Intelligent tax auditing robot system based on NLP and OCR technologies

Abstract

The invention provides an intelligent tax auditing robot system based on NLP and OCR technologies, and relates to the technical field of tax auditing. The data acquisition module collects enterprise tax related data. And the data processing module constructs an enterprise tax knowledge graph. And the risk assessment module generates a risk report and early warning information. And the intelligent interaction module explains the risk assessment result to obtain a decision suggestion. And the report generation module generates an enterprise tax audit report. And the learning optimization module performs real-time optimization and adjustment. According to the method, the dynamic tax rule engine is established through tax regulations and enterprise historical data, and the enterprise tax risk can be accurately identified in combination with the risk assessment model of machine learning. Through real-time comparison and deep analysis, abnormal conditions in the enterprise tax data can be found in time, the risk level is accurately evaluated, tax staff are helped to take measures in advance, enterprise tax risks are reduced, and tax violation behaviors are avoided.

Description

Technical Field

[0001] This invention relates to the field of tax audit technology, and in particular to an intelligent tax audit robot system based on NLP and OCR technologies. Background Technology

[0002] Tax audit refers to the review, verification, and supervision activities conducted on taxpayers and withholding agents regarding their fulfillment of tax obligations and withholding obligations, in accordance with tax laws, regulations, and relevant policies.

[0003] Traditional tax audits are primarily based on fixed rules, lacking flexibility and adaptability, making them ill-equipped to handle complex and ever-changing tax environments. Furthermore, their ability to identify and analyze anomalies is limited, leading to missed or incorrect assessments and hindering the timely and accurate detection of corporate tax risks. Decision-making processes often rely on human experience and subjective judgment, lacking scientific rigor and objectivity. The fragmented and difficult-to-share nature of tax knowledge results in high learning costs for tax personnel and slow improvement in their professional competence. Summary of the Invention

[0004] This invention provides an intelligent tax audit robot system based on NLP and OCR technologies to address the shortcomings of existing technologies.

[0005] This invention provides an intelligent tax audit robot system based on NLP and OCR technologies, comprising: The data acquisition module is used to collect tax-related data from various channels and build a corporate tax data pool.

[0006] The data processing module is used to clean, deduplicate, and normalize corporate tax-related data. At the same time, it uses NLP technology to perform semantic analysis and entity recognition on the text data in the corporate tax-related data to build a corporate tax knowledge graph.

[0007] The risk assessment module is used to establish a dynamic tax rule engine based on tax regulations and historical data of enterprises, to conduct real-time comparison and risk assessment of enterprise tax-related data, and generate risk reports and early warning information.

[0008] The intelligent interaction module provides an intelligent interactive interface for tax officials, supporting natural language question and answer, conversational interaction, interpreting risk assessment results, and providing decision-making suggestions.

[0009] The report generation module is used to generate corporate tax audit reports based on risk assessment results and decision recommendations.

[0010] The learning optimization module is used to optimize and adjust the dynamic tax rules engine in real time based on decision-making suggestions and corporate tax audit reports.

[0011] According to the present invention, an intelligent tax audit robot system based on NLP and OCR technologies is provided. The tax-related data includes paper tax returns uploaded by enterprises, electronic financial documents, tax system interface data, and publicly available enterprise information on the Internet.

[0012] According to the present invention, an intelligent tax audit robot system based on NLP and OCR technologies includes a data acquisition module comprising a channel access unit, a data capture unit, and a data aggregation unit. The channel access unit is used to access multiple channel data sources. The data capture unit is used to capture enterprise tax-related data from multiple channel data sources. The data aggregation unit is used to integrate the enterprise tax-related data captured from multiple channels into a unified data pool, resulting in an enterprise tax data pool.

[0013] According to the present invention, an intelligent tax audit robot system based on NLP and OCR technologies includes a data processing module comprising a basic processing unit, a semantic analysis unit, and an entity recognition unit. The basic processing unit cleans, deduplicates, and normalizes the collected enterprise tax-related data. The semantic analysis unit constructs an NLP model and performs semantic understanding of the text data within the enterprise tax-related data in conjunction with a tax corpus. The entity recognition unit identifies tax-related entities in the text data and constructs an enterprise tax knowledge graph.

[0014] According to the present invention, an intelligent tax audit robot system based on NLP and OCR technologies is provided, and the tax-related entities include tax types, tax rates, and taxpayers.

[0015] According to the present invention, an intelligent tax audit robot system based on NLP and OCR technologies includes a risk assessment module comprising a rule building unit, a real-time comparison unit, and an assessment and early warning unit. The rule building unit establishes a basic rule system for a dynamic tax rule engine based on tax regulations and historical enterprise data. The real-time comparison unit compares the enterprise's tax-related data with the basic rule system in real time, filtering out abnormal data. The assessment and early warning unit constructs a machine learning-based risk assessment model, taking abnormal data as input, outputting a risk level, and triggering an early warning mechanism when the risk level reaches a preset threshold.

