Data processing method and device for tax database, equipment and medium

Abstract

The invention discloses a data processing method and device for a tax database, equipment and a medium, and relates to the technical field of data processing. The method is applied to a tax auditing system and comprises the steps that a to-be-audited table structure design document is acquired, and structured design elements are extracted through a large model analysis engine; based on a pre-constructed standard specification knowledge base and a pre-constructed auditing scheme knowledge base, auditing and judging the structured design elements through a rule inference engine, and generating and outputting an auditing report according to an auditing and judging result; wherein the standard specification knowledge base stores tax auditing standards in a vectorization manner and is associated with dimension tags; the auditing scheme knowledge base contains auditing schemes of different scenes with reference specifications and priorities; during auditing, multiple specifications at the same auditing point are compared according to priority vectors, a semantic model is called to analyze conflicts, and reasonable but non-compliant conflicts and compliant but non-reasonable conflicts are recognized. According to the technical scheme, the complex problems of multiple standards, multiple versions, semantic understanding and the like in the tax industry can be solved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data processing method, apparatus, equipment and medium for a tax database. Background Technology

[0002] As the construction and upgrading of tax industry information systems continue to advance, the requirements for standardization and normalization of database model table design are becoming increasingly stringent, specifically in four aspects: First, it must conform to industry-specific construction standards. For example, the Golden Tax project must comply with the Golden Tax Phase IV model construction standards and complete the review according to its specifications. Second, it must adapt to the construction standards of different systems, such as transactional and analytical systems, and support the application of multiple standards and versions of priority combinations. At the same time, provincial projects must match their own specifications while adhering to the overall standards of Golden Tax Phase IV. Third, there is a semantic pollution problem of "compliant but unreasonable" tables and fields that are compliant in form but incorrect in purpose. Fourth, there are contradictory situations where the design intent is reasonable but is judged as "non-compliant" due to a lack of standard support.

[0003] In response to the aforementioned review requirements, the main technical solutions for reviewing database model tables in the tax industry currently fall into two categories: purely manual review and review assisted by a single standard tool. The former relies on the experience of the approvers, while the latter can only achieve partial review functions for specific scenarios and fixed-format models. However, these solutions have significant drawbacks: purely manual review requires high business response efficiency and a large pool of professional talent, and suffers from low efficiency, high costs, and a tendency to overlook details; the model of multiple teams developing their own tools for multiple scenarios not only results in complex tool usage and an inability to meet complete review needs, but also makes it difficult to adapt to the complex review scenarios in the tax industry with multiple standards, versions, and standard priorities; furthermore, the problem of "reasonable but not compliant" issues caused by untimely updates to standards and specifications is difficult to resolve, hindering system updates and releases.

[0004] Therefore, how to provide a solution to the above-mentioned technical problems is a problem that needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a data processing method, apparatus, device, and medium for a tax database. By organizing discrete standard specifications through multi-dimensional tags and configuring an audit scheme with priority logic, the system can not only perform formal compliance checks during audits but also identify two deeper issues—"unreasonable compliance" and "reasonable non-compliance"—through semantic analysis. This is particularly suitable for complex industry scenarios such as taxation, which have multi-level, multi-version, and strongly compliant requirements, and where standards are dynamically evolving. The specific solution is as follows: In a first aspect, this application discloses a data processing method for a tax database, applied to a tax audit system, wherein the tax audit system includes a standard specification knowledge base, an audit scheme knowledge base, a large model parsing engine, and a rule reasoning engine, and the method includes: Obtain the table structure design document to be reviewed from the tax database, and use the large model parsing engine to parse the table structure design document to extract structured design elements; Based on the pre-built standard specification knowledge base and the audit scheme knowledge base, the structured design elements are audited and judged by the rule reasoning engine, and an audit report is generated and output based on the audit judgment results. The standard specification knowledge base stores multiple standard specifications in the tax field used for reviewing the table structure design documents in a vectorized form, and uses structured indexing technology to associate each standard specification with at least one dimension label used to identify its applicable scenarios; the review scheme knowledge base stores review schemes adapted to different tax business scenarios based on template definitions, including at least one referenced standard specification, and application priority information configured for each standard specification when referencing multiple standard specifications; The review judgment includes: for the same review point in the structured design elements, when the review plan references multiple standards and specifications, performing vectorized comparisons using each standard and specification sequentially according to the application priority information; and... A semantic similarity calculation model is invoked to perform semantic analysis on the review points in order to identify semantic rationality conflicts. Specifically, if the review point conforms to the standard specification and the vector distance between the actual semantics and the expected semantics of the standard specification exceeds a preset threshold, it is determined to be a first type of conflict. If the review point does not conform to the standard specification and the actual semantics meet the preset business rationality conditions, it is determined to be a second type of conflict.

[0006] Optionally, the step of obtaining the table structure design document to be reviewed from the tax database and using the large model parsing engine to parse the table structure design document to extract structured design elements includes: Obtain table structure design documents in different formats from the tax database, and use a large model based on natural language processing and syntax analysis to extract structured design elements, including table names, field names, data types, and annotation semantics, from the table structure design documents. The extracted structured design elements are standardized and format noise is eliminated.

[0007] Optionally, the standard specification knowledge base is constructed, including: Collect and structure standard specifications for table structure design documents from multiple sources in the tax field; the standard specifications include table-level standard specifications and field-level standard specifications; The standard specifications are cleaned and normalized, and structured indexing technology is used to associate and store the standard specifications with preset dimension tags; the dimension tags include project type, system type, database type and version number; The categorized standard specifications are vectorized and stored in a knowledge base to obtain a standard specification knowledge base that supports semantic-based matching and retrieval.

