Financial data management system and method based on block chain

Abstract

The invention discloses a financial data management system and method based on a block chain, and relates to the technical field of financial data management, and the method comprises the steps: dividing a block chain network into a plurality of sub-networks based on the targets of minimizing the network communication delay, maximizing the data storage efficiency and maximizing the calculation capability utilization rate; defining a plurality of credibility evaluation indexes, and for each node, calculating the credibility of the node according to the value of the credibility evaluation index; classifying the transaction data into high-frequency transaction data and low-frequency historical data, executing a full-node storage strategy for the high-frequency transaction data, and executing a distributed storage strategy for the low-frequency historical data; according to the method, load information indexes of block chain sub-networks are collected, a network load value of each sub-network is calculated, the network load value of each sub-network is compared with a network load threshold value, and corresponding processing is performed according to a comparison result, so that the storage efficiency is improved, the computing power consumption is reduced, and the data processing speed and real-time performance are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of financial data management, and specifically provides a blockchain-based financial data management system and method. Background Art

[0002] Financial data management systems not only need to ensure the integrity and accuracy of data, but also provide efficient data processing capabilities and real-time performance to support complex financial analysis and decision-making. Blockchain technology, as a distributed ledger technology, has shown great potential in the financial field with its decentralized, transparent, and immutable characteristics, especially in ensuring transaction security and trust.

[0003] However, when applying blockchain technology to financial data management systems, a series of challenges are faced. Firstly, blockchain requires each node to store all immutable financial transaction records across the network, which poses extremely high requirements for the storage capacity of nodes. As the transaction volume increases, the storage cost rises sharply, and the storage efficiency may be affected. In addition, the consensus mechanism in blockchain, such as Proof of Work (PoW), relies on nodes competing for the right to record new blocks through a large amount of computing power. This process not only consumes a huge amount of energy but also causes a great waste of computing power resources.

[0004] In addition, the scalability issue of the blockchain network is also a major challenge faced by financial data management systems. As the number of network nodes increases, network communication latency and data synchronization problems become increasingly prominent. How to improve the scalability and efficiency of the network while maintaining decentralization and security has become an urgent problem to be solved. Summary of the Invention

[0005] (1) Technical Problems to be Solved

[0006] In view of the deficiencies of the prior art, the present invention provides a blockchain-based financial data management system and method. By intelligently partitioning the blockchain network, evaluating node trustworthiness, implementing differential storage strategies, and dynamically adjusting network load, the storage efficiency can be improved, the computing power consumption can be reduced, and the data processing speed and real-time performance can be enhanced, so as to meet the high standards and strict requirements of financial data management.

[0007] (2) Technical Solutions

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: A blockchain-based financial data management system and method, including:

[0009] Collect node information of all nodes in the blockchain network, and divide the blockchain network into multiple sub-networks based on the goals of minimizing network communication latency, maximizing data storage efficiency, and maximizing computing power utilization;

[0010] Collect the historical financial data, node behavior records, and interaction data with other nodes of all nodes in the blockchain network, define multiple trust evaluation indicators, and for each node, calculate the trust of the node according to the value of its trust evaluation indicators;

[0011] Extract all transaction data from the blockchain network, classify the transaction data into high-frequency transaction data and low-frequency historical data, and for the high-frequency transaction data, implement a full-node storage strategy, and for the low-frequency historical data, implement a distributed storage strategy;

[0012] Collect the load information indicators of the blockchain sub-network, calculate the network load value of each sub-network, compare the network load value of each sub-network with the network load threshold, and make corresponding processing according to the comparison result.

