Large model driven enterprise exhaustion report generation method and system

Abstract

The invention relates to the technical field of enterprise burnout dispatch, in particular to a large-model-driven enterprise burnout dispatch report generation method and system, and the method comprises five steps: data collection and integration, multi-modal processing and standardization, risk analysis and reasoning, dynamic monitoring and updating, and report generation and optimization. Data are obtained through a multi-source heterogeneous interface and credibility scoring is carried out, semantic alignment is realized by adopting a hierarchical fusion architecture, risk identification and association analysis are carried out by utilizing a field enhanced large model, and a report is dynamically updated through an incremental learning mechanism. According to the method, multi-source heterogeneous data can be comprehensively processed, enterprise risk association can be deeply mined, dynamic changes can be responded in real time, the accuracy and practicability of a complete dispatching report are improved, and a reliable basis is provided for financial decision making.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence and big data processing technology, and specifically relates to a large model-driven enterprise due diligence report generation method and system. Background Art

[0002] A due diligence report, conducted by an intermediary with the cooperation of the company, comprehensively reviews the company's historical data and documents, management backgrounds, market risks, management risks, technological risks, and financial risks. This report is often conducted during public offerings and acquisitions. The core purpose of due diligence is to provide the buyer with the most comprehensive understanding of the target company, thereby effectively managing potential risks in the merger and acquisition. For example, due diligence should clarify the accuracy of the target company's historical financial records, the stability of key employees and supply chain relationships after the acquisition, and the existence of significant obligations that could impact operational or financial operations. Identifying these risks and legal issues allows buyers and sellers to negotiate the allocation of responsibilities and provides a basis for subsequent decision-making.

[0003] With the advancement of data analysis technology, existing due diligence methods are gradually incorporating automated tools to improve efficiency. However, current technology still has certain limitations. Manual due diligence relies on analysts to manually collect industrial and commercial, financial, and legal data, taking an average of 2-4 weeks to produce a single report. This is not only costly but also subject to bias due to subjective judgment. While automated tools can generate templated reports, they are insufficient when handling complex correlation analysis (such as supply chain risk transmission). When general large models are directly applied to financial scenarios, they lack adaptability to industry knowledge, resulting in a low recognition rate for financial fraud patterns. Furthermore, existing systems typically only provide static reports and struggle to respond in real time to dynamic events such as industrial and commercial changes and legal additions.

[0004] In recent years, technologies based on the fusion of multi-source heterogeneous data and domain-enhanced big models have provided new directions for due diligence report generation. By integrating multi-dimensional data from industrial and commercial, financial, judicial, and public opinion sources, combined with dynamic update mechanisms, due diligence efficiency and report quality can be improved to a certain extent. However, existing technologies still have room for improvement in areas such as the unified representation and semantic alignment of multi-source heterogeneous data, optimizing the domain adaptability of big models in financial scenarios, and ensuring the interpretability of report conclusions. Furthermore, implementing controllable dynamic report updates triggered by major events remains a pressing issue. Therefore, a more intelligent and dynamic solution is needed to address the complex demands of corporate due diligence. Summary of the Invention

[0005] The present invention relates to the field of enterprise due diligence technology, and discloses a method and system for intelligently generating enterprise due diligence reports based on a large model. The method includes five steps: data collection and integration, multimodal processing and standardization, risk analysis and reasoning, dynamic monitoring and updating, and report generation and optimization. Among them, data collection and integration obtains industrial and commercial, financial, judicial, public opinion and other data in real time through multi-source heterogeneous interfaces, and performs credibility scoring; multimodal processing and standardization adopts a layered fusion architecture to achieve unified representation and semantic alignment of heterogeneous data; risk analysis and reasoning utilizes a domain-enhanced large model to perform core risk identification and association analysis; dynamic monitoring and updating respond to data changes triggered by major events through an incremental learning mechanism; report generation and optimization combine explainable evidence chain tracing technology to generate structured reports and support dynamic adjustment. This method can comprehensively process multi-source heterogeneous data, deeply explore enterprise risk associations, respond to dynamic changes in real time, improve the accuracy and practicality of due diligence reports, and provide a reliable basis for financial decision-making.

