Finance and tax data analysis method and system based on big data

Abstract

The embodiment of the invention provides a big data-based finance and taxation data analysis method and system, and the method comprises the steps: collecting finance and taxation related data flows from a plurality of heterogeneous data sources, constructing a finance and taxation index system, and automatically updating and optimizing the existing finance and taxation indexes and the weights of the existing finance and taxation indexes. Making a finance and taxation index system map the data flow in real time to generate a multi-dimensional finance and taxation index snapshot, calculating finance and taxation health scores under different dimensions by using a preset finance and taxation health degree evaluation model and combining the multi-dimensional finance and taxation index snapshot, and analyzing the internal relation among the finance and taxation health scores by using an association rule mining algorithm to obtain the finance and taxation health degree evaluation result. Determining a potential finance and tax risk early warning signal and an optimized suggested path, and outputting a comprehensive report; according to the technical scheme provided by the embodiment of the invention, comprehensive coverage of finance and taxation information is realized, the timeliness and accuracy of finance and taxation management are improved, finance and taxation risks can be prevented and solved in advance, and enterprises are guided to optimize finance management and operation strategies.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of fiscal and tax data analysis, and in particular, to a method and system for fiscal and tax data analysis based on big data. Background Art

[0002] In the context of modern enterprise management and government supervision, fiscal and tax data analysis plays a crucial role. With the development of economic globalization and information technology, enterprises are facing fierce competition from domestic and international markets and an increasingly complex fiscal and tax regulatory environment;

[0003] Currently, the traditional fiscal and tax data analysis methods adopted by most enterprises and institutions mainly include manual data collation, report generation based on fixed templates, and simple statistical analysis. These methods rely on predefined fiscal and tax indicators and fixed weight allocation rules. Although they can meet the basic data analysis needs to a certain extent, they lack flexibility and adaptive capabilities. In addition, although some existing advanced analysis tools introduce machine learning and big data processing technologies, in actual applications, they often ignore the dynamics and complexity of fiscal and tax data, and fail to fully consider the impact of real-time regulatory requirements, changes in the macroeconomic environment, and adjustments to the enterprise's own business strategies on fiscal and tax indicators. Summary of the Invention

[0004] The embodiments of the present application provide a method and system for fiscal and tax data analysis based on big data, which are used to solve the problems in the prior art that it is impossible to respond to regulatory updates and market changes in real time, resulting in inaccurate assessment of fiscal and tax health, and ignoring the internal connections between different scores and their potential risk warning signals, thereby limiting its ability to provide a comprehensive optimization advice path.

[0005] In a first aspect, the embodiments of the present application provide a method for fiscal and tax data analysis based on big data, including:

[0006] Collecting fiscal and tax-related data streams from multiple heterogeneous data sources;

[0007] Constructing a fiscal and tax indicator system, and using the fiscal and tax indicator system to update and optimize existing fiscal and tax indicators and existing fiscal and tax indicator weights according to real-time regulatory requirements, real-time changes in the macroeconomic environment, and real-time changes in the enterprise's own business strategies, so that the fiscal and tax indicator system can map the fiscal and tax-related data streams in real time to generate multi-dimensional fiscal and tax indicator snapshots;

[0008] Using a preset fiscal and tax health assessment model, and combining the multi-dimensional fiscal and tax indicator snapshots, calculating fiscal and tax health scores in different dimensions;

[0009] Using an association rule mining algorithm to analyze the internal connections between the fiscal and tax health scores, and determining potential fiscal and tax risk warning signals and optimization advice paths;

[0010] Output a comprehensive report containing the aforesaid fiscal and tax health score, the aforesaid potential fiscal and tax risk warning signals, and optimization suggestions.

[0011] 2. The method according to claim 1, characterized in that a fiscal and tax index system is constructed, and using the fiscal and tax index system, according to the real-time regulatory requirements, the real-time changes in the macroeconomic environment, and the real-time transformation of the enterprise's own business strategies, the existing fiscal and tax indexes and the weights of the existing fiscal and tax indexes are updated and optimized, so that the fiscal and tax index system can map the aforesaid fiscal and tax related data streams in real time to generate a multi-dimensional fiscal and tax index snapshot, including:

[0012] Monitor and parse the information sources from the regulatory update interface, the macroeconomic data release platform, and the enterprise internal management strategy change notice to obtain a set of key factors affecting the fiscal and tax indexes;

[0013] Based on the aforesaid set of key factors, evaluate and select the adjustment plan of the fiscal and tax index system through an intelligent decision-making engine to obtain the optimal adjustment plan;

[0014] According to the optimal adjustment plan, use the incremental learning algorithm to optimize the existing fiscal and tax indexes and the weights of the existing fiscal and tax indexes to obtain a dynamic fiscal and tax index system;

[0015] Use the dynamic fiscal and tax index system to map and process the aforesaid fiscal and tax related data streams to generate a multi-dimensional fiscal and tax index snapshot for reflecting the real-time fiscal and tax situation and trends.

[0016] Optionally, according to the optimal adjustment plan, use the incremental learning algorithm to optimize the existing fiscal and tax indexes and the weights of the existing fiscal and tax indexes to obtain a dynamic fiscal and tax index system, including:

[0017] Use the intelligent decision-making engine to determine the existing fiscal and tax indexes and the corresponding weight values of the existing fiscal and tax indexes based on the optimal adjustment plan to obtain a list of fiscal and tax indexes to be optimized;

[0018] According to the list of fiscal and tax indexes to be optimized, use the incremental learning algorithm to conduct an intelligent evaluation and adjustment of the fiscal and tax index system to generate a preliminarily optimized fiscal and tax index system;

[0019] Implement feedback and monitoring processing during the adjustment process of the fiscal and tax index system, track and record the impact of each adjustment on the fiscal and tax index system to obtain a verified fiscal and tax index system;

[0020] Iteratively optimize and converge the verified initial fiscal and tax index system, adjust the existing fiscal and tax indexes and the weights of the existing fiscal and tax indexes until the preset convergence condition is reached to generate a dynamic fiscal and tax index system.

[0021] Optionally, perform iterative optimization and convergence on the verified initial fiscal and tax index system, adjust existing fiscal and tax indexes and the weights of existing fiscal and tax indexes until the preset convergence condition is met, so as to generate a dynamic fiscal and tax index system, including:

[0022] Utilize the real-time economic environment and the internal change rate of the enterprise to perform adaptive adjustment processing on the learning rate of the incremental learning algorithm, and obtain an adaptive learning rate mechanism for responding to the latest data and policy changes;

[0023] According to the adaptive learning rate mechanism, adjust the fiscal and tax indexes and the weights of fiscal and tax indexes in the verified initial fiscal and tax index system to generate an adjusted initial fiscal and tax index system;

[0024] Utilize high-precision monitoring technology to comprehensively evaluate the adjusted initial fiscal and tax index system from multiple dimensions, generate an evaluation result, and feedback the evaluation result to the intelligent decision-making engine to obtain a real-time optimized and evaluation-passed initial fiscal and tax index system;

[0025] Based on the reinforcement learning strategy, combine the historical optimization path and the current environmental state, implement the iterative optimization process of the real-time optimized and evaluation-passed initial fiscal and tax index system, and set strict convergence conditions to generate an optimized fiscal and tax index system;

[0026] Implement an intelligent early warning system to monitor the health of the optimized fiscal and tax index system. When potential risks are detected, trigger an automatic repair mechanism, adjust relevant fiscal and tax indexes and the weights of relevant fiscal and tax indexes to generate an optimized fiscal and tax index system with self-repair ability;

[0027] When the optimized fiscal and tax index system with self-repair ability meets the preset convergence condition, stop the adjustment to generate a dynamic fiscal and tax index system.