[0013] Furthermore, define the objective function, including the objectives of minimizing network communication latency, maximizing data storage efficiency, and maximizing computing power utilization, and set the constraint conditions: each node belongs to and only belongs to one sub-network, and construct an optimization model:

[0014] Constraint conditions: Among them, D represents minimizing network communication latency, d ij represents the communication latency between node i and node j, N represents the set of nodes, x ij , y ik and z ik are binary variables, x ij represents whether node i and node j are in the same sub-network, y ij represents whether node i belongs to sub-network k, z ik represents whether the task is assigned to node i in sub-network k, E represents maximizing data storage efficiency, S i represents the storage capacity of node i, R represents the set of sub-networks, U represents maximizing computing power utilization, C i represents the computing power of node i;

[0015] Use the branch and bound method to solve the optimization model, determine the partitioning scheme of the approximate optimal solution, that is, the sub-network to which each node belongs, and divide the blockchain network into multiple sub-networks.

[0016] Furthermore, according to the historical financial data, node behavior records, and interaction data with other nodes of the nodes, define multiple trust evaluation indicators, including transmission success rate, node online duration rate, power duration ratio, number of communication connections, available storage capacity ratio, as well as abnormal data ratio, packet loss rate, identity fraud ratio, bit error rate, and link establishment delay;

[0017] Further, standardize the trustworthiness evaluation indicators. For the trustworthiness evaluation indicators of benefit-type attributes, the standardization calculation formula is: St_b = (X - Min) / (Max - Min). For the trustworthiness evaluation indicators of cost-type attributes, the standardization calculation formula is: St_c = (Max - X) / (Max - Min). Here, St_b represents the standard value of the trustworthiness evaluation indicator of the benefit-type attribute, St_c represents the standardized value of the trustworthiness evaluation indicator of the cost-type attribute, X represents the trustworthiness evaluation indicator value, and Max and Min respectively represent the maximum and minimum values of the trustworthiness evaluation indicator value.

[0018] Further, for each node, calculate the trustworthiness of the node according to the value of its trustworthiness evaluation indicator: where Tr represents the trustworthiness of the node, St m represents the standardized value of the m-th trustworthiness evaluation indicator, ω m represents the weight corresponding to the m-th trustworthiness evaluation indicator, and M represents the number of trustworthiness evaluation indicators.

[0019] Further, count the number of times each transaction data is accessed, calculate its average access frequency, and compare the average access frequency of each transaction data with the access frequency threshold. If the average access frequency of the transaction data is greater than the access frequency threshold, classify this transaction data as high-frequency transaction data; otherwise, classify this transaction data as low-frequency historical data.

[0020] Further, execute the distributed storage strategy. For each low-frequency historical data, determine the storage node of this low-frequency historical data. If the number of storage nodes is equal to 1, select the sub-network to which the storage node belongs. If the number of storage nodes is not equal to 1, select the sub-network to which the storage node with the highest trustworthiness belongs;

[0021] Obtain the trustworthiness of all nodes in the selected sub-network, preset the trustworthiness threshold, and compare the trustworthiness of each node with the trustworthiness threshold. If the trustworthiness of the node is greater than the trustworthiness threshold, classify this node as a high-trustworthiness node; otherwise, classify this node as a low-trustworthiness node;

[0022] For the high-trustworthiness nodes in the selected sub-network, save the complete copy of the low-frequency historical data. For the low-trustworthiness nodes in the selected sub-network, perform a hash operation on the low-frequency historical data to generate a hash value, and save the hash value.

[0023] Further, collect the load information metrics of the blockchain sub-network, including network bandwidth utilization, CPU utilization, and memory utilization, and calculate the network load value of each sub-network through weighted calculation; if the network load value of the sub-network exceeds the network load threshold, increase the number of nodes participating in bookkeeping of the sub-network by a fixed proportion until the network load value does not exceed the network load threshold or reaches the upper limit of the number of nodes. Otherwise, reduce the number of nodes participating in bookkeeping of the sub-network by a fixed proportion and ensure that the network load value of the sub-network does not exceed the network load threshold.

[0024] Further, when the network load value of the sub-network exceeds the network load threshold, based on the trustworthiness ranking of all nodes in the sub-network, select a fixed proportion of nodes with higher rankings for consensus. When the network load value of the sub-network does not exceed the network load threshold, adopt the full-node consensus mechanism.