[0006] A method and system for intelligently generating enterprise due diligence reports based on a large model, the method comprising the following steps: step S1: data collection and integration, obtaining the original data set required for enterprise due diligence; step S2: multimodal processing and standardization, uniformly representing and semantically aligning the original data set to generate standard structured data; step S3: risk analysis and reasoning, using a domain-enhanced large model to perform core risk identification and association analysis, and generate risk assessment reference data; step S4: dynamic monitoring and updating, responding to data changes through an incremental learning mechanism, and completing report version iteration; step S5: report generation and optimization, generating a structured due diligence report and supporting dynamic adjustment.

[0007] In step S1, the data collection and integration is used to collect the original data set required for corporate due diligence, specifically to obtain industrial and commercial, financial, judicial, public opinion and other data in real time through multi-source heterogeneous interfaces, and perform credibility scoring; the multi-source heterogeneous interfaces include API docking, file upload, direct database connection and other methods; the credibility scoring constructs a scoring model based on three dimensions: source authority, update timeliness, and cross-validation results.

[0008] The unified representation and semantic alignment of the original data set to generate standard structured data includes: establishing a position-content mapping relationship based on the text area position information in the scanned / image data, and extracting key fields by solving the position-content mapping relationship; constructing a table reconstruction algorithm based on the merged cell characteristics in the PDF form, and the table reconstruction algorithm includes cross-page splicing rules and field alignment logic; combining the industry terminology distribution characteristics in the unstructured text to establish a terminology standardization process, and the terminology standardization process includes a terminology disambiguation module and a contextual semantic enhancement mechanism; and layering and fusing the key field extraction results, the table reconstruction output results, and the terminology standardization processing results to generate structured data that complies with the ISO 20022 standard.

[0009] In step S3, the risk analysis and reasoning are used to perform core risk identification and correlation analysis for corporate due diligence scenarios. Specifically, based on the standard structured data, a domain-enhanced large model is used for deep reasoning to generate risk assessment reference data, including the following steps: feature extraction, specifically identifying key indicators from the standard structured data, such as abnormal fluctuations in the debt-to-asset ratio and a sudden increase in the equity pledge ratio; model reasoning, specifically performing risk correlation analysis, such as calculating the correlation between shareholder changes and supply chain stability; content assembly, specifically generating a preliminary report containing risk ratings, visual charts, and evidence indexes according to a preset template; compliance review, specifically automatically matching the regulatory rule base to generate compliance assessment results.

[0010] The dynamic calibration processing of the risk assessment reference data to generate a calibrated risk assessment result includes: extracting abnormal fluctuation points and fluctuation frequencies in the time series data, and calculating the dynamic adjustment amount of the data acquisition parameters changing with time; performing time dimension compensation calculation on the key indicators according to the dynamic adjustment amount to generate calibrated key indicator values; based on the correlation relationship between the fluctuation frequency and data acquisition stability, performing deviation correction processing on the risk association analysis results to generate a calibrated risk association analysis result; performing anti-noise adaptation adjustment processing on the compliance assessment results according to the noise change data under the abnormal fluctuation points to generate a calibrated compliance assessment result; re-inputting the calibrated key indicator values, risk association analysis results and compliance assessment results into the domain enhanced large model to generate a calibrated risk assessment result.