[0028] Optionally, utilize a preset fiscal and tax health evaluation model, combine with the multi-dimensional fiscal and tax index snapshot, and calculate the fiscal and tax health scores in different dimensions, including:

[0029] Utilize a preset fiscal and tax health evaluation model to perform quantitative analysis processing on each fiscal and tax data and index in the multi-dimensional fiscal and tax index snapshot to obtain a preliminary fiscal and tax health score vector;

[0030] According to the weight distribution rule built in the fiscal and tax health evaluation model, assign corresponding weight values to each dimension in the preliminary fiscal and tax health score vector to reflect the importance of each dimension in the overall fiscal and tax health status evaluation, and generate a weighted fiscal and tax health score vector;

[0031] Dynamically adjust the weighted fiscal and tax health score vector in combination with historical fiscal and tax data and the changing trend of the macroeconomic environment to obtain a dynamically adjusted fiscal and tax health score;

[0032] Based on the dynamically adjusted fiscal and tax health score, conduct a comprehensive evaluation according to the interrelationships and comprehensive influencing factors among different dimensions, and calculate a multi-dimensional fiscal and tax health score reflecting the fiscal and tax health of the enterprise.

[0033] Optionally, dynamically adjusting the weighted fiscal and tax health score vector in combination with historical fiscal and tax data and the changing trend of the macroeconomic environment to obtain a dynamically adjusted fiscal and tax health score includes:

[0034] Use historical fiscal and tax data and the changing trend of the macroeconomic environment to perform time series analysis on each score in the weighted fiscal and tax health score vector, identify the trend patterns and periodic fluctuations over time, and obtain the results based on time series analysis;

[0035] According to the results based on time series analysis, in combination with real-time economic policies and market dynamics, conduct scenario simulation adjustment on the weighted fiscal and tax health score vector, simulate the changes outside the preset goals of fiscal and tax health under different economic environments, and generate a scenario simulation adjusted fiscal and tax health score vector;

[0036] Based on machine learning algorithms, use the scenario simulation adjusted fiscal and tax health score vector for training, predict the changing trend of fiscal and tax health within a preset time, generate prediction results, and make a forward-looking adjustment to the scenario simulation adjusted fiscal and tax health score vector according to the prediction results to obtain a forward-looking adjusted fiscal and tax health score vector;

[0037] Comprehensively evaluate the impacts of the results based on time series analysis, the scenario simulation adjusted fiscal and tax health score vector, and the forward-looking adjusted fiscal and tax health score vector, obtain the evaluation results, and dynamically adjust the weighted fiscal and tax health score vector according to the evaluation results to generate a dynamically adjusted fiscal and tax health score.

[0038] Optionally, use the association rule mining algorithm to analyze the internal relationships among the fiscal and tax health scores, and determine potential fiscal and tax risk warning signals and optimization suggestion paths, including:

[0039] Use the association rule mining algorithm to perform in-depth data analysis on the fiscal and tax health scores under different dimensions calculated from the multi-dimensional fiscal and tax indicator snapshots, identify the internal relationships and mutual influence patterns among the fiscal and tax health scores, and obtain a fiscal and tax health score association rule set;

[0040] According to the above-mentioned fiscal and tax health score correlation rule set, evaluate the correlation of fiscal and tax health scores in different dimensions, determine the score combinations that meet the preset conditions, and generate an initial fiscal and tax health score group;

[0041] Based on the initial fiscal and tax health score group, combined with the historical fiscal and tax data and the changing trend of the macroeconomic environment, identify the key factors and warning signals that lead to fiscal and tax risks, and obtain potential fiscal and tax risk warning signals;

[0042] Classify and grade the potential fiscal and tax risk warning signals, and assign corresponding priorities and coping strategies to each warning signal according to the likelihood and impact degree of the risk occurrence, and generate a risk warning system;

[0043] Use the fiscal and tax health score correlation rule set and the risk warning system to generate an optimized recommendation path for enterprise fiscal and tax management.

[0044] In a second aspect, an embodiment of the present application provides a big data-based fiscal and tax data analysis system, including:

[0045] A collection module for collecting fiscal and tax-related data streams from multiple heterogeneous data sources;

[0046] A construction module for constructing a fiscal and tax index system, and using the fiscal and tax index system to update and optimize existing fiscal and tax indexes and existing fiscal and tax index weights according to real-time regulatory requirements, real-time changes in the macroeconomic environment, and real-time changes in the enterprise's own business strategies, so that the fiscal and tax index system can map the fiscal and tax-related data streams in real time to generate multi-dimensional fiscal and tax index snapshots;

[0047] A calculation module for using a preset fiscal and tax health assessment model and combining the multi-dimensional fiscal and tax index snapshots to calculate fiscal and tax health scores in different dimensions;

[0048] An analysis module for using an association rule mining algorithm to analyze the internal relationship between the fiscal and tax health scores, and determining potential fiscal and tax risk warning signals and optimized recommendation paths;

[0049] An output module for outputting a comprehensive report including the fiscal and tax health scores, the potential fiscal and tax risk warning signals, and the optimized recommendations.

[0050] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a big data-based fiscal and tax data analysis method as described in the first aspect above.

[0051] Fourthly, an embodiment of the present application provides a computer storage medium storing a computer program, which when executed by a computer, implements a big data-based fiscal and tax data analysis method as described in the first aspect.

[0052] In an embodiment of the present invention, by collecting fiscal and tax-related data streams from multiple heterogeneous data sources, a fiscal and tax indicator system is constructed. Using the fiscal and tax indicator system, according to real-time regulatory requirements, real-time changes in the macroeconomic environment, and real-time changes in the enterprise's own business strategies, existing fiscal and tax indicators and existing fiscal and tax indicator weights are updated and optimized, so that the fiscal and tax indicator system can map the fiscal and tax-related data streams in real time to generate multi-dimensional fiscal and tax indicator snapshots. Using a preset fiscal and tax health assessment model, combined with the multi-dimensional fiscal and tax indicator snapshots, fiscal and tax health scores in different dimensions are calculated. Using an association rule mining algorithm to analyze the internal relationships between the fiscal and tax health scores, potential fiscal and tax risk warning signals and optimization suggestion paths are determined, and a comprehensive report including the fiscal and tax health scores, the potential fiscal and tax risk warning signals, and optimization suggestions is output. The technical solution provided by the present invention realizes comprehensive coverage of fiscal and tax information, improves the timeliness and accuracy of fiscal and tax management, enables enterprises to understand their own fiscal and tax conditions more comprehensively, provides enterprises with a forward-looking risk management tool, helps prevent and resolve fiscal and tax risks in advance, and at the same time guides enterprises to optimize financial management and business strategies.

