A transaction data analysis system and method based on blockchain

Abstract

The present invention relates to a technical field, specifically a transaction data analysis system and method based on blockchain, the system includes an encryption verification module, a transaction structure analysis module, a dynamic relationship tracking module, a behavior pattern recognition module, a strategy simulation test module, and a transaction data security monitoring module. In the present invention, by performing encrypted hash operations on transaction data and storing the hash value in the blockchain, this solution ensures the integrity and security of the data, systematically retrieves and parses transaction node attributes, allows in-depth classification and sorting of transaction data, improves the intuitiveness and access efficiency of data presentation, monitors changes in the transaction network in real time, quickly captures market fluctuations, supports traders to respond quickly, and effectively identifies regularity and abnormal behavior by analyzing transaction patterns and changes in amounts, enhances risk management, simulates transaction strategies under different market environments and performs real-time optimization, and improves the adaptability and success rate of strategies.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a transaction data analysis system and method based on blockchain. Background Art

[0002] The field of transaction analysis technology involves the use of advanced data analysis tools to parse, evaluate and predict the behavior and trends of transaction data. The technology in this field can not only process and analyze data, but also provide real-time feedback and predictions to help decision makers make more informed choices. In B2B commodity trading, transaction analysis technology is particularly important because such markets are highly volatile and the transaction amounts are huge. The application of technology includes algorithmic trading, risk management, price forecasting and market trend analysis. By integrating data from multiple sources, transaction analysis technology can optimize trading strategies, reduce costs and increase market transparency.

[0003] Among them, the transaction data analysis system and method refers to a set of systems and related methods for analyzing and processing transaction data. Its main purpose is to assist enterprises and individuals in understanding market dynamics and optimizing transaction decisions through in-depth analysis of historical and real-time transaction data. The system uses data mining and statistical analysis techniques to extract valuable information from large-scale transaction data, identify market trends, assess transaction risks, and predict future market behavior. This system is particularly important in the fields of financial markets, stocks and commodity trading, helping traders and enterprises to manage resources more effectively, reduce risks, and improve the success rate of transactions.

[0004] Existing technologies lack sufficient security and real-time performance in the processing and analysis of transaction data. For example, in the B2B commodity trading market, data needs to be processed centrally. This method has deficiencies in data security and real-time response, and is vulnerable to external attacks or internal leaks. Existing technologies cannot effectively update market changes in real time when processing huge amounts of data, resulting in the data on which trading decisions are based no longer reflecting the true state of the current market. This delay leads to significant economic losses or missed opportunities in the decision-making process. Although existing analytical tools can process and analyze data, they lack sufficient flexibility and accuracy in predicting market trends and managing trading risks, and cannot effectively foresee and respond to abnormal behaviors and potential risks in the market. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a transaction data analysis system and method based on blockchain.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution: A transaction data analysis system based on blockchain includes:

[0007] The encryption verification module receives transaction data, performs hash operations on each piece of data, stores the hash value in the blockchain, and securely links the data to generate an encrypted data index;

[0008] The transaction structure analysis module uses the encrypted data index to retrieve transaction nodes and link information, parse node attributes and transaction data, sort and sort by association strength, determine the display method, and obtain the transaction network structure description;

[0009] The dynamic relationship tracking module monitors the node and edge updates in the network based on the transaction network structure description, records state changes, captures dynamic changes, and constructs a dynamic transaction network state;

[0010] The behavior pattern recognition module uses the dynamic transaction network state to analyze the transaction pattern between nodes, identify regularity and abnormal transaction behavior, and obtain behavior pattern analysis results;

[0011] The strategy simulation test module simulates the trading strategy under the differentiated market environment according to the analysis results of the behavior pattern, tests and adjusts the strategy, and forms the optimization results of the trading monitoring strategy;

[0012] The transaction data security monitoring module uses the strategies in the transaction monitoring strategy optimization results to monitor the transaction process, perform security detection, identify and respond to security threats, and form a transaction security assessment result.

[0013] As a further solution of the present invention, the encrypted data index includes transaction ID, hash value, and data block link; the transaction network structure description includes transaction node list, node transaction frequency, and transaction amount correlation; the dynamic transaction network status includes node activity indicators, information on newly added edges, and time stamps of edges; the behavior pattern analysis results include common transaction patterns, abnormal transaction characteristics, and transaction frequency statistics; the transaction monitoring strategy optimization results include transaction threshold adjustment, risk control strategy, and strategy adjustment feedback; the transaction security assessment results include security detection records, abnormal transaction responses, and monitoring strategy updates.

[0014] As a further solution of the present invention, the encryption verification module includes:

[0015] The transaction data receiving submodule receives transaction data, performs format verification and content review, verifies data compliance, classifies and marks the data, and obtains and verifies transaction data;

[0016] The hash operation submodule performs hash conversion on the verification transaction data according to the verification transaction data and adopts the encryption logic of the tag data, strengthens the confidentiality of the data by adjusting the hash parameters, and constructs a transaction hash value;

[0017] The blockchain link storage submodule uses the transaction hash value to perform a link operation with the current blockchain, and performs the operation by comparing and verifying the continuity and integrity of the hash value, thereby strengthening the immutability and traceability of the data and obtaining an encrypted data index.

