System compliance early warning method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of system compliance management, and discloses a system compliance early warning method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting system file data, and carrying out the standardization processing and structural analysis; data preparation is carried out based on the system metadatabase and the supervision fine tuning corpus set, an initial model is constructed and trained, a trained system large model is formed, and version management is carried out; determining early warning types, establishing a system revision early warning model for different early warning types, and triggering early warning reminding according to an output result of the early warning model; performing difference comparative analysis on the same-theme systems based on the system large model, and generating a compliance inspection report; receiving a natural language question, generating an answer and providing system traceability information; a system outline is generated according to system revision requirements, a chapter summary and clause content are generated based on the system outline, and real-time compliance check and clause recommendation are supported. According to the invention, the intelligent demand of enterprise system full-life-cycle management can be met.

Description

Technical Field

[0001] This invention relates to the field of institutional compliance management technology, specifically to an institutional compliance early warning method, system, electronic device, and storage medium. Background Technology

[0002] Corporate compliance management faces numerous challenges in today's complex and ever-changing business environment. As business scale expands and external regulatory requirements become increasingly stringent, corporate policy systems are characterized by their sheer number, complex structure, and frequent updates. Traditional policy management methods, relying primarily on manual review and maintenance, suffer from significant efficiency bottlenecks and inaccuracies.

[0003] Existing technologies suffer from the following significant drawbacks: First, there is a prominent issue of delayed revisions to regulations, failing to keep pace with changes in laws and regulations and adjustments in business needs, leading to a disconnect between the content of regulations and actual requirements. Second, the ability to detect conflicts between regulations is insufficient; inconsistencies or discrepancies often exist between regulations at different levels or between parallel departments, increasing compliance risks. Third, the reliance on manual services for querying and interpreting regulations results in slow response times and inconsistent answer standards, impacting the effectiveness of policy implementation. Fourth, the lack of intelligent auxiliary tools for drafting regulations leads to a time-consuming process and inconsistent quality.

[0004] While some information management systems are currently used for policy management, most are limited to basic functions such as document storage and version control, lacking deep semantic understanding and intelligent analysis capabilities. Existing natural language processing technologies struggle to accurately understand complex policy logic and the connotations of technical terms when processing policy texts, failing to meet the intelligent needs of enterprises for full lifecycle policy management.

[0005] Therefore, this application provides a system compliance early warning method to solve the above-mentioned technical problems. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, electronic device and storage medium for early warning of institutional compliance, so as to solve the technical problem that the existing technology cannot meet the needs of enterprise institutional full life cycle management.

[0007] To address the aforementioned technical problems, this invention provides a method for early warning of institutional compliance, comprising:

[0008] Collect institutional document data, perform standardized processing and structured parsing, extract basic institutional information, chapters and clauses, construct an institutional meta-database, vectorize the institutional content to form an institutional vector library, and construct a supervisory fine-tuning corpus based on the business needs of institutional compliance inspection and early warning.

[0009] Based on the aforementioned institutional metadata database and supervisory fine-tuning corpus, data preparation is carried out, an initial model is constructed and trained, and the capabilities of institutional compliance inspection, institutional Q&A, institutional writing and revision early warning tasks are improved, forming a trained institutional big model and performing version management.

[0010] Analyze the factors that cause changes in institutional documents and policies and regulations, determine the types of early warnings, construct key data for early warnings based on the institutional metadata database, establish institutional revision early warning models for different types of early warnings, and trigger early warning reminders based on the output results of the early warning models;

[0011] Based on the institutional model, we conduct comparative analysis of the differences in the same subject, check the consistency of the systems between superiors and subordinates, check the legal risks of the corporate system clauses, and generate a compliance inspection report.

[0012] It receives natural language questions, matches and retrieves the question content against the institutional metadata database, generates answers through the institutional big data model, and provides institutional traceability information;

[0013] Generate a system outline based on the system revision needs, generate chapter overviews and clause content based on the system outline, and support real-time compliance checks and clause recommendations.

[0014] In some specific embodiments, the collected institutional document data undergoes standardized processing and structured parsing to extract basic institutional information, chapters, and clauses, constructing an institutional metadata database. The institutional content is then vectorized to form an institutional vector library. A supervisory fine-tuning corpus is constructed based on the business needs of institutional compliance checks and early warnings. Further, this includes:

[0015] Collect institutional documents in various electronic document formats, perform text recognition processing on scanned documents, parse the electronic documents to generate standardized text data;

[0016] Standardized texts are structured to identify the hierarchical structure of institutional documents, establish logical relationships between chapters, and generate structured documents.

[0017] Extract basic institutional information from structured documents, establish mapping relationships between chapters and clauses, and construct an institutional meta-database with a multi-level structure;

[0018] Vectorization algorithms are used to semantically encode the content of regulations, and multi-granularity vector indexes are established to form a regulatory vector library.

[0019] In some specific embodiments, data preparation is performed based on the aforementioned institutional metadata database and supervisory fine-tuning corpus; an initial model is constructed and trained to improve the capabilities of institutional compliance checks, institutional Q&A, institutional drafting, and revision early warning tasks; a trained large-scale institutional model is formed and version management is implemented; further including:

[0020] The data in the institutional metadata database is deduplicated and formatted. The monitoring fine-tuning corpus is sample balanced. Task labels for institutional difference checks, consistency checks, and compliance checks are added.

[0021] Configure the training parameters for the large model, load the pre-trained model, and initialize the model parameters;

[0022] A parameter optimization method is adopted to adapt some model parameters to institutional domain tasks.

