A blockchain-based enterprise financing data security management system

Abstract

The present invention relates to the field of data security management technology, and specifically discloses a blockchain-based enterprise financing data security management system, including an enterprise financing data chain module, a transaction anomaly detection and judgment module, an adaptive threshold optimization module, a data transmission leakage monitoring module, a financing data security management module, and a financing data security assessment module; the present invention realizes distributed storage of financing data by constructing monitoring sub-areas through blockchain nodes to ensure that the data cannot be tampered with; adopts an anomaly detection model to analyze on-chain transaction behavior, combines the adaptive threshold optimization algorithm to dynamically adjust the risk judgment standard, and improves the accuracy and timeliness of risk identification; at the same time, integrates the data transmission leakage monitoring module and the comprehensive security assessment framework to form a protection system covering the data management cycle. The present invention breaks through the traditional static risk control paradigm, improves the data security level in the financing scenario, and provides technical support for risk management in the digital financial era.

Description

Technical Field

[0001] The present invention relates to the technical field of data security management, and in particular to a blockchain-based enterprise financing data security management system. Background Art

[0002] With the digital transformation of corporate financing activities, the security and credibility of financing data have become core challenges. Blockchain technology, with its decentralization, immutability, and smart contracts, offers a new paradigm for building trusted data management mechanisms. By enabling multi-party consensus verification through distributed ledger technology and combining cryptographic algorithms to ensure secure transmission and storage, it provides technical support for the full lifecycle management of data in corporate financing scenarios.

[0003] Traditional corporate financing data security management systems typically employ a centralized architecture, with their core logic centered around manual review and offline risk control. The system relies on a single database to store all information submitted by the financing parties, performing compliance verification using a pre-set rule engine. Risk assessment relies primarily on post-audits and spot checks. In multi-party collaboration scenarios, different financial institutions must repeatedly verify the same dataset, resulting in inefficiencies and the creation of information silos. Data transmission lacks end-to-end encryption and real-time monitoring mechanisms, relying solely on passive defenses through firewalls and intrusion detection systems.

[0004] Limitations of traditional enterprise financing data security management systems: First, traditional systems rely on manual review and offline risk control process design to ensure the timeliness of risk prevention and control, resulting in a lag window in risk perception and making it difficult to cope with real-time threats in high-frequency trading scenarios; second, in terms of architectural stability, the centralized storage model of traditional systems forms a single trust anchor, which increases the concentration of data leakage risks; in addition, in terms of risk judgment mechanisms, the static threshold setting of traditional systems cannot adapt to the dynamic changes of different industry cycles and business scenarios, resulting in a high rate of false positives and missed reports, making it difficult to achieve accurate risk warnings. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a blockchain-based enterprise financing data security management system to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: a blockchain-based enterprise financing data security management system, comprising an enterprise financing data on-chain module, a transaction anomaly detection and judgment module, an adaptive threshold optimization module, a data transmission leakage monitoring module, a financing data security management module, and a financing data security assessment module;

[0007] Enterprise financing data chain module: used to upload the target enterprise financing data to the chain, and use the target enterprise financing data blockchain node as each monitoring sub-area;

[0008] Transaction anomaly detection and judgment module: used to obtain on-chain transaction data of each monitoring sub-area, establish a transaction anomaly detection model, analyze and obtain the transaction anomaly monitoring index of each monitoring sub-area, and judge the transaction anomaly. If the judgment result is that the transaction status is abnormal, the adaptive threshold optimization module is further executed. Otherwise, the transaction status is judged to be normal;

[0009] Adaptive Threshold Optimization Module: Based on the transaction anomaly judgment results of the transaction anomaly detection and judgment module, it dynamically optimizes the transaction anomaly monitoring index threshold of each monitoring sub-area with abnormal transaction status through an adaptive threshold algorithm;

[0010] Data transmission leakage monitoring module: used to obtain data during the transmission process of each monitoring sub-area, monitor and analyze the data transmission process, and obtain the transmission leakage risk index of each monitoring sub-area;

[0011] Financing data security management module: Based on the transaction anomaly monitoring index and transmission leakage risk index of each monitoring sub-area, a data security management assessment coefficient model is established to analyze and obtain the data security management assessment coefficient of each monitoring sub-area;

[0012] Financing data security assessment module: used to obtain the data security management assessment coefficient of each monitoring sub-area, assess the security of the target enterprise's financing data, output the financing data security assessment results, and issue early warning information for security abnormal data.

