Intelligent generation method for enterprise final settlement report

Abstract

The invention provides an intelligent generation method for an enterprise final settlement report, and relates to a data processing technology, a server receives initial final settlement data input by an enterprise, captures finance and taxation information in the final settlement data according to information dimensions based on customized information, and sends the finance and taxation information to a server; obtaining finance and taxation information of different information dimensions, and inputting the finance and taxation information into a topology arithmetic unit with a preset value in groups; different arithmetic units of the topology arithmetic unit perform decomposition operation on the finance and taxation information according to a preset operation topology structure to obtain corresponding first finance and taxation sub-information; if it is judged that the first node connected with the graphic rendering generation node exists in the operation topological structure, inputting the first finance and taxation sub-information to the graphic rendering generation node after the first node has the first finance and taxation sub-information; and the graphic rendering generation nodes determine second finance and taxation sub-information corresponding to the first finance and taxation sub-information based on historical interaction data with the first node, and generate a final settlement report based on the second finance and taxation sub-information and the report generation habit of each graphic rendering generation node.

Description

Technical Field

[0001] The present invention relates to data technology, and in particular to a method for intelligently generating a corporate tax settlement report. Background Art

[0002] In today's digital information management system, the importance of an enterprise's year-end tax settlement is self-evident. Tax settlement is the company's summary and review of the previous year's financial data in order to submit accurate tax reports to the tax authorities.

[0003] However, the traditional process of generating tax settlement reports often relies on manual input and data classification, which is not only time-consuming and labor-intensive, but also prone to inaccurate settlement due to human errors. With the increasing demand for enterprise informatization, there is an urgent need for automated, intelligent solutions that can quickly generate accurate tax settlement reports.

[0004] Therefore, how to intelligently generate corresponding tax settlement reports based on the actual financial and tax situation of each enterprise and improve the efficiency and accuracy of tax settlement work has become an urgent problem that needs to be solved. Summary of the invention

[0005] The embodiment of the present invention provides a method for intelligently generating an enterprise tax settlement report, which can intelligently generate a corresponding tax settlement report according to the actual financial and tax situation of each enterprise, thereby improving the efficiency and accuracy of the tax settlement work.

[0006] A first aspect of an embodiment of the present invention provides a method for intelligently generating an enterprise tax settlement report, comprising: The server captures the fiscal and tax information in the tax settlement data according to the information dimension based on the customized information, obtains the fiscal and tax information of different information dimensions, and then groups them and inputs them into the topological operator with preset values; Different operation units of the topological operator perform decomposition operation on the fiscal and tax information according to the operation topological structure of the preset value to obtain the corresponding first fiscal and tax sub-information, wherein the operation topological structure at least includes an input node, an operation node and an output node; If it is determined that there is a first node connected to the graphics rendering generation node in the computational topology structure, then after the first node has the first finance and taxation sub-information, the first node is input to the graphics rendering generation node; The graphic rendering generation node determines the second financial and taxation sub-information corresponding to the first financial and taxation sub-information based on the historical interaction data with the first node, and generates a final settlement report based on the second financial and taxation sub-information and the report generation habits of each graphic rendering generation node.

[0007] Optionally, in a possible implementation of the first aspect, the server captures the fiscal and tax information in the tax settlement data according to the information dimension based on the customized information, obtains the fiscal and tax information of different information dimensions, and then groups and inputs them into a topology operator of a preset value, including: The server receives the tax settlement data in the form of an initial table file input by the enterprise, wherein the tax settlement data in the form of a table file at least includes information dimensions and information values; The server receives at least one tax settlement target through a customized interaction module, and each tax settlement target corresponds to an information value of at least one information dimension; Based on the information dimensions corresponding to all tax settlement targets, the server captures tax settlement data in the form of table files to obtain financial and tax information in different information dimensions; The server adjusts the preset topology operator based on the tax settlement target to obtain the calculation link of the topology operator for this tax settlement.

[0008] Optionally, in a possible implementation manner of the first aspect, the server adjusts a topology operator of a preset value based on a tax settlement target to obtain a calculation link of the topology operator during the current tax settlement, including: Determine the output node corresponding to the tax settlement target in the topological operator as the first output node; Perform link backtracking in a topology operator based on the first output node and the connection path of the first output node to obtain a backtracking start node and a backtracking link; The backtracking start node and the backtracking link are summarized and counted to obtain the operation link of the topology operator during this tax settlement and the operation link table of this time, deactivate the nodes in the non-operation link, and reverse the path of the backtracking link to obtain the operation link.

[0009] Optionally, in a possible implementation manner of the first aspect, the performing link backtracking in a topology operator based on the first output node and the connection path of the first output node to obtain a backtracking start node and a backtracking link includes: Taking each first output node as a corresponding backtracking start node, establishing a calculation link cell corresponding to each backtracking start node in the initial calculation link table; Determine a first information dimension corresponding to the tax settlement target of each first output node, and determine an input node corresponding to the first information dimension as a first input node, and determine the first input node as a backtracking termination node; A corresponding backtracking link is generated based on the backtracking start node, the backtracking end node and the preset backtracking strategy.

[0010] Optionally, in a possible implementation manner of the first aspect, generating a corresponding backtracking link based on the backtracking start node, the backtracking end node, and a preset backtracking strategy includes: Taking the backtracking start node as the starting point, respectively determine the upper dimension operation nodes and / or input nodes that are directly connected and / or indirectly connected, until all the backtracking end nodes are determined, and then stop the link backtracking; The directly connected operation nodes and / or output nodes, and / or the indirectly connected operation nodes and / or output nodes determined between the backtracking start node and the backtracking end node are counted to generate a backtracking link.

[0011] Optionally, in a possible implementation manner of the first aspect, different operation units of the topological operator perform decomposition operations on the fiscal and taxation information according to an operation topological structure of a preset value to obtain corresponding first fiscal and taxation sub-information, and the operation topological structure at least includes an input node, an operation node, and an output node, including: Traversing each operation link cell in the operation link table in turn, determining the corresponding input node in turn according to the operation link in the operation link cell, and determining and inputting the corresponding financial and tax information based on the type of the input node; Based on the calculation of all nodes in the operation link and the connection relationship between nodes, the first financial and taxation sub-information after the operation node and / or output node is calculated and processed is obtained.

[0012] Optionally, in a possible implementation of the first aspect, if it is determined that there is a first node connected to the graphics rendering generation node in the computing topology structure, then after the first node has the first finance and taxation sub-information, the first finance and taxation sub-information is input to the graphics rendering generation node, including: All non-deactivated nodes of the computing links in the computing topology are used as first nodes; Determine the rendering quantity of the graphics rendering generation nodes connected to each first node; If the rendering quantity is 1, then after the first node has the first finance and taxation sub-information, it is input to the graphics rendering generation node; If the number of renderings is multiple, the corresponding graphic rendering generation node is determined based on the current tax settlement target and the first financial and tax sub-information is input. Each tax settlement target has a preset graphic rendering generation node.

