Digital function management method and system based on data mining

Abstract

The invention discloses a digital certificate management method and system based on data mining, and relates to the technical field of digital certificate management, and the method comprises the following steps: obtaining enterprise financial data, and encrypting the enterprise financial data to generate a digital certificate; acquiring strength data and risk influence data in an encryption process, and constructing an encryption risk assessment model based on a bp neural network algorithm; and screening the digital certificate according to the output of the encryption risk assessment model, and carrying out block chain certificate storage processing on the screened digital certificate. According to the invention, the intensity data and the risk influence data in the encryption process are obtained based on the data mining technology, and the encryption risk assessment model is constructed. And by screening the digital function, the encryption risk is effectively reduced, and the data security is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of confirmation management, and more specifically, to a digital confirmation management method and system based on data mining. Background Art

[0002] In today's digital age, corporate financial auditing is facing increasingly complex challenges. Traditional confirmation methods are inefficient and have many drawbacks, such as cumbersome manual processing, untimely information transmission, and difficulty in ensuring data authenticity. With the rapid development of information technology, digital confirmations have emerged. With the help of digital technology, digital confirmations have realized the automation and intelligence of the confirmation process. However, faced with massive financial data and complex business environments, confirmation management faces many problems. On the one hand, the influx of data has generated a large amount of redundant information in the confirmation process, increasing the difficulty of processing; on the other hand, the authenticity and reliability of the data are difficult to guarantee, and there are potential risks. Data mining technology provides a powerful means to solve these problems. By deeply mining corporate financial data, confirmation historical data, etc., key information can be extracted and hidden patterns and laws can be discovered. For example, data mining technology can be used to analyze the transaction patterns and reply laws of the confirmed units, so as to assess the risks of confirmations. At the same time, data mining technology can also help companies optimize the confirmation process and improve the efficiency of confirmations. By analyzing the data, abnormal situations can be discovered in a timely manner, providing decision support for enterprises.

[0003] In addition, with the continuous development of network security technology, digital confirmation faces severe security challenges. Problems such as data leakage and network attacks have brought huge losses to enterprises. Therefore, it is imperative to strengthen the security protection measures of digital confirmation.

[0004] The above disclosed technical solutions have at least the following technical problems: Digital confirmation involves a large amount of sensitive financial information and corporate data. If the platform's security protection measures are not in place, such as network attacks, insufficient data transmission encryption, etc., data leakage may occur, causing serious losses to the enterprise and related parties. In view of the above problems, the present invention proposes a solution. 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 digital confirmation management method and system based on data mining, which constructs an encryption risk assessment model to solve the problem of insufficient data transmission encryption, resulting in data leakage and causing serious losses to enterprises and related parties.

[0006] To achieve the above object, the present invention provides the following technical solutions: A digital confirmation management method based on data mining includes the following steps: obtaining enterprise financial data and encrypting the enterprise financial data to generate digital confirmation; obtaining strength data and risk impact data in the encryption process and building an encryption risk assessment model based on a bp neural network algorithm; screening digital confirmations according to the output of the encryption risk assessment model and performing blockchain storage processing on the screened digital confirmations.

[0007] In a preferred embodiment, the encryption of corporate financial data to generate digital confirmation is specifically as follows: collecting corporate financial data, using the isnull() function to identify missing values ​​in the data and fill them in; identifying outliers based on a box plot, and replacing out-of-range outliers with upper or lower limits to obtain preprocessed corporate financial data; based on a symmetric encryption algorithm, generating a symmetric key of a fixed length; performing an XOR operation on the financial data and the symmetric key to generate a ciphertext; generating an asymmetric key pair based on an asymmetric encryption algorithm, the asymmetric key pair comprising a public key and a private key; encrypting the symmetric key according to the generated public key, storing all data in the financial system after public key encryption, and at the same time, in the financial system, forcing all users to enable multi-factor authentication.