[0013] Preferably, the execution method of the enterprise financing data chain module is as follows:

[0014] The target enterprise financing data is uploaded to the chain, and the enterprise financing data is used as the target monitoring object. It is divided into n monitoring sub-areas according to the blockchain nodes, i=1, 2, 3,..., n, i is the number of each monitoring sub-area.

[0015] Preferably, the execution mode of the transaction anomaly detection and judgment module is as follows:

[0016] Obtaining on-chain transaction data for each monitored sub-region, wherein the on-chain transaction data includes transaction amount and transaction frequency;

[0017] Calculate the transaction amount deviation of each monitoring sub-area. The calculation formula is as follows: ,in, Md i represents the transaction amount deviation of the i-th monitoring sub-area, represents the jth transaction amount in the i-th monitoring sub-area, represents the jth transaction amount in the ith monitoring sub-region, represents the average transaction amount in the ith monitoring sub-region, j = 1, 2, 3, ..., m, j represents the number of each transaction amount, m represents the total number of transactions, i represents the number of each monitoring sub-region, i = 1, 2, 3, ..., n, n represents the total number of monitoring sub-regions;

[0018] Calculate the transaction anomaly monitoring index of each monitoring sub-area using the following formula: represents the average transaction amount of the i-th monitoring sub-area, j = 1, 2, 3, ..., m, j represents the number of each transaction amount, m represents the total number of transactions, i represents the number of each monitoring sub-area, i = 1, 2, 3, ..., n, n represents the total number of monitoring sub-areas;

[0019] Calculate the transaction anomaly monitoring index of each monitoring sub-area. The calculation formula is as follows: ,in, AMI i represents the transaction anomaly monitoring index of the i-th monitoring sub-area, Md i represents the transaction amount deviation of the i-th monitoring sub-area, Mr i represents the transaction frequency mutation rate of the i-th monitoring sub-area, Indicates the deviation from the preset standard transaction amount. It represents the preset standard transaction frequency mutation rate; reads the transaction anomaly monitoring index of each monitoring sub-area, and judges the transaction anomaly status of the target enterprise's financing data. If the transaction anomaly monitoring index of a monitoring sub-area is less than the preset transaction anomaly monitoring index threshold, then the transaction status of the monitoring sub-area is judged to be normal; if the transaction anomaly monitoring index of a monitoring sub-area is greater than or equal to the preset transaction anomaly monitoring index threshold, then the transaction status of the monitoring sub-area is judged to be abnormal, and the adaptive threshold optimization module is further executed.

[0020] Preferably, the calculation formula for the transaction frequency mutation rate of the i-th monitoring sub-area is specifically: ,in, Mr i represents the transaction frequency mutation rate of the i-th monitoring sub-area, Tf i represents the current transaction frequency of the i-th monitoring sub-area, It represents the transaction frequency of the dth historical period in the i-th monitoring sub-area, represents the historical average transaction frequency of the i-th monitoring sub-area, d represents the number of each time period, and W represents the number of historical time periods. Preferably, the execution method of the adaptive threshold optimization module is as follows:

[0021] Receive the transaction anomaly monitoring index of each monitoring sub-area transmitted by the transaction anomaly detection and judgment module, and set the sliding window capacity to K;

[0022] Perform statistical analysis on the transaction anomaly monitoring index within the sliding window of each monitoring sub-area, and calculate the mean value of the transaction anomaly monitoring index within the sliding window of each monitoring sub-area. The calculation formula is: ,in, represents the mean value of the transaction anomaly monitoring index in the sliding window of the i-th monitoring sub-area, Indicates the first monitoring sub-region in the sliding window p The transaction anomaly monitoring index of each data, p Represents the sequence number of the data in the sliding window, P = 1, 2, 3, ..., k;

[0023] Calculate the standard deviation of the transaction anomaly monitoring index within the sliding window of each monitoring sub-area. The calculation formula is: ,in, Represents the standard deviation of the transaction anomaly monitoring index within the sliding window of the i-th monitoring sub-area; through the adaptive threshold algorithm, the dynamic threshold is set, and the calculation formula of the dynamic threshold is: ,in, T i The dynamic threshold of the i-th monitoring sub-area, It represents the adjustment coefficient. The transaction anomaly monitoring index threshold is optimized according to the dynamic threshold of each monitoring sub-area, and the optimized transaction anomaly monitoring index threshold is fed back to the transaction anomaly detection and judgment module.

