A distributed relay-verification fast transaction system

Abstract

The application discloses a kind of distributed relay verification fast transaction systems, belong to data analysis technical field.The system includes transaction control module, security management module, data acquisition module, regional management module, model training analysis module, dynamic selection calculation module;The transaction control module is used to transmit transaction data to different nodes for transaction;The security management module is used to divide the importance level of transaction data;The data acquisition module is used to collect node data and network data;The regional management module is used to calculate the network risk value in region;The model training analysis module is used to analyze the data transmission quality of transaction data under different node quantity and network risk value;The dynamic selection calculation module is used to generate the transaction instruction of different transaction data transmission path;The application improves the self-adaptive ability and anti-interference ability of system to complex network environment by real-time adjustment to transaction data transmission path.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a distributed relay verification fast transaction system. Background Technology

[0002] Distributed relay verification for fast transactions refers to a transaction processing method that relies on a distributed node cluster and relay forwarding mechanism to collaboratively verify and efficiently transmit multi-source transaction requests. The relay node, as a key hub in the network, integrates and collects request information from different transaction initiators and, based on real-time network conditions, quickly and accurately forwards transaction data to appropriate verification nodes. Verification nodes then use parallel computing and a distributed consensus mechanism to verify the security and validity of transactions. Through the optimized scheduling of relay nodes and the parallel processing of verification nodes, the transaction verification time is significantly shortened, achieving rapid transaction processing for the system.

[0003] In the process of relay verification of fast transactions, especially in transaction scenarios with extremely high requirements for timeliness and security, such as financial data, existing technologies mostly adopt a multi-node, multi-path redundant data transmission method. However, there is a lack of regional management of nodes. When some nodes are abnormal, the system usually has difficulty in determining whether it is a single node failure or a regional network failure. When multiple forwarding nodes are in the same network fluctuation area, data transmission delay may still occur due to a regional network failure. Increasing the number of nodes can improve network fault tolerance, but the increase in the number of nodes will cause the data processing time of each node to accumulate. The system has difficulty in assessing the marginal impact of node expansion on the transmission rate, resulting in low data transmission switching efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a technical solution to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a distributed relay verification fast transaction system, which includes a transaction control module, a security management module, a data acquisition module, a region management module, a model training and analysis module, and a dynamic selection calculation module; The transaction control module is used to receive transaction instructions, transmit transaction data to different nodes for transaction according to the transaction instructions, and synchronize the transaction results to the user terminal. The security management module is used to classify the importance levels of different transaction data; and send the classified importance levels of the transaction data to the dynamic selection calculation module. The data acquisition module is used to collect node data from each node during the transaction process and to collect network data generated by the transmission of transaction data in different regions. The node data includes the location information, node weight, and network congestion level of different nodes. The network data includes the transmission area, number of transmission nodes, and data transmission rate of the transaction data. The acquired node data is sent to the region management module and the model training and analysis module. The acquired network data is sent to the model training and analysis module. The region management module is used to divide the region into several independent regions based on the location information of different nodes, generate a mapping table between regions and nodes, analyze the collected node data, calculate the network risk value in different regions, and send the calculated network risk value in different regions to the model training and analysis module. The model training and analysis module is used to establish a network region analysis model based on historical node data and historical network data, and to analyze the data transmission quality of transaction data under different numbers of nodes and network risk values. The dynamic selection calculation module is used to generate trading instructions for different trading data transmission paths based on the importance level of the trading data and the network risk value in different regions, through the network region analysis model established in the model training and analysis module; and send the generated trading instructions to the trading control module.

[0006] Furthermore, the transaction control module includes an instruction receiving unit, a path selection unit, a parallel processing unit, and a feedback control unit; The instruction receiving unit is used to receive transaction instructions sent by the dynamic selection calculation module; The path selection unit is used to determine the network area for transaction data transmission and the minimum number of nodes to be received during the transaction data transmission process, based on the transaction instruction. The parallel processing unit is used to implement parallel processing of transaction data transmission between nodes; the nodes include relay nodes, verification nodes, and target chain nodes. The feedback control unit is used to receive transaction data feedback results from the verification node and the target chain node, and synchronize the transaction results to the user terminal.

[0007] Furthermore, the relay node is used to implement the transaction data transmission process, transmitting the transaction data to other nodes connected to it; the verification node is used to collectively verify the legality of the transaction through a consensus algorithm; the target chain node is used to receive the transaction data transmitted by the relay node, execute the transaction operation, and transmit the transaction result to the feedback control unit through the relay node.

