CN118797531B - A transaction data analysis system and method based on blockchain - Google Patents
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Abstract
本发明涉及技术领域，具体为一种基于区块链的交易数据分析系统及方法，系统包括加密验证模块、交易结构分析模块、动态关系追踪模块、行为模式识别模块、策略模拟测试模块、交易数据安全监控模块。本发明中，通过对交易数据进行加密哈希运算并将哈希值存储在区块链中，本方案确保了数据的完整性和安全性，系统地检索和解析交易节点属性，允许深入分类和整理交易数据，提升了数据呈现的直观性及访问效率，实时监控交易网络变化，迅速捕捉市场波动，支持交易者快速响应，通过分析交易模式和金额变化，有效识别规律性和异常行为，增强风险管理，模拟不同市场环境下的交易策略并进行实时优化，提高了策略的适应性和成功率。
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 organization 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
The invention relates to the technical field, in particular to a transaction data analysis system and method based on a blockchain.
Background
The technical field of transaction analysis involves the use of advanced data analysis tools to analyze, evaluate and predict the behavior and trend of transaction data, and the techniques of the field not only can process and analyze data, but also can provide real-time feedback and predictions to help decision makers make more intelligent choices. In B2B bulk commodity trade, the trade analysis technology is particularly important because of the large volatility of the market and the huge trade amount, and the application of the technology comprises algorithm trade, risk management, price prediction and market trend analysis, and by integrating data from multiple sources, the trade analysis technology can optimize the trade strategy, reduce the cost and increase the market transparency.
The transaction data analysis system and method refer to a set of system and related method for analyzing and processing transaction data, and the system is mainly used for assisting enterprises and individuals to understand market dynamics and optimizing transaction decisions through deep analysis of historical and real-time transaction data, and the system utilizes data mining and statistical analysis technology to extract valuable information from large-scale transaction data, identify market trend, evaluate transaction risk and predict future market behaviors.
The prior art lacks sufficient security and real-time in the processing and analysis of transaction data. For example, in the B2B bulk commodity transaction market, data needs to be processed in a centralized manner, and this method has disadvantages in terms of data security and real-time response, is susceptible to external attacks or risks of internal leakage, and the prior art cannot effectively update market variations in real time when processing huge amounts of data, so that the data based on transaction decisions no longer reflects the actual condition of the current market. Such delays result in significant economic losses or mishaps in the decision making process, and existing analysis tools, while capable of processing and analyzing data, lack sufficient flexibility and accuracy in predicting market trends and managing transaction risk, and are unable to effectively predict and address abnormal behavior and potential risks in the market.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a transaction data analysis system and method based on a blockchain.
In order to achieve the above purpose, the invention adopts the following technical scheme that the transaction data analysis system based on the blockchain comprises:
The encryption verification module performs hash operation on each piece of data by receiving transaction data, stores a hash value into a block chain, and performs secure linking of the data to generate an encrypted data index;
the transaction structure analysis module utilizes the encryption data index to search transaction nodes and link information, analyzes node attributes and transaction data, sorts and sorts the transaction data according to association strength, determines a display mode and acquires transaction network structure description;
The dynamic relation tracking module monitors node and edge updating conditions in a 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 analyzes the transaction pattern among the nodes by utilizing the dynamic transaction network state, recognizes regularity and abnormal transaction behavior, and obtains a behavior pattern analysis result;
The strategy simulation test module simulates a transaction strategy in a differentiated market environment according to the behavior pattern analysis result, tests and adjusts the strategy, and forms a transaction monitoring strategy optimization result;
and the transaction data security monitoring module monitors the transaction process by using the strategy in the transaction monitoring strategy optimization result, performs security detection, recognizes and responds to security threats, and forms a transaction security assessment result.
As a further scheme of the invention, the encryption data index comprises a transaction ID, a hash value and a data block link, the transaction network structure description comprises a transaction node list, node transaction frequency and transaction amount association, the dynamic transaction network state comprises node activity indexes, newly added side information and side time marks, the behavior pattern analysis result comprises common transaction patterns, abnormal transaction characteristics and transaction frequency statistics, the transaction monitoring strategy optimization result comprises transaction threshold adjustment, risk control strategies and strategy adjustment feedback, and the transaction security assessment result comprises security detection records, abnormal transaction responses and monitoring strategy update.
