Financial database table field hierarchical processing method, device, equipment, medium and product

Abstract

The application discloses a financial database table field grading processing method, device, equipment, medium and product. It can be applied to the field of financial technology. The method comprises the following steps: obtaining target financial transaction data in a financial database table, and performing data preprocessing on the target financial transaction data to obtain preprocessed transaction data; inputting the preprocessed transaction data and a preset target prompt word into a preselected intelligent interaction model, supplementing fields of the financial database table based on the target prompt word by the intelligent interaction model, and obtaining a target database table; the target database table comprises supplemented fields and field values corresponding to the supplemented fields; the target prompt word comprises a field grading rule; and determining a financial database table field grading result according to the supplemented fields of the target database table and the field values corresponding to the supplemented fields. The technical scheme can effectively improve the grading efficiency and accuracy of the financial database table fields.

Description

Technical Field

[0001] This invention relates to the field of financial technology, and in particular to a method, apparatus, device, medium, and product for hierarchical processing of financial database table fields. Background Technology

[0002] In the fintech field, within the business scenario of financial database table field classification, the completeness of financial transaction data and the accuracy of field classification are prerequisites for ensuring the validity of the classification results. The rationality of field supplementation directly affects the classification quality. As financial businesses continue to expand, the volume of financial transaction data continues to surge, database table structures become increasingly complex, and the demand for cross-scenario, multi-type transaction data fields has increased significantly.

[0003] Existing methods for classifying fields in financial database tables suffer from issues such as missing fields and non-standard classification logic. They neglect the key correlation between financial transaction data and field classification rules, making it difficult to accurately supplement missing fields and their corresponding values ​​in the database table. Traditional classification methods lack a field supplementation mechanism, which can easily lead to unreasonable field supplementation and classification result deviations. They cannot provide effective support for field classification, and ultimately the field classification results cannot fully adapt to the corresponding business scenarios, making it difficult to meet the accuracy and standardization requirements of field classification in financial database tables. Summary of the Invention

[0004] This invention provides a method, apparatus, device, medium, and product for hierarchical processing of fields in financial database tables, to optimize the scenario of hierarchical processing of fields in financial type database tables in the field of financial technology, thereby ensuring the accuracy and efficiency of financial data hierarchical processing.

[0005] According to one aspect of the present invention, a method for hierarchical processing of financial database table fields is provided, the method comprising:

[0006] Obtain target financial transaction data from a financial database table, and perform data preprocessing on the target financial transaction data to obtain preprocessed transaction data;

[0007] The preprocessed transaction data and preset target prompts are input into a pre-selected intelligent interaction model. The intelligent interaction model then supplements the fields of the financial database table based on the target prompts to obtain a target database table. The target database table includes supplemented fields and their corresponding field values. The target prompts include field hierarchical rules.

[0008] Based on the supplementary fields of the target database table and the corresponding field values, the field classification results of the financial database table are determined.

[0009] According to another aspect of the present invention, a financial database table field hierarchical processing apparatus is provided, the apparatus comprising:

[0010] The preprocessed transaction data acquisition module is used to acquire target financial transaction data from a financial database table and perform data preprocessing on the target financial transaction data to obtain preprocessed transaction data.

[0011] The target database table acquisition module is used to input the preprocessed transaction data and preset target prompt words into a pre-selected intelligent interaction model. The intelligent interaction model supplements the fields of the financial database table based on the target prompt words to obtain the target database table. The target database table includes supplemented fields and the field values ​​corresponding to the supplemented fields. The target prompt words include field hierarchical rules.

[0012] The grading result determination module is used to determine the grading result of the financial database table fields based on the supplementary fields of the target database table and the field values ​​corresponding to the supplementary fields.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory that is communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform a financial database table field hierarchical processing method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute a method for hierarchical processing of financial database table fields according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements a method for hierarchical processing of financial database table fields according to any embodiment of the present invention.

