Financial marketing platform data storage system and method

Abstract

The invention relates to the technical field of financial data processing, in particular to a financial marketing platform data storage system and method, and the system comprises a data prediction regulation and control module, a hotspot recognition module, a storage medium adjustment module, a sensitive encryption module, a data backup module and a performance evaluation module. According to the method, by collecting and calculating the average value of the data traffic and the access time points of the financial marketing platform, the time period with high access frequency can be accurately predicted, the data traffic can be effectively managed, the probability of system overload can be reduced, and by matching suitable storage media such as SSD and HDD for different types of data, optimization is carried out according to the access speed requirement, the access speed of the financial marketing platform can be greatly improved. The data processing speed and cost efficiency are remarkably improved, differentiated encryption processing and backup are performed on the data, and a security policy is designed according to the sensitivity level of the data, so that the operation efficiency of data storage is effectively monitored and improved, and the efficiency and security of a data storage system are improved.

Description

Technical Field

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

[0002] The field of financial data processing technology involves the use of computing methods and systems to collect, store, analyze and manage data used for financial activities. The field ranges from simple database management to complex predictive analysis and real-time data processing. Financial institutions use these technologies to process transaction data, customer information, market data and risk management information. With the development of big data technology, financial data processing is also increasingly using cloud storage, blockchain and artificial intelligence technologies to enhance the speed and security of data processing, while providing more accurate analysis results to help financial institutions make more informed decisions.

[0003] The storage system for financial marketing platform data refers to a data processing and storage solution designed specifically for the financial marketing platform. Such a system needs to process batches of customer data and transaction data to support marketing activities. The main purpose is to optimize the formulation and execution of marketing strategies and customize personalized marketing information by analyzing customer behavior and preferences. Such a system can also help improve the efficiency and effectiveness of marketing activities, enhance customer relationship management, and drive sales growth and brand loyalty.

[0004] The shortcomings of existing financial data processing technologies in processing large-scale financial data include slow data processing speed and easy system congestion during peak hours. In the existing data storage system, there is a lack of prediction and data flow management for high-frequency access periods, resulting in extended system response time and even service unavailability during peak data access periods, which seriously affects the operational efficiency and customer satisfaction of financial institutions. In addition, traditional systems use a unified encryption and backup strategy for data security. This "one-size-fits-all" approach not only increases unnecessary operating costs, but also ignores the special protection needs for highly sensitive data. The lack of differentiated data processing strategies can easily lead to sensitive data leakage or insufficient backup, bringing potential security risks and legal liabilities. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a storage system and method for financial marketing platform data.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a storage system for financial marketing platform data includes: The data prediction and control module collects the access frequency data of the financial marketing platform, calculates the average value of data flow and access time points, analyzes the volatility of access patterns, and obtains peak prediction indicators; The hotspot identification module uses the peak prediction index to filter high-frequency access data points by setting a threshold, applies Euclidean distance to measure the fit between data points, classifies data points according to access frequency, and obtains a data hotspot distribution map; The storage medium adjustment module prioritizes each type of data through the data hotspot distribution map, matches SSD and HDD as storage media according to access speed requirements, and obtains a media allocation plan; The sensitivity encryption module classifies the data in the media allocation scheme, sets a differentiated encryption protocol for each classification according to the sensitivity level of the data, and generates a customized security policy; The data backup module analyzes the storage backup frequency of data with different sensitivity levels according to the customized security policy, determines the backup time, and performs scheduled backup to form a backup execution plan; The performance evaluation module implements the backup execution plan, monitors and dynamically adjusts data storage and backup operations in real time, analyzes the cost-effectiveness of storage operations, checks the operating efficiency of data storage, and obtains storage performance monitoring results.

[0007] As a further solution of the present invention, the peak prediction indicators include predicted high access time periods, estimated peak access volume and access volatility, the data hotspot distribution map includes classified data point distribution, access density of multiple types of data points and key active areas, the media allocation plan includes selected data storage media, data storage ratio and priority ranking of each medium, the customized security policy includes encryption protocols, encryption strength and predetermined encryption operation cycles applied to differentiated data levels, the backup execution plan includes backup schedules, backup selections and backup storage locations for multiple types of data, and the storage performance monitoring results include real-time monitoring of data storage efficiency, cost-benefit analysis of backup operations and performance adjustment plans.

[0008] As a further solution of the present invention, the data prediction and control module includes: The data collection submodule uses real-time monitoring technology to collect the access frequency data of the financial marketing platform, synchronously records the original data of user access in different time periods, screens the effective data for classification and storage, and obtains data summary information; The traffic analysis submodule calculates the data traffic and the average value of the access time points in the differentiated time periods through the data summary information, sorts the access frequencies, identifies the key patterns of data fluctuations, and obtains the volatility analysis results; Based on the volatility analysis results, the peak prediction submodule analyzes the peak data of the financial marketing platform, calculates the peak time points within the next week, evaluates the time periods with high probability, and generates peak prediction indicators.