As a further aspect of the present invention, the encryption authentication module includes:
The transaction data receiving sub-module performs format verification and content auditing and verifies compliance of the data by receiving the transaction data, classifies and marks the data, and acquires verification transaction data;
The hash operation sub-module executes hash conversion on the verification transaction data by adopting encryption logic of marked data according to the verification transaction data, and strengthens confidentiality of the data by adjusting hash parameters to construct a transaction hash value;
And the blockchain link storage submodule performs the link operation with the current blockchain by utilizing the transaction hash value, and the link operation is performed by comparing and verifying the continuity and the integrity of the hash value, so that the non-tamper property and the traceability of the data are enhanced, and the encrypted data index is obtained.
As a further aspect of the present invention, the transaction structure analysis module includes:
The transaction node searching submodule executes the searching of the transaction nodes based on the encryption data index, identifies each node and the interconnected transaction chains, performs scanning and connection path tracking through the node identifiers, and generates a node information map;
The node attribute analysis sub-module analyzes the transaction attribute of each node, including transaction amount and transaction times, compares the data with the transaction frequency and type, distinguishes and classifies the nodes, and performs dynamic data matching and attribute marking to obtain classified associated data;
And the data display customization submodule utilizes the classification associated data to perform data sequencing and display form decision, and obtains the transaction network structure description by analyzing transaction intensity and data concentration optimization display logic among nodes.
As a further aspect of the present invention, the dynamic relationship tracking module includes:
the state recording submodule monitors the change of nodes and edges in the transaction network in real time based on the transaction network structure description, records the network state of each time point, including node increase and decrease, edge change and attribute update, and stores data codes into a structured log to generate a network element state log;
the change capturing submodule identifies key change events through the network element state log, marks the increase and decrease of nodes and the adjustment of connection relations, evaluates the overall network dynamic trend and acquires a network dynamic analysis result;
and the network construction sub-module reconstructs a network structure according to the network dynamic analysis result, updates the connection logic of the nodes and the edges, verifies the consistency of data and reconstructs the network topology, and constructs a dynamic transaction network state.
As a further aspect of the present invention, the behavior pattern recognition module includes:
The transaction mode analysis submodule monitors transaction frequency and amount among nodes based on the dynamic transaction network state, quantitatively analyzes fluctuation of activity period and transaction scale of each node by evaluating transaction amount and time change of frequency, and builds a mode trend map;
the abnormal behavior detection submodule analyzes the difference between the transaction behaviors of multiple nodes and the average mode by utilizing the mode trend map, identifies statistical abnormality by setting a key deviation threshold value, identifies behaviors deviating from the conventional transaction mode and obtains an abnormal behavior mark;
And the behavior result integration sub-module evaluates the transaction behaviors and the abnormal behaviors with strong regularity by applying a random forest algorithm according to the data of the abnormal behavior marks, and forms a behavior pattern analysis result by cross-verifying multiple types of data.
As a further scheme of the invention, the formula of the random forest algorithm is as follows:
Wherein, Is the non-purity of the kene,Is the firstThe proportion of class labels in the nodes,Is the firstThe weight coefficient of the class label is determined,In order for the parameters to be regularized,Is the firstThe standard deviation of the individual features is used,Is the firstAverage of the individual features.
As a further aspect of the present invention, the policy simulation test module includes:
The market environment simulation sub-module sets market fluctuation parameters and transaction behavior modes based on the behavior mode analysis result, simulates differentiated market conditions including high fluctuation and low fluctuation market environments, and generates simulation environment parameters by adjusting market supply and demand dynamics and transaction frequency parameters;
the strategy testing submodule tests various transaction strategies by utilizing the simulation environment parameters, monitors the performances of the strategies under the differential simulation environment, and evaluates the strength and the weakness of the strategies by recording the profit and loss results and the market adaptability reaction of the strategies to obtain strategy testing results;
And the strategy adjustment sub-module analyzes performance indexes and failure points of the strategies according to the data in the strategy test results, adjusts key parameters and transaction logic of the strategies, strengthens market coping capacity, tests and verifies effects of the adjusted strategies, and obtains transaction monitoring strategy optimization results.
