Organization supervision method and system based on alliance chain

Abstract

The application discloses an organization supervision method and system based on a consortium chain, and the method comprises the following steps: each organization node in the consortium chain performs transaction data chaining; in response to successful data synchronization verification between different organization nodes in the same sub-chain and successful cross-chain transaction verification between organization nodes in different sub-chains, a preset supervision process is started; the preset supervision process comprises the following steps: a supervision node calculates the annual supervision rating score of each organization node based on homomorphic mapping and weight confusion; the supervision node performs industry-specific risk assessment and calculates the comprehensive risk coefficient of each organization node; and the supervision node dynamically adjusts the execution frequency and resource proportion of each supervision task in the smart contract based on the annual supervision rating score and the comprehensive risk coefficient of each organization node. The application can effectively solve the problems of data islands, privacy protection and efficiency improvement in the supervision process of local organizations.

Description

Technical Field

[0001] This invention belongs to the field of organizational supervision technology, specifically relating to an organizational supervision method and system based on consortium blockchain. Background Technology

[0002] With the rapid development of local financial organizations (such as microfinance companies and financing guarantee institutions), their regulation faces multiple challenges, including data silos, privacy protection, and regulatory efficiency. In traditional regulatory models, data from these organizations is scattered across various institutions, requiring regulatory authorities to collect and review data manually. This leads to information delays, low collaboration efficiency, and difficulty in achieving real-time risk monitoring. Furthermore, financial data involves sensitive commercial information, and directly sharing raw data can easily result in privacy leaks. Traditional encryption technologies cannot support regulatory calculations in encrypted form, creating a contradiction between "data privacy" and "regulatory transparency."

[0003] The distributed storage and immutability of blockchain technology offer a new approach to solving the aforementioned problems. In existing solutions, at the data management level, independent data storage by different institutions creates information isolation, and traditional encryption methods, while ensuring data privacy, struggle to support efficient cross-institutional regulatory collaboration. At the data collaboration level, reliance on manual data collection and verification can easily lead to information synchronization delays, affecting the timeliness of regulation. In cross-chain business scenarios, existing mechanisms lack sufficient constraints on the behavior of relay nodes, and security risks exist in the transaction verification process, such as the difficulty in effectively curbing double-spending attacks. Regarding the balance between data computation and privacy, the sensitivity of financial data requires avoiding plaintext exposure during use, and there is no optimization tailored to the characteristics of local financial regulatory indicators. The execution efficiency of smart contracts is affected by the regulatory workload, and the dynamic resource allocation mechanism is imperfect, potentially impacting the stability of the regulatory process under high-frequency regulatory operations. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes an organizational oversight method and system based on consortium blockchains, which can effectively solve problems such as data silos, privacy protection, and efficiency improvement that arise during the oversight process.

[0005] To achieve the above-mentioned technical objectives and effects, the present invention is implemented through the following technical solution:

[0006] In a first aspect, the present invention provides an organizational supervision method based on a consortium blockchain, applied to a consortium blockchain including a supervision node and several sub-chains. Within the same sub-chain, the organizational nodes have the same business type, while in different sub-chains, the organizational nodes have different business types. The organizational supervision method is characterized by comprising the following steps:

[0007] Each organizational node in the consortium blockchain uploads transaction data to the blockchain;

[0008] In response to successful data synchronization verification between different organizational nodes in the same subchain and successful cross-chain transaction verification between organizational nodes in different subchains, the preset regulatory process is initiated.

[0009] The preset regulatory process includes:

[0010] The regulatory nodes calculate the annual regulatory rating score for each organization node based on homomorphic mapping and weighted confusion.

[0011] Regulatory nodes conduct industry-specific risk assessments and calculate the comprehensive risk coefficient for each organizational node;

[0012] Based on the annual regulatory rating score and comprehensive risk coefficient of each organization node, the regulatory node dynamically adjusts the execution frequency and resource allocation of each regulatory task in the smart contract.

[0013] Secondly, an organizational supervision system based on a consortium blockchain includes a supervision node and several sub-chains. The supervision node and several sub-chains together constitute a consortium blockchain. The business types of organizational nodes in the same sub-chain are the same, while the business types of organizational nodes in different sub-chains are different.

[0014] The regulatory nodes and organizational nodes are configured to cooperate in implementing the organizational regulatory method described in any one of the first aspects.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0016] This invention addresses the issues of data silos, privacy protection, and efficiency improvement in local financial supervision. Through industry-specific indicator libraries and encrypted consensus optimization, it promotes the lawful data reporting by local organizations, effectively constructing a real-time dynamic supervision mechanism for local organizations. Specifically:

[0017] 1. By dynamically adapting to the regulatory needs of different local organizations through a business-specific indicator library, differentiated indicators are loaded in a targeted manner to avoid a "one-size-fits-all" rating.

[0018] 2. The Paillier homomorphic encryption algorithm is used to calculate regulatory indicators in encrypted form, supporting weighted scoring and other calculations on encrypted data directly, achieving "data usable but not visible", and resolving the contradiction between financial data privacy and regulatory transparency.

[0019] 3. Based on the annual regulatory rating scores and comprehensive risk coefficients of each organizational node, dynamically adjust the execution frequency and resource allocation of each regulatory task in the smart contract to achieve key monitoring under high-risk conditions and resource conservation under low-risk conditions, forming a closed-loop regulatory optimization system.

[0020] 4. By adopting a weighted obfuscation aggregation algorithm and a ciphertext evidence pre-generation mechanism, the calculation process of complex regulatory rules in ciphertext state is optimized, reducing the computational overhead of the entire data.

[0021] By introducing a balanced penalty item to prevent agencies from selectively reporting indicators to embellish results, dynamic penalties are imposed based on the dispersion of the normalized values ​​of the reported indicators and the number of unreported indicators, thereby improving the objectivity and comprehensiveness of risk assessment. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0023] Figure 1 This is a structural block diagram of an organization supervision system based on a consortium blockchain according to an embodiment of the present invention;

[0024] Figure 2 This is a flowchart illustrating an embodiment of the organizational supervision method based on a consortium blockchain according to the present invention. Detailed Implementation

[0025] 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 a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0026] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0027] Example 1

[0028] This invention provides an organizational supervision method based on a consortium blockchain, applied to a consortium blockchain including a supervision node and several sub-chains. Organizational nodes within the same sub-chain have the same business type, while organizational nodes in different sub-chains have different business types. The organizational supervision method includes the following steps:

[0029] (1) Each organization node in the consortium blockchain uploads transaction data to the blockchain;

[0030] (2) In response to the successful data synchronization verification between different organizational nodes in the same subchain and the successful cross-chain transaction verification between organizational nodes in different subchains, the preset regulatory process is initiated.

[0031] (3) The preset regulatory process includes:

[0032] The regulatory nodes calculate the annual regulatory rating score for each organization node based on homomorphic mapping and weighted confusion.