[0018] As a further solution of the present invention, the transaction structure analysis module includes:

[0019] The transaction node retrieval submodule performs the retrieval of transaction nodes based on the encrypted data index, identifies each node and the interconnected transaction chain, scans and tracks the connection path through the node identifier, and generates a node information map;

[0020] The node attribute analysis submodule analyzes the transaction attributes of each node, including the transaction amount and transaction number, through the node information map, compares the data with the transaction frequency and type, distinguishes and classifies the nodes, and performs dynamic data matching and attribute tagging to obtain classified associated data;

[0021] The data display customization submodule uses the classified associated data to determine the data sorting and display format, optimizes the display logic by analyzing the transaction intensity and data density between nodes, and obtains a description of the transaction network structure.

[0022] As a further solution of the present invention, the dynamic relationship tracking module includes:

[0023] The state recording submodule monitors the changes of nodes and edges in the transaction network in real time based on the transaction network structure description, records the network status at each time point, including node addition and reduction, edge change and attribute update, and stores the data in a structured log to generate a network element status log;

[0024] The change capture submodule identifies key change events through the network element status log, marks the increase or decrease of nodes, the adjustment of connection relationships, evaluates the overall network dynamic trend, and obtains network dynamic analysis results;

[0025] The network construction submodule reconstructs the network structure, updates the connection logic of nodes and edges, verifies data consistency and reconstructs the network topology, and constructs a dynamic transaction network state according to the network dynamic analysis results.

[0026] As a further solution of the present invention, the behavior pattern recognition module includes:

[0027] The transaction pattern analysis submodule monitors the transaction frequency and amount between nodes based on the dynamic transaction network status, and quantitatively analyzes the fluctuation of the activity cycle and transaction scale of each node by evaluating the time changes of transaction volume and frequency, and constructs a pattern trend map;

[0028] The abnormal behavior detection submodule uses the pattern trend map to analyze the differences between the transaction behaviors of multiple nodes and the average pattern, identifies statistical anomalies by setting key deviation thresholds, identifies behaviors that deviate from the normal transaction pattern, and obtains abnormal behavior markers;

[0029] The behavior result integration submodule applies the random forest algorithm based on the data marked with abnormal behaviors to evaluate the transaction behaviors with strong regularity and abnormal behaviors, and forms the behavior pattern analysis results by cross-validating multiple types of data.

[0030] As a further solution of the present invention, the formula of the random forest algorithm is as follows:

[0031]

[0032] in, is the Gini impurity, For the The proportion of class labels in nodes, For the The weight coefficient of the class label, is the regularization parameter, For the The standard deviation of the feature, For the The average value of the features.

[0033] As a further solution of the present invention, the strategy simulation test module includes:

[0034] The market environment simulation submodule sets market volatility parameters and transaction behavior patterns based on the behavior pattern analysis results, simulates differentiated market conditions, including high volatility and low volatility market environments, and generates simulation environment parameters by adjusting market supply and demand dynamics and transaction frequency parameters;

[0035] The strategy testing submodule uses the simulation environment parameters to test a variety of trading strategies, monitor the performance of the strategies in the differentiated simulation environment, and evaluate the strength and weakness of the strategies by recording the profit and loss results and market adaptive response of the strategies, and obtain the strategy testing results;

[0036] The strategy adjustment submodule analyzes the performance indicators and failure points of the strategy based on the data in the strategy test results, adjusts the key parameters and trading logic of the strategy and strengthens the market response capability, tests the adjusted strategy and verifies the effect, and obtains the optimization results of the trading monitoring strategy.

[0037] As a further solution of the present invention, the transaction data security monitoring module includes:

[0038] The strategy optimization submodule evaluates the optimization results of the transaction monitoring strategy, adjusts the monitoring parameters and matches the emerging transaction patterns, dynamically updates the strategy parameters to match the current transaction behavior and security requirements, and cyclically adjusts and tests the parameters to obtain the strategy adjustment results;

[0039] The security detection submodule uses the strategy adjustment results to monitor transaction activities, set key monitoring points and capture abnormal transaction behaviors, conduct continuous security assessments on data, including abnormal detection of transaction frequency and amount, and generate security risk identification results;

[0040] The response identification submodule executes a reaction mechanism based on the security risk identification result, responds to the classified threat level, adjusts and optimizes the security defense strategy, responds to and mitigates security threats, and constructs a transaction security assessment result.

[0041] A transaction data analysis method based on blockchain, wherein the transaction data analysis method based on blockchain is performed based on the above-mentioned transaction data analysis system based on blockchain, and comprises the following steps:

[0042] S1: By receiving transaction data, asymmetric encryption is performed on each piece of data, and the encrypted data is recorded one by one in the blockchain database to obtain a secure transaction archive;

[0043] S2: by parsing the encrypted data in the secure transaction archive, identifying the connection between multiple transaction nodes, using a graph data structure to map the node relationship, classifying and weighting the nodes according to the transaction frequency and amount, and constructing a transaction relationship network diagram;

[0044] S3: Based on the transaction relationship network diagram, monitor the dynamic changes of key nodes and connections, including newly established connections and updated transaction nodes, record the changes and analyze the evolution of network behavior to form network change results;

[0045] S4: Analyze the data recorded in the network change results to capture abnormal patterns in transaction frequency and transaction amount, including potential risks and fraudulent behaviors, determine abnormal standards through statistical analysis, and create abnormal activity indicators;