[0008] In a preferred embodiment, the strength data includes a data encryption strength coefficient, and a specific method for obtaining the data encryption strength coefficient is as follows: obtain the key length and algorithm complexity generated during the symmetric encryption process; calculate the size of the key space based on the generated key length; obtain the key strength index based on the size of the key space, the encryption algorithm, and the ratio of the largest key space size among the key lengths; obtain the algorithm strength factor by performing regression analysis based on the complexity of the algorithm and the power operation method used by the algorithm; and obtain the data encryption strength coefficient based on the data encryption strength coefficient calculation formula by combining the key strength index and the algorithm strength factor with the pre-acquired update frequency and security patch response time.

[0009] In a preferred embodiment, the risk impact data includes a key performance delay coefficient, and a specific method for obtaining the key performance delay coefficient is as follows: obtain the key generation delay data in each time period, and perform time series analysis to identify the time period where the system generates a performance bottleneck, and calculate the delay density of each time period; accumulate the delay density in all time periods to obtain the total key generation delay density; calculate the average delay time based on the total key generation delay density and the total duration of system operation; measure the processor load and network bandwidth performance indicators in the simulated environment, calculate the overall processing capacity of the system, and combine the overall processing capacity of the system with the average delay time to calculate the key performance delay coefficient.

[0010] In a preferred embodiment, the risk impact data also includes a key fluctuation impact coefficient, and the specific method for obtaining the key fluctuation impact coefficient is as follows: continuously collect key data at different nodes, and record the key value of each node at each collection time; calculate the entropy value of the key data of each node at each collection time, and mark the key entropy at different nodes at the same time; A detection interval including several collection moments is set, and the average value and standard deviation of the key entropy of each node at different moments are calculated within the detection interval, and the key fluctuation influence coefficient is calculated.

[0011] In a preferred embodiment, the digital confirmation is screened according to the output of the encryption risk assessment model, specifically: a risk threshold is set, the encryption risk assessment coefficient of the digital confirmation is compared with the risk threshold, and a comparison result is obtained, wherein the comparison result includes passing secondary verification and multi-party verification; wherein, in the secondary verification, the digital confirmation is marked as a high-risk confirmation, and a hash function and a symmetric encryption algorithm are selected for re-encryption; in the multi-party verification, the re-encrypted data is split into N fragments, distributed to the end customer through Shamir secret sharing, and a threshold for digital confirmation restoration verification fragments is set. After the verification is passed, a multi-party verification certificate is generated.

[0012] In a preferred embodiment, the blockchain evidence processing of the screened digital confirmation is specifically as follows: the screened digital confirmation is decomposed into time slices and space slices according to the time dimension, classified and structured, key information is extracted and data is stored; a dual-chain hybrid architecture is constructed, the extracted key information is processed, and a unique hash value is generated; a seed is generated by a quantum random number generator, a public-private key pair is generated based on a lattice password, the public key hash is written into the classical chain, and the private key shard is hosted on quantum security hardware; when a quantum computing threat is triggered, the quantum chain verification logic is automatically enabled, and the classical hash verification and quantum signature verification are performed.

[0013] The technical effects and advantages of the digital confirmation management method and system based on data mining of the present invention are as follows: 1. The present invention obtains the strength data and risk impact data in the encryption process based on data mining technology to construct an encryption risk assessment model. By screening digital confirmations, the encryption risk is effectively reduced and data security is ensured. At the same time, the screened digital confirmations are processed by blockchain evidence storage, and the tamper-proof characteristics of blockchain are used to ensure the integrity and traceability of the data. This process not only improves the security of corporate financial data, but also provides a reliable basis for audit work and enhances the trust between enterprises and customers.

[0014] 2. The present invention provides enterprises with quantitative encryption risk assessment indicators by analyzing various factors in the encryption process through data mining. The encryption risk assessment model constructed based on the bp neural network algorithm helps enterprises to be more scientific and reasonable when selecting encryption algorithms. The screening and re-encryption of digital confirmations enable enterprises to adjust encryption strategies according to risk levels and optimize resource allocation. This not only improves encryption efficiency, but also reduces encryption costs and enhances the overall competitiveness of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention is a flowchart of a digital confirmation management method based on data mining.