[0033] Regulatory nodes conduct industry-specific risk assessments and calculate the comprehensive risk coefficient for each organizational node;

[0034] Based on the annual regulatory rating score and comprehensive risk coefficient of each organization node, the regulatory node dynamically adjusts the execution frequency and resource allocation of each regulatory task in the smart contract.

[0035] The organizational supervision method of this invention will be described in detail below with reference to a specific implementation method. In this embodiment, the entity corresponding to the organizational node is a local financial organization, and the entity corresponding to the supervision node is a financial regulatory organization. The organizational node may also correspond to other entity organizations as needed, and can be specifically set according to actual needs.

[0036] The specific steps of this method are as follows:

[0037] S1: Transaction data from each organization's nodes is uploaded to the blockchain. For example... Figure 2 As shown, in the specific implementation process, step S1 is executed in the data uplink module.

[0038] This step securely and immutably records the transaction data of local financial organizations in the consortium blockchain network using encryption and distributed storage technologies, laying the foundation for subsequent data synchronization and verification, and realizing on-chain evidence storage and off-chain storage. The consortium blockchain includes regulatory nodes and several sub-chains. The business types of the organizational nodes within the same sub-chain are the same, while the business types of the organizational nodes in different sub-chains are different.

[0039] The specific implementation steps of S1 are as follows:

[0040] S1.1 Data set definition and encryption initialization.

[0041] The multiple local financial organizations involved in the supervision are represented as a set. ,in, This represents the total number of local financial organizations. The transaction data uploaded by each local financial organization constitutes a set. When local financial organizations organize Upload transaction data At this time, the public key issued by the supervisory node is used first. Transaction data Perform asymmetric encryption to generate encrypted data. , , This indicates an asymmetric encryption operation, ensuring that only the holder of the corresponding private key can perform the encryption. Only regulatory nodes can decrypt and view the original transaction data.

[0042] S1.2 Construction of encrypted data blocks and hash calculation.

[0043] In generating encrypted data Subsequently, local financial organizations Further computation of the encrypted data is required. hash value ,in, Specify the cryptographic hash algorithm. Append a current timestamp. Together they form a complete encrypted data block. This encrypted data block will be broadcast to the consortium blockchain as the basic unit.

[0044] S1.3 Blockchain Hash Index Generation and Storage.

[0045] Encrypted data blocks After being broadcast to the consortium blockchain, the nodes of each organization in the consortium blockchain will analyze the hash values ​​of the data uploaded by all local financial organizations. Perform aggregation calculations. First, construct these hash values. Tree, and calculate its The root is then used to generate a timestamp by combining the root hash with the block (each organizational node on the consortium blockchain is defined as a block). Concatenate the data and use a secure hashing algorithm to generate the final hash index. Hash index This will be stored in the block headers of each organization's nodes, forming an immutable chain structure. Hash Index The formula for generating is defined as follows:

[0046] ,

[0047] in, This represents the Merkle root algorithm.

[0048] S2: Cross-organizational data synchronization verification. For example... Figure 1 As shown, in the specific implementation process, step S2 is executed in the data synchronization module.

[0049] To ensure that data copies among different financial local organizations within the same subchain maintain content and temporal consistency during synchronization, dual-factor authentication is used to effectively prevent data tampering and rehashing attacks as well as time-series forgery attacks, providing a reliable data foundation for core regulatory computations.

[0050] The specific implementation steps of S2 are as follows:

[0051] S2.1 Local financial organizations first employ asymmetric encryption methods to secure local transaction data. Encryption is performed to generate ciphertext data. encrypted data The data is broadcast to the consortium blockchain, forming shared transaction data that can be accessed by other institutions (i.e., nodes of other organizations). Before performing synchronization verification, calculate the local data separately. Shared data to be synchronized The hash value, i.e. and .

[0052] S2.2 Perform data content consistency verification (hash comparison).

[0053] The two hash values ​​obtained by comparing and calculating are used in the data synchronization module. and Are they completely identical? If they are different, data synchronization is immediately deemed a failure, and subsequent processes are interrupted.

[0054] S2.3 Data time sequence consistency verification (time stamp comparison).

[0055] If the hash verification passes, the time-series verification phase begins. Local financial organization nodes obtain data generated via the blockchain time consensus algorithm. Generated local data timestamps and shared data timestamps Calculate the absolute difference between two timestamps and compare this difference with a preset, acceptable time threshold.

[0056] S2.4 Conduct synchronous result determination.

[0057] The consortium blockchain ultimately determines that the cross-institutional data synchronization was successful only if the hash values ​​are identical and the timestamp difference is within the allowed threshold range. The entire verification logic is defined by the following synchronization verification function, which encapsulates the above-mentioned dual verification conditions:

[0058] ,

[0059] In the formula, For a preset, acceptable time threshold, This is a synchronous verification function.

[0060] S3: Two-phase verification for cross-chain transactions. For example... Figure 1 As shown, in the specific implementation process, step S3 is executed in the cross-validation module.

[0061] By introducing a two-stage mechanism that combines source chain signature verification with relay chain proof verification, a dual "identity-data" protection system is constructed to prevent risks such as "malicious relay node behavior" and "double-spending of cross-chain assets".

[0062] The specific implementation steps of S3 are as follows:

[0063] S3.1 Cross-chain transaction data preparation and signature generation.

[0064] In cross-chain (i.e., between different subchains) transaction scenarios, the transaction creator in the source chain (i.e., a certain organization node in the source subchain) generates source-end transaction data containing transaction details. and source transaction data Processed into target-side transaction data The transaction receiver in the target chain (i.e., a node of an organization in the target sub-chain) defines the corresponding target-side transaction data. To ensure the integrity and non-repudiation of transactions, the source chain node first... and Perform data concatenation, and then calculate the hash value of the concatenated data. The transaction creator uses their private key. Digitally sign the hash value to generate a signature. This signature will be linked to the source transaction data. Together they will be passed on to the next stage of the cross-chain process.

[0065] S3.2 Source Chain Digital Signature Verification.

[0066] The identity authentication process verifies whether the initiator of the cross-chain transaction (i.e., the transaction creator in the source chain) is a legitimate and authorized user. The verification node uses the public key corresponding to the transaction creator. The received signature and hash value Verification is performed. Verification nodes are typically supervisory nodes or consensus nodes (referring to any organizational node other than the source and target nodes). The verification process is conducted using a digital signature verification function. Execute and confirm signature It is indeed signed by an entity holding a legitimate private key for specific transaction data. The verification conditions for this stage are defined as follows:

[0067] ,

[0068] If verification fails, the transaction will be immediately terminated and deemed illegal.

[0069] S3.3 Relay Chain Block Proof Verification.

