A financial transaction verification method and system combining large model and blockchain technology

Abstract

The present application provides a financial transaction verification method and system combining a large model with blockchain technology, which associates block nodes of adjacent transaction verification blocks in the blockchain to obtain the node consensus between each group of adjacent transaction verification blocks; determines the node congestion characteristics in the blockchain through serial verification data, and determines the verification credibility of the blockchain during overload verification based on the congestion characteristics of each node and the consensus of all nodes; determines the response hysteresis of all block nodes in each transaction verification block during steady-state verification through transaction throughput and the consensus of each node; when the verification credibility is lower than the preset trust threshold, dynamically adjust the transaction verification block of the target financial transaction platform in the blockchain based on all response hysteresis. Based on the above scheme, dynamic adjustment of the transaction verification block in the blockchain can be achieved, thereby improving the credibility of blockchain verification.

Description

Technical Field

[0001] The present application relates to the field of financial transaction technology, and more specifically, to a financial transaction verification method and system that combines a large model with blockchain technology. Background Art

[0002] With the development of financial technology, financial transactions have become increasingly frequent and complex, and the resulting financial fraud has become increasingly rampant. Financial fraud has not only caused huge economic losses to financial institutions, but also seriously damaged the interests and trust of users, posing a major threat to both financial institutions and individuals. With the popularization of digital payments and online financial services, the means of fraud have become more complex and diverse. Traditional anti-fraud measures mainly rely on rule-based systems to detect abnormal behavior through predefined rules. However, this approach has limitations because fraudsters are able to constantly adjust their strategies to bypass these rules. In addition, rule-based systems are difficult to adapt to changing fraud patterns and require frequent updates and maintenance.

[0003] In existing financial transactions, blockchain transaction verification usually relies on a centralized verification mechanism. In a centralized verification mechanism, all verification power is concentrated on a single verification node. Once the network in the verification node is attacked and a fluctuation failure occurs, the blockchain may face serious security threats, resulting in low credibility of financial transaction security verification. Dynamic adjustment of transaction verification blocks according to network fluctuations of each block node can effectively improve the credibility of blockchain verification. However, how to achieve dynamic adjustment of transaction verification blocks in the blockchain to improve the credibility of blockchain verification is a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a financial transaction verification method and system that combines a large model with blockchain technology, which can realize dynamic adjustment of transaction verification blocks in the blockchain, thereby improving the credibility of blockchain verification.

[0005] In a first aspect, the present application provides a financial transaction verification method combining a large model with blockchain technology, comprising:

[0006] Collect the serial verification records of the target financial transaction platform in the blockchain to obtain serial verification data;

[0007] Based on the consensus model of the blockchain and the serial verification data, block nodes of adjacent transaction verification blocks in the blockchain are associated to obtain the node consensus between each group of adjacent transaction verification blocks;

[0008] Determine the node congestion characteristics of each transaction verification block in the blockchain during overload verification through each serial verification record in the serial verification data, and determine the verification credibility of the blockchain in the target financial transaction platform during overload verification according to the congestion characteristics of each node and the consensus of all nodes;

[0009] Obtain the parallel transaction information of each block node in the blockchain at the transaction layer, determine the transaction throughput of the blockchain in data steady state based on all the parallel transaction information, and then determine the response lag of all block nodes in each transaction verification block during steady state verification through the transaction throughput and the consensus degree of each node;

[0010] When the verification credibility is lower than a preset credibility threshold, the transaction verification block of the target financial transaction platform in the blockchain is dynamically adjusted according to all response hysteresis.

[0011] In some embodiments, based on the consensus model of the blockchain and the serial verification data, block node association is performed on adjacent transaction verification blocks in the blockchain to obtain the node consensus between each group of adjacent transaction verification blocks, specifically including:

[0012] For each group of adjacent transaction verification blocks, obtaining node verification information of the adjacent transaction verification blocks in the serial verification data;

[0013] Based on the consensus model of blockchain, the node association between the verification information of each node is consensus-associated to obtain the consensus relationship between adjacent transaction verification blocks;

[0014] The node consensus between adjacent transaction verification blocks is determined through the consensus relationship, and then the node consensus between each group of adjacent transaction verification blocks is obtained.

[0015] In some embodiments, determining the node congestion characteristics of each transaction verification block in the blockchain during overload verification through each serial verification record in the serial verification data specifically includes:

[0016] For each transaction verification block in the blockchain, all overload verification records are filtered out from each serial verification record of the serial verification data;

[0017] Determine the node congestion value of each block node in the transaction verification block during overload verification based on all overload verification records;

[0018] The node congestion characteristics of the transaction verification block during overload verification are determined according to all node congestion values, and then the node congestion characteristics of each transaction verification block in the blockchain during overload verification are obtained.