[0008] Furthermore, the region management module includes a region division unit, a node data analysis unit, and a network risk value calculation unit; The region division unit is used to divide the transaction data transmission into several independent regions and determine the corresponding nodes in each region; based on the corresponding nodes in each region, a mapping table of regions and nodes is generated. The node data analysis unit is used to analyze node data to obtain the changes in node weight and network congestion level of different nodes with time stamp. The network risk value calculation unit is used to calculate the network risk value of different regions based on the node weights of different nodes and the degree of network congestion, using a mapping table between regions and nodes, according to the calculation formula: ; Where R represents the network risk value within the region; m represents the number of network congested nodes within the region; and n represents the total number of nodes within the region. This represents the sum of the weights of all nodes within the region; This represents the weighted sum of network congestion levels for all congested nodes within the region.

[0009] Furthermore, the indicators for determining the degree of network congestion include transaction data transmission delay, packet loss rate, and bandwidth utilization. Specifically, a node is judged to be congested based on a network congestion monitoring threshold. When the degree of network congestion of a node is less than the network congestion monitoring threshold, the node's network is judged to be normal. When the degree of network congestion of a node is greater than the network congestion monitoring threshold, the node's network is judged to be congested.

[0010] Furthermore, the model training and analysis module includes a historical database, a historical data analysis unit, a transaction node analysis unit, and a model management and analysis unit; The historical database is used to store the collected node data and network data as historical data. The historical data analysis unit is used to retrieve historical node data from the historical database for analysis, and through the network risk value calculation unit, to determine the changes in network risk value with timestamps in different regions; and to retrieve historical network data from the historical database for analysis to determine the changes in the transmission area, number of transmission nodes, and data transmission rate of transaction data with timestamps. The model management and analysis unit is used to establish a network region analysis model. Based on the network risk value in different regions, it analyzes the data transmission quality of transaction data under different numbers of nodes and network risk values ​​using timestamps. The analysis results of historical node data and historical network data in the historical data analysis unit are used as training data to fit a curve showing the relationship between the number of nodes and network risk value on data transmission quality. ; Where V represents the data transmission rate within the region; N represents the number of transmission nodes within the transaction data region; k represents the fan-out coefficient of the node; L represents the basic propagation delay of transaction data between nodes; P represents the processing delay of node transaction data; and a and b are both fitting constants.

[0011] Furthermore, the dynamic selection calculation module includes a node number analysis unit, an intelligent calculation unit, and an intelligent selection unit; The node number analysis unit is used to determine the minimum number of nodes required for transaction data transmission based on the importance level of the transaction data. The intelligent computing unit is used to calculate the fastest data transmission rate of transaction data in different regions based on the current network risk values ​​in different regions, through the established network region analysis module, according to the calculation formula: ; in, This indicates the fastest data transmission rate for transaction data within the region. R0 represents the minimum number of nodes required for transaction data transmission; R0 represents the network risk value within the current region. The intelligent selection unit is used to determine the fastest data transmission rate for transaction data in different regions through the intelligent computing unit. This allows you to obtain the fastest region for transaction data transmission and generate the fastest region's transmission path transaction instruction.

[0012] Furthermore, a system management platform is provided, through which administrators can view historical node data, historical network data, network risk values ​​in different regions, and transaction instructions for different transaction data transmission paths.