[0008] Optionally, the audit scheme knowledge base is constructed, including: Provide tax rule configuration templates for receiving user input to define audit schemes that adapt to different tax business scenarios; The tax rule configuration template receives references to at least one standard specification selected from the standard specification knowledge base; When multiple standard specifications are referenced, the application priority information configured for the multiple standard specifications is received through the tax rule configuration template; The defined audit schemes are vectorized and stored in a knowledge base to obtain an audit scheme knowledge base that supports scheme reuse and version control; wherein, the audit schemes include: single-specification audit schemes that only reference a single standard specification, and multi-specification audit schemes that reference multiple standard specifications and define the application priority information.

[0009] Optionally, for the same review point in the structured design elements, when the review scheme references multiple standards and specifications, the vectorized comparison is performed sequentially using each standard and specification according to the application priority information, including: If the audit point conforms to the standard specification corresponding to the current priority order in the application priority information, then the audit point is deemed compliant and the comparison with the standard specification corresponding to the subsequent priority order is stopped; If the audit point does not conform to the standard specification corresponding to the current priority order in the application priority information, then it continues to be compared with the standard specification corresponding to the next priority order until the standard specification corresponding to all priority orders is compared. The compliance of the audit point is then determined based on the comparison results.

[0010] Optionally, the step of calling the semantic similarity calculation model to perform semantic analysis on the review points to identify semantic rationality conflicts includes: For the first audit point that is determined to conform to the standard specification in the comparison results of the vectorized comparison, the actual semantics of the first audit point in the context are extracted through semantic analysis; Calculate the vector distance between the actual semantics and the expected semantics of the corresponding standard specification in the standard specification knowledge base; When the vector distance exceeds a preset threshold, it is determined that there is a first type of conflict, and the problem is identified and correction suggestions are provided in the audit report.

[0011] Optionally, the step of calling the semantic similarity calculation model to perform semantic analysis on the review points to identify semantic rationality conflicts includes: For the second audit point determined to be inconsistent with the standard specification in the comparison results of the vectorized comparison, a rationality analysis is performed on the second audit point; the rationality analysis includes at least one judgment logic, including: judging whether the naming of the second audit point conforms to the preset naming logic, judging whether the parameters of the second audit point are suitable for the scale of the associated business data, judging whether the contextual logic relationship of the second audit point is consistent, and judging whether there are design elements in the database that can be replaced and reused by the second audit point. When the structured design element is determined to be reasonable based on the rationality analysis, the existence of the second type of conflict is determined, and a supplementary proposal for standard specifications to be approved is automatically generated and added to the review report. After the proposed amendments to the standard specifications are approved, the standard specifications knowledge base is updated according to the proposed amendments.

[0012] Secondly, this application discloses a data processing device for a tax database, applied to a tax audit system. The tax audit system includes a standard specification knowledge base, an audit scheme knowledge base, a large model parsing engine, and a rule reasoning engine. The device includes: The structured parsing module is used to obtain the table structure design document to be reviewed in the tax database, and to use the large model parsing engine to parse the table structure design document to extract structured design elements. The review and judgment module is used to review and judge the structured design elements based on the pre-built standard specification knowledge base and the review scheme knowledge base, through the rule reasoning engine, and generate and output a review report based on the review and judgment results; The standard specification knowledge base stores multiple standard specifications in the tax field used for reviewing the table structure design documents in a vectorized form, and uses structured indexing technology to associate each standard specification with at least one dimension label used to identify its applicable scenarios; the review scheme knowledge base stores review schemes adapted to different tax business scenarios based on template definitions, including at least one referenced standard specification, and application priority information configured for each standard specification when referencing multiple standard specifications; The review judgment includes: for the same review point in the structured design elements, when the review plan references multiple standards and specifications, performing vectorized comparisons using each standard and specification sequentially according to the application priority information; and... A semantic similarity calculation model is invoked to perform semantic analysis on the review points in order to identify semantic rationality conflicts. Specifically, if the review point conforms to the standard specification and the vector distance between the actual semantics and the expected semantics of the standard specification exceeds a preset threshold, it is determined to be a first type of conflict. If the review point does not conform to the standard specification and the actual semantics meet the preset business rationality conditions, it is determined to be a second type of conflict.

[0013] Thirdly, this application discloses an electronic device comprising a processor and a memory; wherein the memory is used to store a computer program, which is loaded and executed by the processor to implement the data processing method for the tax database as described above.

[0014] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the data processing method for the tax database as described above.

[0015] The beneficial effects of this application are as follows: By constructing a multi-dimensional, tagged standard specification knowledge base, the system can automatically and intelligently handle complex application scenarios involving multiple projects, versions, and types of standards in the tax industry, overcoming the shortcomings of traditional manual review or single-standard tools, such as low efficiency, easy omissions, and inability to handle standard conflicts. By comparing various standard specifications sequentially based on priority, the system systematically solves the decision-making problem when multiple standards overlap or conflict at the same review point. This overcomes the shortcomings of single-standard review tools, such as rigidity and inability to adapt to the coexistence of primary and secondary standards unique to the tax industry, making the review results more in line with actual management requirements. The solution identifies and handles semantic reasonableness conflicts, not only conducting formal compliance checks but also proactively discovering two types of deep-seated problems: compliant but unreasonable first-type conflicts and reasonable but non-compliant second-type conflicts. This effectively prevents design errors or obstacles to business innovation caused by mechanically applying rules, upgrading the review from "formal compliance" to "semantic correctness" and "business reasonableness," greatly enhancing the actual value of the review work and user trust.