[0011] In step S4, the dynamic monitoring and updating is used to respond to data changes triggered by major events, specifically based on the risk assessment reference data and the standard structured data, using an incremental learning mechanism for dynamic updating to generate report version iteration data, including the following steps: state parameter space improvement, specifically constructing an optimized state parameter space and improving the system state vector to obtain improved state parameter space data, including industrial and commercial change status, judicial new status and public opinion fluctuation status; multi-layer monitoring network modeling, specifically constructing a meta-monitoring layer, a dynamic optimization layer and an intelligent execution layer in sequence, and performing multi-layer monitoring optimization through the multi-layer monitoring network modeling to obtain an initial monitoring strategy; event impact Function improvement, specifically, improving the event impact function by introducing event impact, fluctuation deviation, feedback index and emergency adjustment indicator function, and obtaining an improved event impact function; disturbance injection improvement, specifically, improving the disturbance injection by introducing disturbance factor control function, and obtaining a disturbance injection function for controlling the degree of disturbance injection; performing disturbance simulation by controlling the disturbance injection function value, including industrial and commercial change disturbance, judicial new disturbance and public opinion fluctuation disturbance; dynamic monitoring optimization, specifically, performing phased optimization of dynamic monitoring optimization by improving the state parameter space, modeling the multi-layer monitoring network, improving the event impact function and improving the disturbance injection, and generating report version iteration data.

[0012] The generation of a management strategy set based on the major event identification includes: calculating the optimal monitoring position based on the identification of the event difference area, and the optimal monitoring position is achieved by adjusting the coverage ratio of adjacent monitoring devices; constructing a database update optimization plan based on the identification of the matching deviation path, and the database update optimization plan includes the selection of event sample supplementary areas and the optimization configuration of sample event parameters; generating a monitoring frequency adjustment strategy based on the identification of the event unstable area, and the monitoring frequency adjustment strategy dynamically adjusts the monitoring interval and monitoring duration according to the event stability prediction value; and prioritizing the optimal monitoring position, the database update optimization plan and the monitoring frequency adjustment strategy to generate a management strategy set including execution timing and implementation parameters.

[0013] In step S5, the report generation and optimization is used to construct an intelligent evaluation method to verify the optimization strategy and improve the report quality. Specifically, based on the report version iteration data, multi-dimensional evaluation indicators are constructed and strategy effectiveness evaluation is performed. By selecting the strategy with the best evaluation effect, an optimized due diligence report is generated; the multi-dimensional evaluation indicators include effectiveness indicators, integrity indicators, compliance indicators and adaptability indicators; the effectiveness indicator specifically refers to the deviation rate between the actual evaluation and the expected evaluation; the integrity indicator specifically refers to the core risk coverage frequency; the compliance indicator specifically refers to the matching degree of regulatory rules; the adaptability indicator specifically refers to the strategy response time.

[0014] The method also includes: collecting the actual due diligence results and risk assessment deviations of the enterprise within a preset calibration period; performing deviation analysis on the actual due diligence results and the predicted confirmation results to generate a first error correction coefficient; performing time domain comparison processing on the risk assessment deviation and the predicted deviation value to generate a second error correction coefficient; adjusting the weight parameters of the domain-enhanced large model according to the first error correction coefficient and the second error correction coefficient to generate an optimized domain-enhanced large model; and applying the optimized domain-enhanced large model to subsequent batches of enterprise due diligence tasks.

[0015] The risk analysis and reasoning module includes a feature extraction unit, a model reasoning unit and a content assembly unit; the feature extraction unit is used to identify key indicators from standard structured data, such as abnormal fluctuations in the debt-to-asset ratio and a sudden increase in the equity pledge ratio; the model reasoning unit is used to perform risk correlation analysis, such as calculating the correlation between shareholder changes and supply chain stability; the content assembly unit is used to generate a preliminary report containing risk ratings, visual charts, and evidence indexes according to a preset template.

[0016] The beneficial effects of this large model-driven enterprise due diligence report generation method and system are mainly reflected in the following aspects: 1. Improve the comprehensiveness and reliability of data processing. This system can access multi-dimensional data such as industrial and commercial, financial, judicial, and public opinion data in real time through multi-source heterogeneous interfaces, covering all key information required for corporate due diligence and preventing data omissions. The acquired data is scored for credibility, and a scoring model is constructed based on three dimensions: source authority, update timeliness, and cross-validation results. This ensures the quality of the original data set and provides a reliable foundation for subsequent analysis.

