A financial data security intelligent management system and method

Abstract

The present invention relates to the field of data security technology, and specifically to an intelligent financial data security management system and method. The system includes: a data security parsing module that performs timestamp extraction based on collected financial data, marks the reception time of each data point, partitions the data points by month and business type, and counts the number of data points and the time span of each partition. In the present invention, by performing timestamp extraction on financial data and performing partition statistics by month and business type, the dynamic monitoring capability of data is enhanced, the detail and accuracy of data integrity detection are effectively improved, the self-attention mechanism is used to assign weights to time points, and key time periods are accurately identified, making risk assessment more targeted and effective. Dynamic threshold management can immediately respond to fluctuations in market and internal data, reducing potential risks caused by delayed responses. Combined with the position encoding method, the analysis of time dependency is enhanced, and the accuracy and applicability of predictions are improved.

Description

Technical Field

[0001] The present invention relates to the field of data security technology, and in particular to a financial data security intelligent management system and method. Background Art

[0002] The field of data security technology involves technologies and measures designed to protect data from unauthorized access, leakage, or corruption. It encompasses aspects such as data encryption, authentication, access control, data integrity protection, and security auditing. These technologies aim to ensure the security of data during storage, transmission, and processing, while complying with laws, regulations, and industry standards. With the advancement of information technology, data security challenges are constantly increasing, making the implementation of innovative security solutions and best practices even more important.

[0003] The Financial Data Security Intelligent Management System is an intelligent management tool that integrates data security technologies to protect the security and integrity of corporate financial data. This system ensures the security of financial information during storage and transmission by implementing measures such as data encryption and access control. Its uses include monitoring access to financial data, preventing data leaks and attacks, and meeting compliance requirements, helping companies maintain data security and enhance decision support.

[0004] Existing technologies lack effective real-time monitoring and response mechanisms in dynamic data environments. They typically rely on pre-set, static rules to manage risk, making them incapable of adapting to rapidly changing data flows. This static management approach prevents timely adjustments when data anomalies occur, increasing the risk of data breaches. Furthermore, existing technologies often fail to consider different timescales in risk analysis, failing to fully assess risk variations across time periods, limiting the effectiveness and foresight of strategies. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a financial data security intelligent management system and method.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: A financial data security intelligent management system includes:

[0007] The data security parsing module extracts timestamps from the collected financial data, marks the receipt time of each data point, partitions the data points by month and business type, counts the number of data points and time span in each partition, and generates partition data integrity results.

[0008] The attention point weight module uses the partition data integrity results to apply self-attention weight calculation to the data partition, assigns differentiated weights to multiple time points, calculates the sum of the weights of each time point, determines the weight distribution of the key time period, and generates a time attention weight matrix;

[0009] The multi-layer risk analysis module uses the time attention weight matrix to process multi-time scale data in parallel, extract short-term, medium-term and long-term risk features respectively, aggregate the risk feature analysis results, and establish a multi-scale risk feature analysis;

[0010] The dynamic threshold management module receives the multi-scale risk feature analysis, sets real-time thresholds for each financial indicator, adjusts the thresholds to match the current fluctuations and future trends of the real-time data, records the decision-making process and results of each threshold adjustment, and generates real-time threshold adjustment records;

[0011] The prediction and response module predicts the financial status in the future period according to the real-time adjustment threshold record, uses the position coding method to analyze the time dependency, merges the prediction results from multiple time scales, and generates future risk prediction results.

[0012] The partition data integrity results are specifically the number of data points and time span of each partition; the time attention weight matrix is ​​specifically the differentiated weights of multiple time points and the weight distribution of key time periods; the multi-scale risk feature analysis includes short-term risk features, medium-term risk features, and long-term risk features; the real-time adjustment threshold record includes real-time threshold, threshold adjustment decision process, and threshold adjustment result; the future risk prediction results are specifically multi-time scale prediction results and time dependency analysis.

[0013] As a further solution of the present invention, the steps for obtaining the partition data integrity result are specifically as follows:

[0014] Extract each data point from the financial database, record its receipt time, and mark the data point according to the business type and receipt month to generate a data record set with time stamp;

[0015] According to the data record set with time stamps, the data is grouped by month and business type, the total number of data points and multiple time spans are calculated for each group, and statistical information for each group is generated;

[0016] Using the statistical information of each group, the data integrity of each group is calculated using the formula:

[0017]

[0018] Generate partition data integrity results;

[0019] in, Representative The total number of data points in a group reflects the amount of data in the group. It is The time span of each group is the length of time from the start to the end of data collection, in days. is the average of all group time spans, used to standardize the time span of each group for comparison. It is an adjustment factor used to balance the impact of the number of data points and the difference in time span.

