Distributed transaction accounting processing method and system based on intelligent data

Abstract

The invention discloses a distributed transaction bookkeeping processing method and system based on intelligent data, and relates to the technical field of warehouse logistics management, and the system comprises a distributed bookkeeping module which is used for recording transaction data in the system, and achieving the collection, classification, numbering and temporary storage of bill information; a data processing storage module; the transaction execution module is used for executing a transaction according to the data provided by the distributed bookkeeping module; a risk supervision module; according to the invention, encryption, decryption and secure storage of data can be solved, the efficiency and security of data management are improved through decentralization processing, high-frequency transaction processing can be supported, the real-time performance of transaction and data consistency are ensured, a transaction process is automatically executed, intermediate links are reduced, the transaction efficiency is improved, and the transaction efficiency is improved. The problem that the final settlement in a distributed account book technology is not clear is solved, and legal and financial risks are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of warehousing logistics management, and more specifically, to a distributed transaction accounting processing method and system based on intelligent data. Background Art

[0002] Distributed ledger technology (DLT) is an important component of the distributed transaction accounting system. Through a decentralized network and consensus mechanism, it enables multiple participants to jointly maintain a ledger without a central agency, and ensures data security and privacy through encryption technology, identity authentication and access control. A Chinese patent with publication number CN110009338B discloses a blockchain-based accounting method, device and machine-readable storage medium. In the present application, the blockchain includes at least one general ledger accounting node and several sub-account accounting nodes as blockchain member nodes. The method includes: the sub-account accounting node receives an accounting transaction initiated by a transaction initiator; wherein the accounting transaction includes a sub-account detail record related to the accounting business corresponding to the sub-account accounting node; the accounting transaction is synchronized with other accounting nodes in the blockchain to initiate consensus processing for the accounting transaction; in response to a notification message that the accounting transaction consensus is passed, the sub-account detail record is stored in the distributed ledger of the blockchain, so that the general ledger accounting node aggregates the sub-accounts based on the distributed ledger of the blockchain to generate a general ledger, so that the general ledger accounting node can complete the general ledger statistics without checking with the sub-account accounting nodes; However, because financial asset transactions involve a large amount of capital flow, require a high degree of data consistency and integrity, and strong regulatory requirements, and because the financial asset trading system needs to be able to handle high-frequency transactions while ensuring the real-time nature of transactions and data consistency, the interconnectivity of financial asset transactions is also crucial. Because distributed ledger technology needs to be able to effectively connect with existing financial systems and payment infrastructure, the implementation of existing technologies is relatively complex. Moreover, in distributed ledger technology, the determination of settlement finality may not be as clear as in traditional systems, which may bring additional legal and financial risks to both parties to the transaction. Summary of the invention

[0003] To solve the above problems, the present invention provides a distributed transaction accounting processing method and system based on intelligent data.

[0004] On the one hand, a distributed transaction accounting processing system based on intelligent data is proposed, including a distributed accounting module, which is used to record transaction data in the system, realize the collection, classification, marking and temporary storage of bill information, specifically: First, collect transaction data and verify it; Package the verified transaction data into blocks and generate corresponding hash values ​​for the blocks ; Make the nodes in the network reach consensus on the packaged blocks; Sort by time, link the generated new block to the previous packaged block; A data processing and storage module, which is used to securely store and process the transaction data temporarily stored by the distributed accounting module; A transaction execution module, which is used to execute transactions according to the data provided by the distributed accounting module and transmit the execution results to the data processing and storage module for storage; A risk supervision module is used to supervise the distributed accounting module and the transaction execution module and manage potential risks.

[0005] Preferably, the distributed accounting module further includes a sharding unit, and the sharding unit works as follows: Determine the number of shards, choose the shard key and distribute the data; Divide the total transaction data volume of the entire network by the number of shards to calculate the target data volume for each shard; According to the calculation result of the shard key, the transaction data is stored in the corresponding shard.

