A bank data warehouse intelligent management system and method

Abstract

The present invention relates to the field of information technology, and in particular to an intelligent management system and method for a bank data warehouse, comprising a data entry module, a preliminary data storage module, a data evaluation module, a data storage module, a data query module, a data download module, and a bank database. The system determines the best entry method for bank data by analyzing the entry efficiency of each entry method, performs preliminary hierarchical storage of bank data according to the business classification of each information file in the bank data, and adjusts the hierarchical storage of each information file of the bank data according to the value of each information file of the bank data. According to the query parameters input by the user, the information file is fuzzy searched and accurately searched, and it is judged whether the user has download authority, and the information file is encrypted according to the encryption key parameters and provided to the user, which helps the bank to better manage and utilize data resources and adapt to the needs of business development and security.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an intelligent management system and method for a bank data warehouse. Background Art

[0002] In today's digital age, enterprises are facing an unprecedented flood of data. Business operations in various industries around the world are constantly generating large amounts of data. These data come from a wide range of sources, including transaction systems, customer relationship management systems, IoT devices, social media, etc. Such a large and complex amount of data has brought tremendous pressure to the data management of enterprises. Traditional data processing and storage methods can no longer meet the needs of enterprises for efficient use and in-depth analysis of data. Enterprises urgently need a solution that can effectively integrate, store and manage these massive data to extract valuable information from them and help strategic decision-making and business development. Driven by this, intelligent management of data warehouses has become the focus of attention and a key development direction to cope with the complex challenges and opportunities faced by data management.

[0003] For example, the existing Chinese patent application number 202411433273.5 discloses a standardized management method for bank data. This solution extracts a root library from the Chinese names of the fields in the business table and the derived table and related information to form an initial business term library, which can be expanded or evaluated and disabled according to actual conditions. Through the key index of business terms, the naming is reviewed based on the root library and the business term library, and non-standard ones are reviewed. A comprehensive inventory is taken of the implementation of standardized management of various business systems, similar business data is aggregated, the owner is clarified and data relationships are established, and a clear semantic system can be established to facilitate the understanding and management of data.

[0004] However, the above patent has the following problems: the solution focuses on the construction of roots and entries and the standardization of naming, and pays attention to standardization and integration at the semantic level, but the reflection of complex data relationships is not direct enough, and insufficient consideration is given to the relationship between quantitative information files. As a result, it is not adaptable enough in some scenarios that require precise quantitative analysis of the degree of data correlation, and it is difficult to fully and accurately reflect the actual complex correlation between information files.

[0005] The existing Chinese patent application number 200810198864.3 discloses an online human resources bank system and a method for querying student data. The solution is to review data through a student data review unit, create a corporate login account through an enterprise account management unit, receive corporate job search information and store it in a human resources bank database through a job search navigation query unit, and query student personal data in a resume database after receiving corporate job search information, thereby achieving standardized review of student data and ensuring data accuracy and reliability.

[0006] However, the above patent has the following problems: the solution does not clearly state the specific standards and methods for student data review, the review process is not clear enough, and there is no detailed explanation of the matching algorithm or mechanism in the query process, which may lead to ambiguity in actual operations. The entire process may lack specific instructions on data security and privacy protection, which may cause security issues such as data leakage. Summary of the invention

[0007] In order to overcome the shortcomings of the background technology, the embodiment of the present invention provides a bank data warehouse intelligent management system and method, which can effectively solve the problems involved in the above-mentioned background technology.

[0008] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an intelligent management system for bank data warehouse, including: a data entry module, used to obtain the number of scanned information files at each time point for each entry method, analyze the entry efficiency of each entry method, and then determine the best entry method for bank data.

[0009] The data preliminary storage module is used to perform preliminary hierarchical storage of bank data according to the business classification of each information file in the bank data.

[0010] The data evaluation module is used to obtain the value parameters of each information file of bank data and analyze the value of each information file of bank data. The value parameters include frequency of use and relevance.

