A method and system for secure storage of financial data

Abstract

The present invention relates to the technical field of financial data processing, and more specifically to a method and system for securely storing financial data. The method comprises: obtaining the salary data of all employees within at least one year in the current month contained in the financial data; calculating the overall salary coreness of each employee based on the salary data; sorting all employees in the current month according to a preset sorting rule based on the total salary of the employees in the salary data to obtain an employee sorting result; calculating the salary similarity between every two employees in each month based on the salary data, the overall salary coreness, and the employee sorting result; clustering the salary data of all employees in each month based on the salary similarity to obtain a clustering result; and securely storing the monthly salary data in the financial data in blocks based on the clustering result. The solution provided by the present invention can reduce the distance between core employees and surrounding data points, improve clustering accuracy, and enhance the storage reliability and security of financial data.

Description

Technical Field

[0001] The present invention relates to the technical field of financial data processing, and in particular to a method and system for securely storing financial data. Background Art

[0002] Financial data typically includes income, expenditure, and asset data. Monthly employee salary data is a core component of this data and is crucial for a company's budgeting, cost control, and financial planning. Furthermore, salary data is sensitive personal information, including employee income and tax status. Companies must properly safeguard this data to prevent data leaks. Therefore, ensuring the secure storage of this monthly salary data within financial data is crucial.

[0003] In related technologies, clustering algorithms are often used to directly classify and process all employee salary data and serve as the basis for block storage. However, due to significant differences in job responsibilities, positions, and experience among different employees, if the total salary of a core employee differs significantly from that of other employees around them, that is, the density of their salaries is low, then directly using the original distance metric may not effectively capture the potential similarities between employees. As a result, the core employee's salary data is easily mislabeled as noise points, making it difficult to form clusters. Furthermore, the accuracy of the clustering results is difficult to guarantee, which in turn affects the storage reliability and security of financial data. Summary of the Invention

[0004] In order to solve the technical problem that traditional block-based secure storage solutions have low clustering accuracy, resulting in insufficient storage reliability and security of financial data, the present invention aims to provide a method and system for secure storage of financial data. The technical solutions adopted are as follows:

[0005] In one aspect, the present invention provides a method for securely storing financial data, the method comprising:

[0006] Get the salary data of all employees in the current month within at least one year from the financial data;

[0007] Based on the salary data, calculate the overall salary coreness of each employee;

[0008] According to the total salary of employees in the salary data, all employees in the current month are sorted according to the preset sorting rules to obtain the employee sorting result;

[0009] Calculate the salary similarity between every two employees in each month based on the salary data, the overall salary coreness, and the employee ranking results;

[0010] Based on the salary similarity, clustering is performed on the salary data of all employees in each month to obtain a clustering result;

[0011] Based on the clustering results, the monthly salary data in the financial data is divided into blocks and securely stored.

[0012] According to a method for securely storing financial data provided by the present invention, the overall salary coreness of each employee is calculated based on the salary data, including:

[0013] Determine the salary component coreness and core contribution corresponding to each salary component value of each employee in the current month based on each salary component value in the current month in the salary data;

[0014] Determine the single core degree of each employee's salary for the current month based on the core degree of the salary component and the core contribution degree;

[0015] The overall salary coreness of each employee is calculated based on the single salary coreness and the monthly total salary of each employee in the salary data.

[0016] According to a method for securely storing financial data provided by the present invention, determining the salary component coreness corresponding to each salary component value of each employee in the current month includes:

[0017] Marking the same wage component value of all employees in the current month in the wage data on a two-dimensional plane to obtain a two-dimensional data distribution map;

[0018] Determining the dispersion of each wage component value based on the two-dimensional data distribution graph;

[0019] Based on each wage component value and the dispersion of each wage component value, the wage component coreness corresponding to each wage component value of each employee in the current month is calculated.

[0020] According to a method for securely storing financial data provided by the present invention, determining the core contribution corresponding to each salary component value of each employee in the current month includes:

[0021] Calculate the average of the same salary component values of all employees in the current month to obtain the average value corresponding to each salary component value;

[0022] Based on the same wage component value of all employees in the current month and the average value corresponding to each wage component value, calculate the overall change of each wage component value in the current month;

[0023] Calculate the sum of the same salary component values for all employees in the current month and obtain the sum value corresponding to each salary component value;

[0024] Based on the overall change in the value of each wage component in the current month and the summed value corresponding to each wage component value, the core contribution corresponding to each wage component value of each employee in the current month is calculated.