[0016] Figure 2 The present invention is a structural schematic diagram of a digital letter of credit management system based on data mining. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0018] Embodiment 1, Figure 1 The present invention provides a digital confirmation management method based on data mining, which includes the following steps: S1, obtain the enterprise financial data, and encrypt the enterprise financial data to generate digital confirmation; The enterprise financial data is encrypted using a hybrid encryption algorithm to generate a digital confirmation. The hybrid encryption algorithm is a combination of a symmetric encryption algorithm and an asymmetric encryption algorithm. In hybrid encryption, the public key of the recipient is used to encrypt the key used by the symmetric encryption algorithm. In this way, only the recipient with the corresponding private key can decrypt the symmetric encryption key and then decrypt the enterprise financial data.

[0019] The advantages of using a hybrid encryption algorithm are: Balance between efficiency and security: Symmetric encryption is fast and can efficiently process large amounts of financial data. Asymmetric encryption provides higher security, especially during the key exchange process, which ensures the secure transmission of symmetric encryption keys. For example, if an enterprise needs to send encrypted financial data to an auditing agency, hybrid encryption can not only quickly encrypt data, but also ensure the security of the key during transmission to prevent data leakage.

[0020] Convenience of key management: In asymmetric encryption, the public key can be widely distributed, while the private key is kept by a specific recipient. This approach makes key management more flexible and secure. For the encryption of corporate financial data, the company can provide the public key to authorized auditors or relevant institutions, while the private key is kept by the company itself or in a designated secure storage location.

[0021] The obtaining of enterprise financial data and encrypting the enterprise financial data are specifically as follows: Collect corporate financial data, use the isnull() function to identify missing values ​​in the data, and use different methods to fill in different missing values; Identify outliers based on the box plot and replace outliers with upper or lower limits to obtain preprocessed corporate financial data; Based on the symmetric encryption algorithm, a symmetric key of fixed length is generated; Perform XOR operation on financial data and symmetric key to generate ciphertext; Generate an asymmetric key pair based on an asymmetric encryption algorithm, wherein the asymmetric key pair includes a public key and a private key; Encrypt the symmetric key based on the generated public key, and store all the data in the financial system after public key encryption. At the same time, in the financial system, multi-factor authentication is mandatory for all users. Specifically, different missing values ​​may be filled using different methods: for numerical data, the mean and median may be used for filling; for character data, the mode may be used for filling.

[0022] Different missing values ​​are filled using different methods, specifically: Classify missing values ​​into numerical data, categorical data, and time series data; For numerical data, use mean filling, median filling, and interpolation methods to fill; For categorical data, mode filling, forward filling and backward filling are used for filling processing; For time series data, linear interpolation and time-weighted averaging are used for filling processing; Input the above filling strategy into the data processing tool to write the code.

[0023] The method of identifying outliers based on the box plot and replacing out-of-range outliers with upper or lower limits is as follows: The box plot describes the data through five key statistics: minimum (Min), first quartile (Q1), median (Q2), third quartile (Q3), and maximum (Max); The outliers of the box plot are defined as low outliers and high outliers; the low outliers are values ​​less than Q1 - 1.5* IQR, and the high outliers are values ​​greater than Q3 + 1.5 * IQR, where IQR is the difference between the third quartile (Q3) and the first quartile (Q1); For low outliers, replace low outliers with Q1, and for high outliers, replace high outliers with Q3.

[0024] The multi-factor authentication includes: Password (Something You Know): The user first enters their password or PIN, which is the most basic form of authentication, but is generally not secure enough.

[0025] Something You Have: Generate a one-time password (OTP) through a mobile phone or other hardware device, such as Google Authenticator, SMS verification code, etc. This adds a physical factor and improves security.

[0026] Biometrics (Something You Are): Use biometric features such as fingerprints, facial recognition, iris scans, etc. for identity verification. The advantage of biometric technology is its uniqueness and difficulty in duplication.

[0027] Hardware Token: Users can authenticate themselves using a physical token, such as a USB security key or smart card. These devices generate dynamic passwords or use encrypted authentication to ensure account security.