[0070] After the previous stage of verification is successful, the verification of data authenticity begins. The cross-chain relay node (a pre-defined functional node in addition to the regulatory and organizational nodes) provides a complete block proof, Relay. This proof contains two key parts: first, the cross-chain transaction's location on both the source and target chains. The verification process involves two aspects: first, proving that the transaction has indeed been included in a block; and second, verifying the number of block confirmations the transaction has received on both the source and target chains, ensuring sufficient network consensus. The verification process is handled by a block proof verification function. Execution, its verification conditions are defined as follows:

[0071] ,

[0072] S3.4 Comprehensive determination of the legality of the transaction.

[0073] Only when both phases of verification are successfully completed is the cross-chain transaction ultimately deemed legitimate and secure, and allowed to continue execution on the target node's (or the transaction recipient's) chain. The complete two-phase verification expression is as follows:

[0074] ,

[0075] in, This is a two-stage verification function.

[0076] S4: Encrypted regulatory rating calculation based on homomorphic mapping and weight confusion. For example... Figure 1 As shown, in the specific implementation process, step S4 is executed in the rating calculation module.

[0077] Based on homomorphic mapping functions, encrypted evidence pre-generation mechanisms, and weighted obfuscation aggregation algorithms, this paper addresses the problem of directly calculating nonlinear local financial regulatory rules in encrypted form.

[0078] The specific implementation steps of S4 are as follows:

[0079] S4.1 Homomorphic mapping of regulatory rules.

[0080] The local financial regulatory indicator system is highly complex and business-related. To calculate regulatory ratings for encrypted data from local financial organizations, the regulatory node first needs to translate plaintext regulatory rules into operations executable in the encrypted domain. Based on the regulatory rules of the local financial organizations, the regulatory node predefines a complete set of rating indicators. Homomorphic mapping functions corresponding to each indicator in the rating indicator set ,in, For the first Indicators represent a specific regulatory dimension; This represents the total number of indicators. At the same time, based on each indicator... The importance of determining the weight coefficient vector of each indicator. And satisfy the condition that the sum of all weights is 1, that is This set of rating indicators and weighted coefficient vectors are securely stored as the benchmark for regulatory calculations. Local financial organizations... Reported indicators Corresponding transaction data Alternatively, qualitative inspection results can be mapped to discrete scores based on business rules. .

[0081] S4.2 Pre-generation of encrypted evidence.

[0082] After the indicators are defined, industry benchmark values ​​are established. This parameter is calculated based on historical regulatory data from provincial financial management departments and is an indicator. Statistical benchmarks within the local financial industry (such as the industry average non-performing loan ratio for microfinance companies). Based on the above business parameters, the characteristics of the Paillier homomorphic encryption algorithm are used for each indicator in this regulatory system. Dynamically generate a unique password generator. Password generator The formula for expressing this is as follows:

[0083] ,

[0084] In the formula, It is the safe modulus of the Paillier algorithm, obtained by multiplying two random large prime numbers p and q (i.e., ). It is a randomly selected integer within the interval [1, n) to ensure that even indicators with the same weights and benchmark values ​​have different generators. Based on customized encryption parameters, a mapping is performed between indicator scores and ciphertext evidence. For the indicator... Its transaction data Through mapping function Convert to discrete score For discrete scores Independently and randomly select an integer ( ∈[1,n). Thereafter, for each discrete score... Pre-generate its corresponding encrypted evidence encrypted evidence The formula for expressing this is as follows:

[0085] ,

[0086] S4.3 Homomorphic interval authentication and dynamic evidence selection for indicator values. This process defines organizational nodes. As the proving party. During rating, the proving party... Randomly select a large integer r∈Z n And using the first generator of the commitment scheme Second generator and another random number Calculate random numbers promise This commitment By the proof party Generates and binds random numbers. and At the same time, the difference is calculated using the homomorphic property. ) promise . The formula for expressing this is as follows:

[0087] ,

[0088] In the formula, The threshold for the scoring interval of the mapping function. Organizational nodes In terms of indicators Encrypted data in this area.

[0089] The regulatory node randomly generates a challenge value. (in (For security parameters), and send them to the proving party. Proof Calculate the first response Second response (in, , It is the difference (related random number), and will respond The response is sent to the validator (another organizational node besides the prover; we are not considering on-chain or off-chain interactions here, but rather the entire consortium network). The custodian node uses the received response to... ,promise and challenge value Verify the equation Whether it is valid or not.

[0090] If the equation holds true, the regulatory node rules out the possibility that local financial organizations deliberately manipulated the data.

[0091] S4.4 weighted obfuscated ciphertext aggregation calculation. Weights for each indicator. Assign a random positive integer confusion factor (A randomly selected positive integer within the interval [1, n)) Calculate the confusing weights And calculate the global confusion coefficient. This is presented as a public parameter. Subsequently, the obfuscated weights are encrypted to obtain the encrypted obfuscated weights. , .

[0092] Encryption obfuscation weight With encrypted evidence Perform homomorphic multiplication and addition operations to obtain the initial aggregated ciphertext. :

[0093] ,

[0094] To aggregate ciphertext from the initial To obtain the true rating score, confounding factors need to be eliminated. The impact. The regulatory node calculates a global correction factor Δ:

[0095] ,

[0096] Ultimately, the monitoring node uses the private key. For the initial aggregated ciphertext By decrypting the global correction factor Δ, the true annual regulatory rating score can be finally restored. These are then mapped to rating levels (A, B, C, and D). The mapping relationship is as follows: Level A: Grade B: Grade C: Grade D: The score may trigger a veto.

[0097] ,

[0098] ,

[0099] ,

[0100] S4.5 Special handling for veto items. For veto-type indicators, when the corresponding triggering event is identified, a veto token with a timestamp is generated, and the hash value of the veto event is recorded. During the final rating phase, the regulatory node first checks whether a valid veto token exists. If it does, the final rating result for the corresponding organization node is forcibly set to the lowest level (Level D).

[0101] S5 Business Specific Risk Assessment. In the specific implementation process, step S5 is executed in the risk assessment module.

[0102] By dynamically loading a dedicated indicator library based on the specific business type of local financial organizations and employing normalization and penalty algorithms, a comprehensive risk coefficient for the risk level of the institution is calculated, providing a quantitative basis for the dynamic optimization of subsequent smart contracts.

[0103] The specific implementation steps of S5 are as follows:

[0104] S5.1 Business Format Identification and Dedicated Indicator Library Loading. When a new organizational node is detected on the consortium blockchain, or an existing organizational node updates its data block, or when a regulatory node issues an assessment instruction through the regulatory platform, a dedicated risk assessment process for the business format is initiated.

[0105] Query the organization's identity identifier stored on the consortium blockchain to obtain the organization's pre-stored identity metadata, i.e., the business type. (For example, "microfinance companies" or "financing guarantee institutions"). Subsequently, according to Dynamically load the corresponding set of rating indicators from the pre-set business-specific indicator library on the blockchain. .