[0019] The technical solution of this invention involves acquiring target financial transaction data from a financial database table, preprocessing the target financial transaction data to obtain preprocessed transaction data, inputting the preprocessed transaction data and preset target prompts into a pre-selected intelligent interaction model, and having the intelligent interaction model supplement the fields of the financial database table based on the target prompts to obtain the target database table; the target database table includes supplemented fields and their corresponding field values; the target prompts include field classification rules; and the field classification result of the financial database table is determined based on the supplemented fields and their corresponding field values. The aforementioned technical solution acquires target financial transaction data from a financial database table and preprocesses it. This effectively filters valid transaction data and eliminates invalid and interfering data, laying a reliable data foundation for subsequent field supplementation and classification. Simultaneously, based on the preprocessed transaction data and preset target prompts containing field classification rules, the solution supplements fields in the financial database table efficiently and accurately, filling in missing fields and their corresponding values. This avoids the issues of missing fields and inaccurate supplementation that exist in traditional field classification methods. By determining the field classification results of the financial database table based on the supplemented fields and their corresponding values, the solution standardizes the field classification process, improves the accuracy and rationality of the classification results, avoids deviations in financial data application caused by missing fields or improper classification, and significantly improves the efficiency and quality of field classification in the financial database table.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for hierarchical processing of financial database table fields according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a method for hierarchical processing of financial database table fields according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a flowchart of a method for hierarchical processing of financial database table fields according to Embodiment 3 of the present invention;

[0025] Figure 4This is a schematic diagram of the structure of a financial database table field hierarchical processing device provided in Embodiment 4 of the present invention;

[0026] Figure 5 This is a schematic diagram of the structure of an electronic device that implements a hierarchical processing method for financial database table fields according to an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1 This is a flowchart of a method for hierarchical processing of financial database table fields provided in Embodiment 1 of the present invention. This embodiment is applicable to scenarios in the fintech field where financial database table fields are hierarchically processed. This method can be executed by a financial database table field hierarchical processing device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0031] S101. Obtain the target financial transaction data from the financial database table, and perform data preprocessing on the target financial transaction data to obtain preprocessed transaction data.

[0032] S102. Input the preprocessed transaction data and the preset target prompt words into the pre-selected intelligent interaction model. The intelligent interaction model supplements the fields of the financial database table based on the target prompt words to obtain the target database table. The target database table includes the supplemented fields and the field values ​​corresponding to the supplemented fields. The target prompt words include field hierarchical rules.

[0033] S103. Determine the field classification results of the financial database table based on the supplementary fields of the target database table and the corresponding field values ​​of the supplementary fields.

[0034] The financial database table is a structured database table used by financial institutions to store customer information, transaction records, and other financial transaction-related data. The target financial transaction data can be data extracted from the financial database table, including fields such as table name, field names, and data types. Preprocessed transaction data is the data obtained after removing redundant fields and selecting key fields from the target financial transaction data.

[0035] For example, structured table data related to financial transactions can be extracted from a financial institution's business database. Sparse data optimization operations can then be performed on the extracted data to obtain preprocessed transaction data after removing invalid and redundant data. Specifically, sparse data optimization can involve a combination of operations such as removing outliers from numeric fields in the target financial transaction data, desensitizing text-sensitive fields, and removing redundant identifier fields from the database tables.

[0036] Furthermore, in order to effectively filter target financial transaction data and eliminate invalid and interfering data, thereby improving the overall quality of the financial database table field hierarchy, in an optional embodiment, preprocessing operations are performed on the target financial transaction data to obtain preprocessed transaction data, including:

[0037] Step a1: Perform sparse data optimization on the target financial transaction data in the financial database table to obtain the target optimized data.

[0038] Step a2: Use the sparse principal component analysis algorithm to process the target optimization data to obtain preprocessed transaction data.

[0039] Among them, target optimized data can be standardized financial transaction data with uniform data format and no interference information after sparse data optimization processing such as outlier removal and data anonymization.

[0040] For example, outliers can be removed from numeric fields in the target financial transaction data using the Raida criterion. For instance, the mean and standard deviation of all numeric fields in the target financial transaction data can be calculated. The normal data range is from the mean - 3 times the standard deviation to the mean + 3 times the standard deviation. If the value of a numeric field in a financial transaction is outside this range, that transaction can be removed. On the other hand, text-based fields in the target financial transaction data can be anonymized. For example, text-based address fields can be anonymized by keeping the province / city and hiding the district / county. Ultimately, this results in optimized target data with a uniform format and no invalid data.

[0041] Among them, sparse principal component analysis algorithm can be used to process target optimization data to obtain preprocessed transaction data.

[0042] For example, sparse principal component analysis (SPM) can be used to process the target optimization data. For instance, the SPM algorithm can be set to process data according to preset parameters, such as 6 principal components and a sparsity of 0.3. The sparsity of 0.3 is used as a sparsity constraint for feature selection, limiting the algorithm to retain only 30% of the non-zero feature coefficients and filtering out 70% of low-weight feature dimensions with no business value when extracting principal components. The non-zero feature coefficients and low-weight feature dimensions are pre-calculated and determined by the algorithm. The number of principal components is used as a threshold for key field selection. Based on the sparsity constraint, the algorithm calculates the variance contribution of each field in the target optimization data, ultimately selecting the top 6 key fields by variance contribution. After removing redundant fields, the core feature data is obtained, which is the preprocessed transaction data.