[0017] 2. Effectively integrate and standardize data. A layered fusion architecture is used to uniformly represent and semantically align multimodal raw data. This includes processing scanned documents or images, PDF form data, and unstructured text data, resolving the difficulty of integrating heterogeneous multi-source data. This generates structured data compliant with the ISO 20022 standard, unifying the data format and facilitating subsequent risk analysis and reasoning.

[0018] 3. Enhance the depth and accuracy of risk analysis. Leveraging domain-enhanced large models for core risk identification and correlation analysis, we can deeply explore potential enterprise risk points, such as abnormal fluctuations in debt-to-asset ratios, sudden increases in equity pledge ratios, and risk correlations between shareholder changes and supply chain stability. Through dynamic calibration, we optimize key indicator values, risk correlation analysis results, and compliance assessments, further improving the accuracy of risk assessments.

[0019] 4. Ensure the dynamic nature and timeliness of reports. Leveraging incremental learning mechanisms, we enable dynamic monitoring and updates. This allows us to respond to data changes triggered by major events, such as industrial and commercial changes, judicial filings, and public opinion fluctuations, completing report iterations promptly to ensure that the report content reflects the company's latest status in real time. By building a multi-layer monitoring network, improving event impact functions, and implementing disturbance injection, we optimize dynamic monitoring strategies, enhancing both the speed of response to dynamic changes and our ability to handle them.

[0020] 5. Improve the practicality and decision-making value of reports. The generated structured due diligence reports include risk ratings, visual charts, evidence indexes, and other content, and support dynamic adjustment. The clear format and comprehensive content make it easy for users to understand and reference. Reports are optimized by combining multi-dimensional evaluation indicators to ensure excellent performance in terms of effectiveness, completeness, compliance, and adaptability, providing a reliable basis for financial decision-making and helping decision-makers make more reasonable judgments.

[0021] 6. It has the ability to continuously optimize. Through the preset calibration cycle, it collects the actual due diligence results and risk assessment deviations of enterprises, calculates the error correction coefficient, adjusts the weight parameters of the domain-enhanced large model, continuously optimizes the model performance, and improves the quality of subsequent batches of enterprise due diligence tasks, so that the system can continuously adapt to the complex needs of the enterprise due diligence field. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Schematic diagram of the process of the present invention.

[0023] Figure 2 This is a structural diagram of the data acquisition and integration module.

[0024] Figure 3 Schematic diagram of the layered fusion architecture of multimodal processing and standardization modules.

[0025] Figure 4 Schematic diagram of the functional composition of the risk analysis and reasoning module.

[0026] Figure 5 Schematic diagram of the incremental learning mechanism for the dynamic monitoring and update module. DETAILED DESCRIPTION

[0027] The present invention provides a method and system for intelligently generating enterprise due diligence reports based on a large model, and its specific implementation is described in detail with reference to the accompanying drawings. Figure 1 As shown in Figure 1, the approach consists of five main steps: data collection and integration, multimodal processing and standardization, risk analysis and reasoning, dynamic monitoring and updating, and report generation and optimization. The following sections will explain the implementation of each step, along with the composition and operating principles of its internal modules.

[0028] First, the data collection and integration module acquires the original data set required for enterprise due diligence in real time through a multi-source heterogeneous interface. As shown in Figure 2 , the multi-source heterogeneous interface includes API docking, file uploading, and database direct connection, etc. methods for obtaining data from different sources such as industry and commerce, finance, judiciary, public opinion, etc. These data will be evaluated by a credibility scoring model after entering the system. The credibility scoring model is based on three dimensions of source authority, update timeliness and cross-validation results to build a scoring mechanism to ensure data quality. For example, data from authoritative agencies is given a higher authority score, while data with lower update frequency is given a lower timeliness score. Finally, all data after scoring is integrated into a unified original data set as the basis for subsequent processing.

