Enterprise tax declaration condition monitoring management system based on big data

Abstract

The invention discloses an enterprise tax declaration condition monitoring management system based on big data, and relates to the technical field of informatization tax management, a data integration module is used for setting a data interface and automatically extracting rules, unified processing of cross-department data is realized, a unique identifier is distributed, and the problems of integration difficulty and data circulation efficiency are effectively solved; in the aspect of abnormity identification, historical records and transaction structures are extracted through a declaration feature extraction unit of an abnormity identification module, risk identification parameters and a risk discrimination ratio are calculated, abnormity is compared and identified through a declaration abnormity threshold value Q, the misjudgment rate is reduced, and the manual rechecking requirement is reduced; and the intelligent analysis module dynamically updates the tax rule base, constructs policy adaptation parameters and rule optimization parameters, automatically adjusts rules in combination with an adaptive algorithm, declarates a compliance threshold Qcg by comparing with comprehensive tax compliance parameters and a risk discrimination ratio, and outputs an optimization instruction, so that the decision support and the automation level of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of information-based tax management, and in particular to a big data-based enterprise tax declaration status monitoring and management system. Background Art

[0002] The background technology of the enterprise tax declaration status monitoring and management system originates from the demand for information-based tax management. With the increase in the number of enterprises and the complexity of tax policies, traditional manual verification and declaration methods are difficult to efficiently meet the compliance needs of enterprises. To this end, tax informatization has been gradually introduced, and the monitoring and management system has come into being by improving declaration efficiency and data accuracy through digital management. The initial system was only used for basic data collection and declaration reminders. With the development of data analysis and artificial intelligence technology, the system has gradually been upgraded to a comprehensive tax compliance monitoring tool that can collect corporate financial data in real time, automatically analyze potential risks, and provide optimization suggestions based on policy changes. Such systems can significantly reduce tax compliance costs both within the enterprise and at the government regulatory level.

[0003] However, the existing enterprise tax declaration status monitoring and management system often has the following technical shortcomings in actual application:

[0004] 1. Difficulty in cross-system integration: The enterprise tax monitoring system needs to be connected with other financial, supply chain and ERP systems. However, due to problems such as inconsistent data formats and incompatible interfaces, integration is difficult, affecting overall data circulation and efficiency.

[0005] 2. Low accuracy of anomaly identification: When dealing with complex transaction structures or cross-border transactions, the system is prone to misjudgment and may identify compliant operations as risks, increasing the cost and workload of manual review.

[0006] 3. Lack of intelligent analysis capabilities: Some systems rely on rule settings to make judgments and are unable to dynamically adapt to emerging tax policies and changing business models. This causes the system to appear rigid when dealing with complex situations and cannot effectively provide decision-making support. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides a big data-based enterprise tax declaration status monitoring and management system, including a data integration module, an abnormality identification module, an intelligent analysis module, a parameter evaluation module and a monitoring optimization module;

[0008] The data integration module is used to automatically extract relevant tax data from different departments within the enterprise, including the enterprise's internal financial data, supply chain data and ERP system data; then integrate and pre-process the relevant tax data of different departments across systems, and finally assign corresponding unique identification marks to the relevant tax data of each department;

[0009] The abnormal identification module is used to extract the declaration feature-related data through big data analysis methods based on the relevant information of historical declaration records and the relevant information of enterprise transaction structure, and preliminarily identify the abnormal declaration operation based on the declaration feature-related data; by constructing the risk identification parameter Ryjc and further calculating the risk discrimination ratio Rpy under the specific transaction mode to filter out abnormal declaration behavior; at the same time, combined with the cross-border tax characteristics, construct multi-dimensional abnormal marking;

[0010] The intelligent analysis module is used to automatically update rules according to the latest tax policy changes. A diversified rule base is set up inside the system to dynamically evaluate the legality and compliance of various tax declarations. By further constructing the policy adaptation parameter Zcsy and the rule optimization parameter Gzyh, combined with the adaptive algorithm to adjust and optimize the rules, it can automatically respond to emerging tax policies and complex transaction patterns.

[0011] The parameter evaluation module is used to calculate the comprehensive tax compliance parameter Twgg, and further calculate the expected tax deviation rate Jfc by comparing the relevant data of historical tax declarations with the actual tax expenditures declared, and preset relevant risk thresholds for evaluation to classify abnormal transaction risk levels;

[0012] The monitoring and optimization module is used to set and update the declaration compliance threshold Qcg, and compare and evaluate it with the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg; based on the evaluation content, it outputs optimization instructions, triggers the corresponding tax declaration optimization process and automatically generates review prompts for related abnormal situations.

[0013] Preferably, the data integration module first sets the data interface and automatic extraction rules, obtains relevant tax data from the data sources of each department at a regular interval, and then realizes the full-process integration of financial, supply chain and ERP data through data crawling and docking; then, the relevant tax data of different departments are uniformly formatted, converted and preprocessed, including data cleaning, normalization, deduplication and missing value filling; after the preprocessing is completed, the relevant tax data of different departments are automatically assigned unique identification marks for data traceability and identification.

