Analysis methods, equipment and storage media for tax-related databases

Abstract

The present application relates to an analysis method, device and storage medium for a tax-related database. The method comprises: constructing a database business data benchmark model adapted to a business object and a database identification engine for extracting database information; for the tax-related database to be analyzed, the database identification engine connects to and extracts database information from the connected tax-related database; and utilizes the business data sub-model within the constructed database business data benchmark model to identify the extracted database information, so as to determine the identification and matching information of any database table within the connected tax-related database after identification and matching, and output the identification and matching information of the determined database table as a business object attribute result; and integrating the output business object attribute result set to form a business object to be displayed after integration. The present application can effectively realize the analysis of the tax-related database and improve the efficiency and reliability of tax audits.

Description

Technical Field

[0001] The present application relates to a database analysis method, and in particular to an analysis method, device and storage medium for a tax-related database. Background Art

[0002] In recent years, enterprise informatization has continued to strengthen, and information systems such as ERP and MES have been widely used. During tax audits, due to limited time, auditors are unable to conduct a detailed on-site review of the auditee's information system. Instead, they typically use methods such as table import and database backup to collect electronic data for detailed analysis.

[0003] Currently, the diverse array of electronic information systems on the market makes it difficult for auditors to effectively analyze databases. Tax-related analysis primarily focuses on financial data or internet data within tax-related data, while analysis of business databases remains a niche area. Summary of the Invention

[0004] The purpose of this application is to overcome the deficiencies in the prior art and to provide a tax-related database analysis method, equipment and storage medium, which can effectively realize the analysis of tax-related databases and improve the efficiency and reliability of tax audits.

[0005] According to the technical solution provided in this application, a tax-related database analysis method includes:

[0006] Construct a database business data benchmark model that is adapted to the business objects and a database identification engine for extracting database information, wherein:

[0007] The constructed database business data benchmark model includes several business data sub-models representing business object attributes. For any business data sub-model, the business data sub-model includes business data identification conditions, business identification information, and model description information based on database available elements associated with the business object;

[0008] For the tax-related database to be analyzed, the database recognition engine connects to and extracts database information from the connected tax-related database, identifies the extracted database information using the business data sub-model within the constructed database business data benchmark model, and determines the identification and matching information of any database table within the connected tax-related database after identification and matching, and outputs the identified identification and matching information of the database table as a business object attribute result;

[0009] The outputted business object attribute result sets are integrated to form a business object to be displayed after integration.

[0010] For any business data sub-model, the model description information includes model table matching information and model column matching information;

[0011] The business identification information includes an identification strategy for identifying data structure and content, an identification weight system for weight measurement when identifying based on model description information, and weight identification parameters for determining the measured weights, wherein:

[0012] The recognition weight system includes information recognition matching weights of several matching information, wherein the matching information includes table name, table comment, number of primary keys contained in the table, table data volume, column name, number of columns, column comment and / or column content;

[0013] The weight identification parameters include a weight identification matching threshold;

[0014] When identifying and matching the extracted database information using the business data sub-model within the constructed database business data benchmark model, the extracted database information is compared one-to-one with the business data sub-model;

[0015] During the comparison, the weight identification value of the extracted database information is determined based on the identification weight system. When the weight identification value matches the weight identification threshold, the identification matching information of any database table in the connected tax-related database is determined.

[0016] When using the database recognition engine to extract database information from the tax-related database to be analyzed, it is first connected to the tax-related database to be analyzed, and after the connection, information is extracted from any database table in the tax-related database, wherein:

[0017] After extracting the tax-related database information, database table extraction information is obtained, and identification and matching is performed based on the database table extraction information and the business data sub-model within the constructed database business data benchmark model;

[0018] The database table extraction information includes basic information of the tax-related database, table information in the tax-related database, and view information of the tax-related database.

[0019] When the database identification engine is connected to the tax-related database, the connection input information input to the database identification engine includes connection string information and the type of the tax-related database to be connected.

[0020] The identified matching information of the determined database table includes the matched business data sub-model, the identified database table name, and the identified database table column set.

[0021] The business objects include vouchers, costs and / or sales flow, wherein:

[0022] When the business object is a voucher, the integrated business objects displayed include the voucher header, voucher entry, voucher auxiliary accounting, and / or voucher cash flow;

[0023] When the business object is cost, the integrated business objects include procurement cost, labor cost, miscellaneous cost and / or loss;

[0024] When the business object is sales flow, the integrated business objects displayed include sales flow.