[0043] The above technical solution, by sequentially performing sparse data optimization and sparse principal component analysis on the target financial transaction data, can remove outliers and invalid values ​​from the data, filter out core key fields, reduce data redundancy, provide an accurate data foundation for subsequent field supplementation work of intelligent interaction models, and avoid field supplementation deviations caused by the messiness of the original data.

[0044] The preset target prompts can be instruction text pre-defined by relevant technical personnel, containing field hierarchy rules. These prompts guide the intelligent interaction model to supplement a financial database table with specified supplementary fields and their corresponding values. The pre-selected intelligent interaction model receives pre-processed transaction data and the target prompts, and completes the field supplementation for the financial database table based on the field hierarchy rules within the prompts. The target database table is the financial database table containing the original fields, the supplementary fields, and their corresponding values ​​after the intelligent interaction model completes the field supplementation.

[0045] For example, preprocessed transaction data and preset prompts are input into a smart interaction model pre-deployed by relevant technical personnel. The smart interaction model supplements each field of the financial database table with specified fields and corresponding values ​​based on the prompt instructions, and then outputs a complete database table containing the supplemented fields and field values.

[0046] The supplementary fields can be feature fields used for field classification, added to the financial database table by the intelligent interaction model based on preset target prompts. The field classification result of the financial database table can be a classification result formed based on the field values ​​of the supplementary fields and the field classification rules.

[0047] For example, a comprehensive analysis and judgment can be performed on the supplementary fields and their specific values ​​in the target database table. Combined with field classification rules, the classification attribute of each field can be determined, and the classification attributes of the supplementary fields can be integrated to generate the corresponding financial database table field classification results. The field classification rules can be preset by relevant technical personnel according to actual needs.

[0048] Furthermore, to standardize the field classification results of financial database tables and improve their accuracy and rationality, in one optional embodiment, supplementary fields include: whether it is personal information, field importance level, field availability level, data classification, confidence level, and classification reason. After determining the field classification results of the financial database table based on the supplementary fields of the target database table and their corresponding field values, the following additional steps are also included:

[0049] Step b1: Based on the data classification in the supplementary field, determine the preset threshold corresponding to the confidence level in the supplementary field.

[0050] Step b2: Mark the fields according to the confidence level and its corresponding preset threshold to obtain the field marking results.

[0051] Step b3: Determine the processing priority of abnormal fields based on data classification.

[0052] Step b4: Configure operation permissions and de-identification rules based on whether the supplementary fields contain personal information, the importance level of the fields, and the availability level of the fields.

[0053] Step b5: Configure storage location and lifecycle based on data classification and field availability level.

[0054] Among them, the preset threshold can be pre-set by relevant technical personnel according to actual needs, and is used to determine whether the field classification results in the financial database table are abnormal.

[0055] For example, based on the data classification field in the supplementary fields, a preset threshold corresponding to the confidence level can be determined. The value of the data classification field includes: general data, important data, and core data. For instance, if the value of the data classification field is general data, the preset threshold corresponding to the confidence level can be 70%; if the value of the data classification field is core data, the preset threshold corresponding to the confidence level can be 90%.

[0056] The field marking result can be used to determine whether the field classification result of the current field is normal. The value of the field marking result includes: normal field classification result and abnormal field classification result.

[0057] Furthermore, in order to accurately identify abnormal fields in financial database tables and clarify the criteria for judging abnormal fields to improve the efficiency of abnormal field processing, in an optional embodiment, fields are marked according to confidence levels and their corresponding preset thresholds to obtain field marking results, including:

[0058] If the confidence level is less than its corresponding preset threshold, the supplementary field corresponding to the confidence level will be marked as an abnormal field.

[0059] Among them, abnormal fields can be financial database table fields whose actual confidence level is lower than their corresponding preset threshold. Such fields need to be manually reviewed and prioritized to avoid classification deviations due to inaccurate field information.

[0060] For example, if the threshold corresponding to the confidence level is set to 80%, and the value of the confidence field in the current field classification result is 70%, since 70% is less than 80%, the field labeling result corresponding to the current field classification result is an abnormal field classification result.

