A financial data management system

Abstract

The present invention belongs to the field of data management, and discloses a financial data management system, including a cloud server and a user terminal; the user terminal is used to send a query instruction to the cloud server; the cloud server includes a database module, a cache management module, an instruction processing module and a communication module; the database module is used to store financial data; the cache module is used to cache the financial data stored in the database module based on an adaptive statistical cycle, and cache the financial data that needs to be cached in the memory of the cloud server; the instruction processing module is used to process the query instruction, and obtain the financial data corresponding to the query instruction from the memory or the database module based on the query instruction; the communication module is used to transmit the financial data obtained by the instruction processing module to the user terminal. The present invention can cache the financial data that needs to be cached more timely, and ensure the reading efficiency of the financial data.

Description

Technical Field

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

[0002] Financial data usually presents certain periodic characteristics in terms of reading and writing. During the period from the end of the previous month to the beginning of the next month, reading and writing are usually more frequent, while in other time periods, they are less frequent. This is because the financial data of the previous statistical period usually needs some time to be counted before it can be stored in the database. In the prior art, in order to improve the reading efficiency of financial data, financial data with an access frequency greater than a set threshold value within a fixed statistical period are usually cached. However, this caching method has certain disadvantages. The statistical period fails to match the periodic characteristics of the reading of financial data. If a statistical period is set too small, although the reading efficiency can be guaranteed when the financial data is frequently read, it will cause the server to allocate more performance for caching the financial data, which reduces the performance of the server in processing other instructions for a long time. If a statistical period is set too large, then the financial data may not be cached in time during the frequent reading stage, which reduces the reading efficiency of the financial data. Summary of the invention

[0003] The purpose of the present invention is to disclose a financial data management system to solve the problem of how to reasonably determine the statistical period so as to improve the reading efficiency of financial data in the frequent reading stage without affecting the performance of the server in processing other instructions for a long time.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] The present invention provides a financial data management system, including a cloud server and a user terminal;

[0006] The user terminal is used to send query instructions to the cloud server;

[0007] The cloud server includes a database module, a cache management module, an instruction processing module and a communication module;

[0008] The database module is used to store financial data;

[0009] The cache module is used to perform cache control on the financial data stored in the database module based on an adaptive statistical cycle, and cache the financial data that needs to be cached in the memory of the cloud server;

[0010] The instruction processing module is used to process the query instruction and obtain the financial data corresponding to the query instruction from the memory or database module based on the query instruction;

[0011] The communication module is used to transmit the financial data obtained by the instruction processing module to the user terminal.

[0012] Preferably, the calculation process of the adaptive statistical period includes:

[0013] The first step, for the k+1th statistical period, the calculation formula is:

[0014]

[0015] stacyc k+1 and stacyc k Respectively represent the kth and k+1th statistical periods, eigval k and eigval k-1 Respectively represent the access index of the kth and k-1th statistical cycles, max means obtaining the larger value in the brackets, tpcyc means the change duration; k is greater than or equal to 2;

[0016] The second step is to determine stacyc k+1 Is it greater than the maximum value of the preset statistical period, stacycma? If so, use the value of stacycma as stacyc k+1 If not, go to step 3;

[0017] The third step is to determine stacyc k+1 Is it less than the preset minimum value of the statistical period stacycmi? If so, use the value of stacycmi as stacyc k+1 The value of .

[0018] Preferably, the calculation formula of the access index is:

[0019]

[0020] eigval k Indicates the access index of the kth statistical period, accnum k Indicates the total number of query commands received by the cloud server in the kth statistical period, dirnum k represents the total number of instructions sent by the user terminal to the cloud server to operate the database module during the kth statistical period, space i It indicates the storage space occupied by the financial data obtained after the instruction processing module processes the query instruction i. spacma indicates the storage space occupied by the financial data obtained after the instruction processing module processes the query instruction i. k After processing query instructions, the maximum value of the storage space occupied by the financial data corresponding to a single query instruction, and λ represents the preset weight value.

