Intelligent finance and tax compliance inspection system based on big data analysis

Abstract

The invention relates to an intelligent finance and tax compliance inspection system based on big data analysis. The system is composed of a data acquisition module, a data preprocessing module, a big data analysis module, an artificial intelligence auxiliary inspection module and a user interaction module. The data acquisition module acquires enterprise finance and taxation data, regulation data and external data in real time; the data preprocessing module is responsible for cleaning, converting and storing data; the big data analysis module discovers potential risks and abnormal behaviors by using a data mining algorithm and performs quantitative evaluation; the artificial intelligence auxiliary inspection module constructs an intelligent inspection model by using a machine learning algorithm, and generates personalized compliance suggestions; and the user interaction module provides a friendly operation interface, displays an inspection result and receives user feedback. The system has an efficient data processing capability, an accurate risk identification function, a real-time regulation updating mechanism and personalized service characteristics, the compliance cost of an enterprise can be remarkably reduced, and the finance and tax management level is improved.

Description

Technical Field

[0001] The present invention belongs to the field of financial and tax management technology, and in particular relates to an intelligent financial and tax compliance inspection system based on big data analysis. Background Art

[0002] In recent years, fields such as big data, artificial intelligence, and machine learning have experienced rapid development. These technologies have been widely applied across multiple industries, significantly improving the efficiency of data processing and analysis. In the field of finance and tax management, big data analytics can process massive amounts of financial and tax data, while machine learning algorithms can automatically identify patterns and abnormal behavior within the data. These technologies provide new approaches and methods for addressing challenges in traditional finance and tax compliance inspections.

[0003] However, the market currently lacks a system specifically designed for financial and tax compliance inspections that deeply integrates big data analysis, artificial intelligence, and machine learning technologies. While existing financial and tax management software can provide basic financial data processing and tax filing capabilities, it still lacks capabilities in intelligent risk identification, regulatory interpretation, and personalized compliance recommendations. Therefore, developing an intelligent financial and tax compliance inspection system based on big data analysis is of great practical significance for improving corporate financial and tax management efficiency and reducing compliance risks. Summary of the Invention

[0004] (1) Purpose of the invention

[0005] In order to overcome the above shortcomings, the purpose of the present invention is to provide an intelligent financial and tax compliance inspection system based on big data analysis to solve the above technical problems.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the technical solutions provided by this application are as follows:

[0008] An intelligent financial and tax compliance inspection system based on big data analysis, including the following modules:

[0009] Data collection module: used to collect corporate financial and tax data, regulatory data and external data;

[0010] Data preprocessing module: used to clean, convert and store the collected data;

[0011] Big data analysis module: used to mine, evaluate and analyze trends of pre-processed data to identify potential financial and tax risks and abnormal behaviors;

[0012] AI-assisted inspection module: used to further analyze and process the results of big data analysis to generate intelligent inspection reports and compliance recommendations;

[0013] User interaction module: used to receive query conditions entered by users, display inspection results, risk assessment reports and compliance recommendations, and receive user feedback.

[0014] Preferably, the data acquisition module includes:

[0015] Enterprise finance and taxation data collection unit: connects to the enterprise's financial system and tax declaration system through an interface to obtain the enterprise's financial statements, account details and tax declaration data in real time;

[0016] Regulatory data collection unit: regularly obtains the latest financial and tax regulations, policy documents and interpretation information from official tax websites and regulatory databases;

[0017] External data collection unit: collects industry data, market data and macroeconomic data to assist in analyzing the rationality of corporate financial and tax data.

[0018] Preferably, the data preprocessing module includes:

[0019] Data cleaning unit: cleans the collected data, removes duplicate data, erroneous data and irrelevant data, and ensures the accuracy and completeness of the data;

[0020] Data conversion unit: converts data from different sources and formats into a unified format to facilitate subsequent analysis and processing;

[0021] Data storage unit: stores cleaned and converted data in a distributed database, supporting efficient storage and fast query of large-scale data.

[0022] Preferably, the big data analysis module includes:

[0023] Data Mining Unit: Uses data mining algorithms to discover potential financial and tax risk patterns and abnormal behaviors from massive amounts of data;

[0024] Risk Assessment Unit: Quantitatively assesses the company's financial and tax compliance risks based on discovered risk patterns and abnormal behaviors, combined with financial and tax regulations and industry standards;

[0025] Trend Analysis Unit: Analyze the changing trends of corporate financial and tax data over time and predict possible compliance risks in the future.

