A blockchain-based data security monitoring and management method

Abstract

This invention relates to the field of data security supervision and discloses a blockchain-based data security monitoring and management method, comprising the following steps: distributing data across numerous nodes of the blockchain to construct a decentralized data storage architecture, thereby reducing the risk of data loss or tampering due to single points of failure or malicious attacks; generating a hash identifier for each data block containing the hash value of the previous data block using hash algorithms and consensus mechanisms, so that changes in the blockchain hash value can be detected in a timely manner when data is tampered with; recording every data operation in detail to construct a traceable system for the entire process of data from generation to final use; determining the encryption method for the data based on the level of data sensitivity during data transmission to prevent data theft or tampering; and establishing a real-time monitoring mechanism to monitor data operations and transmission in real time, detecting and preventing network attacks and malicious tampering.

Description

Technical Field

[0001] This invention relates to the field of data security supervision, and specifically to a data security monitoring and management method based on blockchain. Background Technology

[0002] In today's digital age, the importance of data security is self-evident. Traditional data security monitoring systems primarily rely on centralized network architectures. In this architecture, data is stored on a central server, relying on mechanisms such as firewalls and access controls for security. Firewalls function at the network boundary, blocking unauthorized external access, acting as a protective wall to prevent malicious network traffic intrusion. Access controls, by setting user permissions, define the access levels of different personnel to data, ensuring that sensitive data can only be accessed by authorized personnel. Traditional data encryption algorithms are also a crucial element; for example, symmetric encryption algorithms encrypt data during storage, making it difficult for unauthorized individuals to decipher even if they obtain the data. While this architecture provides a degree of data security, its drawbacks are becoming increasingly apparent with the explosive growth of data volume and the increasing complexity of application scenarios.

[0003] In financial trading scenarios, high-frequency and large-scale transaction data flows continuously. Traditional systems are inadequate to handle such massive amounts of data transmission with extremely high real-time requirements. Frequent cyberattacks mean data is easily tampered with during transmission. Taking stock trading as an example, malicious alteration of core data such as stock prices and trading volumes can trigger severe market fluctuations and seriously disrupt the fair order of the financial market. Traditional systems lack efficient real-time monitoring and anti-tampering mechanisms, making it difficult to detect and prevent such malicious activities in a timely manner.

[0004] The emergence of blockchain technology offers a new approach to solving the aforementioned problems. Blockchain employs a distributed architecture, where data is no longer centrally stored on a single node but distributed across numerous nodes. This decentralized nature significantly reduces the risk of data loss or tampering due to single points of failure or malicious attacks. Its immutability, based on hash algorithms and consensus mechanisms, ensures that each data block contains the hash value of the previous block; if data is tampered with, the hash value of the entire blockchain will change, allowing for rapid detection. Blockchain's traceability, through detailed recording of every transaction or data operation, makes the entire process from data generation to final use clearly traceable. This allows for rapid identification of the source of data problems for data security supervision.

[0005] However, integrating blockchain technology into the data security supervision system also has some drawbacks. For example, when processing large-scale data, blockchain has a slow transaction confirmation speed, which makes it difficult to meet the needs of business scenarios with extremely high real-time requirements, such as high-frequency financial transactions. Summary of the Invention

[0006] The purpose of this invention is to provide a data security monitoring and management method based on blockchain, thereby solving the above-mentioned technical problems.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A blockchain-based data security monitoring and management method includes the following steps:

[0009] S1. Distribute data across numerous nodes of the blockchain to build a decentralized data storage architecture, thereby reducing the risk of data loss or tampering due to single points of failure or malicious attacks.

[0010] S2. By using hash algorithms and consensus mechanisms, a hash identifier containing the hash value of the previous data block is generated for each data block, thereby achieving the immutability of the data. When the data is tampered with, the change in the blockchain hash value can be detected in a timely manner.

[0011] S3. Record every data operation in detail to build a traceable system for the entire process of data from generation to final use, so as to quickly locate the source of data problems; each data operation record includes operation time T, operation subject ID, operation content C and the hash value H(D) of the data block generated by the operation, which is stored in the transaction record of the blockchain to form a chain traceability path.