[0025] According to a method for securely storing financial data provided by the present invention, the single core degree of each employee's salary in the current month is determined based on the core degree of the salary component and the core contribution, including:

[0026] Sum up the core contribution of all salary components of each employee in the current month to get the sum of each employee's core contribution to salary;

[0027] Taking the core contribution degree corresponding to each wage component value of each employee as the quotient and the sum of the core contributions of the wages as the quotient, a core contribution ratio corresponding to each wage component value of each employee is obtained;

[0028] Multiplying the core contribution ratio corresponding to each wage component value of each employee by the core degree of the wage component to obtain the sub-item contribution degree corresponding to each wage component value of each employee;

[0029] Sum up the sub-item contribution corresponding to all salary component values of each employee to obtain the single core degree of each employee's salary in the current month.

[0030] According to a method for securely storing financial data provided by the present invention, the overall salary coreness of each employee is calculated based on the single salary coreness and the monthly total salary of each employee in the salary data, including:

[0031] Average the total salary of each employee in at least one year including the current month to obtain the mean total salary of each employee;

[0032] Calculate the absolute value of the difference between the total monthly salary of each employee and the average total salary for at least one year including the current month to obtain the absolute value of the total monthly salary difference for each employee;

[0033] Sum the absolute values of the total salary differences of each employee in all months within at least one year including the current month to obtain the sum of the total salary differences of each employee;

[0034] Input the sum of the total salary difference of each employee into the maximum and minimum normalization function to obtain the output value of the total salary function for each employee;

[0035] The total salary function output value of each employee is multiplied by the single salary coreness to obtain the overall salary coreness of each employee.

[0036] According to a method for securely storing financial data provided by the present invention, the salary similarity between every two employees in each month is calculated based on the salary data, the overall salary coreness, and the employee ranking result, including:

[0037] Determine the number of intermediate employees between every two employees in each month based on the employee ranking result;

[0038] Based on the number of intermediate employees and the overall coreness of the salary, a similarity correction value between every two employees in each month is calculated;

[0039] Determine the total employee salary corresponding to each two employees in each month based on the salary data;

[0040] Difference the total wages of every two employees in each month and calculate the absolute value to get the absolute value of the total wage difference;

[0041] The salary similarity between every two employees in each month is calculated based on the similarity correction value and the absolute value of the total salary difference.

[0042] According to a method for securely storing financial data provided by the present invention, based on the number of intermediate employees and the overall coreness of the salary, a similarity correction value between every two employees in each month is calculated, including:

[0043] Adding the intermediate number of employees to a preset hyperparameter to obtain a quantity transition value;

[0044] Sum the overall coreness of the salaries of every two employees in each month to get the overall coreness sum value;

[0045] The reciprocal of the quantity transition value is multiplied by the overall coreness sum value to obtain a similarity correction value for every two employees in each month.

[0046] According to a method for securely storing financial data provided by the present invention, the salary data of all employees in each month are clustered according to the salary similarity to obtain a clustering result, including:

[0047] Converting the salary similarity into a corresponding distance value;

[0048] Based on the distance values, a distance matrix corresponding to the salary data of all employees in each month is established;

[0049] Based on the distance matrix, the salary data of all employees in each month are clustered to obtain a clustering result.

[0050] In another aspect, the present invention further provides a financial data security storage system, comprising:

[0051] The acquisition module is used to obtain the salary data of all employees in the current month within at least one year from the financial data;

[0052] A first calculation module is configured to determine, based on the value of each wage component in the current month in the wage data, the wage component coreness and core contribution corresponding to each wage component value of each employee in the current month; determine the single wage coreness of each employee in the current month based on the wage component coreness and the core contribution; and calculate the overall wage coreness of each employee based on the single wage coreness and the total monthly salary of each employee in the wage data;

[0053] A sorting module is used to sort all employees in the current month according to the total salary of employees in the salary data according to the preset sorting rules to obtain an employee sorting result;

[0054] A second calculation module is configured to calculate the salary similarity between every two employees in each month based on the salary data, the overall salary coreness, and the employee ranking result;

[0055] A clustering module is used to cluster the salary data of all employees in each month according to the salary similarity to obtain a clustering result;

[0056] The storage module is used to securely store the monthly salary data in the financial data in blocks according to the clustering results.