[0026] Preferably, the data mining algorithm specifically includes the following steps:

[0027] S1 Data preprocessing: Clean and preprocess the raw data, including processing missing values ​​and outliers;

[0028] S2 prediction model training, using machine learning algorithms to train data and build a prediction model;

[0029] S3 prediction sampling: score the data in the dataset according to the decision tree prediction model and select samples with higher scores for sampling;

[0030] S4 cluster analysis, clustering the sampled data using a clustering algorithm;

[0031] S5 result analysis: analyze the clustering results and extract valuable information.

[0032] Preferably, the clustering algorithm comprises the following steps:

[0033] Initialization: randomly select K data points as the initial cluster centers;

[0034] Assignment, assigning each data point to the cluster represented by the nearest cluster center;

[0035] Update, recalculate the center of each cluster;

[0036] Iteration, repeat the assignment and update steps until the cluster center no longer changes or the preset number of iterations is reached;

[0037] The formula includes the following:

[0038]

[0039] Among them, J represents the minimization of the distance within the cluster, min {Si} represents the optimal cluster partition {S i}, K represents the number of clusters, S i represents the i-th cluster, x represents the data point, ui represents the center of the i-th cluster, ||x-μ i || 2 Represents the square of the Euclidean distance between the data point and the cluster center ui.

[0040] Preferably, the artificial intelligence assisted inspection module includes:

[0041] Natural Language Processing Unit: Performs natural language processing on regulatory data and corporate documents, extracts key information and clauses, and enables intelligent retrieval and interpretation of regulations;

[0042] Machine Learning Unit: Utilizes machine learning algorithms to learn and train existing compliance inspection cases, build intelligent inspection models, and automatically identify anomalies and potential risks in corporate financial and tax data;

[0043] Intelligent Recommendation Unit: Provides personalized compliance advice and solutions to enterprises based on inspection results and risk assessment.

[0044] Preferably, the machine learning algorithm comprises the following steps:

[0045] A1 Data collection and organization: collect historical data and organize the data into a unified format;

[0046] A2 data preprocessing: cleans the data, extracts key features based on the requirements of financial and tax compliance inspections, normalizes the feature data, and scales the data to the same range to improve the efficiency and effectiveness of model training;

[0047] A3 Select a machine learning algorithm. Choose an appropriate algorithm, including the following mathematical model formula:

[0048] Forget Gate:

[0049] f t =σ(W f ·[h t-1 , x t ]+b f )

[0050] Input Gate:

[0051] i t =σ(W i ·[h t-1 , x t ]+b i )

[0052]

[0053] Unit Status:

[0054]

[0055] Output gate:

[0056] o t =σ(W o ·[h t-1 , x t ]+b o )

[0057] h t =o t tanh(C t )

[0058] A4 model training: Split the dataset into training and test sets at an 8:2 ratio, build an LSTM model, train the LSTM model using historical data, optimize model parameters by minimizing the loss function, and evaluate the model performance using cross-validation to ensure the model's generalization ability. Based on the evaluation results, adjust the model parameters or select other algorithms to further optimize the model performance.

[0059] A5 deployment and application integrates the trained LSTM model into the financial and tax compliance inspection system to monitor the company's financial and tax data in real time, promptly detect abnormal behavior and issue early warnings.

[0060] Preferably, the user interaction module includes:

[0061] User interface unit: provides an intuitive and user-friendly interface to facilitate user operations and view inspection results, risk assessment reports and compliance recommendations;

[0062] Report generation unit: Automatically generates detailed tax compliance inspection reports based on user needs. The reports include inspection scope, inspection results, risk assessment, and compliance recommendations.

[0063] Feedback and update unit: Receive user feedback on inspection results, update data and models in the system in a timely manner, and optimize inspection results.

[0064] Beneficial effects:

[0065] 1. The data acquisition module automatically acquires corporate financial and tax data, regulatory data, and external data, eliminating the need for manual collection and organization, significantly reducing the time and labor costs of preliminary data preparation.