[0016] This invention provides an intelligent tax audit robot system based on NLP and OCR technologies. The intelligent interaction module includes an interface presentation unit, a question-and-answer processing unit, and a decision analysis unit. The interface presentation unit constructs a visual interactive interface, providing an intelligent interactive interface for tax personnel and displaying functional information. The question-and-answer processing unit uses an NLP model combined with an enterprise tax knowledge graph to support natural language question-and-answer sessions for tax personnel, answering their questions. The decision analysis unit generates decision suggestions based on the answers from the question-and-answer processing unit and the risk level, using data analysis and decision support algorithms.

[0017] According to the present invention, an intelligent tax audit robot system based on NLP and OCR technologies includes a report generation module comprising an information integration unit, a template application unit, and a report generation unit. The information integration unit extracts and integrates basic enterprise information, tax compliance status, and risk assessment results from the enterprise's tax data pool. The template application unit selects a report template based on different audit scenarios and report types, and fills the integrated information into the report template. The report generation unit adjusts, proofreads, and reviews the content of the applied template to generate an enterprise tax audit report.

[0018] According to the present invention, an intelligent tax audit robot system based on NLP and OCR technologies provides a corporate tax audit report that includes basic corporate information, tax compliance status, risk point analysis, and handling suggestions.

[0019] The present invention provides an intelligent tax audit robot system based on NLP and OCR technologies. The learning optimization module includes a feedback collection unit, a rule adjustment unit, and an effect evaluation unit. The feedback collection unit collects relevant feedback information from decision-making suggestions and corporate tax audit reports. The rule adjustment unit uses machine learning algorithms to adjust and optimize the rules of the dynamic tax rule engine in real time based on the relevant feedback information. The effect evaluation unit evaluates the performance of the optimized rule engine using preset evaluation indicators.

[0020] This invention provides an intelligent tax audit robot system based on NLP and OCR technologies. By accessing multiple data sources, it comprehensively collects enterprise tax-related data. Based on tax regulations and historical enterprise data, it establishes a dynamic tax rule engine and, combined with a machine learning risk assessment model, accurately identifies enterprise tax risks. Through real-time comparison and in-depth analysis, it can promptly detect anomalies in enterprise tax data and accurately assess risk levels, helping tax personnel take proactive measures to reduce tax risks and prevent tax violations. It provides tax personnel with a visual interactive interface and natural language question-and-answer functionality, facilitating their access to necessary information. Decision suggestions are generated based on question-and-answer results and risk levels, providing tax personnel with a more scientific basis for decision-making, reducing subjective judgment errors, and improving the accuracy and rationality of decisions. An enterprise tax knowledge graph is constructed, presenting tax-related entities and relationships in a graphical form, facilitating knowledge retrieval and learning for tax personnel. Simultaneously, the continuous updating and improvement of the knowledge graph contributes to internal knowledge accumulation and sharing, enhancing the professional competence and business capabilities of tax personnel. By collecting feedback information, the rules are adjusted and optimized in real time, continuously adapting to changes in tax regulations and the development of enterprise business. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the structure of an intelligent tax audit robot system based on NLP and OCR technologies provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0023] Figure label: 610. Processor; 620. Communication interface; 630. Memory; 640. Communication bus. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0025] The following is combined with Figures 1-2 This invention describes an intelligent tax audit robot system based on NLP and OCR technologies.

[0026] Figure 1 This is a schematic diagram of the structure of an intelligent tax audit robot system based on NLP and OCR technologies provided in an embodiment of the present invention.

[0027] Figure 2 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0028] like Figures 1-2 As shown in the figure, an intelligent tax audit robot system based on NLP and OCR technology is provided in this embodiment of the invention, including a data acquisition module, a data processing module, a risk assessment module, an intelligent interaction module, a report generation module, and a learning optimization module.

[0029] The data acquisition module is used to collect corporate tax-related data from multiple channels and build a corporate tax data pool.

[0030] Tax-related data includes paper tax returns uploaded by enterprises, electronic financial documents, tax system interface data, and publicly available enterprise information on the Internet.

[0031] The data acquisition module includes a channel access unit, a data capture unit, and a data aggregation unit. The channel access unit connects to multiple data sources. The data capture unit captures tax-related data from these multiple sources. The data aggregation unit integrates the tax-related data captured from these multiple channels into a unified data pool, resulting in a corporate tax data pool.

[0032] The main task of the channel access unit is to establish connections with multiple channel data sources to ensure stable and efficient data acquisition.

[0033] Conduct a comprehensive survey of channels that may provide tax-related data for enterprises, including tax websites, corporate financial systems, third-party tax service platforms, and industry association databases.

[0034] Data sources can be classified based on factors such as their nature, data format, and access method. For example, data sources can be divided into public data sources, internal data sources, and collaborative data sources.

[0035] For web-based data sources, such as public query interfaces, use the HTTP / HTTPS protocol for data requests and responses.

[0036] Establish a connection monitoring mechanism to monitor the connection status with the data source in real time. If a connection anomaly is detected, promptly issue alarms and take appropriate action to ensure the continuity of data collection.