[0025] For any business object, when identifying and matching the corresponding business object attributes with the voucher header based on the business data sub-model, the method for identifying and matching the business object attributes includes:

[0026] Determine the amount of data records corresponding to the database information. If the determined amount of data records meets the business data identification condition, proceed to the step of determining the weight identification value. Otherwise, exit the step of identifying and matching the business object attributes.

[0027] When determining the weight identification value of the database information, the database information is compared with the model table matching information and the model column matching information in the model description information respectively, and the corresponding weight accumulation value is determined based on the constructed identification weight system in the comparison to select the required weight accumulation value as the weight identification value.

[0028] When the business object is a voucher, when identifying and matching the voucher header of the voucher, the business data sub-model used to identify and match the voucher header, the model table matching information in the business data sub-model includes table name synonyms and the number of primary keys included;

[0029] The model column matching information in the business data sub-model includes the account book code, period, voucher word, voucher number, document creator, and document creation date;

[0030] The weight identification values ​​corresponding to multiple database tables in the database information are sorted, and the database table corresponding to the largest weight identification value that is greater than the weight identification matching threshold is selected as the credential header table.

[0031] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that the processor performs the steps of the above method when executing the computer program.

[0032] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0033] Advantages of this application: The database recognition engine connects to and extracts database information from the connected tax-related database, and uses the business data sub-model within the constructed database business data benchmark model to identify the extracted database information, so as to determine the identification matching information of any database table in the connected tax-related database after identification and matching, and output the identification matching information of the determined database table as a business object attribute result;

[0034] The output business object attribute result set is integrated to form a business object to be displayed after integration, which can effectively realize the analysis of tax-related databases and improve the efficiency and reliability of tax audits. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of an analytical method for implementing the present application.

[0036] Figure 2 A schematic diagram of an embodiment of organizing available elements into an analysis structure when constructing a database business data benchmark model for this application.

[0037] Figure 3 A schematic diagram of an embodiment of constructing a database business data benchmark model for this application.

[0038] Figure 4 A schematic diagram of an embodiment of building a database recognition engine for this application.

[0039] Figure 5 This is a schematic diagram of an embodiment of a business data sub-model used for matching and identifying voucher headers in the present application.

[0040] Figure 6 This is an implementation diagram for integrating and displaying the business object voucher for this application.

[0041] Figure 7 This is an implementation diagram for integrated display when the business object of this application is cost. DETAILED DESCRIPTION

[0042] The present application will be further described below with reference to specific drawings and embodiments.

[0043] like Figure 1 As shown, in order to effectively analyze the tax-related database and improve the efficiency and reliability of tax audits, the tax-related database analysis method of this application, specifically, the analysis method includes:

[0044] Construct a database business data benchmark model that is adapted to the business objects and a database identification engine for extracting database information, wherein:

[0045] The constructed database business data benchmark model includes several business data sub-models representing business object attributes. For any business data sub-model, the business data sub-model includes business data identification conditions, business identification information, and model description information based on database available elements associated with the business object;

[0046] For the tax-related database to be analyzed, the database recognition engine connects to and extracts database information from the connected tax-related database, identifies the extracted database information using the business data sub-model within the constructed database business data benchmark model, and determines the identification and matching information of any database table within the connected tax-related database after identification and matching, and outputs the identified identification and matching information of the database table as a business object attribute result;

[0047] The outputted business object attribute result sets are integrated to form a business object to be displayed after integration.

[0048] In specific implementation, the tax-related database is generally the enterprise operation database. The analysis of the tax-related database is mainly convenient for tax audits. Tax auditors can audit the data content corresponding to an enterprise's operation data based on the integrated and displayed business objects, thereby improving the efficiency and reliability of tax audits.

[0049] Based on the characteristics of business objects, this application pre-constructs a database business data benchmark model and a database identification engine. The constructed database business data benchmark model includes several business data sub-models that represent the attributes of business objects. Generally, for any business data sub-model, the business data sub-model includes business data identification conditions, business identification information, and model description information based on the available database elements associated with the business object. The business data identification conditions are used as a pre-condition for identification. Only when the business data identification conditions of the business data sub-model are met will the required identification and matching be performed based on the current business data sub-model. The specific details of the business identification information and model description information will be explained in detail below.