[0013] Optionally, in a possible implementation of the first aspect, the graphic rendering generation node determines, based on historical interaction data with the first node, second financial and taxation sub-information corresponding to the first financial and taxation sub-information, and generates a final settlement report based on the second financial and taxation sub-information and the report generation habit of each graphic rendering generation node, including: The graphic rendering generation node captures historical interaction data of the first node based on the enterprise tag of the enterprise, wherein the historical interaction data includes the historical moment and historical finance and taxation sub-information of each previous generation of the first finance and taxation sub-information; If the graphic rendering generation node determines that the enterprise has not actively configured the report time dimension this time, it obtains the custom time dimension based on the report generation habit of the previous graphic rendering generation node; If the graphics rendering generation node determines that the enterprise actively configures the report time dimension this time, it extracts the corresponding active time dimension; Based on the customary time dimension or the active time dimension, the historical fiscal and taxation sub-information at the historical moment is filtered in the database to obtain the second fiscal and taxation sub-information, the graphic habit of each graphic rendering generation node is obtained, and the reconciliation and settlement report is generated based on the graphic habit and the second fiscal and taxation sub-information.

[0014] Optionally, in a possible implementation manner of the first aspect, if the graphic rendering generation node determines that the enterprise has not actively configured the report time dimension this time, obtaining the custom time dimension based on the report generation habit of the previous graphic rendering generation node includes: The historical time dimensions of all reports of all previous graphics rendering generation nodes are obtained, and the average of all historical time dimensions is rounded up to obtain the customary time dimension.

[0015] Optionally, in a possible implementation of the first aspect, the filtering of historical fiscal and taxation sub-information at historical moments in a database based on the customary time dimension or the active time dimension to obtain second fiscal and taxation sub-information, obtaining a graphic habit of each graphic rendering generation node, and generating a final settlement report based on the graphic habit and the second fiscal and taxation sub-information includes: The second financial and tax sub-information is obtained by filtering the historical financial and tax sub-information at the historical moment based on the customary time dimension or the active time dimension, wherein the customary time dimension or the active time dimension respectively includes a corresponding time period; Obtaining a graphic habit of each graphic rendering generation node, and obtaining a corresponding graphic template based on the graphic habit, wherein each graphic habit has a preset graphic template; The graphic rendering generation node performs fusion rendering based on the graphic template, the first fiscal and taxation sub-information and the second fiscal and taxation sub-information, respectively, and a sub-report of the corresponding graphic rendering generation node is generated; All sub-reports are combined in order of priority to generate a final settlement report.

[0016] Optionally, in a possible implementation of the first aspect, acquiring a graphic habit of each graphic rendering generation node, and obtaining a corresponding graphic template based on the graphic habit, wherein each graphic habit has a preset graphic template, includes: Obtain the historical time dimensions of all sub-reports of all previous graphics rendering generation nodes, count the graphics types of all historical time dimensions, and obtain the graphics template by obtaining the graphics type with the largest number.

[0017] Optionally, in a possible implementation manner of the first aspect, the graphic rendering generation node performs fusion rendering based on the graphic template, the first fiscal and taxation sub-information, and the second fiscal and taxation sub-information, respectively, and a sub-report of a corresponding graphic rendering generation node includes: The graphic rendering generation node determines the graphic to be rendered in the graphic template, and selects a corresponding slot to be rendered in the graphic to be rendered based on the first financial and taxation sub-information and the second financial and taxation sub-information; Based on the sequence number of the slot to be rendered, the time number of the first fiscal and taxation sub-information and the second fiscal and taxation sub-information, the slot to be rendered is processed correspondingly to the first fiscal and taxation sub-information or the second fiscal and taxation sub-information; The rendering pixel points and rendering pixel values ​​of the first fiscal and taxation sub-information and the second fiscal and taxation sub-information are determined based on the numerical value of the first fiscal and taxation sub-information or the second fiscal and taxation sub-information, and a sub-report of the node is generated for graphics rendering based on the rendering pixel points and rendering pixel values.

[0018] According to a fourth aspect of the present invention, a storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the method of the first aspect of the present invention and various possible designs of the first aspect.

[0019] The beneficial effects of the present invention are as follows: 1. The present invention can intelligently generate corresponding tax settlement reports according to the actual financial and taxation situation of each enterprise, thereby improving the efficiency and accuracy of tax settlement work. The present invention can automatically capture and integrate the financial and taxation data uploaded by the enterprise through the server, and use the preset topological operator to perform efficient calculations to achieve rapid processing and accurate calculation of financial and taxation information. It can not only automatically generate the required financial and taxation sub-information, but also has graphic rendering capabilities, and can intuitively present data results, thereby improving the intelligent level of tax settlement report generation.

[0020] 2. The present invention receives the initial tax settlement data uploaded by the enterprise through the server, and automatically captures and groups the data based on customized information, and then uses the preset topological operator to efficiently integrate the financial and tax information of different information dimensions, so as to realize the automation of the whole process from input to output. At the same time, with the help of the flexibility of the topological structure, the operation link can be dynamically adjusted according to the tax settlement target to ensure the pertinence and efficiency of data processing, thereby significantly improving the generation speed and accuracy of the tax settlement report. Among them, the use of the topological operator to decompose the financial and tax information is one of the core advantages of the present invention. By traversing the operation link table, the financial and tax sub-information of the operation nodes and the output nodes is calculated in turn. In this process, according to the type of input node and the associated operation link, the financial and tax sub-information of multiple information dimensions can be quickly generated. By optimizing the operation link and the accuracy of the node calculation, it is ensured that the system can still maintain efficient computing power and accurate calculation results in a complex data environment.

[0021] 3. The present invention also provides a graphics rendering generation function, which can automatically generate a highly visualized tax settlement report based on historical interaction data and generation habits. The present invention can automatically select appropriate graphics templates according to different tax settlement targets, perform data fusion rendering, generate intuitive sub-reports, and finally, form a finalized tax settlement report by prioritizing and integrating all sub-reports, so as to improve the readability and comprehension of the report. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of a method for intelligently generating a corporate tax settlement report provided by the present invention; Figure 2 A schematic diagram of a computing topology structure provided by the present invention; Figure 3 A schematic diagram of a graphics rendering provided by the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

[0025] It should be understood that in various embodiments of the present invention, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0026] It should be understood that in the present invention, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0027] It should be understood that in the present invention, "plurality" refers to two or more than two. "And / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "Contains A, B and C", "Contains A, B, C" means that A, B, and C are all included, "Contains A, B or C" means that one of A, B, and C is included, and "Contains A, B and / or C" means that any one, any two, or any three of A, B, and C are included.