[0016] Furthermore, the data processing apparatus, equipment, and storage medium for a tax database provided in this application correspond to the aforementioned data processing method for a tax database and have the same effect. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 This is a flowchart of a data processing method for a tax database disclosed in this application; Figure 2 A schematic diagram of a design file (C001.TXT) to be reviewed for this application; Figure 3 A schematic diagram of a field standard specification A001 provided for this application; Figure 4 A schematic diagram of a field standard specification A002 provided for this application; Figure 5 A schematic diagram of a table naming convention A003 provided for this application; Figure 6 A schematic diagram of scheme A004, a single specification combination (A001+A003) provided for this application; Figure 7 A schematic diagram of scheme A005 for a single specification combination (A002+A003) provided for this application; Figure 8 A schematic diagram of the A006 scheme, which is a combination of multiple specifications (A001 first, then A002) + A003, provided for this application; Figure 9 A schematic diagram of the A007 scheme, which is a combination of multiple specifications (A002 first, then A001) + A003 provided for this application; Figure 10 A schematic diagram of the conclusion of a standard audit report provided for this application; Figure 11 A schematic diagram of an automatically generated A008 proposal awaiting approval provided for this application; Figure 12 A schematic diagram of the table name specification review result report provided for this application; Figure 13 A schematic diagram of an audit report for a field specification provided in this application; Figure 14 A schematic diagram of an intelligent review technology architecture provided for this application; Figure 15 This is a schematic diagram of the data processing device structure for a tax database disclosed in this application; Figure 16 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0020] Currently, the tax industry system is complex. For example, the "Golden Tax Project" has different versions and types (such as analytical and transactional), and database table designs must comply with multiple standards. However, it currently relies mainly on manual review or simple tools. Purely manual review requires a high level of professional expertise in the tax industry to respond promptly to business changes, and this method is inefficient, costly, and prone to omissions. Different teams using different tools to assist in review in different scenarios can only handle a single standard, but tax scenarios require the application of multiple standards simultaneously, and often encounter situations that are "compliant but unreasonable" or "reasonable but non-compliant."

[0021] To address this, this application provides a data processing solution for a tax database, resolving the problem of rigid single-standard review and solving industry challenges such as semantic blind spots and outdated standards. It achieves a deep semantic understanding adaptation mechanism tailored to tax scenarios, a leap from passive compliance checks to proactive semantic governance and automatic evolution of standards and specifications, enhancing the system's practicality, intelligence, and sustainable development capabilities in complex tax environments.

[0022] This invention discloses a data processing method for a tax database, applied to a tax audit system. The tax audit system includes a standard specification knowledge base, an audit scheme knowledge base, a large model parsing engine, and a rule reasoning engine. (See also...) Figure 1 As shown, the method includes: Step S11: Obtain the table structure design document to be reviewed from the tax database, and use the large model parsing engine to parse the table structure design document to extract structured design elements.

[0023] The process involves acquiring table structure design documents in various formats from a tax database, specifically, design files that record table structures, field attributes, and inter-table relationships within a tax business scenario. These include SQL text files from different databases such as Oracle, HIVE, and MySQL, as well as heterogeneous files like Excel and Word documents. A large model parsing engine is used to perform structured parsing on the imported table structure design documents, extracting structured design elements including table names, field names, data types, and semantic annotations. For example, importable... Figure 2The C001.TXT SQL file shown is parsed.

[0024] The large-scale model parsing engine is a pre-trained model based on natural language processing and SQL syntax analysis, used for semantic understanding and structure extraction of unstructured design documents. After parsing the imported table structure design documents to obtain structured design elements through the large model, the extracted structured design elements are standardized, unified, and format noise and other useless information are eliminated, breaking the strong dependence on structured data.

[0025] As can be seen, when faced with real-world design results in various formats such as SQL scripts, Word documents, and Excel spreadsheets, large-scale modeling technology can achieve automatic structured parsing and eliminate format noise. It can also complete semantic parsing and standardization conversion of unstructured input, resolving format dependencies and overcoming the difficulty of parsing real-world design documents.

[0026] Step S12: Based on the pre-built standard specification knowledge base and the audit scheme knowledge base, the structured design elements are audited and judged through the rule reasoning engine, and an audit report is generated and output according to the audit judgment result.

[0027] In this embodiment, the review system is pre-configured with a standard specification knowledge base and a review scheme knowledge base. Based on a rule-based reasoning engine, the target review scheme in the review scheme knowledge base is invoked, and the structured design elements are compared with the corresponding standard specifications retrieved from the standard specification knowledge base to perform review judgments on the structured design elements. The rule-based reasoning engine is an automated reasoning module that supports conditional judgment, priority matching, and conflict resolution, capable of driving multi-level standard comparison processes. In this way, a large model is first used to process unstructured text, improving the fault tolerance for non-standard SQL; then, the rule engine is used to ensure the determinism and traceability of the review logic, compensating for the illusion of a large model.

[0028] The following section explains the construction process of the standards and specifications knowledge base.

[0029] The standard specification knowledge base in this embodiment is a searchable database that stores various standard specifications (project-level, system type-level, and version number-level) used in the tax field for auditing form structure design documents in a structured and vectorized manner. Simultaneously, the standard specifications are organized using multi-dimensional tags, and structured indexing technology is employed to associate each standard specification with at least one dimension tag used to identify its applicable scenarios.

[0030] Specifically, the process of constructing the aforementioned standard specification knowledge base includes the following steps: Step 1: Collect and structure standard specifications for table structure design documents from multiple sources in the tax field; the standard specifications include table-level standard specifications and field-level standard specifications; Step 2: Clean and normalize the standard specifications, and use structured indexing technology to associate and store the standard specifications with preset dimension tags; the dimension tags include project type, system type, database type and version number; Step 3: Vectorize the categorized standard specifications and store them in a knowledge base to obtain a standard specification knowledge base that supports semantic-based matching and retrieval.

[0031] First, the definition information of table-level and field-level standard specifications required for review is collected. Then, all standard specifications are cleaned, normalized, and vectorized before being stored in a standard specification knowledge base, supporting fuzzy matching, semantic matching, version differentiation, and fast retrieval. This step defines and configures a series of individual standard specifications, forming a standard specification system knowledge base, which serves as the foundation for subsequent flexible combinations and configurations of review solutions for specific scenarios.