[0057] The present invention has the following beneficial effects:

[0058] By calculating the overall coreness of each employee's salary, all employees in the current month are sorted according to the preset sorting rules based on the total employee salary in the salary data to obtain the employee sorting results. Based on the salary data, the overall coreness of the salary, and the employee sorting results, the salary similarity between every two employees in each month is calculated. Then, based on the salary similarity, the salary data of all employees in each month are clustered to obtain the clustering results. Finally, based on the clustering results, the monthly salary data in the financial data is divided into blocks and securely stored. Because the salary similarity based on the clustering process is calculated based on the salary data, the overall coreness of the salary, and the employee sorting results, the distance between the core employee and the surrounding data points can be shortened, and the density of the core employee's salary can be increased, making the core employee's salary data easier to identify as a core point, thereby forming an effective cluster, improving the clustering accuracy, and thus improving the storage reliability and security of the financial data. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 A flowchart of a method for securely storing financial data provided by one embodiment of the present invention;

[0061] Figure 2 This is a system structure diagram of a financial data security storage system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method and system for securely storing financial data according to the present invention, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0063] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0064] The following is combined with Figure 1 and Figure 2 The specific scheme of a financial data secure storage method and system provided by the present invention is specifically described.

[0065] See also Figure 1 , which shows a method flow chart of a method for securely storing financial data provided by an embodiment of the present invention, such as Figure 1 As shown, the above-mentioned method for securely storing financial data includes the following steps:

[0066] Step 110: Obtain the salary data of all employees in the current month within at least one year from the financial data.

[0067] In this embodiment, wage data can be obtained from the company's financial system. The wage data usually includes multiple components such as basic salary, transportation allowance, catering allowance, housing allowance, high temperature allowance, quarterly bonus, performance bonus, overtime pay, commission, year-end bonus, pension insurance, medical insurance and provident fund, and also includes the total salary of employees obtained based on the above multiple components.

[0068] In practical applications, the salary data may be presented in the form of a salary breakdown table. For example, Table 1 below exemplarily shows a salary breakdown table for M employees including N salary component values.

[0069] Table 1 Salary Details

[0070]

[0071] Step 120: Calculate the overall salary coreness of each employee based on the salary data.

[0072] It can be understood that the overall salary coreness can represent the fluctuation of an employee's salary and the importance of the employee's salary in the entire salary data.

[0073] Step 130: Sort all employees in the current month according to the total salary of the employees in the salary data according to the preset sorting rules to obtain the employee sorting result.

[0074] In this embodiment, the total salary of the employees is calculated based on multiple salary component values in the salary data. The preset sorting rule can be to sort the total salary of the employees in descending order, or to sort the total salary of the employees in descending order.

[0075] Step 140: Based on the salary data, the overall salary coreness, and the employee ranking results, calculate the salary similarity between every two employees in each month.

[0076] In this embodiment, salary similarity can represent the proximity of the salaries of two employees. The higher the salary similarity, the closer the salaries of the two employees are. This embodiment uses salary data, overall salary coreness, and employee ranking results to comprehensively analyze and determine the salary similarity between each two employees. Because salary similarity takes multiple factors into consideration during determination, it can more accurately represent the proximity of the salaries of two employees, thereby providing accurate and reliable data for subsequent clustering processing.

[0077] Step 150: Clustering the salary data of all employees in each month based on salary similarity to obtain clustering results.

[0078] In practical applications, a density-based spatial clustering algorithm can be used to cluster the salary data of all employees each month. In this process, the salary similarity can be converted into a distance value. The distance measurement provides an effective data basis for clustering processing, thereby obtaining a more accurate clustering result.

[0079] Step 160: Based on the clustering results, the monthly salary data in the financial data is divided into blocks and securely stored.

[0080] Since the salary data is divided into multiple clusters after clustering, that is, the monthly salary data is divided into multiple categories, it can provide an effective data basis for the subsequent block security storage. This embodiment can provide more accurate data support for data clustering by improving the salary similarity determination scheme, thereby providing protection for the secure storage of financial data.

