ability to manage the validity period of signatures.

[0131] The digital signature integrity index calculation module is used to extract the validity index H and the validity time index , after dimensionless processing, the digital signature integrity index QZd is calculated by the following associated formula:

[0132] ;

[0133] In the formula, and Represents the effectiveness index H and the effective time index respectively The weight coefficient is set by the user. , ,and .

[0134] The third identification unit further includes a reputation index calculation module and a third evaluation module; the reputation index calculation module is used to calculate and obtain the reputation index XYs of the transaction participant through the following formula:

[0135] ;

[0136] In the formula, Indicates the total number of transactions between transaction participants. Indicates the number of successful transactions of the transaction participants, Indicates the number of failed or defaulted transactions by the transaction participants, represents the credit score of the trading party, Indicates the maximum credit score of the transaction party, represents the transaction volume of the largest counterparty of the i-th trading party, j represents the number of the trading counterparties of the trading party, Indicates the total number of delayed transactions by the trading parties; , , and Represents the weight coefficient, which is adjusted by the user. , , , ,and ;

[0137] The third evaluation module is used to preset the integrity threshold E and the reputation threshold R;

[0138] The digital signature integrity index QZd is compared with the integrity threshold E to obtain a third evaluation result, including:

[0139] When the digital signature integrity index QZ is less than the integrity threshold E, indicating that the digital signature is unqualified, the warning unit generates and issues a third warning instruction;

[0140] When the digital signature integrity index QZ ≥ the integrity threshold E, it indicates that the digital signature is qualified, and a third qualified mark is generated; and the participant reputation index XYs is compared with the reputation threshold R to obtain a fourth evaluation result, including:

[0141] When the participant's reputation index XYs is less than the reputation threshold R, it means that the participant's reputation is unqualified, and the early warning unit generates and issues a fourth early warning instruction;

[0142] When the participant's reputation index XYs≥reputation threshold R, it indicates that the participant's reputation is qualified, and a fourth qualified mark is generated.

[0143] In this embodiment, the digital signature integrity index QZd combines the validity index H and the effective time index, and is obtained through weighted calculation. This comprehensive index calculation method can fully reflect the integrity and validity of the signature, and provides the system with a comprehensive digital signature quality assessment tool. The reputation index calculation module generates the reputation index XYs of the participant by analyzing data such as the total number of transactions, the number of successful transactions, the number of failed transactions, and the credit score. This quantitative reputation assessment method ensures a comprehensive evaluation of the transaction participants, more objectively reflects the credit status of the participants, and thus helps identify potential risk parties. When the digital signature integrity index or the participant's reputation index is lower than the threshold, the system will generate a corresponding early warning instruction. This immediate early warning mechanism enables the system to respond quickly to potential risks, take appropriate measures to protect the security of transactions, and avoid transaction failures due to insufficient trust or signature problems.

[0144] Example 6

[0145] See also Figure 1 The strategy unit is used to generate a corresponding strategy according to the first warning instruction, the second warning instruction, the third warning instruction and the fourth warning instruction, including:

[0146] When the first, second, third and fourth qualified marks are obtained at the same time, the system automatically allocates 10%-15% more computing resources and 10%-15% more network bandwidth to transaction processing to promote transaction speed; when all evaluation results are qualified marks, the system automatically allocates additional computing resources and network bandwidth. This optimization measure can improve the speed and efficiency of transaction processing, especially in high-frequency transaction scenarios, ensuring that the system can quickly respond to and process a large number of transaction requests, significantly improving the overall performance of the system and user experience.

[0147] When the first warning instruction is identified, the first strategy is triggered, including: if the transaction match fails, the current transaction is automatically suspended, and the manual review or secondary verification process is entered. At the same time, the system supports the rollback function to safely restore the transaction status when the transaction match fails. This measure can effectively prevent the potential risks caused by the failure of the transaction match and ensure the accuracy and security of the transaction. In addition, the system supports the rollback function, which can safely restore the transaction status when a problem is detected, further ensuring the integrity of the transaction data.

[0148] When identified as the second warning instruction, the second strategy is triggered, including: abnormal fluctuations in transaction amount, reducing the frequency of transaction request processing by 30%-50%, and limiting the number of daily transactions to 1-3 times; this strategy can effectively control the risks caused by abnormal fluctuations in amount, reduce potential losses due to market fluctuations, and ensure the stability and reliability of transactions.

[0149] When it is identified as the third warning instruction, the third strategy is triggered, including: digital signature anomaly, re-verification using multi-layer encryption and identity authentication mechanism, and if the verification still fails, freezing the transaction; this multi-level security measure can enhance the digital signature verification process, ensure the security of the transaction, and prevent fraud or data tampering caused by digital signature problems.