[0009] As a further solution of the present invention, the hotspot identification module includes: The threshold setting submodule adopts the peak prediction index, sets the screening criteria to identify the high-frequency access data points exceeding the set threshold, and filters the high-activity data points through threshold filtering to obtain the threshold filtering results; The data fitting submodule uses the Euclidean distance algorithm to measure the spatial fit of the selected data points based on the threshold filtering result, evaluates the fit between multiple data points through distance calculation, and obtains the fit analysis result; The data clustering classification submodule uses the fit analysis results to classify the access frequencies of the data points, organizes the data points with high fit into groups, performs data clustering operations, and obtains a hotspot distribution map.

[0010] As a further solution of the present invention, the storage medium adjustment module includes: The activity analysis submodule performs activity analysis of data categories based on the hotspot distribution map, sorts data according to the access frequency of each category, and screens data categories with high access frequency through analysis of data access patterns to obtain a priority sorting table; The media matching submodule evaluates the access speed requirements of each type of data according to the priority ranking table, selects the SSD and HDD to be adapted according to the access frequency and response time requirements, and obtains the matching and deployment results by comparing the performance requirements with the cost-effectiveness; The media adjustment submodule configures SSD and HDD for differentiated data categories based on the matching and deployment results, with reference to data security and access efficiency, checks the storage medium optimization for each type of data, and obtains a media allocation plan.

[0011] As a further solution of the present invention, the sensitivity encryption module includes: The data classification submodule implements the media allocation scheme, analyzes the sensitivity characteristics of the financial marketing platform data, classifies the data according to the sensitivity level, and performs sensitivity classification by evaluating the data content and relevance to obtain the sensitivity classification result; The encryption strength matching submodule uses a decision tree algorithm according to the sensitivity classification result to define differentiated encryption standards according to the data sensitivity level to obtain encryption protocol settings; The encryption measures submodule verifies that data of each sensitivity level is protected based on the encryption protocol settings, and obtains a customized security strategy by integrating encryption measures with storage medium characteristics.

[0012] As a further solution of the present invention, the formula of the decision tree algorithm is as follows: ; Among them, E is the quantitative value of the effect of the encryption strategy, represents the information entropy of the overall sensitive data, n represents the total number of data points in the data set, represents the sensitivity value of a single data point, Represents the average sensitivity value of all data points, representing the encryption level of a specific data point. represents the average encryption level, Represents the adjustment factor.

[0013] As a further solution of the present invention, the data backup module includes: The criticality assessment submodule evaluates the risk and criticality of data of different sensitivity levels based on the customized security strategy, performs quantitative analysis on the storage and backup frequency of the financial marketing platform data, determines the backup cycle of multiple types of data by analyzing the frequency and sensitivity of data modification, and obtains backup frequency statistics; The time determination submodule plans the backup time point of each type of data according to the backup frequency statistics, sets the backup execution time by optimizing the backup window and load, checks the balance between performance and data security, and obtains the backup time setting; The automatic control submodule analyzes the data storage backup time and the associated data category based on the backup time setting, performs a scheduled backup operation through automatic control, and obtains a backup execution plan.

[0014] As a further solution of the present invention, the performance evaluation module includes: The plan execution submodule implements the backup execution plan, performs real-time monitoring of data storage and backup operations, adjusts abnormalities and inefficiencies in the backup process, and obtains monitoring and adjustment results; The economic analysis submodule performs a cost and benefit analysis of the data storage operation according to the monitoring and adjustment results, evaluates the economic efficiency of the storage solution by comparing the cost input and data protection effect of differentiated backup strategies and technologies, performs a cost-benefit comparison, and obtains a cost-benefit analysis result; Based on the cost-benefit analysis results, the efficiency check submodule checks the operating efficiency of the storage operation, detects the processing speed and reliability of data storage by analyzing performance indicators and response time, evaluates the overall storage efficiency, and obtains storage performance monitoring results.