[0020] As a further solution of the present invention, the step of obtaining the time attention weight matrix is ​​specifically as follows:

[0021] Extracting data from the partition data integrity results, assigning an initial weight to each time point, adjusting the weight of each time point based on the data integrity results, and generating a preliminary weight distribution;

[0022] Applying a self-attention mechanism to the preliminary weight distribution, iteratively adjusting the weight of each time point by calculating the deviation of the weight of each time point from the global average weight, and generating an adjusted time weight distribution;

[0023] Summarize the adjusted time weight distribution, calculate the sum of the weights of each time point to determine the key time period, and use the formula:

[0024]

[0025] Generate temporal attention weight matrix;

[0026] in, Represents the sum of the weights of the key time periods, reflecting the comprehensive weights of all time points, and is used to determine the key time periods. It is The weight of each time point, is the average value of the weight, and the deviation of the weight from the average value at each time point is calculated. is the standard deviation of the weights, which is used to measure the degree of dispersion among multiple weights.

[0027] As a further solution of the present invention, the steps of obtaining the multi-scale risk feature analysis are specifically as follows:

[0028] Using the time attention weight matrix, the data time scales of short-term, medium-term and long-term are processed in parallel, and key risk features are extracted from each scale to obtain a short-term risk feature set, a medium-term risk feature set and a long-term risk feature set;

[0029] Based on the short-term risk feature set, the medium-term risk feature set, and the long-term risk feature set, the risk features extracted from multiple time scales are aggregated, and the statistical data and influence of multiple features are calculated using the formula:

[0030]

[0031] Evaluate the comprehensive impact of each risk characteristic and obtain the aggregated risk characteristic analysis results at each time scale;

[0032] in, Represents the weighted risk assessment results, reflecting the comprehensive risk impact of multiple time scales, For the The weight of each risk feature reflects the criticality of the risk feature and is used to measure the contribution of differentiated risk features to the risk assessment results. Table No. The value of a risk characteristic represents a measure of risk and is used to quantify the actual observed value of the risk characteristic. For the The standard deviation of each risk characteristic is used to adjust the influence of risk value;

[0033] The results of the aggregated risk feature analysis at each time scale are integrated to analyze the interaction and combined impact of risk features at multiple time scales, identify key risk drivers, and establish a multi-scale risk feature analysis.

[0034] As a further solution of the present invention, the step of obtaining the real-time adjustment threshold record is specifically as follows:

[0035] Receive the results of the multi-scale risk signature analysis, set baseline thresholds based on each financial indicator, analyze short-term, medium-term, and long-term risk factors, determine the value range of preliminary thresholds, and generate a preliminary threshold list;

[0036] Call the real-time data stream to detect current fluctuations and future trends, and adjust the preliminary threshold list using the formula:

[0037]

[0038] Calculate new thresholds and generate an adjusted threshold list;

[0039] in, Represents the adjusted threshold, which is used to reflect the new threshold after considering the current fluctuation and future trend. is the baseline threshold, which specifies the threshold benchmark of the financial indicator in a stable state. is the change in risk characteristics, which measures the change in risk characteristics from the previous assessment cycle to the current one. Is the risk weight coefficient, which is used to adjust the influence of the change in risk characteristics on the threshold. is the time attenuation coefficient, which is used to adjust the sensitivity of the threshold adjustment according to the time variation. It is the absolute value of the time change, reflecting the time span from the last adjustment to the current one;

[0040] Based on the adjusted threshold list, the decision-making process of each threshold adjustment is recorded, the real-time data impact and risk characteristic changes are analyzed, the adjustment information of each threshold is stored, and a real-time adjustment threshold record is generated.

[0041] As a further solution of the present invention, the steps for obtaining the future risk prediction results are specifically as follows:

[0042] Based on the real-time threshold adjustment records, analyzing the current status and changes of multiple financial indicators, setting upper and lower thresholds for each indicator, and generating an initial financial status model;

[0043] The initial financial status model is called and the position encoding method is applied to deal with time dependency using the formula:

[0044]

[0045] Improve the model's processing of time information and generate time-encoded state data;

[0046] in, Indicates time The encoding state is used to represent the unique state of the differentiated time point, which helps the model recognize and match the characteristics of the time series. The frequency parameter representing the encoding determines the periodicity of the time signal and encodes the fluctuation speed and periodicity of the time series data. Represents the phase parameter, which adjusts the starting point of time encoding so that the model can capture the starting phase difference of the time series;

[0047] Using the time-coded state data, the forecast results from the short-term, medium-term and long-term are combined using the formula:

[0048]

[0049] Integrate forecast data at different time scales to generate future risk prediction results;

[0050] in, Represents the risk prediction result, which is a comprehensive reflection of the short-term, medium-term and long-term prediction results. 、 、 They represent the forecast results obtained from the short-term, medium-term and long-term models respectively, which are obtained based on the data analysis of differentiated time scales and reflect the financial risk status within differentiated time ranges.