[0023] Save model versions during the training process and establish a version management mechanism.

[0024] In some specific embodiments, the process includes analyzing factors affecting changes in institutional documents and policies to determine early warning types, constructing key early warning data based on an institutional metadata database, establishing institutional revision early warning models for different early warning types, and triggering early warning alerts based on the output of the early warning models. Further steps include:

[0025] Monitor the release versions and revision history of regulatory documents, track the promulgation, revision and repeal status of relevant laws and regulations, and establish a time-triggered early warning and detection mechanism;

[0026] Calculate the cosine similarity between institutional theme vectors, construct an institutional association graph, calculate the semantic similarity between clause vectors and regulation vectors, and establish a mapping relationship between institutions and regulations;

[0027] Establish a time-based early warning model based on revision cycles, a content-based early warning model based on topic similarity, a dependency-based early warning model based on reference relationships, a compliance-based early warning model based on regulatory changes, and an inspection-based early warning model based on regular checks.

[0028] Set early warning levels and trigger thresholds. When an early warning reminder is detected that a system has not been revised within its expiration period, related systems are outdated, reference systems have been updated, relevant regulations have changed, or compliance checks are abnormal, an early warning reminder will be generated and distributed.

[0029] In some specific embodiments, a comparative analysis of the differences between systems on the same topic is conducted based on a large-scale institutional model, consistency checks are performed on systems at different levels, legal risk checks are conducted on the corporate institutional clauses, and a compliance check report is generated, further including:

[0030] Extract the chapter structure and clause content of the same system, conduct semantic comparison analysis of each clause, identify differences in content expression, normative requirements, and scope of application, and generate a difference comparison table and difference analysis explanation;

[0031] Compare the corresponding clauses of the superior and subordinate systems item by item, detect conflicts in requirements, inconsistencies in standards, and deviations in implementation, identify the location of conflicting clauses, analyze the causes of conflicts, and provide solutions;

[0032] The content of the policy provisions is matched and compared with the latest legal and regulatory provisions to identify provisions that violate mandatory provisions, are inconsistent with legal requirements, or pose legal risks.

[0033] By integrating the results of discrepancy analysis, consistency check, and compliance check, a comprehensive compliance check report is generated, which includes a list of issues, risk levels, legal basis, and improvement recommendations.

[0034] In some specific embodiments, receiving natural language questions, matching the question content with a regulatory metadata database, generating answers through a large-scale regulatory model, and providing regulatory traceability information further includes:

[0035] The natural language questions input by the user are segmented and semantically parsed, and then converted into question vector representations;

[0036] Search the institutional clauses in the institutional vector library that have the highest similarity to the question vector, and set a similarity threshold to filter candidate answers;

[0037] The re-ranking model is used to re-rank the candidate answers based on their relevance, taking into account factors such as semantic relevance, clause authority, and timeliness, and to select the optimal answer fragment.

[0038] The optimal answer fragment is combined with the user's question to construct a prompt text. The input system model generates a structured answer, which is then associated with the corresponding system name, chapter number, and specific clause content.

[0039] In some specific embodiments, a system outline is generated based on the system revision needs, chapter overviews and clause content are generated based on the system outline, and real-time compliance checks and clause recommendations are supported, further including:

[0040] The system analyzes user-inputted requirements for policy drafting, including policy title, scope of application, and target audience, and generates a policy outline framework with a three-level structure of chapters, sections, and articles.

[0041] Based on the chapter titles in the system outline, generate corresponding chapter overview content, including chapter purpose, scope of application, and basic principles;

[0042] The specific clause text is generated item by item based on the chapter overview content, and step-by-step generation is supported according to the chapter order, including definition clauses, normative clauses, liability clauses, and penalty clauses;

[0043] It provides functions for rewriting, continuing, expanding, and abbreviating the text of generated clauses, and checks the compliance of the generated content with existing systems in real time, and recommends relevant clause templates based on the similarity of clause vectors.

[0044] Based on the same concept, the present invention also provides a regulatory compliance early warning system, comprising:

[0045] The data parsing module is configured to collect institutional document data, perform standardized processing and structured parsing, extract basic institutional information, chapters and clauses, construct an institutional meta-database, vectorize the institutional content to form an institutional vector library, and construct a supervisory fine-tuning corpus based on the business needs of institutional compliance inspection and early warning.

[0046] The system large model training module is configured to prepare data based on the system meta-database and the supervision fine-tuning corpus, build an initial model and train it, improve the system compliance check, system Q&A, system writing and revision early warning capabilities, form a trained system large model and perform version management;

[0047] The system revision early warning module is configured to analyze the factors that cause changes in system documents and policies and regulations, determine the type of early warning, construct key early warning data based on the system metadata database, establish system revision early warning models for different early warning types, and trigger early warning reminders based on the output results of the early warning models;

[0048] The compliance inspection report generation module is configured to perform comparative analysis of differences in systems on the same topic based on a large system model, conduct consistency checks on systems at different levels, conduct legal risk checks on corporate system clauses, and generate a compliance inspection report.

[0049] The answer generation module is configured to receive natural language questions, match and retrieve the question content with the institutional metadata database, generate answers through the institutional big data model, and provide institutional traceability information;

[0050] The system drafting module is configured to generate a system outline based on system revision needs, generate chapter overviews and clause content based on the system outline, and support real-time compliance checks and clause recommendations.

[0051] Based on the same concept, the present invention also provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of a regulatory compliance early warning method.

[0052] Based on the same concept, the present invention also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a regulatory compliance early warning method.