[0015] A method for storing financial marketing platform data, the method for storing financial marketing platform data is performed based on the above-mentioned storage system for financial marketing platform data, and comprises the following steps: S1: Collect the access frequency data of the financial marketing platform, calculate the average data flow and access time points, analyze the volatility of the access pattern based on the statistical results, and generate peak prediction indicators; S2: Based on the peak prediction index, set a threshold to filter out high-frequency access data points, use Euclidean distance to measure the fit between data points, classify data points according to access frequency, and obtain a data hotspot distribution map; S3: Prioritize multiple types of data according to the data hotspot distribution map, select SSD and HDD as storage media according to the access speed requirements of each type of data, and formulate a media allocation plan; S4: classifying the data in the media allocation scheme according to the sensitivity level, setting a differentiated encryption protocol for each data level, and generating a customized security policy; S5: According to the customized security policy, analyze the data storage and backup frequency of differentiated sensitivity levels, determine the backup time, and perform scheduled backup. By real-time monitoring and dynamic adjustment of data storage and backup operations, storage performance monitoring results are obtained.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by collecting and calculating the average values ​​of the data flow and access time points of the financial marketing platform, it is possible to accurately predict the time periods with high access frequency, effectively manage the data flow, and reduce the probability of system overload. By applying the Euclidean distance to quantify the degree of fit between data points and classifying the data points by access frequency, data hotspots can be accurately identified, providing a scientific basis for data access optimization. By matching suitable storage media for different types of data, such as SSD and HDD, and optimizing according to access speed requirements, the speed and cost efficiency of data processing can be significantly improved. Differentiated encryption processing and backup of data are performed, and security strategies are designed according to the sensitivity level of the data to improve data security, and data integrity is ensured through scheduled backup. This dynamic storage and backup strategy enables the operating efficiency of data storage to be effectively monitored and improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a system flow chart of the present invention; Figure 2 It is a schematic diagram of the system framework of the present invention; Figure 3 This is a flow chart of the data prediction and control module of the present invention; Figure 4 It is a flow chart of the hotspot identification module of the present invention; Figure 5 It is a flow chart of the storage medium adjustment module of the present invention; Figure 6 This is a flow chart of the sensitive encryption module of the present invention; Figure 7 It is a flow chart of the data backup module of the present invention; Figure 8is a flow chart of the performance evaluation module of the present invention; Fig. 9 It is a schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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.

[0019] 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 indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0020] See also Figure 1 to Figure 2 The present invention provides a technical solution, a storage system for financial marketing platform data includes: The data prediction and control module collects the access frequency data of the financial marketing platform, calculates the average value of data flow and access time points, analyzes the volatility of access patterns, and obtains peak prediction indicators; The hotspot identification module uses peak prediction indicators to filter out high-frequency access data points by setting thresholds, applies Euclidean distance to measure the fit between data points, and classifies data points according to access frequency to obtain data hotspot distribution maps; The storage media adjustment module uses the data hotspot distribution map to filter data points by size and access frequency, prioritizes each type of data, matches SSD and HDD as storage media according to access speed requirements, and obtains a media allocation plan; The sensitivity encryption module uses data tags to classify the data in the media allocation scheme, sets differentiated encryption protocols for each classification according to the sensitivity level of the data, and generates customized security policies; The data backup module analyzes the storage backup frequency of data with different sensitivity levels according to customized security policies, determines the backup time, performs scheduled backup for each sensitivity level, and forms a backup execution plan; The performance evaluation module implements the backup execution plan, monitors and dynamically adjusts data storage and backup operations in real time, analyzes the cost-effectiveness of storage operations, checks the operating efficiency of data storage, and obtains storage performance monitoring results.

[0021] Peak prediction indicators include predicted high-access time periods, estimated peak access volume and access volatility; data hotspot distribution maps include classified data point distribution, access density of multiple types of data points and key active areas; media allocation plans include selected data storage media, data storage ratio and priority ranking of each type of media; customized security policies include encryption protocols, encryption strength and scheduled encryption operation cycles applied to differentiated data levels; backup execution plans include backup schedules, backup selections and backup storage locations for multiple types of data; storage performance monitoring results include real-time monitoring of data storage efficiency, cost-benefit analysis of backup operations and performance adjustment plans.

[0022] See also Figure 2 , 3 , the data prediction and control module includes: The data collection submodule uses real-time monitoring technology to collect the access frequency data of the financial marketing platform, synchronously records the original data of user access in different time periods, screens the valid data for classification and storage, and obtains the execution process of data summary information as follows; Real-time monitoring technology collects access frequency data of the financial marketing platform, realizing accurate data monitoring and collection. The monitoring system is deployed on multiple data nodes to capture the activity records of each user in real time. According to the user's access pattern, the system automatically records the time and duration of each access. These data are transmitted to the main data center for further processing. During the data processing process, noise data is removed through preset data screening, such as abnormal access frequency or access requests in abnormal time periods. According to the characteristics of user access frequency and time, the data will be stored in different data sets in different categories for subsequent data analysis and utilization. This classified storage of data not only improves the efficiency of data query, but also facilitates subsequent data analysis and processing to obtain data summary information.

[0023] The traffic analysis submodule calculates the data traffic and the average value of the access time points in the differentiated time periods through data aggregation information, sorts the access frequencies, identifies the key patterns of data fluctuations, and obtains the execution process of the volatility analysis results as follows; By summarizing the information through data, according to the formula , calculate the average value of data flow and access time point in differentiated time periods. In the formula, Represents a collection of access time points, Representative Access time points, Represents the total number of access time points. Detailed explanation of the formula and formula calculation process: Consider the following access data of the financial marketing platform per hour: 08:00 (200 times), 09:00 (150 times), 10:00 (300 times). Substituting these data into the formula, we get This shows that the average number of visits per hour is about 216.67. This data analysis result helps to identify key patterns of data fluctuations and make more targeted adjustments to marketing strategies.