[0012] S4. During data transmission, the method of encrypting the data is determined according to the level of data sensitivity to prevent the data from being stolen or tampered with during transmission.

[0013] S5. Establish a real-time monitoring mechanism to monitor data operations and transmission in real time, and promptly detect and prevent network attacks and malicious tampering.

[0014] As a further technical solution, in S1: data blocks are evenly distributed to each node through a P2P network protocol, and the nodes maintain network connectivity through a heartbeat detection mechanism;

[0015] Let N be the total number of nodes and M be the number of replicas of each data block stored. M < N and is dynamically adjusted according to the importance of the data to achieve redundant data storage.

[0016] As a further technical solution, in step S2: the SHA-256 hash algorithm is used, and the hash value H(D) of each data block D is calculated as follows:

[0017] H(D) = SHA-256(D||H(prev)) D ));

[0018] Among them, prev DThe hash value H of the previous data block is used, and || represents the data concatenation operation, which involves first concatenating the hash value H of the previous data block. D The hash value H(D) of the current data block is obtained by concatenating the concatenated data with the current data block D and then inputting the concatenated data into the SHA-256 hash function.

[0019] The consensus mechanism adopts the Practical Byzantine Fault Tolerance (PBFT) algorithm, which allows consensus to be reached through message interaction between nodes when the number of Byzantine nodes is f and f < N / 3, thus ensuring the consistency and immutability of blockchain data.

[0020] As a further technical solution, in S3,

[0021] The formula for calculating the hash value of each operation record is: H(Record[i]) = SHA-256(H T ||H I D||H C ||H H(D) ||H(Record[i-1]));

[0022] H T =SHA-256(Record[i].T);

[0023] H I D = SHA-256(Record[i].ID);

[0024] H C =SHA-256(Record[i].C);

[0025] H T =SHA-256(Record[i].H(D));

[0026] Where Record[i].T is the operation time in the i-th operation record, Record[i].ID is the operation subject ID in the i-th operation record, Record[i].C is the operation content in the i-th operation record, Record[i].H(D) is the hash value of the data block generated by the operation in the i-th operation record, and Record[i-1] is the hash value of the previous operation record.

[0027] As a further technical solution, the process of determining the encryption method for the data based on the level of data sensitivity in S4 is as follows:

[0028] When the data block is determined to be low-sensitivity data, the RC4 stream encryption algorithm process is initiated.

[0029] When the data block is determined to be moderately sensitive, the system calls the AES-128 symmetric encryption algorithm.

[0030] When a data block is determined to be highly sensitive, elliptic curve cryptography (ECC) is used to encrypt it based on the secp256k1 curve.

[0031] As a further technical solution, the method for determining the data sensitivity of a data block is as follows:

[0032] Information on data type, usage scenario, and potential impact is collected from the data blocks. After preprocessing each type, a corresponding data type score S is obtained. le Usage scenario rating S ch Potential impact score S qi ;

[0033] Substitute into the formula: In this process, the data sensitivity index S is obtained. I ;

[0034] The data sensitivity index S I With the corresponding data sensitivity interval [S] Imin S Imax Compare them, if S I >S Imax If S I ∈[S Imin S Imax If S..., then the data block is determined to be moderately sensitive data; if S... I Imin If so, the data block is determined to be low-sensitivity data.

[0035] As a further technical solution, the data type information includes account information, transaction amount information, and transaction-related ancillary information;

[0036] Through the formula: The data type score S was calculated. le ;

[0037] Where i represents the i-th data type, n is the total number of data types, and W i P represents the weighting coefficient corresponding to each type of data information. i Sensitivity score for each type of data information;

[0038] The expression for the usage scenario rating is: S ch =CH*W θ CH represents the sensitivity score for financial transaction scenarios, and W... θ These are the weighting coefficients corresponding to financial transaction scenarios.