[0013] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: by managing nodes in a regionalized manner and calculating network risk values ​​in different regions, the impact of regional network failures on transaction data transmission is avoided, and resource waste caused by cross-regional node retrieval is reduced; by establishing a network regional analysis model, the data transmission quality of transaction data under different numbers of nodes and network risk values ​​is analyzed, and the transaction data transmission path is adjusted in real time according to the importance of the transaction data and the network risk values ​​in different regions, thereby improving the system's adaptability and anti-interference ability to complex network environments. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the structure of a distributed relay verification fast transaction system according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 The present invention provides the following technical solution: Please see Figure 1 In this embodiment, a distributed relay verification fast transaction system is provided, which includes a transaction control module, a security management module, a data acquisition module, a region management module, a model training and analysis module, and a dynamic selection calculation module. The transaction control module is used to receive transaction instructions, transmit transaction data to different nodes for transaction according to the transaction instructions, and synchronize the transaction results to the user terminal. The security management module is used to classify the importance levels of different transaction data; and send the classified importance levels of the transaction data to the dynamic selection calculation module. The data acquisition module is used to collect node data from each node during the transaction process and to collect network data generated by the transmission of transaction data in different regions. The node data includes the location information, node weight, and network congestion level of different nodes. The network data includes the transmission area, number of transmission nodes, and data transmission rate of the transaction data. The acquired node data is sent to the region management module and the model training and analysis module. The acquired network data is sent to the model training and analysis module. The region management module is used to divide the region into several independent regions based on the location information of different nodes, generate a mapping table between regions and nodes, analyze the collected node data, calculate the network risk value in different regions, and send the calculated network risk value in different regions to the model training and analysis module. The model training and analysis module is used to establish a network region analysis model based on historical node data and historical network data, and to analyze the data transmission quality of transaction data under different numbers of nodes and network risk values. The dynamic selection calculation module is used to generate trading instructions for different trading data transmission paths based on the importance level of the trading data and the network risk value in different regions, through the network region analysis model established in the model training and analysis module; and send the generated trading instructions to the trading control module.

[0017] Furthermore, the transaction control module includes an instruction receiving unit, a path selection unit, a parallel processing unit, and a feedback control unit; The instruction receiving unit is used to receive transaction instructions sent by the dynamic selection calculation module; The path selection unit is used to determine the network area for transaction data transmission and the minimum number of nodes to be received during the transaction data transmission process, based on the transaction instruction. The parallel processing unit is used to implement parallel processing of transaction data transmission between nodes; the nodes include relay nodes, verification nodes, and target chain nodes. The feedback control unit is used to receive transaction data feedback results from the verification node and the target chain node, and synchronize the transaction results to the user terminal.

[0018] Furthermore, the relay node is used to implement the transaction data transmission process, transmitting the transaction data to other nodes connected to it; the verification node is used to collectively verify the legality of the transaction through a consensus algorithm; the target chain node is used to receive the transaction data transmitted by the relay node, execute the transaction operation, and transmit the transaction result to the feedback control unit through the relay node.

[0019] Furthermore, the region management module includes a region division unit, a node data analysis unit, and a network risk value calculation unit; The region division unit is used to divide the transaction data transmission into several independent regions and determine the corresponding nodes in each region; based on the corresponding nodes in each region, a mapping table of regions and nodes is generated. The node data analysis unit is used to analyze node data to obtain the changes in node weight and network congestion level of different nodes with time stamp. The network risk value calculation unit is used to calculate the network risk value of different regions based on the node weights of different nodes and the degree of network congestion, using a mapping table between regions and nodes, according to the calculation formula: ; Where R represents the network risk value within the region; m represents the number of network congested nodes within the region; and n represents the total number of nodes within the region. This represents the sum of the weights of all nodes within the region; This represents the weighted sum of network congestion levels for all congested nodes within the region.

[0020] It should be noted that by managing nodes in a regionalized manner and calculating network risk values ​​in different regions, the impact of regional network failures on transaction data transmission is avoided, and the waste of resources caused by cross-regional node retrieval is reduced. Transaction data is transmitted in different regions, and even if a failure occurs in one region, it will not spread to the entire network, thus improving the system's fault tolerance and risk resistance.

[0021] Furthermore, the indicators for determining the degree of network congestion include transaction data transmission delay, packet loss rate, and bandwidth utilization. Specifically, a node is judged to be congested based on a network congestion monitoring threshold. When the degree of network congestion of a node is less than the network congestion monitoring threshold, the node's network is judged to be normal. When the degree of network congestion of a node is greater than the network congestion monitoring threshold, the node's network is judged to be congested.

[0022] Furthermore, the model training and analysis module includes a historical database, a historical data analysis unit, a transaction node analysis unit, and a model management and analysis unit; The historical database is used to store the collected node data and network data as historical data. The historical data analysis unit is used to retrieve historical node data from the historical database for analysis, and through the network risk value calculation unit, to determine the changes in network risk value with timestamps in different regions; and to retrieve historical network data from the historical database for analysis to determine the changes in the transmission area, number of transmission nodes, and data transmission rate of transaction data with timestamps. The model management and analysis unit is used to establish a network region analysis model. Based on the network risk value in different regions, it analyzes the data transmission quality of transaction data under different numbers of nodes and network risk values ​​using timestamps. The analysis results of historical node data and historical network data in the historical data analysis unit are used as training data to fit a curve showing the relationship between the number of nodes and network risk value on data transmission quality. ; Where V represents the data transmission rate within the region; N represents the number of transmission nodes within the transaction data region; k represents the fan-out coefficient of the node; L represents the basic propagation delay of transaction data between nodes; P represents the processing delay of node transaction data; and a and b are both fitting constants.