[0024] The peak prediction submodule is based on the volatility analysis results. By analyzing the peak data of the financial marketing platform, it calculates the peak time points in the next week, evaluates the time periods with high probability, and generates the peak prediction indicators. The execution process is as follows; Based on the results of volatility analysis, the peak time points within the next week are calculated. The fluctuation patterns in historical data are used to predict the peak time points through a specially developed algorithm model. The model uses time series analysis methods based on past access data patterns to estimate future data trends. The algorithm model normalizes the data to eliminate the skewness of the data. The model determines the time points that are beyond the normal fluctuation range by calculating the moving average and variance of each time point. The predicted peak time points will be identified based on these statistical indicators. The model also evaluates the probability of occurrence of these peak time points. By comparing the statistical significance of different time points, the time point and probability of each predicted peak are listed in detail, providing decision support for the platform's resource allocation and optimization, and generating peak prediction indicators.

[0025] See also Figure 2 , 4 , the hotspot identification module includes: The threshold setting submodule adopts the peak prediction index, sets the screening criteria to identify the high-frequency access data points that exceed the set threshold, and filters the high-activity data points through threshold filtering to obtain the execution process of the threshold filtering results as follows; Through the peak prediction index, according to the formula , filter out highly active data points. Where, is the new episode after filtering, V represents the access frequency dataset, Represents a single data point The frequency of visits, Represents the set threshold value, is an indicator function, when the condition If satisfied, the value is 1, otherwise it is 0. is the number of data points. Detailed explanation of the formula and the process of formula calculation: Set the access frequency of the monitored data points to: , set the threshold Substitute these data into the formula, filter them, and calculate This shows that the total access frequency of the highly active data points after screening is 700 times, which effectively identifies high-frequency access data points beyond the normal range and provides a key screening basis for subsequent data processing.

[0026] The data fitting submodule uses the Euclidean distance algorithm to measure the spatial fit of the filtered data points based on the threshold filtering results. The fit between multiple data points is evaluated through distance calculation. The execution process of the fit analysis result is as follows; The results are filtered using the threshold, and the Euclidean distance algorithm is used to evaluate the spatial similarity by calculating the geometric distance between data points. The process includes calculating the distance between each pair of data points, and using the distance calculation formula , where x and y represent two data points, and are the coordinates of these points in the cth dimension. This formula is applied to the screened out high-activity data points to calculate the Euclidean distance between them. The system evaluates the fit between the data points based on these calculated distance values. Through evaluation, the system can determine which data points are close to each other in space and analyze the structure and pattern of the data more accurately. The results of the fit analysis will directly affect the quality of data processing and analysis, ensuring the accuracy and reliability of data analysis.

[0027] The data clustering classification submodule uses the fit analysis results to classify the access frequency of data points, organizes the data points with high fit into groups, performs data clustering operations, and obtains the execution process of the hot spot distribution map as follows; According to the results of the fit analysis, the clustering algorithm is used to classify the data points with high fit according to the access frequency. Similar data points are organized into groups according to the access frequency and spatial position of the data points. During the clustering process, the algorithm calculates the central position of the data points in each group and the similarity between the data points in the group to ensure that the data points in each group are highly consistent in access frequency and spatial characteristics. This method can effectively organize the related data points scattered in the large data set, which not only helps to understand the overall distribution pattern of the data, but also provides a basis for further data analysis, ensuring the efficiency and accuracy of data utilization, and forming a hot spot distribution map.

[0028] See also Figure 2 , 5 , the storage medium adjustment module includes: The activity analysis submodule performs activity analysis of data categories based on the hotspot distribution map, sorts data according to the access frequency of each category, and screens data categories with high access frequency through data access pattern analysis to obtain the execution process of the priority sorting table as follows; Use hotspot distribution maps to analyze the activity of data categories and sort each type of data according to the access frequency. During the sorting process, an algorithm is used to calculate the activity of each type of data. This calculation is not only based on the frequency of access, but also considers the time of access and the distribution of users. Through this comprehensive evaluation, data categories with high access frequencies can be identified. These data categories are regarded as information sources that are critical to the business. These data categories are further screened through analysis of data access patterns to ensure that the selected data categories are given priority in future operations and analysis. Through a series of operations, not only the data categories and the corresponding access frequencies are listed, but also the relevant attributes of each data category, such as the data growth rate and the user's activity, etc., which provide an important basis for further data management and analysis and generate a priority sorting table.