[0014] Preferably, the abnormality identification module includes a reporting feature extraction unit and a risk identification unit;

[0015] The declaration feature extraction unit structures the information related to the historical declaration records and the information related to the enterprise transaction structure and forms feature parameters by screening and classifying the information related to the historical declaration records and the information related to the enterprise transaction structure, thereby obtaining declaration feature related data; at the same time, by differentially processing different declaration features in the information related to the historical declaration records, the declaration feature related data includes key features of regular and abnormal declarations;

[0016] Secondly, based on the relevant data of the declared characteristics, the risk identification parameter Ryjc is further constructed, and its specific calculation formula is as follows:

[0017]

[0018] Where, Lsb represents the historical declaration deviation rate in the declaration feature-related data, Jyp represents the transaction frequency abnormal value in the declaration feature-related data, and Sbj represents the declaration amount mutation coefficient in the declaration feature-related data;

[0019] Then, we collect data related to the characteristics of declaration behavior under specific transaction modes, fit them with the risk identification parameter Ryjc, and calculate the risk discrimination ratio Rpy through the following formula:

[0020]

[0021] Wherein, Jyf represents the floating rate of transaction amount in the data related to the characteristics of reporting behavior, and Jys represents the abnormal value of transaction time interval of the floating rate of transaction amount in the data related to the characteristics of reporting behavior.

[0022] Preferably, the risk identification unit is used to compare and evaluate the preset abnormal reporting threshold Q with the risk identification ratio Rpy, and the specific evaluation content is as follows:

[0023] When the risk discrimination ratio Rpy≤the abnormal reporting threshold Q, it means that the current reporting behavior is within the normal range and no additional monitoring or review operations are required;

[0024] When the risk discrimination ratio Rpy>the abnormal reporting threshold Q, it means that the current reporting behavior exceeds the normal range and has deviated from the normal trading mode. The system will mark this reporting behavior as "abnormal" and generate an early warning instruction.

[0025] Preferably, the intelligent analysis module includes a policy adaptation unit and a rule optimization unit;

[0026] The policy adaptation unit is used to update the tax declaration rule base within the system in real time according to the latest tax policy changes; by connecting to the policy database, it automatically monitors and collects tax policy-related data, and obtains the policy adaptation parameter Zcsy through calculation. The specific calculation formula is as follows:

[0027]

[0028] In the formula, Zcy represents the policy impact scope in the tax policy related data, Zcg represents the policy update frequency in the tax policy related data, and Zch represents the policy compliance requirement intensity in the tax policy related data;

[0029] By comparing and analyzing the preset adaptability threshold W and the policy adaptation parameter Zcsy, the following content is generated:

[0030] When the policy adaptation parameter Zcsy>adaptability threshold W, it indicates that the policy change has adaptability requirements. At this time, the existing declaration rules are screened and expanded to generate tax update rules;

[0031] If the policy adaptation parameter Zcsy ≤ the adaptability threshold W, it means that the policy change does not meet the adaptability requirements and does not need to be updated.

[0032] Preferably, the rule optimization unit calculates the rule optimization parameter Gzyh of each rule based on the tax update rule provided by the policy adaptation unit; through the automatic collection and monitoring function of the system, dynamically generates rule usage related data in daily operation, including rule usage frequency Gzs, rule conflict rate Gzc and rule execution efficiency Gzx, and calculates and obtains the rule optimization parameter Gzyh in combination with the following formula:

[0033]

[0034] Then, each rule is sorted from high to low according to the value of the rule optimization parameter Gzyh. The higher the value of the rule optimization parameter Gzyh, the more frequently the current rule is used, and therefore the higher the priority. On the contrary, the lower the value of the rule optimization parameter Gzyh, the lower the priority. Each result sorted by the rule optimization parameter Gzyh is intelligently combined, and the top 50% of the rules are formed into an optimized rule set, and the applicable scenarios and processes are marked in the rule base.

[0035] Finally, in the actual declaration process, the rule optimization parameter Gzyh with the highest value is called first.

[0036] Preferably, the parameter assessment module includes a compliance parameter calculation unit and a risk assessment unit;

[0037] The compliance parameter calculation unit is based on the relevant tax data in the data integration module, and extracts the actual expenditure and declaration difference rate Scc, the historical compliance ratio Lgl and the declaration accuracy Sbz in the relevant tax data, and obtains the comprehensive tax compliance parameter Twgg through the following formula:

[0038]

[0039] Then, by comparing the historical tax declaration and the actual tax expenditure data, we can obtain the historical tax burden Lss and the actual tax expenditure Sjt, and further calculate the expected tax burden deviation rate Jfc through the following formula:

[0040]

[0041] Preferably, the risk assessment unit is used to preset relevant risk thresholds, including a first risk threshold E1 and a second risk threshold E2, to assess the expected tax deviation rate Jfc and classify the abnormal transaction risk level, the specific contents are as follows:

[0042] When the expected tax deviation rate Jfc ≤ the first risk threshold E1, it indicates that the tax deviation is within the normal range, there is no compliance risk, and no further monitoring is required;

[0043] When the first risk threshold E1 < expected tax deviation rate Jfc ≤ second risk threshold E2, it indicates that the tax deviation exceeds the normal range. At this time, the primary risk level is generated, and there is a potential abnormality, but it is within an acceptable range. At this time, the system will generate an internal warning and recommend a review in the next declaration cycle;

[0044] When the expected tax burden deviation rate Jfc>the second risk threshold E2, it indicates that the tax burden deviation exceeds the normal range and there are abnormalities. At this time, a high-level risk level is generated; a high-priority early warning instruction is immediately triggered, and manual review or examination is recommended to further confirm whether there are any declaration errors or violations.

[0045] Preferably, the monitoring optimization module includes a threshold setting unit and an optimization instruction generating unit;

[0046] The threshold setting unit is used to set and dynamically update the declaration compliance threshold Qcg. By comparing historical compliance data and real-time monitoring feedback, combined with the change trend of the comprehensive tax compliance parameter Twgg, the specific value of the declaration compliance threshold Qcg is adjusted in real time, and the range is automatically reset according to policy changes.