[0106] S5.2 Risk Data Collection and Dynamic Threshold Normalization. Regulatory nodes obtain local organization data from the consortium blockchain. The transaction data actually submitted during the current assessment period forms the submitted indicator subset. ,in, This represents the total number of indicators that have been reported. , This represents the total number of indicators that have been reported; for indicators within the subset of reported indicators... Obtain the corresponding transaction data. Loading metrics Corresponding key regulatory thresholds And calculate the normalized dynamic threshold. Normalized dynamic threshold The calculation formula is as follows:

[0107] ,

[0108] In the formula, This represents the standardized deviation value. For positively correlated indicators (the larger the value, the higher the risk). For negatively correlated indicators (the smaller the value, the higher the risk); k is the steepness factor, a configurable constant greater than 0, used to control the S-curve at the threshold. The steepness of the surrounding area.

[0109] S5.3 Calculation of basic risk value and balance penalty term.

[0110] This step calculates the overall risk coefficient. Comprehensive risk coefficient It consists of a base risk value and a balance penalty term.

[0111] Regulatory nodes calculate the basic risk value of reported indicators. The formula is as follows:

[0112] ,

[0113] To prevent local financial organizations from selectively submitting indicators or artificially embellishing results by exploiting the risk characteristics between indicators, a balance-based penalty item is introduced. This penalty is based on the dispersion of the normalized values ​​of the reported indicators, and its calculation formula is as follows:

[0114] ,

[0115] in, yes The standard deviation of is calculated using the following formula:

[0116] ,

[0117] in, yes The mean. The risk imbalance of the reported indicators has been quantified. This is a penalty for failing to report the number of indicators. It is the penalty coefficient, a configurable parameter for the regulatory node.

[0118] Ultimately, the organization node Comprehensive risk coefficient The expression is as follows:

[0119] .

[0120] in, For the first The weights corresponding to each indicator.

[0121] S6: Dynamic optimization of smart contract execution. In specific implementation processes, such as... Figure 1 As shown, step S6 is executed in the contract optimization module.

[0122] By quantifying the historical performance and current risks of local financial organizations, the execution frequency and resource allocation of various regulatory tasks in smart contracts are adaptively adjusted to form a closed-loop regulatory optimization system.

[0123] The specific implementation steps of S6 are as follows:

[0124] S6.1 Smart Contract Task Decomposition and Execution Weight Initialization. After the smart contract is deployed, its total regulatory task is decomposed into Q independent regulatory tasks, denoted as set. Where Q is a positive integer representing the total number of regulatory tasks; each regulatory task (q=1,2,...,Q) corresponds to core operations such as data verification, indicator calculation, risk warning, or cross-chain synchronization; for each regulatory task Set the base execution time (Unit: milliseconds), and assign initial execution weights. ,satisfy , where the initial weight This reflects the resource allocation for regulatory tasks in a non-optimized state.

[0125] S6.2 Construction of the Risk-Rating Two-Factor Coupling Coefficient. Introduction of Dynamic Coupling Coefficient. Used to calculate the comprehensive risk coefficient and annual regulatory rating score Mapped to a regulatory task-specific adjustment factor. Annual regulatory rating score. It needs to be quantified into a numerical value and denoted as (Grade A = 4, Grade B = 3, Grade C = 2, Grade D = 1), and the comprehensive risk coefficient. (Range 0~1) Coupling coefficients are generated through weighted fusion. Coupling coefficient Defined as:

[0126] ,

[0127] in, and It is a weight parameter, set by the regulatory node, and satisfies... This is used to balance the impact of risk and rating factors; These are preset risk thresholds used to distinguish between high and low risk states; It is a smoothing factor used to control the sensitivity when the risk deviates from the threshold, ensuring a smooth and stable adjustment process.

[0128] S6.3 Dynamic adjustment of the execution frequency of regulatory tasks. Based on dynamic coupling coefficient. Regarding regulatory tasks Execution frequency Dynamic adjustments will be made. The adjustment formula employs a segmented strategy, increasing the frequency of key regulatory tasks in high-risk situations and introducing rating feedback to suppress redundancy in low-risk situations. The adjustment formula is as follows:

[0129] ,

[0130] in, It is a regulatory task The initial execution frequency, It is an indicator function; its value is 1 when the condition inside the parentheses is true, and 0 otherwise. Used in high-risk situations ( Frequency adjustment is enhanced by logarithmic growth. Used in low-risk situations ( When introducing rating denominators, frequency boosting is suppressed to avoid wasting resources.

[0131] S6.4 Resource allocation and execution time optimization.

[0132] Based on the adjusted execution frequency Recalculate the resource weights for each regulatory task. This weight reflects the proportion of regulatory tasks in total resources, ensuring that high-risk or low-rated regulatory tasks receive more resources; at the same time, it optimizes the total execution time. Quantify the effectiveness of improved regulatory efficiency. Resource weighting. Total execution time The calculation formula is:

[0133] ,

[0134] ,

[0135] in, Indicating regulatory tasks The optimized resource weights are used to dynamically allocate computing resources during smart contract execution; This represents the number of times the monitoring task is invoked per unit of time. This formula directly reflects the efficiency improvement. The smaller the value, the more significant the optimization effect.

[0136] S6.5 Continuous monitoring and dynamic adjustment mechanism.

[0137] Establish a continuous monitoring cycle to track the comprehensive risk coefficient in real time. and annual regulatory rating score The change in the comprehensive risk coefficient. The absolute value of the change exceeds the reset trigger threshold. Or, when the rating cycle is refreshed, the coupling coefficient is automatically triggered. and execution frequency Recalculation ensures that optimization strategies are synchronized with regulatory requirements in real time. The monitoring mechanism operates on a periodic scanning basis to avoid instability caused by frequent adjustments. The reset condition is... Or the rating cycle update flag is true.

[0138] in, This represents the overall risk coefficient at the current moment; This represents the overall risk coefficient at the previous moment. It is a preset reset trigger threshold, defined by regulatory policy, used to control the sensitivity of the reset mechanism and prevent unnecessary recalculation due to minor fluctuations; the rating cycle update flag is a Boolean variable that is triggered when S4 outputs the new year's regulatory rating score.

[0139] The organizational supervision method in this embodiment of the invention will be described in detail below with reference to a specific implementation method.

[0140] S1: Data is uploaded to the blockchain.

[0141] Taking the supervision of three local financial organizations within a province by a provincial financial regulatory organization as an example, these three organizations include Jincheng Microfinance Co., Ltd., Anxin Financing Guarantee Co., Ltd., and Puhui Rural Mutual Fund. The consortium blockchain network is jointly maintained by the regulatory node (corresponding to the financial regulatory organization) and the three organizational nodes (corresponding to the three local financial organizations). The encryption algorithm used is RSA-2048 asymmetric encryption (public key). (Generated and distributed by the regulatory node).

[0142] The specific implementation steps of S1 are as follows:

[0143] S1.1 Data set definition and encryption initialization.