[0150] When the fourth warning instruction is identified, the fourth strategy is triggered, including: abnormal reputation, automatic partition isolation measures to prevent untrusted participants from affecting other normal trading parties and restrict their operating permissions. This measure can effectively isolate risks, ensure that the normal operations of other trading parties are not affected, and maintain the overall stability and reputation of the system.

[0151] In this embodiment, based on the early warning mechanism, the policy unit of the system can automatically generate and execute corresponding policies according to different early warning instructions (such as unqualified transaction matching, abnormal amount fluctuation, incomplete signature or insufficient credibility of the participating party, etc.), reducing the need for manual intervention. For example, when the transaction matching is unqualified, the system will automatically suspend the transaction and enter the review process; when the transaction amount fluctuates abnormally, the system will reduce the processing frequency; when the digital signature is unqualified, the system will perform multi-layer verification or freeze the transaction, etc. These measures effectively ensure the security, compliance and transparency of financial asset transactions.

[0152] Example 7

[0153] See also Figure 2 , a method for sharing transaction metadata information of financial assets based on blockchain, comprising the following steps:

[0154] S1. Collect metadata information of financial assets and related transaction data to form a set of financial asset metadata, clean and pre-process the set of financial asset metadata, remove invalid information and duplicate data, and convert the set of financial asset metadata into a unified standard format; the financial asset metadata includes asset type data, transaction time data, transaction amount data, transaction participants and digital signature data;

[0155] S2. Extract asset type data, identify and classify different asset type data through text recognition technology, quantify the number of asset transactions, and compare the asset transaction quantity with the account book record to obtain the asset transaction matching index JPs; when the asset transaction matching index JPs is lower than the matching threshold C, the early warning unit generates and issues a first early warning instruction; when the asset transaction matching index JPs is higher than or equal to the matching threshold C, a first qualified mark is generated;

[0156] S3. Extract transaction amount data, use big data analysis technology to compare historical transaction records and market benchmarks, and obtain transaction amount volatility index bdx and transaction abnormality index YCz; when the transaction abnormality index YCz exceeds the set abnormal threshold B, the warning unit generates and issues a second warning instruction; when the transaction amount fluctuation is within a reasonable range, a second qualified mark is generated;

[0157] S4, transaction participants and digital signature data, identify and verify the identity and digital signature of each participant, obtain the digital signature integrity index QZd and the participant reputation index XYs; when the digital signature integrity index QZd is lower than the integrity threshold E, the early warning unit generates and issues a third early warning instruction, otherwise, generates a third qualified mark; when the participant reputation index XYs is lower than the reputation threshold R, the early warning unit generates and issues a fourth early warning instruction, otherwise, generates a fourth qualified mark;

[0158] S5. Generate and execute corresponding strategies according to the first warning instruction, the second warning instruction, the third warning instruction and the fourth warning instruction.