[0081] In one embodiment, the overall salary coreness of each employee is calculated based on the salary data, specifically including:

[0082] First, based on the value of each wage component in the current month in the wage data, determine the wage component coreness and core contribution corresponding to each wage component value of each employee in the current month.

[0083] Understandably, for each salary component, higher values for certain employees often indicate they hold more senior positions or have more significant responsibilities within the company. Consequently, they exert greater influence on the company's operations, indicating a higher degree of core competence. Furthermore, given these larger values, a larger salary gap relative to other employees better reflects the varying contributions of employees at different levels to the company. Companies typically prioritize the roles and work behaviors of core employees with these larger salary gaps, resulting in higher core competence for these employees.

[0084] In a specific implementation, determining the salary component coreness corresponding to each salary component value of each employee in the current month includes:

[0085] The first step is to mark the same salary component value of all employees in the current month in the salary data on a two-dimensional plane to obtain a two-dimensional data distribution map.

[0086] In this embodiment, the same salary component values of all employees may be plotted on a two-dimensional plane, where the horizontal and vertical coordinates of the two-dimensional plane are the same salary component values of the employees.

[0087] The second step is to determine the dispersion of each wage component value based on the two-dimensional data distribution graph.

[0088] In practical applications, the LOF (Local Outlier Factor) algorithm can be used to determine the dispersion of the same wage component values for all employees, thereby determining the dispersion of each wage component value. The LOF algorithm is used to identify local outliers in a dataset. Based on the concept of density, the algorithm determines whether each data point is an outlier by comparing the density of its neighboring points. Based on this theory, the dispersion of each wage component value can be determined.

[0089] The third step is to calculate the wage component coreness corresponding to each wage component value of each employee in the current month based on the value of each wage component and the dispersion of each wage component value.

[0090] In this embodiment, the salary component coreness corresponding to the k-th salary component value of employee Q in the current month can be expressed as follows:

[0091] (1)

[0092] in, represents the salary component coreness corresponding to the k-th salary component value of employee Q, represents the dispersion of the k-th salary component value of employee Q, Represents the maximum and minimum normalization function. The maximum and minimum normalization function is a commonly used data preprocessing method. It can map data to a specified interval, usually [0,1], thereby eliminating the dimensional influence between data features and making data with different features comparable on the same scale. Represents the kth salary component value of employee Q.

[0093] In a specific implementation, determining the core contribution corresponding to each salary component value of each employee in the current month includes:

[0094] The first step is to calculate the average of the same salary component values of all employees in the current month to obtain the average value corresponding to each salary component value.

[0095] The second step is to calculate the overall change of each wage component value in the current month based on the same wage component value of all employees in the current month and the average value corresponding to each wage component value.

[0096] The third step is to calculate the sum of the same salary component values of all employees in the current month and obtain the sum value corresponding to each salary component value.

[0097] The fourth step is to calculate the core contribution of each wage component value of each employee in the current month based on the overall change in the value of each wage component in the current month and the sum of the corresponding values of each wage component.

[0098] It's understandable that for all wage component values, the larger the sum of all employees' wage component values, the more labor costs the company needs to pay for that wage component. This wage component has a decisive impact on the company's compensation strategy, resource allocation, and employee stability. Therefore, the higher the core contribution, the higher the core contribution. Furthermore, if the value fluctuates greatly, it will directly affect employees' short-term performance and better motivate employees, improving overall work motivation and thus promoting the achievement of corporate goals. Therefore, the core contribution corresponding to the kth wage component value can be expressed as follows:

[0099] (2)

[0100] in, Indicates the core contribution corresponding to the k-th salary component value, represents the maximum and minimum normalization function, M represents the total number of employees, represents the kth salary component value of the i-th employee, represents the average value of the kth salary component of M employees, represents the overall change in the kth salary component value of M employees, Represents the sum of the k-th salary component values of M employees.

[0101] Then, based on the core degree of the salary composition and the core contribution, the single core degree of each employee's salary in the current month is determined.

[0102] In a specific implementation, the single core degree of each employee's salary in the current month is determined based on the core degree of the salary component and the core contribution, specifically including:

[0103] The first step is to sum up the core contribution corresponding to all salary components of each employee in the current month to obtain the sum of the core contribution of each employee's salary.

[0104] The second step is to divide the core contribution corresponding to each wage component value of each employee by the sum of the core contribution values of the wages to obtain the core contribution ratio corresponding to each wage component value of each employee.