As a further aspect of the present invention, the transaction data security monitoring module includes:
the strategy optimization sub-module evaluates the transaction monitoring strategy optimization result, adjusts the monitoring parameters and matches the emerging transaction mode, and circularly adjusts and tests the parameters by dynamically updating the strategy parameters to match the current transaction behavior and the security requirement so as to obtain a strategy adjustment result;
the security detection submodule monitors transaction activities by utilizing the strategy adjustment result, sets key monitoring points and captures abnormal transaction behaviors, and carries out continuous security assessment on data, wherein the security detection submodule comprises abnormal detection of transaction frequency and amount and generates a security risk identification result;
And 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, and deals with and relieves the security threat to construct a transaction security assessment result.
A blockchain-based transaction data analysis method, which is executed based on the blockchain-based transaction data analysis system, comprising the following steps:
S1, carrying out asymmetric encryption on each piece of data by receiving transaction data, and recording the encrypted data into a blockchain database one by one to obtain a safe transaction archive;
S2, identifying the connection among the multiple transaction nodes by analyzing the encrypted data in the secure transaction archive, mapping the node relationship by adopting a graph data structure, classifying and distributing weights to the nodes according to the transaction frequency and the amount, and constructing a transaction relationship network graph;
s3, monitoring dynamic changes of key nodes and connections, including newly established connections and updated transaction nodes, by means of the transaction relation network graph, recording changes and analyzing evolution of network behaviors to form a network change result;
s4, analyzing the data recorded in the network change result, capturing abnormal modes of transaction frequency and transaction amount, including potential risks and fraudulent behaviors, determining abnormal standards through statistical analysis, and creating abnormal activity indexes;
and S5, based on the abnormal activity indexes, formulating a corresponding monitoring strategy and resisting the identified risk behaviors, circularly adjusting and optimizing the strategy and matching new transaction environments and potential threats, and establishing a transaction monitoring strategy framework through continuous monitoring and strategy adjustment.
Compared with the prior art, the invention has the advantages and positive effects that:
In the invention, the data integrity and the non-tamper property are ensured by carrying out encryption hash operation on the transaction data and safely storing the hash value in the blockchain, and the strategy allows deeper classification and arrangement on the transaction data by systematically searching and analyzing the attribute of the transaction node, so that the presentation of the data is more visual, thereby optimizing the use efficiency and the access speed of the data. Monitoring dynamic changes in the trading network in real time enables immediate capture of market fluctuations and trends, which is particularly critical in rapidly changing market environments, as traders are allowed to respond quickly to market changes. Through analysis of transaction modes and amount change trends, transaction behaviors with strong regularity and abnormality can be effectively identified, a powerful tool is provided for risk management, and through simulation of transaction strategies in different market environments and optimization of coping mechanisms, the adaptability and effect of the transaction strategies are improved through real-time adjustment and testing, and risks can be better managed and success rate is increased when actual transactions are carried out by using the strategies finally.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a schematic diagram of a system framework of the present invention;
FIG. 3 is a flow chart of the encryption verification module of the present invention;
FIG. 4 is a flow chart of a transaction structure analysis module according to the present invention;
FIG. 5 is a flow chart of the dynamic relationship tracking module of the present invention;
FIG. 6 is a flow chart of a behavior pattern recognition module according to the present invention;
FIG. 7 is a flow chart of a strategy simulation test module according to the present invention;
FIG. 8 is a flow chart of a transaction data security monitoring module according to the present invention;
FIG. 9 is a schematic diagram of the method steps of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1
Referring to fig. 1 and 2, the present invention provides a technical solution, a transaction data analysis system based on blockchain includes:
the encryption verification module performs hash operation on each piece of data by receiving transaction data, stores a hash value into a block chain, and performs secure linking of the data to obtain an encrypted data index;
The transaction structure analysis module retrieves and extracts information of multiple transaction nodes and links through the encryption data index, analyzes the attribute of the transaction nodes, matches the associated transaction data, classifies and sorts the transaction data, sorts the classified and sorted data according to the association strength among the nodes, determines the display form of the data according to the sorting result and the node relation, and acquires the transaction network structure description;
the dynamic relation tracking module monitors the newly added and updated conditions of nodes and edges in the network in real time based on the transaction network structure description, implements continuous state recording, captures the dynamic change of the network and constructs the state of the dynamic transaction network;
The behavior pattern recognition module analyzes the transaction pattern among the nodes by utilizing the dynamic transaction network state, and comprises frequency and change trend of transaction amount, and detects and recognizes the transaction behavior with strong regularity and abnormality to obtain a behavior pattern analysis result;
the strategy simulation test module simulates transaction strategies in various market environments according to the analysis results of the behavior patterns, tests the transaction strategies one by one, and adjusts the coping mechanisms of the strategies according to the test results to form transaction monitoring strategy optimization results;
the transaction data security monitoring module monitors the transaction execution process by using the strategy proposed in the transaction monitoring strategy optimization result, performs security detection on transaction activities, and identifies and responds to potential security threats to form a transaction security assessment result.