[0066] 2. Leveraging machine learning algorithms, the system can automatically learn complex patterns and regularities within data. It can accurately identify anomalies and potential risks in a company's financial and tax data, such as unusual tax filings and financial indicators that don't meet industry standards, thus avoiding potential oversights and errors that can occur during manual inspections.

[0067] 3. The system monitors a company's financial and tax data in real time, and the trend analysis unit analyzes data trends over time. If unusual fluctuations in data or potential compliance risks are detected, the system issues timely warnings, prompting companies to take appropriate measures. This real-time nature enables companies to proactively address risks and avoid unnecessary losses due to non-compliance. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present invention.

[0069] The present invention provides an intelligent financial and tax compliance inspection system based on big data analysis, which includes the following modules:

[0070] Data collection module: used to collect corporate financial and tax data, regulatory data and external data;

[0071] Data preprocessing module: used to clean, convert and store the collected data;

[0072] Big data analysis module: used to mine, evaluate and analyze trends of pre-processed data to identify potential financial and tax risks and abnormal behaviors;

[0073] AI-assisted inspection module: used to further analyze and process the results of big data analysis to generate intelligent inspection reports and compliance recommendations;

[0074] User interaction module: used to receive query conditions entered by users, display inspection results, risk assessment reports and compliance recommendations, and receive user feedback.

[0075] Preferably, the data acquisition module includes:

[0076] Enterprise finance and taxation data collection unit: Connects to the enterprise's financial system and tax declaration system through an interface, and obtains the enterprise's financial statements, account details and tax declaration data in real time, ensuring that the system can obtain the latest enterprise finance and taxation data, avoiding inaccurate inspection results due to data lags. At the same time, it comprehensively covers the enterprise's financial and taxation information, providing a rich data foundation for subsequent analysis. Automated data collection reduces the workload and error rate of manual data input, and improves the efficiency and accuracy of data collection.

[0077] Regulatory Data Collection Unit: Regularly obtains the latest financial and tax laws, policy documents, and interpretation information from official tax websites and regulatory databases; ensures that the system always conducts assessments based on the latest regulatory requirements when conducting financial and tax compliance checks, avoiding compliance risks caused by untimely regulatory updates.

[0078] External Data Collection Unit: This unit collects industry, market, and macroeconomic data to assist in analyzing the rationality of a company's financial and tax data. By introducing external data, the system enriches the data dimensions and enables the system to analyze a company's financial and tax status from a broader perspective.

[0079] Preferably, the data preprocessing module includes:

[0080] Data cleaning unit: cleans the collected data, removes duplicate data, erroneous data, and irrelevant data, ensures data accuracy and completeness, and improves data quality and availability by removing noise and erroneous information.

[0081] Data conversion unit: Converts data from different sources and formats into a unified format. The unified data format enables seamless integration of data from different systems, facilitating unified analysis and processing. This avoids additional processing steps caused by inconsistent data formats and improves the efficiency of data analysis.

[0082] Data storage unit: Stores the cleaned and converted data in a distributed database, supporting efficient storage and fast query of large-scale data. Distributed databases can efficiently store and manage large-scale data, improve the efficiency and reliability of data storage, support fast query, enable the system to quickly respond to user query requests, and improve user experience.

[0083] Preferably, the big data analysis module includes:

[0084] Data Mining Unit: Uses data mining algorithms to discover potential financial and tax risk patterns and abnormal behaviors from massive amounts of data. Through data mining algorithms, it can accurately identify anomalies and potential risks in corporate financial and tax data, avoiding omissions and errors that may occur in manual inspections.

[0085] Risk Assessment Unit: Based on discovered risk patterns and abnormal behaviors, combined with financial and tax regulations and industry standards, we conduct quantitative assessments of a company's financial and tax compliance risks, enabling companies to intuitively understand their own risk levels and facilitate risk management and decision-making.

[0086] Trend Analysis Unit: Analyze the changing trends of corporate financial and tax data over time, predict possible compliance risks in the future, help companies prepare in advance, monitor the company's financial and tax data in real time, promptly identify changing trends in data, and ensure that companies can adjust their strategies in a timely manner.