[0053] Compared with existing technologies, its advantages are as follows:

[0054] This invention discloses a method, system, electronic device, and storage medium for early warning of institutional compliance, which can improve the efficiency of institutional management: by automating institutional parsing, vectorizing processing, and intelligent analysis, it reduces manual operation steps, effectively improving the efficiency of institutional management.

[0055] Enhancing the accuracy of compliance checks: Leveraging the deep semantic understanding capabilities of large-scale models, the system can accurately identify the logical relationships between policy clauses and precisely detect policy conflicts and compliance risks. Through multi-dimensional comparative analysis, the accuracy of policy consistency and compliance checks is improved.

[0056] Achieve dynamic management of regulations: Establish a regulatory revision early warning mechanism that can automatically trigger revision reminders based on external factors such as changes in regulations and business adjustments, ensuring the timeliness and applicability of the regulatory system. Through multi-dimensional analysis including thematic association and reference association, achieve collaborative updates to the regulatory system.

[0057] Provides intelligent assistance and support: It can quickly respond to various policy inquiries, provide accurate answers and policy source tracing; it supports the generation of policy outlines, chapters, clauses, policy source tracing, clause recommendations, and compliance checks, effectively improving the quality and efficiency of policy drafting.

[0058] Building a standardized management system: By establishing a unified institutional metadata database and vectorized representation, we can achieve standardized management and intelligent retrieval of institutional content, provide technical support for the construction of enterprise institutional systems, and promote the institutionalization of management, the standardization of institutional processes, and the informatization of processes.

[0059] Supports full lifecycle management: covering all aspects of policy formulation, implementation, inspection, and revision, forming a complete closed loop of policy management. Through continuous learning and optimization of the large model, the level of intelligence in policy management is constantly improved. Attached Figure Description

[0060] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0061] Figure 1 This is a flowchart illustrating some specific embodiments of the institutional compliance early warning method of the present invention;

[0062] Figure 2 This is one of the flowcharts of another embodiment of the institutional compliance early warning method of the present invention;

[0063] Figure 3 This is a second flowchart illustrating another embodiment of the institutional compliance early warning method of the present invention;

[0064] Figure 4 This is the third flowchart of another embodiment of the institutional compliance early warning method of the present invention;

[0065] Figure 5 This is the fourth flowchart of another embodiment of the institutional compliance early warning method of the present invention;

[0066] Figure 6 This is the fifth flowchart of another embodiment of the institutional compliance early warning method of the present invention;

[0067] Figure 7 This is a flowchart of another embodiment of the institutional compliance early warning method of the present invention;

[0068] Figure 8 This is a schematic diagram of the structure of a regulatory compliance early warning system according to some specific embodiments of the present invention;

[0069] Figure 9 This is a schematic diagram of the structure of an electronic device according to some specific embodiments of the present invention;

[0070] In the diagram, 710 is the processor; 720 is the memory; 730 is the input device; and 740 is the output device. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0072] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0073] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0074] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0075] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0076] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0077] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0078] Reference Figure 1 A method for early warning of institutional compliance, characterized by comprising:

[0079] S101: Collect institutional document data, perform standardized processing and structured parsing, extract basic institutional information, chapters and clauses, construct an institutional meta-database, vectorize the institutional content to form an institutional vector library, and construct a supervision fine-tuning corpus based on the business needs of institutional compliance inspection and early warning.

[0080] S102, Based on the aforementioned institutional metadata database and supervisory fine-tuning corpus, prepare data, construct an initial model and train it to improve the ability to perform tasks such as institutional compliance checks, institutional Q&A, institutional writing and revision early warning, form a trained institutional big model and manage its version.

[0081] S103, analyze the factors that cause changes in institutional documents and policies and regulations, determine the type of early warning, construct key data for early warning based on the institutional metadata database, establish institutional revision early warning models for different types of early warning, and trigger early warning reminders based on the output results of the early warning models;

[0082] S104, based on the institutional model, conducts a comparative analysis of the differences in institutions on the same topic, checks the consistency of institutions at different levels, checks the legal risks of corporate institutional clauses, and generates a compliance inspection report.

[0083] S105 receives natural language questions, matches and retrieves the question content against the institutional metadata database, generates answers through the institutional big data model, and provides institutional traceability information;

[0084] S106 generates a system outline based on the system revision requirements, generates chapter overviews and clause content based on the system outline, and supports real-time compliance checks and clause recommendations.

[0085] Specifically, in this embodiment of the invention, various enterprise policy documents are collected; scanned documents are processed and text content is extracted using optical character recognition (OCR) technology; and electronic documents undergo format parsing and standardization. Structured parsing technology is used to identify the hierarchical structure of policy documents and establish the hierarchical relationships between chapters and clauses. Metadata indexing technology is used to extract basic policy information to form a policy metadata database. Text vectorization algorithms are used to semantically encode policy content, constructing a multi-granularity vector index to form a policy vector library. Based on the business needs of policy compliance inspection, a supervisory fine-tuning corpus containing policy difference checks, consistency checks, and compliance checks is constructed. Data preprocessing and quality verification are performed based on the policy metadata database and the supervisory fine-tuning corpus. Large model training parameters are configured and a pre-trained model is loaded. A parameter-efficient optimization method is used to update some model parameters to adapt to policy domain tasks, constructing a large policy model with capabilities for policy compliance inspection, policy Q&A, policy drafting, and revision early warning. A model version management mechanism is also established. This system analyzes the revision records of institutional documents and changes in policies and regulations, establishing an early warning and detection mechanism based on time triggers and content association. It constructs a similarity calculation model for institutional themes and a regulatory association mapping model using vectorization technology, and establishes a multi-dimensional early warning model with defined trigger conditions. Using the trained institutional model, it performs semantic comparative analysis on institutions with the same theme to identify content differences, compares superior and subordinate institutions clause by clause to detect conflicts, conducts legal risk checks on enterprise institutional clauses, and generates compliance reports. It receives user natural language queries and matches relevant institutional clauses through vector retrieval, optimizes search results using a re-ranking algorithm, generates answers through the institutional model, and provides institutional source information. Based on user institutional revision needs, it generates an institutional outline framework, generates chapter overviews and specific clause content based on the outline, provides text editing functions, and performs real-time compliance checks and clause recommendations.