[0061] The above technical solution compares the confidence level with the preset threshold corresponding to the data classification, clarifies the judgment criteria for abnormal fields, and can accurately identify abnormal fields with inaccurate classification criteria, thereby improving the accuracy of classification results.

[0062] The processing priority of abnormal fields can be determined by the order in which abnormal fields are processed according to the data classification fields.

[0063] For example, if the values ​​of the corresponding data classification fields in the abnormal fields are different, the processing priorities are also different. For instance, if the abnormal field whose data classification field is core data has a higher processing priority than the abnormal field whose data classification field is general data, and the abnormal field whose data classification field is core data needs to be processed immediately.

[0064] Specifically, operation permissions can be configured based on a combination of whether the data is personal information, the field's importance level, and the field's availability level, defining the scope of access, modification, and other operations for fields in a financial database table. Data masking rules can be configured based on a combination of whether the data is personal information, the field's importance level, and the field's availability level, defining rules for masking sensitive fields in a financial database table.

[0065] For example, operation permissions and desensitization rules can be configured for fields in a financial database table based on whether the supplementary field contains personal information, the field's importance level, and the field's availability level. For instance, for fields containing personal information with high importance and availability levels, access permissions that are both accessible and unmodifiable can be configured, while desensitization rules that hide core characters can be used. For fields that are not personal information with low importance and availability levels, operation permissions that are accessible to all relevant technical personnel can be configured, and the complete original value can be viewed without data desensitization.

[0066] The storage location can be the physical storage medium for the data corresponding to the fields in the financial database table, configured according to data classification and field availability level. The lifecycle can be the retention period for the data corresponding to the fields in the financial database table, configured according to data classification and field availability level.

[0067] For example, storage location and lifecycle can be configured for fields in a financial database table based on the data classification and field availability level in the supplementary fields. For instance, a high-performance encrypted disk can be configured as the storage location for core data and fields with high availability levels, and the lifecycle can be set to permanent storage. For general data and fields with low availability levels, a regular storage server can be configured as the storage location, and the corresponding lifecycle can be set to fixed-term storage.

[0068] The above technical solution, based on different combinations of supplementary fields, sequentially completes the determination of abnormal field thresholds, priority division and field operation permissions, desensitization rules, storage location and lifecycle configuration, clarifies the classification standards of each field and subsequent management requirements, improves the management efficiency and security of financial data, and avoids financial data classification deviations caused by improper field classification.

[0069] The technical solution of this invention involves acquiring target financial transaction data from a financial database table, preprocessing the target financial transaction data to obtain preprocessed transaction data, inputting the preprocessed transaction data and preset target prompts into a pre-selected intelligent interaction model, and having the intelligent interaction model supplement the fields of the financial database table based on the target prompts to obtain the target database table; the target database table includes supplemented fields and their corresponding field values; the target prompts include field classification rules; and the field classification result of the financial database table is determined based on the supplemented fields and their corresponding field values. The aforementioned technical solution acquires target financial transaction data from a financial database table and preprocesses it. This effectively filters valid transaction data and eliminates invalid and interfering data, laying a reliable data foundation for subsequent field supplementation and classification. Simultaneously, based on the preprocessed transaction data and preset target prompts containing field classification rules, the solution supplements fields in the financial database table efficiently and accurately, filling in missing fields and their corresponding values. This avoids the issues of missing fields and inaccurate supplementation that exist in traditional field classification methods. By determining the field classification results of the financial database table based on the supplemented fields and their corresponding values, the solution standardizes the field classification process, improves the accuracy and rationality of the classification results, avoids deviations in financial data application caused by missing fields or improper classification, and significantly improves the efficiency and quality of field classification in the financial database table.

[0070] Example 2

[0071] Figure 2 This is a flowchart of a method for classifying financial database table fields according to Embodiment 2 of the present invention. This embodiment optimizes and improves upon the above-mentioned technical solutions. After the step "determine the classification result of the financial database table fields based on the supplementary fields of the target database table and the corresponding field values," a step is added: "obtain the number of modifications to the supplementary fields and their corresponding field values ​​within a preset time period. If the number of modifications reaches a preset threshold, a rule draft is generated. The rule draft is used to update the field classification rules corresponding to the rule draft in the rule base, resulting in updated field classification rules; the rule base stores at least one field classification rule." This improves the update and determination process of the rule base for the method of classifying financial database table fields.

[0072] It should be noted that for parts not described in detail in the embodiments of the present invention, please refer to the descriptions in other embodiments. For example... Figure 2 As shown, the method includes the following specific steps:

[0073] S201. Obtain the target financial transaction data from the financial database table, and perform data preprocessing on the target financial transaction data to obtain preprocessed transaction data.