[0028] S2, obtains the intensity data and risk impact data in the encryption process, and builds an encryption risk assessment model based on the bp neural network algorithm; The strength data includes a data encryption strength coefficient, and the key risk impact data includes a key performance delay coefficient and a key fluctuation impact coefficient; The data mining technology includes association rule mining, classification algorithm and cluster analysis. Association rule mining is used to discover the correlation between different factors in the encryption process, and the classification algorithm is used to classify the encryption strength data and risk impact data. Cluster analysis is used to cluster similar encryption processes, so as to obtain the strength data and risk impact data in the encryption process more comprehensively and accurately.

[0029] The data encryption strength coefficient provides a quantitative reference indicator for enterprises to evaluate the security of encryption algorithms. By calculating this coefficient, enterprises can better understand the reliability of the encryption process and thus choose appropriate encryption algorithms and encryption strategies. In practical applications, this coefficient can help enterprises find a balance between protecting data security and meeting business needs, ensuring the effectiveness and reliability of encryption technology.

[0030] The specific method for obtaining the data encryption strength coefficient is as follows: Get the key length and algorithm complexity generated during symmetric encryption; The size of the key space is calculated based on the generated key length; The key strength index is obtained based on the ratio of the size of the key space to the largest key space size among all encryption algorithms and key lengths; The algorithm strength factor is obtained by performing regression analysis based on the complexity of the algorithm and the power operation method used by the algorithm; The key strength index, algorithm strength factor, and pre-acquired update frequency and security patch response time are combined to obtain the data encryption strength coefficient based on the data encryption strength coefficient calculation formula.

[0031] The power operation algorithm involves the operation of power (exponentiation result) in mathematics, mainly including multiplication, division and exponentiation of power.

[0032] The core idea of ​​the fast power algorithm is to split the exponent into binary and use the power operation rules for fast calculation.

[0033] When the exponent is an even number, the power operation can be converted into the power operation of squaring the base and dividing the exponent by 2; when the exponent is an odd number, the base can be multiplied into the result first, and then the exponent can be subtracted by 1 and converted to an even number.

[0034] The specific steps of the algorithm are: The initialization result is 1; When the index is greater than 0, loop: If the exponent is even, square the base and divide the exponent by 2; If the exponent is an odd number, multiply the base into the result and subtract 1 from the exponent (since the base has been multiplied once, the following operation can be considered as an even power operation after squaring the base); After the loop is completed, the final result is obtained.

[0035] Algorithm complexity: The time complexity of the fast power algorithm is O(logn), where n is the exponent.

[0036] Compared with the O(n) time complexity of brute force solution, the fast power algorithm has significant advantages in dealing with large exponential power operations.

[0037] The calculation formula of the key strength index is:

[0038] The calculation formula of the data encryption strength coefficient is:

[0039] in, is the key strength index, is the key length, is the maximum allowed key space, is the data encryption strength coefficient, is the algorithm strength factor, is the update frequency, Response time for security patches.

[0040] The data encryption strength factor has the following benefits for assessing encryption risk: Assessing security: The data encryption strength coefficient is a quantitative indicator that can intuitively show the security level of the encryption algorithm. When assessing encryption risks, enterprises no longer rely solely on vague qualitative descriptions such as "high risk" and "low risk", but can accurately measure the security of the encryption algorithm through specific coefficient values. For example, a coefficient value of 0.8 may indicate relatively high security, while 0.3 may indicate a high security risk. This quantitative approach helps enterprises more objectively compare the security differences between different encryption algorithms.

[0041] Auxiliary algorithm selection: When faced with multiple encryption algorithms, enterprises can use the data encryption strength coefficient to select the algorithm that best suits their needs. If the data processed by the enterprise is very sensitive, such as financial institutions processing customer fund transaction data, they can choose an encryption algorithm with a higher data encryption strength coefficient to ensure the high security of the data. For some data with relatively low security requirements, such as general notification documents within the enterprise, encryption algorithms with lower costs but sufficient security can be selected based on the coefficient, thereby finding a balance between security and cost.