[0029] The media matching submodule evaluates the access speed requirements of each type of data according to the priority sorting table, and selects the SSD and HDD to adapt according to the access frequency and response time requirements. By comparing the performance requirements with the cost-effectiveness, the execution process of the matching and deployment results is as follows; According to the priority ranking table, according to the formula , select the SSD and HDD to adapt. In the formula, For the cost-effectiveness of storage devices, S represents the access frequency, R represents the response time requirement, and C represents the cost. Detailed explanation of the formula and the process of formula calculation: Consider the needs of two types of data, one is data that is frequently accessed and has high response time requirements, and the other is data that is accessed infrequently but is not sensitive to response time. Set the access frequency of the frequently accessed type to S=1000 times / hour, the response time requirement to R=0.1 second, and the cost to C=50 yuan / GB; the access frequency of the infrequently accessed type to S=100 times / hour, the response time requirement to R=1 second, and the cost to C=5 yuan / GB. Substitute these data into the formula and calculate This shows that SSDs are suitable for storing data that needs to be accessed quickly, while HDDs are suitable for cost-sensitive storage needs. Through this cost-effectiveness comparison, the choice of storage media can be optimized to meet the performance requirements and cost constraints of different data categories.

[0030] Based on the matching and deployment results, the media adjustment submodule configures SSD and HDD for differentiated data categories respectively with reference to data security and access efficiency, checks the storage medium optimization for each type of data, and obtains the execution process of the media allocation plan as follows; The matching and allocation results are used to optimize the data storage media. SSDs and HDDs are configured for different data categories based on data security and access efficiency requirements. During the process, the access frequency, data volume, security requirements and budget constraints of each type of data are analyzed in detail. Based on these analysis results, SSDs are configured for data that requires high-speed access, and HDDs are configured for data that is not frequently accessed but stored in batches. In order to ensure data security, data backup and recovery strategies are also considered, and specific media usage plans are formulated for different types of data. After implementation, the storage media optimization plan for each type of data is evaluated in detail to ensure the efficiency and economy of data storage, while also ensuring data security and fast access capabilities, and obtaining a media allocation plan.

[0031] See also Figure 2 , 6 , the sensitivity encryption module includes: The data classification submodule implements the media allocation scheme, analyzes the sensitivity characteristics of the financial marketing platform data, classifies the data according to the sensitivity level, and performs sensitivity classification by evaluating the data content and relevance. The execution process of obtaining the sensitivity classification result is as follows; According to the media allocation plan, the data in the financial marketing platform will be analyzed and classified according to sensitivity characteristics, and the content characteristics and relevance of each type of data will be evaluated. The evaluation criteria include the privacy risk of the data, the degree of association with the user, and whether it contains sensitive information, such as user identity information, transaction records, etc. During the analysis process, the system identifies the sensitive characteristics of the data through content screening algorithms, and divides them into three levels: high, medium, and low according to the sensitivity level. For highly sensitive data, the system sets more stringent access rights and data protection measures, while for low-sensitivity data, general data access rights and security standards are adopted. The sensitivity grading process ensures that different data categories can obtain appropriate security levels and generate sensitivity classification results.

[0032] The encryption strength matching submodule uses a decision tree algorithm based on the sensitivity classification results to define differentiated encryption standards according to the data sensitivity level, and obtains the execution process of encryption protocol settings as follows; The formula for the decision tree algorithm is as follows: ; Among them, E is the quantitative value of the effect of the encryption strategy, represents the information entropy of the overall sensitive data, n represents the total number of data points in the data set, represents the sensitivity value of a single data point, represents the average sensitivity value of all data points, s represents the encryption level of a specific data point, represents the average encryption level, Represents the adjustment factor.

[0033] Parameter definition and value acquisition: The information entropy represents the overall sensitive data and measures the overall sensitivity of the data. For example, the information entropy of a set of data is calculated by observing the difference between the frequency of occurrence and the expected probability of each data type. Suppose that among 500 data, 100 are marked as highly sensitive, 200 are moderately sensitive, and 200 are lowly sensitive. Calculated to 2.0.

[0034] n is the total number of data points, 500 in this case.

[0035] It is the sensitivity value of a single data point, which is scored according to the data protection level, such as 1, 2, 3, etc. Average is the average of the sensitivity values ​​of all data points.

[0036] s is the encryption level of a particular data point, Similarly graded, is the average of the encryption levels of all data points.

[0037] Quantification When using s, data points are graded according to sensitivity and encryption requirements. For example, if a piece of data contains personally identifiable information, it is assigned a higher sensitivity and encryption level. Quantifying these values ​​is achieved through data monitoring and classification, not through settings.

[0038] Formula Substitution and Calculation set up The actual values ​​of are 1, 2, and 3 (corresponding to low, medium, and high sensitivity). , The value of s is also 1, 2, 3, , Calculating variance The actual value of . Set 100 data points to 1, 200 to 2, and 200 to 3; ; Substituting into the formula: ; Calculated The value is 0.0031, indicating that the overall effect of the encryption policy is weak given the sensitivity distribution of the data and the encryption level adjustment. This result indicates that it is necessary to re-evaluate the encryption protocol settings or adjust the data sensitivity classification and encryption level pairing strategy to improve the protection effect.