[0047] Preferably, the optimization instruction generation unit is used to compare and evaluate the declaration compliance threshold Qcg with the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg, and output the optimization instruction, the specific contents of which are as follows:

[0048] If the comprehensive tax compliance parameter Twgg exceeds the declaration compliance threshold Qcg, but the risk discrimination ratio Rpy is still within the declaration compliance threshold: the system determines that there is a compliance deviation in the current declaration, but the risk assessment shows that its trading behavior does not show abnormal fluctuations; in this case, the system will generate a "first priority" optimization instruction, including adjusting the declaration compliance and conducting subsequent monitoring, without triggering manual review;

[0049] If the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg are both lower than or equal to the declaration compliance threshold Qcg: the system determines that the current declaration does not have compliance deviation, indicating that the declaration data meets the compliance standards and the risk assessment is normal, with no abnormal signs; the system does not trigger any optimization instructions in this case, and the declaration process passes normally;

[0050] If the risk discrimination ratio Rpy exceeds the declaration compliance threshold Qcg, but the comprehensive tax compliance parameter Twgg is still within the declaration compliance threshold: the system determines that the current declaration does not have compliance deviations, but the risk assessment shows that its trading behavior exhibits abnormal fluctuations; in this case, the system generates a "second priority" optimization instruction, including automatically triggering some risk adjustments in the declaration process, and paying attention to the marking and monitoring of potential risk behaviors;

[0051] If the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg both exceed the declaration compliance threshold Qcg: the system determines that the current declaration has compliance deviations and the transaction behavior shows abnormal fluctuations; at this time, the system immediately generates a "third priority" optimization instruction, including forcibly triggering a comprehensive tax declaration optimization process, and generating an emergency review prompt, marking the current declaration as a priority review item, and recommending manual review.

[0052] The present invention provides a big data-based enterprise tax declaration status monitoring and management system, which has the following beneficial effects:

[0053] (1) This enterprise tax declaration status monitoring and management system based on big data, in terms of cross-system integration, automatically extracts the enterprise's internal financial data, supply chain data and ERP system data through the data integration module, sets data interfaces and automatic extraction rules, and realizes unified format conversion and preprocessing of cross-departmental data, including data cleaning, normalization, deduplication and missing value filling, and finally assigns a unique identification mark to each data, effectively solving the problems of difficult cross-system integration, inconsistent data formats and incompatible interfaces, making the overall data flow more efficient and smooth, and improving system integration and data processing efficiency.

[0054] (2) This enterprise tax declaration status monitoring and management system based on big data, in terms of the accuracy of anomaly identification, the system extracts historical declaration records and enterprise transaction structure through the declaration feature extraction unit in the anomaly identification module, generates declaration feature related data, constructs risk identification parameter Ryjc, and further calculates the risk discrimination ratio Rpy by combining the transaction amount fluctuation rate Jyf and the transaction time interval abnormality Jys under the specific transaction mode, and identifies abnormal declaration behaviors that deviate from the normal range through comparison and evaluation of the declaration abnormality threshold Q; the system adopts multi-dimensional labeling and adjusts the accuracy of abnormal declaration behaviors in combination with cross-border tax characteristics, reduces misjudgment, effectively reduces the cost of manual review, and improves the accuracy and automation level of anomaly identification.

[0055] (3) This enterprise tax declaration status monitoring and management system based on big data, in terms of intelligent analysis, is equipped with a policy adaptation unit and a rule optimization unit in the intelligent analysis module. It dynamically evaluates the legality and compliance of various tax declarations according to the latest tax policy changes and diversified rule bases, and automatically responds to emerging tax policies and complex transaction patterns by constructing policy adaptation parameters Zcsy and rule optimization parameters Gzyh, combined with adaptive algorithms to adjust and optimize rules. It also outputs corresponding optimization instructions based on the comparative evaluation of the comprehensive tax compliance parameter Twgg and the risk discrimination ratio Rpy with the declaration compliance threshold Qcg, thereby realizing the dynamic update and optimal configuration of system rules, avoiding the problem of system rigidity, and effectively improving the decision-making support capability and system response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a schematic diagram of the framework structure of a big data-based enterprise tax declaration status monitoring and management system of the present invention;

[0057] Figure 2 This is a simulation experiment data diagram of a big data-based enterprise tax declaration status monitoring and management system of the present invention;

[0058] Figure 3 A graph showing the relationship between risk identification parameters and risk discrimination ratios of a corporate tax declaration status monitoring and management system based on big data according to the present invention;

[0059] Figure 4 It is a distribution chart of the rule optimization parameter Gzyh in the enterprise of the enterprise tax declaration status monitoring and management system based on big data of the present invention;

[0060] Figure 5 The present invention provides a graph showing the relationship between comprehensive tax compliance parameters and expected tax burden deviation rate of an enterprise tax declaration status monitoring and management system based on big data. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Example 1

[0063] See also Figure 1 , a big data-based enterprise tax declaration status monitoring and management system, including a data integration module, an abnormality identification module, an intelligent analysis module, a parameter evaluation module and a monitoring optimization module;

[0064] The data integration module is used to automatically extract relevant tax data from different departments within the enterprise, including the enterprise's internal financial data, supply chain data and ERP system data; then integrate and pre-process the relevant tax data of different departments across systems, and finally assign corresponding unique identification marks to the relevant tax data of each department;

[0065] The abnormal identification module is used to extract the declaration feature-related data through big data analysis methods based on the relevant information of historical declaration records and the relevant information of enterprise transaction structure, and preliminarily identify the abnormal declaration operation based on the declaration feature-related data; by constructing the risk identification parameter Ryjc and further calculating the risk discrimination ratio Rpy under the specific transaction mode to filter out abnormal declaration behavior; at the same time, combined with the cross-border tax characteristics, construct multi-dimensional abnormal marking;

[0066] The intelligent analysis module is used to automatically update rules according to the latest tax policy changes. A diversified rule base is set up inside the system to dynamically evaluate the legality and compliance of various tax declarations. By further constructing the policy adaptation parameter Zcsy and the rule optimization parameter Gzyh, combined with the adaptive algorithm to adjust and optimize the rules, it can automatically respond to emerging tax policies and complex transaction patterns.