[0050] When analyzing tax-related databases, it is necessary to use a database recognition engine to connect to and extract database information from the connected tax-related database. After the database information is extracted, the extracted database information is identified and matched using or based on the business data sub-model within the pre-built database business data benchmark model. After identification and matching, the identification and matching information of any database table in the connected tax-related database is determined, and the identification and matching information of the determined database table is output as a business object attribute result.

[0051] In a specific implementation, since a business object may include multiple attributes, all attributes of the business object identified and matched are integrated to form a business object that can be displayed and output. In one embodiment of the present application, integrating the business object result set specifically refers to combining the attribute results of related business objects according to the characteristics of the business objects, thereby outputting and displaying a business object that is convenient for tax auditors to directly view.

[0052] In one embodiment of the present application, for any business data sub-model, the model description information includes model table matching information and model column matching information;

[0053] The business identification information includes an identification strategy for identifying data structure and content, an identification weight system for weight measurement when identifying based on model description information, and weight identification parameters for determining the measured weights, wherein:

[0054] The recognition weight system includes information recognition matching weights of several matching information, wherein the matching information includes table name, table comment, number of primary keys contained in the table, table data volume, column name, number of columns, column comment and / or column content;

[0055] The weight identification parameters include a weight identification matching threshold;

[0056] When identifying and matching the extracted database information using the business data sub-model within the constructed database business data benchmark model, the extracted database information is compared one-to-one with the business data sub-model;

[0057] During the comparison, the weight identification value of the extracted database information is determined based on the identification weight system. When the weight identification value matches the weight identification threshold, the identification matching information of any database table in the connected tax-related database is determined.

[0058] In order to effectively analyze tax-related databases, when building a database business data benchmark model, it is necessary to analyze the available elements in the enterprise business database; generally, the available elements include the database name, database type, the number of tables contained in the database, the number of views contained in the database, the table name of each table in the database, the description of each table in the database, the number of columns, column names, column types, column aliases, column comments, and data volume contained in each table in the database, the indexes contained in the database, the name of each view in the database, and the number of columns, column names, and aliases contained in each view.

[0059] After analyzing the available elements in the business operation database, the available elements may be organized into a structure for analysis, wherein the structure organized into the structure for analysis may be as follows: Figure 2As shown in the figure, the structure organized for analysis mainly includes database information, database table information, and database view information. Database table information generally includes the database name, database type, number of tables contained in the database, and number of views contained in the database. Database table information includes the table, table comment, column set, table data volume, and index. The column set includes the column name, column type, column alias, column comment, whether it is a primary key, and column content. Indexes include single indexes and composite indexes. Database view information includes the view name, view-containing tables, and view column combinations. The view column set includes the column name and column alias.

[0060] Based on the above organizational structure for analysis, a database business data baseline model is constructed. The more business data sub-models within the database business data baseline model, the broader the ability and scope of identification of tax-related databases. The database business data baseline model can correspond to business objects, with each business data sub-model corresponding to an attribute of a business object. Consequently, each business data sub-model can correspond to a database table within the tax-related database.

[0061] Figure 3 This is an embodiment of constructing a database business data benchmark model, wherein the data model refers to the database business data benchmark model, and the sub-model refers to the business data sub-model. Figure 3 It can be seen that any business data sub-model includes business data identification conditions, business identification information, and model description information based on the available elements of the database associated with the business object; Figure 3 The exclusion conditions are the business data identification conditions, the model description is the model description information, and the identification pattern is the business identification information.

[0062] The business data identification conditions in the business data sub-model may generally include the amount of data in the database table and / or other exclusion conditions. Other exclusion conditions may be selected and determined based on actual application requirements, etc., so as to meet the actual application requirements.

[0063] The model description information includes model table matching information and model column matching information. Figure 3 A specific embodiment of model table matching information and model column matching information is shown in Figure 1. Model table matching information generally includes the table name, the number of primary keys, table comments, and the amount of table data. When matching a table name, synonyms corresponding to the table name can be matched. The number of synonyms can be selected as needed to ensure a valid match for the table name. Of course, table comments also have one or more corresponding synonym matches. The number of primary keys and the amount of table data included in the model table matching information are numerical matches.

[0064] Model column matching information includes column name, column type, column alias, column comment, and column content. There are corresponding synonym matches for column name, column alias, and column comment. The column type refers to the type of the current column, such as string or int. The column content mainly refers to the included keywords or custom rules. The keywords and custom rules can be selected according to actual needs to meet the actual application requirements.