[0021] Preferably, the instructions for operating the database module include query instructions, modification instructions and data addition instructions.

[0022] Preferably, the financial data stored in the database module is cached based on an adaptive statistical cycle, and the financial data to be cached is cached in the memory of the cloud server, including:

[0023] After the current statistical cycle ends, the data statistics phase begins. In the data statistics phase, the cache module calculates the access frequency of each data page in the previous statistical cycle, and calculates the occurrence frequency of each query statement in the previous statistical cycle.

[0024] Determine the financial data that needs to be cached based on the access frequency of the data page in the previous statistical period and the occurrence frequency of the query statement in the previous statistical period;

[0025] The financial data that needs to be cached is read from the database module and cached in the memory of the cloud server.

[0026] Preferably, the cache module is also used to calculate an adaptive statistical period during the data statistics stage;

[0027] After the adaptive statistical cycle calculation is completed and the financial data to be cached is read into the memory of the cloud server for caching, the next statistical cycle begins.

[0028] Preferably, the calculation formula for the access frequency of the data page in the kth statistical period is:

[0029]

[0030] accfreq b Indicates the access frequency of data page b in the kth statistical cycle, resultnum b Indicates the number of times the cache module reads data page b from the database module in the kth statistical period. datpag indicates the set of data pages existing in the kth statistical period. resultnum j Indicates the number of times the cache module reads data page j from the database module during the kth statistical period. b Indicates the modification coefficient of data page b in the kth statistical period, updnum b Indicates the number of modifications to data page b in the kth statistical period, updnum j It indicates the number of times data page j is modified in the kth statistical period, and updthre indicates the preset modification coefficient threshold.

[0031] Preferably, the calculation process of the occurrence frequency of the query statement in the kth statistical period includes:

[0032] Obtain the query statements contained in each query instruction received by the cloud server in the kth statistical period respectively;

[0033] Use the following formula to calculate the frequency of occurrence of the query statement:

[0034]

[0035] enqfreq c Indicates the frequency of occurrence of query statement c in the kth statistical period, consnum c Indicates the number of times the cloud server receives query statement c in the kth statistical period, queins indicates the set of query statements received by the cloud server in the kth statistical period, and consnum u Indicates the number of times the cloud server receives query statement u for the data page existing in the kth statistical period.

[0036] Beneficial effects:

[0037] Compared with the prior art, the present invention does not use a fixed statistical cycle to control the cache of financial data, but controls the cache of financial data by calculating an adaptive statistical cycle, so that during the low query period of financial data, the statistical cycle can be expanded to reduce the impact of data cache on server performance; and during the peak query period of financial data, the statistical cycle can be shortened, so that the financial data that needs to be cached can be cached more timely, ensuring the reading efficiency of the financial data. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0039] Figure 1 A schematic diagram of a financial data management system of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope parameters of the protection of the present invention.

[0041] like Figure 1 In one embodiment shown, the present invention provides a financial data management system, including a cloud server and a user terminal;

[0042] The user terminal is used to send query instructions to the cloud server;

[0043] The cloud server includes a database module, a cache management module, an instruction processing module and a communication module;

[0044] The database module is used to store financial data;

[0045] The cache module is used to perform cache control on the financial data stored in the database module based on an adaptive statistical cycle, and cache the financial data that needs to be cached in the memory of the cloud server;

[0046] The instruction processing module is used to process the query instruction and obtain the financial data corresponding to the query instruction from the memory or database module based on the query instruction;

[0047] The communication module is used to transmit the financial data obtained by the instruction processing module to the user terminal.

[0048] Specifically, the user terminal is a computer device running a financial data management software system.

[0049] After receiving the financial data, the user terminal is also used to display the financial data.