[0024] Preferably, the execution mode of the data transmission leakage monitoring module is as follows:

[0025] Obtain data on the transmission process of each monitoring sub-area, including key update frequency, number of transmission anomalies, and total data transmission times;

[0026] Calculate the data transmission anomaly rate of each monitoring sub-area using the following formula: ,in, Ar i represents the data transmission anomaly rate of the i-th monitoring sub-area, Ae i represents the number of transmission anomalies in the i-th monitoring sub-area, At i represents the total number of data transmissions in the i-th monitoring sub-area;

[0027] Calculate the entropy value of the data transmission path in each monitoring sub-area. The calculation formula is as follows: ,in, Hp i represents the data transmission path entropy value of the i-th monitoring sub-area, represents the number of transmission path nodes of type g in the i-th monitoring sub-area, represents the total number of nodes in the transmission path of the i-th monitoring sub-area, , g Indicates the number of each transmission path node type, g =1, 2, 3, ..., L, where L represents the total number of transmission path node types. The data transmission process is monitored and analyzed to obtain the transmission leakage risk index of each monitoring sub-area. The calculation formula is as follows: ,in, LRI i represents the transmission leakage risk index of the i-th monitoring sub-area, Indicates the maximum entropy value of the data transmission path, kf i represents the key update frequency of the i-th monitoring sub-area, Indicates the maximum value of the key update frequency.

[0028] Preferably, the maximum entropy value of the data transmission path is obtained in the following manner:

[0029] The maximum value of information entropy occurs when all data transmission path node types are evenly distributed, that is, the number of nodes of each data transmission path type is equal. ,but ,When the number of node types in all data transmission paths is exactly the same, the uncertainty of the path, that is, the complexity, is the highest and the entropy value reaches the maximum value. .

[0030] Preferably, the execution method of the financing data security management module is as follows: ,in, MEC i represents the data security management evaluation coefficient of the i-th monitoring sub-area, AMI i represents the transaction anomaly monitoring index of the i-th monitoring sub-area, LRI i represents the transmission leakage risk index of the i-th monitoring sub-area.

[0031] Preferably, the execution method of the financing data security assessment module is as follows: read the data security management assessment coefficient of each monitoring sub-area, and assess the financing data security of the target enterprise; if the data security management assessment coefficient of a monitoring sub-area is greater than the preset security management assessment coefficient threshold, then the data security management status of the monitoring sub-area is judged to be normal; if the data security management assessment coefficient of a monitoring sub-area is less than or equal to the preset security management assessment coefficient threshold, then the data security management status of the monitoring sub-area is judged to be abnormal, and the data security management status abnormality is marked as the financing data security assessment result of the monitoring sub-area of ​​the target enterprise, and an early warning information is issued for the data security assessment result.

[0032] As described above, the blockchain-based enterprise financing data security management system provided by the present invention has at least the following beneficial effects:

[0033] The present invention provides a blockchain-based enterprise financing data security management system, which stores financing data in a distributed manner on blockchain nodes through an enterprise financing data chain module, and uses nodes as monitoring sub-areas; a transaction anomaly detection and judgment module combines on-chain transaction data to build a dynamic detection model, calculates the transaction anomaly monitoring index in real time, and realizes dynamic adjustment of the anomaly threshold through an adaptive threshold optimization module; a data transmission leakage monitoring module quantitatively evaluates the data of the data transmission path and generates a transmission leakage risk index; a financing data security management module integrates the anomaly index and leakage risk to build an assessment coefficient model, and finally the financing data security assessment module outputs a security report and triggers an early warning. The present invention ensures that data cannot be tampered with through decentralized storage, realizes dynamic identification of transaction risks through an adaptive algorithm, and builds a full-process security protection system in combination with multi-dimensional monitoring of transmission links, solving the problems of easy data tampering, delayed monitoring, and weak transmission protection in traditional centralized systems. This invention breaks through the traditional static risk control paradigm, significantly improves the data security level in financing scenarios, and provides technical support for risk management in the digital financial era. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without inventive effort.