[0065] The identification strategy involves configuring the data structure and content to be identified. The data structure weight is set between X and Y for content identification, where X and Y represent the weight range and can be configured based on actual conditions. The identification weight system measures the weight used for identification based on model description information. The identification weight system includes information identification matching weights for several matching information types, corresponding to the model table matching information and model column matching information in the model information type.

[0066] In one embodiment of the present application, the matching information includes a table name, a table comment, the number of primary keys contained in the table, the amount of table data, a column name, the number of columns, a column comment, and / or column content. After configuring the recognition weight system, a weight recognition parameter is configured according to the configured recognition weight system and the characteristics of the business data sub-model, wherein the weight recognition parameter is the configured weight recognition matching threshold.

[0067] After constructing the business data sub-model corresponding to the business object attributes, the construction of the database business data baseline model is realized. Figure 3 In the figure, a business data sub-model is shown. For other business data sub-models, specific configurations can be made by referring to the above description and the attributes of the business objects. Examples will not be given here one by one.

[0068] In one embodiment of the present application, when the extracted database information is identified and matched using the business data sub-model within the constructed database business data benchmark model, the extracted database information is compared one-to-one with the business data sub-model;

[0069] During the comparison, the weight identification value of the extracted database information is determined based on the identification weight system. When the weight identification value matches the weight identification threshold, the identification matching information of any database table in the connected tax-related database is determined.

[0070] Specifically, when the extracted database information is compared against the business data sub-models one by one, the primary focus is determining the business data sub-model that best matches the database information. During this comparison, a weighted identification value for the extracted database information is determined based on an identification weight system. The weighted identification value matches a weighted identification threshold, which, in one embodiment of the present application, specifically refers to a weighted identification value that is greater than the weighted identification threshold.

[0071] If the weight identification value matches the weight identification threshold, the identification matching information of any database table in the connected tax-related database is determined. Otherwise, the identification matching with the next business data sub-model is carried out. When the identification matching with all business data sub-models is carried out, if the corresponding weight identification value cannot match the corresponding weight identification threshold, it means that the extracted database information cannot match the constructed database business data benchmark model, that is, the extracted database information is not the business object attribute required for tax audit.

[0072] In one embodiment of the present application, when using a database recognition engine to extract database information from a tax-related database to be analyzed, the database is first connected to the tax-related database to be analyzed, and after the connection, information is extracted from any database table in the tax-related database, wherein:

[0073] After extracting the tax-related database information, database table extraction information is obtained, and identification and matching is performed based on the database table extraction information and the business data sub-model within the constructed database business data benchmark model;

[0074] The database table extraction information includes basic information of the tax-related database, table information in the tax-related database, and view information of the tax-related database.

[0075] Specifically, when the database identification engine connects to a tax-related database, the connection input information provided to the database identification engine includes connection string information and the type of tax-related database to be connected. Generally, the database identification engine only connects to one tax-related database at a time. The connection string information primarily contains a string of information required for database connection. Different database types have different formats, which are consistent with existing ones. When connecting to a tax-related database, the database backup file (a backup file refers to a file generated by copying the data in the database at a certain moment) must be restored using existing techniques commonly used in this technical field so that the required connection can be made after the restoration.

[0076] After connecting to the tax-related database to be analyzed based on the connection input information, the database identification engine can extract information from the connected tax-related database to obtain database table extraction information. This information includes basic information about the tax-related database, table information within the tax-related database, and view information about the tax-related database. View information specifically refers to a view in a computer database, a virtual table whose content is defined by a query. Like a real table, a view contains a series of named columns and rows of data. However, a view does not exist as a stored set of data values ​​in the database. Instead, the row and column data comes from the table referenced by the query that defines the view and is dynamically generated when the view is referenced, consistent with existing data.

[0077] Figure 4 A specific embodiment of the database identification engine is shown in FIG. The database engine is a database identification engine. The connection input information received by the database identification engine includes connection string information and the type of database. The database identification engine connects to the tax-related database based on the connection input information. After the connection is made, the information of the tax-related database can be extracted. Figure 4 A specific implementation of extracting database table extraction information is shown in FIG, specifically: the database table extraction information includes basic information of the tax-related database, table information in the tax-related database, and view information of the tax-related database.