[0086] In some applications, data from collected policy documents is standardized and structured to extract basic policy information, chapters, and clauses, constructing a policy metadata database. Policy content is then vectorized to form a policy vector library. A supervisory fine-tuning corpus is built based on the business needs of policy compliance checks and early warnings. This includes collecting policy documents in various electronic document formats, performing text recognition processing on scanned documents, parsing the formats of electronic documents, and generating standardized text data; structuring the standardized text to identify the hierarchical structure of policy documents, establishing logical relationships between chapters, and generating structured documents; extracting basic policy information from the structured documents, establishing mapping relationships between chapters and clauses, and constructing a multi-level policy metadata database; and using vectorization algorithms to semantically encode policy content, establishing multi-granularity vector indexes, and forming a policy vector library.

[0087] Understandably, the process involves collecting policy documents in various electronic document formats, using optical character recognition (OCR) technology to process the scanned documents, extracting text content while preserving layout structure information, parsing the electronic documents to remove style tags while retaining semantic content, and generating standardized text data with a unified encoding format. This standardized text is then processed using a combination of rule-based and machine learning methods for structuring. Title pattern recognition and hierarchical analysis techniques are used to identify the chapter-level structure of the policy documents, establishing a logical system of parent-child and sibling relationships between chapters, generating structured documents containing complete structural information. Named entity recognition and information extraction techniques are used to extract basic policy information elements from the structured documents. Semantic role labeling is used to establish a mapping network between chapters and clauses, constructing a multi-level structured policy metadata database containing policy document libraries, chapter libraries, and clause libraries. Deep learning-based vectorization algorithms are used to semantically encode the full text of the policy, chapter titles, and clause texts. Multi-level feature extraction is used to establish a multi-granularity vector index system at the document, chapter, and clause levels, forming a policy vector library with semantic retrieval capabilities.

[0088] In some applications, data preparation is performed based on the aforementioned institutional metadata database and supervisory fine-tuning corpus. An initial model is constructed and trained to improve the capabilities of institutional compliance checks, institutional Q&A, institutional drafting, and revision early warning tasks. A large-scale institutional model is formed after training and version management is implemented. This includes deduplication and format verification of the data in the institutional metadata database, sample balancing of the supervisory fine-tuning corpus, and labeling of institutional difference checks, consistency checks, and compliance checks with task tags. The large-scale model training parameters are configured, the pre-trained model is loaded, and the model parameters are initialized. Parameter optimization methods are used to adapt some model parameters to institutional domain tasks by updating them. The model versions during the training process are saved, and a version management mechanism is established.

[0089] Understandably, data preparation work is carried out based on the institutional metadata database and the supervised fine-tuning corpus. The text data in the institutional metadata database is deduplicated to eliminate duplicate records, and format validation ensures data standardization and uniformity. Oversampling and undersampling techniques are used to balance the samples in the supervised fine-tuning corpus. Multi-label classification is used to label three task categories: institutional difference checking, consistency checking, and compliance checking. Key parameters such as learning rate, batch size, and number of training epochs are configured during large-scale model training. The weights of the base model, pre-trained on massive amounts of text, are loaded, and new model parameters are initialized using a normal distribution initialization method. Efficient parameter optimization methods are employed, updating only a portion of the model parameter layers to adapt to specific tasks in the institutional domain, improving the understanding and processing capabilities of institutional texts while maintaining the model's general capabilities. During training, model checkpoint files are periodically saved to record training loss and changes in evaluation metrics. A version management mechanism based on timestamps and performance metrics is established, supporting the traceability, comparison, and rollback functions of model versions.

[0090] In some applications, the changes in institutional documents and policies are analyzed to determine early warning types. Key early warning data is constructed based on an institutional metadata database. Early warning models for institutional revisions are established for different early warning types. Early warning alerts are triggered based on the output of these models. This includes monitoring the release versions and revision history of institutional documents, tracking the promulgation, revision, and repeal status of relevant laws and regulations, and establishing a time-triggered early warning detection mechanism. The cosine similarity between institutional theme vectors is calculated to construct an institutional association graph. The semantic similarity between clause vectors and regulatory vectors is calculated to establish a mapping relationship between institutions and regulations. Time-based early warning models based on revision cycles, content-based early warning models based on theme similarity, dependency-based early warning models based on reference relationships, compliance-based early warning models based on regulatory changes, and inspection-based early warning models based on periodic checks are established. Early warning levels and trigger thresholds are set. When an institution is detected as overdue for revision, related institutional systems are lagging, reference systems are updated, relevant regulations are changed, or compliance checks are abnormal, early warning alerts are generated and distributed.