The encrypted data index comprises a transaction ID, a hash value and a data block link, the transaction network structure description comprises a transaction node list, node transaction frequency and transaction amount association, the dynamic transaction network state comprises node activity indexes, newly added side information and side time marks, the behavior pattern analysis result comprises common transaction patterns, abnormal transaction characteristics and transaction frequency statistics, the transaction monitoring strategy optimization result comprises transaction threshold adjustment, risk control strategy and strategy adjustment feedback, and the transaction security assessment result comprises security detection records, abnormal transaction response and monitoring strategy update.
Specifically, as shown in fig. 2 and 3, the encryption authentication module includes:
The transaction data receiving sub-module performs format check and content audit and verifies compliance of the data by receiving the transaction data, classifies and marks the data, and obtains an execution flow of verifying the transaction data as follows;
By receiving transaction data, firstly, carrying out accurate verification on the format of the access data to ensure that the data conform to preset templates and type specifications, for example, checking whether the transaction data contain necessary transaction IDs and timestamps, verifying the validity of contents by a system through a series of rules, such as whether transaction amounts conform to legal ranges, activating the states of accounts of both sides, evaluating the compliance of the data, comparing an internal compliance database with external regulations, confirming whether the transaction violates any legal regulations or not, classifying and marking the verified data according to the attributes of the data, such as distinguishing domestic and foreign transactions, large-amount or small-amount transactions, constructing and outputting the verified transaction data by the system for subsequent encryption processing and blockchain recording, and adopting the following formulas:
Wherein, Representing the hash value calculation of the validated transaction data,AndIs the adjustment coefficient of the light source,Is the data security level, the high level enhances the data confidentiality, and the formula passes through the weight coefficientAndAnd (3) dynamically managing the data security, thereby effectively aiming at scenes with different security requirements.
The hash operation sub-module executes hash conversion on the verification transaction data by adopting encryption logic of the marked data according to the verification transaction data, and strengthens the confidentiality of the data by adjusting hash parameters, and an execution flow for constructing a transaction hash value is as follows;
According to verification of transaction data, firstly, preset marked data encryption logic is adopted, for example, SHA-256 algorithm is used for ensuring data integrity and consistency, a module executes hash conversion on each transaction data, the conversion comprises encryption processing of key information such as transaction amount and time stamp, in order to further strengthen data confidentiality, a system dynamically adjusts hash parameters through an interface set by a user, for example, the iteration times or confusion parameters of the hash algorithm are changed, after hash calculation is completed, generated transaction hash values are stored in a safe and irreversible format for a block chain link storage submodule, and the following formula is adopted:
Wherein, Representing a hash operation on the validated transaction data,AndRepresents the adjustment coefficient of the light source,Is an encryption strength parameter in the hash process, and the complexity and the security of the hash can be enhanced by adjusting the parameter.
The block chain link storage submodule performs a link operation with the current block chain by utilizing a transaction hash value, and the execution flow for obtaining the encrypted data index is as follows by comparing and verifying the continuity and the integrity of the hash value to strengthen the non-tamper property and the traceability of the data;
The link operation with the current block chain is executed by utilizing the transaction hash value, and firstly, 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 is not tampered. Based on verifying continuity and integrity, the module locks block data by using an encryption algorithm, so that a newly added block cannot be illegally modified, the system constructs and indexes new block information, and the uniqueness and traceability of the block are ensured by using a specific algorithm, thereby realizing safe storage and efficient indexing of a block chain, and the following formula is adopted:
Wherein, Representing the calculation of hash value continuity from the previous block to the current block,AndIs the adjustment coefficient of the light source,Representing the integrity index of the blockchain, the calculation and protection of the integrity of the blockchain are enhanced by adding weight parameters so as to adapt to the changing safety requirements.