[0074] S202. Input the preprocessed transaction data and preset target prompts into the pre-selected intelligent interaction model. The intelligent interaction model supplements the fields of the financial database table based on the target prompts to obtain the target database table. The target database table includes the supplemented fields and the field values ​​corresponding to the supplemented fields. The target prompts include field hierarchical rules.

[0075] S203. Determine the field classification results of the financial database table based on the supplementary fields of the target database table and the corresponding field values ​​of the supplementary fields.

[0076] S204. Obtain the supplementary fields within the preset time period and the number of times the field values ​​corresponding to the supplementary fields have been modified.

[0077] S205. If the number of modifications reaches the preset modification threshold, a rule draft is generated.

[0078] S206. Update the field hierarchy rule corresponding to the rule draft in the rule base using the rule draft to obtain the updated field hierarchy rule. The rule base stores at least one field hierarchy rule.

[0079] The preset time period can be a time period pre-set by relevant technical personnel. The number of modifications can be the number of times the same supplementary field in the financial database table is modified within the preset time period.

[0080] The modification threshold can be used to determine whether modifications to supplementary fields and field values ​​have reached a pre-defined standard. The rule draft generates preliminary modification rules for the fields requiring adjustment when the number of modifications to supplementary fields and field values ​​reaches the threshold.

[0081] For example, the actual number of times a field is modified can be compared with a preset threshold. For fields whose actual modification count reaches the preset threshold, their modification information is extracted, and a preliminary rule modification plan containing rule adjustment content is generated. For instance, if the preset modification threshold is 2 times, and within a preset time period, the data classification field value corresponding to the field named "Personal Information" in the target database table is successively modified to "General Data," "Important Data," and "Core Data," with a cumulative modification count reaching 3 times, exceeding the preset threshold of 2 times, a rule draft is generated. The rule draft content is: uniformly configure the data classification field of the field named "Personal Information" in all tables in the financial database to "Core Data."

[0082] The rule base can be a pre-built database that stores the classification rules for all financial database table fields. These classification rules can be various standardized rules stored in the rule base for classifying fields in financial database tables.

[0083] For example, the generated rule draft is submitted to relevant technical personnel for review. After the relevant technical personnel approve the rule, it is imported into the rule library. Then, it is matched with the corresponding field classification rule in the rule library, the corresponding content in the original rule is replaced, the rule is saved and the update of the corresponding field classification rule in the rule library is completed, and the updated field classification rule is obtained.

[0084] Furthermore, to improve the accuracy and compliance of the financial database table field classification results, in an optional embodiment, after determining the financial database table field classification results based on the supplementary fields of the target database table and their corresponding field values, the method further includes:

[0085] Step c1: Perform an audit and completion operation on the field classification results of the financial database table to obtain the audit and completion operation results.

[0086] Step c2: Perform an audit and verification operation on the field classification results of the financial database table using the updated field classification rules, and obtain the audit and verification operation results.

[0087] Step c3: Update the field classification results of the financial database table based on the results of the audit completion operation and the audit verification operation.

[0088] The result of the review and completion operation can be the final review conclusion after manually reviewing the classification results of the financial database table fields, supplementing the missing information and correcting the errors in the classification results, including the complete classification information after completion.

[0089] For example, relevant technical personnel will check the field classification results one by one. If any required fields are not filled in correctly, the relevant technical personnel need to supplement the missing information to form a complete and accurate review and completion result. For instance, if the data classification field in the field classification result of the financial database table is not filled in correctly, the relevant technical personnel need to supplement it according to the actual needs to improve the field classification result of the financial database table.

[0090] The audit and verification operation result can be a judgment on whether the classification result obtained after auditing and verifying the classification result of the financial database table fields using the updated field classification rules conforms to the new rules.

[0091] For example, the updated field classification rules are used as the verification standard to check the field classification results of the financial database table one by one. The field classification results of the financial database table that do not meet the field classification rules are modified accordingly to obtain the audit and verification operation results.

[0092] For example, the results of the audit completion operation and the audit verification operation are integrated, the completed information is synchronized to the field classification results of the financial database table, and the corresponding classification configuration of the fields that need to be adjusted is modified according to the new rules. Finally, the updated field classification results are saved.