[0042] Consider update and maintenance factors: This factor takes update frequency and security patch response time into account. This allows enterprises to fully consider the dynamic security of encryption algorithms when assessing encryption risks. Timely updates and fast security patch responses can effectively reduce the risk of encryption algorithms being compromised. For example, although an encryption algorithm initially performs well in key strength index and algorithm strength factor, if its update frequency is very low and the security patch response time is very long, then its data encryption strength coefficient may be reduced, which reminds the enterprise that the encryption algorithm has a high encryption risk.

[0043] Comprehensive reliability assessment: By combining multiple factors such as key length and algorithm complexity to calculate the data encryption strength coefficient, the reliability of the encryption process can be comprehensively evaluated. Key length and algorithm complexity reflect the anti-attack capability of the encryption algorithm itself, while update frequency and security patch response time involve the ability of the encryption algorithm to respond to new threats. Such a comprehensive assessment helps enterprises discover potential risk points of encryption algorithms. For example, an algorithm may have a large enough key space but low algorithm complexity, making it vulnerable to certain types of attacks; or an algorithm may be complex but not updated for a long time, which will also increase encryption risks.

[0044] The key performance delay coefficient is a comprehensive indicator used to evaluate the performance of the key generation system. It takes into account multiple factors such as the delay time of key generation, the overall system processing power, and possible performance bottlenecks. The higher the value of the key performance delay coefficient, the more serious the delay in key generation is under a given system processing power, and the worse the system performance is. Specifically, the key performance delay coefficient calculates the key generation delay density in each time period and combines the system processing power and the weighting factor of the performance bottleneck to comprehensively evaluate the performance of the key generation system. This coefficient not only reflects the direct delay of key generation, but also takes into account the system resource utilization and potential performance limitations.

[0045] The specific method for obtaining the key performance delay coefficient is as follows: Perform key performance evaluation in a simulated environment to obtain key generation delay data in each time period. Perform time series analysis on the measured key generation delay data to identify the time period where the system generates performance bottlenecks and calculate the delay density of each time period. The delay density in all time periods is accumulated to obtain the total key generation delay density; The average delay time is calculated based on the total key generation delay density and the total system operation time; The processor load and network bandwidth performance indicators in the simulation environment are measured to calculate the overall system processing capacity. The key performance delay coefficient is calculated by combining the overall system processing capacity with the average delay time.

[0046] The calculation formula of the key generation delay density is:

[0047] The overall processing capacity of the system is specifically:

[0048] The calculation formula of the key performance delay coefficient is:

[0049] in, For key generation latency density, is the total number of time periods during which the system runs. is the key generation delay time for the i-th time period, is the duration of the i-th time period, is the key performance delay coefficient, For the overall processing capacity of the system, is the processor load in the i-th time period, is the network bandwidth in the i-th time period.

[0050] The key performance delay factor has the following benefits for assessing encryption risk: Accurately locate performance bottlenecks and risky periods: In the process of obtaining the key performance delay coefficient, time series analysis is used to identify the time period where the system has performance bottlenecks. This helps enterprises accurately locate the time periods when risks may occur during the encryption process. For example, during certain peak business hours, the density of key generation delays is high, which may mean that the system is more vulnerable to attacks during these periods. Because delays may cause the encryption process to be unsmooth, attackers may use this time window to conduct man-in-the-middle attacks or other malicious operations. By identifying these high-risk periods, enterprises can strengthen security monitoring and preventive measures during these periods.

[0051] Quantitatively evaluate the performance risk of encryption systems: Calculating the total key generation delay density and average delay time can quantify the delay in the key generation process. Encryption risk is closely related to system performance. Excessive delay may prevent the encryption system from responding to security threats in a timely manner. For example, if the average delay time of an encryption system is too long, it may not be able to quickly update keys or complete encryption operations when facing malicious brute force attacks, thereby increasing the risk of data leakage. Through the key performance delay coefficient, enterprises can quantify this performance risk and intuitively understand the impact of the encryption system's performance on encryption risk.

[0052] Comprehensively consider the relationship between system resources and encryption risk: When calculating the key performance delay coefficient, the overall system processing capacity indicators such as processor load and network bandwidth are also considered. This allows enterprises to comprehensively evaluate the impact of system resources on encryption risk. For example, when the processor load is too high or the network bandwidth is insufficient, the key performance delay coefficient will increase accordingly, which indicates that the encryption risk of the enterprise system will also increase under such resource constraints. Based on this information, enterprises can reasonably allocate system resources, optimize the operating environment of the encryption system, and reduce the encryption risk caused by insufficient system resources.