[0019] In some embodiments, determining the verification credibility of the blockchain in the target financial transaction platform during overload verification based on the congestion characteristics of each node and the consensus of all nodes specifically includes:

[0020] For each block node in the blockchain, the node congestion value of the block node is obtained from the node congestion characteristics of the transaction verification block corresponding to the block node;

[0021] Obtain the node consensus between the transaction verification block corresponding to the block node and the next transaction verification block;

[0022] Determine the consensus verification result of the block node according to the node consensus degree and the node congestion value, and then obtain the consensus verification result of each block node in the blockchain;

[0023] The verification credibility of the blockchain in the target financial transaction platform during overload verification is determined through all consensus verification results.

[0024] In some embodiments, determining the transaction throughput of the blockchain in a data steady state based on all parallel transaction information specifically includes:

[0025] For each block node in the blockchain, all parallel steady-state records are filtered out from the parallel transaction information of the block node;

[0026] Determine the concurrent transaction volume of the block node in the data steady state based on all parallel steady-state records, and then determine the concurrent transaction volume of each block node in the blockchain in the data steady state;

[0027] The transaction throughput of the blockchain in data steady state is determined based on all concurrent throughput.

[0028] In some embodiments, determining the response hysteresis of all block nodes in each transaction verification block during steady-state verification by using the transaction throughput and the consensus of each node specifically includes:

[0029] For each transaction verification block, determining the concurrent throughput of each block node in the transaction verification block in a data steady state according to the transaction throughput;

[0030] The response hysteresis of each block node during steady-state verification is evaluated through each concurrent throughput and the node consensus corresponding to the transaction verification block, and the response hysteresis value of each block node is obtained;

[0031] The response hysteresis of all block nodes in the transaction verification block during steady-state verification is determined according to all response hysteresis values, and then the response hysteresis of all block nodes in each transaction verification block during steady-state verification is obtained.

[0032] In some embodiments, dynamically adjusting the transaction verification block of the target financial transaction platform in the blockchain according to all response hysteresis specifically includes:

[0033] For each transaction verification block, the response hysteresis value of each block node in the transaction verification block is obtained from the response hysteresis of the transaction verification block;

[0034] The response lag nodes in the transaction verification block are screened out through all the response lag values, and then the response lag nodes in each transaction verification block are obtained;

[0035] Based on the pre-trained node allocation model, all response-lagging nodes are reallocated to complete the dynamic adjustment of transaction verification blocks.

[0036] In a second aspect, the present application provides a financial transaction verification system combining a large model with blockchain technology, including:

[0037] A collection module is used to collect serial verification records of the target financial transaction platform in the blockchain to obtain serial verification data;

[0038] A processing module, configured to associate block nodes of adjacent transaction verification blocks in the blockchain based on the consensus model of the blockchain and the serial verification data, and obtain a node consensus degree between each group of adjacent transaction verification blocks;

[0039] The processing module is also used to determine the node congestion characteristics of each transaction verification block in the blockchain during overload verification through each serial verification record in the serial verification data, and determine the verification credibility of the blockchain in the target financial transaction platform during overload verification according to the congestion characteristics of each node and the consensus of all nodes;

[0040] The processing module is also used to obtain parallel transaction information of each block node in the blockchain at the transaction layer, determine the transaction throughput of the blockchain in data steady state according to all the parallel transaction information, and then determine the response hysteresis of all block nodes in each transaction verification block during steady state verification according to the transaction throughput and the consensus degree of each node;

[0041] The execution module is used to dynamically adjust the transaction verification block of the target financial transaction platform in the blockchain according to all response hysteresis when the verification credibility is lower than a preset credibility threshold.

[0042] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned financial transaction verification method combining a big model and blockchain technology.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions or codes. When the instructions or codes are run on a computer, the computer implements the above-mentioned financial transaction verification method combining the big model and blockchain technology when executing.

[0044] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0045] The present application provides a financial transaction verification method and system combining a large model with blockchain technology, which collects serial verification records of a target financial transaction platform in a blockchain to obtain serial verification data; based on a consensus model of the blockchain and the serial verification data, block nodes of adjacent transaction verification blocks in the blockchain are associated to obtain the node consensus between each group of adjacent transaction verification blocks; the node congestion characteristics of each transaction verification block in the blockchain during overload verification are determined through each serial verification record in the serial verification data, and the verification credibility of the blockchain in the target financial transaction platform during overload verification is determined according to the congestion characteristics of each node and the consensus of all nodes; the parallel transaction information of each block node in the blockchain at the transaction layer is obtained, and the transaction throughput of the blockchain in data steady state is determined based on all parallel transaction information, and then the response lag of all block nodes in each transaction verification block during steady-state verification is determined through the transaction throughput and the consensus of each node; when the verification credibility is lower than a preset credibility threshold, the transaction verification block of the target financial transaction platform in the blockchain is dynamically adjusted according to all response lags.