[0046] S5: Based on the abnormal activity indicators, formulate corresponding monitoring strategies and combat the identified risk behaviors, cyclically adjust and optimize strategies and match new trading environments and potential threats, and establish a trading monitoring strategy framework through continuous monitoring and strategy adjustment.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by performing encrypted hash operations on transaction data and securely storing the hash value in the blockchain, data integrity and immutability are ensured. By systematically retrieving and parsing the attributes of transaction nodes, the strategy allows for a deeper classification and organization of transaction data, making the presentation of data more intuitive, thereby optimizing the efficiency of data use and access speed. Real-time monitoring of the dynamic changes of the transaction network can instantly capture market fluctuations and trends, which is particularly critical in a rapidly changing market environment because it allows traders to respond quickly to market changes. By analyzing transaction patterns and amount change trends, it is possible to effectively identify regular and abnormal transaction behaviors, providing a powerful tool for risk management. By simulating transaction strategies under different market environments and optimizing response mechanisms, this real-time adjustment and testing improves the adaptability and effectiveness of transaction strategies, and ultimately uses strategies for actual transactions to better manage risks and increase success rates. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a system flow chart of the present invention;

[0050] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0051] Figure 3 This is a flow chart of the encryption verification module in the present invention;

[0052] Figure 4 This is a flow chart of the transaction structure analysis module in the present invention;

[0053] Figure 5 is a flow chart of the dynamic relationship tracking module in the present invention;

[0054] Figure 6 is a flow chart of the behavior pattern recognition module in the present invention;

[0055] Figure 7 This is a flow chart of the strategy simulation test module in the present invention;

[0056] Figure 8 This is a flow chart of the transaction data security monitoring module in the present invention;

[0057] Fig. 9 It is a schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0060] Embodiment 1

[0061] See also Figure 1 , 2 The present invention provides a technical solution: a transaction data analysis system based on blockchain includes:

[0062] The encryption verification module receives transaction data, performs hash operations on each piece of data, stores the hash value in the blockchain, and securely links the data to obtain the encrypted data index;

[0063] The transaction structure analysis module retrieves and extracts information about multiple transaction nodes and links through encrypted data indexes, parses the attributes of transaction nodes, matches related transaction data, classifies and sorts the transaction data, sorts the sorted data according to the strength of association between nodes, determines the display format of the data based on the sorting results and node relationships, and obtains a description of the transaction network structure;

[0064] The dynamic relationship tracking module is based on the transaction network structure description, monitors the addition and update of nodes and edges in the network in real time, implements continuous status recording, and captures the dynamic changes of the network to build a dynamic transaction network state;

[0065] The behavior pattern recognition module uses the dynamic transaction network status to analyze the transaction patterns between nodes, including the changing trends of frequency and transaction amount, detect and identify regular and abnormal transaction behaviors, and obtain the behavior pattern analysis results;

[0066] The strategy simulation test module simulates trading strategies in various market environments based on the results of behavioral pattern analysis, tests trading strategies one by one, adjusts the strategy response mechanism based on the test results, and forms the optimization results of trading monitoring strategies;

[0067] The transaction data security monitoring module uses the strategies proposed in the transaction monitoring strategy optimization results to monitor the transaction execution process, conduct security checks on transaction activities, identify and respond to potential security threats, and form transaction security assessment results.

[0068] The encrypted data index includes transaction ID, hash value, and data block link. The transaction network structure description includes transaction node list, node transaction frequency, and transaction amount correlation. The dynamic transaction network status includes node activity indicators, information on newly added edges, and edge time stamps. The behavior pattern analysis results include common transaction patterns, abnormal transaction characteristics, and transaction frequency statistics. The transaction monitoring strategy optimization results include transaction threshold adjustment, risk control strategy, and strategy adjustment feedback. The transaction security assessment results include security detection records, abnormal transaction responses, and monitoring strategy updates.

[0069] Specifically, if Figure 2 , 3 As shown, the encryption verification module includes:

[0070] The transaction data receiving submodule receives transaction data, performs format verification and content review, verifies data compliance, classifies and marks the data, and obtains and verifies the execution process of transaction data as follows;

[0071] By receiving transaction data, the format of the access data is first accurately checked to ensure that the data follows the preset template and type specifications, such as checking whether the transaction data contains the necessary transaction ID and timestamp. The system verifies the legality of the content through a series of rules, such as whether the transaction amount is within the legal range, whether the status of the accounts of both parties is activated, and evaluates the compliance of the data. By calling the internal compliance database and comparing it with external regulations, it is confirmed whether the transaction violates any laws and regulations. The verified data is classified and marked according to its attributes, such as distinguishing domestic and foreign transactions, large or small transactions. The system constructs and outputs the verified transaction data for subsequent encryption processing and blockchain records. The formula used is as follows:

[0072]

[0073] in, Represents the hash value calculation of the verified transaction data. and is the adjustment factor, is the data security level. A high level enhances data confidentiality. This formula is calculated by weight coefficient and Through adjustments, data security can be dynamically managed to effectively respond to scenarios with different security requirements.