[0053] The key fluctuation impact coefficient is an important indicator for measuring key stability and security. In the cryptographic system, the key is a key factor in ensuring information security. The key fluctuation impact coefficient is mainly used to evaluate the degree of change of the key under the influence of different factors. From the perspective of stability, it can reflect the fluctuation of the key in time and space dimensions. By calculating the key fluctuation impact coefficient, this change can be quantified. From the security perspective, excessive key fluctuation may mean that the key is more likely to be cracked or attacked. Because the instability of the key state may cause abnormalities in the encryption and decryption process, increasing security risks. This coefficient can help security personnel discover potential security risks in a timely manner and provide an important basis for optimizing the key system.

[0054] The specific method for obtaining the key fluctuation influence coefficient is as follows: At different nodes, continuously collect key data and record the key value of each node at each collection time; the different nodes include key generation nodes, key distribution nodes, and key update nodes; Based on the principle of Shannon entropy, the entropy value of the key data of each node at each collection time is calculated, and the key entropy at different nodes at the same time is marked; Set a detection interval including several collection moments, and calculate the average value and standard deviation of the key entropy of each node at different moments within the detection interval; Calculate the fluctuation coefficient of key entropy at different time nodes according to the mean value and standard deviation of key entropy; The key fluctuation influence coefficient is calculated according to the fluctuation coefficient of the key entropy.

[0055] The selection of the detection interval includes the following requirements: 1. Frequency and requirements of data collection: Frequency requirements: The detection interval should be set based on the frequency of data collection. If data collection is very frequent (e.g., multiple times per second), the detection interval may need to be set shorter to capture data changes in a timely manner. On the contrary, if data collection is less frequent (e.g., once a day), the detection interval can be extended accordingly.

[0056] Demand-oriented: Determine the length of the detection interval based on the specific needs of analysis or monitoring. For example, if you are concerned about short-term fluctuations in data, the detection interval should be shorter; if you are concerned about long-term trends, the detection interval can be longer.

[0057] 2. Data characteristics and stability: Data stability: For data with high stability, a longer detection interval may be more appropriate because such data does not change much in a short period of time. For data with high volatility, a shorter detection interval may more accurately reflect the actual situation of the data.

[0058] Data distribution: Consider the distribution characteristics of the data, such as whether there are periodic changes, seasonal fluctuations, etc. These factors will affect the selection of the detection interval to ensure that these characteristics can be captured within the detection interval.

[0059] 3. Computing resources and efficiency: Computational resources: The setting of the detection interval also needs to consider the available computing resources. A shorter detection interval means that more data points need to be processed, which may increase the computational burden. Therefore, when choosing the detection interval, it is necessary to balance the need for computing resources and data accuracy.

[0060] Efficiency considerations: In order to improve analysis efficiency, an appropriate detection interval length is usually selected to reduce computing time and resource consumption while maintaining data accuracy. The calculation formula of the average value of the key entropy is:

[0061] The calculation formula of the standard deviation of the key entropy is:

[0062] The calculation formula of the key fluctuation influence coefficient is:

[0063] in, is the average value of key entropy, is the total number of moments in the detection interval, is the probability of the key appearing, is the standard deviation of the key entropy, is the key fluctuation influence coefficient.

[0064] The encryption risk assessment model is specifically:

[0065] in, is the encryption risk assessment coefficient, is the preset data encryption strength coefficient weight factor, is the preset key performance delay coefficient weight factor, is the preset key fluctuation influence coefficient weight factor, is the data encryption strength coefficient, is the key performance delay coefficient, is the key fluctuation influence coefficient.

[0066] S3, screens digital confirmations based on the output of the encryption risk assessment model, and performs blockchain storage on the screened digital confirmations.