[0078] Figure 4 The figure shows a specific implementation of extracting database table information. Basic tax database information includes the database name, the number of tables in the database, and the number of views in the database. Table information within the tax database includes the table name, table comment, column set, table data volume, and indexes. The column set includes the column name, column type, column alias, column comment, whether it is a primary key, and column content. Indexes include single indexes and composite indexes. View information includes the view name, view tables, and view column set. The view column set includes the column name, column type, column alias, column comment, whether it is a primary key, and column content.

[0079] Therefore, as shown in the diagram, one implementation of database table extraction information shows that the database table extraction information corresponds to the model description information and identification weight system within the business data sub-model. Therefore, identification and matching can be performed based on the database table extraction information and the business data sub-model within the constructed database business data benchmark model.

[0080] In one embodiment of the present application, the identified matching information of the determined database table includes a matched business data sub-model, an identified database table name, and an identified database table column set.

[0081] As can be seen from the above description, a business object includes one or more business object attributes. Therefore, for any business object, when identifying and matching the corresponding business object attributes in the voucher header based on the business data sub-model, the method for identifying and matching the business object attributes includes:

[0082] Determine the amount of data records corresponding to the database information. If the determined amount of data records meets the business data identification condition, proceed to the step of determining the weight identification value. Otherwise, exit the step of identifying and matching the business object attributes.

[0083] When determining the weight identification value of the database information, the database information is compared with the model table matching information and the model column matching information in the model description information respectively, and the corresponding weight accumulation value is determined based on the constructed identification weight system in the comparison to select the required weight accumulation value as the weight identification value.

[0084] Specifically, after obtaining a weight identification value, when the weight identification value matches the weight identification threshold, the identification matching information of any database table in the connected tax-related database is determined; the specific circumstances of obtaining the identification matching information can be referred to the above description and will not be repeated here. When selecting the desired weight cumulative value as the weight identification value, the largest weight cumulative value is generally used as the weight identification value.

[0085] In one embodiment of the present application, the business objects include vouchers, costs and / or sales flows, wherein:

[0086] When the business object is a voucher, the integrated business objects displayed include the voucher header, voucher entry, voucher auxiliary accounting, and / or voucher cash flow;

[0087] When the business object is cost, the integrated business objects include procurement cost, labor cost, miscellaneous cost and / or loss;

[0088] When the business object is sales flow, the integrated business objects displayed include sales flow.

[0089] From the above description, we can see that when the business object is a voucher, the voucher attributes include voucher header, voucher entry, voucher auxiliary accounting and voucher cash flow; the integrated display is as follows Figure 6 As shown; each voucher attribute corresponds to a database table in the tax-related database.

[0090] When the business object is cost, the attributes of cost include procurement cost, labor cost, miscellaneous cost and / or loss. The integrated display is as follows: Figure 7 When the business object is sales flow, the attributes of sales flow include sales flow.

[0091] When the business object is a voucher, cost, or sales flow, the specific identification and matching of business object attributes is described below using the business object as a voucher and the business object attribute as a voucher header as an example to explain the identification and matching process in detail.

[0092] In one embodiment of the present application, a business data sub-model for identifying and matching a voucher header, model table matching information within the business data sub-model includes table name synonyms and the number of included primary keys;

[0093] The model column matching information in the business data sub-model includes the account book code, period, voucher word, voucher number, document creator, and document creation date;

[0094] The weight identification values ​​corresponding to multiple database tables in the database information are sorted, and the database table corresponding to the largest weight identification value that is greater than the weight identification matching threshold is selected as the credential header table.

[0095] Figure 5 Figure 2 shows an implementation of a business data sub-model for identifying and matching credential headers. The model uses the number of database table records as a business data identification condition. For example, if the number of database table records is less than 100, the matching of the current database table is stopped. If the number of database table records is greater than 100, the corresponding weighted identification value is determined based on the model description information and the business identification information.

[0096] When determining the weight identification value, the specific process includes:

[0097] 1) If the database table name is Voucher or Cher (that is, the voucher header table may be named Voucher or Cher), the cumulative weight value is increased by 10. That is, the information identification matching weight of the database table name is configured as 10.

[0098] 2) If a column in the database table is named accountingbook and the data type of the column is string or integer, it is identified as an account book code, and the cumulative weight value is increased by 3; that is, the information identification matching weight of the account book code is configured to 3 here.

