CN119201007A - A storage system and method for financial marketing platform data - Google Patents
A storage system and method for financial marketing platform data Download PDFInfo
- Publication number
- CN119201007A CN119201007A CN202411696254.1A CN202411696254A CN119201007A CN 119201007 A CN119201007 A CN 119201007A CN 202411696254 A CN202411696254 A CN 202411696254A CN 119201007 A CN119201007 A CN 119201007A
- Authority
- CN
- China
- Prior art keywords
- data
- backup
- storage
- access
- encryption
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
- G06F3/0604—Improving or facilitating administration, e.g. storage management
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
- G06F3/061—Improving I/O performance
- G06F3/0611—Improving I/O performance in relation to response time
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
- G06F3/062—Securing storage systems
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Human Computer Interaction (AREA)
- Computer Security & Cryptography (AREA)
- Bioethics (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
本发明涉及金融数据处理技术领域，具体为一种金融营销平台数据的存储系统及其方法，系统包括数据预测调控模块、热点识别模块、存储介质调整模块、敏感性加密模块、数据备份模块、性能评估模块。本发明中，通过收集和计算金融营销平台的数据流量和访问时间点的平均值，能够精准预测高访问频次的时段，有效管理数据流量，减少系统过载的概率，通过为不同类型的数据匹配合适的存储介质，如SSD和HDD，根据存取速度需求进行优化，显著提升数据处理的速度和成本效率，对数据进行差异化的加密处理和备份，根据数据的敏感性等级设计安全策略，使得数据存储的运行效率得到有效的监控和提升，提高数据存储系统的效率与安全性。
The present invention relates to the technical field of financial data processing, specifically to a storage system and method for financial marketing platform data, the system comprising a data prediction and control module, a hotspot identification module, a storage medium adjustment module, a sensitivity encryption module, a data backup module, and a performance evaluation module. In the present invention, by collecting and calculating the average value of the data flow and access time points of the financial marketing platform, it is possible to accurately predict the time period with high access frequency, effectively manage the data flow, reduce the probability of system overload, match suitable storage media such as SSD and HDD for different types of data, optimize according to the access speed requirements, significantly improve the speed and cost efficiency of data processing, perform differentiated encryption processing and backup of data, design security strategies according to the sensitivity level of the data, so that the operating efficiency of data storage is effectively monitored and improved, and the efficiency and security of the data storage system are improved.
Description
Technical Field
The invention relates to the technical field of financial data processing, in particular to a system and a method for storing financial marketing platform data.
Background
The field of financial data processing technology relates to the use of computing methods and systems to collect, store, analyze and manage data for financial activities. The field includes from simple database management to complex predictive analysis and real-time data processing. Financial institutions utilize these techniques to process transaction data, customer information, market data, and risk management information. With the development of big data technology, the financial data processing also increasingly adopts cloud storage, blockchain and artificial intelligence technology to enhance the speed and safety of data processing, and simultaneously provide more accurate analysis results to help financial institutions make more intelligent decisions.
Wherein, the storage system of the financial marketing platform data refers to a data processing and storage solution specially designed for the financial marketing platform. Such systems require processing of batches of customer data and transaction data to provide support for marketing campaigns. The main purpose is to optimize the formulation and execution of marketing strategies, customizing personalized marketing information by analyzing customer behavior and preferences. Such a system may also help to increase the efficiency and effectiveness of marketing campaigns, enhance customer relationship management, promote sales growth and brand loyalty.
Disadvantages of existing financial data processing techniques in processing large-scale financial data include slow data processing speeds and susceptibility to system congestion during peak hours. In existing data storage systems, lack of prediction and data traffic management for high access frequency periods results in prolonged system response time during data access peaks, even when service is not available, severely affecting financial institution operating efficiency and customer satisfaction. In addition, conventional systems employ unified encryption and backup strategies in terms of data security, which not only adds unnecessary operating costs, but also ignores the special protection requirements for highly sensitive data. Lack of differentiated data processing strategies can easily lead to sensitive data leakage or insufficient backup, bringing potential security risks and legal liabilities.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a system and a method for storing financial marketing platform data.
In order to achieve the above object, the present invention adopts the following technical scheme, and a system for storing financial marketing platform data includes:
The data prediction regulation module collects access frequency data of the financial marketing platform, calculates the average value of data flow and access time points, and analyzes the fluctuation of the access mode to obtain peak prediction indexes;
the hot spot identification module adopts the peak prediction index, screens high-frequency access data points by setting a threshold value, applies the fitting degree among Euclidean distance measurement data points, classifies the data points according to the access frequency, and acquires a data hot spot distribution map;
The storage medium adjusting module performs priority ranking on each type of data through the data hot spot distribution diagram, and matches SSD and HDD as storage media according to the access speed requirement to obtain a media distribution scheme;
the sensitive encryption module classifies data in the medium distribution scheme, and sets a differentiated encryption protocol for each classification according to the sensitivity level of the data to generate a customized security policy;
The data backup module analyzes the storage backup frequency of the differential sensitive level data according to the customized security policy, determines backup time and performs timing backup to form a backup execution plan;
And the performance evaluation module implements the backup execution plan, monitors and dynamically adjusts the data storage and the backup operation in real time, analyzes the cost benefit of the storage operation, and checks the running efficiency of the data storage to obtain a storage performance monitoring result.