[0035] Figure 1 This is a structural diagram of a blockchain-based enterprise financing data security management system of the present invention.

[0036] Figure 2 This is a schematic diagram of the electronic device structure of a blockchain-based enterprise financing data security management system of the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. Example 1

[0038] See also Figure 1 As shown, the present invention provides a blockchain-based enterprise financing data security management system, including an enterprise financing data chain module, a transaction anomaly detection and judgment module, an adaptive threshold optimization module, a data transmission leakage monitoring module, a financing data security management module, and a financing data security assessment module;

[0039] Enterprise financing data chain module: used to upload the target enterprise financing data to the chain, and use the target enterprise financing data blockchain node as each monitoring sub-area;

[0040] In this embodiment, it should be specifically explained that the execution method of the enterprise financing data chain module is as follows:

[0041] The target enterprise financing data is uploaded to the chain, and the enterprise financing data is used as the target monitoring object. It is divided into n monitoring sub-areas according to the blockchain nodes, i=1, 2, 3,..., n, i is the number of each monitoring sub-area.

[0042] It should be specified that the corporate financing data includes but is not limited to equity financing (such as registered capital, shareholder capital increase, IPO and additional issuance of funds, private equity, venture capital, etc.), debt financing (such as bank loans, corporate bonds, corporate bills, trust financing, financial leasing, etc.), other financing (such as government subsidies, industrial funds, supply chain finance, crowdfunding, etc.), as well as key financing-related terms (financing amount, term, interest rate, guarantee method, gambling agreement, etc.) and time nodes (financing time, repayment plan, equity change record, etc.).

[0043] Transaction anomaly detection and judgment module: used to obtain on-chain transaction data of each monitoring sub-area, establish a transaction anomaly detection model, analyze and obtain the transaction anomaly monitoring index of each monitoring sub-area, and judge the transaction anomaly. If the judgment result is that the transaction status is abnormal, the adaptive threshold optimization module is further executed. Otherwise, the transaction status is judged to be normal;

[0044] In this embodiment, it should be specifically explained that the execution method of the transaction anomaly detection and judgment module is as follows:

[0045] Obtaining on-chain transaction data for each monitored sub-region, wherein the on-chain transaction data includes transaction amount and transaction frequency;

[0046] Calculate the transaction amount deviation of each monitoring sub-area. The calculation formula is as follows: ,in, Md i represents the transaction amount deviation of the i-th monitoring sub-area, represents the jth transaction amount in the i-th monitoring sub-area, represents the average transaction amount of the i-th monitoring sub-area, j=1, 2, 3, ..., m, j represents the number of each transaction amount, m represents the total number of transaction amounts, i is the number of each monitoring sub-area, i=1, 2, 3, ..., n, n represents the total number of monitoring sub-areas; it should be specifically noted that the transaction amount deviation is the ratio of the standard deviation of the transaction amount to the mean of the transaction amount, which eliminates the influence of the mean size and can compare the discreteness of the transaction amount at different mean levels; if Md i The larger the value, the greater the fluctuation of the transaction amount in the monitored sub-area relative to the mean, that is, the higher the degree of deviation of the transaction amount.

[0047] Calculate the transaction anomaly monitoring index of each monitoring sub-area. The calculation formula is as follows: ,in, AMI i represents the transaction anomaly monitoring index of the i-th monitoring sub-area, Md i represents the transaction amount deviation of the i-th monitoring sub-area, Mr i represents the transaction frequency mutation rate of the i-th monitoring sub-area, Indicates the deviation from the preset standard transaction amount. It represents the preset standard transaction frequency mutation rate; reads the transaction anomaly monitoring index of each monitoring sub-area, and judges the transaction anomaly status of the target enterprise's financing data. If the transaction anomaly monitoring index of a monitoring sub-area is less than the preset transaction anomaly monitoring index threshold, then the transaction status of the monitoring sub-area is judged to be normal; if the transaction anomaly monitoring index of a monitoring sub-area is greater than or equal to the preset transaction anomaly monitoring index threshold, then the transaction status of the monitoring sub-area is judged to be abnormal, and the adaptive threshold optimization module is further executed.