[0028] It should be understood that in the present invention, "B corresponding to A", "B corresponding to A", "A corresponds to B" or "B corresponds to A" means that B is associated with A and B can be determined based on A. Determining B based on A does not mean determining B based only on A, but B can also be determined based on A and / or other information. A and B match when the similarity between A and B is greater than or equal to a preset threshold.

[0029] Depending on the context, "if" as used herein may be interpreted as "when" or "when" or "in response to determining" or "in response to detecting."

[0030] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0031] like Figure 1 As shown, the present invention provides a method for intelligently generating an enterprise final settlement report, comprising: S1, based on customized information, the server captures the financial and tax information in the tax settlement data according to the information dimension, obtains the financial and tax information of different information dimensions, and then groups them and inputs them into the topological operator with preset values.

[0032] It should be noted that in the existing tax processing methods, a large part of it is still processed manually, and there is no comprehensive application of information technology with electronic computers as the main body in tax work. Data information on final settlement usually requires manual information entry and classification, and data processing automation has not yet been realized. The present invention can automatically extract and integrate the uploaded data to facilitate subsequent calculation of the data, thereby speeding up the generation of final settlement reports.

[0033] It is understandable that the server can receive the initial tax settlement data uploaded by the enterprise, such as an information table containing financial information, which contains a variety of content data. Therefore, it is necessary to extract the financial and tax information in the table according to the customized information required by the tax settlement report. After obtaining the financial and tax information of various dimensions, the information can be grouped and input into a topological operator with preset values ​​so that data calculations can be quickly realized later, thereby improving the efficiency of generating the tax settlement report.

[0034] Among them, the tax settlement data is a table with the data required to generate the tax settlement report, the customized information is the pre-set data type information, such as expenditure data, income data, etc., the financial and tax information is the data information related to finance and taxation, for example, including various information such as income and expenditure, and the topological operator is an operator that uses a topological structure to perform information calculations.

[0035] In some embodiments, the specific implementation of step S1 (the server captures the fiscal and tax information in the tax settlement data according to the information dimension based on the customized information, obtains the fiscal and tax information of different information dimensions, and then groups and inputs them into the topology operator of the preset value) includes: S11, the server receives the tax settlement data in the form of an initial table file input by the enterprise, and the tax settlement data in the form of a table file at least includes information dimensions and information values.

[0036] It is understandable that the server receives the tax settlement data input by the enterprise. These data appear in the form of a table file, similar to a common spreadsheet (such as an Excel file), in which the information dimension is the dimensional information related to finance and taxation, such as operating income, cost, etc., and the information value is the specific value of the corresponding information dimension.

[0037] S12, the server receives at least one tax settlement target through a customized interaction module, and each tax settlement target corresponds to an information value of at least one information dimension.

[0038] It can be understood that the server receives at least one tax settlement target through a customized interaction module, and each tax settlement target is associated with one or more information dimensions.

[0039] Among them, the customized interaction module is a pre-customized module for information interaction, and the tax settlement target is the target information that needs to be displayed, such as the tax rate and tax profit information that needs to be displayed.

[0040] It is not difficult to understand that there can be multiple tax settlement targets, among which the tax settlement target needs to be calculated through the information values ​​of the original relevant information dimensions.

[0041] S13, based on the information dimensions corresponding to all tax settlement targets, the server captures the tax settlement data in the form of a table file to obtain financial and tax information of different information dimensions.

[0042] It can be understood that the server determines the information dimension corresponding to the target of the tax settlement, so that the information of the tax settlement data in the form of a spreadsheet file can be identified and extracted, thereby obtaining financial and tax information of multiple information dimensions.

[0043] It is not difficult to understand that information capture can ensure that the server only focuses on data related to the tax settlement target, improve processing efficiency, and prepare financial and tax information data in different information dimensions for subsequent analysis.

[0044] S14, the server adjusts the topological operator of the preset value based on the final settlement target to obtain the operation link of the topological operator during the final settlement.

[0045] It is understandable that the server can adjust the settings of the preset topology operators according to the tax settlement target. This adjustment includes determining the operation link suitable for this tax settlement, that is, optimizing the data flow path and calculation logic.

[0046] Among them, the operation link is an information link for performing data operations, such as the operation link corresponding to expenditure-income-profit.

[0047] Through the above implementation, the topological operator is adjusted to ensure the pertinence and effectiveness of the calculation process, and more efficient data processing and result generation are achieved through optimized calculation links.

[0048] In some embodiments, the specific implementation of step S14 (the server adjusts the topology operator of the preset value based on the final settlement target to obtain the operation link of the topology operator during the current final settlement) includes: S141, determining the output node corresponding to the tax settlement target in the topology operator as the first output node.

[0049] It can be understood that the output node is the node that outputs the information result in the topological operator, and the first output node is the output node in the topological operator corresponding to the settlement target, that is, the output node corresponding to the required information.

[0050] It is not difficult to understand that there are multiple output nodes in the topological operator, and each node is responsible for outputting different information results. Clearly defining the target output node can ensure that the end point of the operation link is consistent with the settlement target, thereby improving the pertinence and effectiveness of the calculation. In a complex topological network, accurately locating the output node is the key to effective data processing.

[0051] S142: Perform link backtracing in a topology operator based on the first output node and the connection path of the first output node to obtain a backtracing start node and a backtracing link.

[0052] It can be understood that starting from the first output node, the link is traced back through its connection path to find the starting node of the operation link and the operation nodes along the way to form a backtracking link. The backtracking process is actually to trace the path of data flow forward to identify all nodes involved in the calculation, so that irrelevant links and nodes can be optimized later, so as to obtain the operation link that meets the corresponding enterprise, thereby improving the efficiency of generating the final settlement report.

[0053] The connection path is a path connecting nodes, the backtracking start node is a node for link backtracking, and the backtracking link is a connection link between the backtracking start node and the first output node.

[0054] It is not difficult to understand that link backtracking helps to clarify the complete path of data processing, ensure that all relevant nodes are considered, and lay the foundation for subsequent link optimization and path adjustment.

[0055] In some embodiments, a specific implementation of step S142 (the link backtracking in the topology operator based on the first output node and the connection path of the first output node to obtain the backtracking start node and the backtracking link) includes: S1421, taking each first output node as a corresponding backtracking start node, and establishing a calculation link cell corresponding to each backtracking start node in the initial calculation link table.

[0056] It can be understood that each first output node is regarded as a corresponding backtracking start node, and in the initial operation link table, a dedicated operation link cell is created for each backtracking start node to store and organize link information starting from the node.

[0057] The initial operation link table is a blank form, and the operation link cells are cells used to fill in and store operation links.