As a further aspect of the present invention, the peak prediction index includes a predicted high access period, an estimated peak access amount, and an access volatility, the data hotspot distribution map includes a classified data point distribution, an access density of a plurality of types of data points, and a critical active area, the medium allocation scheme includes a selected data storage medium, a data storage proportion of each medium, and a priority order, the customized security policy includes an encryption protocol applied to a differentiated data level, an encryption intensity, and a predetermined encryption operation period, the backup execution plan includes a backup schedule, a backup selection, and a backup storage location of the plurality of types of data, and the storage performance monitoring result includes a data storage efficiency monitored in real time, a cost benefit analysis of the backup operation, and a performance adjustment scheme.
As a further aspect of the present invention, the data prediction regulation module includes:
The data collection submodule collects access frequency data of the financial marketing platform by adopting a real-time monitoring technology, synchronously records original data accessed by users in a differentiated time period, screens effective data and stores the effective data in a classified mode to obtain data summarizing information;
The flow analysis submodule calculates the average value of the data flow and the access time point in the differentiated time period through the data summarizing information, sorts the access frequency, identifies the key mode of data fluctuation and obtains the fluctuation analysis result;
and the peak prediction submodule calculates a peak time point in a future week by analyzing peak data of the financial marketing platform based on the fluctuation analysis result, evaluates a time period with high probability and generates a peak prediction index.
As a further aspect of the present invention, the hotspot identification module includes:
the threshold setting submodule adopts the peak prediction index, sets screening criteria to identify high-frequency access data points exceeding a set threshold, screens high-activity data points through threshold filtering, and obtains a threshold filtering result;
Based on the threshold value filtering result, the data fitting submodule applies Euclidean distance algorithm to carry out spatial fitting degree measurement on the screened data points, and the fitting degree among multiple data points is evaluated through distance calculation to obtain a fitting degree analysis result;
And the data clustering classification submodule classifies the data points according to the access frequency by adopting the fitting degree analysis result, organizes the data points with high fitting degree into groups, and performs data clustering operation to obtain a hot spot distribution diagram.
As a further aspect of the present invention, the storage medium adjustment module includes:
The liveness analysis submodule carries out liveness analysis of data types based on the hot spot distribution map, sorts the data according to the access frequency of each type of data, and screens the data types with high access frequency through analysis of a data access mode to obtain a priority sorting table;
the medium matching sub-module evaluates the access speed requirement of each type of data according to the priority ranking table, selects to adapt to the SSD and the HDD according to the access frequency and the response time requirement, and obtains a matching allocation result through the comparison of the performance requirement and the cost benefit;
And the medium adjustment submodule respectively configures SSD and HDD for the differential data types based on the matching allocation result and referring to the data security and the access efficiency, and checks the storage medium optimization of each data to obtain a medium allocation scheme.
As a further aspect of the present invention, the sensitive encryption module includes:
The data classification submodule implements the medium distribution scheme, analyzes the sensitivity characteristics of the financial marketing platform data, classifies the data according to the sensitivity level, and classifies the sensitivity by evaluating the content and the relevance of the data to obtain a sensitivity classification result;
The encryption strength matching sub-module adopts a decision tree algorithm according to the sensitivity classification result, and defines differentiated encryption standards according to the data sensitivity level to obtain encryption protocol setting;
the encryption measure submodule verifies that the data of each sensitivity level is protected based on the encryption protocol setting, and a customized security policy is obtained by integrating the encryption measure and the storage medium characteristic.
As a further aspect of the present invention, the decision tree algorithm has the following formula:
;
wherein E is the effect quantized value of the encryption strategy, Information entropy representing the overall sensitive data, n represents the total number of data points in the data set,A sensitivity value representing a single data point,Representing the average of all data point sensitivity values, representing the encryption level of a particular data point,Representing the average encryption level of the data stream,Representing the adjustment factor.
As a further aspect of the present invention, the data backup module includes:
The criticality evaluation submodule evaluates the risk and criticality of the differentiated sensitive level data based on the customized security policy, quantitatively analyzes the storage backup frequency of the financial marketing platform data, and determines the backup period of the multi-class data by analyzing the data modification frequency and the sensitivity to obtain the backup frequency statistical information;
The time determination submodule plans the backup time point of each type of data according to the backup frequency statistical information, sets backup execution time by optimizing a backup window and a load, and obtains backup time setting by checking the balance of performance and data safety;
and the automatic control submodule analyzes the time of data storage backup and the associated data category based on the backup time setting, and performs timing backup operation through automatic control to obtain a backup execution plan.