[0074] The hash operation submodule performs hash conversion on the verified transaction data based on the encryption logic of the marked data, and strengthens the confidentiality of the data by adjusting the hash parameters. The execution process of constructing the transaction hash value is as follows;

[0075] According to the verification transaction data, the preset tag data encryption logic is first used, such as the SHA-256 algorithm, to ensure data integrity and consistency. The module performs hash conversion on each transaction data, which includes encryption of key information such as transaction amount and timestamp. In order to further enhance data confidentiality, the system dynamically adjusts hash parameters through the user-set interface, such as changing the number of iterations or obfuscation parameters of the hash algorithm. After the hash calculation is completed, the generated transaction hash value is stored in a secure and irreversible format for use by the blockchain link storage submodule. The formula used is as follows:

[0076]

[0077] in, Represents the hash operation of the verified transaction data. and represents the adjustment factor, It is the encryption strength parameter in the hashing process. Adjusting the parameter can enhance the complexity and security of the hash.

[0078] The blockchain link storage submodule uses the transaction hash value to perform the link operation with the current blockchain, and performs it by comparing and verifying the continuity and integrity of the hash value, strengthening the immutability and traceability of the data. The execution process of obtaining the encrypted data index is as follows;

[0079] The transaction hash value is used to perform the link operation with the current blockchain. First, the system checks the continuity of the new transaction hash value and the hash value of the previous block through a complex hash comparison algorithm to ensure that the data chain is seamless and not tampered with. On the basis of verifying continuity and integrity, the module uses an encryption algorithm to block the block data to ensure that the newly added block cannot be illegally modified. The system builds and indexes new block information, and ensures the uniqueness and traceability of the block through a specific algorithm, thereby achieving secure storage and efficient indexing of the blockchain. The formula used is as follows:

[0080]

[0081] in, Represents the calculation of the hash value continuity from the previous block to the current block, and is the adjustment factor, Representing the integrity indicator of the blockchain, by adding weight parameters, the calculation and protection of blockchain integrity are enhanced to adapt to changing security needs.

[0082] Specifically, if Figure 2 , 4 As shown, the transaction structure analysis module includes:

[0083] The transaction node retrieval submodule performs transaction node retrieval based on the encrypted data index, identifies each node and the interconnected transaction chain, scans and tracks the connection path through the node identifier, and generates the node information map. The execution process is as follows;

[0084] Based on the encrypted data index, we first use an efficient retrieval algorithm to quickly locate the transaction node and perform a deep scan of the encrypted data index. The system uses node identifiers, such as transaction ID and timestamp, to accurately identify each node and its position in the transaction chain. The module uses graph database technology to track the connection paths between nodes to ensure the accuracy and efficiency of the tracking process. The system generates a detailed node information map, which shows all nodes and their connection relationships, providing a basis for further attribute analysis and data matching. The formula used is as follows:

[0085]

[0086] in, Represents the node Function tracking and path determination, and is the adjustment factor, Representing the strength or frequency of the link, the formula enhances the accuracy of node tracking and the information richness of the graph by adjusting the weights of the node connection paths.

[0087] The node attribute analysis submodule analyzes the transaction attributes of each node through the node information map, including the transaction amount and transaction number, compares the data with the transaction frequency and type, distinguishes and classifies the nodes, and performs dynamic data matching and attribute tagging to obtain the execution process of classified associated data as follows;

[0088] Through the node information map, we first analyze the core transaction attributes of each node, such as transaction amount and transaction number. The module uses data mining technology to compare the data of each node with the historical transaction frequency and type in detail, identify the specific behavior patterns and transaction habits of the node, and systematically distinguish and classify different nodes, such as high-frequency transaction nodes and large-amount transaction nodes, and dynamically match and attribute the nodes. The process not only improves the availability of data, but also strengthens the accuracy of node classification. The formula used is as follows:

[0089]

[0090] in, Representative Node Calculation of transaction characteristics, and is the weight coefficient, Represents data matching, which improves the accuracy and efficiency of node attribute parsing by weighting transaction characteristics and data matching.

[0091] The data display customization submodule uses classified associated data to determine the data sorting and display format. It optimizes the display logic by analyzing the transaction intensity and data density between nodes. The execution process of obtaining the transaction network structure description is as follows;

[0092] Using classified associated data, the system first sorts the data, ranking the nodes according to their transaction frequency and transaction amount. The module determines the display form of the data, such as charts or graphical interfaces, which are optimized according to the transaction intensity and data density between nodes. The module effectively displays the structure of the transaction network, making the transaction relationship clear at a glance while optimizing the user's visual experience and data interpretation efficiency. The formula used is as follows:

[0093]

[0094] in, Represents the node The display order, and is the adjustment factor, The choice of representative display media optimizes the logic and effect of data display by adjusting the display order and the weight of the media, and enhances the efficiency of information communication and visual appeal.

[0095] Specifically, if Figure 2 , 5 As shown, the dynamic relationship tracking module includes:

[0096] The status recording submodule monitors the changes of nodes and edges in the transaction network in real time based on the transaction network structure description, records the network status at each time point, including node addition and reduction, edge change and attribute update, and stores the data in structured logs. The execution process of generating network element status logs is as follows;

[0097] Based on the description of the transaction network structure, we first use high-performance monitoring tools to monitor the dynamic changes of nodes and edges in the transaction network in real time. This module carefully records the changes in network status at each time point, including the addition of new nodes, the exit of existing nodes, the increase and decrease of edges, and the update of attributes. Each change is encoded through a specific algorithm and converted into a structured log format that is easy to analyze. The module stores the encoded data in a specially designed log system and generates a detailed network element status log to provide data support for historical tracking and future analysis of network status. The formula used is as follows:

[0098]

[0099] in, Represents the time point The network status code, and is the adjustment factor, Representing the update frequency, the state recording submodule can dynamically adjust the density and detail of log records according to the frequency of network state changes to effectively capture key state changes.