[0067] The digital confirmation is screened according to the output of the encryption risk assessment model, specifically: When the encryption risk assessment coefficient of the digital confirmation is lower than the preset first risk threshold, the digital confirmation is judged to be qualified and passed; When the encryption risk assessment value is lower than the preset second risk threshold and higher than the preset first risk threshold, a local secondary verification is performed, the digital confirmation is marked as a high-risk confirmation, and a hash function and a symmetric encryption algorithm are selected for re-encryption; When the encryption risk assessment value is higher than the preset second risk threshold, re-encryption combined with multi-party verification is triggered, and the re-encrypted data is split into N fragments and distributed to the regulator, auditor, and enterprise through Shamir secret sharing; The digital confirmation restoration verification of the fragments provided by 2 / 3 parties will be obtained. After the verification is passed, all parties will jointly sign to generate a multi-party verification certificate.

[0068] The blockchain evidence storage process of the screened digital letter of credit is specifically as follows: The screened digital confirmations are decomposed into time slices and space slices according to the time dimension, classified and structured, and key information is extracted and stored in the judicial chain, enterprise alliance chain and IPFS respectively. The key information includes the confirmation number, the information of the sender and the receiver, the specific content of the confirmation, and the timestamp; Construct a dual-chain hybrid architecture, which includes a classical chain and a quantum chain, and the quantum chain has a pre-embedded anti-quantum signature layer; The Classic Chain uses the SHA-3 hash algorithm to process the extracted key information and generate a unique hash value; Generate seeds through quantum random number generator, generate public and private key pairs based on lattice cryptography, write public key hash into the classical chain, and shard the private key to quantum security hardware; When a quantum computing threat is triggered, the quantum chain verification logic is automatically enabled, requiring that the stored evidence data must pass both classical hash verification and quantum signature verification.

[0069] Embodiment 2, Figure 2The present invention provides a digital letter of credit management system based on data mining, which includes the following modules: a data encryption module, a risk assessment model building module and a digital letter of credit screening and evidence storage module; Data encryption module: collects and acquires corporate financial data, encrypts the data and generates digital confirmations; Risk assessment model building module: obtains the intensity data and risk impact data in the encryption process, and builds an encryption risk assessment model based on the bp neural network algorithm; Digital confirmation screening and storage module: used to screen digital confirmations based on the output of the encryption risk assessment model, and perform blockchain storage processing on the screened digital confirmations.

[0070] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0071] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0072] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0073] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0074] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0075] 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 protection scope of the present invention.

Claims

1. A digital confirmation management method based on data mining, characterized in that: The following steps are involved: Obtain enterprise financial data, encrypt the enterprise financial data and generate digital confirmation; Obtain the intensity data and risk impact data during the encryption process, and build an encryption risk assessment model based on the bp neural network algorithm; Digital confirmations are screened based on the output of the encrypted risk assessment model, and the screened digital confirmations are processed for blockchain storage.

2. The digital confirmation management method based on data mining according to claim 1 is characterized in that: The encryption of enterprise financial data to generate digital confirmation is specifically as follows: Collect corporate financial data, use the isnull() function to identify missing values ​​in the data and fill them in; Identify outliers based on the box plot and replace outliers with upper or lower limits to obtain preprocessed corporate financial data; Based on the symmetric encryption algorithm, a symmetric key of fixed length is generated; Perform XOR operation on financial data and symmetric key to generate ciphertext; Generate an asymmetric key pair based on an asymmetric encryption algorithm, wherein the asymmetric key pair includes a public key and a private key; The symmetric key is encrypted according to the generated public key, and all data in the financial system is stored after being encrypted with the public key. At the same time, multi-factor authentication is mandatory for all users in the financial system.

3. The digital confirmation management method based on data mining according to claim 2 is characterized in that: The strength data includes a data encryption strength coefficient, and a specific method for obtaining the data encryption strength coefficient is as follows: Get the key length and algorithm complexity generated during symmetric encryption; Calculate the size of the key space based on the generated key length; The key strength index is obtained according to the ratio of the key space size, the encryption algorithm and the largest key space size among the key lengths; The algorithm strength factor is obtained by performing regression analysis based on the complexity of the algorithm and the power operation method used by the algorithm; The key strength index, algorithm strength factor, and pre-acquired update frequency and security patch response time are combined to obtain the data encryption strength coefficient based on the data encryption strength coefficient calculation formula.