[0006] Preferably, the working method of the slicing unit also includes: According to the formula , calculate the set of all shard indexes involved in the transaction ,in Index of all shards involved in the transaction, The balance of all accounts involved in the transaction; According to the formula Get the storage capacity required for the predicted shard ,in is the amount of storage currently used, is the expected growth rate; Based on the predicted storage capacity required for sharding To plan so that it can meet the needs of future data growth.

[0007] Preferably, the working method of the slicing unit also includes: Obtaining a preset load balancing threshold; Get the current load balance; The difference between the load balancing threshold and the load balancing value is obtained, and adjustments are made according to the result of the difference.

[0008] Preferably, the specific working mode of the transaction execution module is as follows: Collect transaction requests and verify the legitimacy of transactions; Package the verified transactions into new blocks and execute the transaction logic; The transaction results in the block are stored in the blockchain, transaction receipts are sent to transaction participants, and the transaction results are ensured to be tamper-proof.

[0009] Preferably, the risk supervision module includes a first evaluation unit and a second evaluation unit, wherein the first evaluation unit is used to evaluate the working process of the sharding unit, and the specific working method is as follows: According to the formula , calculate and obtain the number of nodes for each shard ,in is the preset safety threshold, is the hash power of each node; Get the proportion of malicious nodes in each shard ; According to the formula , calculate and obtain the security of each shard ; Set a security in advance If the security of the shard If the number of nodes in the shard is lower than the corresponding threshold, the number of nodes in the shard will be increased until the security is achieved. Greater than or equal to the corresponding threshold; The second evaluation unit is used to identify risks that occur during the operation of the transaction execution module and evaluate the impact of these risks.

[0010] Preferably, the specific working method of the first evaluation unit also includes: According to the formula , calculate and obtain the number of transactions that each shard can process per second ,in The number of transactions that each node can process per second; Set a transaction quantity value in advance If the transaction number of the shard is If the transaction amount is less than the corresponding threshold, the number of nodes in the shard is increased until the transaction amount reaches Greater than or equal to the corresponding threshold.

[0011] Preferably, the specific working mode of the second evaluation unit is as follows: According to the formula , calculate and obtain the risk quantification value in the work process of the transaction execution module ,in is the probability of default, is the loss given default rate, It is an exposed window. is the risk weight; According to the formula , calculate and obtain the unexpected loss value in the work project of the transaction execution module ,in is the square of the standard deviation of the default loss rate, is the expected value of the default loss rate, is the square of the standard deviation of the probability of default; According to the formula , calculate and obtain the risk reserve .

[0012] Preferably, the specific working method of the second evaluation unit also includes: According to the formula , obtain the maximum expected loss in the transaction execution module work project under a given confidence level ,in is the value corresponding to the confidence level of the normal distribution, which is used to determine how extreme the loss distribution is.

[0013] On the other hand, the present invention also proposes a distributed transaction accounting processing method based on intelligent data, comprising the following steps: Step 1: Record transaction data in the system to collect, classify, mark and temporarily store bill information; Step 2: Safely store and process the temporarily stored transaction data, execute the transaction according to the temporarily stored transaction data, and transmit the execution result for storage; Step 3: Monitor records and transaction processes to manage potential risks; The specific process of step one includes: first collect transaction data and verify it, package the verified transaction data into blocks, and generate corresponding hash values ​​for the blocks. , so that the nodes in the network can reach a consensus on the packaged blocks, and link the generated new blocks to the previous packaged blocks according to the time sorting.