[0032] Standard specifications are organized using multi-dimensional tags. These tags include: [Project Type], [System Type], [Database Type], and [Version Number], for example: Golden Tax Phase IV - Analytical - Oracle - V1.1. In one feasible implementation, data table field specifications are categorized by database type (e.g., analytical Oracle database specifications, transactional MySQL database specifications); by actual project (e.g., analytical State Administration of Taxation project specifications, analytical provincial taxation project specifications); and by actual project region (e.g., analytical Shanghai project specifications, analytical Sichuan project specifications). Under the same circumstances, different requirements at different times, such as different field lengths or field types, will iterate to produce different version numbers of standard specifications applicable to different scenarios. Each standard specification has its own knowledge base created, such as... Figure 3 Data element field standard specification A001 is a specification for analytical table fields. Figure 4 The A002 shown is the field specification for the application transaction type table.

[0033] In another feasible implementation, the table naming standard specification is also categorized according to applicable scenarios: transactional and analytical table naming standards; analytical national bureau projects and analytical provincial bureau projects naming standards; and different versions of the same standard specification from different periods, etc., to create separate knowledge bases. For example... Figure 5 The example shown, A003, illustrates a scenario for creating a knowledge base using table naming conventions.

[0034] In summary, by supporting multi-dimensional tagging of the standard and specification tax knowledge base by project (such as Golden Tax Phase IV), system type (analytic / transactional), database type (Oracle / MySQL / Hive), and version number (V1.0 / V2.1), it supports arbitrary dimension combination retrieval and intelligent recommendation, and can realize version management and cross-project reuse of standard specifications, as well as update mechanisms.

[0035] The following describes the process of building the audit scheme knowledge base.

[0036] In this embodiment, the audit scheme knowledge base stores at least one audit scheme adapted to different tax business scenarios based on template definitions, including at least one referenced standard specification, and application priority information configured for each standard specification when referencing multiple standard specifications; therefore, the audit scheme is a composite audit strategy template composed of multiple standard specifications combined according to priority, supporting single specification and multi-specification nested configuration.

[0037] Specifically, the process of building the knowledge base for the audit scheme includes the following steps: Step 1: Provide a tax rule configuration template for receiving user input to define an audit scheme that adapts to different tax business scenarios; Step 2: Receive references to at least one standard specification selected from the standard specification knowledge base via the tax rule configuration template; Step 3: When referencing multiple standard specifications, receive the application priority information configured for the multiple standard specifications through the tax rule configuration template; Step 4: Vectorize the defined audit scheme and store it in the knowledge base to obtain an audit scheme knowledge base that supports scheme reuse and version control.

[0038] This embodiment defines standard specification review schemes for various types and versions of commonly used solutions in application scenarios. Common scenarios use a single specification scheme, while special scenarios require multiple specification schemes with overlapping priorities for review. When using these schemes, users can flexibly modify them according to scenario requirements using Excel templates (no programming required).

[0039] A single-specification audit scheme references only a single standard specification, while a multi-specification scheme can be arbitrarily combined and configured to define and prioritize standards to form an audit scheme that meets the applicable standard specification requirements for various scenarios. In a multi-specification scheme, each scheme includes the referenced standard specification and the priority of overlapping audit standards (e.g., prioritizing analytical specifications and then transactional supplements). Both schemes are simultaneously stored in the audit scheme knowledge base, providing support for reuse, inheritance, and version control.

[0040] For example, such as Figure 6The example shown is audit scheme A004, which only references the A001 analytical table field specifications and the A003 table naming specifications; or as follows: Figure 7 The example shown is audit scheme A005, which only references the A002 analytical table field specifications and the A003 table naming specifications; or as follows: Figure 8 The following is an audit scheme A006, which prioritizes A001 and then applies A002 table field specifications and A003 table naming specifications if they are not met; or as follows: Figure 9 The diagram shows how to create an audit scheme knowledge base by prioritizing audit scheme A002, then applying the field specifications of table A001 and the naming specifications of table A003 if the requirements are not met. Figure 8 The A006 scheme shown (A001 first, then A002) is applicable in tax scenarios to the audit scheme requirement of "the State Administration of Taxation's standards take precedence, with provincial bureau standards as a fallback." This demonstrates the flexible management methods and capabilities that allow for configuring audit scheme definitions using Excel templates, expressing them as vectors, and storing them in the audit scheme library, without requiring programmer intervention.

[0041] Furthermore, this embodiment describes the review and judgment process.

[0042] In one specific implementation, for review points within the parsed structured design elements, if a review point has only one standard specification, compliance is directly determined by matching and comparing against the relevant standard specification. If multiple standard specifications are referenced in the review scheme for the same review point, each standard specification is used sequentially for vectorized comparison based on its application priority information in the review scheme. In this way, the review scheme can support overlapping reviews of at least two different standard specifications, achieving intelligent decision-making with primary standard priority, secondary standard fallback, and mandatory standard coverage.

[0043] Specifically, the rule-based reasoning engine compares the audit points with the corresponding standard and specification items in vectorized form according to application priority information, and outputs a preliminary compliance judgment based on the comparison results. The process of intelligently comparing and auditing multiple standards and specifications based on overlapping application priority information includes: If the audit point conforms to the standard specification corresponding to the current priority order in the application priority information, then the audit point is deemed compliant and the comparison with the standard specification corresponding to the subsequent priority order is stopped; If the audit point does not conform to the standard specification corresponding to the current priority order in the application priority information, then it continues to be compared with the standard specification corresponding to the next priority order until the standard specification corresponding to all priority orders is compared. The compliance of the audit point is then determined based on the comparison results.

[0044] In this embodiment, based on application priority information, the standard specifications applied first or those ranked higher in the priority order are designated as high priority. If any high-priority standard specification is met, it is determined to be compliant, and the comparison with subsequent low-priority standard specifications is terminated; if it does not meet any high-priority standard specification, the comparison with subsequent low-priority standard specifications continues, and the determination is based on the comparison results.