[0046] It can be seen that in this application, when the verification credibility is lower than the preset credibility threshold, the transaction verification block of the target financial transaction platform in the blockchain is dynamically adjusted according to all response lags; first, determining the verification credibility can obtain an indicator to measure the reliability of consensus verification during overload verification of the blockchain. Combining the node congestion characteristics and the node consensus degree can evaluate the verification credibility of the system under overload conditions. By comprehensively analyzing the performance of the node under high load conditions, its reliability in the verification process is determined to ensure that it can be discovered and adjusted in time when performance anomalies are encountered, thereby improving the accuracy of verification and the overall credibility of the system. Among them, applying the consensus model to associate block nodes helps to determine the consistency of each node in the transaction verification process, which can ensure the consistency and coordination between each transaction verification block, thereby maintaining the security and stability of the system; secondly, determining the response lag can obtain a measure of the transaction verification block in the verification process. The indicator of the degree of response time delay when verifying transactions, combined with transaction throughput and node consensus, can evaluate the response lag of each block node under steady-state verification conditions. By analyzing the processing capacity and response speed of the nodes, the performance bottleneck of the system under normal operation can be effectively identified, which is convenient for subsequent optimization of the division strategy of transaction verification blocks, improving the overall processing efficiency and response capability of the blockchain, and further improving the credibility of verification. Among them, by analyzing the parallel transaction information of each block node under data steady state, the transaction throughput of the blockchain can be determined. The transaction throughput reflects the processing capacity and efficiency of the system under normal operating conditions, which provides a basis for subsequent hysteresis evaluation. Accurately understanding the transaction throughput under data steady state helps to identify and solve performance bottlenecks, thereby maintaining the credibility of blockchain verification in actual operation. In summary, based on the above scheme, dynamic adjustment of transaction verification blocks in the blockchain can be achieved, thereby improving the credibility of blockchain verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 is an exemplary flow chart of a financial transaction verification method combining a big model with blockchain technology according to some embodiments of the present application;

[0049] Figure 2 is a schematic diagram of a process for determining node congestion characteristics according to some embodiments of the present application;

[0050] Figure 3It is a schematic diagram of exemplary hardware and / or software of a financial transaction verification system combining a big model with blockchain technology according to some embodiments of the present application;

[0051] Figure 4 It is a structural diagram of a computer device for implementing a financial transaction verification method combining a big model with blockchain technology as shown in some embodiments of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] The embodiment of the present application provides a financial transaction verification method and system combining a large model with blockchain technology. The core is to associate block nodes of adjacent transaction verification blocks in the blockchain to obtain the node consensus between each group of adjacent transaction verification blocks; determine the node congestion characteristics in the blockchain through serial verification data, and determine the verification credibility of the blockchain during overload verification according to the congestion characteristics of each node and the consensus of all nodes; determine the response hysteresis of all block nodes in each transaction verification block during steady-state verification through transaction throughput and the consensus of each node; when the verification credibility is lower than the preset trust threshold, dynamically adjust the transaction verification block of the target financial transaction platform in the blockchain according to all response hysteresis. Based on the above scheme, dynamic adjustment of the transaction verification block in the blockchain can be achieved, thereby improving the credibility of blockchain verification.

[0054] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a financial transaction verification method combining a large model and blockchain technology according to some embodiments of the present application. The financial transaction verification method combining a large model and blockchain technology mainly includes the following steps:

[0055] In step 101, the serial verification records of the target financial transaction platform in the blockchain are collected to obtain serial verification data.

[0056] In specific implementation, serial verification records in the blockchain within a preset time period (the default is the most recent 1 month) are obtained in the target financial transaction platform, and the set of all serial verification records can be used as serial verification data; it should be noted that in this application, the target financial transaction platform is a selected blockchain transaction platform based on a large model; the serial verification data is a data set consisting of serial verification records within a preset time period, and the serial verification record refers to the record of serial verification of each transaction verification block in the blockchain in sequence during each financial transaction, and each serial verification record contains the verification time, verification occupied resources and verification process, and the verification process refers to the process by which each block node verifies the compliance of the financial transaction.

[0057] In step 102, block nodes of adjacent transaction verification blocks in the blockchain are associated based on the consensus model of the blockchain and the serial verification data to obtain the node consensus between each group of adjacent transaction verification blocks.