[0091] Understandably, by continuously monitoring the release versions and revision history of institutional documents, and tracking the promulgation, revision, and repeal status of relevant laws and regulations in real time, a time-triggered early warning and detection mechanism is established. An institutional association graph is constructed by calculating the cosine similarity between institutional theme vectors, while simultaneously calculating the semantic similarity between clause vectors and regulatory vectors to establish a mapping relationship between institutions and regulations. A time-based early warning model based on revision cycles is established to detect overdue revisions of institutions; a content-based early warning model based on theme similarity is established to identify issues of lagging institutional associations; a dependency-based early warning model based on reference relationships is established to track updates to reference institutions; a compliance-based early warning model based on regulatory changes is established to monitor the impact of relevant regulatory changes; and an inspection-based early warning model based on periodic checks is established to detect anomalies in compliance checks. Three levels of early warning (high, medium, and low) and corresponding trigger thresholds are set. When overdue revisions of institutions, lagging institutional associations, updates to reference institutions, changes to relevant regulations, or anomalies in compliance checks are detected, early warning reminders are automatically generated and distributed to relevant responsible personnel via push notifications and emails.

[0092] In some applications, a comprehensive system model is used to conduct comparative analysis of systems on the same theme, check the consistency of systems at different levels, and conduct legal risk checks on corporate system clauses. This generates a compliance report, including extracting the chapter structure and clause content of systems on the same theme, conducting a clause-by-clause semantic comparative analysis to identify differences in content expression, regulatory requirements, and scope of application, and generating a difference comparison table and explanation of the difference analysis; comparing the corresponding clauses of systems at different levels to detect conflicting requirements, inconsistent standards, and implementation deviations, identifying the location of conflicting clauses, analyzing the causes of conflicts, and providing solutions; matching the system clause content with the latest legal provisions to identify clauses that violate mandatory provisions, are inconsistent with legal requirements, or pose legal risks; and integrating the results of the difference analysis, consistency check, and compliance check to generate a comprehensive compliance report that includes a detailed list of issues, risk levels, legal basis, and improvement suggestions.

[0093] Understandably, based on the trained institutional model, in-depth analysis of systems on the same topic is conducted, extracting the chapter structure and clause content of different institutional documents. Semantic encoding technology is used for clause-by-clause comparison analysis to identify differences in content expression, normative requirements, and scope of application, generating structured difference comparison tables and detailed difference analysis explanations. By comparing the corresponding clauses of superior and subordinate systems clause-by-clause, semantic similarity calculations are used to detect requirement conflicts, standard inconsistencies, and implementation deviations, accurately locating the specific positions of conflicting clauses, analyzing the root causes of conflicts, and providing targeted solutions. Furthermore, the content of corporate institutional clauses is matched and compared with the latest legal provisions in multiple dimensions, using semantic parsing of legal provisions to identify clauses that violate mandatory provisions, are inconsistent with legal requirements, or pose potential legal risks. Finally, the results of difference analysis, consistency checks, and compliance checks are comprehensively integrated to generate a comprehensive compliance check report that includes a detailed list of issues, risk level assessments, legal basis citations, and improvement measure suggestions.

[0094] In some applications, natural language questions are received, and the question content is matched and retrieved against a system metadata database. Answers are generated using a large system model, and system source information is provided. This includes segmenting and semantically parsing the user's natural language question, converting it into a question vector representation; retrieving the system clause with the highest similarity to the question vector from the system vector database, setting a similarity threshold to filter candidate answers; using a re-ranking model to re-rank the candidate answers based on relevance, comprehensively considering semantic relevance, clause authority, and timeliness, selecting the optimal answer fragment; combining the optimal answer fragment with the user's question to construct a prompt text, inputting it into the large system model to generate a structured answer, and associating the answer with the corresponding system name, chapter number, and specific clause content.

[0095] Understandably, the process involves receiving natural language questions from users, breaking them down into semantic units through word segmentation and semantic parsing techniques to understand the question's intent, and then converting the parsed semantic content into a high-dimensional vector representation. A similarity calculation is used to retrieve the most matching policy clause from a policy vector library, and a similarity threshold is set to initially screen candidate answers. A re-ranking model based on multi-feature fusion is then used to perform a secondary ranking of candidate answers, comprehensively considering the feature weights of semantic relevance, clause authority, and timeliness to select the optimal answer fragment. The selected optimal answer fragment is then combined with the original user question to construct a complete prompt text, which is input into a policy model to generate structured answer content. When the answer is output, it is associated with the corresponding policy name, chapter number, and specific clause location information, forming a complete question-and-answer tracing chain.

[0096] In some applications, a system outline is generated based on the system revision needs. Based on the outline, chapter summaries and clause content are generated, and real-time compliance checks and clause recommendations are supported. This includes parsing user-inputted system drafting requirements, including the system title, scope of application, and regulated objects, to generate a system outline framework with a three-level structure of chapters, sections, and clauses. Based on the chapter titles in the outline, corresponding chapter summaries are generated, including the chapter purpose, scope of application, and basic principles. Specific clause texts are generated one by one based on the chapter summaries, supporting step-by-step generation according to chapter order, including definition clauses, normative clauses, liability clauses, and penalty clauses. The system provides functions for rewriting, continuing, expanding, and abbreviating the generated clauses, real-time compliance checks between the generated content and existing systems, and recommendations of relevant clause templates based on clause vector similarity.