[0067] The parameter evaluation module is used to calculate the comprehensive tax compliance parameter Twgg, and further calculate the expected tax deviation rate Jfc by comparing the relevant data of historical tax declarations with the actual tax expenditures declared, and preset relevant risk thresholds for evaluation to classify abnormal transaction risk levels;

[0068] The monitoring and optimization module is used to set and update the declaration compliance threshold Qcg, and compare and evaluate it with the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg; based on the evaluation content, it outputs optimization instructions, triggers the corresponding tax declaration optimization process and automatically generates review prompts for related abnormal situations.

[0069] In this embodiment, the data integration module can automatically extract tax data from the company's internal financial data, supply chain data and ERP system data, and effectively solve the problems of inconsistent data formats and incompatible interfaces in different departments through cross-system integration and preprocessing, thereby improving the efficiency of data circulation and assigning unique identification marks to the data for easy traceability; the anomaly recognition module extracts declaration feature-related data based on historical declaration records and corporate transaction structures, constructs risk identification parameters Ryjc and calculates risk discrimination ratio Rpy, and significantly improves the accuracy of anomaly recognition through big data analysis and multi-dimensional labeling, effectively reducing misjudgment and manual review costs; the intelligent analysis module combines policy adaptation parameters Zcsy and rule optimization parameters Gzyh to The adaptive algorithm automatically updates and optimizes the tax rule base, allowing the system to dynamically adapt to emerging tax policies and complex transaction patterns, improving the system's response speed and compliance assessment capabilities; the parameter assessment module comprehensively evaluates the historical declared tax and actual expenditure data of the declared data through the comprehensive tax compliance parameter Twgg and the expected tax burden deviation rate Jfc, and divides the risk level of abnormal transactions to provide a scientific basis for the identification of abnormal declarations; the monitoring and optimization module sets and updates the declaration compliance threshold Qcg, compares it with the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg, outputs optimization instructions, triggers tax declaration process optimization and abnormal review prompts, and effectively ensures the compliance of corporate tax declarations and risk monitoring efficiency.

[0070] Example 2

[0071] The data integration module first sets the data interface and automatic extraction rules, regularly obtains relevant tax data from the data sources of each department, and then realizes the full-process integration of financial, supply chain and ERP data through data crawling and docking; then the relevant tax data of different departments are uniformly formatted and preprocessed, including data cleaning, normalization, deduplication and missing value filling; after completing the preprocessing, the relevant tax data of different departments are automatically assigned unique identification marks for data traceability and identification.

[0072] In this embodiment, the data integration module obtains relevant tax data from data sources of departments such as the company's finance, supply chain and ERP system on a regular basis by setting data interfaces and automatic extraction rules, and realizes full-process data integration through data crawling and docking, ensuring that the system can fully cover the company's multi-source data; after performing unified format conversion and preprocessing operations, the system cleans, normalizes, deduplicates and fills in missing values ​​for data from different departments, thereby improving the accuracy and consistency of the data; finally, a unique identification mark is assigned to the data of each department to facilitate data tracing and identification in subsequent processing, thereby realizing efficient data management and tracking; this module not only significantly reduces the difficulty of cross-departmental integration and the data circulation barriers caused by interface incompatibility, and ensures the integrity and accuracy of the data, but also provides an accurate and unified tax data foundation for subsequent modules such as abnormal identification, intelligent analysis and risk assessment, and is the basic support for the entire tax declaration status monitoring and management system.

[0073] Example 3

[0074] The anomaly identification module includes a declaration feature extraction unit and a risk identification unit;

[0075] The declaration feature extraction unit structures the information related to the historical declaration records and the information related to the enterprise transaction structure and forms feature parameters by screening and classifying the information related to the historical declaration records and the information related to the enterprise transaction structure, thereby obtaining declaration feature related data; at the same time, by differentially processing different declaration features in the information related to the historical declaration records, the declaration feature related data includes key features of regular and abnormal declarations;

[0076] Secondly, based on the relevant data of the declared characteristics, the risk identification parameter Ryjc is further constructed, and its specific calculation formula is as follows:

[0077]

[0078] Where, Lsb represents the historical declaration deviation rate in the declaration feature-related data, Jyp represents the transaction frequency abnormal value in the declaration feature-related data, and Sbj represents the declaration amount mutation coefficient in the declaration feature-related data;

[0079] Then, we collect data related to the characteristics of declaration behavior under specific transaction modes, fit them with the risk identification parameter Ryjc, and calculate the risk discrimination ratio Rpy through the following formula:

[0080]

[0081] Wherein, Jyf represents the floating rate of transaction amount in the data related to the characteristics of reporting behavior, and Jys represents the abnormal value of transaction time interval of the floating rate of transaction amount in the data related to the characteristics of reporting behavior.

[0082] The risk identification unit is used to compare and evaluate the preset abnormal reporting threshold Q with the risk identification ratio Rpy. The specific evaluation content is as follows:

[0083] When the risk discrimination ratio Rpy≤the abnormal reporting threshold Q, it means that the current reporting behavior is within the normal range and no additional monitoring or review operations are required;

[0084] When the risk discrimination ratio Rpy>the abnormal reporting threshold Q, it means that the current reporting behavior exceeds the normal range and has deviated from the normal trading mode. The system will mark this reporting behavior as "abnormal" and generate an early warning instruction.