[0058] It is not difficult to understand that by starting from the output node and carrying out link analysis one by one, a clearly defined operation path can be sorted out layer by layer. The operation link cells help to systematically manage each path, which is convenient for subsequent analysis and adjustment.

[0059] S1422, determine the first information dimension corresponding to the tax settlement target of each first output node, and determine the input node corresponding to the first information dimension as the first input node, and determine the first input node as the backtracking termination node.

[0060] It can be understood that the relevant first information dimension is determined according to the settlement target of each first output node, and the corresponding input node is identified according to the dimension, which is called the first input node, and is determined as the backtracking termination node.

[0061] Among them, the first information dimension is the information dimension corresponding to the tax settlement target of the first output node. For example, when the tax settlement target is tax profit, the first information dimension corresponding to the tax profit can be the enterprise revenue, that is, the input node corresponding to the enterprise revenue is used as the first input node, and the first input node is determined as the backtracing termination node, so that when the link is subsequently backtraced, the link backtracing is stopped when the backtracing reaches the backtracing termination node.

[0062] Through the above implementation, the relevant information dimensions and input nodes can be accurately located to ensure that each operation link accurately reflects the business objectives.

[0063] S1423: Generate a corresponding backtracking link based on the backtracking start node, the backtracking end node, and a preset backtracking strategy.

[0064] It is understandable that a specific backtracking link is generated using the backtracking start node, the backtracking end node and the preset backtracking strategy, wherein the backtracking strategy may include rules such as path priority and resource restriction, which affect the way of link selection.

[0065] It is not difficult to understand that the introduction of the backtracking strategy makes the link generation process more flexible and intelligent, adapting to different business needs and resource allocations. The generated backtracking link clearly shows the overall picture of data flow and provides a basis for calculation and analysis.

[0066] In some embodiments, a specific implementation of step S1423 (generating a corresponding backtracking link based on the backtracking start node, the backtracking end node and a preset backtracking strategy) includes: Taking the backtracking start node as the starting point, directly connected and / or indirectly connected upper dimension operation nodes and / or input nodes are determined respectively, and link backtracking is stopped after all backtracking termination nodes are determined.

[0067] It can be understood that starting from the backtracking start node, the operation node and / or input node of the upper dimension directly or indirectly connected to it is searched, and this process is continued until all the backtracking end nodes are identified and connected.

[0068] It is not difficult to understand that by gradually identifying the connection path, the integrity and accuracy of the link can be ensured. This step traces back layer by layer to clarify the source and path of each computing node, helping to achieve refined data tracking.

[0069] The directly connected operation nodes and / or output nodes, and / or the indirectly connected operation nodes and / or output nodes determined between the backtracking start node and the backtracking end node are counted to generate a backtracking link.

[0070] It can be understood that all directly and / or indirectly connected operation nodes and output nodes determined between the backtracking start node and the backtracking end node are counted, and a complete backtracking link is generated according to the statistical result.

[0071] It is not difficult to understand that the statistical process ensures that all relevant nodes are taken into account, and the generated back-tracking link shows the full link path from input to output, providing a complete basis for analysis, optimization and decision-making.

[0072] S143, summarize and count the backtracking start nodes and backtracking links, obtain the operation links of the topology operator during the current tax settlement and the operation link table of this time, disable the nodes in the non-operation links, and reverse the path of the backtracking link to obtain the operation link.

[0073] It can be understood that the starting nodes and links obtained by backtracking are summarized and counted, the complete operation link required for this tax settlement is identified, the nodes that are not in the operation link are deactivated, resource utilization is optimized, and the link is traced back by reverse processing to obtain the final operation link, that is, the order of data flow from the starting point to the end point.

[0074] Among them, the operation link is the connection link for the data operation corresponding to the final settlement, and the operation link table is a form showing each operation link.

[0075] Through the above implementation, the operation link table corresponding to the output result can be obtained to ensure that subsequent calculations are performed only on necessary nodes and paths, thereby improving efficiency. Reverse processing ensures that data flows along the optimal path, reducing delays and resource waste.

[0076] S2, different operation units of the topological operator perform decomposition operations on the financial and tax information according to the operation topology structure of the preset value to obtain the corresponding first financial and tax sub-information, and the operation topology structure at least includes an input node, an operation node and an output node.

[0077] It can be understood that the topological operator has multiple different operation units, so that the financial and tax information can be decomposed and operated by the corresponding operation units according to the preset operation topology structure, so as to obtain the corresponding first financial and tax sub-information and provide data results for the subsequent generation of the reconciliation and settlement report.

[0078] The computational topology is the logical structure of the computation performed in the topological operator, such as Figure 2 As shown, similar to a neural network, it at least includes input nodes, operation nodes and output nodes. The input nodes are nodes for inputting information, the operation nodes are nodes for performing data operations, and the output nodes are nodes for outputting information.

[0079] In some embodiments, the specific implementation of step S2 (different operation units of the topological operator perform decomposition operations on the fiscal and tax information according to the operation topological structure of the preset value to obtain the corresponding first fiscal and tax sub-information, and the operation topological structure at least includes an input node, an operation node, and an output node) includes: S21, traverse each operation link cell in the operation link table in turn, determine the corresponding input node in turn according to the operation link in the operation link cell, determine the corresponding financial and tax information based on the type of the input node and input it.

[0080] It can be understood that each operation link cell in the operation link table is traversed in turn, and the relevant input nodes are determined one by one according to the operation link filled in each cell. According to the type of input node, the corresponding financial and tax information is identified and obtained for system input.

[0081] Through the above implementation, by traversing the operation link table and identifying the input nodes, it is ensured that in the entire topology structure, each operation unit can accurately identify and obtain the required financial and tax information. The operation link table is automatically traversed and identified to reduce manual intervention and improve the accuracy and efficiency of information input.

[0082] S22, based on the calculation of all nodes in the operation link and the connection relationship between nodes, obtain the first financial and tax sub-information after the operation node and / or output node is calculated and processed.

[0083] It can be understood that calculations are performed based on the nodes in the operation link and the connection relationships between them, starting from the input node, to generate the first financial and taxation sub-information corresponding to the operation node and / or output node.

[0084] Among them, the first financial and taxation sub-information is the financial and taxation information corresponding to the output of each node. The node can be an operation node or an output node. For example, when the input revenue and cost are transmitted to the operation node, the profit is obtained through difference calculation, and then the profit is multiplied by the corresponding tax rate to obtain the tax payable value corresponding to the tax payable output node. At the same time, the profit at the operation node may also have the corresponding table generation requirement, then the corresponding middle operation node can have the corresponding first financial and taxation sub-information, such as profit.

[0085] It is not difficult to understand that through reasonable topological structure and calculation, the generated financial and taxation sub-information can be ensured to be accurate and reliable, providing high-quality basic data for subsequent financial and taxation information integration and analysis.