[0050] Preferably, the calculation process of the adaptive statistical period includes:

[0051] The first step, for the k+1th statistical period, the calculation formula is:

[0052]

[0053] stacyc k+1 and stacyc k Respectively represent the kth and k+1th statistical periods, eigval k and eigval k-1 Respectively represent the access index of the kth and k-1th statistical cycles, max means obtaining the larger value in the brackets, tpcyc means the change duration; k is greater than or equal to 2;

[0054] The second step is to determine stacyc k+1 Is it greater than the maximum value of the preset statistical period, stacycma? If so, use the value of stacycma as stacyc k+1 If not, go to step 3;

[0055] The third step is to determine stacyc k+1 Is it less than the preset minimum value of the statistical period stacycmi? If so, use the value of stacycmi as stacyc k+1 The value of .

[0056] Specifically, the statistical period of the present invention can be adaptively changed with the changes of the two most recent access indexes. k Less than or equal to eigval k-1 , indicating that the reading frequency of financial data shows a decreasing trend. The present invention will expand the statistical cycle, thereby reducing the frequency of updating the cache and reducing the computing resources used by the cloud server to cache the financial data. k Greater than eigval k-1 , indicating that the reading frequency of financial data is showing an expanding trend, the present invention will shorten the statistical cycle, thereby increasing the frequency of updating the cache, so that the cloud server's memory can cache frequently read data pages in a timely manner. In addition, the magnitude of the change in the statistical cycle of the present invention is also positively correlated with the magnitude of the change in the access index, so that the change in the statistical cycle can be more in line with the change in the reading frequency of financial data, avoiding caching too much or too little financial data in the memory of the cloud server, achieving a more accurate and effective caching effect, and avoiding occupying more computing resources for a long time to achieve the caching of financial data.

[0057] Preferably, the value of the variation duration is 1 hour.

[0058] Preferably, the maximum value of the preset statistical period is 24 hours, and the minimum value of the preset statistical period is 2 hours.

[0059] Preferably, the calculation formula of the access index is:

[0060]

[0061] eigval k Indicates the access index of the kth statistical period, accnum k Indicates the total number of query commands received by the cloud server in the kth statistical period, dirnum k represents the total number of instructions sent by the user terminal to the cloud server to operate the database module during the kth statistical period, space i It indicates the storage space occupied by the financial data obtained after the instruction processing module processes the query instruction i. spacma indicates the storage space occupied by the financial data obtained after the instruction processing module processes the query instruction i. kAfter processing query instructions, the maximum value of the storage space occupied by the financial data corresponding to a single query instruction, and λ represents the preset weight value.

[0062] Specifically, in addition to being related to the number of query instructions, the access index is also related to the size of the financial data corresponding to the query instructions. The larger the number of query instructions and the larger the size of the financial data corresponding to the query instructions, the larger the access index, indicating that the current financial data is accessed more frequently. This calculation method can avoid the impact of repeated query instructions, because when the financial data corresponding to the query instruction is small, even if there are more queries due to reasons such as user terminal freezes, it will not cause the access index to be too large, which is conducive to obtaining a more accurate access coefficient.

[0063] Preferably, the instructions for operating the database module include query instructions, modification instructions and data addition instructions.

[0064] Specifically, the query instruction is used to query the financial data stored in the database module, the modification instruction is used to modify the financial data stored in the database module, and the data addition instruction is used to add financial data to the database module.

[0065] Preferably, the preset weight value is 0.6.

[0066] Preferably, the financial data stored in the database module is cached based on an adaptive statistical cycle, and the financial data to be cached is cached in the memory of the cloud server, including:

[0067] After the current statistical cycle ends, the data statistics phase begins. In the data statistics phase, the cache module calculates the access frequency of each data page in the previous statistical cycle, and calculates the occurrence frequency of each query statement in the previous statistical cycle.

[0068] Determine the financial data that needs to be cached based on the access frequency of the data page in the previous statistical period and the occurrence frequency of the query statement in the previous statistical period;

[0069] The financial data that needs to be cached is read from the database module and cached in the memory of the cloud server.

[0070] Specifically, the value of k is greater than or equal to 2, so the duration of the first statistical cycle and the second statistical cycle are both preset time lengths, such as 24 hours. Therefore, there is no data statistics phase between the first statistical cycle and the second statistical cycle. After the first statistical cycle ends, the second statistical cycle begins.