[0048] In this embodiment, it should be specifically explained that the calculation formula for the transaction frequency mutation rate of the i-th monitoring sub-area is specifically: ,in, Mr i represents the transaction frequency mutation rate of the i-th monitoring sub-area, Tf irepresents the current transaction frequency of the i-th monitoring sub-area, It represents the transaction frequency of the dth historical period in the i-th monitoring sub-area, represents the historical average transaction frequency of the i-th monitoring sub-region, d represents the time period number, and W represents the number of historical time periods. It should be noted that the current transaction frequency is defined as the number of transactions per unit time. The time unit can be adjusted based on the scenario (e.g., hour, day, week), and the time period is defined in units of "hours," "days," or "weeks." When calculating the transaction frequency mutation rate, data from the same time period is used.

[0049] Adaptive Threshold Optimization Module: Based on the transaction anomaly judgment results of the transaction anomaly detection and judgment module, it dynamically optimizes the transaction anomaly monitoring index threshold of each monitoring sub-area with abnormal transaction status through an adaptive threshold algorithm;

[0050] In this embodiment, it should be specifically explained that the execution method of the adaptive threshold optimization module is as follows:

[0051] Receive the transaction anomaly monitoring index of each monitoring sub-area transmitted by the transaction anomaly detection and judgment module, set the sliding window capacity to K, that is, select the data of the latest K monitoring cycles;

[0052] For the i-th monitoring sub-area, if it is currently in a monitoring cycle, the data in the sliding window is expressed as ,in, AMI i represents the transaction anomaly monitoring index of the i-th monitoring sub-area;

[0053] Perform statistical analysis on the transaction anomaly monitoring index within the sliding window of each monitoring sub-area, and calculate the mean value of the transaction anomaly monitoring index within the sliding window of each monitoring sub-area. The calculation formula is: ,in, represents the mean value of the transaction anomaly monitoring index in the sliding window of the i-th monitoring sub-area, Indicates the first monitoring sub-region in the sliding window p The transaction anomaly monitoring index of each data, p Represents the sequence number of the data in the sliding window, P = 1, 2, 3, ..., k;

[0054] Calculate the standard deviation of the transaction anomaly monitoring index within the sliding window of each monitoring sub-area. The calculation formula is: ,in, represents the standard deviation of the transaction anomaly monitoring index within the sliding window of the i-th monitoring sub-area;

[0055] The dynamic threshold is set by the adaptive threshold algorithm. The calculation formula of the dynamic threshold is: ,in,T i The dynamic threshold of the i-th monitoring sub-area, The adjustment coefficient represents the transaction anomaly monitoring index threshold, which is optimized based on the dynamic thresholds of each monitoring sub-area. The optimized transaction anomaly monitoring index threshold is fed back to the transaction anomaly detection and judgment module for subsequent anomaly judgment, forming an optimization closed loop. It should be noted that in one specific embodiment, the adjustment coefficient can be set to 1.5. The adaptive threshold optimization module analyzes the data distribution characteristics based on real-time transaction anomaly data, adaptively adjusting the threshold as the data pattern changes, thereby improving anomaly detection accuracy.

[0056] Data transmission leakage monitoring module: used to obtain data during the transmission process of each monitoring sub-area, monitor and analyze the data transmission process, and obtain the transmission leakage risk index of each monitoring sub-area;

[0057] In this embodiment, it should be specifically explained that the execution method of the data transmission leakage monitoring module is as follows:

[0058] Obtain data on the transmission process of each monitoring sub-area, including key update frequency, number of transmission anomalies, and total data transmission times;

[0059] Calculate the data transmission anomaly rate of each monitoring sub-area using the following formula: ,in, Ar i represents the data transmission anomaly rate of the i-th monitoring sub-area, [[ID=2)1]]Ae i represents the number of transmission anomalies in the i-th monitoring sub-area, At i represents the total number of data transmissions in the i-th monitoring sub-area;

[0060] Calculate the entropy value of the data transmission path in each monitoring sub-area. The calculation formula is as follows: ,in, Hp i represents the data transmission path entropy value of the i-th monitoring sub-area, represents the number of transmission path nodes of type g in the i-th monitoring sub-area, represents the total number of nodes in the transmission path of the i-th monitoring sub-area, , g Indicates the number of each transmission path node type, g =1, 2, 3, ..., L, where L represents the total number of transmission path node types;

[0061] It should be noted that the transmission path node types include but are not limited to routers, switches, and cloud servers.