[0039] The encryption measures submodule verifies that data of each sensitivity level is protected based on encryption protocol settings. By integrating encryption measures with storage media characteristics, the execution process of customized security policies is as follows; According to the encryption protocol settings, the formula E=Encrypt(D,K) is used to verify that data of each sensitivity level is protected. In the formula, E represents the encrypted data, D represents the original data, and K represents the encryption key. Formula explanation and formula calculation derivation process: For highly sensitive data, the AES-256 encryption algorithm is used. Let the data content D be the user transaction record, and the encryption key K=256-bit randomly generated. Substitute these data into the formula and encrypt them to get Set a specific transaction record data as "100 yuan transaction", the corresponding encryption key is a 256-bit string, and the ciphertext generated after encryption It is irreversible encrypted data that can only be decrypted by the corresponding key, effectively preventing unauthorized access. The encryption strategy combines the characteristics of the storage medium, adjusts the encryption strength according to the sensitivity level of the data, and provides a customized security strategy to ensure the security and confidentiality of sensitive data at all levels.

[0040] See also Figure 2 , 7 , the data backup module includes: The criticality assessment submodule evaluates the risk and criticality of data with different sensitivity levels based on customized security policies, conducts quantitative analysis on the storage and backup frequency of financial marketing platform data, determines the backup cycle of multiple types of data by analyzing the frequency and sensitivity of data modification, and obtains the execution process of backup frequency statistics as follows; Based on customized security strategies, risk and criticality assessments are conducted on data of different sensitivity levels. The storage and backup frequency of data is quantitatively analyzed by calculating the modification frequency and sensitivity level of each type of data. The daily modification records and sensitivity levels of the data are collected during the assessment process, and these data are input into the risk assessment model for quantitative analysis. The model combines the modification frequency and access frequency of sensitive data to generate a risk score for the data. The priority and frequency of data backup are determined based on the score. Highly sensitive and frequently modified data are set for daily backup, medium-sensitive and less frequently modified data are set for weekly backup, and low-sensitivity and low-modification-frequency data are backed up monthly, thus obtaining backup frequency statistics.

[0041] The time determination submodule plans the backup time point for each type of data based on the backup frequency statistics, sets the backup execution time by optimizing the backup window and load, and checks the balance between performance and data security. The execution process of backup time setting is as follows; Based on the backup frequency statistics, a backup time point plan is formulated for each type of data. The backup frequency and daily load of each type of data are analyzed. By calculating the execution time window of the backup task, it is ensured that the backup operation will not cause a burden on the daily operation of the system. The backup window is selected according to the active time period of the data to ensure that the backup operation is performed during the low-load time period. For example, high-frequency backup tasks are set to be executed at night to reduce the impact on users. At the same time, the backup plan will be dynamically adjusted to cope with changes in system load, ensuring the stability and efficiency of the backup operation and generating backup time settings.

[0042] The automatic control submodule analyzes the time of data storage backup and the associated data category based on the backup time setting, and performs scheduled backup operations through automatic control to obtain the execution process of the backup execution plan as follows; According to the backup time setting, the relationship between the time arrangement of data storage backup and data category is analyzed, and the automated control process is used to perform scheduled backup operations. An automated scheduling system is created, which takes the backup time setting information as input and triggers the execution of the backup task through programmed time control. The automated system will call the data backup function at the specified time point to ensure that each type of data can complete the backup operation at the established frequency. The automated control system also has an abnormality monitoring function, which monitors the backup status in real time during the backup execution process. Once any abnormal situation occurs, the system will immediately issue an alarm and perform error handling to ensure the integrity and security of the backup process. Through this automated control method, a backup execution plan is generated.

[0043] See also Figure 2 , 8 , the performance evaluation modules include: The plan execution submodule implements the backup execution plan, performs real-time monitoring of data storage and backup operations, and adjusts abnormalities and inefficiencies in the backup process. The execution process of the monitoring and adjustment results is as follows; Implement real-time monitoring of data storage and backup operations based on the backup execution plan. During the execution of the backup task, all storage and backup activities are monitored in real time, and any abnormal conditions or inefficient operations in the backup process are identified through the monitoring system. The backup progress and system resource usage, such as CPU and memory occupancy, are collected regularly, and adjustments are made immediately when abnormalities are detected. The adjustment process includes optimizing the backup speed, reallocating system resources, and adjusting the priority of backup operations. It also has an automatic recovery function. Once an unrecoverable failure occurs, the system will automatically interrupt the backup task and start the recovery process to ensure the integrity of the backup data and the stability of the system, and generate monitoring adjustment results.

[0044] The economic analysis submodule performs a cost-benefit analysis of data storage operations based on the monitoring and adjustment results. By comparing the cost input and data protection effects of differentiated backup strategies and technologies, the economic feasibility of the storage solution is evaluated and a cost-benefit comparison is performed. The execution process of obtaining the cost-benefit analysis results is as follows; By monitoring the adjustment results, a detailed analysis of the costs and benefits of data storage operations is conducted. By analyzing the cost investment of different backup strategies and storage technologies, the performance of each strategy in terms of data protection is compared. During the analysis, the hardware cost, maintenance cost and operating consumption of each backup strategy are calculated in detail, and the improvement in data protection effect is quantitatively evaluated. For example, a quantitative evaluation is conducted through the backup success rate, data recovery time and data integrity, and the performance of different strategies on these indicators is compared. The cost-effectiveness of high-cost backup strategies and economical backup solutions is further compared, and a more economical and effective data storage and backup solution is obtained, resulting in a cost-benefit analysis result.