[0039] ​As a further technical solution, the potential impact information includes information on the impact on personal asset security, the impact on the operation of financial institutions, and the impact on the stability of financial markets;

[0040] Through the formula: The mean value of the evaluation index corresponding to the j-th type of impact is calculated; x k,j Let n be the k-th indicator value of the j-th influence type. j The number of indicators corresponding to the j-th type of influence; j = 1, 2, 3, corresponding to the three types of influence respectively;

[0041] Through the formula: Calculate the standard deviation of the evaluation index corresponding to the j-th type of impact;

[0042] Through the formula: The adjustment coefficient of the evaluation index corresponding to the j-th type of impact is calculated;

[0043] Through the formula:

[0044]

[0045] The potential impact score S was calculated. qi .

[0046] The beneficial effects of this invention are:

[0047] (1) By adopting a decentralized distributed storage architecture, combined with P2P network protocol and heartbeat detection mechanism, as well as dynamically adjusted data block storage replica strategy, the reliability of data storage is greatly improved. Even if some nodes fail or suffer malicious attacks, the data can remain intact and accessible, avoiding data loss due to single point of failure and providing a solid storage foundation for data security;

[0048] (2) The combination of the SHA-256 hash algorithm and the PBFT consensus mechanism gives the data on the blockchain a high degree of integrity and immutability. Data blocks are interconnected through hash values, and any data tampering will cause the hash value to change. The PBFT mechanism ensures that the consistency of blockchain data is maintained when a majority of nodes are normal, thus guaranteeing the authenticity and credibility of the data and effectively preventing malicious data tampering during data storage and operation.

[0049] (3) By meticulously recording each data operation and constructing a chain-like traceability path, the source of data problems can be quickly located. When data anomalies occur, the operation process can be quickly investigated using information such as time, subject ID, and content in the operation record, facilitating timely discovery and resolution of problems, improving the transparency and auditability of data management, and aiding in the investigation of security incidents and the tracing of responsibility;

[0050] (4) Different encryption algorithms are adopted according to the data sensitivity level, realizing refined management of encryption strategies. The lightweight RC4 algorithm is used for low-sensitivity data to improve encryption efficiency while ensuring a certain level of security; the AES-128 symmetric encryption algorithm is used for medium-sensitivity data to balance encryption strength and performance; and the more secure elliptic curve cryptography (ECC) algorithm is used for high-sensitivity data to meet the strict security requirements for critical data, ensure data security during transmission, and reduce the risk of data leakage. At the same time, by using scientific and reasonable methods to judge the sensitivity of data, and comprehensively considering factors such as data type, usage scenario, and potential impact, the calculated data sensitivity index can accurately reflect the potential risks of data. This provides a quantitative basis for data security management, enabling managers to take corresponding security measures according to the risk level, rationally allocate security resources, and improve the efficiency and pertinence of data security management. Attached Figure Description

[0051] The invention will now be further described with reference to the accompanying drawings.

[0052] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Please see Figure 1 As shown, this invention is a data security monitoring and management method based on blockchain, comprising the following steps:

[0055] S1. Distribute data across numerous nodes of the blockchain to build a decentralized data storage architecture, thereby reducing the risk of data loss or tampering due to single points of failure or malicious attacks.

[0056] S2. By using hash algorithms and consensus mechanisms, a hash identifier containing the hash value of the previous data block is generated for each data block, thereby achieving the immutability of the data. When the data is tampered with, the change in the blockchain hash value can be detected in a timely manner.

[0057] S3. Record every data operation in detail to build a traceable system for the entire process of data from generation to final use, so as to quickly locate the source of data problems; each data operation record includes operation time T, operation subject ID, operation content C and the hash value H(D) of the data block generated by the operation, which is stored in the transaction record of the blockchain to form a chain traceability path.

[0058] S4. During data transmission, the method of encrypting the data is determined according to the level of data sensitivity to prevent the data from being stolen or tampered with during transmission.

[0059] S5. Establish a real-time monitoring mechanism to monitor data operations and transmission in real time, and promptly detect and prevent network attacks and malicious tampering.

[0060] This embodiment provides a systematic approach to ensuring data security by addressing key aspects such as data storage, hash identifier generation, operation record tracing, encryption processing, and real-time monitoring. It constructs a data security protection system from multiple dimensions to ensure the security and traceability of data at every stage of storage, operation, and transmission.