[0011] The data storage module is used to adjust the hierarchical storage of each information file of the bank data according to the value of each information file of the bank data.

[0012] The data query module is used to perform fuzzy search and precise search on information files according to the query parameters input by the user, and display the query results to the user.

[0013] The data download module is used to determine whether the user has download authority, and encrypt the information file according to the encryption key parameters and provide it to the user.

[0014] The bank database is used to store the preset value classification of each business category, the value range corresponding to each value classification, the operation records of bank data, and the authority level corresponding to each user identity.

[0015] Preferably, the present invention provides an intelligent management method for a bank data warehouse, and the specific steps of the intelligent management method are as follows: S1. Data warehousing: obtain the number of scanned information files at each time point for each warehousing method, analyze the warehousing efficiency of each warehousing method, and then determine the best warehousing method for bank data.

[0016] S2. Preliminary data storage: Preliminary hierarchical storage of bank data based on the business classification of each information file in the bank data.

[0017] S3. Data evaluation: Obtain the value parameters of each information file of bank data, and analyze the value of each information file of bank data. The value parameters include frequency of use and relevance.

[0018] S4. Data storage: Adjust the hierarchical storage of each information file of bank data according to its value.

[0019] S5. Data query: perform fuzzy search and precise search on information files according to the query parameters input by the user, and display the query results to the user.

[0020] S6. Data download: Determine whether the user has download authority, encrypt the information file according to the encryption key parameters and provide it to the user.

[0021] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: 1. The present invention determines the best storage method for bank data by analyzing the storage efficiency of each storage method, optimizes the data storage process, and improves the efficiency and speed of data storage.

[0022] 2. The present invention performs preliminary hierarchical storage of bank data according to the business classification of each information file in the bank data, and adjusts the hierarchical storage of each information file of the bank data according to the value of each information file of the bank data, which helps banks to better manage and utilize data resources and adapt to the needs of business development and security.

[0023] 3. The present invention performs fuzzy search and precise search on information files according to the query parameters input by the user, which can highlight the management and protection of key data, and at the same time facilitates the rapid positioning and access to important information, and adapts to the data needs of different business scenarios.

[0024] Fourth, the present invention determines whether the user has downloading authority based on the user identity, and encrypts the information file according to the encryption key parameters and provides it to the user, thereby preventing the data from being accessed and abused without authorization and protecting the interests of the bank and customers. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] 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.

[0026] Figure 1This is a module connection diagram for a bank data warehouse intelligent management system.

[0027] Figure 2 for Figure 1 Flow chart of data download module.

[0028] Figure 3 The figure is a flow chart of an intelligent management method for bank data warehouse. DETAILED DESCRIPTION

[0029] 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 of protection of the present invention.

[0030] See also Figure 1 As shown, a bank data warehouse intelligent management system includes a data entry module, a data preliminary storage module, a data evaluation module, a data storage module, a data query module, a data download module, and a bank database.

[0031] The bank database is connected to the data entry module, the data preliminary storage module, the data evaluation module, the data storage module, the data query module, and the data download module, and the data evaluation module is connected to the data preliminary storage module and the data storage module.

[0032] The data entry module is used to obtain the number of scanned information files at each time point for each entry method, analyze the entry efficiency of each entry method, and then determine the best entry method for bank data.

[0033] The specific analysis method of the data entry module is as follows: number each information file in the bank data according to the order corresponding to each entry method, set the number of scanned information files to 0, scan each information file one by one according to the numbering order, and add 1 to the number of scanned information files in real time according to each information file, and select several time points according to the set time length to obtain the number of scanned information files at each time point of each entry method, recorded as , Indicates The number of the storage method, , Indicates The number of the time point, , the total number of information files in the bank data is recorded as , through the formula Get the storage progress of information files at each storage method and time point , and record the set duration as , through the formula Get the storage speed of information files at each storage method and time point , Indicates Type of storage method The progress of the information file entering the database at a certain time point is substituted into the formula Get the storage efficiency of each storage method , Indicates the number of time points, sorts the storage efficiency of each storage method from large to small, takes the storage method corresponding to the first storage efficiency as the best storage method for bank data, and stores bank data according to the best storage method for bank data; helps to improve the overall data processing efficiency and management level, making data storage more scientific and reasonable, and adapting to the needs and development of banking business.