[0053] Furthermore, by monitoring and analyzing information sources from a regulatory update interface, a macroeconomic data release platform, and enterprise internal management strategy change notifications, a set of key factors affecting fiscal and tax indicators is obtained, ensuring the accuracy and pertinence of the adjustment of the fiscal and tax indicator system. Based on the set of key factors, the intelligent decision-making engine evaluates and selects the adjustment plan of the fiscal and tax indicator system to obtain the optimal adjustment plan, enhancing the scientificity and rationality of the adjustment of the fiscal and tax indicator system. Using an incremental learning algorithm to optimize existing fiscal and tax indicators and existing fiscal and tax indicator weights to generate a dynamic fiscal and tax indicator system, realizing the intelligent and automated optimization of the fiscal and tax indicator system, ensuring the flexibility and adaptability of the fiscal and tax indicator system. The finally generated multi-dimensional fiscal and tax indicator snapshots can reflect the latest fiscal and tax conditions and trends in real time, providing enterprises with timely and accurate fiscal and tax status snapshots to support rapid decision-making and response to changes.

[0054] Furthermore, an adaptive learning rate mechanism is introduced to dynamically adjust the learning rate of the incremental learning algorithm according to the real-time economic environment and the internal change rate of the enterprise, enabling the system to accelerate the learning speed in a rapidly changing environment, slow down the adjustment pace during stable periods, and improve the optimization efficiency and response speed. Through high-precision monitoring technology, the fiscal and tax indicator system is comprehensively evaluated from multiple dimensions, and the evaluation results are fed back to the intelligent decision-making engine to ensure that each adjustment can pass strict inspection, improving the accuracy and stability of the fiscal and tax indicator system. Combining the historical optimization path and the current environmental state, reinforcement learning strategies are used to guide the iterative optimization process, accelerating the convergence process and improving the quality of the final fiscal and tax indicator system to ensure that the optimization results are more in line with actual needs. An intelligent early warning system is established to monitor the health of the fiscal and tax indicator system and trigger an automatic repair mechanism when potential risks are detected, quickly adjusting relevant fiscal and tax indicators and their weights to prevent small problems from evolving into major crises, enhancing the reliability and security of the system. When the optimized fiscal and tax indicator system with self-repair ability meets the preset convergence conditions, the adjustment stops, and the final dynamic fiscal and tax indicator system is generated to ensure that the system can not only accurately reflect the current fiscal and tax situation but also has the ability to predict future trends, providing a solid and reliable data basis for subsequent multi-dimensional fiscal and tax indicator snapshots.

[0055] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0057] Figure 1 Shows a flowchart of a method for fiscal and tax data analysis based on big data provided by the present application;

[0058] Figure 2 Shows a schematic structural diagram of a fiscal and tax data analysis system based on big data provided by the present application;

[0059] Figure 3 Shows a schematic structural diagram of a computing device provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] In order to enable those skilled in the art of the present technology to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application.

[0061] In some processes described in the specification, claims, and the above-mentioned drawings of the present application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0062] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0063] Figure 1 The present application provides a flowchart of a method for analyzing fiscal and tax data based on big data, as Figure 1 shown, the method includes:

[0064] Step 101: Collect fiscal and tax-related data streams from multiple heterogeneous data sources;

[0065] In this step, fiscal and tax-related data streams are collected from multiple heterogeneous data sources. These data sources include but are not limited to enterprise internal financial systems, regulatory update interfaces released by the government, macroeconomic data release platforms (such as economic indicators released by the National Bureau of Statistics, the People's Bank of China, etc.), and enterprise internal management strategy change notifications. These data form the basis for comprehensively understanding the fiscal and tax status of the enterprise and are used for subsequent analysis and decision-making support;

[0066] In the field of fiscal and tax data analysis, this step realizes the automatic collection of data through automated data integration tools or API interfaces. The data types cover structured data (such as tables in a database) and unstructured data (such as text files, PDF reports). After data collection, it undergoes cleaning, transformation, and standardization processing to ensure that data from different sources can be effectively analyzed on the same platform;

[0067] For example, when a large manufacturing enterprise implemented this step, it utilized advanced ETL (Extract, Transform, Load) tools to regularly extract the latest data from its ERP system, tax filing system, and external economic data platform. After being cleaned and unified in format, these data were loaded into the enterprise's data center, providing a detailed data foundation for the subsequent construction of the financial and tax index system.

[0068] Step 102: Construct a financial and tax index system, and use the financial and tax index system to update and optimize existing financial and tax indexes and their weights according to real-time regulatory requirements, real-time changes in the macroeconomic environment, and real-time changes in the enterprise's own business strategies, so that the financial and tax index system can map the financial and tax related data streams in real time to generate a multi-dimensional financial and tax index snapshot;

[0069] In this step, a dynamic financial and tax index system is established, which can automatically update and optimize existing financial and tax indexes and their weights according to real-time regulatory requirements, changes in the macroeconomic environment, and changes in the enterprise's business strategies. This system not only reflects the current financial and tax situation but also can predict future trends, generating multi-dimensional financial and tax index snapshots to assist in decision-making;

[0070] During the construction process, first identify the key factors affecting finance and taxation, and then evaluate and select the optimal adjustment plan based on an intelligent decision-making engine. Next, use an incremental learning algorithm to optimize the existing financial and tax indexes and their weights to ensure that each adjustment is the minimum necessary and can quickly respond to the latest changes;

[0071] Continuing from the previous embodiment, after the manufacturing enterprise completed data collection, it used an intelligent decision-making engine to analyze the impact of the newly introduced tax law reform on the enterprise and adjusted the original financial and tax index system in combination with the latest macroeconomic forecasts. For example, new indexes regarding environmental protection tax were added, and the weights of other relevant indexes were adjusted accordingly to reflect the impact of policy changes on the enterprise's cost structure.

[0072] Step 103: Use a preset financial and tax health assessment model to calculate the financial and tax health scores in different dimensions in combination with the multi-dimensional financial and tax index snapshots;

[0073] In this step, a preset financial and tax health assessment model is introduced, and in combination with the multi-dimensional financial and tax index snapshots generated previously, the financial and tax health scores in different dimensions are calculated. These scores are used to quantify the enterprise's performance in aspects such as profitability, debt repayment ability, and operation efficiency, providing a comprehensive view of the health status;

[0074] The financial and tax health assessment model usually includes a series of predefined formulas and algorithms to measure the specific performance of various financial and tax indicators. The model takes into account factors such as the results of time series analysis and the vector of financial and tax health scores adjusted by scenario simulation to ensure that the scoring results not only reflect the current situation but also have forward-lookingness;

[0075] Manufacturing enterprises applied a mature financial and tax health assessment model at this stage and calculated the financial and tax health scores for each dimension based on the adjusted financial and tax indicator system in the previous step. For example, by analyzing key indicators such as cash flow and asset-liability ratio, the enterprise found that although sales increased, the accounts receivable turnover rate decreased, indicating the need to strengthen the management of accounts receivable.