[0014] Beneficial effects: It can solve the encryption, decryption and secure storage of data, improve the efficiency and security of data management through decentralized processing, support high-frequency transaction processing, ensure the real-time and data consistency of transactions, and automatically execute transaction processes, reduce intermediate links, improve transaction efficiency, help solve the problem of unclear settlement finality in distributed ledger technology, and reduce legal and financial risks; By real-time monitoring of trading activities, managing credit and liquidity risks, and ensuring the legal validity and clear responsibilities of transactions, it helps the financial asset trading system to effectively connect with the existing financial system and payment infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the transaction accounting processing system of the present invention; Figure 2 It is a flow chart of the transaction accounting processing method of the present invention. DETAILED DESCRIPTION

[0016] like Figure 1 and Figure 2 As shown, a distributed transaction accounting processing system based on intelligent data includes a distributed accounting module, which is used to record transaction data in the system, realize the collection, classification, marking, numbering and temporary storage of bill information, specifically: First, the transaction data is collected and verified. It should be noted that, in this embodiment, the specific method of verification can be to apply a hash function to the transaction data to obtain the hash value of the data, which can ensure that the transaction data has not been tampered with during transmission. It should be noted that, in this embodiment, the transaction data mainly includes: the addresses of the two parties to the transaction: the blockchain addresses of the sender and the receiver, usually the hash value of the public key, the transaction amount: the amount of assets transferred from the sender to the receiver, the transaction fee: the fee paid to the miner for executing the transaction, the timestamp: the time when the transaction occurred, the additional information of the transaction: such as the remarks or messages that the sender may add, and the digital signature: the signature used to verify the identity of the transaction initiator; In this implementation, the verification method is that when the transaction data is transmitted in the network, the receiver will apply the same hash function to the received data, and compare the hash value of the data during transmission with the hash value obtained by the receiver. If they are the same, it is considered that the data has not been tampered with during the transmission process and the verification is passed. Otherwise, it fails. A hash function is a mathematical function that has the following properties: Determinism: the same input always produces the same output; One-way: it is difficult (practically impossible) to deduce the original input from the output hash value; Uniformity: a small change in the input will result in a significant change in the output; Fast calculation: calculating the hash value is very fast. In this embodiment, the hash function can be , or , by multiplying the transaction data to be hashed by the hash function, the hash value of the transaction data is obtained; Package the verified transaction data into blocks and generate corresponding hash values ​​for the blocks It should be noted that, in this embodiment, the hash value The formula for obtaining is ,in is a hash function, Metadata within the block, such as timestamp, number of transactions, and hash value of the previous block, is the collection of all transaction data in the block. Express and The concatenation operation converts the concatenation result of the block header and block data into a block hash value ; The nodes in the network reach a consensus on the packaged blocks; it should be noted that in this embodiment, the specific steps are to set a formula in advance ,in The difficulty target set for the network, in this embodiment, is a hexadecimal number with a specific number of digits, in order to meet , find a suitable nonce value, add it to the packaged block, and recalculate the corresponding hash value , know satisfaction ; The Nonce value is a random number that is used only once. By requiring a different Nonce value to be used for each transaction or block construction, the same transaction can be prevented from being reused or submitted. The Nonce value of each block is unique, which ensures that the hash value of each block is also unique, thereby preventing the duplication of blocks. According to the time order, the generated new block is linked to the previous packaged block; it should be noted that each new block is linked to the blockchain through the hash value of the previous block, forming an unalterable chain structure; It should also be noted that blockchain technology can be used to record transaction data on multiple nodes to ensure data consistency and integrity, handle large amounts of capital flows, and ensure that transaction records cannot be tampered with, thus meeting the high regulatory requirements for financial asset transactions. It should be noted that distributed ledger technology (DLT) is an important component of the distributed transaction accounting processing system. Through a decentralized network and consensus mechanism, it enables multiple participants to jointly maintain a ledger without a central agency, and ensures the security and privacy of data through encryption technology, identity authentication and access control. Among them, because financial asset transactions involve a large amount of capital flow, require a high degree of data consistency and integrity, and strong regulatory requirements, and because the financial asset trading system needs to be able to handle high-frequency transactions while ensuring the real-time nature of transactions and data consistency, the interconnectivity of financial asset transactions is also crucial, because distributed ledger technology needs to be able to effectively connect with existing financial systems and payment infrastructure, and the implementation of existing technologies is relatively complex; A data processing and storage module, which is used to securely store and process the transaction data temporarily stored by the distributed accounting module; it should be noted that the encryption, decryption and secure storage of data can be solved, the efficiency and security of data management can be improved through decentralized processing, and high-frequency transaction processing can be supported, while ensuring the real-time nature of transactions and data consistency; It should also be noted that the specific steps may include: receiving transaction data from the distributed accounting module, encrypting the transaction data using an encryption algorithm (such as AES, RSA or ECC) to ensure data security, splitting large data sets into smaller data units for distributed storage, distributing and storing the encrypted data on multiple nodes of the blockchain network to improve the data's anti-attack capability and redundancy, creating indexes for the stored data for quick retrieval and query, ensuring the consistency of data stored by all nodes in the network through a consensus mechanism, regularly backing up the stored data to prevent data loss or damage, implementing an access control mechanism to ensure that only authorized users can access or modify the data, data decryption: when data needs to be accessed or processed, decrypt the data using the corresponding key; A transaction execution module, which is used to execute transactions according to the data provided by the distributed accounting module, and transmit the execution results to the data processing and storage module for storage; it should be noted that the transaction process can be automatically executed, the intermediate links can be reduced, the transaction efficiency can be improved, and it is helpful to solve the problem of unclear settlement finality in distributed ledger technology and reduce legal and financial risks; The risk supervision module is used to supervise the distributed accounting module and the transaction execution module and manage potential risks. It should be noted that by real-time monitoring of transaction activities, managing credit and liquidity risks, and ensuring the legal effect and clear responsibilities of transactions, it helps the financial asset trading system to effectively connect with the existing financial system and payment infrastructure.