[0037] The data capture unit is responsible for extracting enterprise tax-related data from various connected data sources.

[0038] Based on business needs, define the scope and conditions of the enterprise tax-related data to be captured, such as enterprise name, tax period, and tax type. When assessing the completeness of the data capture, the formula is as follows:

[0039] In the formula, C represents the data crawling coverage rate. This indicates the number of valid data records actually retrieved. This indicates the total number of records that should theoretically be retrieved from the data source. This represents the data integrity coefficient.

[0040] Clearly define the data format requirements, such as date format and amount format, to facilitate subsequent data processing and analysis.

[0041] For web-based data sources, web crawling techniques are used to extract data. For database-type data sources, SQL statements are used to query data. Appropriate SQL queries are written based on data filtering rules to extract enterprise tax-related data from the database. For data sources that provide API interfaces, the API is called according to the API documentation to obtain the required data.

[0042] The main function of the data aggregation unit is to integrate corporate tax-related data captured from multiple data sources into a unified data pool.

[0043] Since the data structures and field names of different data sources may differ, data field mapping is necessary. This involves mapping fields with the same meaning from different data sources to a unified data model.

[0044] For enterprise tax-related data records from different data sources, matching is required. Matching is performed using key information such as the enterprise's unique identifier and tax period to ensure that tax data from the same enterprise can be correctly merged. During the data matching phase, a matching score is calculated based on key attributes such as the enterprise's unique identifier and tax period. The formula is as follows:

[0045] In the formula, S represents the matching score between two data records, and n represents the number of key attributes involved in the matching. This represents the weight of the i-th attribute. This represents the matching degree of the i-th attribute.

[0046] Based on the matching score, the overall confidence level of the key information is calculated, expressed by the formula:

[0047] in, Indicates the overall confidence level. Indicates the original confidence level. The weights representing the original confidence levels. This indicates the similarity between the current invoice and a known template across multiple data sources. Similarity weights This represents the matching score between two data records. This indicates the matching weight.

[0048] It needs to be explained that in this application, in the formula, The weight represents the original confidence level, and its value ranges from [0,1]. The similarity weights range from [0,1]. This represents the matching weight, with a value range of [0,1]. .

[0049] The matched data will be integrated and merged into a unified dataset. A custom program will be written to implement this data integration.

[0050] The consolidated corporate tax data is stored in a unified data pool. This data pool can be a relational database, a non-relational database, or a data warehouse, etc.

[0051] The data processing module is used to clean, deduplicate, and normalize corporate tax-related data. At the same time, it uses NLP technology to perform semantic analysis and entity recognition on the text data in the corporate tax-related data to build a corporate tax knowledge graph.

[0052] The data processing module includes a basic processing unit, a semantic analysis unit, and an entity recognition unit. The basic processing unit cleans, deduplicates, and normalizes the collected corporate tax-related data. The semantic analysis unit builds an NLP model and uses a tax corpus to perform semantic understanding of the text data within the corporate tax-related data. The entity recognition unit identifies tax-related entities in the text data and constructs a corporate tax knowledge graph.

[0053] Tax-related entities include tax types, tax rates, and taxpayers.

[0054] The basic processing unit cleans, deduplicates, and normalizes the collected corporate tax-related data to improve data quality and prepare for subsequent analysis. In the deduplication process, the formula for calculating the duplication rate between two records is as follows:

[0055] In the formula, This represents the duplication score between two records, where m represents the total number of attributes in the records. and This represents the k-th attribute value of two records. For indicator functions, This represents the importance coefficient of the k-th attribute.

[0056] By traversing the data, find fields in the dataset that contain null values, blank characters, or specific placeholders.

[0057] For numerical data, use the mean, median, or mode to fill in the missing values; for categorical data, use the most frequent values ​​to fill in the missing values, or treat missing values ​​as a separate category. If there are too many missing values ​​and they significantly impact subsequent analysis, consider deleting the record.

[0058] For numerical data, statistical methods are used to determine the normal data range; for categorical data, business rules are used to determine if there are any unreasonable values. Correctable outliers are corrected according to business logic; uncorrectable outliers are either deleted or specially marked. For numerical data, moving averages, weighted averages, and other methods are used to smooth the data and reduce the impact of noise. For text data, regular expressions are used to remove special characters, garbled text, and other noise.

[0059] The semantic analysis unit constructs an NLP model and combines it with a tax corpus to perform semantic understanding of textual data in corporate tax-related data.

[0060] We collect a large amount of tax-related text data, including tax regulations, policy documents, tax return instructions, and tax consultation Q&A.

[0061] The collected corpus is annotated, and the annotation content includes entity type (such as tax type, tax rate, taxpayer, etc.) and semantic relationship (such as "belongs to" "applicable to" etc.).

[0062] Choose an appropriate NLP model based on task requirements and data characteristics, such as recurrent neural networks and their variants, or Transformer architecture models.