[0025] A financial data management system based on blockchain, comprising:

[0026] A network division and optimization module that collects node information of all nodes in the blockchain network and divides the blockchain network into multiple sub-networks based on the goals of minimizing network communication latency, maximizing data storage efficiency, and maximizing computing power utilization;

[0027] A node trustworthiness evaluation module that collects the historical financial data, node behavior records, and interaction data with other nodes of all nodes from the blockchain network, defines multiple trustworthiness evaluation indicators, and calculates the trustworthiness of each node according to the values of its trustworthiness evaluation indicators;

[0028] A data storage strategy module that extracts all transaction data from the blockchain network, classifies the transaction data into high-frequency transaction data and low-frequency historical data, and executes the full-node storage strategy for high-frequency transaction data and the distributed storage strategy for low-frequency historical data;

[0029] A network load optimization module that collects the load information metrics of the blockchain sub-network, calculates the network load value of each sub-network, compares the network load value of each sub-network with the network load threshold, and makes corresponding processing according to the comparison result.

[0030] (III) Beneficial effects

[0031] The present invention provides a financial data management system and method based on blockchain, having the following beneficial effects:

[0032] (1) By dividing the blockchain network into multiple sub - networks, the storage resources of each node can be utilized more effectively. Nodes within each sub - network can jointly undertake storage tasks, thereby dispersing the storage pressure, improving the overall storage efficiency, reducing the communication distance and hop count between nodes, and thus reducing network communication latency.

[0033] (2) By defining multiple trust - degree evaluation indicators and calculating the trust degree of nodes, trustworthy nodes can be screened out. These trustworthy nodes are more likely to abide by the rules in financial data management, reducing the risk of data tampering or malicious attacks. Nodes with a high trust degree are more likely to remain online in the network, providing stable services, thereby enhancing the stability of the entire blockchain network.

[0034] (3) By classifying transaction data into high - frequency transaction data and low - frequency historical data, and implementing full - node storage strategies and distributed storage strategies respectively, storage space can be utilized more effectively. High - frequency transaction data, due to the need for frequent access, full - node storage ensures the rapid availability of data; while low - frequency historical data reduces unnecessary duplicate storage through distributed storage, thereby reducing the overall storage cost. By storing low - frequency historical data on nodes with a relatively high trust degree and only storing the hash values of data on nodes with a low trust degree, not only is the waste of storage space reduced, but also the rational utilization of computing resources and network resources is promoted, avoiding unnecessary computing power consumption and resource waste.

[0035] (4) By dynamically adjusting the number of nodes participating in accounting, it helps to optimize the resource utilization of the blockchain network. Increasing the number of nodes when the load is heavy can make full use of network resources, and reducing the number of nodes when the load is light can reduce operating costs. When the network load is high, by selecting a fixed proportion of nodes with a high trust - degree ranking for consensus, the transaction processing speed and efficiency can be significantly improved. When the network load is low, adopting a multi - node or full - node consensus mechanism helps to maintain the decentralization and security of the blockchain network. Brief Description of the Drawings

[0036] Figure 1 It is a schematic diagram of the steps of the financial data management method based on blockchain of the present invention;

[0037] Figure 2 It is a schematic diagram of the blockchain structure of the present invention;

[0038] Figure 3 It is a schematic diagram of the structure of the financial data management system based on blockchain of the present invention. Detailed Embodiments

[0039] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] Please refer to Figures 1-2 , the present invention provides a financial data management method based on blockchain, including the following steps:

[0041] Step 1: Collect the node information of all nodes in the blockchain network. Based on the goals of minimizing network communication latency, maximizing data storage efficiency, and maximizing computing power utilization, divide the blockchain network into multiple sub-networks;

[0042] The said Step 1 includes the following contents:

[0043] Step 101: Collect the node information of all nodes in the blockchain network. The node information includes but is not limited to the geographical location of the node (such as country, city, data center location, etc.), network bandwidth (including uplink and downlink bandwidth), computing power (such as CPU model, number of cores, memory size, disk read and write speed, etc.), and historical transaction records;