[0034] It should be noted that the storage methods include scanning and storage in the original tree-shaped folder mode, scanning and storage of indifferent data, storage of file screening, and storage of data in a specific location.

[0035] It should be noted that the specific analysis method for scanning and warehousing in the original tree-shaped folder manner is: scan each information file one by one in the order of the numbers, read the hash value of each information file, read the recorded hash value list from the bank database, and compare the hash value of each information file with the recorded hash value list. If the hash value of an information file does not have the same hash value in the hash value list, then the hash value of the information file is added to the hash value list, and the information file is stored in the warehouse. If the hash value of an information file has the same hash value in the hash value list, then the information file is determined to be a duplicate file, and it is marked as processed and subsequent operations are skipped.

[0036] It should be noted that the specific analysis method for the indiscriminate data scanning and warehousing is as follows: clarify the starting point and end point of the scanning range, start scanning, and simultaneously start counting the amount of scanned data, update the scanning speed at a certain frequency, directly warehousing the scanned data, and continuously compare the amount of scanned data with the total amount of data. When the scan reaches the end point of the range, complete the scanning and warehousing operations, and perform final statistics and reports.

[0037] It should be noted that the specific analysis method for file screening and storage is: according to the set screening conditions, traverse the information file storage area, detect each information file, determine whether it meets the screening conditions, read the content of the information file that meets the conditions, process the file content appropriately and then store it in the warehouse, and record the relevant information of the stored information file, such as file name, path, etc.

[0038] It should be noted that the set filtering conditions include the type, size, and format of the file. By traversing the information file storage area, the type, size, and format of each information file are extracted and compared one by one with the set filtering conditions. If the type, size, and format of an information file meet the set filtering conditions, it is determined to meet the conditions and the storage operation is performed; otherwise, it is determined to be not in compliance with the conditions.

[0039] It should be noted that the specific analysis method for storing the data at the specific location is: obtaining parameter information of the specific location, directly locating the specific location, scanning and reading the data at the location, and storing the read data in the database.

[0040] It should be noted that the acquisition of parameter information of a specific location is divided into user input and location parameter reading. User input is the user explicitly providing a specific folder path and database table name to directly locate the specific location. Location parameter reading is to read pre-set location parameters from the configuration file of the banking system.

[0041] The data preliminary storage module is used to perform preliminary hierarchical storage of bank data according to the business classification of each information file in the bank data.

[0042] The specific analysis method of the data preliminary storage module is: reading bank data, obtaining the business classification of each information file in the bank data, recording it as the business classification corresponding to each information file of the bank data, reading the preset value classification of each business classification from the bank database, comparing the business classification corresponding to each information file of the bank data with the preset value classification of each business classification, screening to obtain the value classification of the business classification corresponding to each information file of the bank data, and performing preliminary classification and storage on each information file of the bank data according to the value classification of the business classification corresponding to each information file of the bank data; the bank data can be accurately distinguished according to its business classification, thereby improving the pertinence and accuracy of data management.

[0043] It should be noted that the classification of each information file in the bank data includes savings business, loan business, credit card business, financial management business, etc. The value classification of each information file of the bank data corresponding to the business classification is divided into high value classification, medium value classification, and low value classification. For each information file of the high value classification, high-level encryption technology will be used to encrypt and protect the data, and stored in an independent storage area with strict access control. For each information file of the medium value classification, conventional encryption is used and stored in a specific secure storage area, and data backup and maintenance are performed regularly. For each information file of the low value classification, disk storage is performed, and regular cleaning and sorting are performed to save storage space and improve storage efficiency.