[0086] S3, if it is determined that there is a first node connected to the graphics rendering generation node in the calculation topology structure, then after the first node has the first finance and taxation sub-information, it is input to the graphics rendering generation node.

[0087] It can be understood that the graphics rendering generation node is a node that can generate graphics according to data information and render them.

[0088] It is not difficult to understand that different nodes in the computing topology structure may have graphics rendering generation nodes connected to corresponding connections, so as to receive data information of corresponding connected nodes for graphics generation, so as to facilitate intuitive viewing of corresponding data information.

[0089] In some embodiments, the specific implementation of step S3 (if it is determined that there is a first node connected to the graphics rendering generation node in the computing topology structure, then the first node has the first financial and taxation sub-information and inputs it to the graphics rendering generation node) includes: S31, taking all the non-deactivated nodes of the computing link in the computing topology structure as the first nodes.

[0090] It can be understood that the first node is a node participating in data computing or output in a computing link in a computing topology structure, and the non-deactivated node is a node participating in information computing.

[0091] It is not difficult to understand that ensuring that only valid nodes participating in data operation or output are the first nodes facilitates subsequent data operation and transmission to improve the efficiency of data processing.

[0092] S32: Determine the rendering quantity of the graphics rendering generation nodes connected to each first node.

[0093] It is understandable that the number requirement for generating graphics corresponding to each node may be single or multiple, so that the rendering quantity corresponding to each first node can be obtained, so as to perform data analysis and graphics generation according to the corresponding rendering quantity.

[0094] The rendering quantity is the quantity of the graphics rendering generation nodes connected to each first node.

[0095] S33, if the rendering quantity is 1, then after the first node has the first finance and taxation sub-information, it is input into the graphics rendering generation node.

[0096] It can be understood that when the rendering quantity is 1, the value image rendering generation node can be input after the first node obtains the corresponding first financial and taxation sub-information, so as to subsequently generate the corresponding graphics and render them.

[0097] It is not difficult to understand that when there is only one rendering node, one rendering method is selected to simplify the processing logic, directly transmit data, and reduce latency.

[0098] S34, if the number of renderings is multiple, then determine the corresponding graphic rendering generation node based on the current tax settlement target and input the first fiscal and tax sub-information, and each tax settlement target has a preset graphic rendering generation node.

[0099] It can be understood that when the number of renderings is multiple, the appropriate graphics rendering generation node is selected for data input according to the current tax settlement target, wherein each tax settlement target is pre-configured with a corresponding graphics rendering node.

[0100] Through the above implementation, the present invention can input the first fiscal and tax sub-information corresponding to the tax settlement target into a corresponding graphic rendering node, so as to subsequently generate corresponding graphics and perform graphic rendering to achieve data visualization.

[0101] S4, the graphic rendering generation node determines the second financial and taxation sub-information corresponding to the first financial and taxation sub-information based on the historical interaction data with the first node, and generates a final settlement report based on the second financial and taxation sub-information and the report generation habits of each graphic rendering generation node.

[0102] It can be understood that the graphics rendering generation node refers to the historical interaction data with the first node, determines the second fiscal and taxation sub-information corresponding to the first fiscal and taxation sub-information, and generates the final reconciliation and settlement report based on the second fiscal and taxation sub-information and the report generation habits of the graphics rendering generation node.

[0103] Among them, the historical interaction data is the information data calculated corresponding to the historical time period, and the second fiscal and taxation sub-information is the fiscal and taxation information of the historical time corresponding to the first fiscal and taxation sub-information.

[0104] It is not difficult to understand that generating nodes through graphical rendering provides an intuitive report display method that is easier to understand and apply.

[0105] In some embodiments, the specific implementation of step S4 (the graphic rendering generation node determines the second financial and taxation sub-information corresponding to the first financial and taxation sub-information based on the historical interaction data with the first node, and generates the final settlement report based on the second financial and taxation sub-information and the report generation habits of each graphic rendering generation node) includes: S41, the graphics rendering generation node captures the historical interaction data of the first node based on the enterprise tag of the enterprise, and the historical interaction data includes the historical moment and historical finance and taxation sub-information of each previous generation of the first finance and taxation sub-information.

[0106] It is understandable that this tax reconciliation may be for the tax situation of this year or a quarter. Therefore, the historical interaction data of the previous year or the previous quarter corresponding to the first node can be extracted according to the enterprise label of the enterprise, so as to analyze the financial and tax sub-information and generate the tax reconciliation report based on the historical interaction data and the corresponding report generation logic and method.

[0107] Among them, the enterprise label is a label that indicates the enterprise information, such as the enterprise type or enterprise name corresponding to the enterprise, the historical moment is the time when the first financial and taxation sub-information is generated in the historical interaction data, and the historical financial and taxation sub-information is the financial and taxation sub-information generated corresponding to the historical moment, for example, it can be the corresponding tax payable in the previous year.

[0108] S42, if the graphic rendering generation node determines that the enterprise has not actively configured the report time dimension this time, the customary time dimension is obtained based on the report generation habit of the previous graphic rendering generation node.

[0109] It is understandable that if the enterprise does not actively configure the report time dimension this time, the system will determine a customary time dimension based on the previous report generation habits, so that even if the enterprise does not provide a time dimension preference, it can still generate a report that conforms to the enterprise's habits through experience and historical data.

[0110] For example, when the graphics rendering generation node generates a report, if the report time configured by the enterprise is not received, the time of the reports generated within the historical time period can be referred to. When the reports generated within the historical time period usually refer to the reports of the previous three months before the reports are generated, the reports generated within the previous three months can also be referred to when generating the report this time. In order to obtain the corresponding customary time dimension according to historical habits, it is convenient to configure the time for the generated settlement report.

[0111] Among them, the reporting time dimension is the time dimension for generating the final settlement report, and the customary time dimension is the time dimension for the customary preference report configuration obtained by analyzing the reporting time corresponding to the historical time period.

[0112] In some embodiments, the specific implementation of step S42 (if the graphic rendering generation node determines that the enterprise has not actively configured the report time dimension this time, the custom time dimension is obtained based on the report generation habit of the previous graphic rendering generation node) includes: S421, obtaining the historical time dimensions of all reports of all previous graphics rendering generation nodes, averaging all the historical time dimensions and rounding them up to obtain the customary time dimension.

[0113] It is understandable that the time of reference data for reports generated at historical moments may be 2 months, 3 months, etc., and the reference time dimensions of reports generated in historical time periods may be inconsistent. All historical time dimensions may then be averaged and rounded up to obtain the final customary time dimension. For example, when the historical time dimension obtained is 2-3 months, the customary time dimension obtained after rounding up is 3 months.