[0071] Preferably, the cache module is also used to calculate an adaptive statistical period during the data statistics stage;

[0072] After the adaptive statistical cycle calculation is completed and the financial data to be cached is read into the memory of the cloud server for caching, the next statistical cycle begins.

[0073] Preferably, the calculation formula for the access frequency of the data page in the kth statistical period is:

[0074]

[0075] accfreq b Indicates the access frequency of data page b in the kth statistical cycle, resultnum b Indicates the number of times the cache module reads data page b from the database module in the kth statistical period. datpag indicates the set of data pages existing in the kth statistical period. resultnum j Indicates the number of times the cache module reads data page j from the database module during the kth statistical period. b Indicates the modification coefficient of data page b in the kth statistical period, updnum b Indicates the number of modifications to data page b in the kth statistical period, updnum j It indicates the number of times data page j is modified in the kth statistical period, and updthre indicates the preset modification coefficient threshold.

[0076] Specifically, when calculating the access frequency, the present invention sets the access coefficient of the data page whose modification coefficient is greater than the preset modification coefficient threshold to 0, mainly because when a data page is modified too frequently, the cache in the memory needs to be frequently updated, which will occupy more computing resources of the cloud server. Therefore, the present invention only caches relatively stable data pages, thereby reducing the occupation of computing resources of the cloud server and achieving the use of fewer computing resources to maintain the cache of financial data.

[0077] Preferably, the preset modification coefficient threshold is 0.1.

[0078] Preferably, the calculation process of the occurrence frequency of the query statement in the kth statistical period includes:

[0079] Obtain the query statements contained in each query instruction received by the cloud server in the kth statistical period respectively;

[0080] Use the following formula to calculate the frequency of occurrence of the query statement:

[0081]

[0082] enqfreqc Indicates the frequency of occurrence of query statement c in the kth statistical period, consnum c Indicates the number of times the cloud server receives query statement c in the kth statistical period, queins indicates the set of query statements received by the cloud server in the kth statistical period, and consnum u Indicates the number of times the cloud server receives query statement u for the data page existing in the kth statistical period.

[0083] Preferably, determining the financial data to be cached based on the access frequency of the data page in the previous statistical period and the occurrence frequency of the query statement in the previous statistical period includes:

[0084] Rank the data pages according to the access frequency from high to low, and rank the pages with a ranking less than The data pages are stored in the set datpagu, datpagnum represents the number of different data pages in the kth statistical period, and z represents the preset proportional coefficient;

[0085] Rank the query statements according to the order of occurrence frequency from high to low, and rank less than The query statements are sent to the set queinsu, queinsnum represents the total number of different query statements received by the cloud server in the kth statistical period;

[0086] Store the data pages corresponding to the query statements in the set queinsu into the set datpagt;

[0087] Calculate the cache coefficient of each data page in datpagu respectively;

[0088] The cache factor with the largest data pages as the financial data that needs to be cached, and y represents the adaptive cache ratio coefficient.

[0089] When determining the data that needs to be cached, the present invention does not rely solely on the access frequency as the basis for judgment, because there may be a situation where the access frequency is high, but the number of corresponding query statements is large. In this way, the frequency of the same query statement appearing in the next query cycle is reduced. In other words, the access frequency calculated in the previous statistical cycle has a certain amount of water. Determining the financial data that needs to be cached based solely on the access frequency will result in a large portion of the cached data not being accessed in the next statistical cycle.

[0090] Therefore, in addition to ranking the access frequency of data pages, the present invention also ranks query statements, and comprehensively calculates the cache coefficient based on the ranking results of the access frequency and the ranking results of the query statements. The financial data that ultimately needs to be cached is determined based on the cache coefficient, thereby improving the effectiveness of the cached financial data and facilitating improving the reading efficiency of the financial data.

[0091] Preferably, the preset proportionality factor is 0.1.