[0061] In S1: Data blocks are evenly distributed to each node through the P2P network protocol, and the nodes maintain network connectivity through a heartbeat detection mechanism.

[0062] Let N be the total number of nodes and M be the number of replicas of each data block stored. M < N and is dynamically adjusted according to the importance of the data to achieve redundant data storage.

[0063] In this embodiment, the data blocks are evenly distributed through the P2P network protocol, the network connectivity between nodes is maintained by the heartbeat detection mechanism, and the number of data block storage replicas is dynamically adjusted according to the importance of the data. This enhances the stability and reliability of data storage, effectively reduces the risk of data loss due to node failure or attacks, and ensures high data availability.

[0064] In S2: the SHA-256 hash algorithm is used, and the hash value H(D) of each data block D is calculated as follows:

[0065] H(D) = SHA-256(D||H(prev)) D ));

[0066] Among them, prev D The hash value H of the previous data block is used, and || represents the data concatenation operation, which involves first concatenating the hash value H of the previous data block. D The hash value H(D) of the current data block is obtained by concatenating the concatenated data with the current data block D and then inputting the concatenated data into the SHA-256 hash function.

[0067] The consensus mechanism adopts the Practical Byzantine Fault Tolerance (PBFT) algorithm, which allows consensus to be reached through message interaction between nodes when the number of Byzantine nodes is f and f < N / 3, thus ensuring the consistency and immutability of blockchain data.

[0068] In this embodiment, through the calculation method of H(D)=SHA-256(D||H(prev D )), the hash value of each data block depends not only on its own data content but also on the hash value of the previous data block, forming a chained hash association structure; in terms of the consensus mechanism, the Practical Byzantine Fault Tolerance algorithm (PBFT) is selected. In a blockchain network composed of N nodes, the number of Byzantine nodes allowed is f (where the condition f < N / 3 needs to be satisfied); when a node proposes to create a new data block, it broadcasts a pre-prepared message containing the content of the new data block and the hash value of the previous data block to other nodes; after receiving the pre-prepared message, other nodes will verify the message, including checking the legitimacy of the proposing node, the compliance of the data block content, and the correctness of the hash value, etc.; after passing the verification, the node broadcasts a prepare message to the network. When a node collects 2f + 1 prepare messages (including its own message), it is considered that the data block has been recognized by the majority of nodes. At this time, the node broadcasts a commit message. When a node collects 2f + 1 commit messages, it adds the data block to the local blockchain; through this multi-round message interaction method, the PBFT algorithm ensures that in the presence of some malicious nodes (Byzantine nodes), the blockchain network can still reach an agreement on the content and order of the data blocks, thereby ensuring the consistency and immutability of the blockchain data; once the data in a certain data block is tampered with, its corresponding hash value H(D) will inevitably change. Since the hash values of subsequent data blocks depend on the hash value of the tampered data block, the hash values of all subsequent data blocks in the entire blockchain will change accordingly. By regularly verifying the blockchain hash values by each node, the situation of data tampering can be detected in a timely manner.

[0069] In this embodiment, the specific algorithms and consensus mechanisms adopted for hash identification generation are described. The SHA-256 hash algorithm associates the data block hash value with the previous data block, forming a chained hash structure and enhancing the immutability of the data; the Practical Byzantine Fault Tolerance algorithm PBFT reaches a consensus through message interaction between nodes under the condition of allowing a certain number of Byzantine nodes to exist, ensuring the consistency of the blockchain data and further improving the security and integrity of the data.

[0070] In S3,

[0071] The calculation formula for the hash value of each operation record is: H(Record[i]) = SHA-256(H T ||H I D||H C ||H H(D) ||H(Record[i - 1]));

[0072] H T=SHA-256(Record[i].T);

[0073] H I D = SHA-256(Record[i].ID);

[0074] H C =SHA-256(Record[i].C);

[0075] H T =SHA-256(Record[i].H(D));

[0076] Where Record[i].T is the operation time in the i-th operation record, Record[i].ID is the operation subject ID in the i-th operation record, Record[i].C is the operation content in the i-th operation record, Record[i].H(D) is the hash value of the data block generated by the operation in the i-th operation record, and Record[i-1] is the hash value of the previous operation record.