[0044] Step 102: Preprocess the collected node information, including data cleaning (removing duplicate, invalid or abnormal data), data formatting (unifying data formats and standards), and data normalization (converting data with different dimensions into the same dimension for subsequent analysis);

[0045] Step 103: Define the objective function, including the goals of minimizing network communication latency, maximizing data storage efficiency, and maximizing computing power utilization, and set the constraint conditions: each node belongs to and only belongs to one sub-network, and construct an optimization model:

[0046] Constraint conditions: Among them, D represents minimizing network communication latency, d ij represents the communication latency between node i and node j, N represents the set of nodes, x ij , y ik and z ik are binary variables, x ij represents whether node i and node j are in the same sub-network, y ij represents whether node i belongs to sub-network k, z ik represents whether the task is assigned to node i in sub-network k, E represents maximizing data storage efficiency, S iDenote the storage capacity of node \(i\), \(R\) represents the set of sub - networks, \(U\) represents maximizing the utilization rate of computing power, and \(C\) i represents the computing power of node \(i\);

[0047] It should be noted that the computing power can be obtained through benchmark scores. Benchmark testing is a method of evaluating the computing power of nodes by running specific tasks. Common benchmark tests include CPU benchmark tests (such as SPECCPU2006), memory benchmark tests (such as STREAM), and disk benchmark tests (such as DD or FIO). The final computing power score is obtained by performing weighted calculations on the results of different benchmark tests;

[0048] Step 104: Select a solution algorithm (such as the branch - and - bound method, genetic algorithm, etc.) to solve the optimization model, obtain the optimal solution or approximate optimal solution, that is, the partitioning scheme of which sub - network each node belongs to, and divide the blockchain network into multiple sub - networks intelligently;

[0049] When in use, combine the content of Steps 101 to 104:

[0050] By dividing the blockchain network into multiple sub - networks, the storage resources of each node can be utilized more effectively. The nodes within each sub - network can jointly undertake the storage tasks, thereby dispersing the storage pressure, improving the overall storage efficiency, reducing the communication distance and hop count between nodes, and thus reducing the network communication latency.

[0051] Step Two: Collect the historical financial data, node behavior records, and interaction data with other nodes of all nodes from the blockchain network, define multiple trust - degree evaluation indicators, and for each node, calculate the trust degree of the node according to the values of its trust - degree evaluation indicators;

[0052] The above - mentioned Step Two includes the following content:

[0053] Step 201: Collect the historical financial data, node behavior records, and interaction data with other nodes of all nodes from the blockchain network, including the transaction success rate, transaction response time, node online duration, abnormal behavior records, etc.;

[0054] Step 202: According to the historical financial data, node behavior records, and interaction data with other nodes of the nodes, define multiple trust - degree evaluation indicators, including the transmission success rate, node online duration rate, power duration ratio, number of communication connections, available storage capacity ratio (benefit - type attribute, the larger the value, the better), and abnormal data ratio, packet loss rate, identity fraud ratio, bit error rate, link - establishment delay (cost - type attribute, the smaller the value, the better), etc.;

[0055] Transmission success rate: It refers to the proportion of data successfully transmitted by a node, reflecting the communication stability and reliability of the node. Node online duration rate: It refers to the proportion of the time the node is online to the total time, reflecting the activity and availability of the node. Battery duration ratio (applicable to mobile devices or IoT nodes): It refers to the proportion of the time the node's battery lasts to the total charging time, reflecting the energy management efficiency of the node. Number of communication connections: It refers to the number of communication connections established by the node simultaneously, reflecting the communication ability and network participation of the node. Available storage capacity ratio: It refers to the proportion of the currently available storage capacity of the node to the total storage capacity, reflecting the storage ability and resource utilization rate of the node;