[0063] The model is trained using a pre-built tax corpus, and its parameters are adjusted to improve its semantic understanding of tax text data. The pre-processed text is then input into the trained NLP model, which outputs semantic information such as sentence topic, sentiment, and key information extraction.

[0064] The entity recognition unit is used to identify tax-related entities in text data and build a corporate tax knowledge graph.

[0065] Features for identifying tax-related entities, such as part-of-speech tags, word frequency, and contextual information, are extracted from text data. A supervised learning method is used to train the labeled tax corpus to obtain an entity recognition model. Enterprise tax text data is then input into the trained model, which outputs the location and type of tax-related entities (tax type, tax rate, taxpayer) within the text.

[0066] After identifying tax-related entities, the relationships between them are analyzed, such as "taxpayer - payment - tax type" and "tax type - corresponding - tax rate". The identified entities and relationships are stored in a graph database to represent the enterprise tax knowledge graph in graph form. Nodes in the graph represent entities, and edges represent relationships between entities.

[0067] The risk assessment module is used to establish a dynamic tax rule engine based on tax regulations and historical data of enterprises, to conduct real-time comparison and risk assessment of enterprise tax-related data, and generate risk reports and early warning information.

[0068] The risk assessment module includes a rule building unit, a real-time comparison unit, and an assessment and early warning unit. The rule building unit establishes the basic rule system for a dynamic tax rule engine based on tax regulations and historical enterprise data. The real-time comparison unit compares the enterprise's tax-related data with the basic rule system in real time, filtering out abnormal data. The assessment and early warning unit builds a machine learning-based risk assessment model, taking abnormal data as input, outputting a risk level, and triggering an early warning mechanism when the risk level reaches a preset threshold.

[0069] The rule building unit primarily relies on tax regulations and historical enterprise data to construct the foundational rule system for the dynamic tax rule engine. The specific process is as follows: It comprehensively collects various local tax regulations, policy documents, and implementation rules, covering provisions related to various taxes such as value-added tax, corporate income tax, and individual income tax.

[0070] We meticulously reviewed the collected regulations, extracted key clauses and rules, and clarified various tax requirements, such as tax filing deadlines, tax bases, and applicable tax rates.

[0071] Integrate the company's tax filing data, financial statement data, invoice data, etc. over a period of time to form a complete historical dataset.

[0072] Data mining techniques are used to analyze patterns and regularities in a company's historical tax data, such as fluctuations in tax payments at different times and the proportional relationships of various tax indicators.

[0073] Based on tax regulations and historical data mining results, specific tax rules are defined. For example, if a company's VAT burden rate is lower than the industry average by a certain percentage for three consecutive months, it is considered abnormal; or if the deduction ratio for a certain expense exceeds the legally stipulated limit, it is also considered abnormal. When defining the rule that the VAT burden rate is lower than the industry average, the Z-score is used to quantify the deviation, expressed by the formula:

[0074] In the formula, Z represents the deviation of the tax burden rate, and x represents the actual VAT burden rate of the enterprise. This indicates the average VAT burden rate for the same industry during the same period. It represents the standard deviation of the VAT burden rate of the same industry during the same period.

[0075] The defined rules are converted into code that computers can recognize, thus building a rule engine.

[0076] We will continue to monitor changes in tax regulations and promptly incorporate new regulatory requirements into the rule system.

[0077] Based on feedback from enterprises' actual tax payment situation and risk assessment results, the rules are adjusted and optimized to ensure the effectiveness and adaptability of the rule system.

[0078] The task of the real-time comparison unit is to compare the company's tax-related data with the basic rule system in real time and filter out abnormal data. The specific operation process is as follows: It can obtain the latest tax-related data of enterprises in real time, including tax declaration data, invoice data, financial statement data, etc., to ensure the timeliness and completeness of the data.

[0079] The data received is cleaned, transformed, and standardized to meet the requirements of rule comparison.

[0080] The preprocessed corporate tax data is matched against each rule in the rule system in turn.

[0081] The company's tax data is assessed based on the conditions and logic outlined in the rules. For example, if the rule stipulates that "the ratio of a company's monthly VAT output tax to its input tax should be within a certain range," then the company's actual output tax and input tax ratio are compared to see if they fall within that range.

[0082] When a company's tax data does not meet the rules, the data is marked as abnormal. Information related to the abnormal data is recorded, such as the data source, data content, and the rules violated.

[0083] The filtered abnormal data is categorized, for example by tax type or anomaly type, for subsequent analysis and processing.

[0084] The assessment and early warning unit analyzes abnormal data by constructing a machine learning-based risk assessment model, outputs a risk level, and triggers an early warning mechanism when a preset threshold is reached. The specific steps are as follows: Collect and filter out the abnormal data, and organize it into a dataset suitable for training machine learning models. This includes feature extraction and labeling. Features are the various attributes of the abnormal data, such as tax amount, tax burden rate, number of invoices, etc., and labels are the risk level (e.g., low risk, medium risk, high risk).