[0093] The above technical solution performs audit completion and audit verification operations on the classification results of financial database table fields in sequence, and then updates the classification results by combining the results of the two operations. This effectively corrects missing information and rule deviations in the classification results, ensures the completeness of the final field classification results, and further improves the accuracy, completeness and compliance of the classification results of financial database table fields.

[0094] The technical solution of this invention accurately obtains the differences in field classification rules by statistically analyzing the number of times supplementary fields and field values ​​are modified within a preset time period. A rule draft is generated based on the number of modifications as a trigger condition, and then the corresponding field classification rules in the rule base are updated in a targeted manner using the rule draft. This makes the field classification rules of the financial database table adapt to changes in business needs, while further improving the accuracy and compliance of field classification results, and enhancing the flexibility and reliability of financial data classification management.

[0095] Example 3

[0096] Figure 3 This is a flowchart illustrating a hierarchical processing method for financial database table fields according to Embodiment 3 of the present invention. This embodiment provides a preferred example based on the above embodiments.

[0097] S301. Obtain the target financial transaction data from the financial database table, and perform sparse data optimization processing on the target financial transaction data in the financial database table to obtain the target optimized data.

[0098] S302. The sparse principal component analysis algorithm is used to process the target optimization data to obtain preprocessed transaction data.

[0099] S303. Input the preprocessed transaction data and the preset target prompt words into the pre-selected intelligent interaction model. The intelligent interaction model supplements the fields of the financial database table based on the target prompt words to obtain the target database table. The target database table includes the supplemented fields and the field values ​​corresponding to the supplemented fields. The target prompt words include field hierarchical rules.

[0100] S304. Determine the field classification results of the financial database table based on the supplementary fields of the target database table and the corresponding field values ​​of the supplementary fields.

[0101] S305. Based on the data classification in the supplementary field, determine the preset threshold corresponding to the confidence level in the supplementary field.

[0102] S306. Determine the confidence level and its corresponding preset threshold. If the confidence level is less than the corresponding preset threshold, execute S307-S308; otherwise, execute S309.

[0103] S307. Mark the supplementary field corresponding to the confidence level as an abnormal field.

[0104] S308. Based on data classification, determine the processing priority of abnormal fields.

[0105] S309. The current field is a normal field.

[0106] S310. Configure operation permissions and de-identification rules based on whether the supplementary field contains personal information, the importance level of the field, and the availability level of the field.

[0107] S311. Configure storage location and lifecycle based on data classification and field availability level.

[0108] S312. Obtain the supplementary fields within the preset time period and the number of times the field values ​​corresponding to the supplementary fields have been modified.

[0109] S313. If the number of modifications reaches the preset modification threshold, a rule draft is generated.

[0110] S314. Update the field hierarchy rule corresponding to the rule draft in the rule base using the rule draft to obtain the updated field hierarchy rule; the rule base stores at least one field hierarchy rule.

[0111] S315. Perform an audit and completion operation on the hierarchical results of the financial database table fields to obtain the audit and completion operation results.

[0112] S316. Perform an audit and verification operation on the field classification results of the financial database table using the updated field classification rules, and obtain the audit and verification operation results.

[0113] S317. Update the field classification results of the financial database table based on the results of the audit completion operation and the audit verification operation.

[0114] The information collected in the above embodiments of the present invention is all information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse.

[0115] Example 4

[0116] Figure 4This is a schematic diagram of a financial database table field hierarchical processing device provided in Embodiment 4 of the present invention. The financial database table field hierarchical processing device provided in this embodiment of the present invention is applicable to scenarios in the financial technology field where financial database table fields are processed hierarchically. This device can be implemented in hardware and / or software, and can be applied to a financial database table field hierarchical processing method. Specifically, it can be configured in a controller, such as... Figure 4 As shown, the device includes: a preprocessing transaction data acquisition module 401, a target database table acquisition module 402, and a grading result determination module 403. Wherein:

[0117] The preprocessing transaction data acquisition module 401 is used to acquire target financial transaction data from a financial database table and perform data preprocessing on the target financial transaction data to obtain preprocessed transaction data.

[0118] The target database table acquisition module 402 is used to input preprocessed transaction data and preset target prompt words into a pre-selected intelligent interaction model. The intelligent interaction model supplements the fields of the financial database table based on the target prompt words to obtain the target database table. The target database table includes supplemented fields and the field values ​​corresponding to the supplemented fields. The target prompt words include field hierarchical rules.

[0119] The grading result determination module 403 is used to determine the grading result of the financial database table fields based on the supplementary fields of the target database table and the field values ​​corresponding to the supplementary fields.