[0056] Abnormal data ratio: It refers to the proportion of abnormal data generated by the node to the total data, reflecting the data quality and stability of the node. Packet loss rate: It refers to the proportion of data packets lost by the node during transmission, reflecting the communication quality and network stability of the node. Identity fraud ratio: It refers to the proportion of the node impersonating other nodes or conducting identity fraud in the blockchain network, reflecting the integrity and security of the node. Bit error rate: It refers to the proportion of error data generated by the node during transmission, reflecting the communication accuracy and data integrity of the node. Link establishment delay: It refers to the time required for the node to establish a communication link, reflecting the response speed and network efficiency of the node;

[0057] Step 203: Standardize the trustworthiness evaluation indicators. Among them, for the trustworthiness evaluation indicators of benefit-type attributes, the standardization calculation formula is: St_b = (X - Min) / (Max - Min). For the trustworthiness evaluation indicators of cost-type attributes, the standardization calculation formula is: St_c = (Max - X) / (Max - Min). Where St_b represents the standard value of the trustworthiness evaluation indicator of the benefit-type attribute, St_c represents the standardized value of the trustworthiness evaluation indicator of the cost-type attribute, X represents the trustworthiness evaluation indicator value, and Max and Min respectively represent the maximum and minimum values of the trustworthiness evaluation indicator value;

[0058] Benefit-type attributes: Refer to those trustworthiness evaluation indicators with larger values being better, such as transmission success rate, node online duration rate, battery duration ratio, number of communication connections, available storage capacity ratio, etc. Cost-type attributes: Refer to those trustworthiness evaluation indicators with smaller values being better, such as abnormal data ratio, packet loss rate, identity fraud ratio, bit error rate, link establishment delay, etc.;

[0059] Step 204: For each node, calculate the trustworthiness of the node according to the value of its trustworthiness evaluation indicator. The weighted average method or other suitable algorithms can be used to calculate the trustworthiness of the node. For example, the weighted average method is used to calculate the trustworthiness of the node: Where Tr represents the trustworthiness of the node, St mrepresents the standardized value of the m-th trust evaluation index, ω m represents the weight corresponding to the m-th trust evaluation index, and M represents the number of trust evaluation indices;

[0060] It should be noted that the weight setting can construct a hierarchical structure model through the analytic hierarchy process, decompose complex problems into multiple levels and factors, and then use the comparison matrix and consistency test to determine the weight, or determine the weight according to the information entropy of each index;

[0061] When in use, combine the content of steps 201 to 204:

[0062] By defining multiple trust evaluation indices and calculating the trust of nodes, credible nodes can be screened out. These credible nodes are more likely to comply with the rules in financial data management, reducing the risk of data being tampered with or maliciously attacked. Nodes with high trust are more likely to remain online in the network and provide stable services, thus enhancing the stability of the entire blockchain network.

[0063] Step 3: Extract all transaction data from the blockchain network, classify the transaction data into high-frequency transaction data and low-frequency historical data. For high-frequency transaction data, implement the full-node storage strategy, and for low-frequency historical data, implement the distributed storage strategy;

[0064] The said Step 3 includes the following content:

[0065] Step 301: Use a blockchain browser (such as Etherscan, Blockchain.info, etc.) or an API (such as the API provided by Alchemy, Infura, etc.) to extract all transaction data from the blockchain network, count the number of times each transaction data is accessed, and calculate its average access frequency;

[0066] Step 302: Preset an access frequency threshold, compare the average access frequency of each transaction data with the access frequency threshold. If the average access frequency of the transaction data is greater than the access frequency threshold, classify this transaction data as high-frequency transaction data; otherwise, classify this transaction data as low-frequency historical data. For high-frequency transaction data, implement the full-node storage strategy, that is, each node (or most nodes) will store a complete copy of these data. For low-frequency historical data, implement the distributed storage strategy;

[0067] The setting of the access frequency threshold determines which transaction data are regarded as high-frequency transaction data and which are regarded as low-frequency historical data. The access frequency threshold should be set according to the specific situation of the blockchain network and business requirements. For example, if the blockchain network is mainly used to process high-frequency transactions (such as financial transactions), the access frequency threshold may be set relatively high; if it is mainly used to store historical data (such as document notarization), the access frequency threshold may be set relatively low.