[0100] The change capture submodule uses the network element status log to identify key change events, mark the increase or decrease of nodes, and the adjustment of connection relationships, evaluate the overall network dynamic trend, and obtain the execution process of network dynamic analysis results as follows;

[0101] Through the network element status log, the system first uses advanced data analysis technology to identify key change events, such as the increase or decrease of nodes and important adjustments to connection relationships. The module analyzes the impact of these key events on the transaction network and evaluates the dynamic trend of the overall network. The module marks the active nodes and key connection paths in the network, and then generates comprehensive network dynamic analysis results. The results help network administrators understand the evolution of the network and provide a scientific basis for subsequent network optimization and decision-making. The formula used is as follows:

[0102]

[0103] in, Representative log Evaluation of change events in and is the weight coefficient, Represents the time window. By adjusting the time window and the evaluation weight, the change capture submodule can more accurately identify and respond to important changes in the network.

[0104] The network construction submodule reconstructs the network structure, updates the connection logic of nodes and edges, verifies data consistency and reconstructs the network topology according to the results of network dynamic analysis, and the execution process of constructing the dynamic transaction network state is as follows;

[0105] According to the results of network dynamic analysis, the connection logic of network nodes and edges that need to be updated or reconstructed is first determined. The module uses algorithms to detect data consistency to ensure that all network activities and changes meet predefined data standards. The module reconstructs the network topology and adjusts the relationship between nodes and edges according to real-time analysis results to build a network state that conforms to the current transaction dynamics. By simulating and verifying various network construction schemes, it ensures that the newly constructed network state is both efficient and stable. The formula used is as follows:

[0106]

[0107] in, Represents the results of dynamic analysis The network model construction, and is the adjustment factor, Represents the balance of topology construction. By weighing the dynamic analysis results and topology balance, the network construction process is optimized to ensure that the new network can efficiently respond to transaction needs.

[0108] Specifically, if Figure 2 , 6 As shown, the behavior pattern recognition module includes:

[0109] The transaction pattern analysis submodule monitors the transaction frequency and amount between nodes based on the dynamic transaction network status. By evaluating the time changes of transaction volume and frequency, it quantitatively analyzes the activity cycle of each node and the fluctuation of transaction scale. The execution process of constructing the pattern trend map is as follows;

[0110] Based on the dynamic transaction network status, we first use a complex monitoring algorithm to continuously monitor the transaction frequency and amount between nodes in the network. The system collects and analyzes data to evaluate the changes in transaction volume and frequency in different time periods. Using time series analysis technology, the module quantitatively analyzes the activity cycle of each node and tracks the fluctuation trend of transaction scale. The module constructs a pattern trend map, showing the periodicity of node activities in the entire transaction network and the trend of scale changes, which provides a basis for further behavioral analysis. The formula used is as follows:

[0111]

[0112] in, Represents the quantitative analysis results of transaction frequency, The quantitative analysis results of the representative transaction amount, and It is an adjustment factor that enhances the understanding and prediction of transaction pattern trends by combining analysis of transaction frequency and amount.

[0113] The abnormal behavior detection submodule uses the pattern trend map to analyze the differences between the transaction behaviors of multiple nodes and the average pattern. It identifies statistical anomalies by setting key deviation thresholds and marks behaviors that deviate from the normal transaction pattern. The execution process of obtaining abnormal behavior tags is as follows;

[0114] Using the pattern trend map, the system first conducts a detailed analysis of the transaction behaviors of multiple nodes and compares the differences between these behaviors and the average transaction pattern. By setting key deviation thresholds, the module can identify statistically significant anomalies, such as nodes with sudden increases or decreases in transaction frequency or amount. The identification process is based on advanced statistical analysis technology, which can effectively identify transaction patterns that deviate from the norm and provide important information for further risk management and monitoring. The formula used is as follows:

[0115]

[0116] in, represents the analysis of deviations from the average pattern, represents the deviation analysis of a single node, and is the weight coefficient. The formula enhances the recognition accuracy and response speed of abnormal behaviors by fine-tuning the deviation threshold.

[0117] The behavior result integration submodule uses the random forest algorithm to evaluate the regular trading behaviors and abnormal behaviors based on the data marked by abnormal behaviors. By cross-validating multiple types of data, the execution process of the behavior pattern analysis results is as follows;

[0118] Based on the data marked with abnormal behavior, the system uses advanced data analysis models, such as logistic regression, to evaluate regular trading behaviors and identify abnormal behaviors. The module cross-validates multiple types of data and comprehensively analyzes the patterns of trading behaviors to form comprehensive behavior pattern analysis results. Through algorithm optimization and model adjustment, the accuracy and reliability of the analysis results are ensured, providing a scientific basis for security monitoring and decision support of trading networks. By weighting the verification results of normal and abnormal behaviors, the comprehensiveness and depth of behavior pattern analysis are improved.

[0119] The formula for the random forest algorithm is as follows:

[0120]

[0121] in, is the Gini impurity, For the The proportion of class labels in nodes, For the The weight coefficient of the class label, is the regularization parameter, For the The standard deviation of the feature, For the The average value of the features.