[0062] The present invention provides another specific embodiment, specifically: if the transmission path of the target enterprise financing data is: enterprise gateway to operator router and then to cloud server, then , , ,According to the data transmission path entropy value calculation formula, the transmission path entropy value of the target enterprise financing data in a monitoring sub-area is obtained as follows: ;

[0063] The data transmission process is monitored and analyzed to obtain the transmission leakage risk index of each monitoring sub-area. The calculation formula is as follows: ,in, LRI i represents the transmission leakage risk index of the i-th monitoring sub-area, Indicates the maximum entropy value of the data transmission path, kf i represents the key update frequency of the i-th monitoring sub-area, Indicates the maximum value of the key update frequency; In this embodiment, it should be specifically noted that, in the formula, the data transmission abnormality rate of the i-th monitoring sub-area Ar i The larger the data transmission path entropy value is, the Hp i The larger the value, the more frequent the key update. kf i The smaller the value is, the higher the transmission leakage risk index of the i-th monitoring sub-area is. LRI i The larger the value is, the greater the risk of data transmission leakage in the monitoring sub-area.

[0064] It should be noted that the maximum entropy value of the data transmission path is obtained in the following manner:

[0065] The maximum value of information entropy occurs when all data transmission path node types are evenly distributed, that is, the number of nodes of each data transmission path type is equal. ,but ,When the number of node types in all data transmission paths is exactly the same, the uncertainty of the path, that is, the complexity, is the highest and the entropy value reaches the maximum value. .

[0066] Financing data security management module: Based on the transaction anomaly monitoring index and transmission leakage risk index of each monitoring sub-area, a data security management assessment coefficient model is established to analyze and obtain the data security management assessment coefficient of each monitoring sub-area;

[0067] In this embodiment, it should be specifically explained that the execution method of the financing data security management module is as follows: ,in, MEC irepresents the data security management evaluation coefficient of the i-th monitoring sub-area, AMI i represents the transaction anomaly monitoring index of the i-th monitoring sub-area, LRI i represents the transmission leakage risk index of the ith monitoring sub-area; in this embodiment, it should be specifically noted that, in the formula, the transaction anomaly monitoring index of the ith monitoring sub-area AMI i The smaller 、 Transmission Leakage Risk Index LRI i The smaller the value is, the greater the data security management evaluation coefficient of the i-th monitoring sub-area is. MEC i The larger the value is, the smaller the data security management requirement of the monitoring sub-area is, and the transaction anomaly monitoring index of the i-th monitoring sub-area is AMI i Transmission Leakage Risk Index LRI i Financing Data Security Assessment Module: This module is used to obtain the data security management assessment coefficients for each monitoring sub-area, assess the security of the target enterprise's financing data, output the financing data security assessment results, and issue early warning information for security anomalies.

[0068] In this embodiment, it should be specifically explained that the execution method of the financing data security assessment module is as follows:

[0069] The data security management assessment coefficient of each monitoring sub-area is read to assess the financing data security of the target enterprise. If the data security management assessment coefficient of a monitoring sub-area is greater than the preset security management assessment coefficient threshold, the data security management status of the monitoring sub-area is judged to be normal. If the data security management assessment coefficient of a monitoring sub-area is less than or equal to the preset security management assessment coefficient threshold, the data security management status of the monitoring sub-area is judged to be abnormal, and the abnormal data security management status is marked as the financing data security assessment result of the monitoring sub-area of ​​the target enterprise, and an early warning information is issued for the data security assessment result. Example 2

[0070] According to an exemplary embodiment, an electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0071] The processor executes the above-mentioned blockchain-based enterprise financing data security management system by calling the computer program stored in the memory.

[0072] Figure 2This is a structural diagram of an electronic device provided in an embodiment of the present application. The electronic device may have relatively large differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement a blockchain-based enterprise financing data security management system provided in the above-mentioned various method embodiments.

[0073] The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also include components such as a wired or wireless network interface and an input / output interface for input and output.

[0074] This embodiment also provides a computer program product stored on a computer-readable medium, including a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement the blockchain-based enterprise financing data security management system.