[0045] The efficiency check submodule checks the operating efficiency of storage operations based on the cost-benefit analysis results. By analyzing performance indicators and response time, it detects the processing speed and reliability of data storage, evaluates the overall storage efficiency, and obtains the execution process of storage performance monitoring results as follows; Based on the results of the cost-benefit analysis, the operating efficiency of data storage operations is checked, and various performance indicators and response times of data storage are analyzed in detail. By monitoring indicators such as data access speed, system processing load, and concurrent operation capabilities, the processing speed and reliability of data storage operations are evaluated. The response time of data storage is also collected regularly, and the performance of different storage strategies in various performance indicators is compared. The analysis results show the advantages and disadvantages of each storage solution in terms of performance and economy, providing a reference basis for optimizing storage performance and obtaining storage performance monitoring results.

[0046] See also Fig. 9 A method for storing financial marketing platform data is provided. The method for storing financial marketing platform data is performed based on the storage system for the financial marketing platform data, and includes the following steps: S1: Collect the access frequency data of the financial marketing platform, calculate the average data flow and access time points, analyze the volatility of the access pattern based on the statistical results, and generate peak prediction indicators; S2: Based on the peak prediction index, set the threshold to filter the high-frequency access data points, use the Euclidean distance to measure the fit between the data points, classify the data points according to the access frequency, and obtain the data hotspot distribution map; S3: Prioritize multiple types of data based on the data hotspot distribution map, select SSD and HDD as storage media based on the access speed requirements of each type of data, and formulate a media allocation plan; S4: classify the sensitivity level of the data in the media allocation scheme, set differentiated encryption protocols for each data level, and generate customized security policies; S5: According to the customized security policy, analyze the data storage and backup frequency of differentiated sensitivity levels, determine the backup time, and perform scheduled backup. Through real-time monitoring and dynamic adjustment of data storage and backup operations, storage performance monitoring results are obtained.

[0047] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A storage system for financial marketing platform data, characterized in that: The system comprises: The data prediction and control module collects the access frequency data of the financial marketing platform, calculates the average value of data flow and access time points, analyzes the volatility of access patterns, and obtains peak prediction indicators; The hotspot identification module uses the peak prediction index to filter high-frequency access data points by setting a threshold, applies Euclidean distance to measure the fit between data points, classifies data points according to access frequency, and obtains a data hotspot distribution map; The storage medium adjustment module prioritizes each type of data through the data hotspot distribution map, matches SSD and HDD as storage media according to access speed requirements, and obtains a media allocation plan; The sensitivity encryption module classifies the data in the media allocation scheme, sets a differentiated encryption protocol for each classification according to the sensitivity level of the data, and generates a customized security policy; The data backup module analyzes the storage backup frequency of data with different sensitivity levels according to the customized security policy, determines the backup time, and performs scheduled backup to form a backup execution plan; The performance evaluation module implements the backup execution plan, monitors and dynamically adjusts data storage and backup operations in real time, analyzes the cost-effectiveness of storage operations, checks the operating efficiency of data storage, and obtains storage performance monitoring results.

2. The storage system for financial marketing platform data according to claim 1, characterized in that: The peak prediction indicators include predicted high-access time periods, estimated peak access volume and access volatility; the data hotspot distribution map includes classified data point distribution, access density of multiple types of data points and key active areas; the media allocation plan includes selected data storage media, data storage ratio and priority ranking of each type of media; the customized security policy includes encryption protocols, encryption strength and predetermined encryption operation cycles applied to differentiated data levels; the backup execution plan includes backup schedules, backup selections and backup storage locations for multiple types of data; the storage performance monitoring results include real-time monitoring of data storage efficiency, cost-benefit analysis of backup operations and performance adjustment plans.

3. The storage system for financial marketing platform data according to claim 1, characterized in that: The data prediction and control module includes: The data collection submodule uses real-time monitoring technology to collect the access frequency data of the financial marketing platform, synchronously records the original data of user access in different time periods, screens the effective data for classification and storage, and obtains data summary information; The traffic analysis submodule calculates the data traffic and the average value of the access time points in the differentiated time periods through the data summary information, sorts the access frequencies, identifies the key patterns of data fluctuations, and obtains the volatility analysis results; Based on the volatility analysis results, the peak prediction submodule analyzes the peak data of the financial marketing platform, calculates the peak time points within the next week, evaluates the time periods with high probability, and generates peak prediction indicators.