Specifically, as shown in fig. 2 and 4, the transaction structure analysis module includes:
The transaction node retrieval sub-module performs retrieval of transaction nodes based on the encrypted data index, identifies each node and a transaction chain connected with each other, performs scanning and connection path tracking through a node identifier, and generates an execution flow of a node information map as follows;
Based on the encryption data index, firstly, the high-efficiency searching algorithm is utilized to perform quick positioning of transaction nodes, the encryption data index is subjected to deep scanning, the system accurately identifies each node and the position of each node in a transaction chain through node identifiers such as transaction IDs and timestamps, the module utilizes the graph database technology to perform connection path tracking among the nodes, the accuracy and the high efficiency of the tracking process are ensured, the system generates an exhaustive node information map which shows all nodes and connection relations thereof, a foundation is provided for further attribute analysis and data matching, and the following formula is adopted:
Wherein, Representative pair nodeIs provided with a functional tracking and path determination,AndIs the adjustment coefficient of the light source,The intensity or frequency of the links is represented, and the formula strengthens the accuracy of node tracking and the information richness of the map by adjusting the weight of the node connection paths.
The node attribute analysis sub-module analyzes the transaction attribute of each node, including transaction amount and transaction times, compares the data with the transaction frequency and type, distinguishes and classifies the nodes, and performs dynamic data matching and attribute marking to obtain the execution flow of classified associated data as follows;
Through the node information map, core transaction attributes of each node, such as transaction amount and transaction times, are analyzed first. The module adopts a data mining technology to compare the data of each node with the historical transaction frequency and type in detail, identifies the specific behavior mode and transaction habit of the node, distinguishes and classifies different nodes such as high-frequency transaction nodes and large transaction nodes by a system, carries out dynamic data matching and attribute marking on the nodes, improves the usability of the data, strengthens the accuracy of node classification, and adopts the following formula:
Wherein, Representative nodeIs provided for the calculation of the transaction characteristics of (a),AndIs the weight coefficient of the weight of the object,Representing the data matching degree, and improving the accuracy and efficiency of node attribute analysis by weighting the transaction characteristics and the data matching degree.
The data display customization submodule utilizes the classified associated data to carry out data sequencing and display form decision, and the transaction intensity and data concentration among the nodes are analyzed to optimize display logic, so that the execution flow of the transaction network structure description is obtained as follows;
By using the classified associated data, the system firstly sorts the data, and arranges the data according to the transaction frequency and the transaction amount of the nodes, the module determines the display form of the data, such as a graph or a graphic interface, and optimizes according to the transaction intensity and the data density among the nodes, the module effectively displays the structure of the transaction network, so that the transaction relationship is clear and simultaneously optimizes the visual experience of the user and the interpretation efficiency of the data, and the method adopts the following formula:
Wherein, Representative pair nodeIs provided with a display ordering of (c) in the order,AndIs the adjustment coefficient of the light source,Representing the selection of the display media, the logic and effect of data display are optimized by adjusting the display ordering and the weight of the media, and the transmission efficiency and visual appeal of information are enhanced.
Specifically, as shown in fig. 2 and 5, the dynamic relationship tracking module includes:
The state recording submodule monitors the change of nodes and edges in the transaction network in real time based on the transaction network structure description, records the network state of each time point, including node increase and decrease, edge change and attribute update, and stores data codes into a structured log to generate an execution flow of the network element state log as follows;
based on the transaction network structure description, the dynamic changes of nodes and edges in the transaction network are monitored in real time by utilizing a high-performance monitoring tool, and the module carefully records the network state changes at each time point, including the addition of new nodes, the withdrawal of existing nodes, the increase and decrease of edges and the update of attributes. Each change is coded through a specific algorithm and converted into a structured log format which is easy to analyze, the coded data is stored into a specially designed log system by a module, a detailed network element state log is generated, data support is provided for historical tracking and future analysis of the network state, and the following formula is adopted:
Wherein, Representative pair of time pointsIs used for the network state encoding of (a),AndIs the adjustment coefficient of the light source,The state record sub-module can dynamically adjust the density and the detail degree of log records according to the frequency of network state change to effectively capture key state change.