[0068] Step 303: Execute the distributed storage strategy. For each piece of low-frequency historical data, determine the storage node of this piece of low-frequency historical data. If the number of storage nodes is equal to 1, select the sub-network to which the storage node belongs; if the number of storage nodes is not equal to 1, select the sub-network to which the storage node with the highest trust level belongs.

[0069] Step 304: Obtain the trust levels of all nodes in the selected sub-network. Preset a trust level threshold, and compare the trust level of each node with the trust level threshold. If the trust level of a node is greater than the trust level threshold, classify this node as a high-trust-level node; otherwise, classify this node as a low-trust-level node.

[0070] The setting of the trust level threshold, similar to the access frequency threshold, determines which nodes are regarded as high-trust-level nodes and which are regarded as low-trust-level nodes. Initially set a trust level threshold, which can be a relatively conservative value. Through experiments and tests, adjust the trust level threshold to determine the final trust level threshold.

[0071] Step 305: For the high-trust-level nodes in the selected sub-network, save the complete copy of this piece of low-frequency historical data. For the low-trust-level nodes in the selected sub-network, perform a hash operation on this piece of low-frequency historical data to generate a hash value, and save the hash value.

[0072] When in use, combine the content of Steps 301 to 305:

[0073] By classifying transaction data into high-frequency transaction data and low-frequency historical data, and respectively executing the full-node storage strategy and the distributed storage strategy, storage space can be utilized more effectively. Since high-frequency transaction data needs to be accessed frequently, full-node storage ensures the rapid availability of data; while low-frequency historical data reduces unnecessary duplicate storage through distributed storage, thereby reducing the overall storage cost. By storing low-frequency historical data on nodes with higher trust levels and only storing the hash values of data on low-trust-level nodes, not only is the waste of storage space reduced, but also the reasonable utilization of computing resources and network resources is promoted, avoiding unnecessary computing power consumption and resource waste.

[0074] Step 4: Collect the load information metrics of the blockchain sub-network, calculate the network load value of each sub-network, compare the network load value of each sub-network with the network load threshold, and make corresponding processing according to the comparison result.

[0075] The above Step 4 includes the following:

[0076] Step 401: Use monitoring tools to collect the load information metrics of the blockchain sub-network in real time, including network bandwidth utilization rate, CPU utilization rate, memory utilization rate, etc., and calculate the network load value of each sub-network through weighted calculation based on the load information metrics;

[0077] Step 402: Preset the network load threshold, compare the network load value of each sub-network with the network load threshold. If the network load value of the sub-network exceeds the network load threshold, increase the number of nodes participating in accounting of the sub-network by a fixed ratio (such as 5% of the total number of current nodes) until the network load value does not exceed the network load threshold or reaches the node number limit. On the contrary, reduce the number of nodes participating in accounting of the sub-network by a fixed number, and ensure that the network load value of the sub-network does not exceed the network load threshold;

[0078] The setting of the network load threshold directly affects the performance and resource utilization efficiency of the system, and is adjusted according to the specific business requirements of the financial data management system, such as transaction processing speed, real-time requirements, etc. Adjust the load threshold. If the system has high requirements for transaction processing speed, the load threshold can be appropriately reduced to trigger the increase of the number of nodes earlier;

[0079] Step 403: When the network load value of the sub-network exceeds the network load threshold, based on the trustworthiness ranking of all nodes in the sub-network, select a fixed ratio of nodes with higher rankings for consensus, such as the top 20%, to improve the transaction processing speed and efficiency. When the network load value of the sub-network does not exceed the network load threshold, adopt a multi-node or full-node consensus mechanism to maintain the decentralization and security of the network;

[0080] When in use, combine the content of Steps 401 to 403:

[0081] By dynamically adjusting the number of nodes participating in accounting, it helps to optimize the resource utilization of the blockchain network. Increasing nodes when the load is heavy can make full use of network resources, reducing nodes when the load is light can reduce operating costs. When the network load is high, by selecting a fixed ratio of nodes with higher trustworthiness rankings for consensus, the transaction processing speed and efficiency can be significantly improved. When the network load is low, adopting a multi-node or full-node consensus mechanism helps to maintain the decentralization and security of the blockchain network.