[0114] Among them, the historical time dimension is the time dimension of the report corresponding to the historical time.

[0115] It is worth mentioning that through statistical analysis of historical data, a representative time dimension is generated to ensure that even if the enterprise does not actively configure it, the system can provide a reasonable time frame based on historical habits.

[0116] S43, if the graphics rendering generation node determines that the enterprise actively configures the report time dimension this time, it extracts the corresponding active time dimension.

[0117] It is understandable that when an enterprise proactively configures the time dimension of a report, the system can directly extract the corresponding configured report time dimension to meet the needs of the corresponding enterprise.

[0118] Among them, the active time dimension is the time dimension actively configured by the enterprise.

[0119] S44, based on the customary time dimension or the active time dimension, the historical fiscal and taxation sub-information at the historical moment is filtered in the database to obtain the second fiscal and taxation sub-information, the graphic habit of each graphic rendering generation node is obtained, and the final settlement report is generated based on the graphic habit and the second fiscal and taxation sub-information.

[0120] It can be understood that the historical fiscal and taxation sub-information corresponding to the historical moments in the database is filtered according to the acquired customary time dimension or active time dimension, so that the filtered historical fiscal and taxation sub-information is used as the second fiscal and taxation sub-information, and the graphic habit of each graphic rendering generation node is obtained, so that the final reconciliation and settlement report can be generated according to the graphic habit and the second fiscal and taxation sub-information.

[0121] The second finance and taxation sub-information is the historical finance and taxation sub-information selected from the data according to the customary time dimension or the active time dimension, and the graphic habit is the habit of generating graphics, for example, a histogram or a tree diagram is usually generated.

[0122] It is not difficult to understand that by filtering relevant data and applying graphic habits, generating personalized and visual reports can help companies understand their financial status more intuitively.

[0123] In some embodiments, the specific implementation of step S44 (the filtering of historical fiscal and taxation sub-information at historical moments in the database based on the customary time dimension or the active time dimension to obtain second fiscal and taxation sub-information, obtaining the graphic habit of each graphic rendering generation node, and generating a final settlement report based on the graphic habit and the second fiscal and taxation sub-information) includes: S441, determining the historical finance and taxation sub-information at the historical moment based on the customary time dimension or the active time dimension to filter out the second finance and taxation sub-information, wherein the customary time dimension or the active time dimension respectively includes the corresponding time period.

[0124] It can be understood that the customary time dimension or the active time dimension respectively includes corresponding time periods to ensure that the financial and tax sub-information included in the report accurately reflects the financial and tax situation within the time period based on the defined time dimension.

[0125] S442, obtaining a graphic habit of each graphic rendering generation node, and obtaining a corresponding graphic template based on the graphic habit, wherein each graphic habit has a preset graphic template.

[0126] It is understandable that the graphic habit of each graphic rendering generation node is obtained so as to obtain the corresponding graphic template according to the graphic habit, so as to facilitate the subsequent rendering of the corresponding graphic template according to the financial and taxation sub-information.

[0127] Among them, the graphic habit has preset graphic templates, which are templates for different data analysis charts, such as a bar chart template without data, a pie chart template, etc.

[0128] In some embodiments, the specific implementation of step S442 (obtaining the graphic habit of each graphic rendering generation node, obtaining a corresponding graphic template based on the graphic habit, and each graphic habit having a preset graphic template) includes: S4421, obtaining the historical time dimensions of all sub-reports of all previous graphics rendering generation nodes, counting the graphics types of all historical time dimensions, and obtaining the graphics type with the largest number to obtain a graphics template.

[0129] It is understandable that the graphic information of the historical report, including the time dimension and the graphic type, is extracted from the database, the extracted data is analyzed, the usage frequency of each graphic type is counted, and based on the statistical results, the graphic type with the most occurrences is selected as the graphic template.

[0130] The graphic type is the type of data graphic, such as tree diagram, histogram, pie chart, etc.

[0131] It is not difficult to understand that by analyzing historical data, the most commonly used graphic templates that are most in line with user habits are automatically selected to ensure that the selected graphic templates are consistent with the user's historical usage habits to optimize the report presentation effect and user satisfaction. This not only improves the efficiency of report generation, but also ensures report quality and consistency.

[0132] S443, the graphic rendering generation node performs fusion rendering based on the graphic template, the first financial and taxation sub-information and the second financial and taxation sub-information, respectively, and generates a sub-report of the corresponding graphic rendering generation node.

[0133] It can be understood that when there are multiple graphic rendering generation nodes, they will be fused and rendered according to the graphic template, the first financial and taxation sub-information and the second financial and taxation sub-information respectively to obtain sub-reports corresponding to each graphic generation node, so as to be subsequently sorted and combined to obtain a final settlement report.

[0134] In some embodiments, the specific implementation of step S443 (the graphic rendering generation node performs fusion rendering based on the graphic template, the first fiscal and taxation sub-information and the second fiscal and taxation sub-information, respectively, corresponding to a sub-report of the graphic rendering generation node) includes: S4431, the graphics rendering generation node determines the graphics to be rendered in the graphics template, and selects a corresponding slot to be rendered in the graphics to be rendered based on the first finance and taxation sub-information and the second finance and taxation sub-information.

[0135] It is understandable that some graphic templates are standard, and their base plates have color display, so no color rendering is required, but some graphic models are related to data. Therefore, color rendering is required according to the corresponding data, so that the graphics to be rendered that need to be rendered can be determined, and the corresponding slots to be rendered can be selected according to the corresponding data quantity of the first fiscal and taxation sub-information and the second fiscal and taxation sub-information.

[0136] For example, when the graphic template is a histogram, and the corresponding first fiscal and taxation sub-information is the tax amount for June, and the second fiscal and taxation sub-information includes the tax amounts for March, April, and May, 4 slots can be selected in the histogram as the slots to be dyed for subsequent color rendering.

[0137] The graphics to be rendered are graphics that need to be rendered in color, and the slots to be rendered are slots that need to be rendered in color.

[0138] Through the above implementation, the corresponding graphics to be rendered and the slots to be rendered are determined so as to facilitate subsequent color rendering, thereby ensuring that the financial and tax information is accurately and effectively presented in the report.

[0139] S4432, based on the sequence number of the slot to be rendered, the time number of the first fiscal and taxation sub-information and the second fiscal and taxation sub-information, the slot to be rendered is processed correspondingly to the first fiscal and taxation sub-information or the second fiscal and taxation sub-information respectively.

[0140] It can be understood that the slots to be rendered are numbered in order from left to right, and the time for obtaining the data corresponding to the first fiscal and taxation sub-information and the second fiscal and taxation sub-information are numbered in order from early to late, so that the sequence number can be corresponded to the time number, so that the corresponding slot can be rendered later according to the fiscal and taxation data of the corresponding time.