[0085] The intelligent analysis module includes a policy adaptation unit and a rule optimization unit;

[0086] The policy adaptation unit is used to update the tax declaration rule base within the system in real time according to the latest tax policy changes; by connecting to the policy database, it automatically monitors and collects tax policy-related data, and obtains the policy adaptation parameter Zcsy through calculation. The specific calculation formula is as follows:

[0087]

[0088] In the formula, Zcy represents the policy impact scope in the tax policy related data, Zcg represents the policy update frequency in the tax policy related data, and Zch represents the policy compliance requirement intensity in the tax policy related data;

[0089] By comparing and analyzing the preset adaptability threshold W and the policy adaptation parameter Zcsy, the following content is generated:

[0090] When the policy adaptation parameter Zcsy>adaptability threshold W, it indicates that the policy change has adaptability requirements. At this time, the existing declaration rules are screened and expanded to generate tax update rules;

[0091] If the policy adaptation parameter Zcsy ≤ the adaptability threshold W, it means that the policy change does not meet the adaptability requirements and does not need to be updated.

[0092] The rule optimization unit calculates the rule optimization parameter Gzyh of each rule based on the tax update rules provided by the policy adaptation unit; through the automatic collection and monitoring function of the system, it dynamically generates rule usage related data in daily operation, including rule usage frequency Gzs, rule conflict rate Gzc and rule execution efficiency Gzx, and calculates the rule optimization parameter Gzyh in combination with the following formula:

[0093]

[0094] Then, each rule is sorted from high to low according to the value of the rule optimization parameter Gzyh. The higher the value of the rule optimization parameter Gzyh, the more frequently the current rule is used, and therefore the higher the priority. On the contrary, the lower the value of the rule optimization parameter Gzyh, the lower the priority. Each result sorted by the rule optimization parameter Gzyh is intelligently combined, and the top 50% of the rules are formed into an optimized rule set, and the applicable scenarios and processes are marked in the rule base.

[0095] Finally, in the actual declaration process, the rule optimization parameter Gzyh with the highest value is called first.

[0096] The parameter assessment module includes a compliance parameter calculation unit and a risk assessment unit;

[0097] The compliance parameter calculation unit is based on the relevant tax data in the data integration module, and extracts the actual expenditure and declaration difference rate Scc, the historical compliance ratio Lgl and the declaration accuracy Sbz in the relevant tax data, and obtains the comprehensive tax compliance parameter Twgg through the following formula:

[0098]

[0099] Then, by comparing the historical tax declaration and the actual tax expenditure data, we can obtain the historical tax burden Lss and the actual tax expenditure Sjt, and further calculate the expected tax burden deviation rate Jfc through the following formula:

[0100]

[0101] The risk assessment unit is used to preset relevant risk thresholds, including a first risk threshold E1 and a second risk threshold E2, to assess the expected tax deviation rate Jfc and to classify the abnormal transaction risk levels. The specific contents are as follows:

[0102] When the expected tax deviation rate Jfc ≤ the first risk threshold E1, it indicates that the tax deviation is within the normal range, there is no compliance risk, and no further monitoring is required;

[0103] When the first risk threshold E1 < expected tax deviation rate Jfc ≤ second risk threshold E2, it indicates that the tax deviation exceeds the normal range. At this time, the primary risk level is generated, and there is a potential abnormality, but it is within an acceptable range. At this time, the system will generate an internal warning and recommend a review in the next declaration cycle;

[0104] When the expected tax burden deviation rate Jfc>the second risk threshold E2, it indicates that the tax burden deviation exceeds the normal range and there are abnormalities. At this time, a high-level risk level is generated; a high-priority early warning instruction is immediately triggered, and manual review or examination is recommended to further confirm whether there are any declaration errors or violations.

[0105] In this embodiment, the core function of the anomaly identification module is to identify and evaluate the risks in tax declaration behavior. The declaration feature extraction unit selects key feature parameters from past declaration information, such as the historical declaration deviation rate Lsb, the transaction frequency abnormal value Jyp and the declaration amount mutation coefficient Sbj, to construct the risk identification parameters Ryjc for diversified declaration scenarios; these parameters help to clarify the abnormal characteristics in the declaration behavior, and combine with the characteristics of the transaction behavior to form a risk discrimination ratio Rpy, which is used to finally determine whether there is an abnormality in the declaration; the risk discrimination unit compares and evaluates the preset declaration abnormality threshold Q with the risk discrimination ratio Rpy, thereby realizing automatic identification of normal and abnormal behaviors; if the risk discrimination ratio Rpy exceeds the threshold Q, it will trigger an early warning to prevent potential risks, which significantly improves the system's ability to identify abnormal declaration behaviors and accuracy; among them, the historical declaration The reporting deviation rate Lsb is calculated by counting the deviations between the tax data reported by the enterprise in the past and its actual operating conditions, using the historical data comparison method; the transaction frequency anomaly Jyp is based on the time series analysis of the enterprise's transaction records, calculates the frequency of transactions, and detects whether there is a short-term transaction concentration. Common methods include mean deviation calculation or standard deviation screening; the declared amount mutation coefficient Sbj uses a sliding window algorithm or a trend change detection method to calculate the sudden increase or decrease in the declared amount; and the transaction amount floating rate Yf is based on the enterprise's transaction data, and calculates the rate of change of the transaction amount in different time windows, usually using the change ratio or relative growth rate calculation method; the transaction time interval anomaly Jys analyzes the transaction time distribution and calculates whether the time interval between adjacent transactions conforms to the normal business logic. Usually, mean deviation or standardized transformation is used for anomaly detection;