[0044] The data evaluation module is used to obtain the value parameters of each information file of bank data and analyze the value of each information file of bank data. The value parameters include frequency of use and relevance.

[0045] The specific analysis method of the value parameters of each information file of the bank data is as follows: the first step is to obtain the operation records of the bank data from the bank database, and extract the search times and operation times of each information file of the bank data in each month, quarter and year respectively, and record the search times of each information file of the bank data in each month, quarter and year as ,in Indicates The number of the information file, , Indicates Monthly number, , Indicates The number of the quarter, , Indicates The number of the year, , and record the average monthly duration as , substituting it into the formula Get the search frequency of each information file of bank data , Indicates the number of months, represents the number of quarters, Indicates the number of years, The weight factors represent the monthly, quarterly and annual search times of bank data respectively. According to the method of analyzing the search frequency of each information file of bank data, the operation frequency of each information file of bank data is analyzed in each month, quarter and year, and the operation frequency of each information file of bank data is obtained, which is recorded as , substituting it into the formula Get the usage frequency of each information file of bank data , They represent the weight factors of the preset search frequency and operation frequency respectively; they can provide an in-depth understanding of the usage popularity and importance of each information file in the bank data in different time dimensions, which helps banks better plan resources, optimize business processes and identify key data.

[0046] It should be noted that, in a specific embodiment, It can be set to 0.4. It can be set to 0.3. It can be set to 0.2. Monthly data can reflect recent business dynamics and user behavior changes more timely, and has more direct guiding significance for current decision-making and adjustments. The weight of annual search times is relatively small. Although it can show the long-term overall situation, it may be relatively insensitive and has a slightly weaker influence on real-time decision-making. Therefore, the weight corresponding to the monthly search times of bank data is larger, and the weight corresponding to the annual search times is smaller.

[0047] It should be noted that, in a specific embodiment, It can be set to 0.6. It can be set to 0.4. Search frequency can more directly reflect the user's attention to and demand for specific information. It is often the first step for users to obtain information and is of great significance for understanding users' interests and business priorities. The operation frequency focuses on the execution of specific business processes. Relatively speaking, search frequency can reflect potential demand trends and business trends earlier, so the weight corresponding to search frequency is slightly larger.

[0048] The second step is to read each information file of the bank data, and annotate each information file of the bank data with an attribute label according to the preset classification. An information file has at least one attribute label. Each attribute label is associated with each information file annotated with the attribute label to obtain each information file corresponding to each attribute label. Any information file is recorded as a designated information file. According to the information file corresponding to each attribute label, the same attribute labels of the designated information file and other information files are obtained by querying. According to the setting, weights are assigned to each attribute label. By accumulating the weights corresponding to the same attribute labels of the designated information file and other information files, the same attribute label weights of the designated information file and other information files are obtained. According to the same method, the same attribute label weights of the bank data information files and other information files are obtained, which are recorded as , Indicates The number of the additional information file, , substituting it into the formula Get the correlation of each information file of bank data ; It can build a network of associations between information files and present the relationship between them more clearly, so that banks can accurately grasp the closeness between different information files, which plays an important guiding role in data integration, risk assessment and other aspects.

[0049] The specific analysis method of the value of each information file of the bank data is: reading the usage frequency of each information file of the bank data respectively , correlation , substituting it into the formula Get the value of each information file of bank data , They respectively represent the frequency of use and weight factors of the relevance of the preset information files; they can more comprehensively reflect the importance and value of the information files, and provide a more accurate basis for banks in data management, resource allocation, risk prevention and control, etc.

[0050] It should be noted that, in a specific embodiment, It can be set to 0.5. It can be set to 0.5. Information files with high usage frequency are usually closely related to core businesses, such as customer transaction records, account information, etc. These frequently used files are directly related to the development of daily business and operational efficiency. Their value is intuitive and prominent, and their relevance is also relatively important. For example, the high correlation between certain files can help banks better conduct risk assessment and customer portrait construction, which is of great significance for accurate decision-making and optimization of business processes. Therefore, the frequency of use and relevance of information files have equal weights.