[0051] A method for intelligent financial data security management is provided, which is based on the above-mentioned intelligent financial data security management system and includes the following steps:

[0052] S1: Collect financial data, extract the timestamp of each data point, mark the receipt time, partition the data points by month and business type, count the number of data points and time span in each partition, and generate partition data integrity results;

[0053] S2: Based on the partition data integrity results, apply self-attention weight calculation to multiple partitions, assign weights to differentiated time points, calculate the sum of the weights of each time point, determine the weight distribution of key time periods, and generate a time attention weight matrix;

[0054] S3: Using the time attention weight matrix, processing financial data at multiple time scales in parallel, extracting short-term risk characteristics, medium-term risk characteristics, and long-term risk characteristics, aggregating the analysis results of multiple risk characteristics, and establishing a multi-scale risk characteristic analysis;

[0055] S4: Based on the multi-scale risk feature analysis, set real-time thresholds for each financial indicator, adjust the thresholds to match the fluctuations and trends of real-time data, record the decision-making process and results of each threshold adjustment, generate real-time threshold adjustment records, and predict the financial status in future periods based on the records to generate future risk prediction results.

[0056] Compared with the prior art, the advantages and positive effects of the present invention are:

[0057] This invention enhances dynamic data monitoring capabilities by extracting timestamps from financial data and performing statistics partitioned by month and business type, effectively improving the detail and accuracy of data integrity detection. A self-attention mechanism is used to assign weights to time points, accurately identifying key time periods and making risk assessment more targeted and effective. Dynamic threshold management can instantly reflect fluctuations in market and internal data, reducing potential risks caused by delayed responses. Combined with positional encoding methods, this method enhances analysis of time dependencies and improves the accuracy and applicability of predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a system flow chart of the present invention;

[0059] Figure 2 Flowchart of the steps for obtaining the partition data integrity result of the present invention;

[0060] Figure 3 Flowchart of the steps for obtaining the time attention weight matrix of the present invention;

[0061] Figure 4 Flowchart of the steps for obtaining multi-scale risk signature analysis of the present invention;

[0062] Figure 5 A flow chart of the steps for obtaining a record of real-time threshold adjustment according to the present invention;

[0063] Figure 6 This is a flow chart of the steps for obtaining future risk prediction results of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0066] Example 1

[0067] See also Figure 1 The present invention provides a technical solution: a financial data security intelligent management system comprising:

[0068] The data security parsing module extracts timestamps from the collected financial data, marks the receipt time of each data point, partitions the data points by month and business type, counts the number of data points and time span in each partition, and generates partition data integrity results.

[0069] The attention point weight module uses the partition data integrity results to apply self-attention weight calculation to the data partition, assigns differentiated weights to multiple time points, calculates the sum of the weights of each time point, determines the weight distribution of the key time period, and generates the time attention weight matrix;

[0070] The multi-layer risk analysis module uses a time-attention weight matrix to process multi-time-scale data in parallel, extracting short-term, medium-term, and long-term risk features respectively, aggregating the risk feature analysis results, and establishing a multi-scale risk feature analysis;

[0071] The dynamic threshold management module receives multi-scale risk feature analysis, sets real-time thresholds for each financial indicator, adjusts the thresholds to match the current fluctuations and future trends of real-time data, records the decision-making process and results of each threshold adjustment, and generates real-time threshold adjustment records;

[0072] The prediction and response module adjusts the threshold records in real time to predict the financial status in the future period, uses the position coding method to analyze the time dependency, merges the prediction results from multiple time scales, and generates the future risk prediction results.

[0073] The partition data integrity results specifically include the number of data points and time span of each partition. The time attention weight matrix specifically includes the differentiated weights of multiple time points and the weight distribution of key time periods. The multi-scale risk feature analysis includes short-term risk features, medium-term risk features, and long-term risk features. The real-time adjustment threshold records include real-time thresholds, threshold adjustment decision-making process, and threshold adjustment results. The future risk prediction results specifically include multi-time scale prediction results and time dependency analysis.

[0074] See also Figure 2 , the specific steps for obtaining the partition data integrity results are:

[0075] Extract each data point from the financial database, record its receipt time, and mark the data point according to the business type and receipt month to generate a data record set with time stamp;

[0076] Extract each data point from the financial database, record its receipt time, and mark the data point according to the business type and receipt month. First, verify the format and storage location of the required data. By querying the corresponding database table, obtain information including the receipt time and business type fields. Then, perform preliminary cleaning on the obtained data to eliminate missing values ​​and outliers to ensure data accuracy. Then, use a program or script to classify and organize the data by business type and receipt month, record the time information of each data piece, and mark the corresponding business type and month to form a data record set with time stamps for subsequent data processing and analysis.