[0077] In this way, each operation record is closely linked to the previous record, forming a complete chain traceability path. When it is necessary to locate the source of a data problem, one can start from the hash value of the problematic data block and search backwards in the blockchain transaction record according to the chain traceability path. By comparing information such as operation time, operation subject ID, and operation content, the specific data operation that caused the problem can be quickly determined, thereby accurately locating the source of the data problem.

[0078] This embodiment clarifies the calculation method for the hash value of the operation record. By hashing and concatenating information such as the operation time, operation subject ID, operation content, and hash value of the data block generated by the operation, and then combining it with the hash value of the previous operation record, a complete chain-like traceability path is formed. This facilitates the quick and accurate location of the specific operation when data problems occur, achieving full traceability of data operations.

[0079] The process in S4 of determining the encryption method for the data based on the level of data sensitivity is as follows:

[0080] When the data block is determined to be low-sensitivity data, the RC4 stream encryption algorithm process is initiated.

[0081] The RC4 stream encryption algorithm expands the input key K into a key stream of the same length as the plaintext data P using a key stream generator. Then, it XORs the key stream with the plaintext data bitwise to obtain the ciphertext C. Let the plaintext data be P, with a length of n bits, and the key be K, with a length of m bits (m is generally not less than 64 bits). First, the key K is initialized using a key scheduling algorithm (KSA) to generate an initial permutation S-box. Then, a pseudo-random number generation algorithm (PRGA) is used to generate a key stream based on the initial permutation S-box. Let the generated key stream be Z, also with a length of n bits. The final formula for calculating the encrypted ciphertext C is: Where i = 0, 1, ..., n-1, Indicates a bitwise OR operation;

[0082] When a data block is determined to be moderately sensitive, the system invokes the AES-128 symmetric encryption algorithm. The AES-128 symmetric encryption algorithm divides the plaintext data P into 128-bit blocks, with the number of blocks being k, i.e., P = P0||P1||...||P K-1 Each group P j The key is 128 bits long, j = 0, 1, ..., n-1; the key K is also 128 bits long; the encryption process is implemented through a series of round transformations, including byte substitution, row shifting, column obfuscation, and round key addition. Each round of encryption is calculated based on the result of the previous round and the round key of the current round; let the result after the r-th round of encryption be... initial value For the r-th round, where r = 1, 2, ..., 10, AES-128 encryption is used for 10 rounds. The encryption calculation formula is as follows:

[0083] Byte substitution: right Replace each byte.

[0084] Row shift: Perform a row shift operation on the result after byte substitution.

[0085] Column obfuscation: Perform column obfuscation on the result after row shifting.

[0086] Round key addition:

[0087] K r Let K be the round key for the r-th round, which is generated from the initial key K using a key expansion algorithm.

[0088] The encrypted ciphertext,

[0089] When a data block is determined to be highly sensitive, Elliptic Curve Cryptography (ECC) based on the secp256k1 curve is used for encryption. Let the private key be d, a random integer within a certain range, and the public key be Q = d * G, where G is a base point on the secp256k1 curve. For plaintext data P, it is first encoded as a point M on the curve. Then, a random number k is chosen, and two points C1 = k * G and C2 = M + k * Q are calculated. The encrypted ciphertext C consists of these two points, i.e., C = (C1, C2). At the receiving end, the private key d is used to calculate C2 - d * C1 = M + k * Q / k * k * G = M + k * d * G / d * k * G = M, thus reconstructing the plaintext data P.

[0090] This embodiment specifically illustrates the encryption method based on the sensitivity of the data. RC4 stream encryption, AES-128 symmetric encryption, and elliptic curve cryptography (ECC) are employed for data of varying sensitivity, providing corresponding strengths of encryption protection based on data importance and potential risks. This effectively prevents data from being stolen or tampered with during transmission, ensuring data transmission security.