[0082] Please refer to Figure 3, the present invention also provides a financial data management system based on blockchain, including: a network division and optimization module, a node trustworthiness evaluation module, a data storage strategy module, and a network load optimization module; wherein,

[0083] The network division and optimization module collects the node information of all nodes in the blockchain network, and divides the blockchain network into multiple sub-networks based on the goals of minimizing network communication latency, maximizing data storage efficiency, and maximizing computing power utilization;

[0084] The node trustworthiness evaluation module collects the historical financial data, node behavior records, and interaction data with other nodes of all nodes from the blockchain network, defines multiple trustworthiness evaluation indicators, and calculates the trustworthiness of each node according to the values of its trustworthiness evaluation indicators;

[0085] The data storage strategy module extracts all transaction data from the blockchain network, classifies the transaction data into high-frequency transaction data and low-frequency historical data, and executes a full-node storage strategy for high-frequency transaction data and a distributed storage strategy for low-frequency historical data;

[0086] The network load optimization module collects the load information indicators of the blockchain sub-networks, calculates the network load values of each sub-network, compares the network load values of each sub-network with the network load threshold, and makes corresponding processing according to the comparison results.

[0087] In the application, several formulas involved are calculated by taking their numerical values after dimensionless, and the formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation, and the coefficients in the formula are set by those skilled in the art according to the actual situation.

[0088] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution.

[0089] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] As described above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application.

Claims

1. A financial data management system and method based on blockchain, characterized by: include: Collect node information of all nodes in the blockchain network, and divide the blockchain network into multiple sub-networks based on the goals of minimizing network communication delay, maximizing data storage efficiency, and maximizing computing power utilization; Collect historical financial data, node behavior records, and interaction data with other nodes from all nodes in the blockchain network, define multiple trust evaluation indicators, and calculate the trust of each node based on the value of its trust evaluation indicator; Extract all transaction data from the blockchain network and classify the transaction data into high-frequency transaction data and low-frequency historical data. For high-frequency transaction data, implement the full-node storage strategy, and for low-frequency historical data, implement the distributed storage strategy; Collect the load information indicators of the blockchain subnetwork, calculate the network load value of each subnetwork, compare the network load value of each subnetwork with the network load threshold, and make corresponding processing based on the comparison results.

2. A blockchain-based financial data management system and method according to claim 1, characterized in that: Define the objective function, including minimizing network communication delay, maximizing data storage efficiency, and maximizing computing power utilization, and set constraints: each node belongs to and only belongs to one subnetwork, and build an optimization model: Constraints: Where D represents minimizing network communication delay, d ij represents the communication delay between node i and node j, N represents the set of nodes, x ij ,y ik and z ik is a binary variable, x ij Indicates whether nodes i and j are in the same subnetwork, y ij Indicates whether node i belongs to subnetwork k, z ik indicates whether the task is assigned to node i in subnetwork k, E represents maximizing data storage efficiency, S i represents the storage capacity of node i, R represents the set of sub-networks, U represents the maximum computing power utilization, C i represents the computing power of node i; The branch and bound method is used to solve the optimization model and determine the partitioning scheme that approximates the optimal solution, that is, the sub-network to which each node belongs, and divide the blockchain network into multiple sub-networks.

3. A blockchain-based financial data management system and method according to claim 1, characterized in that: Based on the node's historical financial data, node behavior records, and interaction data with other nodes, multiple trust evaluation indicators are defined, including transmission success rate, node online time rate, power duration ratio, number of communication connections, available storage capacity ratio, as well as abnormal data ratio, packet loss rate, identity fraud ratio, bit error rate, and link establishment delay.