[0159] The threshold is set to facilitate comparison. The threshold depends on the amount of sample data and the number of bases set by technicians in this field for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0160] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A system for sharing transaction metadata information of financial assets based on blockchain, characterized by: It includes a blockchain collection unit, a first identification unit, a second identification unit, a third identification unit, an early warning unit and a strategy unit; The blockchain collection unit is used to collect metadata information of financial assets and related transaction data to form a financial asset metadata set, and clean and pre-process the financial asset metadata set to remove invalid information and duplicate data, and then convert the financial asset metadata set into a unified standard format; the financial asset metadata includes asset type data, transaction time data, transaction amount data, transaction participants and digital signature data; The first identification unit is used to extract asset type data, identify and classify different asset type data through text recognition technology, quantify the number of asset transactions, and compare the asset transaction quantity with the account book record to obtain the asset transaction matching index JPs; when the asset transaction matching index JPs is lower than the matching threshold C, the early warning unit generates and issues a first early warning instruction; when the asset transaction matching index JPs is higher than or equal to the matching threshold C, a first qualified mark is generated; The first identification unit includes a first data extraction module and a first statistics module; The first data extraction module is used to extract asset type data from the financial asset metadata set, extract and classify different asset type data from the account book through text recognition technology, and obtain the number of account book asset transactions Tbx, the total amount of account book transactions Zje, the transaction time error index Jysj and the transaction participant matching index Jycyf through the first statistical module through the following formula; ； ； ； ； In the formula, N represents the number of transactions of this asset type recorded in the ledger. represents the i-th transaction record, Indicates the amount data value of the i-th transaction, is the timestamp of the i-th transaction recorded in the system, is the timestamp of the i-th transaction recorded in the ledger, is the number of participants in the i-th transaction recorded by the system that matches the ledger record, The total number of participants in the i-th system record and the ledger, taking the maximum value; The first identification unit further includes a first association module and a first judgment module; The first association module is used to extract the number of ledger asset transactions Tbx, the total amount of ledger transactions Zje, the transaction time error index Jysj and the transaction participant matching index Jycyf. After dimensionless processing, the asset transaction matching index JPs is calculated by the following formula: ； In the formula, , , and They represent the weight coefficients of the number of ledger asset transactions Tbx, the total amount of ledger transactions Zje, the transaction time error index Jysj, and the transaction participant matching index Jycyf, respectively. , , , ,and , represents the first correction constant; The first judgment module is used to preset a matching threshold C, and compare and judge the asset transaction matching index JPs with the matching threshold C to obtain a first evaluation result, including: When the asset transaction matching index JPs ≥ matching threshold C, it means that the transaction matching degree is qualified, and the first qualified mark is generated, and it continues or enters the next stage; When the asset transaction matching index JPs is less than the matching threshold C, it means that the transaction matching degree is unqualified, triggering the first warning instruction to remind relevant personnel or the system to pay attention to the abnormal transaction matching problem; The second identification unit is used to extract transaction amount data, compare historical transaction records with market benchmarks using big data analysis technology, and obtain transaction amount volatility index bdx and transaction abnormality index YCz; when the transaction abnormality index YCz exceeds the set abnormal threshold B, the warning unit generates and issues a second warning instruction; when the transaction amount fluctuation is within a reasonable range, a second qualified mark is generated; The second identification unit includes a second data extraction module and a second evaluation module; The second data extraction module is used to extract relevant transaction amount data from the transaction record, input the original transaction data and timestamp, and output the extracted transaction amount data set , N represents the number of transactions of this asset type recorded in the account book, and the transaction amount volatility index bdx and transaction anomaly index YCz are calculated by the following formula: ； ； In the formula, Indicates the amount data value of the i-th transaction, Represents the mean of transaction amount data; Indicates the market benchmark volatility threshold; The second evaluation module is used to preset an abnormality threshold B, and compare and evaluate the transaction abnormality index YCz with the abnormality threshold B to obtain a second evaluation result, including: When the transaction abnormality index YCz ≥ abnormal threshold B, it means that the transaction amount is abnormal, and a second warning instruction is generated; When the transaction anomaly index YCz is less than the anomaly threshold B, it means that the transaction amount fluctuation is within a reasonable range, and a second qualified mark is generated; The third identification unit is used for transaction participants and digital signature data, identifying and verifying the identity and digital signature of each participant, and obtaining the digital signature integrity index QZd and the participant reputation index XYs; when the digital signature integrity index QZd is lower than the integrity threshold E, the early warning unit generates and issues a third early warning instruction, otherwise, a third qualified mark is generated; when the participant reputation index XYs is lower than the reputation threshold R, the early warning unit generates and issues a fourth early warning instruction, otherwise, a fourth qualified mark is generated; The third identification unit includes a third data extraction module and a digital signature integrity index calculation module; The third data extraction module is used to identify and verify the identity and digital signature of each transaction participant, extract and verify each transaction data of each transaction participant, and the transaction data includes: original information, digital signature, public key of the signer and signature timestamp ; The original message hash value is calculated using the same hash function SHA-256 as the digital signature algorithm ; Decrypt the digital signature using the sender's public key to obtain the hash value calculated when the digital signature was signed ; and compare the original message hash Calculate the hash value after digital signature , and obtain the effectiveness index H: ； when =1, it means that the original information is consistent with the hash value obtained after decryption. When ≠1, the message is considered to be tampered; And based on the signature timestamp and effective time threshold , calculate the effective time index using the following formula ; ； T represents the current time, the effective time threshold Set up for 3-7 days; The digital signature integrity index calculation module is used to extract the validity index H and the validity time index , after dimensionless processing, the digital signature integrity index QZd is calculated by the following associated formula: ； In the formula, and Represents the effectiveness index H and the effective time index respectively The weight coefficient is set by the user. , ,and ; The third identification unit further includes a reputation index calculation module and a third evaluation module; the reputation index calculation module is used to calculate and obtain the reputation index XYs of the transaction participant through the following formula: ； In the formula, Indicates the total number of transactions between transaction participants. Indicates the number of successful transactions of the transaction participants, Indicates the number of failed or defaulted transactions by the transaction participants, represents the credit score of the trading party, Indicates the maximum credit score of the transaction party, represents the transaction volume of the largest counterparty of the i-th trading party, j represents the number of the trading counterparties of the trading party, Indicates the total number of delayed transactions by the trading parties; , , and Represents the weight coefficient, which is adjusted by the user. , , , ,and ; The third evaluation module is used to preset an integrity threshold E and a reputation threshold R; The digital signature integrity index QZd is compared with the integrity threshold E to obtain a third evaluation result, including: When the digital signature integrity index QZ is less than the integrity threshold E, indicating that the digital signature is unqualified, the warning unit generates and issues a third warning instruction; When the digital signature integrity index QZ ≥ the integrity threshold E, it indicates that the digital signature is qualified, and a third qualified mark is generated; and the participant reputation index XYs is compared with the reputation threshold R to obtain a fourth evaluation result, including: When the participant's reputation index XYs is less than the reputation threshold R, it means that the participant's reputation is unqualified, and the warning unit generates and issues a fourth warning instruction; When the participant's reputation index XYs ≥ the reputation threshold R, it means that the participant's reputation is qualified, and a fourth qualified mark is generated; The strategy unit is used to generate and execute corresponding strategies according to the first warning instruction, the second warning instruction, the third warning instruction and the fourth warning instruction.