[0091] The method for determining the data sensitivity of a data block is as follows:

[0092] Information on data type, usage scenario, and potential impact is collected from the data blocks. After preprocessing each type, a corresponding data type score S is obtained. le Usage scenario rating S ch Potential impact score S qi ;

[0093] Substitute into the formula: In this process, the data sensitivity index S is obtained. I ;

[0094] The data sensitivity index S I With the corresponding data sensitivity interval [S] Imin S Imax Compare them, if S I >S Imax If S I ∈[S Imin S Imax If S, then the data block is determined to be moderately sensitive data; if S I Imin If so, the data block is determined to be low-sensitivity data.

[0095] ​This embodiment provides a specific method for determining the sensitivity of data blocks. By collecting and preprocessing information on data type, usage scenario, and potential impact, a corresponding score is calculated, resulting in a data sensitivity index. This index is then compared with a preset sensitivity range to determine the data sensitivity level, providing a scientific basis for subsequent targeted encryption measures and enabling refined management of data encryption.

[0096] The data type information includes account information, transaction amount information, and transaction-related ancillary information;

[0097] Through the formula: The data type score S was calculated. le ;

[0098] Where i represents the i-th data type, n is the total number of data types, and W i The weighting coefficients for each type of data are determined based on a combination of empirical and historical data. P i Sensitivity score for each type of data information;

[0099] The expression for the usage scenario rating is: S ch =CH*W θ CH represents the sensitivity score for financial transaction scenarios, and W... θ The weighting coefficients for financial transaction scenarios are determined based on historical data analysis.

[0100] In this embodiment, the calculation methods for data type scoring and usage scenario scoring are refined. Data type scoring is calculated by multiplying the weight coefficients and sensitivity scores of different types of data to highlight the impact of different data types on the overall sensitivity. Usage scenario scoring is determined based on the sensitivity score and corresponding weight coefficient of financial transaction scenarios, accurately reflecting the contribution of usage scenarios to data sensitivity, making data sensitivity assessment more accurate and reasonable.

[0101] The potential impact information includes information on the impact on personal asset security, the impact on the operation of financial institutions, and the impact on the stability of financial markets;

[0102] Through the formula: The mean value of the evaluation index corresponding to the j-th type of impact is calculated; x k,j Let n be the k-th indicator value of the j-th influence type. j The number of indicators corresponding to the j-th type of influence; j = 1, 2, 3, corresponding to the three types of influence respectively;

[0103] Through the formula: The standard deviation of the evaluation index corresponding to the j-th impact type is calculated; k represents the k-th index.

[0104] Through the formula: The adjustment coefficient of the evaluation index corresponding to the j-th type of impact is calculated;

[0105] Through the formula:

[0106]

[0107] The potential impact score S was calculated. qi W k,j The weighting coefficients for each type of impact are determined based on historical data analysis.

[0108] This embodiment details the calculation method for the potential impact score. By calculating the mean, standard deviation, and adjustment coefficient of the evaluation indicators, and comprehensively considering the importance of different impact types and the degree of data dispersion, it comprehensively measures the impact on personal asset security, financial institution operations, and financial market stability. This makes the potential impact score more scientifically reflect the potential risks of the data and provides a more accurate basis for judging the sensitivity of the data.

[0109] It should be noted that the calculation formulas and all parameters involved in the calculations in this invention have been dimensionless beforehand. The process of dimensionless processing is well known in the industry and will not be described here.