[0085] The dataset is divided into training, validation, and test sets. Based on the data characteristics and problem type, an appropriate machine learning model is selected, such as logistic regression, decision tree, random forest, support vector machine, or neural network. The selected model is trained using the training set. By adjusting the model's parameters, it is made capable of accurately predicting risk levels based on abnormal data. The formula for calculating the probability of high risk based on the logistic regression model is expressed as:

[0086] In the formula, This indicates the probability that a company is classified as high-risk. Indicates a high-risk label. This represents the j-th anomalous feature. For bias terms, Let represent the coefficient of the j-th feature, and p represent the number of anomalous features.

[0087] The trained model is evaluated using validation and test sets. Common evaluation metrics include accuracy, recall, and F1 score. Based on the evaluation results, the model is optimized, such as adjusting model parameters, adding or removing features, or employing ensemble learning methods to improve its performance and stability. New outlier data is then input into the trained model, and the model outputs a corresponding risk level, where the risk level formula is expressed as:

[0088] in, Indicates the risk level. This represents the total number of risk rules in the basic rule system. This indicates the first in the basic rule system. The weight of each rule, Represents the model balance coefficients. This indicates the first in the basic rule system. The trigger risk value of the rule, This indicates the first in the basic rule system. The rules are used to calculate the model analysis risk probability in the logistic regression model.

[0089] It needs to be explained that in this application, in the formula, This represents the model balance coefficient, with a value range of [0,1].

[0090] Different risk levels are set as warning thresholds. When the risk level reaches or exceeds the preset threshold, the warning mechanism is triggered. Warning methods include email notifications, SMS alerts, and system pop-ups, promptly informing relevant personnel of potential tax risks to the company.

[0091] The intelligent interaction module provides an intelligent interactive interface for tax officials, supporting natural language question and answer, conversational interaction, interpreting risk assessment results, and providing decision-making suggestions.

[0092] The intelligent interaction module comprises an interface presentation unit, a question-and-answer processing unit, and a decision analysis unit. The interface presentation unit constructs a visual interactive interface, providing an intelligent interaction platform for tax personnel and displaying functional information. The question-and-answer processing unit uses an NLP model combined with an enterprise tax knowledge graph to support natural language question-and-answer sessions for tax personnel, answering their questions. The decision analysis unit generates decision recommendations based on the answers from the question-and-answer processing unit and the risk level, using data analysis and decision support algorithms.

[0093] The interface presentation unit aims to build a visually appealing interactive interface, providing tax officials with a superior intelligent interactive experience and showcasing functional information. The following is a detailed implementation process: Communicate with tax officials to understand their workflows, information needs, and operating habits, and determine the main content and functions that the interface needs to display, such as corporate tax data statistics charts, risk assessment results, and Q&A interaction areas.

[0094] Based on the requirements analysis, design the interface prototype and plan the layout and interaction methods of each functional module. Use tools such as Axure and Sketch to create low-fidelity or high-fidelity prototypes to demonstrate the general structure and interaction flow of the interface.

[0095] Choose the appropriate front-end technology stack, such as HTML, CSS, JavaScript, etc., and use popular front-end frameworks to build the interface.

[0096] It interfaces with the backend data interface to obtain the functional information and data to be displayed. Asynchronous data requests are implemented using technologies such as AJAX and FetchAPI to ensure that the interface can update data in real time.

[0097] Use the visualization library to develop various statistical charts and visualization components to present corporate tax data in an intuitive graphical format.

[0098] Conduct comprehensive testing on the completed interface, including functional testing, compatibility testing, and performance testing, to ensure that the interface can be displayed and used normally in different browsers, devices, and network environments.

[0099] The question-answering unit leverages an NLP model combined with an enterprise tax knowledge graph to support tax personnel in conducting natural language question-and-answer sessions and answering their questions. The specific implementation method is as follows: Preprocessing is performed on the natural language questions input by tax officials, including word segmentation, part-of-speech tagging, removal of stop words, and normalization.

[0100] Use a trained NLP model to perform semantic parsing on the preprocessed question to understand the question's intent and key information.

[0101] Based on the stated intent and key information, the natural language understanding accuracy of the NLP model is analyzed. Combining the natural language understanding accuracy with the knowledge graph, the effectiveness of the preprocessed question is calculated using the following formula:

[0102] in, Indicates degree of validity. This indicates the total number of question-and-answer rounds in the question-and-answer processing unit. Indicates the accuracy of natural language understanding. Indicates the first Cosine similarity between round-robin responses and the knowledge graph. Indicates An exponential function with base 0. This represents the response delay penalty coefficient. Indicates the first The response time for round-robin responses.

[0103] Based on the aforementioned effectiveness, tax-related entities (such as tax types, tax rates, and taxpayers) are identified from the question, and matched against the enterprise tax knowledge graph to find relevant entity nodes. According to the semantics of the question and the entity relationships in the knowledge graph, relational reasoning is performed to search for relevant knowledge and information. After retrieving relevant knowledge and information from the knowledge graph, it is filtered and integrated to generate an accurate and concise answer. The generated answer is converted into natural language text and replied to tax officials in an appropriate language style.