4. A blockchain-based financial data management system and method according to claim 3, characterized in that: The trust evaluation index is standardized. For the trust evaluation index of benefit-type attributes, the standardized calculation formula is: St_b = (X-Min) / (Max-Min), and for the trust evaluation index of cost-type attributes, the standardized calculation formula is: St_c = (Max-X) / (Max-Min), where St_b represents the standard value of the trust evaluation index of benefit-type attributes, St_c represents the standardized value of the trust evaluation index of cost-type attributes, X represents the trust evaluation index value, and Max and Min represent the maximum and minimum values ​​of the trust evaluation index value, respectively.

5. A blockchain-based financial data management system and method according to claim 4, characterized in that: For each node, the trustworthiness of the node is calculated according to the value of its trustworthiness evaluation index: Among them, Tr represents the trust of the node, St m represents the standardized value of the mth trust evaluation index, ω m represents the weight corresponding to the mth trust evaluation index, and M represents the number of trust evaluation indicators.

6. A blockchain-based financial data management system and method according to claim 1, characterized in that: Count the number of times each transaction data is accessed, calculate its average access frequency, and compare the average access frequency of each transaction data with the access frequency threshold. If the average access frequency of the transaction data is greater than the access frequency threshold, the transaction data is classified as high-frequency transaction data; otherwise, the transaction data is classified as low-frequency historical data.

7. A blockchain-based financial data management system and method according to claim 6, characterized in that: Execute the distributed storage strategy. For each piece of low-frequency historical data, determine the storage node of the low-frequency historical data. If the number of storage nodes is equal to 1, select the sub-network to which the storage node belongs. If the number of storage nodes is not equal to 1, select the sub-network to which the storage node with the highest trust belongs. Obtain the trust of all nodes in the selected subnetwork, pre-set a trust threshold, compare the trust of each node with the trust threshold, and if the trust of the node is greater than the trust threshold, classify the node as a high-trust node, otherwise, classify the node as a low-trust node; For high-trust nodes in the selected sub-network, a complete copy of the low-frequency historical data is saved, and for low-trust nodes in the selected sub-network, a hash operation is performed on the low-frequency historical data to generate a hash value, and the hash value is saved.

8. A blockchain-based financial data management system and method according to claim 7, characterized in that: Collect the load information indicators of blockchain subnetworks, including network bandwidth utilization, CPU utilization, and memory utilization, and obtain the network load value of each subnetwork through weighted calculation; if the network load value of a subnetwork exceeds the network load threshold, increase the number of nodes participating in the bookkeeping of the subnetwork at a fixed ratio until the network load value does not exceed the network load threshold or reaches the upper limit of the number of nodes; otherwise, reduce the number of nodes participating in the bookkeeping of the subnetwork at a fixed ratio and ensure that the network load value of the subnetwork does not exceed the network load threshold.

9. A blockchain-based financial data management system and method according to claim 8, characterized in that: When the network load value of a sub-network exceeds the network load threshold, a fixed proportion of nodes with the highest ranking are selected for consensus based on the trust ranking of all nodes in the sub-network. When the network load value of a sub-network does not exceed the network load threshold, a full-node consensus mechanism is adopted.

10. A financial data management system based on blockchain, used to implement the method described in any one of claims 1 to 9, characterized in that: include: The network partitioning and optimization module collects node information of all nodes in the blockchain network and divides the blockchain network into multiple sub-networks based on the goals of minimizing network communication delay, maximizing data storage efficiency, and maximizing computing power utilization; The node trust evaluation module collects the historical financial data, node behavior records and interaction data of all nodes from the blockchain network, defines multiple trust evaluation indicators, and calculates the trust of each node based on the value of its trust evaluation indicator; The data storage strategy module extracts all transaction data from the blockchain network and classifies the transaction data into high-frequency transaction data and low-frequency historical data. For high-frequency transaction data, the full-node storage strategy is implemented, and for low-frequency historical data, the distributed storage strategy is implemented; The network load optimization module collects the load information indicators of the blockchain subnetwork, calculates the network load value of each subnetwork, compares the network load value of each subnetwork with the network load threshold, and makes corresponding processing based on the comparison results.

Images