[0045] It should be noted that when multiple standards (such as the Golden Tax System Phase IV standard A001 and the provincial tax bureau standard A002) conflict at the same audit point, the system executes intelligent decisions based on a priority conflict resolution algorithm. For example, based on the principle of "policy rigidity priority" (e.g., the State Taxation Administration standard has greater weight than the provincial tax bureau standard), the implementation plan can be automatically optimized using a weighted voting algorithm, rather than simply manually defining the execution order. For example... Figure 8 , Figure 9 Examples of multiple specification combinations in related documents.

[0046] In another specific implementation, for the formal compliance review portion of the aforementioned preliminary compliance assessment, a semantic similarity calculation model is invoked to perform semantic analysis on the review points to identify semantic reasonableness conflicts. Semantic reasonableness conflicts include a first type of conflict and a second type of conflict. The first type of conflict corresponds to the "compliant but unreasonable" problem at the system operation level, where the design conforms to preset rules but has flaws and does not meet actual requirements. The second type of conflict corresponds to the "reasonable but non-compliant" problem where the design intent is reasonable and without conflict, but due to a lack of corresponding standard support, especially in the review of new business applications where the design is reasonable but the standards are not updated in a timely manner.

[0047] (1) For the first type of conflict, for the parts that are determined to comply with the standard specifications in the preliminary compliance assessment, a semantic reasonableness assessment is performed based on the collected business requirements or sample data. By calculating the vector distance between the actual semantic information of the fields extracted by the large model parsing engine and the expected semantic information defined in the standard specification entries, compliant but unreasonable conflicts are identified where the audit point complies with the standard specifications, but the vector distance between the actual semantics and the expected semantics of the standard specifications exceeds a preset threshold. Then, the problem is marked and correction suggestions are provided in the audit report.

[0048] As can be seen, through formal compliance auditing, the vector distance between the actual semantics of the audit point in the context and the expected semantics of the corresponding standard specification in the standard specification knowledge base is calculated. Based on semantic vector comparison, deviations between its actual use and the standard definition are identified, rather than relying on rule matching, thus achieving the handling of "compliant but unreasonable" cases. Through deep semantic analysis, the auditing focuses on issues such as incorrect use of standard data elements in table fields (e.g., taxpayer names being mistakenly stored as codes). This effectively prevents auditing loopholes of "semantic confusion under the guise of compliance" and has the capability to audit and handle misuse of standard data elements.

[0049] (2) For the second type of conflict, for the parts determined to be non-compliant with standards and specifications in the preliminary compliance assessment, a business rationality analysis is performed through a rule reasoning engine. The rationality analysis includes at least one of the following judgment logics: determining whether the naming of the second audit point conforms to the preset naming logic and analyzing naming standardization; determining whether the parameters of the second audit point are suitable for the scale of the associated business data, such as the rationality of field type length matching the business order of magnitude; determining whether the contextual logical relationship of the second audit point is consistent; and determining whether there are design elements in the database that can be reused as a substitute for the second audit point. If the audit point meets the preset rationality analysis conditions, it is identified as a second type of conflict (a reasonable but non-compliant conflict), and a standard supplementation process is triggered, generating a standard specification supplementation proposal to be approved. After approval, the standard knowledge base and associated audit scheme are automatically updated. This proactively triggers the version approval and update synchronization capability of the standard specification knowledge base, solving the industry problem of tax standards lagging behind business development.

[0050] Finally, based on the vectorized comparison results and the handling results of semantic reasonableness conflicts, an interpretable audit report is generated and output. The audit report includes at least the final compliance conclusion, the highest priority standard references that led to the non-compliance and modification suggestions, the compliance review results of all standards at each audit point in order of optimization level, and supplementary standard proposals generated for the second type of conflict. For example... Figure 10 The generated modification suggestions are shown below. Figure 11 The image shows the automatically generated A008 proposal awaiting approval.

[0051] As can be seen, this embodiment can output a four-dimensional interpretable report that includes audit conclusions, decision-making paths, modification suggestions, and standard evolution proposals. It upgrades the audit from simply assessing "compliance" to addressing "why it is compliant / non-compliant, how to improve, and whether the standard should be updated," providing a step-by-step audit basis chain and operational suggestions, thereby improving the credibility and operability of the audit results.

[0052] The beneficial effects of this application are as follows: By constructing a multi-dimensional, tagged standard specification knowledge base, the system can automatically and intelligently handle complex application scenarios involving multiple projects, versions, and types of standards in the tax industry, overcoming the shortcomings of traditional manual review or single-standard tools, such as low efficiency, easy omissions, and inability to handle standard conflicts. By comparing various standard specifications sequentially based on priority, the system systematically solves the decision-making problem when multiple standards overlap or conflict at the same review point. This overcomes the shortcomings of single-standard review tools, such as rigidity and inability to adapt to the coexistence of primary and secondary standards unique to the tax industry, making the review results more in line with actual management requirements. The solution identifies and handles semantic reasonableness conflicts, not only conducting formal compliance checks but also proactively discovering two types of deep-seated problems: compliant but unreasonable first-type conflicts and reasonable but non-compliant second-type conflicts. This effectively prevents design errors or obstacles to business innovation caused by mechanically applying rules, upgrading the review from "formal compliance" to "semantic correctness" and "business reasonableness," greatly enhancing the actual value of the review work and user trust.

[0053] Furthermore, an example will be used to illustrate the review process of database table structure design documents: 1) Define the data element names, Chinese names, and data types of the standard data elements in the database model tables using an Excel file (or by calling relevant system interfaces), and then transfer them to the knowledge base for subsequent use by larger models. For example, standard specification data element A001 and standard specification data element A002 are defined as follows: Figure 3 , Figure 4 As shown.