[0077] Based on the data record set with time stamps, the data is grouped by month and business type, the total number of data points and multiple time spans are calculated for each group, and statistical information for each group is generated;

[0078] Based on the data record set, the data is grouped by month and business type. First, each record in the data set is traversed to extract its business type and receiving month information. Then, based on the receiving month and business type, a group statistical structure is created to calculate the total number of data points in each group and the corresponding time span. By comparing the time information of each group, the receiving time range of the data points is calculated, and statistical information for each group is generated to ensure the integrity and accuracy of the statistical data.

[0079] Using the statistical information of each group, calculate the data integrity of each group using the formula:

[0080]

[0081] Generate partition data integrity results;

[0082] in, Representative The total number of data points in a group reflects the amount of data in the group. It is The time span of each group is the length of time from the start to the end of data collection, in days. is the average of all group time spans, used to standardize the time span of each group for comparison. It is an adjustment factor used to balance the impact of the number of data points and the difference in time span.

[0083] formula:

[0084]

[0085] The benefit of the formula is that by quantifying the relationship between the total number of data points and the time span, the data integrity of each group can be evaluated, and the applicability and flexibility of the formula can be increased by referring to the changes in the number of data points and the time span.

[0086] Detailed explanation of the formula and the process of formula calculation and derivation:

[0087] In the formula, Representative The total number of data points in a group is obtained through the above statistical process. If a group has 80 data points, then , It is the time span of the group, which can be calculated by obtaining the earliest and latest receiving time of the group, including the earliest time of January 1, 2023 and the latest time of January 31, 2023. sky, It is the average time span of all groups. After setting the calculation, the average value is 28 days. is the adjustment coefficient, which takes a value of 1. Substitute into the formula for calculation:

[0088]

[0089] The results show that the data integrity index is 46.19, which reflects a high level of data integrity in the grouping, which is helpful for subsequent analysis.

[0090] See also Figure 3 , the steps to obtain the time attention weight matrix are as follows:

[0091] Extract data from the partition data integrity results, assign initial weights to each time point, adjust the weights of each time point based on the data integrity results, and generate a preliminary weight distribution;

[0092] Extract data from the partition data integrity results and assign initial weights to each time point. These weights are adjusted based on the level of data integrity. This process involves analyzing the data integrity scores to ensure that the weight assignments are consistent with the actual data. The adjustment process refers to the integrity of the data at multiple time points to ensure the rationality and fairness of the weight assignments. In this way, the initial weight distribution not only reflects the data integrity at multiple time points, but also provides the basis for subsequent weight adjustments.

[0093] Apply the self-attention mechanism to the preliminary weight distribution, calculate the deviation of the weight of each time point from the global average weight, iteratively adjust the weight of each time point, and generate the adjusted time weight distribution;

[0094] The initial weight distribution is processed by the self-attention mechanism. This technology iteratively adjusts the weights by comparing the differences between the weights of multiple time points and the global average weight. The focus is on identifying relatively critical time points and thus adjusting their weights upward. This process not only increases the model's matching of the criticality of data time points, but also improves the accuracy of the weight distribution. The adjusted time weight distribution can more accurately reflect the critical time points, providing a basis for identifying critical time periods.

[0095] Summarize the adjusted time weight distribution, calculate the sum of the weights of each time point to determine the key time period, and use the formula:

[0096]

[0097] Generate temporal attention weight matrix;

[0098] in, Represents the sum of the weights of the key time periods, reflecting the comprehensive weights of all time points, and is used to determine the key time periods. It is The weight of each time point, is the average value of the weight, and the deviation of the weight from the average value at each time point is calculated. is the standard deviation of the weights, which is used to measure the degree of dispersion among multiple weights.

[0099] formula:

[0100]

[0101] The benefit of the formula is that by introducing the standard deviation and mean deviation of the time point weights, the accuracy and sensitivity of the time attention weight matrix are improved, enabling the model to more accurately identify and weight those time points that exhibit abnormalities in data completeness and variability.

[0102] Detailed explanation of the formula and the process of formula calculation and derivation:

[0103] Set the total number of time points , the weight at each time point , the average value of the weights , the standard deviation of the weights , calculate multiple weight bias adjustment values:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] Total weight:

[0110]

[0111] The results show that by adjusting the weights, the generated time attention weight matrix can effectively reflect the criticality of multiple time points and provide a basis for the identification and decision-making of critical time periods.

[0112] See also Figure 4 ,The steps for obtaining multi-scale risk feature analysis are as follows:

[0113] Using the time attention weight matrix, we parallelly process the data time scales of short-term, medium-term, and long-term, extract key risk features from each scale, and obtain the short-term risk feature set, medium-term risk feature set, and long-term risk feature set.