As a further aspect of the present invention, the performance evaluation module includes:
The plan execution submodule implements the backup execution plan, carries out real-time monitoring of data storage and backup operation, and adjusts abnormality and inefficiency in the backup process to obtain a monitoring and adjusting result;
The economic analysis submodule carries out cost and benefit analysis of data storage operation according to the monitoring and adjusting result, evaluates the economic performance of a storage scheme by comparing the cost investment and the data protection effect of a differential backup strategy and technology, and carries out cost benefit comparison to obtain a cost benefit analysis result;
And the efficiency checking sub-module checks the running efficiency of the storage operation based on the cost benefit analysis result, and detects the processing speed and reliability of the data storage by analyzing the performance index and the response time, and evaluates the overall storage efficiency to obtain the storage performance monitoring result.
A storage method of financial marketing platform data is executed based on a storage system of the financial marketing platform data, and comprises the following steps:
S1, collecting access frequency data of a financial marketing platform, carrying out statistics calculation on the data to obtain average data flow and access time points, and analyzing the volatility of an access mode according to a statistics result to generate a peak prediction index;
S2, based on the peak prediction index, setting a threshold value to screen high-frequency access data points, and classifying the data points according to the access frequency by utilizing the fitting degree among Euclidean distance measurement data points to obtain a data hotspot distribution map;
S3, sorting the priority of the multiple types of data according to the data hotspot distribution diagram, selecting SSD and HDD as storage media according to the access speed requirement of each type of data, and making a media distribution scheme;
s4, classifying the sensitivity level of the data in the medium distribution scheme, setting a differentiated encryption protocol for each data level, and generating a customized security policy;
S5, analyzing the data storage and backup frequency of the differential sensitivity level according to the customized security policy, determining the backup time, executing the timing backup, and obtaining the storage performance monitoring result through real-time monitoring and dynamic adjustment of the data storage and backup operation.
Compared with the prior art, the invention has the advantages and positive effects that:
According to the invention, the data flow of the financial marketing platform and the average value of the access time point are collected and calculated, so that the time period of high access frequency can be accurately predicted, the data flow is effectively managed, and the probability of system overload is reduced. And the Euclidean distance is used for quantifying the fitting degree among the data points, and the data points are classified through the access frequency, so that the data hot points can be accurately identified, and a scientific basis is provided for data access optimization. By matching appropriate storage media, such as SSDs and HDDs, for different types of data, the speed and cost effectiveness of data processing is significantly improved by optimizing according to access speed requirements. And carrying out differentiated encryption processing and backup on the data, designing a security policy according to the sensitivity level of the data, improving the security of the data, and ensuring the integrity of the data through timing backup. The dynamic storage and backup strategy effectively monitors and improves the operation efficiency of data storage.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a schematic diagram of a system framework of the present invention;
FIG. 3is a flow chart of a data predictive regulation module of the present invention;
FIG. 4 is a flowchart of a hotspot identification module according to the present invention;
FIG. 5 is a flow chart of a storage medium adjustment module of the present invention;
FIG. 6 is a flow chart of a sensitive encryption module of the present invention;
FIG. 7 is a flow chart of a data backup module according to the present invention;
FIG. 8 is a flow chart of a performance evaluation module according to the present invention;
FIG. 9 is a schematic diagram of the method steps of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Referring to fig. 1 to 2, the present invention provides a technical solution, a system for storing data of a financial marketing platform, comprising:
The data prediction regulation module collects access frequency data of the financial marketing platform, calculates the average value of data flow and access time points, and analyzes the fluctuation of the access mode to obtain peak prediction indexes;
the hot spot identification module adopts a peak prediction index, screens high-frequency access data points by setting a threshold value, measures the fitting degree among the data points by applying Euclidean distance, classifies the data points according to the access frequency, and acquires a data hot spot distribution map;
The storage medium adjusting module screens the data points according to the size and the access frequency through the data hot spot distribution diagram, applies priority ordering to each type of data, and matches SSD and HDD as storage media according to the access speed requirement to obtain a medium distribution scheme;
the sensitive encryption module classifies data in the medium distribution scheme by adopting a data tag, and sets a differentiated encryption protocol for each classification according to the sensitivity level of the data to generate a customized security policy;
the data backup module analyzes the storage backup frequency of the differential sensitive level data according to the customized security policy, determines backup time, and executes timed backup aiming at each sensitive level to form a backup execution plan;
And the performance evaluation module implements a backup execution plan, monitors and dynamically adjusts the data storage and backup operation in real time, analyzes the cost benefit of the storage operation, and checks the running efficiency of the data storage to obtain a storage performance monitoring result.
The peak prediction index comprises a predicted high access time period, an estimated peak access amount and access volatility, the data hotspot distribution map comprises classified data point distribution, access density of multiple types of data points and key active areas, the medium distribution scheme comprises selected data storage media, data storage proportion and priority ordering of each media, the customized security policy comprises an encryption protocol applied to differentiated data levels, encryption intensity and a preset encryption operation period, the backup execution plan comprises a backup schedule of multiple types of data, backup selection and backup storage positions, and the storage performance monitoring result comprises real-time monitored data storage efficiency, cost benefit analysis of backup operation and a performance adjustment scheme.