[0058] In some embodiments, based on the consensus model of the blockchain and the serial verification data, block node association is performed on adjacent transaction verification blocks in the blockchain, and the node consensus between each group of adjacent transaction verification blocks is obtained by the following steps:

[0059] For each group of adjacent transaction verification blocks, obtaining node verification information of the adjacent transaction verification blocks in the serial verification data;

[0060] Based on the consensus model of blockchain, the node association between the verification information of each node is consensus-associated to obtain the consensus relationship between adjacent transaction verification blocks;

[0061] The node consensus between adjacent transaction verification blocks is determined through the consensus relationship, and then the node consensus between each group of adjacent transaction verification blocks is obtained.

[0062] It should be noted that in this application, node consensus is an indicator that measures the consistency of adjacent transaction verification blocks in the blockchain during the node consensus process; the consensus model of the blockchain is a mechanism used to reach consensus among multiple nodes in the blockchain. The consensus model of the blockchain can ensure the validity and immutability of transactions. Common consensus models include proof of work, proof of stake, and Byzantine fault-tolerant protocols. In each consensus model, nodes reach consensus through certain rules.

[0063] In the specific implementation, first, for each group of adjacent transaction verification blocks, the node verification record of the transaction verification block in the adjacent transaction verification block is obtained in each serial verification record of the serial verification data as the block verification record, and the set of all block verification records can be used as the node verification information of the corresponding transaction verification block; then, a blockchain consensus model based on a neural network is initialized, and the verification time and verification result in each node verification information are used as the evaluation data source of the node association in the blockchain consensus model, and the node consensus process between adjacent transaction verification blocks is consensus-associated, and the result of the consensus association can be used as the consensus relationship between adjacent transaction verification blocks. The consensus relationship is the degree to which the nodes participating in the verification in the adjacent transaction verification blocks reach a consensus in the blockchain consensus process. The consensus relationship is composed of the node associations between the nodes of each block, and each node association is a decimal between 0 and 1; finally, the mean of all node associations in the consensus relationship can be used as the node consensus degree between adjacent transaction verification blocks. The node consensus degree between each group of adjacent transaction verification blocks can be obtained in the above manner.

[0064] In step 103, the node congestion characteristics of each transaction verification block in the blockchain during overload verification are determined through each serial verification record in the serial verification data, and the verification credibility of the blockchain in the target financial transaction platform during overload verification is determined based on the congestion characteristics of each node and the consensus of all nodes.

[0065] In some embodiments, the node congestion characteristics of each transaction verification block in the blockchain during overload verification are determined by each serial verification record in the serial verification data, referring to Figure 2 As described above, the figure is a schematic diagram of a process for determining node congestion characteristics in some embodiments of the present application. In this embodiment, determining the node congestion characteristics can be implemented by the following steps:

[0066] In step 1031, for each transaction verification block in the blockchain, all overload verification records are filtered out from each serial verification record of the serial verification data;

[0067] In step 1032, the node congestion value of each block node in the transaction verification block during overload verification is determined according to all overload verification records;

[0068] In step 1033, the node congestion characteristics of the transaction verification block during overload verification are determined based on all node congestion values, and then the node congestion characteristics of each transaction verification block in the blockchain during overload verification are obtained.

[0069] It should be noted that in this application, the node congestion feature is a set of indicators that reflect the importance of each transaction verification block in fraud verification; the overload verification record refers to the verification record generated when the set time or resource threshold is exceeded during the transaction verification process, reflecting the working status of the block node under high load; the node congestion value is an indicator used to quantify the degree of congestion of the block node during overload verification. The higher the node congestion value, the worse the working performance of the block node under overload conditions.

[0070] In the specific implementation, first, for each transaction verification block in the blockchain, the node verification record of the transaction verification block is obtained from each serial verification record of the serial verification data as the block verification record, and all block verification records whose verification resources exceed the overload verification threshold can be used as overload verification records; then, for each block node in the transaction verification block, the verification time of the block node during overload verification is obtained from each overload verification record, and the ratio of the verification time to the standard time can be used as the node congestion value of the block node during overload verification. The node congestion value of each block node in the transaction verification block during overload verification can be obtained in the above manner; finally, the set of all node congestion values ​​can be used as the node congestion feature of the transaction verification block during overload verification, and the node congestion feature of each transaction verification block during overload verification can be obtained in the above manner.

[0071] In some embodiments, determining the verification credibility of the blockchain in the target financial transaction platform during overload verification based on the congestion characteristics of each node and the consensus of all nodes can be achieved by the following steps:

[0072] For each block node in the blockchain, the node congestion value of the block node is obtained from the node congestion characteristics of the transaction verification block corresponding to the block node;

[0073] Obtain the node consensus between the transaction verification block corresponding to the block node and the next transaction verification block;

[0074] Determine the consensus verification result of the block node according to the node consensus degree and the node congestion value, and then obtain the consensus verification result of each block node in the blockchain;

[0075] The verification credibility of the blockchain in the target financial transaction platform during overload verification is determined through all consensus verification results.