[0144] Define the set of local financial organizations as ,in, It is Jincheng Microfinance Co., Ltd. For Anxin Financing Guarantee Co., Ltd. This is for rural mutual financial institutions to benefit all. Each local financial organization needs to upload the following set of transaction data. Specific data examples are as follows:

[0145] (correspond Uploaded transaction data): Transaction data for the first quarter of 2025, including 150 loan transactions, a total loan amount of RMB 50 million, and a non-performing loan ratio of 2.1%.

[0146] (correspond Uploaded transaction data): Guarantee business data for the first quarter of 2025, including a guarantee balance of 30 million yuan and a compensation rate of 1.5%.

[0147] (correspond Uploaded transaction data): Mutual aid fund data for the first quarter of 2025, including deposits of 20 million yuan and a loan ratio of 60%.

[0148] The monitoring node generates an RSA-2048 key pair and sends the public key to the monitoring node. Distribute to all organizational nodes. Organizational nodes Upload data When using a public key Data Perform asymmetric encryption to generate encrypted data. .For example, Data Encrypted data is obtained after encryption. .

[0149] S1.2 Construction of encrypted data blocks and hash calculation.

[0150] Each organization node Calculate encrypted data SHA-256 hash value and append the current timestamp (Organization Node) The timestamp is 2025-03-01 10:00:00, which constitutes a complete encrypted data block. .in, It is a 64-bit hexadecimal string.

[0151] S1.3 Blockchain Hash Index Generation and Storage.

[0152] In a consortium blockchain, the hash values ​​of all organizational nodes (including organizational nodes and supervisory nodes) are aggregated. , and , build Tree. Calculate the root hash. Combine the root hash with the block generation timestamp. (2025-03-01 10:05:00 UTC) Concatenate the data and use the SHA3-256 algorithm to generate the final hash index. The final hash index is stored in the block header, forming an immutable chain structure.

[0153] S1.4 IPFS Distributed Storage and Related Retrieval

[0154] Encrypted data blocks It is stored in the IPFS distributed storage system. The IPFS distributed storage system generates a unique content identifier for each encrypted data block. (e.g., QmXyz123...), for example, encrypted data blocks The hash index is "QmXyz123abc". This is the hash index stored on the consortium blockchain. Content identifiers in the IPFS distributed storage system Associations are established using key-value pairs. When a monitoring node needs to retrieve organization nodes... When processing data, the system queries the hash index on the blockchain. Locating the corresponding IPFS distributed storage system Obtain the encrypted data block and decrypt it for verification.

[0155] S2: Cross-organizational data synchronization verification.

[0156] This step ensures that different organizations within the same subchain network (such as Jincheng Microfinance Co., Ltd.) Anxin Financing Guarantee Co., Ltd. The data replicas maintain content and temporal consistency during synchronization and provide a reliable data foundation for core regulatory computing through dual-factor verification.

[0157] The specific implementation steps of S2 are as follows:

[0158] S2.1 Double encryption and hash calculation.

[0159] To organize nodes (Jincheng Microfinance Co., Ltd.) to the organizational node (Anxin Financing Guarantee Co., Ltd.) Taking synchronized data as an example. Organizational nodes. Local transaction data is That is, as defined in S1 (Data for the first quarter of 2025: 150 loans, total amount of RMB 50 million, non-performing loan ratio of 2.1%, etc.) Transaction data Perform asymmetric encryption (using the public key of the monitoring node) Generate shared data After encryption, shared data Broadcast to the consortium blockchain network for organization nodes. Access. Organization node Calculate local data SHA-256 hash value Simultaneously, it obtains shared data from the consortium blockchain network. hash value .

[0160] S2.2 Data content consistency verification (hash comparison).

[0161] Compare hash values ​​using the data synchronization module and If the two are completely identical, content verification passes; otherwise, synchronization is immediately aborted. If the hash value... and Differences (e.g., due to transmission tampering) (If the value becomes invalid), the system records a synchronization failure and triggers an alarm mechanism.

[0162] S2.3 Data Time Consistency Verification (Timestamp Comparison). The system obtains timestamps from the blockchain and uses the blockchain time consensus generation algorithm. Local data timestamps Taken from the organization node in S1 Upload timestamp (example: 2025-03-01 10:00:00 UTC), shared data timestamp Record timestamps for the consortium blockchain network (example: 2025-03-01 10:02:00 UTC).

[0163] Preset time threshold The time is 5 minutes. Calculate the absolute difference. The timestamp is converted to a Unix timestamp (seconds) for calculation:

[0164] (Corresponds to 2025-03-01 10:00:00 UTC)

[0165] (Corresponds to 2025-03-01 10:02:00 UTC).

[0166] The absolute difference is 1740830400 - 1740830520 = 120 seconds (2 minutes). Since 120 seconds < 300 seconds (5 minutes), the timing verification is successful.

[0167] S2.4 Synchronization result determination.

[0168] The system determines that the cross-institutional data synchronization was successful only if the hash values ​​match and the timestamp difference is within the allowed threshold range. The entire verification logic is defined by the following synchronization verification function:

[0169] ,

[0170] True, and If true, the synchronous verification function returns. The cross-institutional data synchronization was deemed successful.

[0171] S3: Cross-chain transaction two-stage verification.

[0172] By introducing a two-stage mechanism that combines source chain signature verification with relay chain proof verification, a dual "identity-data" protection system is constructed to prevent risks such as "malicious relay node behavior" and "double-spending of cross-chain assets".

[0173] S3.1 Cross-chain transaction data preparation and signature generation.

[0174] To organize nodes Taking Jincheng Microfinance Co., Ltd. as the source chain node, as an example, when initiating a cross-chain transaction to the target chain network, the source chain node generates source-side transaction data. Specifically: based on the organization nodes in S1 Data An example of a loan disbursement record: Loan Number L001, Amount 1 million RMB, Borrower ID, Timestamp 2025-03-01 10:00:00. The target chain network defines the target-side transaction data. Example of an asset transfer record: target chain address Addr_XYZ, transfer amount 1 million yuan, recipient information. Source transaction data... and target-side transaction data Perform data concatenation (string concatenation) and calculate its SHA-256 hash value. .For example:

[0175] ={"L001,1000000, borrower_123,2025-03-01 10:00:00"},

[0176] ={"Addr_XYZ,1000000,receiver_456"},

[0177] , It is a 64-bit hexadecimal number obtained based on the SHA-256 encryption algorithm.

[0178] Source chain node (organization node) ) Use its own private key For hash value Perform digital signature, the signature function is: Generate signature and transaction data They will be passed on to the next stage of the cross-chain process.

[0179] S3.2 Source Chain Digital Signature Verification.

[0180] Validator nodes use source chain nodes public key For signature and hash value Verification is required. If the signature is valid, the transaction is considered to have been initiated by a legitimate authorized user; otherwise, the transaction is immediately terminated and deemed illegal.

[0181] S3.3 Relay Chain Block Proof Verification.