[0099] 3) If a column in the database table is named "period" and the data type of the column is string or integer, it is identified as a period, and the cumulative weight value is increased by 3; that is, the information identification matching weight of the period is configured as 3 here.

[0100] 4) If a column in the data table is named group or name, and the column data type is string, it is identified as a voucher word, and the cumulative weight value is increased by 3; that is, the information identification matching weight of the voucher word is configured to 3 here.

[0101] 5) If a column in the database table is named num and the data type of the column is string or integer, it is identified as a voucher number, and the cumulative weight value is increased by 3; here, the information identification matching weight of the voucher number is configured to 3.

[0102] 6) If a column in the data table is named prepared and the data type of the column is string, it is identified as the person who made the order, and the cumulative weight value is increased by 3; that is, the information identification matching weight of the person who made the order is configured to 3 here.

[0103] 7) If a column in a data table is named "preparedate" and its data type is "Date" or "stringDate," it is identified as the order preparation date, and the cumulative weight is increased by 3. In other words, the identification and matching weight for the order preparation date is configured to 3. If the data type is "stringDate," it is a string type in the database, but the stored value is a date. The recognized date format is either yyyy-MM-dd or yy / MM / dd. If the value stored in the database is in either of these formats, it is considered a string type value storing a date format.

[0104] After the above identification, a weighted cumulative value is obtained. When the weighted cumulative value is greater than 15, the table is temporarily identified as the voucher header and the remaining tables are identified. In this case, 15 is the configured weighted identification threshold.

[0105] If the cumulative weight value is between 10 and 15, the column contents of the database table will be identified. If the contents of a column of the database table contain any one of the following: receipt, payment, transfer, or record, and the data type of the column is string, the column will be identified as a voucher number, and the cumulative weight value will be increased by 5. If the contents of a column of the data contain consecutive numbers 1-50, such as the column content in the first row is 1, the column content in the second row is 2, the column content in the third row is 3, and so on, and the column type is string or integer, the column will be identified as a voucher number, and the cumulative weight value will be increased by 5. After identifying the content, the weights are added. If the cumulative weight value is greater than 15, it is identified as a voucher header table. Continue to identify the remaining tables to obtain the corresponding weight identification values.

[0106] By identifying the tables in the database, the tables identified as voucher header tables are sorted according to weights, and the database table with the largest weight identification value is identified as the voucher header table.

[0107] For specific identification and matching of other voucher attributes, voucher entries, voucher auxiliary accounting, and voucher cash flow, refer to the description of the voucher header to ensure effective identification and matching.

[0108] When the business object is cost and the attribute is purchase cost, if the business data identification condition is that the number of data records is less than 10, then exclude, that is, stop comparing the current database table. The model table matching information in the model description information includes table name synonyms and the number of primary keys. Model column matching information includes purchase order number, purchase product name, purchase quantity, purchase unit, purchase amount, purchase time, and purchaser. For the specific details of the identification strategy, identification weight system, and weight identification parameters in the business identification information, please refer to the description of the voucher header, so as to achieve the identification and matching of the corresponding database table of the purchase cost based on the characteristics of the purchase cost.

[0109] When the business object is cost and the attribute is labor cost, if the business data identification condition is that the number of data records is less than 500, then exclude, that is, stop comparing the current database table. The model table matching information in the model description information includes table name matching. Model column matching information includes personnel, salary, bonus, subsidy, three insurances and one fund amount, and payment date. For specific details about the identification strategy, identification weight system, and weight identification parameters in the business identification information, please refer to the description of the voucher header, so as to achieve identification and matching of the corresponding database table of human cost based on the characteristics of human cost.

[0110] When the business object is cost and the attributes are miscellaneous costs and losses, please refer to the description of procurement costs and labor costs for details, which will not be repeated here.

[0111] When the business object is a sales flow and the attribute is also a sales flow, if the business data identification condition is that the number of data records is less than 10,000, it is excluded, that is, the comparison of the current database table is stopped. The model table matching information in the model description information includes table name synonyms and the number of primary keys. The model column matching information includes sales order number, customer name, product type, product code, product name, quantity, unit price, execution price, payment method, and payment on schedule. For the specific situation of the identification strategy, identification weight system, and weight identification parameters in the business identification information, please refer to the description of the voucher header, so as to realize the identification and matching of the corresponding database table of the sales flow based on the characteristics of the sales flow.