[0051] The data storage module is used to adjust the hierarchical storage of each information file of the bank data according to the value of each information file of the bank data.

[0052] The specific analysis method of the data storage module is: reading the value of each information file of the bank data, and at the same time reading the value range corresponding to each value grade from the bank database, comparing the value of each information file of the bank data with the value range corresponding to each value grade, matching the value range to which the value of each information file of the bank data belongs, and then obtaining the corresponding value grade, which is recorded as the value grade of each information file of the bank data, and adjusting the hierarchical storage of each information file of the bank data according to the value grade of each information file of the bank data; adjusting the hierarchical storage of information files according to the value grade can achieve more efficient storage management, and for files with higher value, more reliable and faster storage methods and more resource guarantees can be provided to ensure their security and availability, while for files with lower value, a relatively economical storage strategy can be adopted.

[0053] It should be noted that the specific analysis method for adjusting the hierarchical storage of each information file of bank data is: reading the value classification of each information file of bank data corresponding to the business classification, and comparing the value classification of each information file of bank data with the value classification of the business classification corresponding to each information file of bank data one by one; if the value classification of an information file of bank data is the same as the value classification of its corresponding business classification, then it is determined that the storage of the information file does not need to be adjusted; if the value classification of an information file of bank data is different from the value classification of its corresponding business classification, then the storage method of the information file is adjusted to the storage method corresponding to the value classification of the information file.

[0054] The data query module is used to perform fuzzy search and precise search on information files according to the query parameters input by the user, and display the query results to the user.

[0055] It should be noted that the query parameters include attribute tags, keywords, and time ranges. If the query parameters input by the user only include the attribute tag, it is determined as a fuzzy search, otherwise it is determined as an exact search.

[0056] The specific analysis method of the fuzzy search is as follows: the first step is to obtain the attribute tags that the user needs to query and screen them in the bank data. If the number of attribute tags is 1, the value of each information file of the bank data is read, and the value of each information file corresponding to the attribute tag that needs to be queried is screened out. The information files corresponding to the attribute tag that needs to be queried are sorted in order from large to small in value and displayed to the user. If the number of attribute tags is greater than 1, the second step is executed; a simple and direct value screening and sorting display is performed for a single attribute tag to facilitate users to quickly obtain relevant information.

[0057] In the second step, the attribute tags that need to be queried are recorded as query attribute tags, and the information files corresponding to the query attribute tags are screened out from the information files of the bank data to form a set of query attribute tag information files. The intersection of the query attribute tag information file sets is obtained and recorded as the query attribute tag information file set. The value of each information file in the query attribute tag information file set is screened out from the value of each information file of the bank data, and the information files in the query attribute tag information file set are sorted in descending order of value and displayed to the user; the information files that meet multiple attribute tags at the same time can be accurately found, and displayed to the user according to the value sorting, which improves the accuracy and pertinence of the query.

[0058] The specific analysis method of the precise search is: obtaining the query parameters input by the user, combining the query parameters into a query expression, executing the query expression in the bank data, and obtaining the parameter sorting method from the user's preset, sorting the queried data according to the parameter sorting method, and displaying the final sorted query results to the user; the qualified data can be accurately screened out from the bank data, so that the presentation of the results is more in line with the user's needs and expectations, thereby improving the user experience.

[0059] It should be noted that, in a specific embodiment, the query parameters input by the user are: keyword "Zhang San", the time range is "January 2024 to December 2024", and the attribute label is "savings business". The query expression is constructed according to the query parameters: name = 'Zhang San' AND date ≥ '2024-1-1' AND date ≤ '2024-13-31' AND attribute label = 'savings business', and the sorting parameter is set to "in descending order of transaction amount". The query operation is executed, and the sorted query results, Zhang San's specific information and the corresponding transaction amount are presented to the user in a list form on the interface.