[0122] The execution process is as follows:

[0123] Initialize each category label Weight , weights can be set based on the frequency of the category or other business logic, and then the proportion of each category in the node is calculated , using the weight coefficient With proportion The square of is multiplied to adjust the influence of each category in the impurity calculation and introduce a regularization term, where The value is determined by cross-validation to optimize the generalization ability of the model and calculate each feature Standard Deviation and average , and the ratio multiplied by the regularization parameter is added to the Gini impurity formula to increase the model's sensitivity to the distribution of data features. The entire process combines the newly introduced parameters with the original Gini impurity calculation to achieve the goal of more accurately evaluating decision tree nodes.

[0124] Specifically, if Figure 2 , 7 As shown, the strategy simulation test module includes:

[0125] The market environment simulation submodule sets market volatility parameters and trading behavior patterns based on the behavior pattern analysis results, simulates differentiated market conditions, including high volatility and low volatility market environments, and generates the execution process of simulation environment parameters by adjusting market supply and demand dynamics and trading frequency parameters as follows;

[0126] Based on the results of behavioral pattern analysis, market volatility parameters are first set. The parameters are based on historical data and forecasting models to determine the characteristics of high-volatility and low-volatility market environments. The module simulates differentiated market conditions and simulates economic activities under different market conditions by dynamically adjusting market supply and demand dynamics and transaction frequency parameters. The process generates simulation environment parameters through real-time data streams and market simulation algorithms to ensure that the simulated market environment can accurately reflect the real trading environment under different market volatility conditions. The improved formula is:

[0127]

[0128] in, Represents the generated simulation environment parameters, represents the market volatility parameter, represents the transaction frequency parameter, and is the adjustment coefficient. This formula adjusts the weights of market volatility and transaction frequency to make the simulation environment more realistically reflect market conditions.

[0129] The strategy testing submodule uses simulation environment parameters to test a variety of trading strategies, monitor the performance of strategies in differentiated simulation environments, and evaluate the strength and weakness of strategies by recording the profit and loss results and market adaptability of strategies. The execution process for obtaining strategy testing results is as follows;

[0130] Using simulation environment parameters, a variety of trading strategies are fully tested. This module monitors the performance of each strategy in a differentiated simulation environment, including the profit and loss results of the strategy and the market adaptability response. Through testing, the module evaluates the strength and weakness of each strategy, systematically collects and analyzes strategy performance data, and generates strategy test results. The process not only verifies the effectiveness of the strategy, but also provides data support for further optimization. The formula used is as follows:

[0131]

[0132] in, represents the strategy testing results, represents the profit and loss results of the strategy, represents the market's adaptive response, and is the adjustment factor. This formula effectively evaluates the overall performance of the strategy by weighting the profit and loss results and market adaptability.

[0133] The strategy adjustment submodule analyzes the performance indicators and failure points of the strategy based on the data in the strategy test results, adjusts the key parameters and trading logic of the strategy and strengthens the market response capabilities, tests the adjusted strategy and verifies the effect, and obtains the execution process of the transaction monitoring strategy optimization results as follows;

[0134] According to the data in the strategy test results, the performance indicators and failure points of the strategy are analyzed first. The module adjusts the key parameters and trading logic of the strategy to strengthen the market response ability of the strategy. Through further testing and effect verification of the adjusted strategy, the module ensures that the strategy optimization can improve trading efficiency and profitability in the actual market environment. The module continuously iterates and optimizes the strategy, and finally obtains the optimization results of the trading monitoring strategy. The formula used is as follows:

[0135]

[0136] in, Represents the optimization results of the transaction monitoring strategy, Represents performance indicators, represents the effect of strategy adjustment, and is the adjustment coefficient. This formula ensures the effectiveness of strategy adjustment and the ability to adapt to the market by optimizing the weights of performance indicators and adjustment effects.

[0137] Specifically, if Figure 2 , 8 As shown, the transaction data security monitoring module includes:

[0138] The strategy optimization submodule evaluates the optimization results of the transaction monitoring strategy, adjusts the monitoring parameters and matches the emerging transaction patterns, dynamically updates the strategy parameters to match the current transaction behavior and security requirements, and cyclically adjusts and tests the parameters to obtain the execution process of the strategy adjustment results as follows;

[0139] To evaluate the optimization results of the transaction monitoring strategy, first adjust the monitoring parameters to match the emerging transaction patterns. The module dynamically updates the strategy parameters to ensure that the strategy can adapt to the current transaction behavior and security requirements. By cyclically adjusting and testing each parameter, the module refines and optimizes the strategy to ensure that each adjustment is based on the feedback of actual transaction data and patterns, thereby continuously improving the effectiveness of the strategy and finally obtaining the strategy adjustment results. The formula used is as follows:

[0140]

[0141] in, Represents the result of strategy adjustment, Represents the monitoring parameter adjustment, Represents the adaptability of emerging transaction models, and is the adjustment coefficient. The formula ensures that the strategy can respond to market changes efficiently by adjusting the weights of monitoring parameters and adapting trading patterns.