[0017] As an optional embodiment: the distributed accounting module further includes a sharding unit, and the sharding unit works as follows: It should be noted that in actual use, the blockchain network needs to process more and more transactions, but the size of each block and the time interval for block generation are limited, which limits the network's ability to process transactions. As the number of users increases and the volume of transactions grows, the network may become congested, transaction fees rise, and confirmation time lengthens; Determine the number of shards, select a shard key and allocate data; It should be noted that, in this embodiment, the number of shards is determined by system requirements and expected network capacity. The specific process of selecting a shard key and allocating data is that for each transaction data, the data key is used to allocate it to the corresponding shard through a hash function, and the index of the shard in which the data should be stored is calculated. The specific calculation method is to multiply the key of the transaction data item by the hash function and then calculate the remainder according to the number of shards, and map the hash value to a smaller data range. The index value calculated in this way indicates the shard number where the data should exist; This ensures that data is evenly distributed across shards, so that each transaction data item is assigned to a specific shard, which helps maintain data consistency and allows the system to quickly locate data based on the shard index; Divide the total transaction data volume of the entire network by the number of shards to calculate the target data volume for each shard. It should be noted that, for example, if a blockchain network has a total of 1TB of data and is divided into 10 shards, each shard should store an average of about 100GB of data to achieve a balanced state. This calculation helps network maintainers understand the ideal data carrying capacity of each shard, and monitor and adjust data distribution accordingly to ensure that no shard is overloaded or resources are wasted. According to the calculation result of the shard key, the transaction data is stored in the corresponding shard.