[0076] Step 104: Use the association rule mining algorithm to analyze the internal relationships among the financial and tax health scores, and determine potential financial and tax risk warning signals and optimization suggestion paths;

[0077] In this step, the association rule mining algorithm is used to deeply analyze the relationships among the financial and tax health scores to determine potential financial and tax risk warning signals and optimization suggestion paths. This step reveals the interactions among different financial and tax health scores, helps to identify possible risk points in advance, and formulates targeted improvement measures;

[0078] The association rule mining algorithm can find significant correlations among different scores and identify key factors that may lead to financial and tax risks. Through classification and grading, priorities and response strategies can be assigned to each warning signal, thus forming a complete risk warning system;

[0079] Based on the results of the financial and tax health scores, manufacturing enterprises further applied the association rule mining algorithm and found a strong correlation between the accounts receivable turnover rate and the inventory level. This discovery prompted the management to take actions to optimize the supply chain management and sales collection process to reduce the potential risk of capital chain breakage.

[0080] Step 105: Output a comprehensive report containing the financial and tax health scores, the potential financial and tax risk warning signals, and optimization suggestions;

[0081] In this step, a comprehensive report is generated, summarizing the financial and tax health scores, potential financial and tax risk warning signals, and specific optimization suggestions. This report provides comprehensive and intuitive information support for enterprise decision-makers, helping them make wise choices in the complex market environment;

[0082] The compilation of the comprehensive report is a process of information integration, which presents all the analysis results in the form of charts, written explanations, etc. The report not only shows the current financial and tax situation but also puts forward clear improvement suggestions to guide the enterprise on how to better plan for the future;

[0083] The comprehensive report of manufacturing enterprises details the fiscal and tax health scores in various dimensions, the detected risk signals (such as accounts receivable management problems), and the corresponding optimization suggestions (such as accelerating the recovery of accounts receivable and optimizing inventory management). This report has become an important reference for the enterprise management to hold quarterly financial meetings, helping the enterprise adjust its strategies in a timely manner and improve the efficiency of financial management.

[0084] Through the implementation of the above steps, the intelligentization, automation, and refinement of fiscal and tax data analysis have been realized, significantly improving the timeliness and accuracy of fiscal and tax management. In particular, by responding in real time to regulatory changes, macroeconomic fluctuations, and the adjustment of the enterprise's own business strategies, the fiscal and tax indicator system always remains up-to-date, ensuring the accuracy of fiscal and tax health assessment. The application of the association rule mining algorithm enables the enterprise to identify potential risks in advance, formulate scientific and reasonable optimization paths, and the finally generated comprehensive report provides strong support for enterprise decision-makers, enabling them to make more informed decisions in the complex and changeable economic environment. This method not only improves the enterprise's fiscal and tax management level but also enhances its market competitiveness and risk resistance ability.

[0085] To solve the problem that the traditional fiscal and tax indicator system cannot respond in real time to regulatory changes, macroeconomic fluctuations, and enterprise business strategy adjustments, in some embodiments, the construction of the fiscal and tax indicator system described in step 102 uses the fiscal and tax indicator system to update and optimize the existing fiscal and tax indicators and the weights of the existing fiscal and tax indicators according to the real-time regulatory requirements, the real-time changes in the macroeconomic environment, and the real-time transformation of the enterprise's own business strategy, so that the fiscal and tax indicator system can map the fiscal and tax-related data streams in real time to generate a multi-dimensional fiscal and tax indicator snapshot, specifically including:

[0086] Monitor and parse the information sources from the regulatory update interface, the macroeconomic data release platform, and the enterprise internal management strategy change notice to obtain a set of key factors affecting the fiscal and tax indicators; based on the set of key factors, evaluate and select the adjustment plan of the fiscal and tax indicator system through the intelligent decision-making engine to obtain the optimal adjustment plan; according to the optimal adjustment plan, use the incremental learning algorithm to optimize the existing fiscal and tax indicators and the weights of the existing fiscal and tax indicators to obtain a dynamic fiscal and tax indicator system; use the dynamic fiscal and tax indicator system to map the fiscal and tax-related data streams to generate a multi-dimensional fiscal and tax indicator snapshot for reflecting the real-time fiscal and tax status and trends;

[0087] In this embodiment, the regulation update interface refers to a data interface connected to official tax or legislative bodies, which is used to obtain the latest laws and regulations information. The macroeconomic data release platform covers economic indicators released by the National Bureau of Statistics, the People's Bank of China, etc., such as GDP growth rate, inflation rate, etc. These data reflect the changing trends of the overall economic environment. The enterprise internal management strategy change notice refers to the notice system of enterprise internal policy changes, such as new sales strategies, cost control measures, etc. These information directly affect the financial status and operation efficiency of the enterprise. The key factor set refers to the set of factors that have a significant impact on financial and tax indicators identified through monitoring and parsing the above information sources, such as tax rate adjustment, market demand change, cost structure change, etc.;

[0088] In the embodiment of the present application, first, the system continuously monitors the regulation update interface, the macroeconomic data release platform, and the enterprise internal management strategy change notice to capture any key factors that may affect financial and tax indicators. Then, the intelligent decision-making engine analyzes these factors and evaluates multiple possible adjustment plans according to preset rules and models, and selects the optimal plan from them. Then, an incremental learning algorithm is used to fine-tune the existing financial and tax indicators and their weights to ensure that each adjustment is the minimum necessary and can quickly respond to the latest changes. Finally, the optimized financial and tax indicator system is applied to real-time map the financial and tax related data streams to generate a multi-dimensional financial and tax indicator snapshot reflecting the current financial and tax status and future trends, providing an instant and accurate view of the financial and tax status of the enterprise.

[0089] To improve the accuracy and adaptability of the financial and tax indicator system, according to the previous embodiment, according to the optimal adjustment plan, an incremental learning algorithm is used to optimize the existing financial and tax indicators and the weights of the existing financial and tax indicators to obtain a dynamic financial and tax indicator system, specifically including:

[0090] Using the intelligent decision-making engine, based on the optimal adjustment plan, determine the existing financial and tax indicators and the corresponding weight values of the existing financial and tax indicators to obtain a list of financial and tax indicators to be optimized; according to the list of financial and tax indicators to be optimized, use an incremental learning algorithm to perform intelligent evaluation and adjustment on the financial and tax indicator system to generate a preliminarily optimized financial and tax indicator system; implement feedback and monitoring processing during the adjustment process of the financial and tax indicator system, track and record the impact of each adjustment on the financial and tax indicator system to obtain a verified financial and tax indicator system; perform iterative optimization and convergence on the verified initial financial and tax indicator system, adjust the existing financial and tax indicators and the weights of the existing financial and tax indicators until the preset convergence condition is reached to generate a dynamic financial and tax indicator system;

[0091] In this embodiment, the list of fiscal and tax indicators to be optimized refers to the specific fiscal and tax indicators and their corresponding weight values that need to be added, modified, or removed after being evaluated by the intelligent decision-making engine. The selection of these indicators is based on the set of key factors affecting fiscal and tax, and is obtained through analysis by the intelligent decision-making engine, ensuring that each adjustment is the minimum necessary and targeted. The high-precision monitoring technology is used to monitor the adjustment effect of the fiscal and tax indicator system in real time, providing evaluation data in multiple dimensions (such as accuracy, stability, and forward-looking), ensuring that each adjustment can be accurately tracked and recorded. The verified initial fiscal and tax indicator system refers to the fiscal and tax indicator system that has been preliminarily optimized and verified for its effectiveness by the high-precision monitoring technology, which provides a reliable basis for subsequent iterative optimization;