[0092] Preferably, the data pages corresponding to the query statements in the set queinsu are stored in the set datpagt, including:

[0093] Each query statement in queinsu is processed as follows:

[0094] Get the set of data pages datpagtemp corresponding to the query statement;

[0095] Store the data pages in datpagtemp that do not belong to datpagt into datpagt.

[0096] Preferably, the cache coefficient of each data page in datpagu is calculated separately, including:

[0097] For data page b in datpagu, its corresponding data page cache coefficient cacfac b The calculation formula is as follows:

[0098] cacfac b =accfreq b +judidx b

[0099] cacfac b Indicates the cache coefficient of data page b; judidx b Indicates the judgment value. If data page b belongs to the set datpagt, the judgment value is 1, otherwise, the judgment value is 0.

[0100] When calculating the cache coefficient, if data page b also exists in datpagt, it means that the query statement corresponding to data page b also appears more frequently, so the cache coefficient of data page b is larger. This helps avoid caching data pages with a low probability of being accessed in the next statistical cycle, thereby improving the effectiveness of the cache.

[0101] Preferably, the calculation process of the adaptive cache proportional coefficient includes:

[0102] y=eigval k ×bsy

[0103] eigval k represents the access index of the k-th statistical period, and bsy represents a preset value.

[0104] Specifically, the adaptive cache ratio coefficient is related to the access index of the previous statistical period. The larger the access index, the larger the adaptive cache ratio coefficient; the smaller the access index, the smaller the adaptive cache ratio coefficient. Thus, when the access pressure of financial data is smaller, the space occupied by the cached financial data in the memory is smaller, while ensuring the reading efficiency, reducing the memory occupancy, and enabling the cloud server to have stronger performance in processing other instructions. When the access pressure of financial data is larger, the space occupied by the cached financial data in the memory is larger, effectively ensuring the reading efficiency. A balance between the space occupancy ratio and ensuring the reading efficiency is achieved.

[0105] Preferably, the preset value is 0.5.

[0106] Preferably, the query instruction is processed to obtain the financial data corresponding to the query instruction from the memory or the database module, including:

[0107] Obtain the query statement included in the query instruction;

[0108] Obtain the page number of the data page corresponding to the query statement;

[0109] Perform the following processing on each page number respectively:

[0110] Judge whether the data page corresponding to the page number is cached in the memory; if so, directly obtain the data page corresponding to the page number from the memory, if not, obtain the data page corresponding to the page number from the database module;

[0111] Take the data in the data pages corresponding to all the page numbers of the query statement as the financial data corresponding to the query instruction.

[0112] For example, for the following query statement:

[0113] SELECT `sname` AS 'Li Hua', `class_id` AS 'Class 2, Grade 3' FROM student;

[0114] After receiving the query statement, the instruction processing module searches for the page number of the data page where the data with `sname` being 'Li Hua' and `class_id` being 'Class 2, Grade 3' is located according to the index scan;

[0115] If the page number is the page number of the data page cached in the memory, directly read the data page from the memory, otherwise, read the data page from the database module.

[0116] Compared with the prior art, the present invention does not use a fixed statistical cycle to control the cache of financial data, but controls the cache of financial data by calculating an adaptive statistical cycle, so that during the low query period of financial data, the statistical cycle can be expanded to reduce the impact of data cache on server performance; and during the peak query period of financial data, the statistical cycle can be shortened, so that the financial data that needs to be cached can be cached more timely, ensuring the reading efficiency of the financial data.