[0141] Among them, the sequential numbers are the corresponding numbers of the slots to be rendered, such as the 1st, 2nd, 3rd, etc., and the time numbers are the numbers arranged in sequence according to time, such as March, April, May, etc.

[0142] For example, when the second fiscal and taxation sub-information corresponds to March, April, and May, and the first fiscal and taxation sub-information corresponds to June, the first slot to be rendered can be corresponded to the second fiscal and taxation sub-information of March, the second slot to be rendered can be corresponded to the second fiscal and taxation sub-information of April, the third slot to be rendered can be corresponded to the second fiscal and taxation sub-information of May, and the fourth slot to be rendered can be corresponded to the second fiscal and taxation sub-information of June.

[0143] It is not difficult to understand that through orderly arrangement and correspondence, it is ensured that the information is accurately filled into the correct position of the graphic template.

[0144] S4433, determining the rendering pixel points and rendering pixel values ​​of the first fiscal and taxation sub-information and the second fiscal and taxation sub-information based on the numerical value of the first fiscal and taxation sub-information or the second fiscal and taxation sub-information, and generating a sub-report of the node for graphics rendering based on the rendering pixel points and rendering pixel values.

[0145] It can be understood that different numerical values ​​have corresponding rendering pixel points, so that the rendering pixel points of the first fiscal and taxation sub-information and the second fiscal and taxation sub-information can be determined according to the numerical value of the first fiscal and taxation sub-information or the second fiscal and taxation sub-information, so as to perform pixel rendering on the corresponding rendering pixel points.

[0146] The rendered pixel point is a pixel point for color rendering, and the rendered pixel value is a pixel value for rendering, such as red, blue, etc.

[0147] In addition, the total number of pixels in the slot to be rendered can be counted, and the largest value between the first fiscal and taxation sub-information and the second fiscal and taxation sub-information can be selected as the total value. The benchmark rendering pixel number can be obtained according to the ratio of the total number of pixels and the total value, so as to facilitate the subsequent ratio of the values ​​of each of the first fiscal and taxation sub-information and the second fiscal and taxation sub-information to the total value to obtain the quantity ratio, so that the rendering pixel points corresponding to the values ​​of each fiscal and taxation sub-information can be determined by multiplying the quantity ratio and the benchmark rendering pixel number.

[0148] Among them, the total number of pixels is the sum of the number of pixels in the slots to be rendered, and the total value is the largest value in the first fiscal and taxation sub-information and the second fiscal and taxation sub-information. For example, the corresponding value for March is 400,000, the corresponding value for April is 200,000, the corresponding value for May is 600,000, and the corresponding value for June is 1 million. The total value is the largest 1 million. The benchmark rendering pixel number is the number of pixels corresponding to the unit value. For example, when the total number of pixels is 1,000 and the total value is 1 million, the corresponding benchmark rendering pixel number is 10, that is, a value of 10,000 corresponds to rendering 1 pixel.

[0149] At the same time, the quantity ratio is the ratio of the number of pixel points rendered in the rendering slots to the corresponding values ​​of each financial and taxation sub-information. For example, when the value of March corresponding to the second financial and taxation sub-information is 400,000, the quantity ratio can be obtained as 40÷100=2 / 5, and then the corresponding rendering pixel points in March are 2 / 5×1000=400.

[0150] It is worth mentioning that when rendering pixels, pixels can be distinguished according to the value size, for example, Figure 3As shown, a value of 500,000 can be preset manually. When the value of the corresponding financial and taxation sub-information is less than or equal to 50, the corresponding rendering pixel point can be rendered in yellow. When the value of the corresponding financial and taxation sub-information is greater than 50, the corresponding rendering pixel point can be rendered in red, so that it can be clearly distinguished and it is easy to observe the value size, thereby intuitively reflecting the financial and taxation situation of the enterprise.

[0151] S444, all sub-reports are combined in order of priority to generate a final settlement report.

[0152] It is understandable that all sub-reports are combined in a preset priority order to generate a final final settlement report.

[0153] It is not difficult to understand that by prioritizing, the most important information is presented first, optimizing the logical structure and information delivery of the report.

[0154] The present invention also provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the various embodiments described above.

[0155] Among them, the storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also exist in a communication device as discrete components. The storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0156] The present invention also provides a program product, which includes an execution instruction, which is stored in a storage medium. At least one processor of a device can read the execution instruction from the storage medium, and at least one processor executes the execution instruction so that the device implements the methods provided in the above various embodiments.

[0157] In the above-mentioned terminal or server embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. The intelligent generation method of enterprise tax settlement report is characterized by: include: The server captures the fiscal and tax information in the tax settlement data according to the information dimension based on the customized information, obtains the fiscal and tax information of different information dimensions, and then groups them and inputs them into the topological operator with preset values; Different operation units of the topological operator perform decomposition operation on the financial and tax information according to the operation topological structure of the preset value to obtain the corresponding first financial and tax sub-information, wherein the operation topological structure at least includes an input node, an operation node and an output node; If it is determined that there is a first node connected to the graphics rendering generation node in the computational topology structure, then after the first node has the first finance and taxation sub-information, the first node is input to the graphics rendering generation node; The graphic rendering generation node determines the second financial and taxation sub-information corresponding to the first financial and taxation sub-information based on the historical interaction data with the first node, and generates a final settlement report based on the second financial and taxation sub-information and the report generation habits of each graphic rendering generation node.

2. The intelligent generation method of enterprise tax settlement report according to claim 1 is characterized in that: The server captures the fiscal and tax information in the tax settlement data according to the information dimension based on the customized information, obtains the fiscal and tax information of different information dimensions, and then groups and inputs them into the topological operator of the preset value, including: The server receives the tax settlement data in the form of an initial table file input by the enterprise, wherein the tax settlement data in the form of a table file at least includes information dimensions and information values; The server receives at least one tax settlement target through a customized interaction module, and each tax settlement target corresponds to an information value of at least one information dimension; Based on the information dimensions corresponding to all tax settlement targets, the server captures tax settlement data in the form of table files to obtain financial and tax information in different information dimensions; The server adjusts the preset topology operator based on the tax settlement target to obtain the calculation link of the topology operator for this tax settlement.

3. The intelligent generation method of enterprise tax settlement report according to claim 2 is characterized in that: The server adjusts the topological operator of the preset value based on the final settlement target to obtain the operation link of the topological operator during the final settlement, including: Determine the output node corresponding to the tax settlement target in the topological operator as the first output node; Perform link backtracking in a topology operator based on the first output node and the connection path of the first output node to obtain a backtracking start node and a backtracking link; The backtracking start node and the backtracking link are summarized and counted to obtain the operation link of the topology operator during this tax settlement and the operation link table of this time, deactivate the nodes in the non-operation link, and reverse the path of the backtracking link to obtain the operation link.