[0110] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1.A blockchain-based method for data security monitoring and management, characterized in that, Comprise the following steps: S1, the data is stored in the blockchain of a plurality of nodes, to build a decentralized data storage architecture, to reduce the risk of data loss or tampering due to single point failure or malicious attack; S2, using hash algorithm and consensus mechanism for each data block to generate a hash identification containing the previous data block hash value, to realize the tamper-proof characteristics of data, when data tampering, can detect the change of the blockchain hash value in time; S3, detailed record of each data operation, to build a whole process traceable system from the generation to the final use of data, in order to quickly locate the source of data problem; Each data operation record contains operation time T, operation subject ID, operation content C and operation data block hash value H(D), stored in the transaction record of the blockchain, forming a chain traceability path; S4, in the process of data transmission, according to the level of data sensitivity to determine the encryption processing mode of the data, to prevent data from being stolen or tampered in the transmission process; S5, establish real-time monitoring mechanism, real-time monitoring of data operation and transmission, timely discovery and prevention of network attack and malicious tampering behavior; The method for judging the data sensitivity of the data block is: Collect the data type, use scene and potential impact information in the data block, and classify and preprocess respectively to obtain the corresponding data type score , use scene score , potential impact score ; Substitute into the formula: In the formula, the data sensitivity index is obtained ; sensitive index corresponding data sensitive interval comparison, if the data block is judged as high sensitive data; if the data block is judged as medium sensitive data; if the data block is judged as low sensitive data; The data type information includes account information, transaction amount information and transaction related auxiliary information; The data type score is calculated by the formula: ;​ wherein, represents the first data type, is the total number of data types, is the weight coefficient corresponding to each data type information, is the sensitive score corresponding to each data type information; The expression of the use scenario score is: , is a sensitive score of a financial transaction scenario, is a weight coefficient corresponding to the financial transaction scenario; The potential impact information includes the impact on personal asset security, the impact on financial institution operation and the impact on financial market stability information; The average of the evaluation indexes corresponding to the first influence type is calculated by the formula: The average of the evaluation indexes corresponding to the first influence type is calculated by the formula: The average of the evaluation indexes corresponding to the first influence type is calculated by the formula: The average of the evaluation indexes corresponding to the first influence type is calculated by the formula: The average of the evaluation indexes corresponding to the first influence type is calculated by the formula: The average of the evaluation indexes corresponding to the first influence type is calculated by the formula: The average of the evaluation indexes corresponding to the first influence type is calculated by the formula: The average of the evaluation indexes corresponding to the first influence type is calculated by the formula: , respectively corresponding to three influence types; The standard deviation of the evaluation index corresponding to the influence type is calculated by the formula: ​​ Through the formula: Calculate the first Adjustment coefficients for the evaluation indicators corresponding to each type of impact; Through the formula: ； calculating a potential impact score ; wherein, is the weight coefficient corresponding to each influence type, determined based on historical data analysis. 2.The blockchain-based data security monitoring and management method of claim 1, wherein, In the S1, the data block is evenly distributed to each node through the P2P network protocol, and the network connectivity between nodes is maintained through the heartbeat detection mechanism; The total number of nodes is N, and the number of copies of each data block is M; Satisfy M < N and adjust dynamically according to the importance of data to realize the redundant storage of data. 3.The blockchain-based data security monitoring and management method of claim 2, wherein, In S2, the SHA-256 hash algorithm is used to calculate the hash value of each data block The calculation method is as follows:​ ； wherein, is the hash value of the previous data block, is a concatenation operation of the data, i.e., the hash value of the previous data block is concatenated with the current data block , and the concatenated whole data is input into the SHA-256 hash function, thereby obtaining the hash value of the current data block . ; The consensus mechanism adopts the practical Byzantine fault tolerance algorithm PBFT, which allows the number of Byzantine nodes to be f and f < N / 3, and reaches consensus through message interaction between nodes to ensure the consistency and tamper-proof of blockchain data. 4.The blockchain-based data security monitoring and management method of claim 3, wherein, In the S3, The calculation formula of the hash value of each operation record is: ； ； ； ； ； wherein, is an operation time in the i-th operation record, is an operation subject ID in the i-th operation record, is an operation content in the i-th operation record, is a data block hash value generated by the i-th operation record, is a hash value of a previous operation record. 5.The blockchain-based data security monitoring and management method of claim 4, wherein, The process of determining the encryption processing mode of the data according to the level of data sensitivity in the S4 is: When judging the data block as low sensitive data, start RC4 stream encryption algorithm process; When judging the data block as medium sensitive data, the system calls AES-128 symmetric encryption algorithm; When judging the data block as high sensitive data, use elliptic curve encryption algorithm (ECC) based on secp256k1 curve for encryption.

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