Referring to fig. 2 and 3, the data prediction regulation module includes:
the data collection submodule collects the access frequency data of the financial marketing platform by adopting a real-time monitoring technology, synchronously records the original data accessed by users in a differentiated time period, screens the effective data and stores the effective data in a classified mode, and an execution flow of obtaining data summary information is as follows;
The real-time monitoring technology collects access frequency data of the financial marketing platform, accurate data monitoring and acquisition are achieved, the monitoring system is deployed on a plurality of data nodes, activity records of each user are captured in real time, according to access modes of the users, the system can automatically record time and duration of each access, the data are transmitted to a main data center for further processing, noise data such as abnormal access frequency or abnormal access request of an abnormal time period are removed through preset data screening in the data processing process, the data are stored in different data sets in a classified mode according to the characteristics of the frequency and time of the access of the users, so that subsequent data analysis and utilization are facilitated, and the classified storage of the data not only improves data query efficiency, but also facilitates subsequent data analysis and processing work, and data summarization information is obtained.
The flow analysis submodule calculates the average value of the data flow and the access time point in the differentiated time period through the data summarizing information, sorts the access frequency, identifies the key mode of data fluctuation and obtains the execution flow of the fluctuation analysis result as follows;
by summarizing the information, the method is as follows The average of the data traffic and access time points for the differentiated time period is calculated. In the formula,Representing a set of access points in time,Represents the firstThe point in time of the access is a point in time,Representing the total number of access points in time. Formulation details and formulation calculation derivation process consider the financial marketing platform access data per hour as follows: 08:00 (200 times), 09:00 (150 times), 10:00 (300 times). Substituting the data into a formula to calculate. This shows that the average number of accesses per hour is about 216.67 times, and the data analysis results help identify key modes of data fluctuation, and make more targeted adjustments to marketing strategies.
The peak prediction sub-module calculates peak time points in the future week by analyzing peak data of the financial marketing platform based on the fluctuation analysis result, evaluates a time period with high probability, and generates an execution flow of a peak prediction index as follows;
Calculating peak time points in the future week based on a fluctuation analysis result, predicting the peak time points by utilizing a fluctuation mode in historical data through a specially developed algorithm model, estimating the future data trend by using a time sequence analysis method according to a past access data mode, wherein the algorithm model carries out normalization processing on the data so as to eliminate the bias influence of the data, determining the time points exceeding the conventional fluctuation range by calculating the moving average value and the variance of each time point, identifying the predicted peak time points according to the statistical indexes, evaluating the occurrence probability of the peak time points by the model, and providing decision support for resource allocation and optimization of a platform by comparing the statistical significance of different time points to list the time points and the probability of each predicted peak in detail so as to generate a peak prediction index.
Referring to fig. 2 and 4, the hotspot identification module includes:
the threshold setting submodule adopts a peak prediction index, sets screening criteria to identify high-frequency access data points exceeding a set threshold, screens high-activity data points through threshold filtering, and obtains the execution flow of a threshold filtering result as follows;
Through the peak prediction index, the method is according to the formula High active data points are screened. In the formula,For the new number of episodes after filtering, V represents the access frequency dataset,Representing a single data pointIs used for the access frequency of (a),Representing the set threshold value of the threshold,Is an indication function, when the condition isWhen the value is satisfied, the value is 1, otherwise, the value is 0,Number of data points. The formula details and formula calculation deduction process is that the monitored data point access frequency is set as follows: setting a threshold value . Substituting the data into a formula, screening, and calculating to obtain. This shows that the total access frequency of the screened high-activity data points is 700 times, so that the high-frequency access data points beyond the common range are effectively identified, and a key screening basis is provided for subsequent data processing.
The data fitting sub-module carries out spatial fitting degree measurement on the screened data points by applying a Euclidean distance algorithm based on a threshold filtering result, and evaluates the fitting degree among multiple data points through distance calculation to obtain an execution flow of a fitting degree analysis result as follows;
using a threshold filtering result, using a Euclidean distance algorithm that evaluates spatial similarity by calculating geometric distances between data points, the process including calculating distances between pairs of data points, and using a distance calculation formula Where x and y represent two data points,AndThe coordinate of the points in the c dimension is applied to the screened high-activity data points, euclidean distance between the data points is calculated, the system evaluates the fitting degree between the data points according to the calculated distance values, through evaluation, the system can determine which data points are close to each other in space, the structure and the mode of the data are analyzed more accurately, the fitting degree analysis result directly influences the quality of data processing and analysis, and the accuracy and the reliability of data analysis are ensured.