[0097] Understandably, by parsing the user-input elements of the policy drafting requirements, including the policy title, scope of application, and regulated objects, the system generates a policy outline framework with a three-level structure of chapters and clauses based on the semantic understanding capabilities of the policy framework model. Based on the content of each chapter title in the policy outline, it automatically generates corresponding chapter summaries, including the purpose and significance of the chapter, its specific scope of application, and the basic principles to be followed. Based on the summaries of each chapter, it generates specific clause text content one by one, supporting generation sequentially according to chapter order or selective generation of specific chapters. The generated clause types include terminology definition clauses, behavioral norms clauses, liability division clauses, and violation penalty clauses. It provides various text editing functions for rewriting, adjusting, continuing, supplementing, expanding, refining, and abbreviating the generated clause text. During the editing process, it checks the compliance of the generated content with the existing policy system in real time and recommends relevant standard clause templates for reference based on clause vector similarity calculations.

[0098] The following is combined Figures 2-7 Another embodiment of the regulatory compliance early warning method of the present invention is described below:

[0099] like Figure 2 and Figure 3 As shown, this implementation includes:

[0100] By employing institutional document governance techniques, including but not limited to OCR parsing, XML parsing, metadata indexing, vectorization, Prompt engineering, and other NLP techniques, we construct a digital, structured, fragmented, clause-based, and vectorized institutional metadata database and a high-quality corpus.

[0101] like Figure 4 As shown, the construction of the institutional metadata database and high-quality corpus includes:

[0102] Collect policy document data in formats including PDF, Word, EXCEL, and TXT, and standardize the data of various policy formats to form a library of original policy documents. This step involves collecting policy documents in complex and diverse formats. Scanned PDFs need to be analyzed using OCR layout technology to recognize the text content and generate double-copyable PDF policy documents to facilitate subsequent analysis and processing of policy document content.

[0103] Using XML parsing technology, the original documents of the aforementioned regulations are structured to form a standardized XML document library. Metadata indexing technology, including but not limited to knowledge element extraction, named entity recognition, and text classification, along with other machine learning and natural language processing techniques, is used to create a regulatory metadata database, including a basic information database, a chapter database, and a clause database. Vectorization technology is used to perform vector processing on the regulatory metadata database, including the basic information database, chapter database, and clause database, across multiple dimensions such as the full text of the regulations, chapters, clauses, and themes, to create a regulatory vector database. Finally, using large-scale model Prompt engineering technology, a high-quality SFT corpus is constructed based on the business needs of regulatory compliance inspection and early warning.

[0104] Based on large-scale model training and fine-tuning technology, and utilizing data such as institutional metadata databases and high-quality corpora, we construct a large-scale institutional model for institutional compliance management by fine-tuning and optimizing the capabilities of multiple institutional vertical tasks.

[0105] like Figure 5 As shown, the optimization of capabilities for multiple institutional vertical tasks and the construction of a large institutional model include:

[0106] Based on the aforementioned institutional metadata database and high-quality SFT corpus, data preparation for model training and fine-tuning was completed using various data processing methods such as data augmentation and data cleaning. The high-quality SFT corpus covers multiple capabilities including institutional compliance, institutional Q&A, institutional drafting, and revision warning. An example of the institutional compliance check SFT corpus is shown in Table 1 below.

[0107] Institutional Differences Check (Example) System consistency check (example) System compliance check (example) System Character Design You are an expert in comparing and analyzing institutional differences. By comparing and analyzing the differences between two sets of regulations, focusing primarily on the clauses with high relevance, summarize the differences one by one and provide an analysis of these differences. Note that you should only analyze the differences and distinctions; the summary of differences, the specific clauses, and the analysis should be accurate, logically sound, and rigorously presented. You are an expert in ensuring the consistency and compliance of regulations. You are able to conduct compliance checks on the regulations of subordinate units by comparing and analyzing the regulations of higher and lower-level units. This includes checking for incompleteness, non-compliance, conflicts with higher-level unit regulations, violations of higher-level unit requirements, and inconsistencies with higher-level unit regulations. Pay attention to the accuracy, rationality, and rigor of problem identification, specific clause content, and root cause analysis. You are an expert in policy compliance review, capable of analyzing the legal risks and legality issues of policy provisions based on relevant national policies, laws, and regulations. For each legal risk or illegality issue identified in the policy, you will summarize and provide a detailed risk analysis. Note that the core of compliance review is to identify potential legal risks and illegal content in policy provisions and to provide reasonable solutions and suggestions for improvement. Model Input System content one: XXXXXXXXXXXXXXXXXXX System content two: XXXXXXXXXXXXXXXXXXX Content of the superior unit's regulations: XXXXXXXXXXXXXXXXXXXXX Content of the subordinate unit's regulations: XXXXXXXXXXXXXXXXXXX The contents of the system are as follows: XXXXXXXXXXXXXXXXXXX The contents of the policies and regulations are as follows: XXXXXXXXXXXXXXXXXXX Model output Based on the content of the two regulations mentioned above, this paper elaborates on the differences in terms of the main content and detailed requirements of the regulations. First, it selects the highly relevant clauses from both regulations and compares and analyzes their differences one by one. Finally, it summarizes the differences between the two regulations and their specific contents. A sample format for the difference analysis is as follows: Difference point n: xxxxx Regulations Content 1: Article x xxxxxxxxx Regulations Content 2: Article x xxxxxxxxxxxx Difference Analysis: xxxxxxxxxxxx Finally, it summarizes and outlines the differences between all relevant clauses of Regulations Content 1 and Regulations Content 2. Based on the provided superior-subordinate regulations, compare them with the superior unit's regulations, focusing on checking for inconsistencies or conflicts in the subordinate unit's regulations. Please first provide an overall overview of any conflicts or inconsistencies. Then, elaborate on each clause, referring to the following format for each conflict point: Conflict Point n xxxxx Superior Unit Regulations: Chapter x xxxxx Article x xxxxx Subordinate Unit Regulations: Chapter x xxxxx Article x xxxxx Conflict Explanation: xxxxxxxxxx Finally, provide a summary. Based on the relevant national policies and regulations you provided, the following is a legal risk assessment of the "xxxxxx" system. The potential legal risks and illegal clauses are analyzed as follows: Article x of Chapter x, which mentions "xxxxxx," is suspected of "xxxxxx." Article x restricts "xxxxxx," which may lead to "xxxxxx." Article x stipulates "xxxxxx," which may involve "xxxxxx" issues. Article x of Chapter x, which stipulates "xxxxxx," may generate "xxxxxx" disputes. Article x stipulates "xxxxxx," which may lead to "xxxxxx" impacts. Article x's provisions related to "xxxxxx" may affect the fairness and impartiality of "xxxxxx." Article x's content regarding "xxxxxx" may violate the legal provisions of "xxxxxx" regarding "xxxxxx." Article x's "xxxxxx" violates "xxxxxx." In conclusion, this summarizes the potential legal risks and illegal content of the current system, recommends improvements for enterprises, suggests considering specific situations, and recommends adjustments and improvements based on the latest laws and regulations.