[0106] The intelligent analysis module includes a policy adaptation unit and a rule optimization unit, which are used to dynamically update and optimize the tax rule system to adapt to the latest policy changes; the policy adaptation unit is based on tax policy-related data, such as policy impact scope Zcy, policy update frequency Zcg and policy compliance requirement intensity Zch, and calculates the policy adaptation parameter Zcsy to determine whether the declaration rules need to be screened and expanded, thereby forming new tax update rules; the rule optimization unit further calculates the rule optimization parameter Gzyh based on parameters including rule usage frequency Gzs, rule conflict rate Gzc and rule execution efficiency Gzx, and prioritizes the rules to form an optimized rule set, so that the system can give priority to the application of the most suitable rules in different scenarios, thereby improving the execution efficiency of the rules and the compliance of the declaration. The policy impact scope Zcy is based on the scope of tax policy changes, combined with the analysis of policy and regulatory texts, to conduct labeling analysis on applicable enterprise types, industry fields, etc. The policy update frequency Zcg analyzes the issuance frequency of tax policies and uses time series analysis to calculate their update trends. The intensity of policy compliance requirements Zch uses text mining and rule analysis methods to classify and evaluate key requirements in tax policies. The rule usage frequency Gzs counts the number of times enterprises use various rules in the declaration process and calculates the frequency based on log data. The rule conflict rate Gzc cross-validates the execution results of different rules and calculates the conflict rate. The rule execution efficiency Gzx uses time records or task completion rate analysis to calculate the execution speed and impact of rules in the declaration process.

[0107] The parameter evaluation module includes a compliance parameter calculation unit and a risk assessment unit, which are used to deeply analyze the actual compliance status of tax declarations; the compliance parameter calculation unit calculates the comprehensive tax compliance parameter Twgg through the historical compliance ratio Lgl, the declaration accuracy Sbz and the actual expenditure and declaration difference rate Scc to ensure the accuracy of the declaration; the risk assessment unit calculates the expected tax burden deviation rate Jfc by comparing the historical declared tax burden Lss with the actual tax expenditure tax burden Sjt, and conducts a graded assessment of tax risks based on this value; if the tax burden deviation rate Jfc is higher than the set first risk threshold E1 and the second risk threshold E2, different levels of risk warnings will be triggered, thereby helping the system to identify potential compliance risks early and provide corresponding response suggestions, so that the overall compliance of tax declarations is effectively improved;

[0108] Among them, the actual expenditure and declared difference rate Scc is calculated by comparing the actual operating expenditure of the enterprise with the declared data, using the ratio analysis method. The historical compliance ratio Lgl is based on the enterprise's past tax compliance records to calculate the proportion of compliant behaviors. The declaration accuracy Sbz combines the historical declaration data with the audit feedback information to calculate the accuracy level of the declared data. The historical declared tax burden Lss statistics the enterprise's past tax declaration burden and uses historical data to calculate retrospectively. The actual tax expenditure tax burden Sjt is calculated based on the taxes actually paid by the enterprise, usually using invoice verification or financial statement analysis.

[0109] Example 4

[0110] The monitoring optimization module includes a threshold setting unit and an optimization instruction generating unit;

[0111] The threshold setting unit is used to set and dynamically update the declaration compliance threshold Qcg. By comparing historical compliance data and real-time monitoring feedback, combined with the change trend of the comprehensive tax compliance parameter Twgg, the specific value of the declaration compliance threshold Qcg is adjusted in real time, and the range is automatically reset according to policy changes.

[0112] The optimization instruction generation unit is used to compare and evaluate the declaration compliance threshold Qcg with the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg, and output the optimization instruction, the specific contents of which are as follows:

[0113] If the comprehensive tax compliance parameter Twgg exceeds the declaration compliance threshold Qcg, but the risk discrimination ratio Rpy is still within the declaration compliance threshold: the system determines that there is a compliance deviation in the current declaration, but the risk assessment shows that its trading behavior does not show abnormal fluctuations; in this case, the system will generate a "first priority" optimization instruction, including adjusting the declaration compliance and conducting subsequent monitoring, without triggering manual review;

[0114] If the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg are both lower than or equal to the declaration compliance threshold Qcg: the system determines that the current declaration does not have compliance deviation, indicating that the declaration data meets the compliance standards and the risk assessment is normal, with no abnormal signs; the system does not trigger any optimization instructions in this case, and the declaration process passes normally;

[0115] If the risk discrimination ratio Rpy exceeds the declaration compliance threshold Qcg, but the comprehensive tax compliance parameter Twgg is still within the declaration compliance threshold: the system determines that the current declaration does not have compliance deviations, but the risk assessment shows that its trading behavior exhibits abnormal fluctuations; in this case, the system generates a "second priority" optimization instruction, including automatically triggering some risk adjustments in the declaration process, and paying attention to the marking and monitoring of potential risk behaviors;

[0116] If the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg both exceed the declaration compliance threshold Qcg: the system determines that the current declaration has compliance deviations and the transaction behavior shows abnormal fluctuations; at this time, the system immediately generates a "third priority" optimization instruction, including forcibly triggering a comprehensive tax declaration optimization process, and generating an emergency review prompt, marking the current declaration as a priority review item, and recommending manual review.