[0120] The technical solution of this invention involves acquiring target financial transaction data from a financial database table, preprocessing the target financial transaction data to obtain preprocessed transaction data, inputting the preprocessed transaction data and preset target prompts into a pre-selected intelligent interaction model, and having the intelligent interaction model supplement the fields of the financial database table based on the target prompts to obtain the target database table; the target database table includes supplemented fields and their corresponding field values; the target prompts include field classification rules; and the field classification result of the financial database table is determined based on the supplemented fields and their corresponding field values. The aforementioned technical solution acquires target financial transaction data from a financial database table and preprocesses it. This effectively filters valid transaction data and eliminates invalid and interfering data, laying a reliable data foundation for subsequent field supplementation and classification. Simultaneously, based on the preprocessed transaction data and preset target prompts containing field classification rules, the solution supplements fields in the financial database table efficiently and accurately, filling in missing fields and their corresponding values. This avoids the issues of missing fields and inaccurate supplementation that exist in traditional field classification methods. By determining the field classification results of the financial database table based on the supplemented fields and their corresponding values, the solution standardizes the field classification process, improves the accuracy and rationality of the classification results, avoids deviations in financial data application caused by missing fields or improper classification, and significantly improves the efficiency and quality of field classification in the financial database table.

[0121] Optionally, the preprocessing transaction data acquisition module 401 includes:

[0122] The target optimization data acquisition unit is used to perform sparse data optimization processing on the target financial transaction data in the financial database table to obtain target optimized data.

[0123] The preprocessing transaction data acquisition unit is used to process the target optimization data using the sparse principal component analysis algorithm to obtain preprocessed transaction data.

[0124] Optionally, the device further includes:

[0125] The preset threshold determination module is used to determine the preset threshold corresponding to the confidence level in the supplementary field based on the data classification in the supplementary field.

[0126] The field labeling result acquisition module is used to label fields based on confidence level and its corresponding preset threshold, and obtain field labeling results.

[0127] The module for determining the processing priority of abnormal fields is used to determine the processing priority of abnormal fields based on data hierarchy.

[0128] The module for obtaining operation permissions and de-identification rules is used to configure operation permissions and de-identification rules based on whether the supplementary fields contain personal information, the importance level of the fields, and the availability level of the fields.

[0129] The storage location and lifecycle configuration module is used to configure the storage location and lifecycle based on data hierarchy and field availability level.

[0130] Optional, the field tagging result retrieval module is specifically used for:

[0131] If the confidence level is less than its corresponding preset threshold, the supplementary field corresponding to the confidence level will be marked as an abnormal field.

[0132] Optionally, the device further includes:

[0133] The supplementary field acquisition module is used to obtain the supplementary fields within a preset time period and the number of times the field values ​​corresponding to the supplementary fields have been modified.

[0134] The rule draft generation module is used to generate a rule draft if the number of modifications reaches a preset threshold.

[0135] The field-level rule update module is used to update the field-level rules in the rule base corresponding to the rule draft using the rule draft, so as to obtain the updated field-level rules. The rule base stores at least one field-level rule.

[0136] Optionally, the device further includes:

[0137] The module for obtaining the results of the audit completion operation is used to perform audit completion operations on the hierarchical results of the fields in the financial database table and obtain the audit completion operation results.

[0138] The module for obtaining the results of the audit and verification operation is used to perform audit and verification operations on the field classification results of the financial database table using the updated field classification rules, and obtain the audit and verification operation results.

[0139] The grading result update module is used to update the grading results of the financial database table fields based on the results of the audit completion operation and the audit verification operation.

[0140] The financial database table field hierarchical processing device provided in this embodiment of the invention can execute a financial database table field hierarchical processing method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0141] Example 5

[0142] Figure 5A schematic diagram of an electronic device 50 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0143] like Figure 5 As shown, the electronic device 50 includes at least one processor 51 and a memory, such as a read-only memory (ROM) 52 and a random access memory (RAM) 53, communicatively connected to the at least one processor 51. The memory stores computer programs executable by the at least one processor. The processor 51 can perform various appropriate actions and processes based on the computer program stored in the ROM 52 or loaded from storage unit 58 into the RAM 53. The RAM 53 can also store various programs and data required for the operation of the electronic device 50. The processor 51, ROM 52, and RAM 53 are interconnected via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0144] Multiple components in electronic device 50 are connected to I / O interface 55, including: input unit 56, such as keyboard, mouse, etc.; output unit 57, such as various types of monitors, speakers, etc.; storage unit 58, such as disk, optical disk, etc.; and communication unit 59, such as network card, modem, wireless transceiver, etc. Communication unit 59 allows electronic device 50 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0145] Processor 51 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 51 performs the various methods and processes described above, such as the hierarchical processing method for financial database table fields.