[0029] Next, the multi-modal processing and standardization module unifies the representation and semantic alignment of the original data set to generate standard structured data. As shown in Figure 3 , the hierarchical fusion architecture is the core component of this module, which designs different processing paths for scanned documents / pictures, PDF forms and unstructured text. For scanned documents / pictures, a position-content mapping relationship is established based on the text area position information, and the key fields are extracted by solving the mapping relationship. For data in PDF forms, a table reconstruction algorithm is used to handle the characteristics of merged cells, which includes cross-page splicing rules and field alignment logic, so as to realize accurate parsing of complex table data. For unstructured text, a term standardization process is established based on the distribution characteristics of industry terms, which includes a term disambiguation module and a context semantic enhancement mechanism to eliminate term ambiguity and enhance semantic consistency. The above processing results are processed by hierarchical fusion to generate structured data conforming to the ISO 20022 standard, providing a high-quality data basis for subsequent risk analysis.

[0030] Subsequently, the risk analysis and reasoning module uses a domain-enhanced large model to perform core risk identification and correlation analysis based on the standard structured data. As shown in Figure 4As shown in the figure, this module consists of a feature extraction unit, a model inference unit, and a content assembly unit. The feature extraction unit identifies key indicators from standard structured data, such as abnormal fluctuations in the debt-to-asset ratio or a sudden increase in the equity pledge ratio. The model inference unit performs risk correlation analysis, calculates the correlation between shareholder changes and supply chain stability, and generates risk assessment reference data. The content assembly unit generates a preliminary report containing risk ratings, visual charts, and evidence indexes according to a preset template. At the same time, it automatically matches the regulatory rule base to generate compliance assessment results to ensure that the report content complies with relevant laws and regulations. In addition, by analyzing the abnormal fluctuation points and fluctuation frequencies in the time series data, the dynamic adjustment amount of the data acquisition parameters over time is calculated, and then the key indicator values ​​and risk correlation analysis results are calibrated to generate a calibrated risk assessment result.

[0031] In the dynamic monitoring and update module, the system responds to data changes triggered by major events through an incremental learning mechanism and completes report version iteration. Figure 5 As shown in the figure, this module mainly includes four parts: state parameter space improvement, multi-layer monitoring network modeling, event impact function improvement, and disturbance injection improvement. State parameter space improvement constructs an optimized state parameter space and improves the system state vector to obtain improved state parameter space data, including industrial and commercial change status, judicial addition status, and public opinion fluctuation status. Multi-layer monitoring network modeling sequentially constructs the meta-monitoring layer, dynamic optimization layer, and intelligent execution layer, and generates the initial monitoring strategy through multi-layer monitoring optimization. Event impact function improvement improves the event impact function by introducing event impact, fluctuation deviation, feedback indicators, and emergency adjustment indicator functions to enhance monitoring accuracy. Disturbance injection improvement simulates industrial and commercial change disturbances, judicial addition disturbances, and public opinion fluctuation disturbances by introducing disturbance factor control functions, thereby testing the system's anti-interference capabilities. Finally, through the coordinated work of these improvement measures, report version iteration data is generated.

[0032] Finally, the report generation and optimization module constructs multidimensional evaluation indicators based on the report version iteration data and conducts a strategy effectiveness evaluation to generate an optimized due diligence report. The multidimensional evaluation indicators include effectiveness indicators, integrity indicators, compliance indicators, and adaptability indicators. The effectiveness indicator refers to the deviation rate between the actual and expected assessments, the integrity indicator refers to the frequency of core risk coverage, the compliance indicator refers to the degree of matching with regulatory rules, and the adaptability indicator refers to the time required for strategy response. By selecting the strategy with the best evaluation effect, the final due diligence report is generated. In addition, the system also regularly collects the actual due diligence results and risk assessment deviations of enterprises, performs deviation analysis on the actual due diligence results and the predicted confirmation results, and generates a first error correction coefficient; the risk assessment deviation and the predicted deviation values ​​are compared in the time domain to generate a second error correction coefficient. Based on these two error correction coefficients, the weight parameters of the domain-enhanced large model are adjusted to generate an optimized domain-enhanced large model to improve the accuracy of subsequent batches of enterprise due diligence tasks.