[0018] As an optional embodiment: the working method of the sharding unit also includes: It should be noted that, in the specific use process, there may be multiple shards involved in the transaction process, and it is difficult to ensure that the data of each participant in the transaction is correctly processed, and the storage demand of the shards will increase over time. It is difficult to plan sufficient storage resources in advance with the existing method; According to the formula , calculate the set of all shard indexes involved in the transaction ,in Index of all shards involved in the transaction, The balance of all accounts involved in the transaction. It should be noted that if multiple accounts are involved in a transaction, each account may belong to a different shard. We need to determine the shards where all these accounts are located to ensure that the transaction can be correctly routed and processed. This set will help the system identify which shards need to participate in the verification of the transaction and the final ledger update. According to the formula Get the storage capacity required for the predicted shard ,in is the amount of storage currently used, is the expected growth rate; it should be noted that, in this embodiment, The value of is 56%. It should also be noted that as the blockchain network runs, the amount of data will gradually increase, which requires more storage space to save new blocks, transaction records and other information. Through the current storage capacity and the expected growth rate, we can predict the storage capacity required in the future, so as to plan storage resources in advance and ensure the stable operation of the system. Based on the predicted storage capacity required for sharding To plan so that it can meet the needs of future data growth. It should be noted that in this embodiment, the specific methods of planning are as follows: Hardware procurement: if the current storage resources are insufficient, purchase additional storage hardware or cloud storage services according to the predicted value; system upgrade, increase storage capacity on existing hardware, or migrate to a system with larger storage capacity; cost assessment: evaluate future costs based on predicted storage needs, including the purchase cost, maintenance cost, and energy cost of storage devices; data management strategy: formulate or adjust data retention strategies based on storage demand forecasts, for example, determine which data needs to be stored for a long time and which data can be archived or deleted.

[0019] As an optional embodiment: the working method of the sharding unit also includes: It should be noted that, in actual use, there will be differences between the current load and the balancing threshold of the shard, which may affect the distribution of transaction data and the overall performance; Obtain a preset load balancing threshold; it should be noted that it is used to define the ideal state of system load distribution. This threshold may represent the maximum allowable value of the load difference between all shards to ensure the balance and efficiency of the network; Get the current load balance; it should be noted that it indicates the distribution of the actual load of each shard in the current network, involving the number of transactions processed by each shard, the amount of data stored, or other performance indicators; Obtain the difference between the load balancing threshold and the load balancing, and make adjustments based on the difference. It should be noted that in this embodiment, the adjustment result is usually triggered in the following situations: when the network detects that the load of some shards is significantly higher than that of other shards; when the overall network load increases and the load needs to be redistributed to avoid overloading some shards; when the network introduces new shards or the capacity of some shards changes; It should also be noted that the following operations are performed based on the result of the difference: Data migration: Migrate data from shards with high load to shards with low load, Load redistribution: Adjust transaction routing rules to direct new transactions or queries to shards with lower load, Shard merging / splitting: In some cases, it may be necessary to merge smaller shards or split larger shards to optimize the load distribution of the overall network; The ultimate goal of the adjustment is to achieve optimal allocation of network resources, improve system throughput, reduce latency, and ensure that the performance of all shards is as balanced as possible, thereby improving the stability and scalability of the entire blockchain network. The blockchain network can adaptively respond to changing load conditions and ensure that the network continues to operate efficiently.

[0020] As an optional embodiment: the specific working mode of the transaction execution module is as follows: Collect transaction requests and verify the legitimacy of transactions. It should be noted that this includes checking signatures, transaction formats, and the account status of both parties to the transaction. The verified transactions are packaged into new blocks and the transaction logic is executed. It should be noted that the ledger status is updated at the same time, and the validity of the block is ensured through the consensus mechanism. The specific method of ensuring the validity of the block through the consensus mechanism is the same as the above block hash calculation. The transaction results in the block are stored in the blockchain, transaction receipts are sent to transaction participants, and the transaction results are ensured to be tamper-proof.