[0075] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0076] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0077] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0078] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A blockchain-based enterprise financing data security management system, characterized by: include: Enterprise financing data chain module: used to upload the target enterprise financing data to the chain, and use the target enterprise financing data blockchain node as each monitoring sub-area; Transaction anomaly detection and judgment module: used to obtain on-chain transaction data of each monitoring sub-area, establish a transaction anomaly detection model, analyze and obtain the transaction anomaly monitoring index of each monitoring sub-area, and judge the transaction anomaly. If the judgment result is that the transaction status is abnormal, the adaptive threshold optimization module is further executed. Otherwise, the transaction status is judged to be normal; The execution method of the transaction anomaly detection and judgment module is as follows: Obtaining on-chain transaction data for each monitored sub-region, wherein the on-chain transaction data includes transaction amount and transaction frequency; Calculate the transaction amount deviation of each monitoring sub-area. The calculation formula is as follows: ,in, Md i represents the transaction amount deviation of the i-th monitoring sub-area, represents the jth transaction amount in the i-th monitoring sub-area, represents the average transaction amount of the i-th monitoring sub-area, j = 1, 2, 3, ..., m, j represents the number of each transaction amount, m represents the total number of transactions, i is the number of each monitoring sub-area, i = 1, 2, 3, ..., n, n represents the total number of monitoring sub-areas; Calculate the transaction anomaly monitoring index of each monitoring sub-area. The calculation formula is as follows: ,in, AMI i represents the transaction anomaly monitoring index of the i-th monitoring sub-area, Md i represents the transaction amount deviation of the i-th monitoring sub-area, Mr i represents the transaction frequency mutation rate of the i-th monitoring sub-area, Indicates the deviation from the preset standard transaction amount. Indicates the preset standard transaction frequency mutation rate; Read the transaction anomaly monitoring index of each monitoring sub-area and judge the abnormal transaction status of the target enterprise's financing data. If the transaction anomaly monitoring index of a monitoring sub-area is less than the preset transaction anomaly monitoring index threshold, the transaction status of the monitoring sub-area is judged to be normal; if the transaction anomaly monitoring index of a monitoring sub-area is greater than or equal to the preset transaction anomaly monitoring index threshold, the transaction status of the monitoring sub-area is judged to be abnormal, and the adaptive threshold optimization module is further executed; Adaptive Threshold Optimization Module: Based on the transaction anomaly judgment results of the transaction anomaly detection and judgment module, it dynamically optimizes the transaction anomaly monitoring index threshold of each monitoring sub-area with abnormal transaction status through an adaptive threshold algorithm; Data transmission leakage monitoring module: used to obtain data during the transmission process of each monitoring sub-area, monitor and analyze the data transmission process, and obtain the transmission leakage risk index of each monitoring sub-area; Financing data security management module: Based on the transaction anomaly monitoring index and transmission leakage risk index of each monitoring sub-area, a data security management assessment coefficient model is established to analyze and obtain the data security management assessment coefficient of each monitoring sub-area; Financing data security assessment module: used to obtain the data security management assessment coefficient of each monitoring sub-area, assess the security of the target enterprise's financing data, output the financing data security assessment results, and issue early warning information for security abnormal data.

2. The blockchain-based enterprise financing data security management system according to claim 1, characterized in that: The execution method of the enterprise financing data chain module is as follows: The target enterprise financing data is uploaded to the chain, and the enterprise financing data is used as the target monitoring object. It is divided into n monitoring sub-areas according to the blockchain nodes, i=1, 2, 3,..., n, i is the number of each monitoring sub-area.

3. The blockchain-based enterprise financing data security management system according to claim 1, characterized in that: The calculation formula for the transaction frequency mutation rate of the i-th monitoring sub-area is specifically: ,in, Mr i represents the transaction frequency mutation rate of the i-th monitoring sub-area, Tf i represents the current transaction frequency of the i-th monitoring sub-area, It represents the transaction frequency of the dth historical period in the i-th monitoring sub-area, represents the historical average transaction frequency of the i-th monitoring sub-area, d represents the number of each time period, and W represents the number of historical time periods.