[0033] Throughout the system's operation, modules collaborate closely through data flow and signal transmission. For example, the raw data set output by the Data Collection and Integration Module is directly fed into the Multimodal Processing and Standardization Module for processing, while the standard structured data generated by the latter serves as input to the Risk Analysis and Reasoning Module. Similarly, the report version iteration data generated by the Dynamic Monitoring and Update Module provides the necessary update basis for the Report Generation and Optimization Module. Through this inter-module collaboration, the system achieves full automation from data collection to report generation, significantly improving the efficiency and quality of corporate due diligence reports.

[0034] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is supplemented below with reference to a specific application scenario.

[0035] In step S1, the data collection and integration module obtains the original data set required for enterprise due diligence in real time through multi-source heterogeneous interfaces. Figure 2 As shown, the system obtains enterprise registration information and change records from the industrial and commercial authorities through API connections. It also extracts data such as balance sheets and income statements from the financial system through direct database connections, and imports scanned copies of judicial decisions through file uploads. Once this data enters the system, a credibility scoring model evaluates it based on three dimensions: source authority, update timeliness, and cross-validation results. For example, data from the industrial and commercial authorities is given a high score due to its high authority, while certain public opinion data with lower update frequency is rated lower due to its lack of timeliness. Ultimately, all scored data is integrated into a unified raw data set, which serves as the basis for subsequent processing.

[0036] Subsequently, in step S2, the multimodal processing and standardization module uses a layered fusion architecture to process different types of raw data. Taking the PDF format financial statements provided by a certain company as an example, the system first identifies the merged cell characteristics, and uses the table reconstruction algorithm to parse the cross-page splicing rules and field alignment logic, so as to accurately extract key financial indicators. For scanned copies of judicial judgments, the system establishes a position-content mapping relationship based on the text area position information, and extracts key fields such as case number and judgment date. In addition, for unstructured text data, the system combines the distribution characteristics of industry terms, and uses the term disambiguation module and contextual semantic enhancement mechanism to eliminate term ambiguity and ensure semantic consistency. The above processing results generate structured data that complies with the ISO 20022 standard through layered fusion, providing a high-quality foundation for subsequent risk analysis.

[0037] In step S3, the risk analysis and reasoning module performs core risk identification and correlation analysis based on the generated standard structured data. Figure 4 As shown, the feature extraction unit identified abnormal fluctuations in the debt-to-asset ratio exceeding 30% from the financial data and marked them as key indicators. The model inference unit further calculated the correlation between shareholder change events and supply chain stability, finding that the company had undergone multiple equity changes in the past year and that its relationships with key suppliers were becoming unstable. Based on this analysis, the content assembly unit generated a preliminary report using a preset template, including risk ratings, visualization charts, and evidence indexes. The system also automatically matched the regulatory rule base to generate compliance assessment results, ensuring that the report content complies with relevant laws and regulations.

[0038] In step S4, the dynamic monitoring and updating module responds to data changes triggered by major events through an incremental learning mechanism. Figure 5 As shown in the figure, when the system detects a business registration change at a certain enterprise, the state parameter space improvement immediately optimizes the state vector of the event and generates improved state parameter space data. Multi-layer monitoring network modeling sequentially constructs a meta-monitoring layer, a dynamic optimization layer, and an intelligent execution layer to form an initial monitoring strategy. The event impact function improvement improves monitoring accuracy by introducing parameters such as event impact and fluctuation deviation. The disturbance injection improvement simulates disturbances caused by business registration changes, judicial additions, and public opinion fluctuations to test the system's anti-interference capabilities. Finally, through this collaborative workflow, the system generates report version iteration data to ensure that the report reflects the latest developments in real time.