[0117] In this embodiment, the monitoring and optimization module effectively improves the compliance management and risk control capabilities of the system through the combination of a threshold setting unit and an optimization instruction generation unit; the threshold setting unit dynamically adjusts the declaration compliance threshold Qcg according to the change trend of the comprehensive tax compliance parameter Twgg and real-time monitoring feedback, ensuring that a reasonable range is automatically set when the policy changes, making the compliance assessment standard more flexible and accurate; the optimization instruction generation unit compares and evaluates the declaration compliance threshold Qcg with the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg, and can generate corresponding priority optimization instructions according to different assessment situations; among them, the system generates a "first priority" instruction to adjust compliance and conduct subsequent monitoring; the system generates a "second priority" instruction to mark and monitor potential risks; the system generates a "third priority" instruction to force optimization of the declaration process and generate an emergency review prompt; it plays an important role in automated risk screening and graded optimization, ensuring timely response and efficient management of corporate declaration compliance and risk control.

[0118] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A big data-based enterprise tax declaration status monitoring and management system, characterized by: It includes data integration module, anomaly identification module, intelligent analysis module, parameter evaluation module and monitoring optimization module; The data integration module is used to automatically extract relevant tax data from different departments within the enterprise, including the enterprise's internal financial data, supply chain data and ERP system data; then integrate and pre-process the relevant tax data of different departments across systems, and finally assign corresponding unique identification marks to the relevant tax data of each department; The abnormal identification module is used to extract the declaration feature-related data through big data analysis methods based on the relevant information of historical declaration records and the relevant information of enterprise transaction structure, and preliminarily identify the abnormal declaration operation based on the declaration feature-related data; by constructing the risk identification parameter Ryjc and further calculating the risk discrimination ratio Rpy under the specific transaction mode to filter out abnormal declaration behavior; at the same time, combined with the cross-border tax characteristics, construct multi-dimensional abnormal marking; The intelligent analysis module is used to automatically update rules according to the latest tax policy changes. A diversified rule base is set up inside the system to dynamically evaluate the legality and compliance of various tax declarations. By further constructing the policy adaptation parameter Zcsy and the rule optimization parameter Gzyh, combined with the adaptive algorithm to adjust and optimize the rules, it can automatically respond to emerging tax policies and complex transaction patterns. The parameter evaluation module is used to calculate the comprehensive tax compliance parameter Twgg, and further calculate the expected tax deviation rate Jfc by comparing the relevant data of historical tax declarations with the actual tax expenditures declared, and preset relevant risk thresholds for evaluation to classify abnormal transaction risk levels; The monitoring and optimization module is used to set and update the declaration compliance threshold Qcg, and compare and evaluate it with the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg; based on the evaluation content, it outputs optimization instructions, triggers the corresponding tax declaration optimization process and automatically generates review prompts for related abnormal situations.

2. According to the big data-based enterprise tax declaration status monitoring and management system of claim 1, it is characterized by: The data integration module first sets up data interfaces and automated extraction rules, obtains relevant tax data from data sources of various departments on a regular basis, and then achieves full-process integration of financial, supply chain and ERP data through data crawling and docking; then performs unified format conversion and preprocessing operations on relevant tax data of different departments, including data cleaning, normalization, deduplication and missing value filling; After preprocessing is completed, unique identification marks for data traceability and identification are automatically assigned to relevant tax data of different departments.

3. According to the big data-based enterprise tax declaration status monitoring and management system of claim 1, it is characterized by: The anomaly identification module includes a declaration feature extraction unit and a risk identification unit; The declaration feature extraction unit structures the information related to the historical declaration records and the information related to the enterprise transaction structure and forms feature parameters by screening and classifying the information related to the historical declaration records and the information related to the enterprise transaction structure, thereby obtaining declaration feature related data; at the same time, by differentially processing different declaration features in the information related to the historical declaration records, the declaration feature related data includes key features of regular and abnormal declarations; Secondly, based on the relevant data of the declared characteristics, the risk identification parameter Ryjc is further constructed, and its specific calculation formula is as follows: Where, Lsb represents the historical declaration deviation rate in the declaration feature-related data, Jyp represents the transaction frequency abnormal value in the declaration feature-related data, and Sbj represents the declaration amount mutation coefficient in the declaration feature-related data; Then, we collect data related to the characteristics of declaration behavior under specific transaction modes, fit them with the risk identification parameter Ryjc, and calculate the risk discrimination ratio Rpy through the following formula: Wherein, Jyf represents the floating rate of transaction amount in the data related to the characteristics of reporting behavior, and Jys represents the abnormal value of transaction time interval of the floating rate of transaction amount in the data related to the characteristics of reporting behavior.

4. According to the big data-based enterprise tax declaration status monitoring and management system of claim 3, it is characterized by: The risk identification unit is used to compare and evaluate the preset abnormal reporting threshold Q with the risk identification ratio Rpy. The specific evaluation content is as follows: When the risk discrimination ratio Rpy≤the abnormal reporting threshold Q, it means that the current reporting behavior is within the normal range and no additional monitoring or review operations are required; When the risk discrimination ratio Rpy>the abnormal reporting threshold Q, it means that the current reporting behavior exceeds the normal range and has deviated from the normal trading mode. The system will mark this reporting behavior as "abnormal" and generate an early warning instruction.

5. According to the big data-based enterprise tax declaration status monitoring and management system of claim 1, it is characterized by: The intelligent analysis module includes a policy adaptation unit and a rule optimization unit; The policy adaptation unit is used to update the tax declaration rule base within the system in real time according to the latest tax policy changes; by connecting to the policy database, it automatically monitors and collects tax policy-related data, and obtains the policy adaptation parameter Zcsy through calculation. The specific calculation formula is as follows: In the formula, Zcy represents the policy impact scope in the tax policy related data, Zcg represents the policy update frequency in the tax policy related data, and Zch represents the policy compliance requirement intensity in the tax policy related data; By comparing and analyzing the preset adaptability threshold W and the policy adaptation parameter Zcsy, the following content is generated: When the policy adaptation parameter Zcsy>adaptability threshold W, it indicates that the policy change has adaptability requirements. At this time, the existing declaration rules are screened and expanded to generate tax update rules; If the policy adaptation parameter Zcsy ≤ the adaptability threshold W, it means that the policy change does not meet the adaptability requirements and does not need to be updated.