[0105] The third step is to multiply the core contribution ratio corresponding to each wage component value of each employee by the wage component coreness to obtain the sub-item contribution corresponding to each wage component value of each employee.

[0106] The fourth step is to sum up the sub-item contribution corresponding to all salary component values of each employee to obtain the single core degree of each employee's salary in the current month.

[0107] In this embodiment, the single coreness of employee Q's salary in the current month can be expressed as follows:

[0108] (3)

[0109] in, It represents the single core degree of employee Q’s salary in the current month, and N represents the total number of items in a salary component value. Indicates the core contribution corresponding to the k-th salary component value, represents the salary component coreness corresponding to the k-th salary component value of employee Q, Indicates the wage component coreness corresponding to the mth wage component value, Represents the sum of the core contribution to the salary, and both m and k satisfy the value range of [1, N].

[0110] Finally, the overall salary coreness of each employee is calculated based on the single salary coreness and the total monthly salary of each employee in the salary data.

[0111] In a specific implementation, the overall salary coreness of each employee is calculated based on the single salary coreness and the monthly total salary of each employee in the salary data, specifically including:

[0112] The first step is to average the total salary of each employee in at least one year including the current month to obtain the mean total salary of each employee.

[0113] The second step is to calculate the absolute value of the difference between the total salary of each employee in each month and the average total salary for at least one year including the current month, and obtain the absolute value of the total salary difference of each employee in each month.

[0114] The third step is to sum the absolute values of the total salary differences of each employee in all months within at least one year including the current month to obtain the sum of the total salary differences of each employee.

[0115] The fourth step is to input the sum of the total salary difference of each employee into the maximum and minimum normalization function to obtain the output value of the total salary function of each employee.

[0116] The fifth step is to multiply the total salary function output value of each employee by the single salary coreness to obtain the overall salary coreness of each employee.

[0117] It is understandable that if, based on the single coreness of an employee's salary, there are employees with significant overall salary volatility within the past year, including the current month, this is a concern for the company. This is because high salary volatility often reflects issues with performance management, incentive mechanisms, and the company's financial situation, and may also bring about the risk of employee turnover. The company should evaluate the reasons behind this volatility, ensure the fairness and transparency of the compensation system, and strengthen long-term incentives and stability support for employees to maintain a high-efficiency work atmosphere and employee satisfaction. Therefore, the greater the salary volatility, the greater the final overall salary coreness based on the single coreness of the salary. Based on this, the overall salary coreness of employee Q can be expressed as follows:

[0118] (4)

[0119] in, represents the overall coreness of employee Q’s salary, Represents the maximum and minimum normalization function, 12 represents a total of 12 months including the current month, It represents the total salary of employee Q in the vth month before and including the current month. Indicates the average total salary of employee Q in the 12 months before and including the current month. Indicates the single core degree of employee Q's salary in the current month.

[0120] In one embodiment, based on the salary data, the overall salary coreness, and the employee ranking results, the salary similarity between every two employees in each month is calculated, specifically including:

[0121] The first step is to determine the number of intermediate employees between every two employees in each month based on the employee ranking results.

[0122] In this embodiment, the employee sorting result includes the sorting information of all employees based on their total salary. For example, the employee sorting result may include the sorting order of all employees sorted in descending order of their total salary. For any two employees, the number of employees between the two employees, i.e., the number of intermediate employees, can be determined based on the sorting order of all employees in the employee sorting result.

[0123] In the second step, based on the number of middle employees and the overall coreness of their wages, the similarity correction value between every two employees in each month is calculated.

[0124] In a specific implementation, based on the number of intermediate employees and the overall coreness of their salaries, a similarity correction value between every two employees is calculated each month, specifically including:

[0125] First, the intermediate number of employees is added to the preset hyperparameter to obtain the transition value of the number.

[0126] Then, the overall coreness of the salaries of every two employees in each month is summed up to obtain the overall coreness sum value.

[0127] Finally, multiply the inverse of the quantity transition value by the overall coreness sum to obtain the similarity correction value for every two employees in each month.

[0128] It can be understood that since this embodiment requires adjusting the salary similarity measurement method for the salary data of core employees with low surrounding density and difficult to form clusters, it is necessary to reduce the distance between the core employees and the surrounding data points and increase the density of the salaries of these core employees, so that they can be more easily identified as core data points, thereby forming effective clustering.