The data clustering classification submodule classifies the data points with access frequency by adopting a fitting degree analysis result, organizes the data points with high fitting degree into groups, performs data clustering operation, and obtains an execution flow of a hot spot distribution map as follows;
Operating according to the analysis result of the fitting degree, classifying the data points with high fitting degree according to the visit frequency by adopting a clustering algorithm, organizing similar data points into groups according to the visit frequency and the space position of the data points, calculating the center position of the data points in each group and the similarity between the data points in the group by the algorithm in the clustering process, the method ensures that the data points gathered by each group have high consistency in access frequency and spatial characteristics, and can effectively organize the related data points scattered in a large data set, thereby not only being beneficial to understanding the overall distribution mode of the data, but also providing a basis for further data analysis, ensuring the high efficiency and accuracy of data utilization and forming a hot spot distribution map.
Referring to fig. 2 and 5, the storage medium adjusting module includes:
the liveness analysis submodule carries out liveness analysis of data types based on the hot spot distribution diagram, sorts the data according to the access frequency of each type of data, screens the data types with high access frequency through analysis of the data access mode, and obtains an execution flow of a priority sorting table as follows;
The method comprises the steps of analyzing the liveness of data categories by using a hot spot distribution map, sorting each type of data according to access frequency, calculating the liveness of each type of data by adopting an algorithm in the sorting process, wherein the calculation is not only based on the access frequency, but also considers the access time and the user distribution, the data categories with high access frequency can be identified through the comprehensive evaluation, the data categories are regarded as information sources which are vital to business, the data categories are further screened through analysis of a data access mode, the selected data categories are ensured to be processed preferentially in future operation and analysis, and through series operation, not only the data categories and the corresponding access frequency are listed, but also related attributes of each data category, such as the growth rate of the data, the liveness of the user and the like, an important basis is provided for further data management and analysis, and a priority sorting table is generated.
The medium matching submodule evaluates the access speed requirement of each type of data according to the priority ranking table, selects and adapts to the SSD and the HDD according to the access frequency and the response time requirement, and obtains the execution flow of a matching allocation result through the comparison of the performance requirement and the cost benefit as follows;
According to the priority order table and the formula The SSD and HDD are selected for adaptation. In the formula,For cost effectiveness of the storage device, S represents the access frequency, R represents the response time requirement, and C represents the cost. The formulation details and formulation calculation derivation process considers the requirements of two data types, one being frequently accessed and response time demanding data and the other being low access frequency but not response time sensitive data. Setting the access frequency S=1000 times/hour of the frequent access type, wherein the response time is required to be R=0.1 second and the cost is C=50 yuan/GB, and the access frequency S=100 times/hour of the low-frequency access type is required to be R=1 second and the cost is C=5 yuan/GB. Substituting the data into a formula to calculate. This suggests that SSDs are suitable for storing data that requires fast access, whereas HDDs are suitable for cost-sensitive storage requirements, by which cost-effective comparison the selection of storage media can be optimized to meet the performance requirements and cost constraints of different data categories.
The medium adjustment submodule respectively configures SSD and HDD for the differential data types based on the matching allocation result and referring to the data security and the access efficiency, and verifies the optimization of the storage medium of each data to obtain the execution flow of the medium allocation scheme as follows;
The data storage medium is optimized by utilizing the matching allocation result, SSDs and HDDs are configured for different data types according to the safety and access efficiency requirements of the data, the access frequency, the data quantity, the safety requirement and the budget limit of each data are analyzed in detail in the process, the SSDs are configured for the data which need to be accessed at high speed according to the analysis results, the HDDs are configured for the data which are not accessed frequently but are stored in batches, in order to ensure the safety of the data, the backup and recovery strategies of the data are considered, the specific medium use scheme is formulated for the data of different types, the storage medium optimization scheme of each data is subjected to detailed effect evaluation after implementation, the efficiency and the economical efficiency of the data storage are ensured, and meanwhile, the safety and the quick access capability of the data are ensured, so that the medium allocation scheme is obtained.
Referring to fig. 2 and 6, the sensitive encryption module includes:
The data classification sub-module implements a medium distribution scheme, analyzes the sensitivity characteristics of the financial marketing platform data, classifies the data according to sensitivity grades, and classifies the sensitivity by evaluating the content and the relevance of the data to obtain an execution flow of a sensitivity classification result as follows;
According to the medium distribution scheme, data in a financial marketing platform are analyzed and classified according to sensitivity characteristics, content characteristics and relevance of each type of data are evaluated, evaluation standards comprise privacy risks of the data, relevance degree of the data and whether sensitive information such as identity information and transaction records of the user is contained, in the analysis process, a system identifies the sensitive characteristics of the data through a content screening algorithm and classifies the sensitive characteristics into three levels of high, medium and low according to sensitivity grades, for the data with high sensitivity, the system sets more strict access rights and data protection measures, for the data with low sensitivity, general data access rights and security standards are adopted, and a sensitivity classification process ensures that different data types can obtain proper security levels to generate sensitivity classification results.