[0104] It needs to be explained that in this application, in the formula, This represents the response delay penalty coefficient, with a value of 0.01.

[0105] Based on the responses and risk levels from the question-and-answer processing unit, the decision analysis unit generates decision recommendations using data analysis and decision support algorithms. The specific steps are as follows: The system collects the answers from the question-and-answer processing unit, the risk levels output by the risk assessment module, and related corporate tax data, and integrates them into a unified dataset.

[0106] Data analytics techniques are used to conduct in-depth analysis of the integrated data, uncovering potential information and patterns. Based on the type of decision problem and the characteristics of the data, appropriate decision support algorithms are selected, such as multi-attribute decision algorithms (e.g., analytic hierarchy process, TOPSIS) and machine learning algorithms (e.g., decision trees, random forests). Historical data is used to train and optimize the selected decision support algorithms, adjusting model parameters to improve accuracy and reliability. The analysis results and risk levels are input into the trained decision support model, which outputs decision recommendations for different situations. The generated decision recommendations are presented to tax officials in an intuitive way, along with explanations of the basis and rationale, to help them understand and accept the recommendations.

[0107] The report generation module is used to generate corporate tax audit reports based on risk assessment results and decision recommendations.

[0108] The report generation module includes an information integration unit, a template application unit, and a report generation unit. The information integration unit extracts and integrates basic enterprise information, tax compliance status, and risk assessment results from the enterprise's tax data pool. The template application unit selects a report template based on different audit scenarios and report types, and then fills the integrated information into the template. The report generation unit adjusts, proofreads, and reviews the content of the applied template to generate the enterprise tax audit report, which includes basic enterprise information, tax compliance status, risk point analysis, and handling recommendations.

[0109] The information integration unit is primarily responsible for extracting and integrating the necessary information from the enterprise's tax data pool, providing a foundation for subsequent report generation. Its specific implementation process is as follows: Based on the requirements of the enterprise's tax audit report, determine the information that needs to be extracted from the enterprise's tax data pool, including basic enterprise information (such as enterprise name, taxpayer identification number, registered address, etc.), tax compliance status (such as tax declaration records, tax payment status, etc.), and risk assessment results (such as risk level, risk point details, etc.).

[0110] Use database query languages ​​(such as SQL) to extract relevant data from the enterprise tax data pool. Set filtering criteria based on the characteristics of different information to ensure that the extracted data is accurate and meets reporting requirements.

[0111] The extracted data is cleaned to remove duplicate, invalid, and erroneous data. Data in different formats is converted and standardized to ensure consistency.

[0112] Based on the relationships between the data, the extracted basic enterprise information, tax compliance status, and risk assessment results are integrated. The integrated data is then summarized and statistically analyzed to generate the key indicators and statistical data required for the report.

[0113] The template application unit selects an appropriate report template based on different audit scenarios and report types, and fills the template with the integrated information. The specific implementation method is as follows: Design corresponding report templates based on different audit scenarios and report types (such as annual audit reports, special audit reports, etc.). Categorize and manage templates, establishing a template library for easy retrieval and selection later. Regularly maintain and update templates to ensure their format, content, and style comply with the latest audit requirements and industry standards. Identify the required report template type based on the specific requirements and scenario of the audit task. Select a report template from the template library that matches the current audit scenario and report type. Map the integrated information to the fields in the report template to determine the population position of each information item in the template. Use programming techniques to automatically populate the integrated information into the corresponding fields of the report template.

[0114] The report generation unit adjusts the format, proofreads, and reviews the content after applying the template, and finally generates a corporate tax audit report. The specific process is as follows: Optimize the layout of the template-based report, adjusting font, font size, line spacing, and margins to ensure a visually appealing and clear overall structure. If the report includes charts (such as tax trend graphs or tax type ratio charts), insert and adjust them accordingly. Carefully proofread the report content, checking the accuracy of the data, the logical coherence, and the standardization of the wording. Establish a review process, inviting relevant personnel to review the report. Reviewers will provide feedback and suggestions, leading to further revisions and improvements. After formatting adjustments, proofreading, and review, save the report in the specified file format. Distribute the generated corporate tax audit report to relevant personnel.

[0115] The learning optimization module is used to optimize and adjust the dynamic tax rules engine in real time based on decision-making suggestions and corporate tax audit reports.

[0116] The learning optimization module includes a feedback collection unit, a rule adjustment unit, and a performance evaluation unit. The feedback collection unit gathers feedback information related to decision-making suggestions and corporate tax audit reports. The rule adjustment unit uses machine learning algorithms to adjust and optimize the rules of the dynamic tax rule engine in real time based on the feedback information. The performance evaluation unit evaluates the performance of the optimized rule engine using preset evaluation metrics.