The change capturing submodule identifies key change events through a network element state log, marks the increase and decrease of nodes and the adjustment of connection relations, evaluates the overall network dynamic trend and obtains the execution flow of the network dynamic analysis result as follows;
through the network element state log, the system firstly uses advanced data analysis technology to identify key change events, such as increase and decrease of nodes and important adjustment of connection relations, a module analyzes the influence of the key events on a transaction network, evaluates the dynamic trend of the whole network, marks active nodes and key connection paths in the network, further generates comprehensive network dynamic analysis results, helps a network administrator to understand the evolution process of the network, provides scientific basis for subsequent network optimization and decision, and adopts the following formula:
Wherein, Representative pair logIs an evaluation of the change event in the middle,AndIs the weight coefficient of the weight of the object,Representing the time window, the change capture sub-module may more accurately identify and reflect important changes in the network by adjusting the time window and the estimated weights.
The network construction sub-module reconstructs a network structure according to a network dynamic analysis result, updates the connection logic of nodes and edges, verifies data consistency and reconstructs network topology, and constructs an execution flow of a dynamic transaction network state as follows;
According to the network dynamic analysis result, firstly determining the connection logic of the network nodes and edges which need to be updated or reconstructed, detecting the data consistency by using an algorithm by a module, ensuring that all network activities and changes meet the predefined data standard, reconstructing the network topology by the module, adjusting the relation between the nodes and the edges according to the real-time analysis result, constructing the network state which meets the current transaction dynamic, and ensuring that the newly constructed network state is efficient and stable by simulating and verifying various network construction schemes by adopting the following formula:
Wherein, Representative based on dynamic analysis resultsIs used for the construction of a network model of (a),AndIs the adjustment coefficient of the light source,Representing the balance of topology construction, optimizing the network construction process by balancing dynamic analysis results and topology balance, and ensuring that a new network can efficiently respond to transaction demands.
Specifically, as shown in fig. 2 and 6, the behavior pattern recognition module includes:
The transaction mode analysis submodule monitors transaction frequency and amount among nodes based on a dynamic transaction network state, quantitatively analyzes fluctuation of activity period and transaction scale of each node by evaluating transaction amount and time change of the frequency, and constructs an execution flow of a mode trend map as follows;
Based on the dynamic transaction network state, firstly, the transaction frequency and the transaction amount among all nodes in the network are continuously monitored by using a complex monitoring algorithm. The system collects and analyzes data to evaluate the change condition of transaction amount and frequency in different time periods, a time sequence analysis technology is utilized, a module quantitatively analyzes the activity period of each node and tracks the fluctuation trend of transaction scale, the module constructs a pattern trend map, shows the periodic and scale change trend of node activity in the whole transaction network, provides a basis for further behavior analysis, and adopts the following formula:
Wherein, Representing the quantitative analysis result of the transaction frequency,Representing the result of the quantitative analysis of the transaction amount,AndThe method is an adjustment coefficient, and by combining analysis of transaction frequency and amount, understanding and prediction capability of transaction mode trend are enhanced.
The abnormal behavior detection submodule utilizes a pattern trend map to analyze the difference between the transaction behaviors of multiple nodes and an average pattern, identifies statistical abnormality by setting a key deviation threshold value, identifies behaviors deviating from a conventional transaction pattern, and obtains an execution flow of an abnormal behavior mark as follows;
By using the pattern trend spectrum, the system firstly analyzes the transaction behaviors of multiple nodes in detail and compares the differences between the behaviors and the average transaction pattern. By setting a key deviation threshold, the module can identify statistically significant anomalies such as nodes with sudden increase or decrease of transaction frequency or amount, the identification process can effectively identify deviations from a conventional transaction mode based on an advanced statistical analysis technology, and important information is provided for further risk management and monitoring, and the following formula is adopted:
Wherein, Representing an analysis of the deviation from the average pattern,Representing an analysis of the deviations of the individual nodes,AndThe weight coefficient is adopted, and the formula enhances the recognition accuracy and the response speed of abnormal behaviors by fine adjustment of the deviation threshold.
The behavior result integration submodule evaluates the transaction behaviors and the abnormal behaviors with strong regularity by applying a random forest algorithm according to the data of the abnormal behavior marks, and forms an execution flow of a behavior pattern analysis result by cross-verifying multiple types of data as follows;
According to the data of the abnormal behavior mark, the system adopts an advanced data analysis model, such as logistic regression, to evaluate the transaction behavior with strong regularity and identify the abnormal behavior, the module comprehensively analyzes the patterns of the transaction behavior to form a comprehensive behavior pattern analysis result by cross-verifying multiple types of data, the accuracy and the reliability of the analysis result are ensured by algorithm optimization and model adjustment, scientific basis is provided for safety monitoring and decision support of a transaction network, and the comprehensiveness and the depth of behavior pattern analysis are improved by weighting the verification results of normal and abnormal behaviors.