The encryption strength matching sub-module adopts a decision tree algorithm according to the sensitivity classification result, and defines differentiated encryption standards according to the data sensitivity level, so as to obtain an execution flow of encryption protocol setting as follows;
The formula of the decision tree algorithm is as follows:
;
wherein E is the effect quantized value of the encryption strategy, Information entropy representing the overall sensitive data, n represents the total number of data points in the data set,A sensitivity value representing a single data point,Representing the average of all data point sensitivity values, s represents the encryption level for a particular data point,Representing the average encryption level of the data stream,Representing the adjustment factor.
Parameter definition and value acquisition:
Information entropy representing the overall sensitive data, and sensitivity of the data overall is measured. For example, the information entropy of a set of data is calculated by observing the frequency of occurrence and expected probability differences for each data type. Of the 500 pieces of data, 100 pieces are marked as highly sensitive, 200 pieces as moderately sensitive, 200 pieces as lowly sensitive, Calculated as 2.0.
N is the total number of data points, in this case 500.
Is the sensitivity value of a single data point, scored according to the data protection class, e.g., 1,2,3, etc. Average value ofIs the average of all data point sensitivity values.
S is the encryption level of a particular data point, andThe classification is performed in a similar way,Is the average of all data point encryption levels.
QuantizationAnd s, the data points are ranked according to sensitivity and encryption requirements. For example, if a piece of data contains personal identity information, a higher sensitivity and encryption level are assigned. These values are quantified by data monitoring and classification, not by setting.
Formula substitution and calculation
Setting upThe actual value distribution of (a) is 1,2,3 (corresponding to low, medium, high sensitivity),,
S is also 1,2,3,,
Calculating varianceIs a real value of (c). 100 data points were set 1,200 2,200 3;
;
Substitution formula:
;
Calculated A value of 0.0031 indicates that the overall effect of the encryption policy is weaker given the sensitivity distribution of the data and encryption level adjustment. This result indicates that the encryption protocol settings need to be re-evaluated or the sensitivity classification and encryption level pairing policy of the data adjusted to improve the protection effect.
The encryption measure submodule verifies that the data of each sensitivity level is protected based on encryption protocol setting, and the execution flow of the customized security policy is obtained by integrating the characteristics of the encryption measure and the storage medium as follows;
according to the encryption protocol settings, data of each sensitivity level is verified to be protected according to the formula e=encrypter (D, K). Wherein E represents encrypted data, D represents original data, and K represents an encryption key. Formula details and formula calculation deduction process, namely, for high-sensitivity data, adopting AES-256 encryption algorithm, setting data content D as user transaction record, randomly generating encryption key K=256-bits, substituting the data into a formula, and encrypting to obtain . Setting a specific transaction record data as 100-element transaction, and setting a corresponding encryption key as a 256-bit character string and generating ciphertext after encryptionThe encryption strategy combines the characteristics of the storage medium, adjusts encryption intensity in a grading way according to the sensitivity of the data, provides a customized security strategy and ensures the security and confidentiality of sensitive data of all levels.
Referring to fig. 2 and 7, the data backup module includes:
the criticality evaluation submodule evaluates the risk and criticality of the differentiated sensitive level data based on the customized security policy, quantitatively analyzes the storage backup frequency of the financial marketing platform data, and determines the backup period of the multi-class data by analyzing the data modification frequency and the sensitivity, so as to obtain the execution flow of the backup frequency statistical information as follows;
According to customized security policy, risk and criticality of data with different sensitivity levels are evaluated, the data storage backup frequency is quantitatively analyzed by calculating the modification frequency and the sensitivity level of each type of data, the daily modification record and the sensitivity level of the data are collected in the evaluation process, the data are input into a risk evaluation model for quantitative analysis, the model combines the modification frequency and the access frequency of the sensitive data to generate a risk score of the data, the priority and the frequency of the data backup are determined according to the score, the data with high sensitivity and high modification frequency are set as daily backup, the data with medium sensitivity and lower modification frequency are set as weekly backup, and the data with low sensitivity and low modification frequency are set as monthly backup, so that backup frequency statistic information is obtained.
The time determining submodule plans the backup time point of each type of data according to the backup frequency statistical information, sets backup execution time by optimizing a backup window and a load, checks the balance of performance and data safety, and obtains the execution flow of backup time setting as follows;
According to the statistical information of the backup frequency, a backup time point plan of each type of data is formulated, the backup frequency and daily load condition of each type of data can be analyzed, the backup operation is ensured not to cause burden on the daily operation of the system by calculating the execution time window of the backup task, the backup window is selected according to the active time period of the data, the backup operation is ensured to be carried out in a low-load time period, for example, the high-frequency backup task is set to be executed at night so as to reduce the influence on a user, and meanwhile, the backup plan can be dynamically adjusted to deal with the change of the system load, the stability and the high efficiency of the backup operation are ensured, and the backup time setting is generated.