[0142] The security detection submodule uses the strategy adjustment results to monitor transaction activities, set key monitoring points and capture abnormal transaction behaviors, and conduct continuous security assessments on data, including abnormal detection of transaction frequency and amount. The execution process of generating security risk identification results is as follows;

[0143] Using the strategy adjustment results, the module monitors trading activities. The module sets key monitoring points to capture abnormal trading behaviors, including abnormal detection of transaction frequency and amount. Through continuous data analysis and security assessment, the module can timely discover potential security risks and generate security risk identification results. The formula used is as follows:

[0144]

[0145] in, Represents the security risk identification result, Anomaly detection results representing transaction frequency, The anomaly detection result representing the amount, and is the adjustment factor. The formula improves the accuracy and timeliness of risk identification by detecting anomalies in weighted frequency and amount.

[0146] The response identification submodule executes the reaction mechanism based on the security risk identification results, responds to the classified threat levels, adjusts and optimizes the security defense strategy, and responds to and mitigates security threats. The execution process of constructing the transaction security assessment results is as follows;

[0147] Based on the security risk identification results, the response mechanism is executed. The module responds to the classified threat level and adjusts and optimizes the security defense strategy to respond to and mitigate security threats. Through measures, the module constructs the transaction security assessment results to ensure the continuous security and stability of the transaction environment. The formula used is as follows:

[0148]

[0149] in, Represents the transaction security assessment result, stands for Threat Classification Response, Represents the adjustment of defense strategy. and is the adjustment coefficient. This formula effectively improves the security and response efficiency of the trading environment by optimizing the weights of threat response and defense strategies.

[0150] See also Fig. 9 , a transaction data analysis method based on blockchain, the transaction data analysis method based on blockchain is executed based on the above-mentioned transaction data analysis system based on blockchain, and includes the following steps:

[0151] S1: By receiving transaction data, asymmetric encryption is performed on each piece of data, and the encrypted data is recorded one by one in the blockchain database to obtain a secure transaction archive;

[0152] S2: By parsing the encrypted data in the secure transaction archive, identifying the connections between multiple transaction nodes, using a graph data structure to map the node relationships, classifying and weighting the nodes according to transaction frequency and amount, and constructing a transaction relationship network diagram;

[0153] S3: Based on the transaction relationship network diagram, monitor the dynamic changes of key nodes and connections, including newly established connections and updated transaction nodes, record changes and analyze the evolution of network behavior to form network change results;

[0154] S4: Analyze the data recorded in the network change results to capture abnormal patterns in transaction frequency and transaction amount, including potential risks and fraudulent behaviors, determine abnormal standards through statistical analysis, and create abnormal activity indicators;

[0155] S5: Based on abnormal activity indicators, formulate corresponding monitoring strategies and combat identified risky behaviors, cyclically adjust and optimize strategies and match new trading environments and potential threats, and establish a trading monitoring strategy framework through continuous monitoring and strategy adjustment.

[0156] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A transaction data analysis system based on blockchain, characterized by: The system comprises: The encryption verification module receives transaction data, performs hash operations on each piece of data, stores the hash value in the blockchain, and securely links the data to generate an encrypted data index; The transaction structure analysis module uses the encrypted data index to retrieve transaction nodes and link information, parse node attributes and transaction data, sort and sort by association strength, determine the display method, and obtain the transaction network structure description; The dynamic relationship tracking module monitors the node and edge updates in the network based on the transaction network structure description, records state changes, captures dynamic changes, and constructs a dynamic transaction network state; The behavior pattern recognition module uses the dynamic transaction network state to analyze the transaction pattern between nodes, identify regularity and abnormal transaction behavior, and obtain behavior pattern analysis results; The strategy simulation test module simulates the trading strategy under the differentiated market environment according to the analysis results of the behavior pattern, tests and adjusts the strategy, and forms the optimization results of the trading monitoring strategy; The transaction data security monitoring module uses the strategy in the transaction monitoring strategy optimization result to monitor the transaction process, perform security detection, identify and respond to security threats, and form a transaction security assessment result; The strategy simulation test module includes: The market environment simulation submodule sets market volatility parameters and transaction behavior patterns based on the behavior pattern analysis results, simulates differentiated market conditions, including high volatility and low volatility market environments, and generates simulation environment parameters by adjusting market supply and demand dynamics and transaction frequency parameters; The strategy testing submodule uses the simulation environment parameters to test a variety of trading strategies, monitor the performance of the strategies in the differentiated simulation environment, and evaluate the strength and weakness of the strategies by recording the profit and loss results and market adaptive response of the strategies, and obtain the strategy testing results; The strategy adjustment submodule analyzes the performance indicators and failure points of the strategy based on the data in the strategy test results, adjusts the key parameters and trading logic of the strategy and strengthens the market response capability, tests the adjusted strategy and verifies the effect, and obtains the optimization results of the trading monitoring strategy.

2. The transaction data analysis system based on blockchain according to claim 1, characterized in that: The encrypted data index includes transaction ID, hash value, and data block link; the transaction network structure description includes transaction node list, node transaction frequency, and transaction amount correlation; the dynamic transaction network status includes node activity indicators, information on newly added edges, and edge time stamps; the behavior pattern analysis results include common transaction patterns, abnormal transaction characteristics, and transaction frequency statistics; the transaction monitoring strategy optimization results include transaction threshold adjustment, risk control strategy, and strategy adjustment feedback; the transaction security assessment results include security detection records, abnormal transaction responses, and monitoring strategy updates.