[0182] After S3.2 verification is successful, the cross-chain relay node (a pre-defined functional node in addition to the regulatory node and organization node) provides a complete block proof. . It consists of two parts:

[0183] Proof: Prove that the cross-chain transaction has been packaged into a block on both the source and target chains. For example, at block height 1001 of the source chain. Prove that the target chain block height is 805. prove.

[0184] Block confirmation count: The number of confirmations a transaction has received on the source chain and the target chain (e.g., 6 confirmations on the source chain and 4 confirmations on the target chain), ensuring that the transaction has sufficient consensus on the network.

[0185] If the verification passes, then the verification function... .

[0186] S3.4 Comprehensive Judgment of Transaction Legality. Only when the verification in the above two stages is successful and the following comprehensive verification function returns true, is the cross-chain transaction finally deemed legal and secure, and allowed to continue execution on the target chain.

[0187] ,

[0188] S4: Calculation of encrypted regulatory ratings based on homomorphic mapping and weight confusion.

[0189] This step is based on the information provided by Jincheng Microfinance Co., Ltd. Taking (e.g.) as an example, based on the homomorphic mapping function, the encrypted evidence pre-generation mechanism, and the weighted obfuscation aggregation algorithm, the regulatory rating calculation in the encrypted state is realized.

[0190] The specific implementation steps of S4 are as follows:

[0191] S4.1 Homomorphic mapping of regulatory rules.

[0192] Regulatory authorities pre-set a set of rating indicators based on local financial regulatory rules. This includes quantitative indicators, qualitative indicators, compliance indicators, and veto indicators. The weight of each indicator is as follows: satisfy For organizational nodes Transaction data Through mapping function The indicator values ​​are converted into discrete fractions. The regulatory indicator library is shown in Tables 1 and 2:

[0193] Table 1 Regulatory Indicator Database

[0194] Table 2 Regulatory Indicator Library

[0195] .

[0196] Organization Nodes The specific data reported includes:

[0197] Quantitative indicator data:

[0198] (Non-performing loan ratio): 2.1% (reported value, industry benchmark B1=2.5%)

[0199] (Capital Adequacy Ratio): 11% (reported value, industry benchmark B2=12%)

[0200] (Single-household loan concentration): 8% (reported value, industry benchmark B3=10%)

[0201] (Proportion of inclusive micro and small enterprise loans): 65% (reported value, industry benchmark B4=60%)

[0202] (Proportion of agricultural loans): 55% (reported value, industry benchmark B5=50%)

[0203] (Loan loss provision adequacy ratio): 110% (reported value, industry benchmark B6=100%)

[0204] (Related party transaction percentage): 15% (reported value, industry benchmark B7=20%)

[0205] Qualitative indicator data:

[0206] (Corporate Governance Structure): The qualitative check result is "is_qualified";

[0207] (Soundness of financial management system): The qualitative inspection result is "is_sound";

[0208] (Authenticity of shareholder contributions): The qualitative check result is "is_authentic";

[0209] Compliance indicator data:

[0210] (Timeliness of information reporting): The qualitative inspection result is "is_no_time";

[0211] (Interest rate compliance): Annualized interest rate of 30% (reported value, threshold 36%).

[0212] Veto indicator

[0213] (Illegal fundraising): Not triggered (check result is "!exists");

[0214] (Violent debt collection): Not triggered (check result is "!exists");

[0215] The score set after mapping is:

[0216] { , , , , , , , , , , , } serves as the basis for encrypted text rating.

[0217] S4.2 Pre-generation of encrypted evidence.

[0218] Employing the Paillier homomorphic encryption algorithm, with a security modulus of [missing information]. ( , , For each metric Dynamically generate custom password generator .by generator For example, , , Let be a random integer (let) ),have to:

[0219] .

[0220] Subsequently, encrypted evidence is pre-generated for each score. : Score Random selection ∈[1,n) (e.g.) ),calculate All evidence is stored on the consortium blockchain for quick retrieval during the aggregation phase.

[0221] S4.3 Homomorphic Interval Authentication and Dynamic Evidence Selection for Indicator Values. Proof Method Randomly select an integer Using the first generator of the commitment scheme Second generator (System preset parameters) and random numbers Calculate commitment Meanwhile, regarding the indicator thresholds (e.g., non-performing loan ratio threshold) =0.02), calculate the difference commitment. The monitoring node sends a challenge value c (let's say...). ), Calculate the response and, verify If the equation holds true, then certification is passed if and only if the company... Honestly used the correct (i.e., the true difference between the reported data and the threshold) and the random number used in the initial commitment. , To calculate the response and Only then will the verification be successful.

[0222] S4.4 Ciphertext Aggregation Calculation with Weighted Obfuscation

[0223] For each weight Assign random confusion factor Calculate the weights after confusion The specific details are shown in Table 3.

[0224] Table 3 Weighted Obfuscation Configuration Table

[0225]

[0226] Global confusion coefficient For each obfuscated weight Perform Paillier encryption:

[0227] , to encrypt the obfuscation weight With the encrypted evidence selected from phase S4.2 Perform homomorphic multiplication and addition operations to obtain the initial aggregated ciphertext. : ,

[0228] Substitution The specific scores are calculated in detail. =13.92.

[0229] To aggregate ciphertext from the initial To obtain a true annual regulatory rating score, confounding factors need to be eliminated. The impact. The regulatory node calculates a global correction factor Δ, the formula for which Δ is calculated is:

[0230] ,

[0231] Substitution Specific data .

[0232] Ultimately, the monitoring node uses the private key. right By decrypting Δ, the true annual regulatory rating score can be obtained. Each level is then mapped to a rating grade (Level A, Level B, Level C, and Level D).

[0233] ,

[0234] ,

[0235] Calculate the final rating score according to the formula: ;

[0236] According to the rating mapping rules: Grade A: Grade B: ≥8.0 points; Grade B: 6.0≤ <8.0 points; Grade C: 4.0≤ <6.0 points; Grade D: A score of less than 4.0 may trigger a veto. The score is ∈ [6.0, 8.0), so it is mapped to a B-level rating.

[0237] S5 Business Model-Specific Risk Assessment. This step is based on the experience of Jincheng Microfinance Co., Ltd. Taking a specific example, based on its business type (micro-loan company), a dedicated indicator library is dynamically loaded and a normalization and penalty algorithm is used to calculate the comprehensive risk coefficient of the institution's risk level.

[0238] The specific implementation method of S5 is as follows:

[0239] S5.1 Business Format Identification and Dedicated Indicator Library Loading. When a local financial organization is detected... The new data block for (Jincheng Microfinance Co., Ltd.) has been stably confirmed on the blockchain (based on the S1 block timestamp 2025-03-01 10:05:00UTC). The regulatory node has issued an assessment instruction through the regulatory platform to initiate the risk assessment process. The organization's identity identifier stored on the blockchain is queried to obtain its pre-stored business type. The system determines the type based on the type. Dynamically load the corresponding set of rating indicators from the pre-set industry-specific indicator library on the blockchain. .