2. The system for sharing transaction metadata information of financial assets based on blockchain according to claim 1, characterized in that: The blockchain acquisition unit includes a data acquisition module, a data preprocessing module and a data verification module; The data acquisition module is used to obtain metadata information of financial assets and related transaction data in real time through API external system and financial platform interface interaction technology and blockchain technology by accessing blockchain nodes or using blockchain API to form a financial asset metadata set; The data preprocessing module is used to clean and preprocess the financial asset metadata set, remove invalid information and duplicate data, convert the financial asset metadata set into a unified standard format, and convert the transaction data including time format and currency unit into a unified standard; The data verification module is used to perform data format verification on the financial asset data set, including date format verification, amount format verification and digital signature format verification, and mark the data that does not conform to the format as abnormal data and issue a correction alarm, which requires manual confirmation and manual correction.

3. The system for sharing transaction metadata information of financial assets based on blockchain according to claim 1, characterized in that: The strategy unit is used to generate corresponding strategies according to the first warning instruction, the second warning instruction, the third warning instruction and the fourth warning instruction, including: When the first qualified mark, the second qualified mark, the third qualified mark and the fourth qualified mark are obtained at the same time, the system automatically allocates 10%-15% more computing resources and 10%-15% more network bandwidth to the transaction processing to promote the transaction speed; When the first warning instruction is identified, the first strategy is triggered, including: if the transaction match fails, the current transaction is automatically suspended, and the manual review or secondary verification process is entered. At the same time, the system supports the rollback function to safely restore the transaction status when the transaction match fails; When the second warning instruction is identified, the second strategy is triggered, including: abnormal fluctuations in transaction amount, reducing the frequency of transaction request processing by 30%-50%, and limiting the number of daily transactions to 1-3 times; When the third warning instruction is identified, the third strategy is triggered, including: digital signature anomaly, re-verification using multi-layer encryption and identity authentication mechanism, and freezing the transaction if the verification still fails; When it is identified as the fourth warning instruction, the fourth strategy is triggered, including: credit abnormality, automatic partition isolation measures to prevent untrustworthy participants from affecting other normal transaction parties and restricting their operating permissions.

4. A method for sharing transaction metadata information of financial assets based on blockchain, applied to a system for sharing transaction metadata information of financial assets based on blockchain as claimed in any one of claims 1 to 3, characterized in that: The following steps are involved: S1. Collect metadata information of financial assets and related transaction data to form a set of financial asset metadata, clean and pre-process the set of financial asset metadata, remove invalid information and duplicate data, and convert the set of financial asset metadata into a unified standard format; the financial asset metadata includes asset type data, transaction time data, transaction amount data, transaction participants and digital signature data; S2. Extract asset type data, identify and classify different asset type data through text recognition technology, quantify the number of asset transactions, and compare the asset transaction quantity with the account book record to obtain the asset transaction matching index JPs; when the asset transaction matching index JPs is lower than the matching threshold C, the early warning unit generates and issues a first early warning instruction; when the asset transaction matching index JPs is higher than or equal to the matching threshold C, a first qualified mark is generated; S3. Extract transaction amount data, use big data analysis technology to compare historical transaction records and market benchmarks, and obtain transaction amount volatility index bdx and transaction abnormality index YCz; when the transaction abnormality index YCz exceeds the set abnormal threshold B, the warning unit generates and issues a second warning instruction; when the transaction amount fluctuation is within a reasonable range, a second qualified mark is generated; S4, transaction participants and digital signature data, identify and verify the identity and digital signature of each participant, obtain the digital signature integrity index QZd and the participant reputation index XYs; when the digital signature integrity index QZd is lower than the integrity threshold E, the early warning unit generates and issues a third early warning instruction, otherwise, generates a third qualified mark; when the participant reputation index XYs is lower than the reputation threshold R, the early warning unit generates and issues a fourth early warning instruction, otherwise, generates a fourth qualified mark; S5. Generate and execute corresponding strategies according to the first warning instruction, the second warning instruction, the third warning instruction and the fourth warning instruction.

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