The formula of the random forest algorithm is as follows:
Wherein, Is the non-purity of the kene,Is the firstThe proportion of class labels in the nodes,Is the firstThe weight coefficient of the class label is determined,In order for the parameters to be regularized,Is the firstThe standard deviation of the individual features is used,Is the firstAverage of the individual features.
The execution process is as follows:
Initializing each category label Weights of (2)The weights may be set based on the frequency of the categories or other business logic, and then calculate the proportion of each category in the nodeUsing weighting coefficientsAnd ratio ofTo adjust the influence of each category in the calculation of the unrepeace, introducing a regularization term, whereinThe values are determined by cross-validation to optimize the generalization ability of the model, computing each featureStandard deviation of (2)Average value ofAnd multiplying the ratio by a regularization parameter to be added into a base-Ni-based unrepeace formula, so that consideration of sensitivity of the model to data characteristic distribution is increased, and the whole process combines newly-introduced parameters and original base-Ni unrepeace calculation to achieve the aim of more accurately evaluating decision tree nodes.
Specifically, as shown in fig. 2 and 7, the policy simulation test module includes:
The market environment simulation submodule sets market fluctuation parameters and transaction behavior modes based on the behavior mode analysis result, simulates differentiated market conditions including high fluctuation and low fluctuation market environments, and generates execution flows of simulation environment parameters by adjusting market supply and demand dynamics and transaction frequency parameters, wherein the execution flows are as follows;
Based on the analysis result of the behavior mode, firstly setting market fluctuation parameters, and determining the characteristics of high fluctuation and low fluctuation market environments based on historical data and a prediction model by the parameters. The module simulates differentiated market conditions, economic activities under different market conditions are simulated by dynamically adjusting market supply and demand dynamics and transaction frequency parameters, the process generates simulation environment parameters through real-time data flow and a market simulation algorithm, and the simulated market environment can be ensured to accurately reflect the actual transaction environment under different market fluctuation conditions. The improved formula is:
Wherein, Representing the parameters of the simulated environment that are generated,Representing the parameters of the market fluctuation,Representing a transaction frequency parameter,AndThe simulation environment is an adjustment coefficient, and the simulation environment reflects the market condition more realistically by adjusting the weight of market fluctuation and transaction frequency.
The strategy testing sub-module tests various transaction strategies by using simulation environment parameters, monitors the performance of the strategies under the differential simulation environment, and evaluates the strength and the weakness of the strategies by recording the profit and loss results and market adaptability reaction of the strategies, so as to obtain the execution flow of the strategy testing results as follows;
The method comprises the steps of comprehensively testing various transaction strategies by using simulation environment parameters, monitoring performances of each strategy under a differential simulation environment by using a module, including the earning and depletion results and market adaptability reactions of the strategies, evaluating the strength and weakness of each strategy by using the module through testing, systematically collecting and analyzing strategy performance data, generating strategy test results, verifying the effectiveness of the strategies by using the process, providing data support for further optimization, and adopting the following formula:
Wherein, Representing the result of the policy test,Representing the result of the earning and depletion of the policy,Representing a market-adaptive response to the changes in the market,AndIs an adjustment coefficient, and the formula effectively evaluates the comprehensive performance of the strategy through weighting the profit and loss results and the market adaptability.
The strategy adjustment submodule analyzes performance indexes and failure points of strategies according to data in strategy test results, adjusts key parameters and transaction logic of the strategies, strengthens market coping capacity, tests and verifies effects of the adjusted strategies, and obtains execution flows of transaction monitoring strategy optimization results as follows;
According to the data in the policy test result, firstly analyzing the performance index and failure point of the policy. The module adjusts key parameters and transaction logic of the strategy to strengthen market coping capability of the strategy, the module ensures that the strategy optimization can improve transaction efficiency and profitability in an actual market environment by further testing and verifying effects of the adjusted strategy, the module continuously iterates and optimizes the strategy, and finally obtains a transaction monitoring strategy optimization result, and the method adopts the following formula:
Wherein, On behalf of the transaction monitoring policy optimization results,Represents the performance index of the product,Representing the effect of the policy adjustment,AndIs an adjustment coefficient, and the formula ensures the effectiveness of strategy adjustment and the capability of adapting to the market by optimizing the performance index and the weight of the adjustment effect.