[0117] The feedback collection unit is responsible for collecting feedback information related to decision-making recommendations and corporate tax audit reports. The specific process is as follows: Communicate with internal personnel involved in the tax audit, such as auditors and tax experts. Distribute questionnaires or conduct interviews with the audited companies to understand their perceptions of the audit report's content and decision-making recommendations, their views on their practicality, and the actual impact of these recommendations on the company's tax management.

[0118] Design targeted questionnaires covering issues such as the accuracy and practicality of decision-making recommendations, and the completeness and readability of audit reports.

[0119] Organize face-to-face or telephone interviews to engage in in-depth discussions with key feedback personnel and obtain their detailed opinions and suggestions on decision-making recommendations and audit reports.

[0120] Record user interactions with decision-making suggestions and audit reports in relevant business systems, such as viewing time and operation records, and obtain indirect feedback by analyzing these log information.

[0121] The collected feedback information is categorized according to different themes, such as decision-making recommendations, audit report content, and rule applicability.

[0122] The feedback information is cleaned to remove invalid, duplicate, or irrelevant information, ensuring the accuracy and effectiveness of the feedback.

[0123] The compiled feedback information is stored in a dedicated database or file system for subsequent analysis and use.

[0124] Based on feedback, the rule adjustment unit uses machine learning algorithms to adjust and optimize the rules of the dynamic tax rule engine in real time. The specific steps are as follows: Extract features related to rule adjustments from the feedback information, such as error type, scope of impact, and frequency of occurrence.

[0125] By using data analysis techniques and machine learning algorithms, patterns and regularities can be identified in the feedback information.

[0126] Based on the needs of rule adjustment and the characteristics of feedback information, select appropriate machine learning algorithms, such as decision trees, neural networks, and genetic algorithms.

[0127] Using historical feedback information and corresponding rule adjustment results as training data, a selected machine learning algorithm is trained to obtain a rule adjustment model. By continuously adjusting the model's parameters, the model's accuracy and generalization ability are improved.

[0128] The model is adjusted based on the trained rules, and the rules of the dynamic tax rule engine are updated.

[0129] Before officially applying the adjusted rules, validate them. Use methods such as simulation testing and historical data verification to ensure that the rule adjustments do not introduce new problems and effectively improve the performance of the rule engine.

[0130] The performance evaluation unit evaluates the optimized rule engine's performance using preset evaluation metrics. The specific process is as follows: The accuracy of a rule engine in identifying abnormal tax situations is measured by metrics such as precision, recall, and F1 score. Precision reflects the proportion of abnormal situations correctly identified by the rule engine, while recall reflects the proportion of abnormal situations that the rule engine can identify relative to actual abnormal situations.

[0131] Evaluate the operational efficiency of the rule engine, such as processing time and resource utilization. Processing time reflects the speed at which the rule engine analyzes and judges tax data, while resource utilization reflects the use of system resources by the rule engine during operation.

[0132] Examine the stability of the rule engine under different data volumes and complex conditions, such as fluctuations in false positive rate and false negative rate.

[0133] Select representative test data, including both normal and abnormal tax data. The test data should cover different business scenarios and data characteristics to ensure the comprehensiveness and reliability of the evaluation results.

[0134] The test data is input into the optimized rule engine, and the results of the rule engine's operation are recorded, including information such as identified anomalies, processing time, and resource consumption.

[0135] Based on the recorded execution results, preset evaluation index values ​​are calculated, and the evaluation results are analyzed. The effectiveness of the rule adjustment is evaluated by comparing the performance with that of the rule engine before optimization.

[0136] Based on the evaluation results, the identified problems and improvement suggestions are fed back to the rule adjustment unit for further optimization of the rules. If the evaluation results indicate that the accuracy of the rule engine is still not ideal, the parameters of the machine learning algorithm are further adjusted or the training data is increased.

[0137] In summary, this embodiment provides an intelligent tax audit robot system based on NLP and OCR technologies. By accessing multiple data sources, it comprehensively collects enterprise tax-related data, providing rich information for subsequent analysis. A dynamic tax rule engine is established based on tax regulations and historical enterprise data. Combined with a machine learning-based risk assessment model, it can accurately identify enterprise tax risks. Through real-time comparison and in-depth analysis, anomalies in enterprise tax data can be detected promptly, and risk levels can be accurately assessed, helping tax personnel take proactive measures to reduce enterprise tax risks and prevent tax violations. A visual interactive interface and natural language question-and-answer function are provided for tax personnel to facilitate access to necessary information. Decision suggestions are generated based on question-and-answer results and risk levels, providing tax personnel with a more scientific basis for decision-making, reducing subjective judgment errors, and improving the accuracy and rationality of decisions. An enterprise tax knowledge graph is constructed, presenting tax-related entities and relationships in a graphical form, facilitating knowledge retrieval and learning for tax personnel. Simultaneously, the continuous updating and improvement of the knowledge graph contributes to internal knowledge accumulation and sharing within the enterprise, enhancing the professional competence and business capabilities of tax personnel. By collecting feedback and adjusting and optimizing the rules in real time, the system continuously adapts to changes in tax regulations and the development of business operations. This continuous improvement mechanism ensures that the tax management system remains effective and adaptable, providing strong support for the long-term stable development of enterprises.