[0092] In the embodiment of the present application, first, the intelligent decision-making engine determines which fiscal and tax indicators and their weights need to be adjusted according to the optimal adjustment plan, forming a list of fiscal and tax indicators to be optimized. Then, the incremental learning algorithm will conduct intelligent evaluation and fine-tuning for each indicator in this list, gradually generating a preliminarily optimized initial fiscal and tax indicator system. Then, through the high-precision monitoring technology, the system continuously collects feedback information during the adjustment process, ensuring that each adjustment step is fully verified and recording the impact of all changes on the overall fiscal and tax indicator system. Finally, based on the iterative results of the incremental learning algorithm, the system continuously optimizes the fiscal and tax indicators and their weights until the preset convergence condition is met, and finally generates a dynamic fiscal and tax indicator system that can accurately reflect the current fiscal and tax situation and has the ability to predict future trends.

[0093] To improve the self-adaptability and prediction ability of the fiscal and tax indicator system, according to the previous embodiment, the verified initial fiscal and tax indicator system is iteratively optimized and converged, adjusting the existing fiscal and tax indicators and the weights of the existing fiscal and tax indicators until the preset convergence condition is reached to generate a dynamic fiscal and tax indicator system, specifically including:

[0094] Utilize the real-time economic environment and the internal change rate of the enterprise to adaptively adjust the learning rate of the incremental learning algorithm, and obtain an adaptive learning rate mechanism for responding to the latest data and policy changes; according to the adaptive learning rate mechanism, adjust the fiscal and tax indicators and the weights of the fiscal and tax indicators in the verified initial fiscal and tax indicator system to generate an adjusted initial fiscal and tax indicator system; use high-precision monitoring technology to comprehensively evaluate the adjusted initial fiscal and tax indicator system from multiple dimensions, generate an evaluation result, and feedback the evaluation result to the intelligent decision-making engine to obtain a real-time optimized and evaluated initial fiscal and tax indicator system; based on the reinforcement learning strategy, combine the historical optimization path and the current environmental state to implement the iterative optimization process of the real-time optimized and evaluated initial fiscal and tax indicator system, and set strict convergence conditions to generate an optimized fiscal and tax indicator system; implement an intelligent early warning system to monitor the health of the optimized fiscal and tax indicator system, trigger an automatic repair mechanism when potential risks are detected, adjust relevant fiscal and tax indicators and the weights of relevant fiscal and tax indicators to generate an optimized fiscal and tax indicator system with self-repair ability; when the optimized fiscal and tax indicator system with self-repair ability meets the preset convergence conditions, stop the adjustment and generate a dynamic fiscal and tax indicator system;

[0095] In this embodiment, the adaptive learning rate mechanism refers to dynamically adjusting the learning speed of the incremental learning algorithm according to the real-time economic environment and the internal change rate of the enterprise. This mechanism ensures that the algorithm can accelerate the learning speed in a rapidly changing environment and slow down the adjustment pace during stable periods, so as to respond more effectively to the latest data and policy changes. The intelligent early warning system is an automated system for continuously monitoring the health of the fiscal and tax indicator system, which can identify potential risks and trigger an automatic repair mechanism when necessary to maintain the robustness and reliability of the fiscal and tax indicator system. The automatic repair mechanism refers to that when anomalies or potential risks are detected, the system automatically adjusts relevant fiscal and tax indicators and their weights to prevent small problems from evolving into major crises and ensure that the fiscal and tax indicator system is always in the best state;

[0096] In the embodiments of the present application, first, the system dynamically adjusts the learning rate of the incremental learning algorithm according to the real-time economic environment and the internal change rate of the enterprise, forming an adaptive learning rate mechanism, so that each adjustment can efficiently respond to the latest changes. Next, according to this adaptive learning rate mechanism, the system fine-tunes the financial and tax indicators and their weights in the verified initial financial and tax indicator system to generate an adjusted initial financial and tax indicator system. Then, the high-precision monitoring technology is used to comprehensively evaluate the adjusted financial and tax indicator system from multiple dimensions to ensure its accuracy, stability and forward-lookingness, and the evaluation results are fed back to the intelligent decision-making engine to obtain an initially optimized and evaluated financial and tax indicator system. Subsequently, based on the reinforcement learning strategy, combined with the historical optimization path and the current environmental state, the system implements an iterative optimization process and sets strict convergence conditions, and finally generates an optimized financial and tax indicator system. Finally, the intelligent early warning system continuously monitors the health of the optimized financial and tax indicator system. Once potential risks are detected, the automatic repair mechanism is immediately triggered to adjust the relevant financial and tax indicators and their weights, generating an optimized financial and tax indicator system with self-repair ability. When this optimized financial and tax indicator system with self-repair ability meets the preset convergence conditions, the system stops adjusting and generates the final dynamic financial and tax indicator system.

[0097] In order to improve the accuracy and comprehensiveness of the financial and tax health assessment and solve the problem that static scoring in the prior art cannot reflect dynamic changes in a timely manner, as another embodiment, according to step 103, using a preset financial and tax health assessment model, combined with the multi-dimensional financial and tax indicator snapshots, calculate the financial and tax health scores in different dimensions, specifically including:

[0098] Using a preset financial and tax health assessment model, perform quantitative analysis and processing on the financial and tax data and indicators in the multi-dimensional financial and tax indicator snapshots to obtain a preliminary financial and tax health score vector; according to the weight distribution rules built into the financial and tax health assessment model, assign corresponding weight values to each dimension in the preliminary financial and tax health score vector to reflect the importance of each dimension in the overall financial and tax health status assessment, and generate a weighted financial and tax health score vector; combine the historical financial and tax data and the changing trends of the macroeconomic environment to dynamically adjust the weighted financial and tax health score vector to obtain a dynamically adjusted financial and tax health score; based on the dynamically adjusted financial and tax health score, conduct a comprehensive assessment according to the mutual relationship between different dimensions and the comprehensive influencing factors, and calculate the multi-dimensional financial and tax health score reflecting the financial and tax health of the enterprise;

[0099] In this embodiment, the financial and tax health assessment model is a set of predefined algorithms and formula collections, which are used to measure the performance of an enterprise in multiple key financial and tax dimensions such as profitability, debt repayment ability, and operation efficiency. The preliminary financial and tax health score vector refers to a series of initial scores obtained through quantitative analysis and processing, and these scores reflect the specific performance of each key financial and tax dimension. The weight allocation rule is a part of the financial and tax health assessment model, which stipulates the relative importance of different dimensions in the overall assessment of the financial and tax health status, ensuring that the scoring results can truly reflect the actual situation of the enterprise. Dynamic adjustment means combining the historical financial and tax data and the changing trend of the macroeconomic environment to further optimize the financial and tax health score vector, making it more in line with the current economic environment and the enterprise operation status. The multi-dimensional financial and tax health score is the final assessment result, which comprehensively considers the mutual relationship between different dimensions and their impact on the overall financial and tax health of the enterprise, providing a comprehensive view of the financial health for the management;