[0087] Preferably, the data mining algorithm specifically includes the following steps:

[0088] S1 Data preprocessing: Clean and preprocess the raw data, including processing missing values ​​and outliers;

[0089] S2 prediction model training, using machine learning algorithms to train data and build a prediction model;

[0090] S3 prediction sampling: score the data in the dataset according to the decision tree prediction model and select samples with higher scores for sampling;

[0091] S4 cluster analysis, clustering the sampled data using a clustering algorithm;

[0092] S5 result analysis: analyze the clustering results and extract valuable information.

[0093] Preferably, the clustering algorithm comprises the following steps:

[0094] Initialization: randomly select K data points as the initial cluster centers;

[0095] Assignment, assigning each data point to the cluster represented by the nearest cluster center;

[0096] Update, recalculate the center of each cluster;

[0097] Iteration, repeat the assignment and update steps until the cluster center no longer changes or the preset number of iterations is reached;

[0098] The formula includes the following:

[0099]

[0100] Among them, J represents the minimization of the distance within the cluster, min {Si} represents the optimal cluster partition {S i}, K represents the number of clusters, S i represents the i-th cluster, x represents the data point, ui represents the center of the i-th cluster, ||x-μ i || 2 Represents the square of the Euclidean distance between the data point and the cluster center ui.

[0101] Preferably, the artificial intelligence assisted inspection module includes:

[0102] Natural Language Processing Unit: This unit performs natural language processing on regulatory data and corporate documents, extracts key information and clauses, and enables intelligent retrieval and interpretation of regulations. This reduces the workload and error rate of manual interpretation, ensures that regulatory clauses are correctly understood and applied, and improves the accuracy of compliance checks.

[0103] Machine Learning Unit: This unit uses machine learning algorithms to learn and train existing compliance inspection cases, builds intelligent inspection models, and automatically identifies anomalies and potential risks in corporate financial and tax data. It can automatically learn and adapt to new data and regulatory changes, maintaining the accuracy and timeliness of inspections. It automatically identifies anomalies and potential risks in corporate financial and tax data, improving the efficiency and accuracy of risk identification.

[0104] Intelligent recommendation unit: Based on inspection results and risk assessment, it provides enterprises with personalized compliance advice and solutions to help them better deal with compliance risks, provide strong decision-making support for the management of the enterprise, and help them formulate reasonable financial and tax management strategies.

[0105] Preferably, the machine learning algorithm comprises the following steps:

[0106] A1 Data collection and organization: collect historical data and organize the data into a unified format;

[0107] A2 data preprocessing: cleans the data, extracts key features based on the requirements of financial and tax compliance inspections, normalizes the feature data, and scales the data to the same range to improve the efficiency and effectiveness of model training;

[0108] A3 Select a machine learning algorithm. Choose an appropriate algorithm, including the following mathematical model formula:

[0109] Forget Gate:

[0110] f t =σ(W f ·[h t-1 , x t ]+b f )

[0111] Input Gate:

[0112] i t =σ(W i ·[h t-1 , x t ]+b i )

[0113]

[0114] Unit Status:

[0115]

[0116] Output gate:

[0117] o t =σ(W o ·[h t-1 , x t ]+b o )

[0118] h t =o t tanh(C t )

[0119] A4 model training: Split the dataset into training and test sets at an 8:2 ratio, build an LSTM model, train the LSTM model using historical data, optimize model parameters by minimizing the loss function, and evaluate the model performance using cross-validation to ensure the model's generalization ability. Based on the evaluation results, adjust the model parameters or select other algorithms to further optimize the model performance.

[0120] A5 deployment and application integrates the trained LSTM model into the financial and tax compliance inspection system to monitor the company's financial and tax data in real time, promptly detect abnormal behavior and issue early warnings.

[0121] Preferably, the user interaction module includes:

[0122] User interface unit: provides an intuitive and user-friendly interface to facilitate user operations and view inspection results, risk assessment reports and compliance recommendations. The intuitive and user-friendly interface enables users to easily operate and query, improving user experience;

[0123] Report generation unit: Automatically generates detailed financial and tax compliance inspection reports based on user needs. The reports include inspection scope, inspection results, risk assessment, and compliance recommendations. Automatically generating detailed inspection reports reduces the workload and error rate of manual report writing. The comprehensive reports provide enterprises with complete compliance inspection records.