[0129] Therefore, when correcting the salary similarity between two employees, the higher the degree to which they are cored and the lower the density with surrounding data points, the higher the salary similarity should be. Based on this, the similarity correction value corresponding to the salary data of employee A and employee B can be expressed as follows:

[0130] (5)

[0131] in, Indicates the similarity correction value corresponding to the salary data of employee A and employee B, Indicates the number of intermediate employees between employee A and employee B determined based on the employee sorting results. represents a non-zero hyperparameter, Indicates the overall coreness of employee A’s salary, Indicates the overall coreness of employee B’s salary. The value is 0.01. In specific applications, implementers can set it according to specific circumstances.

[0132] The third step is to determine the total salary of every two employees in each month based on the salary data.

[0133] The fourth step is to subtract the total wages of every two employees in each month and calculate the absolute value to obtain the absolute value of the total wage difference.

[0134] The fifth step is to calculate the salary similarity between every two employees in each month based on the similarity correction value and the absolute value of the total salary difference.

[0135] It is understandable that for the salary data of employee A and employee B, the initial similarity is the similarity of the total salary of the employees in the current month. From the above description, it can be seen that the higher the similarity correction value, the higher the initial similarity should be. Based on this, the salary similarity between employee A and employee B can be expressed as follows:

[0136] (6)

[0137] in, Indicates the salary similarity between employee A and employee B, Indicates the similarity correction value of the salary data of employee A and employee B. represents a hyperparameter, represents the total salary of employee A, represents the total salary of employee B, represents a non-zero hyperparameter, represents the initial similarity between the salary data of employee A and employee B, and norm() represents the normalization function. In this embodiment, The value of is 0.1, The value is 0.01. In specific applications, implementers can set it according to specific circumstances.

[0138] In some embodiments, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm can be used to implement clustering operations. The DBSCAN clustering algorithm can divide data points into core points, boundary points, and noise points, can discover clusters of any shape, and identify noise points in the data set.

[0139] In one embodiment, the salary data of all employees in each month are clustered based on salary similarity to obtain clustering results, which specifically include:

[0140] First, convert the salary similarity into the corresponding distance value.

[0141] It is understandable that since the DBSCAN clustering algorithm relies on a distance metric, a high salary similarity means that the salaries of two employees are closer, that is, the distance between the two data points is smaller. Therefore, the distance value between the data points corresponding to employee A and employee B can be expressed as follows:

[0142] (7)

[0143] in, Represents the distance value between the data points corresponding to employee A and employee B, Indicates the salary similarity between employee A and employee B.

[0144] Then, based on the distance values, a distance matrix corresponding to the salary data of all employees in each month is established.

[0145] Finally, based on the distance matrix, the salary data of all employees in each month are clustered to obtain the clustering results.

[0146] In practical applications, before clustering processing, it is necessary to define the key parameters of the DBSCAN clustering algorithm. The two key parameters of the DBSCAN clustering algorithm include the neighborhood radius and the minimum number of neighbors required for the core point. In this embodiment, the minimum number of neighbors required for the core point is defined as 3, and then the K-Distance graph is used to determine the neighborhood radius.

[0147] Subsequently, the distance matrix can be imported into the DBSCAN clustering algorithm and the clustering process can be executed to obtain the clustering results. The clustering results are then processed using a hash function to securely store the monthly salary data in the financial data in blocks.

[0148] In some embodiments, the process of processing the clustering results using a hash function is as follows:

[0149] First, a hash function is used to perform hash calculation on each category of salary data in the clustering results to generate a hash value of fixed length.

[0150] Then, each category of salary data in the clustering result is encrypted to obtain encrypted salary data. In practical applications, the salary data can be encrypted using the AES (Advanced Encryption Standard) encryption algorithm.

[0151] Finally, the generated hash value and encrypted salary data are stored in a distributed storage system (such as a distributed file system or a cloud storage system, etc.) to improve the storage security and reliability of the salary data in the financial data.

[0152] At this point, the hash function can be used to securely store the salary data in the classified financial data in blocks, better ensuring the integrity and security of the monthly salary data of all employees in the financial data and reducing the risk of data leakage or tampering.