[0054] 2) Define the standard specifications, priorities, and priority descriptions for the applicable standard specifications of the database model tables using an Excel file. Select predefined review schemes or add / modify new review schemes as needed. Vectorize the review schemes with different priorities for different scenarios and save them to the knowledge base for easy access in subsequent large models. For example, the definitions of schemes applicable to single specification combinations A004 and A005 are as follows: Figure 6 , Figure 7 As shown, the definitions of the multi-specification combination schemes A006 and A007 are as follows: Figure 8 , Figure 9 As shown.

[0055] 3) Define the naming conventions, data layering, naming rules, and data classification for the database model tables using an Excel file, and then transfer the data to a knowledge base for easy access by large models. For example, the naming conventions for the A003 database model table are defined as follows: Figure 5 As shown.

[0056] 4) A large-scale model analysis is performed on the SQL statements and other text of the database model tables to be reviewed, and standardized elements of the database model tables are parsed and organized for subsequent review and comparison. For example, the C001.TXT file contains the SQL statements for the newly created database model tables, such as... Figure 2 As shown.

[0057] 5) When configuring data according to standard specifications, conduct audits and comparisons, and output audit reports and process suggestions, including table name specification audit, field specification audit, audit conclusions, etc. For example... Figure 12 The image shown is the report on the review results of the table name specification. For example... Figure 13 The image shown is a partial report of the audit of table field specifications. For example... Figure 10 The image shown is the conclusion section of the database model audit report; as follows: Figure 11 The image shows the automatically generated A008 proposal awaiting approval.

[0058] As can be seen, this invention is flexible in configuration, easy to use, and highly practical. It can collect standard and specification systems through an Excel spreadsheet, flexibly configure multiple standard and specification priorities to form an audit plan, and intelligently audit table design documents in the form of SQL according to the audit plan. It is not just a mechanical rule execution check, but an intelligent audit officer that can understand intent, weigh priorities, and promote the evolution of standards.

[0059] like Figure 14 The diagram shows a three-layer intelligent auditing technology architecture based on the aforementioned implementation process: a standard knowledge layer, which builds a standardized knowledge base covering field specifications, naming specifications, performance specifications, etc.; a strategy configuration layer, which defines auditing schemes that combine multiple specification priorities; and an execution application layer, which completes the parsing of the model to be audited, rule comparison, and result generation.

[0060] After the process starts, the initialization of standards and specifications (associated with the standard knowledge layer) is performed first: First, the pre-defined tax database standard specification scheme definition and configuration information are loaded, completing the initial preparation of the audit basis. Next, the application layer file processing stage is executed: The tax database model table files to be audited (such as SQL DDL statements and Excel table design documents) are imported into the system. The large model parsing engine performs semantic understanding and syntax parsing on the files, extracting structured elements such as table names, field names, field types, and foreign key relationships between tables, forming standardized audit input data. Next, the audit comparison stage is executed: If a single standard specification scheme is used, the extracted structured elements are directly compared based on the initialized standard specification to complete the basic compliance judgment; if a multi-standard specification scheme (associated with the strategy configuration layer) is used, priority overlap audit is performed: Structured elements are compared sequentially according to the pre-defined standard specification application priority order. If the higher-priority standard specification comparison passes, the corresponding audit point is deemed compliant. Then, the semantic conflict identification step (application layer) is executed: based on the compliance judgment, two types of semantic reasonableness conflicts are further identified: if the structured elements conform to the standard specifications in form, but the actual business semantics do not match the expected semantics of the standard specifications, it is judged as "compliant but unreasonable"; if the structured elements do not conform to the standard specifications in form, but the design meets the preset tax business reasonableness conditions, it is judged as "reasonable but non-compliant". Finally, the above audit comparison results and semantic conflict identification results are integrated to generate and output an audit report containing compliant items, non-compliant items, semantic conflict items and corresponding optimization suggestions, and the process ends.

[0061] Accordingly, this application also discloses a data processing device for a tax database, see [link to relevant documentation]. Figure 15 As shown, this device is applied to a tax audit system, which includes a standard specification knowledge base, an audit scheme knowledge base, a large model parsing engine, and a rule reasoning engine. The device includes: The structured parsing module 11 is used to obtain the table structure design document to be reviewed in the tax database, and use the large model parsing engine to parse the table structure design document to extract structured design elements. The review and judgment module 12 is used to review and judge the structured design elements based on the pre-built standard specification knowledge base and the review scheme knowledge base, through the rule reasoning engine, and generate and output a review report based on the review and judgment results. The standard specification knowledge base stores multiple standard specifications in the tax field used for reviewing the table structure design documents in a vectorized form, and uses structured indexing technology to associate each standard specification with at least one dimension label used to identify its applicable scenarios; the review scheme knowledge base stores review schemes adapted to different tax business scenarios based on template definitions, including at least one referenced standard specification, and application priority information configured for each standard specification when referencing multiple standard specifications; The review judgment includes: for the same review point in the structured design elements, when the review plan references multiple standards and specifications, performing vectorized comparisons using each standard and specification sequentially according to the application priority information; and... A semantic similarity calculation model is invoked to perform semantic analysis on the review points in order to identify semantic rationality conflicts. Specifically, if the review point conforms to the standard specification and the vector distance between the actual semantics and the expected semantics of the standard specification exceeds a preset threshold, it is determined to be a first type of conflict. If the review point does not conform to the standard specification and the actual semantics meet the preset business rationality conditions, it is determined to be a second type of conflict.

[0062] For more detailed information on the working process of each of the above modules, please refer to the relevant content disclosed in the foregoing embodiments, which will not be repeated here.