6. According to the big data-based enterprise tax declaration status monitoring and management system of claim 5, it is characterized by: The rule optimization unit calculates the rule optimization parameter Gzyh of each rule based on the tax update rules provided by the policy adaptation unit; through the automatic collection and monitoring function of the system, it dynamically generates rule usage related data in daily operation, including rule usage frequency Gzs, rule conflict rate Gzc and rule execution efficiency Gzx, and calculates the rule optimization parameter Gzyh in combination with the following formula: Then, each rule is sorted from high to low according to the value of the rule optimization parameter Gzyh. The higher the value of the rule optimization parameter Gzyh, the more frequently the current rule is used, and therefore the higher the priority. On the contrary, the lower the value of the rule optimization parameter Gzyh, the lower the priority. Each result sorted by the rule optimization parameter Gzyh is intelligently combined, and the top 50% of the rules are formed into an optimized rule set, and the applicable scenarios and processes are marked in the rule base. Finally, in the actual declaration process, the rule optimization parameter Gzyh with the highest value is called first.

7. According to the big data-based enterprise tax declaration status monitoring and management system of claim 1, it is characterized by: The parameter assessment module includes a compliance parameter calculation unit and a risk assessment unit; The compliance parameter calculation unit is based on the relevant tax data in the data integration module, and extracts the actual expenditure and declaration difference rate Scc, the historical compliance ratio Lgl and the declaration accuracy Sbz in the relevant tax data, and obtains the comprehensive tax compliance parameter Twgg through the following formula: Then, by comparing the historical tax declaration and the actual tax expenditure data, we can obtain the historical tax burden Lss and the actual tax expenditure Sjt, and further calculate the expected tax burden deviation rate Jfc through the following formula:

8. The enterprise tax declaration status monitoring and management system based on big data according to claim 7 is characterized by: The risk assessment unit is used to preset relevant risk thresholds, including a first risk threshold E1 and a second risk threshold E2, to assess the expected tax deviation rate Jfc and to classify the abnormal transaction risk levels. The specific contents are as follows: When the expected tax deviation rate Jfc ≤ the first risk threshold E1, it indicates that the tax deviation is within the normal range, there is no compliance risk, and no further monitoring is required; When the first risk threshold E1 < expected tax deviation rate Jfc ≤ second risk threshold E2, it indicates that the tax deviation exceeds the normal range. At this time, the primary risk level is generated, and there is a potential abnormality, but it is within an acceptable range. At this time, the system will generate an internal warning and recommend a review in the next declaration cycle; When the expected tax deviation rate Jfc> the second risk threshold E2, it indicates that the tax deviation exceeds the normal range and there is an abnormality, and a high-level risk level is generated at this time; A high-priority warning instruction is immediately triggered, and manual review or examination is recommended to further confirm whether there are any reporting errors or violations.

9. The enterprise tax declaration status monitoring and management system based on big data according to claim 1 is characterized by: The monitoring optimization module includes a threshold setting unit and an optimization instruction generating unit; The threshold setting unit is used to set and dynamically update the declaration compliance threshold Qcg. By comparing historical compliance data and real-time monitoring feedback, combined with the change trend of the comprehensive tax compliance parameter Twgg, the specific value of the declaration compliance threshold Qcg is adjusted in real time, and the range is automatically reset according to policy changes.

10. The enterprise tax declaration status monitoring and management system based on big data according to claim 9 is characterized by: The optimization instruction generation unit is used to compare and evaluate the declaration compliance threshold Qcg with the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg, and output the optimization instruction, the specific contents of which are as follows: If the comprehensive tax compliance parameter Twgg exceeds the declaration compliance threshold Qcg, but the risk discrimination ratio Rpy is still within the declaration compliance threshold: the system determines that there is a compliance deviation in the current declaration, but the risk assessment shows that its trading behavior does not show abnormal fluctuations; in this case, the system will generate a "first priority" optimization instruction, including adjusting the declaration compliance and conducting subsequent monitoring, without triggering manual review; If the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg are both lower than or equal to the declaration compliance threshold Qcg: the system determines that the current declaration does not have compliance deviation, indicating that the declaration data meets the compliance standards and the risk assessment is normal, with no abnormal signs; the system does not trigger any optimization instructions in this case, and the declaration process passes normally; If the risk discrimination ratio Rpy exceeds the declaration compliance threshold Qcg, but the comprehensive tax compliance parameter Twgg is still within the declaration compliance threshold: the system determines that the current declaration does not have compliance deviations, but the risk assessment shows that its trading behavior exhibits abnormal fluctuations; in this case, the system generates "second priority" optimization instructions, including automatically triggering some risk adjustments in the declaration process, and focusing on the marking and monitoring of potential risk behaviors; If the risk discrimination ratio Rpy and the comprehensive tax compliance parameter Twgg both exceed the declaration compliance threshold Qcg: the system determines that the current declaration has compliance deviations and the transaction behavior shows abnormal fluctuations; at this time, the system immediately generates a "third priority" optimization instruction, including forcibly triggering a comprehensive tax declaration optimization process, and generating an emergency review prompt, marking the current declaration as a priority review item, and recommending manual review.

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