[0100] In the embodiment of the present application, first, the system uses the preset financial and tax health assessment model to perform quantitative analysis and processing on multiple key financial and tax dimensions (such as profit level, debt ratio, cash flow, etc.) based on the financial and tax data and indicators included in the multi-dimensional financial and tax index snapshot, and generates a preliminary financial and tax health score vector. Then, according to the weight allocation rule built in the financial and tax health assessment model, the system assigns corresponding weight values to each dimension in the preliminary financial and tax health score vector to generate a weighted financial and tax health score vector, ensuring that the importance of each dimension can be reflected in the final score. Then, combining the historical financial and tax data and the changing trend of the macroeconomic environment, the system dynamically adjusts the weighted financial and tax health score vector, so that the scoring result not only reflects the current situation but also can adapt to possible future changes. Finally, based on the dynamically adjusted financial and tax health score, the system conducts a comprehensive assessment according to the mutual relationship between different dimensions and the comprehensive influencing factors, and calculates the final multi-dimensional financial and tax health score, providing a comprehensive and accurate view of the financial and tax health status for the enterprise.

[0101] To enhance the accuracy and response speed of the financial and tax health score and overcome the limitation that traditional static scoring methods are difficult to capture the dynamic changes of the economic environment, as described in the previous embodiment, the weighted financial and tax health score vector is dynamically adjusted in combination with the historical financial and tax data and the changing trend of the macroeconomic environment to obtain the dynamically adjusted financial and tax health score, which specifically includes:

[0102] Using the historical fiscal and tax data and the changing trends of the macroeconomic environment, perform time series analysis on each score in the weighted fiscal and tax health score vector to identify the trend patterns and periodic fluctuations over time, and obtain the results based on time series analysis; according to the results based on time series analysis, combined with real-time economic policies and market dynamics, perform scenario simulation adjustment on the weighted fiscal and tax health score vector, simulate the changes beyond the preset goals of fiscal and tax health under different economic environments, and generate the fiscal and tax health score vector after scenario simulation adjustment; based on machine learning algorithms, use the fiscal and tax health score vector after scenario simulation adjustment for training, predict the changing trends of fiscal and tax health within a preset time, generate prediction results, and perform forward-looking adjustment on the fiscal and tax health score vector after scenario simulation adjustment according to the prediction results to obtain the fiscal and tax health score vector after forward-looking adjustment; comprehensively evaluate the impacts of the results based on time series analysis, the fiscal and tax health score vector after scenario simulation adjustment, and the fiscal and tax health score vector after forward-looking adjustment to obtain evaluation results, and perform dynamic adjustment on the weighted fiscal and tax health score vector according to the evaluation results to generate the dynamically adjusted fiscal and tax health score;

[0103] In this embodiment, time series analysis is a statistical method used to identify trend patterns and periodic fluctuations over time from historical data, which helps to understand the historical evolution law of the fiscal and tax health score. Scenario simulation adjustment refers to constructing different economic scenarios (such as economic growth, recession, etc.) based on current economic policies and market dynamics, and simulating the possible changes in fiscal and tax health under these scenarios, so as to generate a more realistic fiscal and tax health score vector after scenario simulation adjustment. Forward-looking adjustment is to predict the changing trends of future fiscal and tax health through machine learning algorithms and adjust the fiscal and tax health score vector accordingly to ensure that the score can reflect the future economic environment changes in advance. The evaluation result is a comprehensive consideration of the results based on time series analysis, the fiscal and tax health score vector after scenario simulation adjustment, and the fiscal and tax health score vector after forward-looking adjustment, and is ultimately used to generate a more accurate dynamically adjusted fiscal and tax health score;

[0104] In the embodiments of the present application, first, the system performs time series analysis processing on each score in the weighted fiscal and tax health score vector by using historical fiscal and tax data and the changing trend of the macroeconomic environment, identifies the trend patterns and periodic fluctuations changing over time, and obtains the result based on time series analysis. This step helps to discover the internal law of the score evolution over time and provides a basis for subsequent adjustments. Next, according to the result based on time series analysis and combined with real-time economic policies and market dynamics, the system performs scenario simulation adjustment on the weighted fiscal and tax health score vector, simulates the changes outside the preset goals of fiscal and tax health under different economic environments, and generates the fiscal and tax health score vector after scenario simulation adjustment. This process enables the fiscal and tax health score to better adapt to the constantly changing external environment. Then, based on the machine learning algorithm, the system uses the fiscal and tax health score vector after scenario simulation adjustment for training, predicts the changing trend of fiscal and tax health within the preset time, generates the prediction result, and makes a forward-looking adjustment to the fiscal and tax health score vector after scenario simulation adjustment according to the prediction result, obtaining the fiscal and tax health score vector after forward-looking adjustment. This step ensures that the fiscal and tax health score not only reflects the current situation but also has the ability to predict the future. Finally, the system comprehensively evaluates the impacts of the result based on time series analysis, the fiscal and tax health score vector after scenario simulation adjustment, and the fiscal and tax health score vector after forward-looking adjustment, obtains the evaluation result, and makes a dynamic adjustment to the weighted fiscal and tax health score vector according to the evaluation result, generating the final dynamically adjusted fiscal and tax health score to ensure its accuracy and forward-lookingness.

[0105] In order to improve the predictability and accuracy of fiscal and tax risk management and solve the problems of difficult identification of potential risks and lack of targeted optimization suggestions in the prior art, in another embodiment, according to what is described in step 104, the association rule mining algorithm is used to analyze the internal relationships between the fiscal and tax health scores, and potential fiscal and tax risk warning signals and optimization suggestion paths are determined, specifically including:

[0106] Using the association rule mining algorithm, based on the fiscal and tax health scores calculated from the multi-dimensional fiscal and tax indicator snapshots under different dimensions, perform in-depth data analysis and processing to identify the internal connections and mutual influence patterns among the fiscal and tax health scores, and obtain the fiscal and tax health score association rule set; according to the fiscal and tax health score association rule set, evaluate the correlation of the fiscal and tax health scores in different dimensions, determine the score combinations that meet the preset conditions, and generate the initial fiscal and tax health score group; based on the initial fiscal and tax health score group, combined with the historical fiscal and tax data and the changing trends of the macroeconomic environment, identify the key factors and warning signals that lead to fiscal and tax risks, and obtain the potential fiscal and tax risk warning signals; classify and grade the potential fiscal and tax risk warning signals, and assign corresponding priorities and response strategies to each warning signal according to the likelihood and impact degree of the risk occurrence, and generate the risk warning system; use the fiscal and tax health score association rule set and the risk warning system to generate an optimized recommendation path for enterprise fiscal and tax management;