[0114] The process of using the time attention weight matrix to parallel process multi-time scale data involves data management and analysis technology. First, the system needs to classify the data in the time attention weight matrix and identify short-term, medium-term and long-term data segments. This process is completed by analyzing the data timestamp to determine which time period each data point belongs to. Then, differentiated data processing algorithms are applied to the data in each time period to extract risk characteristics. Short-term data pays more attention to changing risk indicators, including the impact of emergencies, while long-term data analysis focuses on slower trend changes, including gradually rising or falling risk trends. Data processing at each time scale requires targeted algorithm settings and parameter adjustments to ensure that the risk characteristics of the corresponding time scale can be accurately extracted. The key to this step is to adjust and apply multiple data processing technologies, match the data characteristics of differentiated time scales, and aggregate these data to form a risk characteristic analysis model. The model can reflect various risk changes from short-term to long-term, providing support for subsequent risk management and decision-making.

[0115] Based on the short-term risk feature set, the medium-term risk feature set, and the long-term risk feature set, the risk features extracted from multiple time scales are aggregated and the statistical data and influence of multiple features are calculated using the formula:

[0116]

[0117] Evaluate the comprehensive impact of each risk characteristic and obtain the aggregated risk characteristic analysis results at each time scale;

[0118] in, Represents the weighted risk assessment results, reflecting the comprehensive risk impact of multiple time scales, For the The weight of each risk feature reflects the criticality of the risk feature and is used to measure the contribution of differentiated risk features to the risk assessment results. Table No. The value of a risk characteristic represents a measure of risk and is used to quantify the actual observed value of the risk characteristic. For the The standard deviation of each risk characteristic is used to adjust the influence of risk value;

[0119] formula:

[0120]

[0121] The benefit of the formula is that by introducing standard deviation to adjust the impact of each risk feature, it can effectively balance the weights of multiple features in the overall assessment, thereby providing a more accurate and fair risk assessment result.

[0122] Detailed explanation of the formula and the process of formula calculation and derivation:

[0123] There are three risk characteristics, and their weights They are 0.3, 0.4, and 0.3 respectively, and the risk value 10, 15, 20 respectively, standard deviation They are 2, 3, and 5 respectively. The calculation process is as follows:

[0124] 1. Calculate the weighted value of the first risk feature:

[0125] 2. Calculate the weighted value of the second risk feature:

[0126] 3. Calculate the weighted value of the third risk characteristic:

[0127] 4. Summarize the weighted values ​​of all risk characteristics:

[0128] The results show that after integrating multiple risk characteristics, the total risk score is 4.7. This score can help decision makers understand the overall risk level and then take corresponding management measures.

[0129] Integrate the aggregated risk feature analysis results of each time scale, analyze the interaction and comprehensive impact of risk features at multiple time scales, identify key risk drivers, and establish a multi-scale risk feature analysis.

[0130] By analyzing the interactions of risk characteristics at multiple time scales, this step focuses on integrating the risk characteristic results at multiple time scales and evaluating the relationships and interactions between them. This requires the application of correlation analysis and regression analysis to determine which risk characteristics have the greatest impact on the overall risk assessment. In addition, parameters need to be adjusted to match the data type and scale of differentiated risk characteristics to ensure the accuracy and reliability of the analysis results. The results of the multi-scale risk characteristic analysis will provide decision-makers with a panoramic risk assessment, assist in identifying risk hotspots, optimize resource allocation, and formulate more effective risk management strategies.

[0131] See also Figure 5 The specific steps for obtaining real-time threshold adjustment records are as follows:

[0132] Receive the results of the multi-scale risk signature analysis, set baseline thresholds based on each financial indicator, analyze short-term, medium-term, and long-term risk factors, determine the range of preliminary thresholds, and generate a preliminary threshold list;

[0133] Receive the results of multi-scale risk feature analysis, set baseline thresholds based on each financial indicator, analyze short-term, medium-term and long-term risk factors, determine the value range of the preliminary thresholds, and through real-time monitoring of market data, summarize the data of multiple indicators, perform weighted processing, and obtain a preliminary threshold list, thereby providing a basis for subsequent adjustments. Monitor current fluctuations and future trends in real time, and cyclically update thresholds based on current market data. At the same time, conduct comparative analysis on previous data to ensure that the updated thresholds are reasonable and effective.

[0134] Call the real-time data stream to detect current fluctuations and future trends, adjust the preliminary threshold list, and use the formula:

[0135]

[0136] Calculate new thresholds and generate an adjusted threshold list;

[0137] in, Represents the adjusted threshold, which is used to reflect the new threshold after considering the current fluctuation and future trend. is the baseline threshold, which specifies the threshold benchmark of the financial indicator in a stable state. is the change in risk characteristics, which measures the change in risk characteristics from the previous assessment cycle to the current one. Is the risk weight coefficient, which is used to adjust the influence of the change in risk characteristics on the threshold. is the time attenuation coefficient, which is used to adjust the sensitivity of the threshold adjustment according to the time variation. It is the absolute value of the time change, reflecting the time span from the last adjustment to the current one;

[0138] formula:

[0139]

[0140] The benefit of the formula is that it makes the threshold adjustment more flexible and dynamic by combining the change in risk characteristics with time decay.