[0076] It should be noted that verification credibility is an indicator to measure the reliability of consensus verification of blockchain during overload verification; the consensus verification result is a quantitative value of the credibility of block nodes in the node consensus process of overload verification.

[0077] In the specific implementation, first, for each block node in the blockchain, the node congestion value of the block node is obtained from the node congestion characteristics of the transaction verification block corresponding to the block node; secondly, the node consensus between the transaction verification block corresponding to the block node and the next transaction verification block is obtained; then, the ratio of the node consensus degree to the node congestion value can be used as the consensus verification result of the block node. The consensus verification result of each block node in the blockchain can be obtained by the above method. In other embodiments, other methods can also be used to determine the consensus verification result. Through the node congestion value and the node consensus degree, each block node can be more comprehensively evaluated. The performance under high load conditions is calculated to obtain the consensus verification result of each node; finally, the mean of all consensus verification results can be used as the verification credibility of the blockchain in the target financial transaction platform during overload verification. In order to improve the accuracy of the verification credibility, in other embodiments, the importance of each block node in the blockchain can be used as a weight to calculate the weighted average of all consensus verification results as the verification credibility of the blockchain in the target financial transaction platform during overload verification, where the importance of the block node in the blockchain can be determined by the computing power of the block node. The higher the computing power, the higher the importance of the block node in the blockchain.

[0078] In step 104, the parallel transaction information of each block node in the blockchain at the transaction layer is obtained, and the transaction throughput of the blockchain in the data steady state is determined based on all the parallel transaction information, and then the response lag of all block nodes in each transaction verification block during steady-state verification is determined through the transaction throughput and the consensus of each node.

[0079] It should be noted that in the present application, parallel transaction information is a data set consisting of parallel transaction records, each parallel transaction record contains the number of parallel transactions, the execution time of each parallel transaction and the amount of resources consumed; in specific implementation, for each block node in the blockchain, the parallel transaction records of the block nodes in the blockchain at the transaction layer are obtained in the target financial transaction platform, and the set of all parallel transaction records can be used as the parallel transaction information of the block nodes at the transaction layer. The parallel transaction information of each block node in the blockchain at the transaction layer can be obtained in the above manner.

[0080] In some embodiments, determining the transaction throughput of the blockchain in a data steady state based on all parallel transaction information may be implemented by the following steps:

[0081] First, for each block node in the blockchain, all parallel steady-state records are filtered out from the parallel transaction information of the block node;

[0082] Secondly, the concurrent transaction volume of the block node in the data steady state is determined based on all parallel steady-state records, and then the concurrent transaction volume of each block node in the blockchain in the data steady state is determined;

[0083] Finally, the transaction throughput of the blockchain in data steady state is determined based on all concurrent throughputs.

[0084] It should be noted that in this application, transaction throughput is a key performance indicator to measure the system processing capability; parallel steady-state records are transaction processing records under data steady-state; and concurrent transaction volume refers to the number of transactions that a block node can process simultaneously per unit time.

[0085] In the specific implementation, first, for each block node in the blockchain, the resource consumption and execution time in each parallel transaction record can be obtained from the parallel transaction information of the block node, the ratio of the resource consumption to the execution time can be used as the resource utilization rate of each parallel transaction record, and the parallel transaction record with a resource utilization rate higher than the preset standard can be used as a parallel steady-state record; then, the number of parallel transactions in each parallel steady-state record is counted as the parallel transaction volume, and the maximum value of all parallel transaction volumes can be used as the concurrent transaction volume of the block node in the data steady state. The concurrent transaction volume of each block node in the data steady state can be obtained in the above manner; finally, the set of all concurrent transaction volumes can be used as the value range of the transaction throughput, so as to obtain the transaction throughput of the blockchain in the data steady state, and the transaction throughput represents the total amount of transactions that the blockchain can process per unit time.

[0086] It should be noted that in this application, by screening parallel steady-state records, determining the concurrent transaction volume of each block node, and then calculating the overall transaction throughput of the blockchain system based on the concurrent transaction volume of all nodes, it is possible to comprehensively evaluate the processing capabilities of the blockchain under data steady-state, thereby optimizing the blockchain's transaction parallel processing capabilities, improving resource utilization, and enhancing the system's throughput performance.

[0087] In some embodiments, determining the response hysteresis of all block nodes in each transaction verification block during steady-state verification by using the transaction throughput and the consensus of each node can be implemented by the following steps:

[0088] For each transaction verification block, determining the concurrent throughput of each block node in the transaction verification block in a data steady state according to the transaction throughput;

[0089] The response hysteresis of each block node during steady-state verification is evaluated through each concurrent throughput and the node consensus corresponding to the transaction verification block, and the response hysteresis value of each block node is obtained;

[0090] The response hysteresis of all block nodes in the transaction verification block during steady-state verification is determined according to all response hysteresis values, and then the response hysteresis of all block nodes in each transaction verification block during steady-state verification is obtained.