[0240] S5.2 Risk Data Acquisition and Dynamic Threshold Normalization. The system obtains organizational data from the blockchain. The indicator data actually submitted during the current assessment period (first quarter of 2025) forms the submitted indicator subset. For each metric Obtain its original value Synchronously load each metric Corresponding key regulatory thresholds .

[0241] For all thresholds according to Normalization processing, among which, Steepness factor (configurable parameter). This represents the standardized deviation value. Calculate the standardized deviation for each indicator. :

[0242] ; ; ; ,

[0243] ; ; ; ,

[0244] ; ; ; .

[0245] S5.3 Calculation of basic risk value and balance penalty term.

[0246] This step calculates the overall risk coefficient. Based on the basic risk value and balance penalty item constitute.

[0247] First, calculate the base risk value:

[0248] ,

[0249] Substitute weights and corrections :

[0250] Calculate the balance penalty term: ,in =0.1 (penalty coefficient), T=14, U=12. First calculate the normalized value of the reported indicators. mean and standard deviation :

[0251] , ,

[0252] The conclusion is ,final Overall risk factor: .

[0253] S6: Dynamic optimization of smart contract execution.

[0254] This step is based on the information provided by Jincheng Microfinance Co., Ltd. Taking [a specific example] as an example, by quantifying its historical regulatory rating score and current comprehensive risk coefficient, the execution frequency and resource allocation of each regulatory task in the smart contract (which includes regulatory tasks + the execution frequency and resource allocation of each regulatory task) are adaptively adjusted to form a closed-loop regulatory optimization system. This implementation method is based on the rating results output by S4 ( rating score Mapped to Level B, quantified as numerical values ​​(3) and calculated using S5, the actual comprehensive risk coefficient. This ensures that the optimization process is synchronized with regulatory requirements in real time.

[0255] S6.1 Smart Contract Oversight Task Decomposition and Execution Weight Initialization. After the smart contract is deployed, the system decomposes its total oversight task into... Each independent regulatory task is denoted as a set. ,in, Corresponding data validation operation, Calculation of corresponding indicators. Corresponding risk warning actions. For each regulatory task. Set the base execution time (Unit: milliseconds): ; ; Assign initial execution weights. ,satisfy , , , Initial execution frequency Set as the baseline value of 1 time / cycle to reflect the resource ratio under no-optimization conditions.

[0256] S6.2: Construction of the Risk-Rating Two-Factor Coupling Coefficient. The system introduces a dynamic coupling coefficient. Used to calculate the real-time comprehensive risk coefficient of S5. And the quantitative results of the annual regulatory rating of S4 (Level B) is mapped to a task-specific adjustment factor. Weight parameters. , (Set by the regulatory node to meet) ), risk threshold (Preset value), Smoothing factor (Control sensitivity), substitute and :

[0257] ,

[0258] S6.3 Dynamic adjustment of task execution frequency based on coupling coefficient For each regulatory task Execution frequency Dynamic adjustments are made. The adjustment formula employs a piecewise strategy because... Using the low-risk state term, substitute it into the frequency adjustment formula:

[0259] ,

[0260] Therefore, the adjusted frequency of all regulatory tasks ≈1.077 times / cycle.

[0261] S6.4 Resource Allocation and Execution Time Optimization. Based on the adjusted frequency. =1.077, recalculate the computational resource weights for each regulatory task. Total execution time The resource weight formula is:

[0262] ,

[0263] Substitute into the calculation of each regulatory task :

[0264] ,

[0265] ,

[0266] ,

[0267] The overall execution time optimization result is as follows:

[0268] ,

[0269] Total execution time before optimization (based on initial frequency) The original time was 100+150+200=450ms, which was reduced to 417.83ms after optimization, resulting in an efficiency improvement of about 7.1%.

[0270] S6.5 Continuous monitoring and dynamic adjustment mechanism.

[0271] Establish a continuous monitoring cycle to track the comprehensive risk coefficient in real time. and rating Changes. Setting a reset trigger threshold. The monitoring mechanism operates on a periodic scanning basis to avoid frequent adjustments. A recalculation is automatically triggered when the following conditions are detected:

[0272] Risk change conditions: ,in, This represents the overall risk coefficient at the current moment; This represents the overall risk coefficient at the previous moment.

[0273] Rating cycle update: When S4 outputs a new year's regulatory rating score (rating cycle update flag is true).

[0274] For example, if the overall risk coefficient for the next cycle becomes Then the absolute value of the change Trigger coupling coefficient and execution frequency The recalculation ensures that the optimization strategy is synchronized with regulatory requirements in real time.

[0275] Example 2

[0276] This invention provides an organization supervision system based on a consortium blockchain, including a supervision node and several sub-chains. The supervision node and several sub-chains together constitute a consortium blockchain. The business types of the organization nodes in the same sub-chain are the same, while the business types of the organization nodes in different sub-chains are different.

[0277] The regulatory node and each organizational node are configured to cooperate in executing the organizational regulatory method described in any one of Embodiment 1.

[0278] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0279] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0280] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0281] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0282] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

[0283] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for organizational supervision based on a consortium blockchain, applied to a consortium blockchain comprising a supervisory node and several sub-chains, wherein the business types of organizational nodes within the same sub-chain are the same, and the business types of organizational nodes in different sub-chains are different, characterized in that... The organizational oversight method includes the following steps: Each organizational node in the consortium blockchain uploads transaction data to the blockchain; In response to successful data synchronization verification between different organizational nodes in the same subchain and successful cross-chain transaction verification between organizational nodes in different subchains, the preset regulatory process is initiated. The preset regulatory process includes: The regulatory nodes calculate the annual regulatory rating score for each organization node based on homomorphic mapping and weighted confusion. Regulatory nodes conduct industry-specific risk assessments and calculate the comprehensive risk coefficient for each organizational node; Based on the annual regulatory rating score and comprehensive risk coefficient of each organization node, the regulatory node dynamically adjusts the execution frequency and resource allocation of each regulatory task in the smart contract.