[0146] In some embodiments, a method for hierarchical processing of financial database table fields can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 58. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 50 via ROM 52 and / or communication unit 59. When the computer program is loaded into RAM 53 and executed by processor 51, one or more steps of the hierarchical processing method for financial database table fields described above can be performed. Alternatively, in other embodiments, processor 51 can be configured as a method for hierarchical processing of financial database table fields by any other suitable means (e.g., by means of firmware).

[0147] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0148] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0149] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0150] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0151] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0152] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0153] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0154] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for hierarchical processing of table fields in a financial database, characterized in that, include: Obtain target financial transaction data from a financial database table, and perform data preprocessing on the target financial transaction data to obtain preprocessed transaction data; The preprocessed transaction data and preset target prompts are input into a pre-selected intelligent interaction model, which then supplements the fields of the financial database table based on the target prompts to obtain the target database table. The target database table includes supplementary fields and the field values ​​corresponding to the supplementary fields; The target prompts include field hierarchical rules; Based on the supplementary fields of the target database table and the corresponding field values, the field classification results of the financial database table are determined.

2. The method according to claim 1, characterized in that, Perform preprocessing operations on the target financial transaction data to obtain preprocessed transaction data, including: The target financial transaction data in the financial database table is subjected to sparse data optimization processing to obtain the target optimized data. The target optimization data is processed using a sparse principal component analysis algorithm to obtain preprocessed transaction data.

3. The method according to claim 1, characterized in that, The supplementary fields include: whether it is personal information, field importance level, field availability level, data classification, confidence level, and classification reason; correspondingly, after determining the field classification result of the financial database table based on the supplementary fields of the target database table and the field values ​​corresponding to the supplementary fields, the method further includes: Based on the data classification in the supplementary field, determine the preset threshold corresponding to the confidence level in the supplementary field; The fields are labeled based on the confidence level and its corresponding preset threshold to obtain the field labeling results; Based on the data classification, the processing priority of abnormal fields is determined; Based on whether the supplementary fields contain personal information, the importance level of the fields, and the availability level of the fields, configure operation permissions and de-identification rules; Configure the storage location and lifecycle based on the data classification and the field availability level.

4. The method according to claim 3, characterized in that, The step of labeling fields based on the confidence level and its corresponding preset threshold to obtain field labeling results includes: If the confidence level is less than its corresponding preset threshold, the supplementary field corresponding to the confidence level is marked as an abnormal field.

5. The method according to claim 1, characterized in that, After determining the field classification result of the financial database table based on the supplementary fields of the target database table and the field values ​​corresponding to the supplementary fields, the method further includes: Obtain the supplementary fields within a preset time period and the number of times the field values ​​corresponding to the supplementary fields have been modified; If the number of modifications reaches a preset threshold, a rule draft is generated. The field classification rule corresponding to the rule draft in the rule base is updated using the rule draft to obtain the updated field classification rule; the rule base stores at least one field classification rule.

6. The method according to claim 5, characterized in that, After determining the field classification result of the financial database table based on the supplementary fields of the target database table and the field values ​​corresponding to the supplementary fields, the method further includes: The hierarchical results of the fields in the financial database table are reviewed and completed to obtain the review and completion results. The updated field classification rules are used to audit and verify the field classification results of the financial database table to obtain the audit and verification results. Based on the results of the audit completion operation and the audit verification operation, update the field classification results of the financial database table.

7. A device for hierarchical processing of table fields in a financial database, characterized in that, include: The preprocessed transaction data acquisition module is used to acquire target financial transaction data from a financial database table and perform data preprocessing on the target financial transaction data to obtain preprocessed transaction data. The target database table acquisition module is used to input the preprocessed transaction data and preset target prompt words into a pre-selected intelligent interaction model, and the intelligent interaction model supplements the fields of the financial database table based on the target prompt words to obtain the target database table; The target database table includes supplementary fields and the field values ​​corresponding to the supplementary fields; The target prompts include field hierarchical rules; The grading result determination module is used to determine the grading result of the financial database table fields based on the supplementary fields of the target database table and the field values ​​corresponding to the supplementary fields.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the financial database table field hierarchical processing method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the hierarchical processing method for financial database table fields as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the hierarchical processing method for financial database table fields according to any one of claims 1-6.

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