[0039] Finally, in step S5, the report generation and optimization module constructs multidimensional evaluation indicators based on the report version iteration data and evaluates the effectiveness of the strategy. For example, the system calculates a first error correction factor by comparing the actual due diligence results with the forecast confirmation results; a second error correction factor is generated by comparing the risk assessment deviation with the time-domain forecast value. Based on these two error correction factors, the weight parameters of the domain-enhanced large model are adjusted to generate an optimized domain-enhanced large model. Ultimately, the system selects the strategy with the best evaluation results to generate a due diligence report, and supports dynamic adjustment to ensure that the report content is comprehensive, accurate, and interpretable.

[0040] Throughout the entire process, modules collaborate closely through data flows. For example, the raw data set output by the Data Collection and Integration Module is directly fed into the Multimodal Processing and Standardization Module for processing, while the standardized structured data generated by the latter serves as input to the Risk Analysis and Reasoning Module. Similarly, the report version iteration data generated by the Dynamic Monitoring and Update Module provides the necessary update basis for the Report Generation and Optimization Module. Through this inter-module collaboration, the system achieves full automation from data collection to report generation, significantly improving the efficiency and quality of corporate due diligence reports.

[0041] 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 any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0042] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A large model-driven enterprise due diligence report generation method, characterized by: The method comprises the following steps: Step S1: Data collection and integration: Real-time acquisition of industrial and commercial, financial, judicial, and public opinion data through multi-source heterogeneous interfaces, and credibility scoring to obtain the original data set required for enterprise due diligence; Step S2: multimodal processing and standardization, performing unified representation and semantic alignment on the original data set to generate standard structured data; Step S3: Risk analysis and reasoning, using a domain-enhanced large model to perform core risk identification and correlation analysis to generate risk assessment reference data; Step S4: Dynamic monitoring and updating, responding to data changes triggered by major events through incremental learning mechanisms, and completing report version iterations; Step S5: Report generation and optimization, generate a structured due diligence report and support dynamic adjustments.

2. The large model-driven enterprise due diligence report generation method according to claim 1 is characterized by: In step S1, the multi-source heterogeneous interface includes API docking, file upload and database direct connection; the credibility score constructs a scoring model based on three dimensions: source authority, update timeliness and cross-validation results.

3. The large model-driven enterprise due diligence report generation method according to claim 1 is characterized by: In step S2, the multimodal processing and standardization includes the following steps: Based on the location information of text areas in scanned documents or image data, a location-content mapping relationship is established to extract key fields. Based on the merge cell feature in PDF forms, a table reconstruction algorithm is built, including cross-page splicing rules and field alignment logic; Based on the distribution characteristics of industry terms in unstructured text, a term standardization process is established, including a term disambiguation module and a contextual semantic enhancement mechanism; The key field extraction results, table reconstruction output results and terminology standardization processing results are layered and fused to generate structured data that complies with the ISO 20022 standard.

4. The large model-driven enterprise due diligence report generation method according to claim 1 is characterized by: In step S3, the risk analysis and reasoning includes the following steps: Identify key indicators from the standard structured data, including abnormal fluctuations in debt-to-asset ratios and sudden increases in equity pledge ratios; Perform risk correlation analysis, including calculation of correlation between shareholder changes and supply chain stability; Generate a preliminary report containing risk ratings, visual charts, and evidence indexes according to pre-set templates; Automatically match the regulatory rule base to generate compliance assessment results.