[0021] As an optional embodiment: the risk supervision module includes a first evaluation unit and a second evaluation unit, and the first evaluation unit is used to evaluate the working process of the slicing unit. The specific working method is as follows: According to the formula , calculate and obtain the number of nodes U of each shard, where D is the preset security threshold and Z is the hash power of each node; it should be noted that in this embodiment, the security threshold is usually based on the degree of decentralization and anti-attack capability required by the network. For example, a network may want to resist an attack that accounts for 51% of the total computing power, so D can be set to more than 50% of the total computing power in this embodiment; It should also be noted that the hash rate of each node refers to the number of hash calculations that the node can perform per second, which is determined by benchmarking the nodes, that is, measuring the amount of work completed by the node in a unit of time; In actual deployment, D and Z may need to be adjusted according to the specific situation of the network. For example, if the network is small or has higher security requirements, the value of D may need to be increased. If the hash power Z of the node is found to be lower than expected, the number of nodes may need to be increased or node performance may need to be optimized. Get the proportion of malicious nodes in each shard It should be noted that, in this embodiment, the proportion of malicious nodes By continuously monitoring network activity, it is possible to identify abnormal behavior, which may indicate the presence of malicious nodes, analyze the network's historical data, including past attack events and dishonest behavior, to estimate the proportion of potential malicious nodes, and conduct regular security audits to identify and assess security vulnerabilities and potential malicious activities in the network; According to the formula , calculate and obtain the security of each shard ; It should be noted that the number of honest nodes is obtained by subtracting the number of potential malicious nodes from the total number of nodes, and then the number of honest nodes is divided by the total number of nodes to obtain a ratio representing security; if the security Close to 1, it means that the vast majority of nodes are honest and the sharding is very safe. Close to 0, it means that most nodes may be malicious and the security of the shard is low; Through this quantitative security index, network operators can more intuitively understand the security status of the shards and make corresponding adjustments and optimizations accordingly; Set a security in advance If the security of the shard If the number of nodes in the shard is lower than the corresponding threshold, the number of nodes in the shard will be increased until the security is achieved. Greater than or equal to the corresponding threshold; It should be noted that it can ensure that the shard maintains normal working performance and makes the malicious nodes in the shard within the controllable range; The second evaluation unit is used to identify risks that occur during the operation of the transaction execution module and evaluate the impact of these risks.

[0022] As an optional embodiment: the specific working method of the first evaluation unit also includes: According to the formula , calculate and obtain the number of transactions that each shard can process per second ,in The number of transactions that each node can process per second. It should be noted that by calculating the transaction processing capacity of the shard, the throughput of the entire blockchain network can be evaluated to understand the maximum number of transactions that the network can process within a specific time. It should also be noted that the number of transactions each node can process per second The method of obtaining can be: benchmarking the nodes in the network to determine the number of transactions a single node can handle under ideal conditions. This includes measuring the time it takes for the node to process a transaction and its performance under different loads; Set a transaction quantity value in advance If the transaction number of the shard is If the transaction amount is less than the corresponding threshold, the number of nodes in the shard is increased until the transaction amount reaches Greater than or equal to the corresponding threshold. It should be noted that if the transaction processing capacity is lower than expected, the network performance can be optimized by increasing the number of nodes in the shard or improving the processing speed of a single node to ensure that the network can meet the growing transaction needs.

[0023] As an optional embodiment: the specific working mode of the second evaluation unit is as follows: According to the formula , calculate and obtain the risk quantification value in the work process of the transaction execution module ,in is the probability of default, is the loss given default rate, It is an exposed window. is the risk weight; it should be noted that the probability of default , the probability that the lender will fail to meet its repayment obligations, which is usually estimated based on historical default data and statistical models, and can be predicted using methods such as mobility models, survival models, or deep learning; Loss Given Default , which indicates the proportion of loan losses when default occurs, usually depends on the value of the collateral and the recovery rate, and can be obtained by analyzing the losses of borrowers after default through historical data; Exposure Window It refers to the amount of risk exposure faced by the bank in the event of a default. It is related to factors such as loan balance and loan amount, and can be achieved through internal risk measurement models, such as Monte Carlo simulation. Risk Weight is a preset proportionality coefficient, and in this embodiment, the value may be 0.986; According to the formula , calculate and obtain the unexpected loss value in the work project of the transaction execution module ,in is the square of the standard deviation of the default loss rate, is the expected value of the default loss rate, It is the square of the standard deviation of the probability of default. It should be noted that by considering the probability of default, the default loss rate and its volatility, the potential losses beyond the expected losses, namely the unexpected losses, are quantified. According to the formula , calculate and obtain the risk reserve It should be noted that represents the expected loss. By adding the expected loss to the unexpected loss, the loss value in the transaction process can be estimated in advance. The main function of the risk reserve is to provide a financial buffer for the enterprise to cope with possible future losses by pre-calculating and setting risk reserves. , when risk events occur, quick action can be taken to reduce losses, and risk reserves can also help reflect potential risks in financial statements; Based on the results of risk quantification, taking measures to reduce or transfer identified risks can protect the assets and interests of users.