The automatic control submodule analyzes the time of data storage backup and the associated data category based on backup time setting, and performs timing backup operation through automatic control to obtain the execution flow of a backup execution plan as follows;
According to the backup time setting, the association relation between the time arrangement of data storage backup and the data category is analyzed, the timing backup operation is carried out by adopting an automatic control flow, an automatic scheduling system is created, backup time setting information is used as input, the execution of backup tasks is triggered by programmed time control, the automatic system can call the data backup function at a designated time point, each type of data can be ensured to finish the backup operation according to a set frequency, the automatic control system also has an abnormality monitoring function, the backup state is monitored in real time in the backup execution process, and once any abnormal condition occurs, the system immediately gives an alarm and carries out error processing, the integrity and the safety of the backup process are ensured, and a backup execution plan is generated by the automatic control mode.
Referring to fig. 2 and 8, the performance evaluation module includes:
the plan execution submodule executes a backup execution plan, performs real-time monitoring of data storage and backup operation, adjusts abnormality and inefficiency in the backup process, and obtains an execution flow of a monitoring and adjusting result as follows;
The method comprises the steps of implementing real-time monitoring of data storage and backup operation based on a backup execution plan, monitoring all storage and backup activities in real time in the backup task execution process, identifying any abnormal situation or low-efficiency operation in the backup process through a monitoring system, acquiring the backup progress and the use condition of system resources, such as CPU and memory occupancy rate, timely adjusting when detecting the abnormality, wherein the adjusting process comprises optimizing the backup speed, reallocating system resources and adjusting the priority of the backup operation, and the method further comprises an automatic recovery function.
The economic analysis submodule carries out cost and benefit analysis of data storage operation according to the monitoring and adjusting result, evaluates the economic performance of the storage scheme by comparing the cost investment and the data protection effect of the differentiated backup strategy and technology, and carries out cost benefit comparison to obtain an execution flow of the cost benefit analysis result as follows;
By monitoring the adjustment result, the cost and benefit of the data storage operation are analyzed in detail, the performance of different backup strategies and the cost input condition of the storage technology in terms of the data protection effect is compared, in the analysis process, the hardware cost, the maintenance cost and the operation consumption of each backup strategy are calculated in detail, the improvement of the data protection effect is evaluated in a quantitative manner, for example, the performance of different strategies in terms of the indexes is compared through the quantitative evaluation of the backup success rate, the data recovery time and the data integrity, the cost benefit between the high-cost backup strategy and the economic backup scheme is further compared, and more economic and effective data storage and backup schemes are obtained, so that the cost benefit analysis result is obtained.
The efficiency checking submodule checks the running efficiency of the storage operation based on the cost-benefit analysis result, and detects the processing speed and reliability of the data storage by analyzing the performance index and the response time, and evaluates the overall storage efficiency to obtain the execution flow of the storage performance monitoring result as follows;
according to the cost benefit analysis result, the running efficiency inspection of the data storage operation is carried out, each performance index and response time of the data storage are analyzed in detail, the processing speed and reliability of the data storage operation are evaluated through monitoring the indexes such as the data access speed, the processing load of the system and the concurrent operation capability, the response time of the data storage operation is also collected at fixed time, the performance of different storage strategies on each performance index is compared, the analysis result shows the advantages and disadvantages of each storage scheme on performance and economy, and a reference basis is provided for optimizing the storage performance, so that the storage performance monitoring result is obtained.
Referring to fig. 9, a method for storing financial marketing platform data is executed based on the storage system of the financial marketing platform data, and includes the following steps:
S1, collecting access frequency data of a financial marketing platform, carrying out statistics calculation on the data to obtain average data flow and access time points, and analyzing the volatility of an access mode according to a statistics result to generate a peak prediction index;
S2, based on peak prediction indexes, setting a threshold value to screen high-frequency access data points, and classifying the data points according to the access frequency by utilizing the fitting degree among Euclidean distance measurement data points to obtain a data hotspot distribution map;
s3, sorting the priority of multiple types of data according to the data hotspot distribution diagram, selecting SSD and HDD as storage media according to the access speed requirement of each type of data, and making a media distribution scheme;
S4, classifying the sensitivity level of the data in the medium distribution scheme, setting a differentiated encryption protocol for each data level, and generating a customized security policy;