[0153] To sum up, the financial data security storage method provided in this embodiment addresses the situation where it is difficult to form clusters when directly using the DBSCAN clustering algorithm due to the low density around the core employees' wages. By adjusting the measurement method of wage similarity, the distance between core employees and surrounding data points is shortened, and the density around the wages of these core employees is increased, so that the wage data of core employees can be more easily identified as core points, thereby forming effective clusters, improving clustering accuracy, and further enhancing the storage security and reliability of financial data.

[0154] Based on the same general inventive concept, the present invention also protects a financial data security storage system. The financial data security storage system provided by the present invention is described below. The financial data security storage system described below and the financial data security storage method described above can be referenced to each other.

[0155] See also Figure 2 , which shows a system structure diagram of a financial data security storage system provided by an embodiment of the present invention, such as Figure 2 As shown, the above system specifically includes:

[0156] The acquisition module 210 is used to acquire the salary data of all employees in the current month within at least one year from the financial data.

[0157] The first calculation module 220 is used to determine the salary component coreness and core contribution corresponding to each salary component value of each employee in the current month based on each salary component value of the current month in the salary data; determine the single salary coreness of each employee in the current month based on the salary component coreness and the core contribution; and calculate the overall salary coreness of each employee based on the single salary coreness and the total monthly salary of each employee in the salary data.

[0158] The sorting module 230 is used to sort all employees in the current month according to the total wages of the employees in the wage data and according to the preset sorting rules to obtain the employee sorting result.

[0159] The second calculation module 240 is configured to calculate the salary similarity between every two employees in each month based on the salary data, the overall salary coreness, and the employee ranking results.

[0160] The clustering module 250 is used to cluster the salary data of all employees in each month according to salary similarity to obtain a clustering result.

[0161] The storage module 260 is used to divide the monthly salary data in the financial data into blocks and store them securely according to the clustering results.

[0162] It can be seen that the financial data security storage system provided by this embodiment can reduce the distance between core employees and surrounding data points, increase the density of core employees' wages, and make the core employees' wage data easier to be identified as core points, thereby forming effective clustering, improving clustering accuracy, and improving the storage reliability and security of financial data.

[0163] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0164] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0165] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for secure storage of financial data, characterized in that: The method comprises: Get the salary data of all employees in the current month within at least one year from the financial data; Based on the value of each wage component in the current month in the wage data, the wage component coreness and core contribution corresponding to each wage component value of each employee in the current month are determined respectively; based on the wage component coreness and the core contribution, the single wage coreness of each employee in the current month is determined; based on the single wage coreness and the monthly total wage of each employee in the wage data, the overall wage coreness of each employee is calculated, and the overall wage coreness is used to characterize the employee's wage fluctuation and the importance of the employee's wage in the wage data; According to the total salary of employees in the salary data, all employees in the current month are sorted according to the preset sorting rules to obtain the employee sorting result; Based on the salary data, the overall salary coreness, and the employee ranking result, calculating the salary similarity between every two employees in each month, the salary similarity being used to characterize the proximity of the salaries between the two employees, the salary similarity being determined based on a similarity correction value and an absolute value of a total salary difference between every two employees in each month, the similarity correction being determined based on the number of intermediate employees between every two employees in each month and the overall salary coreness, the number of intermediate employees being determined based on the employee ranking result, the absolute value of the total salary difference being obtained by taking the absolute value of the difference between the total salaries corresponding to every two employees in each month, and the total salary of each employee being determined based on the salary data; Based on the salary similarity, clustering is performed on the salary data of all employees in each month to obtain a clustering result; Based on the clustering results, the monthly salary data in the financial data is divided into blocks and securely stored.

2. A method for securely storing financial data according to claim 1, characterized in that: Determine the salary component coreness corresponding to each salary component value of each employee in the current month, including: Marking the same wage component value of all employees in the current month in the wage data on a two-dimensional plane to obtain a two-dimensional data distribution map; Determining the dispersion of each wage component value based on the two-dimensional data distribution graph; Based on each wage component value and the dispersion of each wage component value, the wage component coreness corresponding to each wage component value of each employee in the current month is calculated.