4. The blockchain-based enterprise financing data security management system according to claim 1, characterized in that: The execution mode of the adaptive threshold optimization module is as follows: Receive the transaction anomaly monitoring index of each monitoring sub-area transmitted by the transaction anomaly detection and judgment module, and set the sliding window capacity to K; Perform statistical analysis on the transaction anomaly monitoring index within the sliding window of each monitoring sub-area, and calculate the mean value of the transaction anomaly monitoring index within the sliding window of each monitoring sub-area. The calculation formula is: ,in, represents the mean value of the transaction anomaly monitoring index in the sliding window of the i-th monitoring sub-area, Indicates the first monitoring sub-region in the sliding window p The transaction anomaly monitoring index of each data, p Represents the sequence number of the data in the sliding window, P = 1, 2, 3, ..., k; Calculate the standard deviation of the transaction anomaly monitoring index within the sliding window of each monitoring sub-area. The calculation formula is: ,in, represents the standard deviation of the transaction anomaly monitoring index within the sliding window of the i-th monitoring sub-area; The dynamic threshold is set by the adaptive threshold algorithm. The calculation formula of the dynamic threshold is: ,in, T i The dynamic threshold of the i-th monitoring sub-area, It represents the adjustment coefficient. The transaction anomaly monitoring index threshold is optimized according to the dynamic threshold of each monitoring sub-area, and the optimized transaction anomaly monitoring index threshold is fed back to the transaction anomaly detection and judgment module.

5. The blockchain-based enterprise financing data security management system according to claim 1, characterized in that: The execution mode of the data transmission leakage monitoring module is as follows: Obtain data on the transmission process of each monitoring sub-area, including key update frequency, number of transmission anomalies, and total data transmission times; Calculate the data transmission anomaly rate of each monitoring sub-area using the following formula: ,in, Ar i represents the data transmission anomaly rate of the i-th monitoring sub-area, Ae i represents the number of transmission anomalies in the i-th monitoring sub-area, At i represents the total number of data transmissions in the i-th monitoring sub-area; Calculate the entropy value of the data transmission path in each monitoring sub-area. The calculation formula is as follows: ,in, Hp i represents the data transmission path entropy value of the i-th monitoring sub-area, represents the number of transmission path nodes of type g in the i-th monitoring sub-area, represents the total number of nodes in the transmission path of the i-th monitoring sub-area, , g Indicates the number of each transmission path node type, g =1, 2, 3, ..., L, where L represents the total number of transmission path node types; The data transmission process is monitored and analyzed to obtain the transmission leakage risk index of each monitoring sub-area. The calculation formula is as follows: ,in, LRI i represents the transmission leakage risk index of the i-th monitoring sub-area, Indicates the maximum entropy value of the data transmission path, kf i represents the key update frequency of the i-th monitoring sub-area, Indicates the maximum value of the key update frequency.

6. The blockchain-based enterprise financing data security management system according to claim 5, characterized in that: The method for obtaining the maximum entropy value of the data transmission path is as follows: The maximum value of information entropy occurs when all data transmission path node types are evenly distributed, that is, the number of nodes of each data transmission path type is equal. ,but ,When the number of node types in all data transmission paths is exactly the same, the uncertainty of the path, that is, the complexity, is the highest and the entropy value reaches the maximum value. .

7. The blockchain-based enterprise financing data security management system according to claim 1, characterized in that: The execution method of the financing data security management module is as follows: ,in, MEC i represents the data security management evaluation coefficient of the i-th monitoring sub-area, AMI i represents the transaction anomaly monitoring index of the i-th monitoring sub-area, LRI i represents the transmission leakage risk index of the i-th monitoring sub-area.

8. The blockchain-based enterprise financing data security management system according to claim 1, characterized in that: The execution method of the financing data security assessment module is as follows: The data security management assessment coefficient of each monitoring sub-area is read to assess the financing data security of the target enterprise. If the data security management assessment coefficient of a monitoring sub-area is greater than the preset security management assessment coefficient threshold, the data security management status of the monitoring sub-area is judged to be normal. If the data security management assessment coefficient of a monitoring sub-area is less than or equal to the preset security management assessment coefficient threshold, the data security management status of the monitoring sub-area is judged to be abnormal, and the abnormal data security management status is marked as the financing data security assessment result of the monitoring sub-area of ​​the target enterprise, and an early warning information is issued for the data security assessment result.

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