[0107] In this embodiment, the association rule mining algorithm is a data mining method for discovering interesting relationships among items in a large dataset. It can reveal the hidden internal connections and mutual influence patterns among different fiscal and tax health scores, and help identify score combinations that may jointly indicate potential risks or opportunities. The fiscal and tax health score association rule set refers to a series of association rules among scores extracted from the multi-dimensional fiscal and tax indicator snapshots by applying the association rule mining algorithm, which describe how different scores interact and influence each other. The initial fiscal and tax health score group is a score combination determined to meet specific conditions (such as significant correlation or outliers) through correlation evaluation, and they may be early indicators of potential risks. The potential fiscal and tax risk warning signals refer to the key factors and signs that may lead to fiscal and tax risks identified through in-depth analysis of the initial fiscal and tax health score group, combined with historical fiscal and tax data and the changing trends of the macroeconomy. The risk warning system classifies and grades the potential fiscal and tax risk warning signals, and sets priorities and formulates response strategies for each warning signal to ensure that the enterprise can take appropriate actions promptly when facing risks. The optimized recommendation path is specific fiscal and tax management improvement measures provided for the enterprise based on the fiscal and tax health score association rule set and the risk warning system, aiming to reduce risks and improve the fiscal and tax health status;

[0108] In the embodiments of the present application, first, the system uses an association rule mining algorithm to perform in-depth data analysis and processing based on the financial and tax health scores in different dimensions calculated from the multi-dimensional financial and tax index snapshots, identify the internal relationships and mutual influence patterns among the financial and tax health scores, and form a financial and tax health score association rule set. Then, based on this association rule set, the system evaluates the correlation of the financial and tax health scores in different dimensions, determines the score combinations that meet the preset conditions, and generates an initial financial and tax health score group. These score combinations may imply potential risk points. Then, based on the initial financial and tax health score group, the system further analyzes in combination with the historical financial and tax data and the changing trends of the macroeconomic environment to identify the key factors and warning signals leading to financial and tax risks, thereby obtaining potential financial and tax risk warning signals. Subsequently, the system classifies and grades these potential financial and tax risk warning signals, assigns corresponding priorities and response strategies to each warning signal according to the likelihood and impact degree of the risk occurrence, and constructs a risk warning system. Finally, the system uses the financial and tax health score association rule set and the risk warning system to provide specific optimization suggestion paths for enterprise financial and tax management. These suggestions not only help reduce risks but also guide enterprises on how to adjust their financial and tax strategies to improve the overall health status.

[0109] Figure 2 FIG. is a schematic structural diagram of a financial and tax data analysis system based on big data provided by an embodiment of the present application, as Figure 2 shown, the device includes:

[0110] A collection module 21 for collecting financial and tax-related data streams from multiple heterogeneous data sources;

[0111] A construction module 22 for constructing a financial and tax index system, and using the financial and tax index system to update and optimize existing financial and tax indexes and existing financial and tax index weights according to real-time regulatory requirements, real-time changes in the macroeconomic environment, and real-time changes in the enterprise's own business strategies, so that the financial and tax index system can map the financial and tax-related data streams in real time to generate multi-dimensional financial and tax index snapshots;

[0112] A calculation module 23 for calculating financial and tax health scores in different dimensions by using a preset financial and tax health degree evaluation model in combination with the multi-dimensional financial and tax index snapshots;

[0113] An analysis module 24 for analyzing the internal relationships among the financial and tax health scores by using an association rule mining algorithm to determine potential financial and tax risk warning signals and optimization suggestion paths;

[0114] An output module 25 for outputting a comprehensive report including the financial and tax health scores, the potential financial and tax risk warning signals, and optimization suggestions.

[0115] Figure 2The described fiscal and tax data analysis system based on big data can execute Figure 1 A fiscal and tax data analysis method according to the embodiment shown, the implementation principle and technical effects of which will not be elaborated. For the fiscal and tax data analysis system in the above embodiment, the specific manners in which each module and unit perform operations have been described in detail in the embodiment related to the method, and will not be elaborated herein.

[0116] In a possible design, Figure 2 The fiscal and tax data analysis system according to the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0117] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0118] The processing component 32 is used for the above Figure 1 A fiscal and tax data analysis method according to the embodiment.

[0119] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0120] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0121] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0122] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0123] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0124] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device can refer to a cloud server. The above-mentioned processing components, storage components, etc. can be basic server resources leased or purchased from a cloud computing platform.

[0125] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the XX method in the above-mentioned Figure 1 illustrated embodiment.

[0126] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0127] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0128] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A financial and tax data analysis method based on big data, characterized in that: include: Collect finance and tax-related data streams from multiple heterogeneous data sources; Constructing a fiscal and taxation indicator system, using the fiscal and taxation indicator system to update and optimize existing fiscal and taxation indicators and existing fiscal and taxation indicator weights according to real-time regulatory requirements, real-time changes in the macroeconomic environment, and real-time changes in the enterprise's own business strategy, so that the fiscal and taxation indicator system maps the fiscal and taxation-related data streams in real time to generate a multi-dimensional fiscal and taxation indicator snapshot; Using the preset fiscal and tax health assessment model, combined with the multi-dimensional fiscal and tax indicator snapshots, calculate the fiscal and tax health scores under different dimensions; Utilize association rule mining algorithms to analyze the inherent connections between the financial and tax health scores, and determine potential financial and tax risk warning signals and optimization suggestion paths; The output is a comprehensive report including the financial and tax health score, the potential financial and tax risk warning signals and optimization suggestions.

2. The method according to claim 1, characterized in that: Construct a fiscal and taxation indicator system, and use the fiscal and taxation indicator system to update and optimize existing fiscal and taxation indicators and existing fiscal and taxation indicator weights according to real-time regulatory requirements, real-time changes in the macroeconomic environment, and real-time changes in the company's own business strategies, so that the fiscal and taxation indicator system maps the fiscal and taxation related data streams in real time to generate a multi-dimensional fiscal and taxation indicator snapshot, including: Monitor and analyze information sources from regulatory update interfaces, macroeconomic data release platforms, and internal corporate management strategy change notifications to obtain a set of key factors that affect fiscal and taxation indicators; Based on the set of key factors, the adjustment plan of the fiscal and taxation indicator system is evaluated and selected through the intelligent decision-making engine to obtain the optimal adjustment plan; According to the optimal adjustment plan, the existing fiscal and taxation indicators and the weights of the existing fiscal and taxation indicators are optimized using an incremental learning algorithm to obtain a dynamic fiscal and taxation indicator system; The dynamic fiscal and taxation indicator system is used to map the fiscal and taxation related data streams to generate a multi-dimensional fiscal and taxation indicator snapshot that reflects real-time fiscal and taxation status and trends.

3. The method according to claim 2, characterized in that According to the optimal adjustment plan, the incremental learning algorithm is used to optimize the existing fiscal and taxation indicators and the existing fiscal and taxation indicator weights to obtain a dynamic fiscal and taxation indicator system, including: Using the intelligent decision-making engine, based on the optimal adjustment plan, the existing fiscal and taxation indicators and the weight values ​​corresponding to the existing fiscal and taxation indicators are determined to obtain a list of fiscal and taxation indicators to be optimized; According to the list of fiscal and taxation indicators to be optimized, an incremental learning algorithm is used to intelligently evaluate and adjust the fiscal and taxation indicator system to generate a preliminary optimized fiscal and taxation indicator system; Implement feedback and monitoring during the adjustment of the fiscal and taxation indicator system, track and record the impact of each adjustment on the fiscal and taxation indicator system, and obtain a verified fiscal and taxation indicator system; The verified initial fiscal and taxation indicator system is iteratively optimized and converged, and the existing fiscal and taxation indicators and the weights of the existing fiscal and taxation indicators are adjusted until the preset convergence conditions are reached to generate a dynamic fiscal and taxation indicator system.