[0060] The data download module is used to determine whether the user has download authority, and encrypt the information file according to the encryption key parameters and provide it to the user.

[0061] See also Figure 2 As shown, the specific analysis method of the data download module is: the first step is to obtain the user identity, and at the same time extract the authority level corresponding to each user identity from the bank database, compare and obtain the authority level corresponding to the user identity, and record it as the user authority level; ensuring that subsequent operations are based on the user's legal identity and the granted authority.

[0062] It should be noted that in a specific embodiment, there is an information file that needs to be encrypted. A 256-bit AES encryption key is obtained from the bank database through specific authority verification, the document content of the information file is read line by line into the memory, the data of the information file is encrypted using the AES algorithm, the read document content is grouped, and each group is encrypted using the encryption key according to the rules of the encryption algorithm to obtain encrypted document content groups, which are written into a new encrypted file in turn for storage, and the encrypted file is sent to a designated user through the bank's internal secure file transfer system, and the user is informed of the decryption key through a separate secure channel. The user uses the provided key to decrypt and obtain the original information file.

[0063] The second step is to obtain the permission level of the information file to be downloaded, and compare the user permission level with the permission level of the information file to be downloaded. If the user permission level is greater than or equal to the permission level of the information file to be downloaded, it means that the user has the permission to download the information file, and execute the third step. If the user permission level is less than the permission level of the information file to be downloaded, it means that the user has no permission to download the information file and downloading is not allowed. Through clear comparison rules, it can be clearly defined whether the user is qualified to download the corresponding file, which helps to prevent unauthorized access and data leakage, and maintain the confidentiality and integrity of the data.

[0064] The third step is to obtain the encryption key parameters and read the information file to be downloaded, use the encryption key and the preset encryption algorithm to encrypt the data of the information file, send the encrypted information file to the user, and the user uses the provided key to decrypt the original information file; this provides a reliable guarantee for the confidentiality of the data, ensuring that the bank's information assets can be properly protected and managed, while also meeting the reasonable information needs of different users within the scope of their legal authority.

[0065] The bank database is used to store the preset value classification of each business category, the value range corresponding to each value classification, the operation records of bank data, and the authority level corresponding to each user identity.

[0066] See also Figure 3 As shown, in addition, the present invention provides an intelligent management method for a bank data warehouse, and the specific steps of the intelligent management method are as follows: S1. Data warehousing: Obtain the number of scanned information files at each time point for each warehousing method, analyze the warehousing efficiency of each warehousing method, and then determine the best warehousing method for bank data.

[0067] S2. Preliminary data storage: Preliminary hierarchical storage of bank data based on the business classification of each information file in the bank data.

[0068] S3. Data evaluation: Obtain the value parameters of each information file of bank data, and analyze the value of each information file of bank data. The value parameters include frequency of use and relevance.

[0069] S4. Data storage: Adjust the hierarchical storage of each information file of bank data according to its value.

[0070] S5. Data query: perform fuzzy search and precise search on information files according to the query parameters input by the user, and display the query results to the user.

[0071] S6. Data download: Determine whether the user has download authority, encrypt the information file according to the encryption key parameters and provide it to the user.

[0072] This system determines the best storage method for bank data by analyzing the storage efficiency of each storage method, performs preliminary hierarchical storage of bank data according to the business classification of each information file in the bank data, and adjusts the hierarchical storage of each information file of the bank data according to the value of each information file of the bank data. It performs fuzzy search and precise search on information files according to the query parameters entered by the user, determines whether the user has download permission, and encrypts the information files according to the encryption key parameters and provides them to the user, which helps banks better manage and utilize data resources and adapt to the needs of business development and security.

[0073] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention and they are still covered by the protection scope of the present invention.