[0024] As an optional embodiment: the specific working method of the second evaluation unit also includes: According to the formula , obtain the maximum expected loss VR in the work process of the transaction execution module under a given confidence level, where It is the value corresponding to the confidence level of the normal distribution, which is used to determine the extreme degree of loss distribution. It should be noted that by continuously monitoring risk indicators to ensure that risks are within controllable ranges, reporting to relevant parties, reporting risk indicators regularly, and enhancing transparency, it is possible to better understand and manage the market risks it faces; It should also be noted that, in this embodiment, The value of can be determined by first determining the desired confidence level, such as 95% or 99%. This confidence level determines the range of losses we want VR to cover; Use a standard normal distribution table or statistical software to find the value that corresponds to the chosen confidence level. For example, for a 95% confidence level, is approximately 1.65; for a 99% confidence level, The value is approximately 2.33.

[0025] On the other hand, the present invention also proposes a distributed transaction accounting processing method based on intelligent data, comprising the following steps: Step 1: Record transaction data in the system to collect, classify, mark and temporarily store bill information; Step 2: Safely store and process the temporarily stored transaction data, execute the transaction according to the temporarily stored transaction data, and transmit the execution result for storage; Step 3: Monitor records and transaction processes to manage potential risks; The specific process of step one includes: first collect transaction data and verify it, package the verified transaction data into blocks, and generate a corresponding hash value B for the block, so that the nodes in the network can reach a consensus on the packaged blocks, and sort by time, link the generated new block to the previous packaged block.

[0026] How it works It can solve the encryption, decryption and secure storage of data, improve the efficiency and security of data management through decentralized processing, support high-frequency transaction processing, ensure the real-time and data consistency of transactions, and automatically execute transaction processes, reduce intermediate links, improve transaction efficiency, and help solve the problem of unclear settlement finality in distributed ledger technology, reducing legal and financial risks; By real-time monitoring of trading activities, managing credit and liquidity risks, and ensuring the legal validity and clear responsibilities of transactions, it helps the financial asset trading system to effectively connect with the existing financial system and payment infrastructure.

[0027] The above are only preferred implementations of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical staff in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of this template.

Claims

1. A distributed transaction accounting processing system based on intelligent data, characterized in that: It includes a distributed accounting module, which is used to record transaction data in the system and realize the collection, classification, marking and temporary storage of bill information, specifically: First, collect transaction data and verify it; Package the verified transaction data into blocks and generate corresponding hash values ​​for the blocks ; Make the nodes in the network reach consensus on the packaged blocks; Sort by time, link the generated new block to the previous packaged block; A data processing and storage module, which is used to securely store and process the transaction data temporarily stored by the distributed accounting module; A transaction execution module, which is used to execute transactions according to the data provided by the distributed accounting module and transmit the execution results to the data processing and storage module for storage; A risk supervision module is used to supervise the distributed accounting module and the transaction execution module and manage potential risks.

2. A distributed transaction accounting processing system based on intelligent data according to claim 1, characterized in that: The distributed accounting module also includes a sharding unit, and the sharding unit works as follows: Determine the number of shards, choose the shard key and distribute the data; Divide the total transaction data volume of the entire network by the number of shards to calculate the target data volume for each shard; According to the calculation result of the shard key, the transaction data is stored in the corresponding shard.