[0091] It should be noted that response hysteresis is an indicator to measure the degree of response time delay of the transaction verification block when verifying transactions. In specific implementation, first, for each transaction verification block, all corresponding block nodes in the transaction verification block are obtained, and the value of the transaction throughput corresponding to each block node can be used as the concurrent throughput of the corresponding block node in the transaction verification block under data steady state; then, for each block node, a hysteresis evaluation model based on a neural network is initialized, and each concurrent throughput is used as the evaluation parameter of the throughput in the hysteresis evaluation model, and the node consensus corresponding to the transaction verification block is used as the response parameter in the hysteresis evaluation model. The hysteresis evaluation parameter of the response time is used to evaluate the response of the hysteresis of the block node during steady-state verification. The hysteresis value of the response evaluation can be used as the response hysteresis value of the block node. The response hysteresis value of each block node can be obtained in the above manner. The response hysteresis value is the difference between the actual processing capacity and the average processing capacity of the block node in transaction verification. Finally, the set of all response hysteresis values ​​can be used as the response hysteresis of all block nodes in the transaction verification block during steady-state verification. The response hysteresis of all block nodes in each transaction verification block during steady-state verification can be obtained in the above manner.

[0092] In step 105, when the verification credibility is lower than a preset credibility threshold, the transaction verification block of the target financial transaction platform in the blockchain is dynamically adjusted according to all response hysteresis.

[0093] In some embodiments, dynamically adjusting the transaction verification block of the target financial transaction platform in the blockchain according to all response hysteresis can be implemented by the following steps:

[0094] For each transaction verification block, the response hysteresis value of each block node in the transaction verification block is obtained from the response hysteresis of the transaction verification block;

[0095] The response lag nodes in the transaction verification block are screened out through all the response lag values, and then the response lag nodes in each transaction verification block are obtained;

[0096] Based on the pre-trained node allocation model, all response-lagging nodes are reallocated to complete the dynamic adjustment of transaction verification blocks.

[0097] In the specific implementation, first, for each transaction verification block, the response lag value of each block node in the transaction verification block is obtained from the response lag of the transaction verification block; then, all block nodes whose response lag value is lower than the preset lag threshold can be used as the response lag nodes in the transaction verification block, and all response lag nodes in each transaction verification block can be obtained in the above manner; finally, a node allocation model based on a neural network is initialized, and the node allocation model is used to reallocate all response lag nodes until the verification credibility is greater than or equal to the preset credibility threshold, thereby completing the dynamic adjustment of the transaction verification block.

[0098] In the present application, when the verification credibility is lower than the preset credibility threshold, the transaction verification block of the target financial transaction platform in the blockchain is dynamically adjusted according to all response lags; first, by determining the verification credibility, an indicator for measuring the reliability of consensus verification during overload verification of the blockchain can be obtained. The verification credibility of the system under overload conditions can be evaluated by combining the node congestion characteristics with the node consensus. By comprehensively analyzing the performance of the node under high load conditions, its reliability in the verification process is determined to ensure that performance anomalies can be discovered and adjusted in a timely manner, thereby improving the accuracy of verification and the overall credibility of the system. Among them, applying the consensus model to associate block nodes helps to determine the consistency of each node in the transaction verification process, which can ensure the consistency and coordination between each transaction verification block, thereby maintaining the security and stability of the system; secondly, determining the response lag can obtain an indicator for measuring the reliability of the transaction verification block in the verification process. The indicator of the degree of response time delay during service operation, combined with transaction throughput and node consensus, can evaluate the response lag of each block node under steady-state verification conditions. By analyzing the processing capacity and response speed of the nodes, the performance bottleneck of the system under normal operation can be effectively identified, which is convenient for subsequent optimization of the division strategy of transaction verification blocks, improving the overall processing efficiency and response capability of the blockchain, and further improving the credibility of verification. Among them, by analyzing the parallel transaction information of each block node under data steady state, the transaction throughput of the blockchain can be determined. The transaction throughput reflects the processing capacity and efficiency of the system under normal operating conditions, which provides a basis for subsequent hysteresis evaluation. Accurately understanding the transaction throughput under data steady state helps to identify and solve performance bottlenecks, thereby maintaining the credibility of blockchain verification in actual operation. In summary, based on the above scheme, dynamic adjustment of transaction verification blocks in the blockchain can be achieved, thereby improving the credibility of blockchain verification.