[0023] It should be noted that, in fitting the relationship curves of the impact of the number of nodes and network risk value on data transmission quality, the network risk value in different regions is used as the training data for R, the number of transmission nodes in the transmission region is used as the training data for N, and the data transmission rate in the transmission region is used as the training data for V. The values ​​of constants a and b are determined according to the least squares method. In this embodiment, the fan-out coefficient k of the node, the basic propagation delay L of the transaction data between nodes, and the processing delay P of the node transaction data are all fixed values.

[0024] It should be noted that by establishing a network area analysis model, the data transmission quality of transaction data under different numbers of nodes and network risk values ​​can be analyzed, thereby achieving load balancing of transaction data transmission across the entire network, reducing data congestion caused by local network risks, and improving the overall transaction data throughput of the system.

[0025] Furthermore, the dynamic selection calculation module includes a node number analysis unit, an intelligent calculation unit, and an intelligent selection unit; The node number analysis unit is used to determine the minimum number of nodes required for transaction data transmission based on the importance level of the transaction data. The intelligent computing unit is used to calculate the fastest data transmission rate of transaction data in different regions based on the current network risk values ​​in different regions, through the established network region analysis module, according to the calculation formula: ; in, This indicates the fastest data transmission rate for transaction data within the region. R0 represents the minimum number of nodes required for transaction data transmission; R0 represents the network risk value within the current region. The intelligent selection unit is used to determine the fastest data transmission rate for transaction data in different regions through the intelligent computing unit. This allows you to obtain the fastest region for transaction data transmission and generate the fastest region's transmission path transaction instruction.

[0026] It should be noted that when the number of nodes for transaction data transmission increases, data can be transmitted through multiple independent paths. In this case, attackers need to compromise more nodes simultaneously to achieve their attack objectives. However, the increased number of nodes requires more verification time, which will affect the timeliness of data transmission. Based on the different security levels of transaction data, the minimum number of nodes required for transaction data transmission is determined. By combining the network risk values ​​in different regions, the transaction data transmission is adjusted in real time. Under the premise of ensuring data transmission security, the data transmission rate is increased, thereby improving the system's adaptability and anti-interference capabilities in complex network environments.

[0027] Furthermore, a system management platform is provided, through which administrators can view historical node data, historical network data, network risk values ​​in different regions, and transaction instructions for different transaction data transmission paths.

[0028] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A distributed relay verification fast transaction system, characterized in that: The system includes a transaction control module, a security management module, a data acquisition module, a region management module, a model training and analysis module, and a dynamic selection calculation module; The transaction control module is used to receive transaction instructions, transmit transaction data to different nodes for transaction according to the transaction instructions, and synchronize the transaction results to the user terminal. The security management module is used to classify the importance levels of different transaction data; and send the classified importance levels of the transaction data to the dynamic selection calculation module. The data acquisition module is used to collect node data from each node during the transaction process and to collect network data generated by the transmission of transaction data in different regions. The node data includes the location information, node weight, and network congestion level of different nodes. The network data includes the transmission area, number of transmission nodes, and data transmission rate of the transaction data. The acquired node data is sent to the region management module and the model training and analysis module. The acquired network data is sent to the model training and analysis module. The region management module is used to divide the region into several independent regions based on the location information of different nodes and generate a mapping table between the regions and the nodes. Analyze the collected node data to calculate network risk values ​​in different areas; The calculated network risk values ​​for different regions are sent to the model training and analysis module. The model training and analysis module is used to establish a network region analysis model based on historical node data and historical network data, and to analyze the data transmission quality of transaction data under different numbers of nodes and network risk values. The model training and analysis module includes a historical database, a historical data analysis unit, and a model management and analysis unit. The historical database is used to store the collected node data and network data as historical data. The historical data analysis unit is used to retrieve historical node data from the historical database for analysis to determine the changes in network risk values ​​in different regions with timestamps; and to retrieve historical network data from the historical database for analysis to determine the changes in the transmission area, number of transmission nodes, and data transmission rate of transaction data with timestamps. The model management and analysis unit is used to establish a network region analysis model. Based on the network risk value in different regions, it analyzes the data transmission quality of transaction data under different numbers of nodes and network risk values ​​using timestamps. The analysis results of historical node data and historical network data in the historical data analysis unit are used as training data to fit a curve showing the relationship between the number of nodes and network risk value on data transmission quality. ; Where V represents the data transmission rate within the region; N represents the number of transmission nodes within the transaction data region; k represents the fan-out coefficient of the node; L represents the basic propagation delay of transaction data between nodes; P represents the processing delay of node transaction data; a and b are both fitting constants; R represents the network risk value within the region. The dynamic selection calculation module is used to generate trading instructions for different trading data transmission paths based on the importance level of the trading data and the network risk value in different regions, through the network region analysis model established in the model training and analysis module; and send the generated trading instructions to the trading control module.