3. A distributed transaction accounting processing system based on intelligent data according to claim 2, characterized in that: The working method of the sharding unit also includes: According to the formula , calculate the set of all shard indexes involved in the transaction ,in Index of all shards involved in the transaction, The balance of all accounts involved in the transaction; According to the formula Get the storage capacity required for the predicted shard ,in is the amount of storage currently used, is the expected growth rate; Based on the predicted storage capacity required for sharding To plan so that it can meet the needs of future data growth.

4. A distributed transaction accounting processing system based on intelligent data according to claim 2, characterized in that: The working method of the sharding unit also includes: Obtaining a preset load balancing threshold; Get the current load balance; The difference between the load balancing threshold and the load balancing value is obtained, and adjustments are made according to the result of the difference.

5. The distributed transaction accounting processing system based on intelligent data according to claim 1 is characterized in that: The specific working mode of the transaction execution module is as follows: Collect transaction requests and verify the legitimacy of transactions; Package the verified transactions into new blocks and execute the transaction logic; The transaction results in the block are stored in the blockchain, transaction receipts are sent to transaction participants, and the transaction results are ensured to be tamper-proof.

6. A distributed transaction accounting processing system based on intelligent data according to claim 1, characterized in that: The risk supervision module includes a first evaluation unit and a second evaluation unit. The first evaluation unit is used to evaluate the working process of the sharding unit. The specific working method is as follows: According to the formula , calculate and obtain the number of nodes for each shard ,in is the preset safety threshold, is the hash power of each node; Get the proportion of malicious nodes in each shard ; According to the formula , calculate and obtain the security of each shard ; Set a security in advance If the security of the shard If the number of nodes in the shard is lower than the corresponding threshold, the number of nodes in the shard will be increased until the security is achieved. Greater than or equal to the corresponding threshold; The second evaluation unit is used to identify risks that occur during the operation of the transaction execution module and evaluate the impact of these risks.

7. A distributed transaction accounting processing system based on intelligent data according to claim 6, characterized in that: The specific working method of the first evaluation unit also includes: According to the formula , calculate and obtain the number of transactions that each shard can process per second ,in The number of transactions that each node can process per second; Set a transaction quantity value in advance If the transaction number of the shard is If the transaction amount is less than the corresponding threshold, the number of nodes in the shard is increased until the transaction amount reaches Greater than or equal to the corresponding threshold.

8. A distributed transaction accounting processing system based on intelligent data according to claim 6, characterized in that: The specific working mode of the second evaluation unit is as follows: According to the formula , calculate and obtain the risk quantification value in the work process of the transaction execution module ,in is the probability of default, is the loss given default rate, It is an exposed window. is the risk weight; According to the formula , calculate and obtain the unexpected loss value in the work project of the transaction execution module ,in is the square of the standard deviation of the default loss rate, is the expected value of the default loss rate, is the square of the standard deviation of the probability of default; According to the formula , calculate and obtain the risk reserve .

9. A distributed transaction accounting processing system based on intelligent data according to claim 8, characterized in that: The specific working method of the second evaluation unit also includes: According to the formula , obtain the maximum expected loss in the transaction execution module work project under a given confidence level ,in is the value corresponding to the confidence level of the normal distribution, which is used to determine how extreme the loss distribution is.

10. A distributed transaction accounting processing method based on intelligent data, applicable to a distributed transaction accounting processing system based on intelligent data according to claim 1, characterized in that: The following steps are involved: Step 1: Record transaction data in the system to collect, classify, mark and temporarily store bill information; Step 2: Safely store and process the temporarily stored transaction data, execute the transaction according to the temporarily stored transaction data, and transmit the execution result for storage; Step 3: Monitor records and transaction processes to manage potential risks; The specific process of step one includes: first collect transaction data and verify it, package the verified transaction data into blocks, and generate corresponding hash values ​​for the blocks. , so that the nodes in the network can reach a consensus on the packaged blocks, and link the generated new blocks to the previous packaged blocks according to the time sorting.

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