[0099] In addition, in another aspect of the present application, in some embodiments, the present application provides a financial transaction verification system combining a large model with blockchain technology, referring to Figure 3, which is a schematic diagram of exemplary hardware and / or software of a financial transaction verification system combining a large model and blockchain technology according to some embodiments of the present application. The financial transaction verification system combining a large model and blockchain technology includes: a collection module 201, a processing module 202 and an execution module 203, which are described as follows:

[0100] Collection module 201, in this application, collection module 201 is mainly used to collect serial verification records of the target financial transaction platform in the blockchain to obtain serial verification data;

[0101] Processing module 202, in the present application, is used to associate block nodes of adjacent transaction verification blocks in the blockchain based on the consensus model of the blockchain and the serial verification data, and obtain the node consensus between each group of adjacent transaction verification blocks;

[0102] It should be noted that the processing module 202 is also used to determine the node congestion characteristics of each transaction verification block in the blockchain during overload verification through each serial verification record in the serial verification data, and determine the verification credibility of the blockchain in the target financial transaction platform during overload verification according to the congestion characteristics of each node and the consensus of all nodes;

[0103] In addition, the processing module 202 is also used to obtain the parallel transaction information of each block node in the blockchain at the transaction layer, determine the transaction throughput of the blockchain in the data steady state according to all the parallel transaction information, and then determine the response hysteresis of all block nodes in each transaction verification block during the steady state verification through the transaction throughput and the consensus degree of each node;

[0104] Execution module 203, in the present application, execution module 203 is mainly used to dynamically adjust the transaction verification block of the target financial transaction platform in the blockchain according to all response lags when the verification credibility is lower than a preset credibility threshold.

[0105] The above describes in detail the examples of the financial transaction verification method and system combined with the large model and blockchain technology provided by the embodiments of the present application. It can be understood that the corresponding device includes a hardware structure and / or software module corresponding to each function in order to realize the above functions. It should be easily appreciated by those skilled in the art that the present application can be implemented in the form of hardware or a combination of hardware and computer software in combination with the units and algorithm steps of each example described in the embodiments disclosed herein. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0106] In some embodiments, the present application also provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned financial transaction verification method combining the big model and blockchain technology.

[0107] In some embodiments, reference Figure 4 , the dotted line in the figure indicates that the unit or module is optional. The figure is a schematic diagram of the structure of a computer device for implementing a financial transaction verification method combining a large model with blockchain technology according to an embodiment of the present application. The financial transaction verification method combining a large model with blockchain technology described in the above embodiment can be Figure 4 The computer device shown in the figure is implemented, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device can be a terminal device, a server or a chip.

[0108] The processor 301 may be a general-purpose processor or a special-purpose processor. For example, the processor 301 may be a central processing unit (CPU), which may be used to control the computer device, execute software programs, and process data of the software programs. The computer device may also include a communication unit 305 to implement signal input (reception) and output (transmission).

[0109] For example, the computer device may be a chip, the communication unit 305 may be an input and / or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other devices.

[0110] For another example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0111] The computer device may include one or more memories 302, on which a program 304 is stored. The program 304 can be executed by the processor 301 to generate instructions 303, so that the processor 301 performs the method described in the above method embodiment according to the instructions 303. Optionally, data (such as a target audit model) can also be stored in the memory 302. Optionally, the processor 301 can also read the data stored in the memory 302, and the data can be stored at the same storage address as the program 304, or the data can be stored at a different storage address from the program 304.

[0112] The processor 301 and the memory 302 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of the terminal device.

[0113] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0114] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0115] For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned financial transaction verification method combining the big model and blockchain technology when executing.