[0112] In summary, a computer device is obtained, including a memory and a processor, wherein the memory stores a computer program, and is characterized in that the processor executes the steps of the above method when executing the computer program.

[0113] And, a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0114] In one embodiment of the present application, if implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the method described in each embodiment of the present invention.

[0115] Specifically, the memory and storage medium may be computer-readable storage media, for example, the computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

[0116] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for analyzing a tax-related database, characterized in that: The analysis method comprises: Construct a database business data benchmark model that is adapted to the business objects and a database identification engine for extracting database information, wherein: The constructed database business data benchmark model includes several business data sub-models representing business object attributes. For any business data sub-model, the business data sub-model includes business data identification conditions, business identification information, and model description information based on database available elements associated with the business object; For the tax-related database to be analyzed, the database recognition engine connects to and extracts database information from the connected tax-related database, identifies the extracted database information using the business data sub-model within the constructed database business data benchmark model, and determines the identification and matching information of any database table within the connected tax-related database after identification and matching, and outputs the identified identification and matching information of the database table as a business object attribute result; Integrating the outputted business object attribute result set to form a business object to be displayed after integration; For any business data sub-model, the model description information includes model table matching information and model column matching information; The business identification information includes an identification strategy for identifying data structure and content, an identification weight system for weight measurement when identifying based on model description information, and weight identification parameters for determining the measured weights, wherein: The recognition weight system includes information recognition matching weights of several matching information, wherein the matching information includes table name, table comment, number of primary keys contained in the table, table data volume, column name, number of columns, column comment and / or column content; The weight identification parameters include a weight identification matching threshold; When identifying and matching the extracted database information using the business data sub-model within the constructed database business data benchmark model, the extracted database information is compared one-to-one with the business data sub-model; During the comparison, a weighted identification value of the extracted database information is determined based on an identification weight system. When the weighted identification value matches a weighted identification threshold, identification matching information of any database table in the connected tax-related database is determined; The identified matching information of the determined database table includes the matched business data sub-model, the identified database table name, and the identified database table column set.

2. The tax-related database analysis method according to claim 1, characterized in that: When using the database recognition engine to extract database information from the tax-related database to be analyzed, it is first connected to the tax-related database to be analyzed, and after the connection, information is extracted from any database table in the tax-related database, wherein: After extracting the tax-related database information, database table extraction information is obtained, and identification and matching is performed based on the database table extraction information and the business data sub-model within the constructed database business data benchmark model; The database table extraction information includes basic information of the tax-related database, table information in the tax-related database, and view information of the tax-related database.

3. The tax-related database analysis method according to claim 2, characterized in that: When the database identification engine is connected to the tax-related database, the connection input information input to the database identification engine includes connection string information and the type of the tax-related database to be connected.

4. The tax-related database analysis method according to any one of claims 2 to 3, characterized in that: The business objects include vouchers, costs and / or sales flow, wherein: When the business object is a voucher, the integrated business objects displayed include the voucher header, voucher entry, voucher auxiliary accounting, and / or voucher cash flow; When the business object is cost, the integrated business objects include procurement cost, labor cost, miscellaneous cost and / or loss; When the business object is sales flow, the integrated business objects displayed include sales flow.

5. The tax-related database analysis method according to claim 4 is characterized in that: For any business object, when identifying and matching the corresponding business object attributes with the voucher header based on the business data sub-model, the method for identifying and matching the business object attributes includes: Determine the amount of data records corresponding to the database information. If the determined amount of data records meets the business data identification condition, proceed to the step of determining the weight identification value. Otherwise, exit the step of identifying and matching the business object attributes. When determining the weight identification value of the database information, the database information is compared with the model table matching information and the model column matching information in the model description information respectively, and the corresponding weight accumulation value is determined based on the constructed identification weight system in the comparison to select the required weight accumulation value as the weight identification value.

6. The tax-related database analysis method according to claim 5, characterized in that: When the business object is a voucher, when identifying and matching the voucher header of the voucher, the business data sub-model used to identify and match the voucher header, the model table matching information in the business data sub-model includes table name synonyms and the number of primary keys included; The model column matching information in the business data sub-model includes the account book code, period, voucher word, voucher number, document creator, and document creation date; The weight identification values ​​corresponding to multiple database tables in the database information are sorted, and the database table corresponding to the largest weight identification value that is greater than the weight identification matching threshold is selected as the credential header table.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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