Specifically, as shown in fig. 2 and 8, the transaction data security monitoring module includes:
The strategy optimization sub-module evaluates the transaction monitoring strategy optimization result, adjusts the monitoring parameters and matches the emerging transaction mode, and circularly adjusts and tests the parameters by dynamically updating the strategy parameters to match the current transaction behavior and the security requirement, so as to obtain an execution flow of the strategy adjustment result as follows;
Evaluating the optimization result of the transaction monitoring strategy, firstly adjusting monitoring parameters to match an emerging transaction mode, ensuring that the strategy can adapt to the current transaction behavior and safety requirements by dynamically updating strategy parameters by a module, refining and optimizing the strategy by the module by circularly adjusting and testing each parameter, ensuring that each adjustment is based on the feedback of actual transaction data and modes, thereby continuously improving the effect of the strategy, finally obtaining the strategy adjustment result, and adopting the following formula:
Wherein, Representing the result of the policy adjustment,Representing the adjustment of the monitoring parameters,Representing the fitness of the emerging transaction patterns,AndThe method is to adjust the coefficient, and the formula ensures that the strategy can respond to market change efficiently by adjusting the monitoring parameters and adapting the weight of the transaction mode.
The security detection submodule monitors transaction activities by utilizing strategy adjustment results, sets key monitoring points and captures abnormal transaction behaviors, carries out continuous security assessment on data, comprises abnormal detection on transaction frequency and amount, and generates an execution flow of a security risk identification result as follows;
The method comprises the steps of monitoring transaction activities by using a strategy adjustment result, setting key monitoring points by a module, capturing abnormal transaction behaviors, including abnormal detection of transaction frequency and amount, timely discovering potential safety risks by continuous data analysis and safety evaluation, generating a safety risk identification result, and adopting the following formula:
Wherein, Representing the result of the security risk identification,An anomaly detection result representing the transaction frequency,An abnormality detection result representing the amount of money,AndThe method is an adjustment coefficient, and the formula improves the accuracy and timeliness of risk identification through the abnormal detection of the weighted frequency and the amount.
The response identification sub-module is used for executing a reaction mechanism based on the security risk identification result, responding to the classified threat level, adjusting and optimizing the security defense strategy, coping with and relieving the security threat, and constructing an execution flow of the transaction security assessment result as follows;
Based on the security risk identification result, executing a reaction mechanism, responding and adjusting the classified threat level by a module and optimizing a security defense strategy to cope with and alleviate the security threat, constructing a transaction security assessment result by measures, and ensuring the continuous security and stability of a transaction environment, wherein the following formula is adopted:
Wherein, On behalf of the outcome of the transaction security assessment,Representing a threat classification response,Representing the adjustment of the defending policy,AndThe method is an adjustment coefficient, and the security and response efficiency of the transaction environment are effectively improved by optimizing the weights of threat response and defense strategies.
Referring to fig. 9, a blockchain-based transaction data analysis method is performed based on the blockchain-based transaction data analysis system, and includes the following steps:
S1, carrying out asymmetric encryption on each piece of data by receiving transaction data, and recording the encrypted data into a blockchain database one by one to obtain a safe transaction archive;
s2, identifying the connection among the multiple transaction nodes by analyzing the encrypted data in the secure transaction archive, mapping the node relationship by adopting a graph data structure, classifying and weight distributing the nodes according to the transaction frequency and the amount, and constructing a transaction relationship network graph;
S3, monitoring dynamic changes of key nodes and connections, including newly established connections and updated transaction nodes, by means of a transaction relation network diagram, recording changes and analyzing evolution of network behaviors to form a network change result;
S4, analyzing data recorded in a network change result, capturing abnormal modes of transaction frequency and transaction amount, including potential risks and fraudulent behaviors, determining abnormal standards through statistical analysis, and creating abnormal activity indexes;
And S5, based on abnormal activity indexes, formulating a corresponding monitoring strategy and resisting the identified risk behaviors, circularly adjusting and optimizing the strategy, matching new transaction environment and potential threat, and establishing a transaction monitoring strategy framework through continuous monitoring and strategy adjustment.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
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