S5, analyzing the data storage and backup frequency of the differential sensitivity level according to the customized security policy, determining the backup time, executing the timing backup, and obtaining the storage performance monitoring result through real-time monitoring and dynamic adjustment of the data storage and backup operation.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411696254.1A CN119201007A (en) | 2024-11-25 | 2024-11-25 | A storage system and method for financial marketing platform data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411696254.1A CN119201007A (en) | 2024-11-25 | 2024-11-25 | A storage system and method for financial marketing platform data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN119201007A true CN119201007A (en) | 2024-12-27 |
Family
ID=94058642
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411696254.1A Pending CN119201007A (en) | 2024-11-25 | 2024-11-25 | A storage system and method for financial marketing platform data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119201007A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119759297A (en) * | 2025-03-06 | 2025-04-04 | 深圳印智互联信息技术有限公司 | A data asset modeling method and system |
| CN120179184A (en) * | 2025-05-22 | 2025-06-20 | 杭州易康信科技有限公司 | Cloud-based multimodal data dynamic archiving system for big data |
| CN120335720A (en) * | 2025-04-01 | 2025-07-18 | 上饶市大万网络科技有限公司 | A big data storage service method and system |
| CN120541864A (en) * | 2025-06-30 | 2025-08-26 | 广州大一互联网络科技有限公司 | Data security management method and system for server |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080046483A1 (en) * | 2006-08-16 | 2008-02-21 | Lehr Douglas L | Method and system for selecting the timing of data backups based on dynamic factors |
| CN115050472A (en) * | 2022-06-07 | 2022-09-13 | 西安鹫一卓越软件科技有限公司 | Method and system for hypertension risk level assessment and risk early warning |
| CN118157907A (en) * | 2024-01-26 | 2024-06-07 | 重庆嗨客网络科技有限公司 | Intelligent interaction method and system for serving big data information security of financial institution |
| CN118244994A (en) * | 2024-05-27 | 2024-06-25 | 深圳市今古科技有限公司 | Historical data storage method and device based on cloud computing |
| CN118261695A (en) * | 2024-03-26 | 2024-06-28 | 中国工商银行股份有限公司 | Risk assessment method and device, computer storage medium and electronic equipment |
-
2024
- 2024-11-25 CN CN202411696254.1A patent/CN119201007A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080046483A1 (en) * | 2006-08-16 | 2008-02-21 | Lehr Douglas L | Method and system for selecting the timing of data backups based on dynamic factors |
| CN115050472A (en) * | 2022-06-07 | 2022-09-13 | 西安鹫一卓越软件科技有限公司 | Method and system for hypertension risk level assessment and risk early warning |
| CN118157907A (en) * | 2024-01-26 | 2024-06-07 | 重庆嗨客网络科技有限公司 | Intelligent interaction method and system for serving big data information security of financial institution |
| CN118261695A (en) * | 2024-03-26 | 2024-06-28 | 中国工商银行股份有限公司 | Risk assessment method and device, computer storage medium and electronic equipment |
| CN118244994A (en) * | 2024-05-27 | 2024-06-25 | 深圳市今古科技有限公司 | Historical data storage method and device based on cloud computing |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119759297A (en) * | 2025-03-06 | 2025-04-04 | 深圳印智互联信息技术有限公司 | A data asset modeling method and system |
| CN120335720A (en) * | 2025-04-01 | 2025-07-18 | 上饶市大万网络科技有限公司 | A big data storage service method and system |
| CN120179184A (en) * | 2025-05-22 | 2025-06-20 | 杭州易康信科技有限公司 | Cloud-based multimodal data dynamic archiving system for big data |
| CN120541864A (en) * | 2025-06-30 | 2025-08-26 | 广州大一互联网络科技有限公司 | Data security management method and system for server |
| CN120541864B (en) * | 2025-06-30 | 2026-01-13 | 广州大一互联网络科技有限公司 | A data security management method and system for servers |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN119201007A (en) | A storage system and method for financial marketing platform data | |
| US8260622B2 (en) | Compliant-based service level objectives | |
| US20060074621A1 (en) | Apparatus and method for prioritized grouping of data representing events | |
| CN118469681B (en) | IT equipment time-sharing leasing solution system | |
| WO2021243508A1 (en) | Scanning for information according to scan objectives | |
| CN112380569A (en) | Data analysis method based on block chain and edge computing server | |
| CN118965441B (en) | A financial data privacy protection method and system based on large model | |
| CN119538316B (en) | Electronic commerce platform information security desensitization scheme analysis system based on artificial intelligence | |
| CN117195292B (en) | Power business evaluation method based on data fusion and edge calculation | |
| CN119088761B (en) | An electronic management method and system for international standardization organization system documents | |
| CN119416260B (en) | Differential Privacy Algorithm Optimization Method and Device for Enterprise Credit Data Privacy Protection | |
| CN119597210B (en) | A CD-ROM cartridge storage management system based on hierarchical management | |
| CN118861781A (en) | An office management analysis system based on big data | |
| CN116070249B (en) | Asset data intelligent monitoring management system and method | |
| CN117057849A (en) | A processing method, system and storage medium based on data classification and classification | |
| CN116797388A (en) | Business operation and financial health index generation system | |
| CN120046138A (en) | Method and system for managing rights based on multiple clusters of users | |
| CN119766478A (en) | Data encryption method, device, computer program product and data encryption system | |
| CN118822588A (en) | A data mart trading platform based on power metering | |
| CN118394851A (en) | Data management and control method based on block chain | |
| CN114596162B (en) | A smart carbon trading service management system and method | |
| CN118229117B (en) | Territorial space planning implementation monitoring model | |
| CN121077826B (en) | Intelligent equipment management method and system based on big data processing | |
| CN119473786B (en) | A method and system for analyzing mobile application usage data | |
| CN120086098B (en) | File storage real-time monitoring method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20241227 |