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0118] The present application is described with reference to the flowcharts and block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and box in the flowchart and block diagram, as well as the combination of the processes and boxes in the flowchart and block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and block diagram. Figure 1 Process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 Process or multiple processes and boxes Figure 1 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 Process or multiple processes and boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A financial data management system, characterized in that: Including cloud server and user terminal; The user terminal is used to send query instructions to the cloud server; The cloud server includes a database module, a cache management module, an instruction processing module and a communication module; The database module is used to store financial data; The cache module is used to perform cache control on the financial data stored in the database module based on an adaptive statistical cycle, and cache the financial data that needs to be cached in the memory of the cloud server; The instruction processing module is used to process the query instruction and obtain the financial data corresponding to the query instruction from the memory or database module based on the query instruction; The communication module is used to transmit the financial data obtained by the instruction processing module to the user terminal; The calculation process of the adaptive statistical period includes: The first step, for the k+1th statistical period, the calculation formula is: stacyc k+1 and stacyc k Respectively represent the kth and k+1th statistical periods, eigval k and eigval k-1 Respectively represent the access index of the kth and k-1th statistical cycles, max means obtaining the larger value in the brackets, tpcyc means the change duration; k is greater than or equal to 2; The second step is to determine stacyc k+1 Is it greater than the preset maximum value of the statistical period stacycma? If so, use the value of stacycma as stacyc k+1 If not, go to step 3; The third step is to determine stacyc k+1 Is it less than the preset minimum value of the statistical period stacycmi? If so, use the value of stacycmi as stacyc k+1 The value of The calculation formula of the access index is: eigval k Indicates the access index of the kth statistical period, accnum k Indicates the total number of query commands received by the cloud server in the kth statistical period, dirnum k represents the total number of instructions sent by the user terminal to the cloud server to operate the database module during the kth statistical period, space i It indicates the storage space occupied by the financial data obtained after the instruction processing module processes the query instruction i. spacma indicates the storage space occupied by the financial data obtained after the instruction processing module processes the query instruction i. k After processing query instructions, the maximum value of the storage space occupied by the financial data corresponding to a single query instruction, and λ represents the preset weight value.

2. A financial data management system according to claim 1, characterized in that: The instructions for operating the database module include query instructions, modification instructions and data addition instructions.

3. A financial data management system according to claim 1, characterized in that: The financial data stored in the database module is cached based on an adaptive statistical cycle, and the financial data that needs to be cached is cached in the memory of the cloud server, including: After the current statistical cycle ends, the data statistics phase begins. In the data statistics phase, the cache module calculates the access frequency of each data page in the previous statistical cycle, and calculates the occurrence frequency of each query statement in the previous statistical cycle. Determine the financial data that needs to be cached based on the access frequency of the data page in the previous statistical period and the occurrence frequency of the query statement in the previous statistical period; The financial data that needs to be cached is read from the database module and cached in the memory of the cloud server.

4. A financial data management system according to claim 3, characterized in that: The cache module is also used to calculate the adaptive statistical period during the data statistics stage; After the adaptive statistical cycle calculation is completed and the financial data to be cached is read into the memory of the cloud server for caching, the next statistical cycle begins.

5. A financial data management system according to claim 3, characterized in that: The calculation formula for the access frequency of a data page in the kth statistical period is: accfreq b Indicates the access frequency of data page b in the kth statistical cycle, resultnum b Indicates the number of times the cache module reads data page b from the database module in the kth statistical period. datpag indicates the set of data pages existing in the kth statistical period. resultnum j Indicates the number of times the cache module reads data page j from the database module during the kth statistical period. b Indicates the modification coefficient of data page b in the kth statistical period, updnum b Indicates the number of modifications to data page b in the kth statistical period, updnum j It indicates the number of times data page j is modified in the kth statistical period, and updthre indicates the preset modification coefficient threshold.

6. A financial data management system according to claim 3, characterized in that: The calculation process of the occurrence frequency of the query statement in the kth statistical period includes: Obtain the query statements contained in each query instruction received by the cloud server in the kth statistical period respectively; Use the following formula to calculate the frequency of occurrence of the query statement: enqfreq c Indicates the frequency of occurrence of query statement c in the kth statistical period, consnum c Indicates the number of times the cloud server receives query statement c in the kth statistical period, queins indicates the set of query statements received by the cloud server in the kth statistical period, and consnum u Indicates the number of times the cloud server receives query statement u for the data page existing in the kth statistical period.

Images