[0141] Detailed explanation of the formula and the process of formula calculation and derivation:

[0142] Setting baseline thresholds , risk characteristic change , time variation , risk weight coefficient , time decay coefficient . Calculate according to the formula:

[0143] 1. Calculation

[0144] 2. Calculation

[0145] 3. Substitute the above values ​​into the formula:

[0146]

[0147] The results show that the adjusted threshold It is 103.48, reflecting the matching adjustment in the current risk environment.

[0148] Based on the adjusted threshold list, record the decision-making process of each threshold adjustment, analyze the real-time data impact and risk characteristic changes, store the adjustment information of each threshold, and generate real-time adjustment threshold records.

[0149] Record the decision-making process of each threshold adjustment, including the analysis of real-time data and risk characteristics. Each adjustment step is based on real-time monitoring data and previously set risk analysis models, referring to market dynamics and changes in internal indicators, so as to make the decision-making process transparent. Each adjustment record includes the reason for the adjustment, the adjustment value and the expected impact. The records are stored and managed through the database system to facilitate audits and evaluations in future time periods. This method not only enhances the matching and response speed of risk management, but also improves the accuracy of strategy adjustments, ensuring that each threshold adjustment is based on multi-dimensional data analysis and accurate trend forecasting. The generated real-time adjustment threshold records reflect the circumstances and decision-making basis of each adjustment. These records provide key information for senior management and decision makers, optimizing risk management and decision-making processes in future time periods.

[0150] See also Figure 6 , the specific steps for obtaining future risk prediction results are:

[0151] Based on real-time threshold adjustment records, analyze the current status and changes of multiple financial indicators, set upper and lower thresholds for each indicator, and generate an initial financial status model;

[0152] Based on real-time threshold adjustment records, analyze the current status and changes of multiple financial indicators, set the upper and lower thresholds for each indicator, identify the fluctuation patterns and trends of each financial indicator through previous data analysis, and determine the matching thresholds. This process involves retrospective analysis of batches of previous financial data, determining the fluctuation range through standard deviation and mean calculations, and optimizing threshold settings with reference to seasonal adjustments and outlier processing to ensure that the thresholds reflect recent market conditions and real-time changes in the company's financial status. The set thresholds are then used to monitor future financial deviations and their potential impact on the company's financial health.

[0153] Call the initial financial status model and apply the position encoding method to deal with time dependency using the formula:

[0154]

[0155] Improve the model's processing of time information and generate time-encoded state data;

[0156] in, Indicates time The encoding state is used to represent the unique state of the differentiated time point, which helps the model recognize and match the characteristics of the time series. The frequency parameter representing the encoding determines the periodicity of the time signal and encodes the fluctuation speed and periodicity of the time series data. Represents the phase parameter, which adjusts the starting point of time encoding so that the model can capture the starting phase difference of the time series;

[0157] formula:

[0158]

[0159] The formula is useful in that by adjusting the frequency and phase Parameters,the formula can simulate the periodic changes of time series data,,thereby improving the analysis accuracy of the time dependence of financial,indicators, matching the data characteristics of differentiated time scales, and,enhancing the model's ability to predict future changes in financial,status.

[0160] Detailed explanation of the formula and the process of formula calculation and derivation:

[0161] Set the time during the analysis period For 6 months, that is, 180 days, select Let's refer to three key frequencies, , , , phase , , , and calculate:

[0162]

[0163]

[0164]

[0165]

[0166] The results show that by accumulating the sine waves of three key frequencies, the time The comprehensive position encoding state , the value represents the composite time dependence of financial status data within 180 days, providing a quantitative basis for subsequent risk analysis and decision-making.

[0167] Using time-coded state data, we combine the forecast results from the short-term, medium-term, and long-term using the formula:

[0168]

[0169] Integrate forecast data at different time scales to generate future risk prediction results;

[0170] in, Represents the risk prediction result, which is a comprehensive reflection of the short-term, medium-term and long-term prediction results. 、 、 They represent the forecast results obtained from the short-term, medium-term and long-term models respectively, which are obtained based on the data analysis of differentiated time scales and reflect the financial risk status within differentiated time ranges.

[0171] formula:

[0172]

[0173] The benefit of the formula is that it can reflect future risk conditions by combining financial forecast data with different time scales.

[0174] Detailed explanation of the formula and the process of formula calculation and derivation:

[0175] Setting short-term forecasts , medium-term forecast , long-term forecast , the prediction result is obtained by adding these three and dividing by three. The calculation process is as follows:

[0176]

[0177] The results show that the predicted value of financial risk in the future time period is 1000, and the prediction of integrated differentiated time frames can provide a basis for decision-making more accurately.