2. The organizational supervision method based on consortium blockchain according to claim 1, characterized in that: The regulatory node defines a corresponding set of rating indicators based on the regulatory rules for the organization node. , , For the organization node One indicator, The total number of indicators; all organizational nodes of the same business type use the same set of rating indicators; Define the homomorphic mapping function for each index. ; According to each indicator The importance of each factor determines its corresponding weighting coefficient. Generate weight coefficient vector , And satisfy ; The Paillier homomorphic encryption algorithm is used to dynamically generate a unique cryptographic generator for each indicator. , In the formula, It is the security modulus of the Paillier homomorphic encryption algorithm, which is obtained by multiplying two random large prime numbers p and q. This is the industry benchmark value; It is an integer randomly selected within the interval [1, n); For indicators The corresponding transaction data is mapped using a homomorphic mapping function. Convert to discrete score ; For discrete scores Randomly select an integer , ; For discrete scores Pre-generate its corresponding encrypted evidence , ; Definition of the first Organizational nodes To prove this, during the rating process, the organization nodes... Randomly select a large integer And using the first generator of the commitment scheme Second generator Calculate random numbers promise , ; Commitment to calculating the difference using homomorphic properties , In the formula, For organization nodes No. Encrypted data for each indicator, This represents the Paillier homomorphic encryption function. The threshold for the scoring interval of the mapping function. The difference. For organization nodes No. The transaction data corresponding to each indicator The public key for the monitoring node; The regulatory node randomly generates a challenge value. , ,in, As security parameters, and send them to the organization node. Organizational nodes Calculate the first response Second response , , ,in, , It is the difference The relevant random number will be generated by the first response. Second response The constituted response pair Send to the monitoring node; The regulatory node uses the received response to ,promise and challenge value Verify the equation Is it valid? If the equation holds true, the regulatory node excludes the possibility that the organization node deliberately manipulated the data; Weight for each indicator Assign a random positive integer confusion factor , Let be a positive integer randomly selected from the interval [1, n). Calculate the weights after confusion and global confusion coefficient As a public parameter, among which, , ; Weights after obfuscation Encryption is performed to obtain the encryption and obfuscation weight. , ; Encryption obfuscation weight With encrypted evidence Perform homomorphic multiplication and addition operations to obtain the initial aggregated ciphertext. ; The regulatory node calculates a global correction factor Δ; Regulatory node usage For the initial aggregated ciphertext By decrypting the global correction factor Δ, the true annual regulatory rating score can be obtained. And mapped to rating levels, where, and All of these are private key parameters; For veto-type indicators, when the corresponding triggering event is identified, a veto token with a timestamp is generated. During the final rating phase, the existence of a valid veto token is checked first. If it exists, the final rating result of the corresponding organization node is forcibly set to the lowest level.

3. The organizational supervision method based on consortium blockchain according to claim 2, characterized in that: The initial aggregated ciphertext The calculation formula is: 。 4. The organizational supervision method based on consortium blockchain according to claim 3, characterized in that: The formula for calculating the global correction factor Δ is as follows: 。 5. The organizational supervision method based on consortium blockchain according to claim 4, characterized in that: True annual regulatory rating score The calculation formula is: ， in, ， 。 6. The organizational supervision method based on consortium blockchain according to claim 2, characterized in that: When a new organizational node is detected on the consortium blockchain or an existing organizational node updates its data block, and this has been stably confirmed on the consortium blockchain, or when an assessment instruction is issued by a regulatory node, a business-specific risk assessment process is initiated. The specific risk assessment process for the aforementioned business type includes: Query the organization identity identifier stored on the consortium blockchain by the organization node, and obtain the pre-stored identity metadata of the organization node, wherein the identity metadata is the business type. ; According to business type Dynamically load the corresponding set of rating indicators from the pre-set industry-specific indicator library on the consortium blockchain. ; Obtaining local organizations from consortium blockchains The transaction data actually submitted during the current assessment period forms the submitted indicator subset. ,in, This represents the total number of indicators that have been reported. For indicators in the already reported subset of indicators Obtain the corresponding transaction data. Loading metrics Corresponding key regulatory thresholds And calculate the normalized dynamic threshold. , In the formula, For positively correlated indicators, the standardized deviation value is... For negatively correlated indicators, ; Steepness factor; Calculate the comprehensive risk coefficient The comprehensive risk coefficient It consists of a base risk value and a balance penalty term; Calculate the basic risk value of the reported indicators , ; Calculate the balance penalty term , , yes standard deviation , yes The mean, It is a penalty for failing to report the number of indicators. It is the penalty coefficient; Based on the aforementioned basic risk value and balance penalty item Calculate organization nodes Comprehensive risk coefficient Comprehensive risk coefficient The calculation formula is: ， in, For the first The weights corresponding to the reported indicators.

7. The organizational supervision method based on consortium blockchain according to claim 1, characterized in that: After the smart contract is deployed, the consortium blockchain breaks down its overall oversight tasks into... Each independent regulatory task is denoted as a set. , Where Q is a positive integer representing the total number of regulatory tasks; each regulatory task Corresponding to the corresponding operations; for each regulatory task Set the base execution time and assign initial execution weights. ,satisfy Initial weights This reflects the resource allocation for regulatory tasks in a non-optimized state. Introducing dynamic coupling coefficients This is used to calculate the real-time comprehensive risk coefficient. and annual regulatory rating score Mapped to a regulatory task-specific adjustment factor; the annual regulatory rating score Quantify into numerical values ​​and record them as With the comprehensive risk coefficient Dynamic coupling coefficients are generated through weighted fusion. Dynamic coupling coefficient The calculation formula is: ， In the formula, and It is a weighting parameter, set by the regulatory node, and satisfies β1+β2=1, used to balance the impact of risk and rating factors; These are preset risk thresholds used to distinguish between high and low risk states; It is a smoothing factor; Based on dynamic coupling coefficient Regarding regulatory tasks The initial execution frequency is dynamically adjusted to obtain the adjusted execution frequency. The formula used for dynamic adjustment is: ， in, It is a regulatory task The initial execution frequency, It is an indicator function; its value is 1 if the condition inside the parentheses is true, and 0 otherwise. Used in Frequency adjustment is enhanced by logarithmic growth; Used in The introduction of rating denominator to suppress frequency increase; Based on the adjusted execution frequency Calculate regulatory tasks Adjusted resource weights and adjusted total execution time .

8. The organizational supervision method based on consortium blockchain according to claim 7, characterized in that: The formulas for calculating the adjusted resource weights and the adjusted total execution time are as follows: ， ， in, Used to dynamically allocate computing resources during the execution of smart contracts; The smaller the value, the more significant the optimization effect.

9. A method for organizational supervision based on a consortium blockchain according to claim 8, characterized in that: The organizational oversight methods also include: Establish a continuous monitoring cycle to track the comprehensive risk coefficient in real time. and annual regulatory rating score Changes in the comprehensive risk coefficient, when detected The absolute value of the change exceeds the reset trigger threshold. Or, when the rating period is refreshed, the dynamic coupling coefficient is automatically triggered. and execution frequency The recalculation is triggered by the following expression: Or the rating cycle update flag is true; in, This represents the overall risk coefficient at the current moment; This represents the overall risk coefficient at the previous moment; It is a preset reset trigger threshold; the rating cycle update flag is a Boolean variable that is true when the new year's regulatory rating score is output.

10. An organizational oversight system based on a consortium blockchain, characterized in that, It includes a regulatory node and several sub-chains, which together constitute a consortium blockchain. The business types of the organizational nodes in the same sub-chain are the same, while the business types of the organizational nodes in different sub-chains are different. The regulatory node and each organizational node are configured to cooperate in executing the organizational regulatory method as described in any one of claims 1-9.

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