3. The transaction data analysis system based on blockchain according to claim 1, characterized in that: The encryption verification module comprises: The transaction data receiving submodule receives transaction data, performs format verification and content review, verifies data compliance, classifies and marks the data, and obtains and verifies transaction data; The hash operation submodule performs hash conversion on the verification transaction data according to the verification transaction data and adopts the encryption logic of the tag data, strengthens the confidentiality of the data by adjusting the hash parameters, and constructs a transaction hash value; The blockchain link storage submodule uses the transaction hash value to perform a link operation with the current blockchain, and performs the operation by comparing and verifying the continuity and integrity of the hash value, thereby strengthening the immutability and traceability of the data and obtaining an encrypted data index.

4. The transaction data analysis system based on blockchain according to claim 1, characterized in that: The transaction structure analysis module includes: The transaction node retrieval submodule performs the retrieval of transaction nodes based on the encrypted data index, identifies each node and the interconnected transaction chain, scans and tracks the connection path through the node identifier, and generates a node information map; The node attribute analysis submodule analyzes the transaction attributes of each node, including the transaction amount and transaction number, through the node information map, compares the data with the transaction frequency and type, distinguishes and classifies the nodes, and performs dynamic data matching and attribute tagging to obtain classified associated data; The data display customization submodule uses the classified associated data to determine the data sorting and display format, optimizes the display logic by analyzing the transaction intensity and data density between nodes, and obtains a description of the transaction network structure.

5. The transaction data analysis system based on blockchain according to claim 1, characterized in that: The dynamic relationship tracking module includes: The state recording submodule monitors the changes of nodes and edges in the transaction network in real time based on the transaction network structure description, records the network status at each time point, including node addition and reduction, edge change and attribute update, and stores the data in a structured log to generate a network element status log; The change capture submodule identifies key change events through the network element status log, marks the increase or decrease of nodes, the adjustment of connection relationships, evaluates the overall network dynamic trend, and obtains network dynamic analysis results; The network construction submodule reconstructs the network structure, updates the connection logic of nodes and edges, verifies data consistency and reconstructs the network topology, and constructs a dynamic transaction network state according to the network dynamic analysis results.

6. The transaction data analysis system based on blockchain according to claim 1, characterized in that: The behavior pattern recognition module includes: The transaction pattern analysis submodule monitors the transaction frequency and amount between nodes based on the dynamic transaction network status, and quantitatively analyzes the fluctuation of the activity cycle and transaction scale of each node by evaluating the time changes of transaction volume and frequency, and constructs a pattern trend map; The abnormal behavior detection submodule uses the pattern trend map to analyze the differences between the transaction behaviors of multiple nodes and the average pattern, identifies statistical anomalies by setting key deviation thresholds, identifies behaviors that deviate from the normal transaction pattern, and obtains abnormal behavior markers; The behavior result integration submodule applies the random forest algorithm based on the data marked with abnormal behaviors to evaluate the transaction behaviors with strong regularity and abnormal behaviors, and forms the behavior pattern analysis results by cross-validating multiple types of data.

7. The transaction data analysis system based on blockchain according to claim 6, characterized in that: The formula of the random forest algorithm is as follows: Among them, G′ is the Gini impurity, p l is the proportion of the i-th label in the node, w l is the weight coefficient of the i-th class label, λ is the regularization parameter, σ j is the standard deviation of the jth feature, is the average value of the jth feature.

8. The transaction data analysis system based on blockchain according to claim 1, characterized in that: The transaction data security monitoring module includes: The strategy optimization submodule evaluates the optimization results of the transaction monitoring strategy, adjusts the monitoring parameters and matches the emerging transaction patterns, dynamically updates the strategy parameters to match the current transaction behavior and security requirements, and cyclically adjusts and tests the parameters to obtain the strategy adjustment results; The security detection submodule uses the strategy adjustment results to monitor transaction activities, set key monitoring points and capture abnormal transaction behaviors, conduct continuous security assessments on data, including abnormal detection of transaction frequency and amount, and generate security risk identification results; The response identification submodule executes a reaction mechanism based on the security risk identification results, responds to the classified threat levels, adjusts and optimizes security defense strategies, responds to and mitigates security threats, and constructs transaction security assessment results.

9. A transaction data analysis method based on blockchain, characterized in that: The blockchain-based transaction data analysis system according to any one of claims 1 to 8 comprises the following steps: By receiving transaction data, asymmetric encryption is implemented on each piece of data, and the encrypted data is recorded one by one in the blockchain database to obtain a secure transaction archive; By parsing the encrypted data in the secure transaction archive, identifying the connections between multiple transaction nodes, using a graph data structure to map the node relationships, classifying and weighting the nodes according to transaction frequency and amount, and constructing a transaction relationship network diagram; Relying on the transaction relationship network diagram, monitor the dynamic changes of key nodes and connections, including newly established connections and updated transaction nodes, record the changes and analyze the evolution of network behavior to form network change results; Analyze the data recorded in the network change results to capture abnormal patterns in transaction frequency and transaction amount, including potential risks and fraudulent behavior, determine abnormal standards through statistical analysis, and create indicators of abnormal activity; Based on the abnormal activity indicators, formulate corresponding monitoring strategies and combat identified risky behaviors, cyclically adjust and optimize strategies and match new trading environments and potential threats, and establish a trading monitoring strategy framework through continuous monitoring and strategy adjustment.

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