Claims

1. A bank data warehouse intelligent management system, characterized in that: The system specifically includes the following modules: The data entry module is used to obtain the number of scanned information files at each time point for each entry method, analyze the entry efficiency of each entry method, and then determine the best entry method for bank data. The specific analysis method is as follows: Number the bank data information files in the order corresponding to each storage method, set the number of scanned information files to 0, scan each information file one by one in the order of numbers and add 1 to the number of scanned information files in turn, select several time points according to the set duration, and obtain the number of scanned information files at each time point for each storage method , Indicates The number of the storage method, , Indicates The number of the time point, , the total number of information files in the bank data is recorded as , through the formula Get the storage progress of information files at each storage method and time point , and record the set duration as , so as to obtain the storage speed of information files at each storage method and time point , and then analyze the efficiency of each warehousing method , sort the storage efficiency of each storage method from large to small, take the storage method corresponding to the first storage efficiency as the best storage method for bank data, and store the bank data according to the best storage method for bank data; A preliminary data storage module is used to perform preliminary hierarchical storage of bank data according to the business classification of each information file in the bank data; The data evaluation module is used to obtain the value parameters of each information file of the bank data and analyze the value of each information file of the bank data. The value parameters include the frequency of use and the degree of relevance. A data storage module, used for adjusting the hierarchical storage of each information file of the bank data according to the value of each information file of the bank data; The data query module is used to perform fuzzy search and precise search on information files according to the query parameters input by the user, and display the query results to the user; The data download module is used to determine whether the user has the download permission, and encrypt the information file according to the encryption key parameters and provide it to the user; The bank database is used to store the preset value classification of each business category, the value range corresponding to each value classification, the operation records of bank data, and the authority level corresponding to each user identity; The specific analysis method of the data preliminary storage module is: Read bank data, obtain the business classification of each information file in the bank data, record it as the business classification corresponding to each information file of the bank data, read the preset value classification of each business classification from the bank database, compare the business classification corresponding to each information file of the bank data with the preset value classification of each business classification, screen and obtain the value classification of the business classification corresponding to each information file of the bank data, and perform preliminary classification and storage on each information file of the bank data according to the value classification of the business classification corresponding to each information file of the bank data.

2. The bank data warehouse intelligent management system according to claim 1, characterized in that: The analysis formula for the storage speed of information files at each time point in each storage method is: ,in Indicates Type of storage method The storage progress of information files at each time point; the storage efficiency of each storage method The analysis formula is: , Indicates the number of time points.

3. The bank data warehouse intelligent management system according to claim 1, characterized in that: The specific analysis method of the value parameters of each information file of the bank data is as follows: The first step is to obtain the operation records of bank data from the bank database, and extract the search times and operation times of each information file of bank data for each month, quarter and year respectively, and record the search times of each information file of bank data for each month, quarter and year as ,in Indicates The number of the information file, , Indicates Monthly number, , Indicates The number of the quarter, , Indicates The number of the year, , and record the average monthly duration as , substituting it into the formula Get the search frequency of each information file of bank data , Indicates the number of months, represents the number of quarters, Indicates the number of years, The weight factors of the monthly, quarterly and annual search times of bank data are respectively represented. According to the method of analyzing the search frequency of each information file of bank data, the operation frequency of each information file of bank data is analyzed in each month, quarter and year, and the operation frequency of each information file of bank data is obtained, which is recorded as , substituting it into the formula Get the usage frequency of each information file of bank data , Respectively represent the weight factors of the preset search frequency and operation frequency; The second step is to read each information file of the bank data, and annotate each information file of the bank data with an attribute label according to the preset classification. An information file has at least one attribute label. Each attribute label is associated with each information file annotated with the attribute label to obtain each information file corresponding to each attribute label. Any information file is recorded as a designated information file. According to the information file corresponding to each attribute label, the same attribute labels of the designated information file and other information files are obtained by querying. According to the setting, weights are assigned to each attribute label. By accumulating the weights corresponding to the same attribute labels of the designated information file and other information files, the same attribute label weights of the designated information file and other information files are obtained. According to the same method, the same attribute label weights of the bank data information files and other information files are obtained, which are recorded as , Indicates The number of the additional information file, , substituting it into the formula Get the correlation of each information file of bank data .