[0063] Therefore, the above-described solution in this embodiment, by constructing a multi-dimensional, tagged standard specification knowledge base, enables the system to automatically and intelligently handle complex application scenarios involving multiple projects, versions, and types of standards in the tax industry. This overcomes the shortcomings of traditional manual review or single-standard tools, which are inefficient, prone to omissions, and unable to handle standard conflicts. By comparing various standard specifications sequentially based on priority, the system systematically solves the decision-making problem when multiple standards overlap or conflict at the same review point. This overcomes the shortcomings of single-standard review tools, which are rigid and unable to adapt to the coexistence of primary and secondary standards unique to the tax industry, making the review results more in line with actual management requirements. The solution identifies and handles semantic reasonableness conflicts, not only performing formal compliance checks but also proactively discovering two types of deeper problems: compliant but unreasonable first-type conflicts and reasonable but non-compliant second-type conflicts. This effectively prevents design errors or obstacles to business innovation caused by mechanically applying rules, upgrading the review from "formal compliance" to "semantic correctness" and "business reasonableness," greatly enhancing the actual value of the review work and user trust.

[0064] In one feasible implementation, the data processing apparatus for the tax database further includes: The standard and specification knowledge base construction module is used for: Collect and structure standard specifications for table structure design documents from multiple sources in the tax field; the standard specifications include table-level standard specifications and field-level standard specifications; The standard specifications are cleaned and normalized, and structured indexing technology is used to associate and store the standard specifications with preset dimension tags; the dimension tags include project type, system type, database type and version number; The categorized standard specifications are vectorized and stored in a knowledge base to obtain a standard specification knowledge base that supports semantic-based matching and retrieval.

[0065] The audit scheme knowledge base construction module is used for: Provide tax rule configuration templates for receiving user input to define audit schemes that adapt to different tax business scenarios; The tax rule configuration template receives references to at least one standard specification selected from the standard specification knowledge base; When multiple standard specifications are referenced, the application priority information configured for the multiple standard specifications is received through the tax rule configuration template; The defined audit schemes are vectorized and stored in a knowledge base to obtain an audit scheme knowledge base that supports scheme reuse and version control; wherein, the audit schemes include: single-specification audit schemes that only reference a single standard specification, and multi-specification audit schemes that reference multiple standard specifications and define the application priority information.

[0066] The multi-standard specification review and comparison module is used for: If the audit point conforms to the standard specification corresponding to the current priority order in the application priority information, then the audit point is deemed compliant and the comparison with the standard specification corresponding to the subsequent priority order is stopped; If the audit point does not conform to the standard specification corresponding to the current priority order in the application priority information, then it continues to be compared with the standard specification corresponding to the next priority order until the standard specification corresponding to all priority orders is compared. The compliance of the audit point is then determined based on the comparison results.

[0067] The first type of conflict identification module is used for: For the first audit point that is determined to conform to the standard specification in the comparison results of the vectorized comparison, the actual semantics of the first audit point in the context are extracted through semantic analysis; Calculate the vector distance between the actual semantics and the expected semantics of the corresponding standard specification in the standard specification knowledge base; When the vector distance exceeds a preset threshold, it is determined that there is a first type of conflict, and the problem is identified and correction suggestions are provided in the audit report.

[0068] The second type of conflict identification module is used for: For the second audit point determined to be inconsistent with the standard specification in the comparison results of the vectorized comparison, a rationality analysis is performed on the second audit point; the rationality analysis includes at least one judgment logic, including: judging whether the naming of the second audit point conforms to the preset naming logic, judging whether the parameters of the second audit point are suitable for the scale of the associated business data, judging whether the contextual logic relationship of the second audit point is consistent, and judging whether there are design elements in the database that can be replaced and reused by the second audit point. When the structured design element is determined to be reasonable based on the rationality analysis, the existence of the second type of conflict is determined, and a supplementary proposal for standard specifications to be approved is automatically generated and added to the review report. After the proposed amendments to the standard specifications are approved, the standard specifications knowledge base is updated according to the proposed amendments.

[0069] Furthermore, embodiments of this application also disclose an electronic device, Figure 16 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0070] Figure 16 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the data processing method for the tax database disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be a computer.

[0071] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0072] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk, or optical disk, etc. The resources stored on it can include an operating system 221, computer programs 222, and data 223, etc. The data 223 can include various types of data. The storage method can be temporary storage or permanent storage.

[0073] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the data processing method for the tax database executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0074] Furthermore, this application also discloses a computer-readable storage medium, which includes random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, magnetic disks, optical disks, or any other form of storage medium known in the art. The computer program, when executed by a processor, implements the aforementioned data processing method for the tax database. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0075] Furthermore, embodiments of this application also provide a computer program product, including a computer program / instructions, which, when executed by a processor, implements any of the above-described methods for processing data in the tax database.

[0076] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0077] The steps of the data processing methods or algorithms for tax databases described in conjunction with the embodiments disclosed herein can be implemented directly using hardware, software modules executed by a processor, or a combination of both. The software modules can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art.

[0078] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0079] The above provides a detailed description of the data processing method, apparatus, device, and medium for a tax database provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only intended to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A data processing method for a tax database, characterized in that, Applied to a tax audit system, which includes a standard specification knowledge base, an audit scheme knowledge base, a large model parsing engine, and a rule reasoning engine, the method includes: Obtain the table structure design document to be reviewed from the tax database, and use the large model parsing engine to parse the table structure design document to extract structured design elements; Based on the pre-built standard specification knowledge base and the audit scheme knowledge base, the structured design elements are audited and judged by the rule reasoning engine, and an audit report is generated and output based on the audit judgment results. The standard specification knowledge base stores multiple standard specifications in the tax field used for reviewing the table structure design documents in a vectorized form, and uses structured indexing technology to associate each standard specification with at least one dimension label used to identify its applicable scenarios; the review scheme knowledge base stores review schemes adapted to different tax business scenarios based on template definitions, including at least one referenced standard specification, and application priority information configured for each standard specification when referencing multiple standard specifications; The review judgment includes: for the same review point in the structured design elements, when the review plan references multiple standards and specifications, performing vectorized comparisons using each standard and specification sequentially according to the application priority information; and... A semantic similarity calculation model is invoked to perform semantic analysis on the review points in order to identify semantic rationality conflicts. Specifically, if the review point conforms to the standard specification and the vector distance between the actual semantics and the expected semantics of the standard specification exceeds a preset threshold, it is determined to be a first type of conflict. If the review point does not conform to the standard specification and the actual semantics meet the preset business rationality conditions, it is determined to be a second type of conflict.