[0108] Utilizing open-source large-scale model training platforms and Huawei Cloud's large-scale model suite, a complete model training and fine-tuning solution is constructed through parameter adjustment, model monitoring, progress viewing, status detection, and process browsing. This efficiently trains initial industry models for regulations. Multiple mainstream large-scale model fine-tuning methods, including full-scale training, Adapter-Tuning, P-Tuning v2, LoRA, and QLoRA, are employed to optimize and fine-tune capabilities for various vertical tasks related to regulations, such as compliance, question answering, drafting, and revision alerts, constructing a large-scale regulatory model for compliance management. The training and fine-tuning of large-scale models, including the Huazhi large-scale model and the Zhipu AI model, utilizes various mainstream large-scale model fine-tuning methods based on SFT corpora for multiple tasks such as compliance, question answering, drafting, and revision alerts to complete the optimization and fine-tuning of the large-scale regulatory model.

[0109] The system provides unified management of the trained large-scale system model and historical version model files, and can back up multiple process model files for repeated optimization and testing. The aforementioned large-scale system model is used for AIGC services in system compliance management, and is a vertical large-scale system model fine-tuned for application in enterprise system lifecycle management scenarios.

[0110] Based on institutional vectorization technology and a large institutional model, an early warning model for institutional revision is constructed from multiple dimensions, including revision cycle, theme association, reference association, regulatory association, and compliance inspection.

[0111] like Figure 6 As shown, the construction of the early warning model for system revision includes:

[0112] Based on changes in institutional documents, policies, and regulations, such as amendments and repeals, we analyze the application scenarios for early warning of institutional revisions to obtain early warning types. The major categories of early warning types include revision cycle, thematic association, reference association, regulatory association, and compliance inspection.

[0113] Based on the institutional metadata database, vectorization technology is used to construct key early warning data such as institutional theme categories, theme vectors, reference institutions, institutional vectors, related regulations, and regulation vectors;

[0114] Based on key early warning data, a system revision early warning model is designed and implemented for each type of early warning. This model promptly notifies and reminds relevant personnel in the company or its subsidiaries to revise and update the system documents, ensuring consistency between the documents and the actual situation. The system revision early warning models include a revision cycle early warning model, a topic-related early warning model, a reference-related early warning model, a regulatory-related early warning model, and a compliance inspection early warning model.

[0115] Develop a system for checking and warning compliance based on a large model, targeting the application of enterprise system full life cycle management scenarios;

[0116] like Figure 7 As shown, the system compliance inspection and early warning management system based on a large model includes:

[0117] The system compliance inspection module is used for system compliance inspection, including comparative analysis of differences in systems on the same topic, consistency inspection of systems between superiors and subordinates, and legal risk inspection of the content of corporate system clauses.

[0118] The system revision early warning module is used to provide early warnings and reminders for relevant systems caused by factors such as system updates, repeals, and compliance anomalies, providing enterprises with real-time dynamic risk monitoring.

[0119] The intelligent Q&A module for regulations is designed to meet the precise question-and-answer needs of enterprise managers and employees regarding the content of regulations. It uses natural language interaction to ask and answer questions about the specific content of various policies and regulations, and provides accurate answers and tracing of the regulations' origins.

[0120] The system drafting module intelligently generates system outlines and clauses based on the needs of system revision, assisting system drafters in completing the initial draft of the system, and supporting real-time system compliance checks, clause recommendations, and system tracing.

[0121] The system compliance inspection module is implemented through three dimensions: difference, consistency, and conformity, including:

[0122] The system difference analysis mainly focuses on comparing and contrasting the content of systems on the same topic, highlighting the differences and generating a difference analysis report. The system consistency check mainly focuses on checking the consistency of the content of systems between superiors and subordinates, highlighting whether the content of the subsidiary's system clauses is consistent with the requirements of the parent company's system clauses, whether there are any conflicting contents, and analyzing each conflict point to generate a consistency check report. The system compliance check mainly focuses on whether there are any legal risks in the content of the company's system clauses, highlighting the potential risks in the content of the company's system clauses, analyzing each risk point, and generating a compliance check report.