4. The intelligent generation method of enterprise tax settlement report according to claim 3 is characterized in that: The step of performing link backtracking in a topology operator based on the first output node and the connection path of the first output node to obtain a backtracking start node and a backtracking link includes: Taking each first output node as a corresponding backtracking start node, establishing a calculation link cell corresponding to each backtracking start node in the initial calculation link table; Determine a first information dimension corresponding to the tax settlement target of each first output node, and determine an input node corresponding to the first information dimension as a first input node, and determine the first input node as a backtracking termination node; A corresponding backtracking link is generated based on the backtracking start node, the backtracking end node and the preset backtracking strategy.

5. The intelligent generation method of enterprise tax settlement report according to claim 4 is characterized in that: The generating of a corresponding backtracking link based on the backtracking start node, the backtracking end node and a preset backtracking strategy includes: Taking the backtracking start node as the starting point, respectively determine the upper dimension operation nodes and / or input nodes that are directly connected and / or indirectly connected, until all the backtracking end nodes are determined, and then stop the link backtracking; The directly connected operation nodes and / or output nodes, and / or the indirectly connected operation nodes and / or output nodes determined between the backtracking start node and the backtracking end node are counted to generate a backtracking link.

6. The intelligent generation method of enterprise tax settlement report according to claim 4 is characterized in that: Different operation units of the topological operator perform decomposition operation on the fiscal and tax information according to the operation topological structure of the preset value to obtain the corresponding first fiscal and tax sub-information, wherein the operation topological structure at least includes an input node, an operation node and an output node, including: Traversing each operation link cell in the operation link table in turn, determining the corresponding input node in turn according to the operation link in the operation link cell, and determining and inputting the corresponding financial and tax information based on the type of the input node; Based on the calculation of all nodes in the operation link and the connection relationship between nodes, the first financial and taxation sub-information after the operation node and / or output node is calculated and processed is obtained.

7. The method for intelligently generating enterprise tax settlement report according to claim 6 is characterized in that: If it is determined that there is a first node connected to the graphics rendering generation node in the computing topology structure, then after the first node has the first finance and taxation sub-information, the first node is inputted into the graphics rendering generation node, including: All non-deactivated nodes of the computing links in the computing topology are used as first nodes; Determine the rendering quantity of the graphics rendering generation nodes connected to each first node; If the rendering quantity is 1, then after the first node has the first finance and taxation sub-information, it is input to the graphics rendering generation node; If the number of renderings is multiple, the corresponding graphic rendering generation node is determined based on the current tax settlement target and the first financial and tax sub-information is input. Each tax settlement target has a preset graphic rendering generation node.

8. The method for intelligently generating enterprise tax settlement report according to claim 1 is characterized in that: The graphic rendering generation node determines the second financial and taxation sub-information corresponding to the first financial and taxation sub-information based on the historical interaction data with the first node, and generates a final settlement report based on the second financial and taxation sub-information and the report generation habit of each graphic rendering generation node, including: The graphic rendering generation node captures historical interaction data of the first node based on the enterprise tag of the enterprise, wherein the historical interaction data includes the historical moment and historical finance and taxation sub-information of each previous generation of the first finance and taxation sub-information; If the graphic rendering generation node determines that the enterprise has not actively configured the report time dimension this time, it obtains the custom time dimension based on the report generation habit of the previous graphic rendering generation node; If the graphics rendering generation node determines that the enterprise actively configures the report time dimension this time, it extracts the corresponding active time dimension; Based on the customary time dimension or the active time dimension, the historical fiscal and taxation sub-information at the historical moment is filtered in the database to obtain the second fiscal and taxation sub-information, the graphic habit of each graphic rendering generation node is obtained, and the reconciliation and settlement report is generated based on the graphic habit and the second fiscal and taxation sub-information.

9. The method for intelligently generating enterprise tax settlement report according to claim 8 is characterized in that: If the graphic rendering generation node determines that the enterprise has not actively configured the report time dimension this time, the custom time dimension is obtained based on the report generation habit of the previous graphic rendering generation node, including: The historical time dimensions of all reports of all previous graphics rendering generation nodes are obtained, and the average of all historical time dimensions is rounded up to obtain the customary time dimension.

10. The method for intelligently generating enterprise tax settlement report according to claim 8 is characterized in that: The step of filtering the historical fiscal and taxation sub-information at the historical moment in the database based on the customary time dimension or the active time dimension to obtain the second fiscal and taxation sub-information, obtaining the graphic habit of each graphic rendering generation node, and generating a final settlement report based on the graphic habit and the second fiscal and taxation sub-information includes: The second financial and tax sub-information is obtained by filtering the historical financial and tax sub-information at the historical moment based on the customary time dimension or the active time dimension, wherein the customary time dimension or the active time dimension respectively includes a corresponding time period; Obtaining a graphic habit of each graphic rendering generation node, and obtaining a corresponding graphic template based on the graphic habit, wherein each graphic habit has a preset graphic template; The graphic rendering generation node performs fusion rendering based on the graphic template, the first fiscal and taxation sub-information and the second fiscal and taxation sub-information, respectively, and a sub-report of the corresponding graphic rendering generation node is generated; All sub-reports are combined in order of priority to generate a final settlement report.

11. The method for intelligently generating enterprise tax settlement report according to claim 10 is characterized in that: The step of obtaining the graphic habit of each graphic rendering generation node and obtaining a corresponding graphic template based on the graphic habit, wherein each graphic habit has a preset graphic template, includes: Obtain the historical time dimensions of all sub-reports of all previous graphics rendering generation nodes, count the graphics types of all historical time dimensions, and obtain the graphics template by obtaining the graphics type with the largest number.

12. The method for intelligently generating enterprise tax settlement report according to claim 10 is characterized in that: The graphic rendering generation node performs fusion rendering based on the graphic template, the first fiscal and taxation sub-information, and the second fiscal and taxation sub-information, respectively. A sub-report of a corresponding graphic rendering generation node includes: The graphic rendering generation node determines the graphic to be rendered in the graphic template, and selects a corresponding slot to be rendered in the graphic to be rendered based on the first financial and taxation sub-information and the second financial and taxation sub-information; Based on the sequence number of the slot to be rendered, the time number of the first fiscal and taxation sub-information and the second fiscal and taxation sub-information, the slot to be rendered is processed correspondingly to the first fiscal and taxation sub-information or the second fiscal and taxation sub-information; The rendering pixel points and rendering pixel values ​​of the first fiscal and taxation sub-information and the second fiscal and taxation sub-information are determined based on the numerical value of the first fiscal and taxation sub-information or the second fiscal and taxation sub-information, and a sub-report of the node is generated for graphics rendering based on the rendering pixel points and rendering pixel values.

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