[0124] Feedback and Update Unit: Receive user feedback on inspection results, update data and models in the system in a timely manner, optimize inspection results, encourage users to actively participate in system optimization and improvement, and improve user satisfaction and loyalty.

[0125] The method of using the present invention includes:

[0126] System initialization: The user installs and starts the system, which automatically connects to the company's financial system, tax declaration system, and regulatory database to complete initial data collection;

[0127] Data collection and preprocessing: The system automatically collects corporate financial and tax data, regulatory data, and external data on a regular basis, and performs data cleaning, conversion, and storage.

[0128] Data analysis and evaluation: When the user starts the data analysis module, the system automatically runs data mining, risk assessment and trend analysis algorithms to generate preliminary inspection results.

[0129] Intelligent inspection and recommendations: The system uses artificial intelligence-assisted inspection modules to further analyze preliminary inspection results and generate intelligent inspection reports and personalized compliance recommendations.

[0130] User interaction and feedback: Users view inspection results and compliance recommendations through the user interface, query and operate the system, and provide feedback. The system promptly updates data and models based on user feedback.

[0131] Report generation and export: Users can generate detailed financial and tax compliance inspection reports as needed. The system supports exporting reports in multiple formats for easy storage and use.

[0132] In practice, this system can be deployed on an enterprise's internal server or a cloud server, and enterprise users can access the system through a browser or client software. The system allows for flexible configuration of parameters such as data collection scope, analysis model, and inspection frequency based on the enterprise's size and business needs. For example, large enterprises can conduct a comprehensive financial and tax compliance inspection daily; small and medium-sized enterprises can conduct inspections weekly or monthly, depending on their specific circumstances. Furthermore, the system can be seamlessly integrated with the enterprise's financial and tax filing software, enabling automated data exchange and sharing, further improving work efficiency.

[0133] The intelligent tax compliance inspection system of the present invention realizes the efficiency, intelligence and precision of tax compliance inspection through modular design and integration of cutting-edge technologies. The system adopts technologies such as big data analysis, artificial intelligence, machine learning and natural language processing to monitor corporate tax data in real time and warn of potential risks, while providing personalized compliance recommendations. Its modular structure is easy to customize and expand, and the distributed database ensures data security and reliability. The system can automatically obtain the latest regulatory information and adjust the inspection model to maintain regulatory adaptability, reduce corporate compliance costs, and promote industry innovation and development. The comprehensive benefits brought by these features give the system significant advantages in improving the level of corporate financial and tax management, reducing risks, and enhancing competitiveness, providing strong support for the digital transformation of the financial and tax management industry.

[0134] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0135] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent tax compliance inspection system based on big data analysis, characterized by: Includes the following modules: Data collection module: used to collect corporate financial and tax data, regulatory data and external data; Data preprocessing module: used to clean, convert and store the collected data; Big data analysis module: used to mine, evaluate and analyze trends of pre-processed data to identify potential financial and tax risks and abnormal behaviors; AI-assisted inspection module: used to further analyze and process the results of big data analysis to generate intelligent inspection reports and compliance recommendations; User interaction module: used to receive query conditions entered by users, display inspection results, risk assessment reports and compliance recommendations, and receive user feedback.

2. The intelligent tax compliance inspection system based on big data analysis according to claim 1 is characterized in that: The data acquisition module includes: Enterprise finance and taxation data collection unit: connects to the enterprise's financial system and tax declaration system through an interface to obtain the enterprise's financial statements, account details and tax declaration data in real time; Regulatory data collection unit: regularly obtains the latest financial and tax regulations, policy documents and interpretation information from official tax websites and regulatory databases; External data collection unit: collects industry data, market data and macroeconomic data to assist in analyzing the rationality of corporate financial and tax data.

3. The intelligent tax compliance inspection system based on big data analysis according to claim 1 is characterized in that: The data preprocessing module includes: Data cleaning unit: cleans the collected data, removes duplicate data, erroneous data and irrelevant data, and ensures the accuracy and completeness of the data; Data conversion unit: converts data from different sources and formats into a unified format to facilitate subsequent analysis and processing; Data storage unit: stores cleaned and converted data in a distributed database, supporting efficient storage and fast query of large-scale data.