4. The method according to claim 3, characterized in that Iteratively optimize and converge the verified initial fiscal and taxation indicator system, adjust the existing fiscal and taxation indicators and the weights of the existing fiscal and taxation indicators until the preset convergence conditions are reached, so as to generate a dynamic fiscal and taxation indicator system, including: By using the real-time economic environment and the rate of change within the enterprise, the learning rate of the incremental learning algorithm is adaptively adjusted to obtain an adaptive learning rate mechanism for responding to the latest data and policy changes. According to the adaptive learning rate mechanism, the fiscal and taxation indicators and the fiscal and taxation indicator weights in the verified initial fiscal and taxation indicator system are adjusted to generate an adjusted initial fiscal and taxation indicator system; Using high-precision monitoring technology, comprehensively evaluate the adjusted initial fiscal and taxation indicator system from multiple dimensions, generate evaluation results, and feed the evaluation results back to the intelligent decision-making engine to obtain a real-time optimized and evaluated initial fiscal and taxation indicator system; Based on the reinforcement learning strategy, combined with the historical optimization path and the current environment status, the iterative optimization process of the initial fiscal and taxation indicator system that has been optimized and evaluated in real time is implemented, and strict convergence conditions are set to generate an optimized fiscal and taxation indicator system; Implementing an intelligent early warning system to monitor the health of the optimized fiscal and taxation indicator system, triggering an automatic repair mechanism when potential risks are detected, adjusting relevant fiscal and taxation indicators and relevant fiscal and taxation indicator weights, and generating an optimized fiscal and taxation indicator system with self-repair capabilities; When the optimized fiscal and taxation indicator system with self-repairing capability meets the preset convergence conditions, the adjustment is stopped and a dynamic fiscal and taxation indicator system is generated.

5. The method according to claim 1, characterized in that Using the preset fiscal and tax health assessment model, combined with the multi-dimensional fiscal and tax indicator snapshots, the fiscal and tax health scores under different dimensions are calculated, including: Using the preset fiscal and tax health assessment model, we conduct quantitative analysis and processing on various fiscal and tax data and indicators in the multi-dimensional fiscal and tax indicator snapshot to obtain a preliminary fiscal and tax health score vector; According to the weight allocation rules built into the fiscal and tax health assessment model, each dimension in the preliminary fiscal and tax health score vector is assigned a corresponding weight value to reflect the importance of each dimension in the overall fiscal and tax health assessment, and a weighted fiscal and tax health score vector is generated; In combination with historical fiscal and taxation data and the changing trend of the macroeconomic environment, the weighted fiscal and taxation health score vector is dynamically adjusted to obtain a dynamically adjusted fiscal and taxation health score; Based on the dynamically adjusted financial and tax health score, a comprehensive assessment is conducted according to the interrelationships between different dimensions and comprehensive influencing factors to calculate a multi-dimensional financial and tax health score that reflects the financial and tax health of the enterprise.

6. The method according to claim 5, characterized in that Combined with the historical fiscal and taxation data and the changing trend of the macroeconomic environment, the weighted fiscal and taxation health score vector is dynamically adjusted to obtain a dynamically adjusted fiscal and taxation health score, including: Using historical fiscal and taxation data and the changing trends of the macroeconomic environment, a time series analysis is performed on each score in the weighted fiscal and taxation health score vector to identify trend patterns and periodic fluctuations that change over time, and obtain results based on time series analysis; According to the results of the time series analysis, combined with real-time economic policies and market dynamics, the weighted fiscal and tax health score vector is adjusted by scenario simulation, the changes outside the preset target of fiscal and tax health under different economic environments are simulated, and the fiscal and tax health score vector adjusted by scenario simulation is generated; Based on the machine learning algorithm, the financial and tax health score vector adjusted by the scenario simulation is used for training to predict the trend of financial and tax health changes within a preset time, generate a prediction result, and make a forward-looking adjustment to the financial and tax health score vector adjusted by the scenario simulation according to the prediction result to obtain a forward-looking adjusted financial and tax health score vector; Comprehensively evaluate the impact of the results based on time series analysis, the fiscal and tax health score vector adjusted by scenario simulation, and the fiscal and tax health score vector adjusted forward-lookingly to obtain an evaluation result, and dynamically adjust the weighted fiscal and tax health score vector according to the evaluation result to generate a dynamically adjusted fiscal and tax health score.

7. The method according to claim 1, characterized in that The association rule mining algorithm is used to analyze the internal relationship between the financial and tax health scores to determine the potential financial and tax risk warning signals and optimization suggestion paths, including: Using an association rule mining algorithm, based on the fiscal and tax health scores in different dimensions calculated in the multi-dimensional fiscal and tax indicator snapshot, in-depth data analysis and processing are performed to identify the internal connections and mutual influence patterns between the fiscal and tax health scores, and obtain a set of association rules for the fiscal and tax health scores; According to the financial and tax health score association rule set, the financial and tax health scores of different dimensions are evaluated for relevance, a score combination that meets the preset conditions is determined, and an initial financial and tax health score group is generated; Based on the initial fiscal and tax health score group, combined with historical fiscal and tax data and the changing trend of the macroeconomic environment, key factors and early warning signals leading to fiscal and tax risks are identified to obtain potential fiscal and tax risk early warning signals; Classify and grade the potential financial and tax risk warning signals, assign corresponding priorities and response strategies to each warning signal according to the possibility of risk occurrence and the degree of impact, and generate a risk warning system; The financial and tax health score association rule set and risk warning system are used to generate an optimization recommendation path for corporate financial and tax management.

8. A financial and tax data analysis system based on big data, characterized in that: include: The collection module is used to collect finance and taxation related data streams from multiple heterogeneous data sources; A construction module is used to construct a financial and taxation indicator system, and to use the financial and taxation indicator system to update and optimize existing financial and taxation indicators and existing financial and taxation indicator weights according to real-time regulatory requirements, real-time changes in the macroeconomic environment, and real-time changes in the business strategy of the enterprise itself, so that the financial and taxation indicator system maps the financial and taxation related data streams in real time to generate a multi-dimensional financial and taxation indicator snapshot; A calculation module, used to calculate the financial and tax health scores under different dimensions by using a preset financial and tax health assessment model and combining the multi-dimensional financial and tax indicator snapshots; An analysis module, for analyzing the internal connections between the financial and tax health scores using an association rule mining algorithm, and determining potential financial and tax risk warning signals and optimization suggestion paths; The output module is used to output a comprehensive report including the financial and tax health score, the potential financial and tax risk warning signal and optimization suggestions.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a financial and tax data analysis method based on big data as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a financial and tax data analysis method based on big data as described in any one of claims 1 to 7 is implemented.

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