4. The storage system for financial marketing platform data according to claim 1, characterized in that: The hotspot identification module includes: The threshold setting submodule adopts the peak prediction index, sets the screening criteria to identify the high-frequency access data points exceeding the set threshold, and filters the high-activity data points through threshold filtering to obtain the threshold filtering results; The data fitting submodule uses the Euclidean distance algorithm to measure the spatial fit of the selected data points based on the threshold filtering result, evaluates the fit between multiple data points through distance calculation, and obtains the fit analysis result; The data clustering classification submodule uses the fit analysis results to classify the access frequencies of the data points, organizes the data points with high fit into groups, performs data clustering operations, and obtains a hotspot distribution map.

5. The storage system for financial marketing platform data according to claim 1, characterized in that: The storage medium adjustment module includes: The activity analysis submodule performs activity analysis of data categories based on the hotspot distribution map, sorts data according to the access frequency of each category, and screens data categories with high access frequency through analysis of data access patterns to obtain a priority sorting table; The media matching submodule evaluates the access speed requirements of each type of data according to the priority ranking table, selects the SSD and HDD to be adapted according to the access frequency and response time requirements, and obtains the matching and deployment results by comparing the performance requirements with the cost-effectiveness; The media adjustment submodule configures SSD and HDD for differentiated data categories based on the matching and deployment results, with reference to data security and access efficiency, checks the storage medium optimization for each type of data, and obtains a media allocation plan.

6. The storage system for financial marketing platform data according to claim 1, characterized in that: The sensitivity encryption module includes: The data classification submodule implements the media allocation scheme, analyzes the sensitivity characteristics of the financial marketing platform data, classifies the data according to the sensitivity level, and performs sensitivity classification by evaluating the data content and relevance to obtain the sensitivity classification result; The encryption strength matching submodule uses a decision tree algorithm according to the sensitivity classification result to define differentiated encryption standards according to the data sensitivity level to obtain encryption protocol settings; The encryption measures submodule verifies that data of each sensitivity level is protected based on the encryption protocol settings, and obtains a customized security strategy by integrating encryption measures with storage medium characteristics.

7. The storage system for financial marketing platform data according to claim 6, characterized in that: The formula of the decision tree algorithm is as follows: ； Among them, E is the quantitative value of the effect of the encryption strategy, represents the information entropy of the overall sensitive data, n represents the total number of data points in the data set, represents the sensitivity value of a single data point, Represents the average sensitivity value of all data points, representing the encryption level of a specific data point. represents the average encryption level, Represents the adjustment factor.

8. The storage system for financial marketing platform data according to claim 1, characterized in that: The data backup module comprises: The criticality assessment submodule evaluates the risk and criticality of data of different sensitivity levels based on the customized security strategy, performs quantitative analysis on the storage and backup frequency of the financial marketing platform data, determines the backup cycle of multiple types of data by analyzing the frequency and sensitivity of data modification, and obtains backup frequency statistics; The time determination submodule plans the backup time point of each type of data according to the backup frequency statistics, sets the backup execution time by optimizing the backup window and load, checks the balance between performance and data security, and obtains the backup time setting; The automatic control submodule analyzes the data storage backup time and the associated data category based on the backup time setting, performs a scheduled backup operation through automatic control, and obtains a backup execution plan.

9. The storage system for financial marketing platform data according to claim 1, characterized in that: The performance evaluation module includes: The plan execution submodule implements the backup execution plan, performs real-time monitoring of data storage and backup operations, adjusts abnormalities and inefficiencies in the backup process, and obtains monitoring and adjustment results; The economic analysis submodule performs a cost and benefit analysis of the data storage operation according to the monitoring and adjustment results, evaluates the economic efficiency of the storage solution by comparing the cost input and data protection effect of differentiated backup strategies and technologies, performs a cost-benefit comparison, and obtains a cost-benefit analysis result; Based on the cost-benefit analysis results, the efficiency check submodule checks the operating efficiency of the storage operation, detects the processing speed and reliability of data storage by analyzing performance indicators and response time, evaluates the overall storage efficiency, and obtains storage performance monitoring results.

10. A method for storing financial marketing platform data, characterized in that: The storage system for financial marketing platform data according to any one of claims 1 to 9 is implemented, comprising the following steps: Collect the access frequency data of the financial marketing platform, calculate the average data flow and access time points, analyze the volatility of the access pattern based on the statistical results, and generate peak prediction indicators; Based on the peak prediction index, a threshold is set to filter out high-frequency access data points, the Euclidean distance is used to measure the fit between data points, the data points are classified according to the access frequency, and a data hotspot distribution map is obtained; According to the data hotspot distribution map, multiple types of data are prioritized, and according to the access speed requirements of each type of data, SSD and HDD are selected as storage media, and a media allocation plan is formulated; Classifying the data in the media allocation scheme according to the sensitivity level, setting differentiated encryption protocols for each data level, and generating customized security policies; According to the customized security policy, the data storage and backup frequency of differentiated sensitivity levels are analyzed, the backup time is determined, and scheduled backup is performed. The storage performance monitoring results are obtained through real-time monitoring and dynamic adjustment of data storage and backup operations.

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