4. The digital confirmation management method based on data mining according to claim 3 is characterized in that: The risk impact data includes a key performance delay coefficient, and a specific method for obtaining the key performance delay coefficient is as follows: Obtain key generation delay data in each time period, perform time series analysis, identify the time period where the system generates performance bottlenecks, and calculate the delay density of each time period; The delay density in all time periods is accumulated to obtain the total key generation delay density; Calculate the average delay time based on the total key generation delay density and the total system operation time; Measure the processor load and network bandwidth performance indicators in the simulation environment, calculate the overall system processing capacity, combine the overall system processing capacity with the average delay time, and calculate the key performance delay coefficient.

5. The digital confirmation management method based on data mining according to claim 4 is characterized in that: The risk impact data also includes a key fluctuation impact coefficient, and a specific method for obtaining the key fluctuation impact coefficient is as follows: At different nodes, key data is continuously collected, and the key value of each node at each collection moment is recorded; Calculate the entropy value of the key data of each node at each collection time, and mark the key entropy at different nodes at the same time; A detection interval including several collection moments is set, and the average value and standard deviation of the key entropy of each node at different moments are calculated within the detection interval, and the key fluctuation influence coefficient is calculated.

6. The digital confirmation management method based on data mining according to claim 5 is characterized in that: The digital confirmation is screened according to the output of the encryption risk assessment model, specifically: Setting a risk threshold, comparing the encrypted risk assessment coefficient of the digital confirmation with the risk threshold, and obtaining a comparison result, wherein the comparison result includes secondary verification and multi-party verification; Among them, in the secondary verification, the digital confirmation is marked as a high-risk confirmation, and a hash function and a symmetric encryption algorithm are selected for re-encryption; In multi-party verification, the re-encrypted data is split into N fragments and distributed to end customers through Shamir secret sharing. A threshold for digital confirmation restoration verification fragments is set. After the verification is passed, a multi-party verification certificate is generated.

7. The digital confirmation management method based on data mining according to claim 6 is characterized in that: The blockchain evidence storage process of the screened digital letter of credit is specifically as follows: The screened digital confirmations are decomposed into time slices and space slices according to the time dimension, classified and structured, and key information is extracted and stored; Build a dual-chain hybrid architecture, extract key information for processing, and generate a unique hash value; Generate seeds through quantum random number generator, generate public and private key pairs based on lattice cryptography, write public key hash into the classical chain, and shard the private key to quantum security hardware; When a quantum computing threat is triggered, the quantum chain verification logic is automatically enabled, through classical hash verification and quantum signature verification.

8. The digital confirmation management method based on data mining according to claim 7 is characterized in that: The calculation formula of the key generation delay density is: The calculation formula of the key performance delay coefficient is: in, For key generation latency density, is the total number of time periods during which the system is running, is the key generation delay time for the i-th time period, is the duration of the i-th time period, is the key performance delay coefficient, For the overall processing capacity of the system, is the processor load in the i-th time period, is the network bandwidth in the i-th time period.

9. The digital confirmation management method based on data mining according to claim 8 is characterized in that: The calculation formula of the average value of the key entropy is: The calculation formula of the standard deviation of the key entropy is: The calculation formula of the key fluctuation influence coefficient is: in, is the average value of key entropy, is the total number of moments in the detection interval, is the probability of the key appearing, is the standard deviation of the key entropy, is the key fluctuation influence coefficient.

10. A system for digital confirmation management method based on data mining according to any one of claims 1 to 9, characterized in that: It includes the following modules: data encryption module, risk assessment model building module and digital confirmation screening and evidence storage module; Data encryption module: collects and acquires corporate financial data, encrypts the data and generates digital confirmations; Risk assessment model building module: obtains the intensity data and risk impact data in the encryption process, and builds an encryption risk assessment model based on the bp neural network algorithm; Digital confirmation screening and storage module: used to screen digital confirmations based on the output of the encryption risk assessment model, and perform blockchain storage processing on the screened digital confirmations.

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