2. The data processing method for the tax database according to claim 1, characterized in that, The process involves obtaining the table structure design document from the tax database that is awaiting review, and then using the large model parsing engine to parse the table structure design document to extract structured design elements, including: Obtain table structure design documents in different formats from the tax database, and use a large model based on natural language processing and syntax analysis to extract structured design elements, including table names, field names, data types, and annotation semantics, from the table structure design documents. The extracted structured design elements are standardized and format noise is eliminated.

3. The data processing method for the tax database according to claim 1, characterized in that, Constructing the aforementioned standard specification knowledge base includes: Collect and structure standard specifications for table structure design documents from multiple sources in the tax field; the standard specifications include table-level standard specifications and field-level standard specifications; The standard specifications are cleaned and normalized, and structured indexing technology is used to associate and store the standard specifications with preset dimension tags; the dimension tags include project type, system type, database type and version number; The categorized standard specifications are vectorized and stored in a knowledge base to obtain a standard specification knowledge base that supports semantic-based matching and retrieval.

4. The data processing method for the tax database according to claim 1, characterized in that, Constructing the knowledge base for the aforementioned audit scheme includes: Provide tax rule configuration templates for receiving user input to define audit schemes that adapt to different tax business scenarios; The tax rule configuration template receives references to at least one standard specification selected from the standard specification knowledge base; When multiple standard specifications are referenced, the application priority information configured for the multiple standard specifications is received through the tax rule configuration template; The defined audit schemes are vectorized and stored in a knowledge base to obtain an audit scheme knowledge base that supports scheme reuse and version control; wherein, the audit schemes include: single-specification audit schemes that only reference a single standard specification, and multi-specification audit schemes that reference multiple standard specifications and define the application priority information.

5. The data processing method for the tax database according to claim 1, characterized in that, When multiple standards and specifications are referenced in the audit plan for the same audit point in the structured design elements, the vectorized comparison is performed sequentially using each standard and specification according to the application priority information, including: If the audit point conforms to the standard specification corresponding to the current priority order in the application priority information, then the audit point is deemed compliant and the comparison with the standard specification corresponding to the subsequent priority order is stopped; If the audit point does not conform to the standard specification corresponding to the current priority order in the application priority information, then it continues to be compared with the standard specification corresponding to the next priority order until the standard specification corresponding to all priority orders is compared. The compliance of the audit point is then determined based on the comparison results.

6. The data processing method for a tax database according to any one of claims 1 to 5, characterized in that, The step of calling the semantic similarity calculation model to perform semantic analysis on the review points to identify semantic rationality conflicts includes: For the first audit point that is determined to conform to the standard specification in the comparison results of the vectorized comparison, the actual semantics of the first audit point in the context are extracted through semantic analysis; Calculate the vector distance between the actual semantics and the expected semantics of the corresponding standard specification in the standard specification knowledge base; When the vector distance exceeds a preset threshold, it is determined that there is a first type of conflict, and the problem is identified and correction suggestions are provided in the audit report.

7. The data processing method for a tax database according to any one of claims 1 to 5, characterized in that, The step of calling the semantic similarity calculation model to perform semantic analysis on the review points to identify semantic rationality conflicts includes: For the second audit point determined to be inconsistent with the standard specification in the comparison results of the vectorized comparison, a rationality analysis is performed on the second audit point; the rationality analysis includes at least one judgment logic, including: judging whether the naming of the second audit point conforms to the preset naming logic, judging whether the parameters of the second audit point are suitable for the scale of the associated business data, judging whether the contextual logic relationship of the second audit point is consistent, and judging whether there are design elements in the database that can be replaced and reused by the second audit point. When the structured design element is determined to be reasonable based on the rationality analysis, the existence of the second type of conflict is determined, and a supplementary proposal for standard specifications to be approved is automatically generated and added to the review report. After the proposed amendments to the standard specifications are approved, the standard specifications knowledge base is updated according to the proposed amendments.

8. A data processing device for a tax database, characterized in that, Applied to a tax audit system, the tax audit system includes a standard specification knowledge base, an audit scheme knowledge base, a large model parsing engine, and a rule reasoning engine. The device includes: The structured parsing module is used to obtain the table structure design document to be reviewed in the tax database, and to use the large model parsing engine to parse the table structure design document to extract structured design elements. The review and judgment module is used to review and judge the structured design elements based on the pre-built standard specification knowledge base and the review scheme knowledge base, through the rule reasoning engine, and generate and output a review report based on the review and judgment results; The standard specification knowledge base stores multiple standard specifications in the tax field used for reviewing the table structure design documents in a vectorized form, and uses structured indexing technology to associate each standard specification with at least one dimension label used to identify its applicable scenarios; the review scheme knowledge base stores review schemes adapted to different tax business scenarios based on template definitions, including at least one referenced standard specification, and application priority information configured for each standard specification when referencing multiple standard specifications; The review judgment includes: for the same review point in the structured design elements, when the review plan references multiple standards and specifications, performing vectorized comparisons using each standard and specification sequentially according to the application priority information; and... A semantic similarity calculation model is invoked to perform semantic analysis on the review points in order to identify semantic rationality conflicts. Specifically, if the review point conforms to the standard specification and the vector distance between the actual semantics and the expected semantics of the standard specification exceeds a preset threshold, it is determined to be a first type of conflict. If the review point does not conform to the standard specification and the actual semantics meet the preset business rationality conditions, it is determined to be a second type of conflict.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory; wherein the memory is used to store a computer program, which is loaded and executed by the processor to implement the data processing method for the tax database as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein the computer programs, when executed by a processor, implement the data processing method for the tax database as described in any one of claims 1 to 7.

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