3. A method for securely storing financial data according to claim 1, characterized in that: Determine the core contribution of each employee's salary component value for the current month, including: Calculate the average of the same salary component values of all employees in the current month to obtain the average value corresponding to each salary component value; Based on the same wage component value of all employees in the current month and the average value corresponding to each wage component value, calculate the overall change of each wage component value in the current month; Calculate the sum of the same salary component values for all employees in the current month and obtain the sum value corresponding to each salary component value; Based on the overall change in the value of each wage component in the current month and the summed value corresponding to each wage component value, the core contribution corresponding to each wage component value of each employee in the current month is calculated.

4. A method for securely storing financial data according to claim 1, characterized in that: Based on the salary component coreness and the core contribution, determine the single coreness of each employee's salary for the current month, including: Sum up the core contribution of all salary components of each employee in the current month to get the sum of each employee's core contribution to salary; Taking the core contribution degree corresponding to each wage component value of each employee as the quotient and the sum of the core contributions of the wages as the quotient, a core contribution ratio corresponding to each wage component value of each employee is obtained; Multiplying the core contribution ratio corresponding to each wage component value of each employee by the core degree of the wage component to obtain the sub-item contribution degree corresponding to each wage component value of each employee; Sum up the sub-item contribution corresponding to all salary component values of each employee to obtain the single core degree of each employee's salary in the current month.

5. A method for securely storing financial data according to claim 1, characterized in that: Based on the single coreness of the salary and the monthly total salary of each employee in the salary data, the overall coreness of the salary of each employee is calculated, including: Average the total salary of each employee in at least one year including the current month to obtain the mean total salary of each employee; Calculate the absolute value of the difference between the total monthly salary of each employee and the average total salary for at least one year including the current month to obtain the absolute value of the total monthly salary difference for each employee; Sum the absolute values of the total salary differences of each employee in all months within at least one year including the current month to obtain the sum of the total salary differences of each employee; Input the sum of the total salary difference of each employee into the maximum and minimum normalization function to obtain the output value of the total salary function for each employee; The total salary function output value of each employee is multiplied by the single salary coreness to obtain the overall salary coreness of each employee.

6. A method for securely storing financial data according to claim 1, characterized in that: Based on the number of intermediate employees and the overall coreness of the salary, a similarity correction value between every two employees in each month is calculated, including: Adding the intermediate number of employees to a preset hyperparameter to obtain a quantity transition value; Sum the overall coreness of the salaries of every two employees in each month to get the overall coreness sum value; The reciprocal of the quantity transition value is multiplied by the overall coreness sum value to obtain a similarity correction value for every two employees in each month.

7. A method for securely storing financial data according to claim 1, characterized in that: Based on the salary similarity, the salary data of all employees in each month are clustered to obtain clustering results, including: Converting the salary similarity into a corresponding distance value; Based on the distance values, a distance matrix corresponding to the salary data of all employees in each month is established; Based on the distance matrix, the salary data of all employees in each month are clustered to obtain a clustering result.

8. A financial data security storage system, characterized in that: The system comprises: The acquisition module is used to obtain the salary data of all employees in the current month within at least one year from the financial data; A first calculation module is configured to determine, based on the value of each wage component in the current month in the wage data, the wage component coreness and core contribution corresponding to each wage component value of each employee in the current month; determine, based on the wage component coreness and core contribution, the single wage coreness of each employee in the current month; and calculate, based on the single wage coreness and the monthly total wage of each employee in the wage data, the overall wage coreness of each employee, where the overall wage coreness is used to characterize the employee's wage fluctuation and the importance of the employee's wage in the wage data; A sorting module is used to sort all employees in the current month according to the total salary of employees in the salary data according to the preset sorting rules to obtain an employee sorting result; a second calculation module for calculating, based on the salary data, the overall salary coreness, and the employee ranking result, the salary similarity between every two employees each month, the salary similarity being used to characterize the proximity of the salaries between two employees, the salary similarity being determined based on a similarity correction value and an absolute value of a total salary difference between every two employees each month, the similarity correction being determined based on the number of intermediate employees between every two employees each month and the overall salary coreness, the number of intermediate employees being determined based on the employee ranking result, the absolute value of the total salary difference being obtained by taking the absolute value of the difference between the total salaries corresponding to every two employees each month, the total salary of each employee being determined based on the salary data; A clustering module is used to cluster the salary data of all employees in each month according to the salary similarity to obtain a clustering result; The storage module is used to securely store the monthly salary data in the financial data in blocks according to the clustering results.

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