4. The bank data warehouse intelligent management system according to claim 3 is characterized by: The specific analysis method of the value of each information file of the bank data is as follows: Read the usage frequency of each information file of bank data separately , correlation , substituting it into the formula Get the value of each information file of bank data , They respectively represent the usage frequency and the weight factor of the relevance of the preset information files.

5. The bank data warehouse intelligent management system according to claim 4 is characterized by: The specific analysis method of the data storage module is: The value of each information file of the bank data is read, and at the same time, the value range corresponding to each value grade is read from the bank database, and the value of each information file of the bank data is compared with the value range corresponding to each value grade, and the value range to which the value of each information file of the bank data belongs is matched, and then the corresponding value grade is obtained, which is recorded as the value grade of each information file of the bank data, and the hierarchical storage of each information file of the bank data is adjusted according to the value grade of each information file of the bank data.

6. The bank data warehouse intelligent management system according to claim 1, characterized in that: The specific analysis method of the fuzzy search is: The first step is to obtain the attribute tags that the user needs to query and filter them in the bank data. If the number of attribute tags is 1, read the value of each information file of the bank data, filter out the value of each information file corresponding to the attribute tag to be queried, sort the information files corresponding to the attribute tag to be queried in descending order of value and display them to the user. If the number of attribute tags is greater than 1, execute the second step; In the second step, the attribute tags that need to be queried are recorded as query attribute tags, and the information files corresponding to the query attribute tags are screened out from the information files of the bank data to form a set of query attribute tag information files. The intersection of the query attribute tag information file sets is obtained and recorded as the query attribute tag information file set. The value of each information file in the query attribute tag information file set is screened out from the value of each information file of the bank data, and the information files in the query attribute tag information file set are sorted in descending order of value and displayed to the user.

7. The intelligent management system for bank data warehouse according to claim 6 is characterized by: The specific analysis method for the precise search is: Get the query parameters input by the user, combine the query parameters into a query expression, execute the query expression in the bank data, and get the parameter sorting method from the user's preset, sort the queried data according to the parameter sorting method, and display the final sorted query results to the user.

8. The bank data warehouse intelligent management system according to claim 1 is characterized by: The specific analysis method of the data download module is: The first step is to obtain the user identity and extract the permission level corresponding to each user identity from the bank database. The permission level corresponding to the user identity is obtained by comparison and recorded as the user permission level; The second step is to obtain the permission level of the information file to be downloaded, and compare the user permission level with the permission level of the information file to be downloaded. If the user permission level is greater than or equal to the permission level of the information file to be downloaded, it means that the user has the permission to download the information file, and the third step is executed. If the user permission level is less than the permission level of the information file to be downloaded, it means that the user has no permission to download the information file and the download is not allowed; The third step is to obtain the encryption key parameters, read the information file to be downloaded, use the encryption key and the preset encryption algorithm to encrypt the data of the information file, send the encrypted information file to the user, and the user uses the provided key to decrypt it to obtain the original information file.

9. A bank data warehouse intelligent management method, characterized in that: The intelligent management method is implemented by using a bank data warehouse intelligent management system as described in any one of claims 1 to 8, and the specific steps of the intelligent management method are as follows: S1. Data storage: Obtain the number of scanned information files at each time point for each storage method, analyze the storage efficiency of each storage method, and then determine the best storage method for bank data; S2. Preliminary data storage: Preliminary hierarchical storage of bank data according to the business classification of each information file in the bank data; S3. Data evaluation: Obtain the value parameters of each information file of bank data, and analyze and obtain the value of each information file of bank data. The value parameters include usage frequency and relevance; S4. Data storage: adjust the hierarchical storage of each information file of bank data according to the value of each information file of bank data; S5. Data query: perform fuzzy search and precise search on information files according to the query parameters entered by the user, and display the query results to the user; S6. Data download: Determine whether the user has download authority, encrypt the information file according to the encryption key parameters and provide it to the user.

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