[0178] A method for intelligent financial data security management is provided. The method is based on the above-mentioned intelligent financial data security management system and includes the following steps:

[0179] S1: Collect financial data, extract the timestamp of each data point, mark the receipt time, partition the data points by month and business type, count the number of data points and time span in each partition, and generate partition data integrity results;

[0180] S2: Based on the partition data integrity results, apply self-attention weight calculation to multiple partitions, assign weights to differentiated time points, calculate the sum of the weights of each time point, determine the weight distribution of key time periods, and generate a temporal attention weight matrix;

[0181] S3: Use the time attention weight matrix to process financial data at multiple time scales in parallel, extract short-term risk characteristics, medium-term risk characteristics, and long-term risk characteristics, aggregate the analysis results of multiple risk characteristics, and establish a multi-scale risk characteristic analysis;

[0182] S4: Based on multi-scale risk feature analysis, set real-time thresholds for each financial indicator, adjust the thresholds to match the fluctuations and trends of real-time data, record the decision-making process and results of each threshold adjustment, generate real-time threshold adjustment records, and predict the financial status in future periods based on the records to generate future risk prediction results.

[0183] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A financial data security intelligent management system, characterized in that: The system comprises: The data security parsing module extracts timestamps from the collected financial data, marks the receipt time of each data point, partitions the data points by month and business type, counts the number of data points and time span in each partition, and generates partition data integrity results. The attention point weight module uses the partition data integrity results to apply self-attention weight calculation to the data partition, assigns differentiated weights to multiple time points, calculates the sum of the weights of each time point, determines the weight distribution of the key time period, and generates a time attention weight matrix; The multi-layer risk analysis module uses the time attention weight matrix to process multi-time scale data in parallel, extract short-term, medium-term and long-term risk features respectively, aggregate the risk feature analysis results, and establish a multi-scale risk feature analysis; The dynamic threshold management module receives the multi-scale risk feature analysis, sets real-time thresholds for each financial indicator, adjusts the thresholds to match the current fluctuations and future trends of the real-time data, records the decision-making process and results of each threshold adjustment, and generates real-time threshold adjustment records; The prediction and response module predicts the financial status in the future period according to the real-time adjustment threshold record, uses the position coding method to analyze the time dependency, merges the prediction results from multiple time scales, and generates future risk prediction results.

2. The financial data security intelligent management system according to claim 1, characterized in that: The partition data integrity results are specifically the number of data points and time span of each partition; the time attention weight matrix is ​​specifically the differentiated weights of multiple time points and the weight distribution of key time periods; the multi-scale risk feature analysis includes short-term risk features, medium-term risk features, and long-term risk features; the real-time adjustment threshold record includes real-time threshold, threshold adjustment decision process, and threshold adjustment result; the future risk prediction results are specifically multi-time scale prediction results and time dependency analysis.

3. The financial data security intelligent management system according to claim 2, characterized in that: The steps for obtaining the partition data integrity result are specifically as follows: Extract each data point from the financial database, record its receipt time, and mark the data point according to the business type and receipt month to generate a data record set with time stamp; According to the data record set with time stamps, the data is grouped by month and business type, the total number of data points and multiple time spans are calculated for each group, and statistical information for each group is generated; Using the statistical information of each group, the data integrity of each group is calculated using the formula: Generate partition data integrity results; in, Representative The total number of data points in a group reflects the amount of data in the group. It is The time span of each group is the length of time from the start to the end of data collection, in days. is the average of all group time spans, used to standardize the time span of each group for comparison. It is an adjustment factor used to balance the impact of the number of data points and the difference in time span.

4. The financial data security intelligent management system according to claim 3 is characterized in that: The steps for obtaining the time attention weight matrix are specifically as follows: Extracting data from the partition data integrity results, assigning an initial weight to each time point, adjusting the weight of each time point based on the data integrity results, and generating a preliminary weight distribution; Applying a self-attention mechanism to the preliminary weight distribution, iteratively adjusting the weight of each time point by calculating the deviation of the weight of each time point from the global average weight, and generating an adjusted time weight distribution; Summarize the adjusted time weight distribution, calculate the sum of the weights of each time point to determine the key time period, and use the formula: Generate temporal attention weight matrix; in, Represents the sum of the weights of the key time periods, reflecting the comprehensive weights of all time points, and is used to determine the key time periods. It is The weight of each time point, is the average value of the weight, and the deviation of the weight from the average value at each time point is calculated. is the standard deviation of the weights, which is used to measure the degree of dispersion among multiple weights.