4. The intelligent tax compliance inspection system based on big data analysis according to claim 1 is characterized in that: The big data analysis module includes: Data Mining Unit: Uses data mining algorithms to discover potential financial and tax risk patterns and abnormal behaviors from massive amounts of data; Risk Assessment Unit: Quantitatively assesses the company's financial and tax compliance risks based on discovered risk patterns and abnormal behaviors, combined with financial and tax regulations and industry standards; Trend Analysis Unit: Analyze the changing trends of corporate financial and tax data over time and predict possible compliance risks in the future.

5. The intelligent tax compliance inspection system based on big data analysis according to claim 1 is characterized in that: The data mining algorithm specifically includes the following steps: S1 Data preprocessing: Clean and preprocess the raw data, including processing missing values ​​and outliers; S2 prediction model training, using machine learning algorithms to train data and build a prediction model; S3 prediction sampling: score the data in the dataset according to the decision tree prediction model and select samples with higher scores for sampling; S4 cluster analysis, clustering the sampled data using a clustering algorithm; S5 result analysis: analyze the clustering results and extract valuable information.

6. The intelligent tax compliance inspection system based on big data analysis according to claim 5 is characterized in that: The clustering algorithm comprises the following steps: Initialization: randomly select K data points as the initial cluster centers; Assignment, assigning each data point to the cluster represented by the nearest cluster center; Update, recalculate the center of each cluster; Iteration, repeat the assignment and update steps until the cluster center no longer changes or the preset number of iterations is reached; The formula includes the following: Among them, J represents the minimization of the distance within the cluster, min {Si} represents the optimal cluster partition {S i }, K represents the number of clusters, S i represents the i-th cluster, x represents the data point, ui represents the center of the i-th cluster, ||x-μ i || 2 Represents the square of the Euclidean distance between the data point and the cluster center ui.

7. The intelligent tax compliance inspection system based on big data analysis according to claim 1 is characterized in that: The artificial intelligence assisted inspection module includes: Natural Language Processing Unit: Performs natural language processing on regulatory data and corporate documents, extracts key information and clauses, and enables intelligent retrieval and interpretation of regulations; Machine Learning Unit: Utilizes machine learning algorithms to learn and train existing compliance inspection cases, build intelligent inspection models, and automatically identify anomalies and potential risks in corporate financial and tax data; Intelligent Recommendation Unit: Provides personalized compliance advice and solutions to enterprises based on inspection results and risk assessment.

8. The intelligent tax compliance inspection system based on big data analysis according to claim 7 is characterized in that: The machine learning algorithm includes the following steps: A1 Data collection and organization: collect historical data and organize the data into a unified format; A2 data preprocessing: cleans the data, extracts key features based on the requirements of financial and tax compliance inspections, normalizes the feature data, and scales the data to the same range to improve the efficiency and effectiveness of model training; A3 Select a machine learning algorithm. Choose an appropriate algorithm, including the following mathematical model formula: Forget Gate: f t =σ(W f ·[h t-1 ,x t ]+b f ) Input Gate: i t =σ(W i ·[h t-1 ,x t ]+b i ) Unit Status: Output gate: the t =σ(W o ·[h t-1 ,x t ]+b o ) h t =o t ·tanh(C t ) A4 model training: Split the dataset into training and test sets at an 8:2 ratio, build an LSTM model, train the LSTM model using historical data, optimize model parameters by minimizing the loss function, and evaluate the model performance using cross-validation to ensure the model's generalization ability. Based on the evaluation results, adjust the model parameters or select other algorithms to further optimize the model performance. A5 deployment and application integrates the trained LSTM model into the financial and tax compliance inspection system to monitor the company's financial and tax data in real time, promptly detect abnormal behavior and issue early warnings.

9. The intelligent tax compliance inspection system based on big data analysis according to claim 1 is characterized in that: The user interaction module includes: User interface unit: provides an intuitive and user-friendly interface to facilitate user operations and view inspection results, risk assessment reports and compliance recommendations; Report generation unit: Automatically generates detailed tax compliance inspection reports based on user needs. The reports include inspection scope, inspection results, risk assessment, and compliance recommendations. Feedback and update unit: Receive user feedback on inspection results, update data and models in the system in a timely manner, and optimize inspection results.

Images