5. The large model-driven enterprise due diligence report generation method according to claim 4 is characterized by: The dynamic calibration process of the risk assessment reference data comprises the following steps: Extract abnormal fluctuation points and fluctuation frequencies in time series data, and calculate the dynamic adjustment of data acquisition parameters over time; Performing time dimension compensation calculation on the key indicator according to the dynamic adjustment amount to generate a calibrated key indicator value; Based on the correlation between fluctuation frequency and data acquisition stability, the risk association analysis results are corrected for deviations to generate calibrated risk association analysis results; Based on the noise change data at the abnormal fluctuation point, the compliance assessment results are adjusted for noise resistance to generate calibrated compliance assessment results. The calibrated key indicator values, risk correlation analysis results, and compliance assessment results are re-input into the domain-enhanced big model to generate calibrated risk assessment results.

6. The large model-driven enterprise due diligence report generation method according to claim 1 is characterized by: In step S4, the dynamic monitoring and updating includes the following steps: Construct an optimized state parameter space and improve the system state vector to obtain improved state parameter space data, including industrial and commercial change status, judicial addition status, and public opinion fluctuation status; Construct the meta-monitoring layer, dynamic optimization layer, and intelligent execution layer in sequence, perform multi-layer monitoring optimization, and obtain the initial monitoring strategy; By introducing event impact, fluctuation deviation, feedback index and emergency adjustment indicator function, the event impact function is improved to obtain the improved event impact function; By introducing the disturbance factor control function, the disturbance injection function is improved to control the degree of disturbance injection; By controlling the disturbance injection function value, disturbance simulation is performed, including disturbances caused by industrial and commercial changes, judicial additions, and public opinion fluctuations; Through state parameter space improvement, multi-layer monitoring network modeling, event impact function improvement and disturbance injection improvement, dynamic monitoring optimization is carried out in stages to generate report version iteration data.

7. The large model-driven enterprise due diligence report generation method according to claim 6, characterized in that: Generating a management policy set according to a major event identifier includes the following steps: Based on the identification of event difference areas, the optimal monitoring location is calculated and achieved by adjusting the coverage ratio of adjacent monitoring devices; Based on the identification of the matching deviation path, a database update optimization plan is constructed, including the selection of event sample supplementary areas and the optimization configuration of sample event parameters; Based on the identification of unstable event areas, a monitoring frequency adjustment strategy is generated, and the monitoring interval and monitoring duration are dynamically adjusted according to the predicted value of event stability; The optimal monitoring location, database update optimization plan and monitoring frequency adjustment strategy are prioritized to generate a management strategy set including execution timing and implementation parameters.

8. The large model-driven enterprise due diligence report generation method according to claim 1, characterized in that: In step S5, the report generation and optimization includes the following steps: Constructing multi-dimensional evaluation indicators and conducting strategy effectiveness evaluation, wherein the multi-dimensional evaluation indicators include effectiveness indicators, integrity indicators, compliance indicators and adaptability indicators; The effectiveness indicator mentioned refers to the deviation rate between the actual evaluation and the expected evaluation; The said completeness indicator refers to the frequency of core risk coverage; The compliance indicators mentioned above refer to the degree of matching with regulatory rules; The adaptability index refers to the time it takes for the strategy to respond; Select the strategy with the best evaluation effect and generate an optimized due diligence report.

9. The large model-driven enterprise due diligence report generation method according to claim 1, characterized in that: The method further comprises the following steps: Collect the actual due diligence results and risk assessment deviations of the enterprise within the preset calibration period; Perform deviation analysis on the actual due diligence results and the forecast confirmation results to generate a first error correction coefficient; Performing time domain comparison processing on the risk assessment deviation and the predicted deviation value to generate a second error correction coefficient; Adjusting the weight parameters of the domain-enhanced large model according to the first error correction coefficient and the second error correction coefficient to generate an optimized domain-enhanced large model; The optimized domain-enhanced big model will be applied to subsequent batches of corporate due diligence tasks.

10. A large model-driven enterprise due diligence report generation system, characterized by: It includes data collection and integration module, multimodal processing and standardization module, risk analysis and reasoning module, dynamic monitoring and update module and report generation and optimization module.

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