[0138] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0139] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An intelligent tax audit robot system based on NLP and OCR technologies, characterized in that, include: The data acquisition module is used to collect corporate tax-related data from multiple channels and build a corporate tax data pool; The data processing module is used to clean, deduplicate, and normalize the enterprise's tax-related data. At the same time, it uses NLP technology to perform semantic analysis and entity recognition on the text data in the enterprise's tax-related data to construct an enterprise tax knowledge graph. The risk assessment module is used to establish a dynamic tax rule engine based on tax regulations and historical data of enterprises, to perform real-time comparison and risk assessment of the enterprise's tax-related data, and to generate risk reports and early warning information. The intelligent interaction module provides an intelligent interactive interface for tax officials, supporting natural language question and answer, conversational interaction, interpreting risk assessment results, and obtaining decision-making suggestions. The report generation module is used to generate corporate tax audit reports based on the risk assessment results and the aforementioned decision recommendations; The learning optimization module is used to optimize and adjust the dynamic tax rule engine in real time based on the decision recommendations and the enterprise tax audit report.

2. The intelligent tax audit robot system based on NLP and OCR technology according to claim 1, characterized in that, The tax-related data includes paper tax returns, electronic financial documents, tax system interface data, and publicly available company information uploaded by enterprises.

3. The intelligent tax audit robot system based on NLP and OCR technology according to claim 1, characterized in that, The data acquisition module includes a channel access unit, a data capture unit, and a data aggregation unit. The channel access unit is used to access multiple channel data sources. The data capture unit is used to capture enterprise tax-related data from multiple channel data sources. The data aggregation unit is used to integrate the enterprise tax-related data captured from multiple channels into a unified data pool to obtain an enterprise tax data pool.

4. The intelligent tax audit robot system based on NLP and OCR technology according to claim 1, characterized in that, The data processing module includes a basic processing unit, a semantic analysis unit, and an entity recognition unit. The basic processing unit is used to clean, deduplicate, and normalize the collected corporate tax-related data. The semantic analysis unit is used to construct an NLP model and perform semantic understanding of the text data in the corporate tax-related data in conjunction with a tax corpus. The entity recognition unit is used to identify tax-related entities in the text data and construct a corporate tax knowledge graph.

5. The intelligent tax audit robot system based on NLP and OCR technology according to claim 4, characterized in that, The tax-related entities include tax types, tax rates, and taxpayers.

6. The intelligent tax audit robot system based on NLP and OCR technology according to claim 1, characterized in that, The risk assessment module includes a rule building unit, a real-time comparison unit, and an assessment and early warning unit. The rule building unit is used to establish a basic rule system for a dynamic tax rule engine based on tax regulations and historical enterprise data. The real-time comparison unit is used to compare the enterprise's tax-related data with the basic rule system in real time and filter out abnormal data in the enterprise's tax-related data. The assessment and early warning unit is used to build a risk assessment model based on machine learning, take the abnormal data as input, output the risk level, and trigger an early warning mechanism when the risk level reaches a preset threshold.

7. The intelligent tax audit robot system based on NLP and OCR technology according to claim 6, characterized in that, The intelligent interaction module includes an interface presentation unit, a question-and-answer processing unit, and a decision analysis unit. The interface presentation unit is used to construct a visual interactive interface, providing an intelligent interactive interface for tax personnel and displaying functional information. The question-and-answer processing unit is used to support tax personnel in conducting natural language question-and-answer sessions by combining an NLP model with an enterprise tax knowledge graph, answering the questions asked by tax personnel. The decision analysis unit is used to generate decision suggestions based on the answer results of the question-and-answer processing unit and the risk level, using data analysis and decision support algorithms.

8. The intelligent tax audit robot system based on NLP and OCR technology according to claim 1, characterized in that, The report generation module includes an information integration unit, a template application unit, and a report generation unit; the information integration unit is used to extract basic enterprise information, tax compliance status, and risk assessment results from the enterprise tax data pool and integrate them. The template application unit is used to select a report template according to different audit scenarios and report types, and to fill the integrated information into the report template; The report generation unit generates a corporate tax audit report by adjusting, proofreading, and reviewing the content of the template.

9. The intelligent tax audit robot system based on NLP and OCR technology according to claim 1, characterized in that, The corporate tax audit report includes basic corporate information, tax compliance status, risk point analysis, and handling suggestions.

10. The intelligent tax audit robot system based on NLP and OCR technology according to claim 1, characterized in that, The learning optimization module includes a feedback collection unit, a rule adjustment unit, and an effect evaluation unit; the feedback collection unit is used to collect relevant feedback information on the decision suggestions and the enterprise tax audit report. The rule adjustment unit is used to adjust and optimize the rules of the dynamic tax rule engine in real time based on the relevant feedback information and using machine learning algorithms; the effect evaluation unit is used to evaluate the performance of the optimized rule engine through preset evaluation indicators.

Images