[0116] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0117] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A financial transaction verification method combining a large model with blockchain technology, characterized in that: The steps include: Collect the serial verification records of the target financial transaction platform in the blockchain to obtain serial verification data; Based on the consensus model of the blockchain and the serial verification data, block nodes of adjacent transaction verification blocks in the blockchain are associated to obtain the node consensus between each group of adjacent transaction verification blocks; Determine the node congestion characteristics of each transaction verification block in the blockchain during overload verification through each serial verification record in the serial verification data, and determine the verification credibility of the blockchain in the target financial transaction platform during overload verification according to the congestion characteristics of each node and the consensus of all nodes; Obtain the parallel transaction information of each block node in the blockchain at the transaction layer, determine the transaction throughput of the blockchain in data steady state based on all the parallel transaction information, and then determine the response lag of all block nodes in each transaction verification block during steady state verification through the transaction throughput and the consensus degree of each node; When the verification credibility is lower than a preset credibility threshold, dynamically adjusting the transaction verification block of the target financial transaction platform in the blockchain according to all response hysteresis; Among them, based on the consensus model of the blockchain and the serial verification data, the block nodes of adjacent transaction verification blocks in the blockchain are associated to obtain the node consensus between each group of adjacent transaction verification blocks, which specifically includes: For each group of adjacent transaction verification blocks, obtaining node verification information of the adjacent transaction verification blocks in the serial verification data; Based on the consensus model of blockchain, the node association between the verification information of each node is consensus-associated to obtain the consensus relationship between adjacent transaction verification blocks; Determine the node consensus between adjacent transaction verification blocks through the consensus relationship, and then obtain the node consensus between each group of adjacent transaction verification blocks; Among them, determining the node congestion characteristics of each transaction verification block in the blockchain during overload verification through each serial verification record in the serial verification data specifically includes: For each transaction verification block in the blockchain, all overload verification records are filtered out from each serial verification record of the serial verification data; Determine the node congestion value of each block node in the transaction verification block during overload verification based on all overload verification records; Determine the node congestion characteristics of the transaction verification block during overload verification based on all node congestion values, and then obtain the node congestion characteristics of each transaction verification block in the blockchain during overload verification; Among them, dynamically adjusting the transaction verification block of the target financial transaction platform in the blockchain according to all response lags specifically includes: For each transaction verification block, the response hysteresis value of each block node in the transaction verification block is obtained from the response hysteresis of the transaction verification block; The response lag nodes in the transaction verification block are screened out through all the response lag values, and then the response lag nodes in each transaction verification block are obtained; Based on the pre-trained node allocation model, all response-lagging nodes are reallocated to complete the dynamic adjustment of transaction verification blocks.

2. The method according to claim 1, characterized in that The verification credibility of the blockchain in the target financial transaction platform during overload verification is determined based on the congestion characteristics of each node and the consensus of all nodes, including: For each block node in the blockchain, the node congestion value of the block node is obtained from the node congestion characteristics of the transaction verification block corresponding to the block node; Obtain the node consensus between the transaction verification block corresponding to the block node and the next transaction verification block; Determine the consensus verification result of the block node according to the node consensus degree and the node congestion value, and then obtain the consensus verification result of each block node in the blockchain; The verification credibility of the blockchain in the target financial transaction platform during overload verification is determined through all consensus verification results.

3. The method according to claim 1, characterized in that Determining the transaction throughput of the blockchain in a data steady state based on all parallel transaction information specifically includes: For each block node in the blockchain, all parallel steady-state records are filtered out from the parallel transaction information of the block node; Determine the concurrent transaction volume of the block node in the data steady state based on all parallel steady-state records, and then determine the concurrent transaction volume of each block node in the blockchain in the data steady state; The transaction throughput of the blockchain in data steady state is determined based on all concurrent throughput.

4. The method according to claim 1, characterized in that Determining the response hysteresis of all block nodes in each transaction verification block during steady-state verification through the transaction throughput and the consensus of each node specifically includes: For each transaction verification block, determining the concurrent throughput of each block node in the transaction verification block in a data steady state according to the transaction throughput; The response hysteresis of each block node during steady-state verification is evaluated through each concurrent throughput and the node consensus corresponding to the transaction verification block, and the response hysteresis value of each block node is obtained; The response hysteresis of all block nodes in the transaction verification block during steady-state verification is determined according to all response hysteresis values, and then the response hysteresis of all block nodes in each transaction verification block during steady-state verification is obtained.

5. A financial transaction verification system combining a large model and blockchain technology, which uses the method described in any one of claims 1 to 4 to verify financial transactions, characterized in that: The financial transaction verification system comprises: A collection module is used to collect serial verification records of the target financial transaction platform in the blockchain to obtain serial verification data; A processing module, configured to associate block nodes of adjacent transaction verification blocks in the blockchain based on the consensus model of the blockchain and the serial verification data, and obtain a node consensus degree between each group of adjacent transaction verification blocks; The processing module is also used to determine the node congestion characteristics of each transaction verification block in the blockchain during overload verification through each serial verification record in the serial verification data, and determine the verification credibility of the blockchain in the target financial transaction platform during overload verification according to the congestion characteristics of each node and the consensus of all nodes; The processing module is also used to obtain parallel transaction information of each block node in the blockchain at the transaction layer, determine the transaction throughput of the blockchain in data steady state according to all the parallel transaction information, and then determine the response hysteresis of all block nodes in each transaction verification block during steady state verification according to the transaction throughput and the consensus degree of each node; The execution module is used to dynamically adjust the transaction verification block of the target financial transaction platform in the blockchain according to all response hysteresis when the verification credibility is lower than a preset credibility threshold.

6. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the financial transaction verification method combining a big model and blockchain technology as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the financial transaction verification method combining a big model with blockchain technology as described in any one of claims 1 to 4.

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