2. The distributed relay verification fast transaction system according to claim 1, characterized in that: The transaction control module includes an instruction receiving unit, a path selection unit, a parallel processing unit, and a feedback control unit; The instruction receiving unit is used to receive transaction instructions sent by the dynamic selection calculation module; The path selection unit is used to determine the network area for transaction data transmission and the minimum number of nodes to be received during the transaction data transmission process, based on the transaction instruction. The parallel processing unit is used to implement parallel processing of transaction data transmission between nodes; the nodes include relay nodes, verification nodes, and target chain nodes. The feedback control unit is used to receive transaction data feedback results from the verification node and the target chain node, and synchronize the transaction results to the user terminal.

3. The distributed relay verification fast transaction system according to claim 2, characterized in that: The relay node is used to transmit transaction data to other connected nodes; the verification node is used to collectively verify the legality of the transaction through a consensus algorithm; the target chain node is used to receive the transaction data transmitted by the relay node, execute the transaction operation, and transmit the transaction result to the feedback control unit through the relay node.

4. The distributed relay verification fast transaction system according to claim 1, characterized in that: The regional management module includes a regional division unit, a node data analysis unit, and a network risk value calculation unit. The region division unit is used to divide the transaction data transmission into several independent regions and determine the corresponding nodes in each region. Generate a mapping table of regions and nodes based on the corresponding nodes in each region; The node data analysis unit is used to analyze node data to obtain the changes in node weight and network congestion level of different nodes with time stamp. The network risk value calculation unit is used to calculate the network risk value of different regions based on the node weights of different nodes and the degree of network congestion, using a mapping table between regions and nodes, according to the calculation formula: ; Where R represents the network risk value within the region; m represents the number of network congested nodes within the region; and n represents the total number of nodes within the region. This represents the sum of the weights of all nodes within the region; This represents the weighted sum of network congestion levels for all congested nodes within the region.

5. A distributed relay verification fast transaction system according to claim 4, characterized in that: The indicators for determining the degree of network congestion include transaction data transmission delay, packet loss rate, and bandwidth utilization. Specifically, a network congestion level is used to determine whether a node is experiencing network congestion. When the degree of network congestion of a node is less than the network congestion monitoring threshold, the node's network is considered to be normal. When the degree of network congestion of a node is greater than the network congestion monitoring threshold, the node's network is considered to be experiencing network congestion.

6. A distributed relay verification fast transaction system according to claim 4, characterized in that: The dynamic selection calculation module includes a node number analysis unit, an intelligent calculation unit, and an intelligent selection unit. The node number analysis unit is used to determine the minimum number of nodes required for transaction data transmission based on the importance level of the transaction data. The intelligent computing unit is used to calculate the fastest data transmission rate of transaction data in different regions based on the current network risk values ​​in different regions, through the established network region analysis module, according to the calculation formula: ; in, This indicates the fastest data transmission rate for transaction data within the region. R0 represents the minimum number of nodes required for transaction data transmission; R0 represents the network risk value within the current region. The intelligent selection unit is used to determine the fastest data transmission rate for transaction data in different regions through the intelligent computing unit. This allows you to obtain the fastest region for transaction data transmission and generate the fastest region's transmission path transaction instruction.

7. A distributed relay verification fast transaction system according to claim 1, characterized in that: It provides a system management platform, through which administrators can view historical node data, historical network data, network risk values ​​in different regions, and transaction instructions for different transaction data transmission paths.

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