5. The financial data security intelligent management system according to claim 4 is characterized in that: The steps for obtaining the multi-scale risk feature analysis are specifically as follows: Using the time attention weight matrix, the data time scales of short-term, medium-term and long-term are processed in parallel, and key risk features are extracted from each scale to obtain a short-term risk feature set, a medium-term risk feature set and a long-term risk feature set; Based on the short-term risk feature set, the medium-term risk feature set, and the long-term risk feature set, the risk features extracted from multiple time scales are aggregated, and the statistical data and influence of multiple features are calculated using the formula: Evaluate the comprehensive impact of each risk characteristic and obtain the aggregated risk characteristic analysis results at each time scale; in, Represents the weighted risk assessment results, reflecting the comprehensive risk impact of multiple time scales, For the The weight of each risk feature reflects the criticality of the risk feature and is used to measure the contribution of differentiated risk features to the risk assessment results. Table No. The value of a risk characteristic represents a measure of risk and is used to quantify the actual observed value of the risk characteristic. For the The standard deviation of each risk characteristic is used to adjust the influence of risk value; The results of the aggregated risk feature analysis at each time scale are integrated to analyze the interaction and combined impact of risk features at multiple time scales, identify key risk drivers, and establish a multi-scale risk feature analysis.

6. The financial data security intelligent management system according to claim 5, characterized in that: The steps for obtaining the real-time adjustment threshold record are specifically as follows: Receive the results of the multi-scale risk signature analysis, set baseline thresholds based on each financial indicator, analyze short-term, medium-term, and long-term risk factors, determine the value range of preliminary thresholds, and generate a preliminary threshold list; Call the real-time data stream to detect current fluctuations and future trends, and adjust the preliminary threshold list using the formula: Calculate new thresholds and generate an adjusted threshold list; in, Represents the adjusted threshold, which is used to reflect the new threshold after considering the current fluctuation and future trend. is the baseline threshold, which specifies the threshold benchmark of the financial indicator in a stable state. is the change in risk characteristics, which measures the change in risk characteristics from the previous assessment cycle to the current one. Is the risk weight coefficient, which is used to adjust the influence of the change in risk characteristics on the threshold. is the time attenuation coefficient, which is used to adjust the sensitivity of the threshold adjustment according to the time variation. It is the absolute value of the time change, reflecting the time span from the last adjustment to the current one; Based on the adjusted threshold list, the decision-making process of each threshold adjustment is recorded, the real-time data impact and risk characteristic changes are analyzed, the adjustment information of each threshold is stored, and a real-time adjustment threshold record is generated.

7. The financial data security intelligent management system according to claim 6, characterized in that: The steps for obtaining the future risk prediction results are specifically as follows: Based on the real-time threshold adjustment records, analyzing the current status and changes of multiple financial indicators, setting upper and lower thresholds for each indicator, and generating an initial financial status model; The initial financial status model is called and the position encoding method is applied to deal with time dependency using the formula: Improve the model's processing of time information and generate time-encoded state data; in, Indicates time The encoding state is used to represent the unique state of the differentiated time point, which helps the model recognize and match the characteristics of the time series. The frequency parameter representing the encoding determines the periodicity of the time signal and encodes the fluctuation speed and periodicity of the time series data. Represents the phase parameter, which adjusts the starting point of time encoding so that the model can capture the starting phase difference of the time series; Using the time-coded state data, the forecast results from the short-term, medium-term and long-term are combined using the formula: Integrate forecast data at different time scales to generate future risk prediction results; in, Represents the risk prediction result, which is a comprehensive reflection of the short-term, medium-term and long-term prediction results. 、 、 They represent the forecast results obtained from the short-term, medium-term and long-term models respectively, which are obtained based on the data analysis of differentiated time scales and reflect the financial risk status within differentiated time ranges.

8. A method for intelligent and secure management of financial data, characterized in that: The financial data security intelligent management system according to any one of claims 1 to 7 is implemented, comprising the following steps: Collect financial data, extract the timestamp of each data point, mark the receipt time, partition the data points by month and business type, count the number of data points and time span in each partition, and generate partition data integrity results; Based on the partition data integrity results, apply self-attention weight calculation to multiple partitions, assign weights to differentiated time points, calculate the sum of the weights of each time point, determine the weight distribution of key time periods, and generate a time attention weight matrix; Using the time attention weight matrix, multi-time-scale financial data is processed in parallel to extract short-term risk characteristics, medium-term risk characteristics, and long-term risk characteristics, and the analysis results of multiple risk characteristics are aggregated to establish a multi-scale risk characteristic analysis; Based on the multi-scale risk feature analysis, a real-time threshold is set for each financial indicator, the threshold is adjusted to match the fluctuations and trends of the real-time data, the decision-making process and results of each threshold adjustment are recorded, a real-time adjustment threshold record is generated, and the financial status in the future period is predicted based on the record to generate future risk prediction results.

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