[0123] The system revision early warning module is implemented through five dimensions: revision cycle, topic relevance, reference relevance, regulatory relevance, and compliance check, including:

[0124] The system includes several early warning models: a revision cycle model (based on the release date, issuing authority, and revision deadline) and a compliance inspection model. The theme-related early warning model identifies when new regulations will cause delays in existing regulations related to the same theme within the company and its subsidiaries, and issues revision warnings based on theme category and theme vector. A reference-related early warning model identifies when revisions will cause delays in existing regulations that referenced previous versions of the same regulations, and issues revision warnings based on reference regulations and system vector. A regulatory-related early warning model identifies when the release of laws, regulations, and new policies will cause delays in related business regulations, and issues revision warnings based on related regulations and regulatory vectors. Finally, a compliance inspection early warning model periodically checks all current regulations in the historical system database based on a comprehensive system model, generates compliance inspection reports, and issues revision warnings for regulations with compliance issues.

[0125] The intelligent Q&A module for regulations enhances Q&A through an external knowledge base and content generation, including:

[0126] Using vectorization technology, pre-set questions from business scenarios or user queries are vectorized, and the most similar regulations or clauses are retrieved from the regulatory metadata database described in S1. The retrieved regulations or clauses are reordered using a reordering model. The Prompt prompts, retrieved regulations or clauses, and questions are reorganized and input into the regulatory big model. The big model's powerful understanding and generalization capabilities are used to provide answers. Finally, the answers and associated original regulations are combined to obtain accurate answers and regulatory origins.

[0127] The policy drafting module generates the full text of policies through a combination of large-scale model fine-tuning and human-computer interaction, including:

[0128] Enter the policy title, subject matter, or other content requirements to generate a policy outline with one click; generate an overview of each chapter's table of contents based on the policy outline and insert it into the document; generate the full text with one click based on the policy outline and overview, or generate it step by step by chapter; rewrite, continue, expand, abbreviate, polish, recommend clauses, trace the policy's origin, and conduct compliance checks on the generated chapters or clauses; after the full policy text is generated, it supports compliance checks on the entire text and can also be saved and exported using a user-defined template;

[0129] For the purpose of simplicity, the method steps disclosed in the above embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0130] like Figure 8 As shown, the present invention also provides a regulatory compliance early warning system, comprising:

[0131] The data parsing module 201 is configured to collect institutional document data, perform standardized processing and structured parsing, extract basic institutional information, chapters and clauses, construct an institutional meta-database, vectorize the institutional content to form an institutional vector library, and construct a supervision fine-tuning corpus based on the institutional compliance inspection and early warning business needs.

[0132] The institutional big model training module 202 is configured to prepare data based on the institutional meta-database and the supervision fine-tuning corpus, build an initial model and train it, improve the ability of institutional compliance inspection, institutional Q&A, institutional writing and revision early warning tasks, form a trained institutional big model and perform version management;

[0133] The system revision early warning module 203 is configured to analyze the factors that cause changes in system documents and policies and regulations, determine the type of early warning, construct key data for early warning based on the system metadata database, establish system revision early warning models for different types of early warning, and trigger early warning reminders based on the output results of the early warning models;

[0134] The compliance inspection report generation module 204 is configured to perform a comparative analysis of the differences between systems on the same topic based on the system model, conduct consistency checks on systems at different levels, conduct legal risk checks on the clauses of the enterprise system, and generate a compliance inspection report.

[0135] The answer generation module 205 is configured to receive natural language questions, match and retrieve the question content with the institutional metadata database, generate answers through the institutional big data model, and provide institutional traceability information;

[0136] The system drafting module 206 is configured to generate a system outline based on system revision needs, generate chapter overviews and clause content based on the system outline, and support real-time compliance checks and clause recommendations.

[0137] It is worth noting that although only some basic functional modules are disclosed in the embodiments of this invention, it does not mean that the composition of this system is limited to the above-mentioned basic functional modules. On the contrary, what this embodiment intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with existing technology to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. The fact that this embodiment only discloses a few basic functional modules should not be considered as the scope of protection of the claims of this invention being limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above device is described separately according to its functions as various units and modules. Of course, in implementing this invention, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0138] like Figure 9 As shown, the present invention also provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of a regulatory compliance early warning method.

[0139] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. For example... Figure 9 The structure shown in this embodiment of the invention includes an electronic device comprising one or more processors 710 and a memory 720; the processors 710 in this electronic device may be one or more. Figure 9 Taking a processor 710 as an example; a memory 720 is used to store one or more programs; the one or more programs are executed by the one or more processors 710, so that the one or more processors 710 implement a system compliance early warning method as described in any one of the embodiments of the present invention.

[0140] The electronic device may also include an input device 730 and an output device 740.

[0141] The processor 710, memory 720, input device 730, and output device 740 in this electronic device can be connected via a bus or other means. Figure 9 Taking the example of a connection between China and Israel via a bus.

[0142] The memory 720 in this electronic device serves as a computer-readable storage medium, capable of storing one or more programs. These programs can be software programs, computer-executable programs, or modules, such as the program instructions / modules corresponding to the regulatory compliance early warning method provided in this embodiment of the invention. The processor 710 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 720, thereby implementing the regulatory compliance early warning method described in the above embodiment.

[0143] The memory 720 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 720 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 720 may further include memory remotely located relative to the processor 710, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0144] Input device 730 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the electronic device. Output device 740 may include display devices such as a display screen.

[0145] The present invention also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a regulatory compliance early warning method.

[0146] Specifically, the computer storage medium in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A system compliance early warning method, characterized in that, Comprise: Collecting system document data for standardized processing and structured analysis, extracting system basic information, chapters and clauses, constructing system metadata database, vectorizing system content to form system vector library, and constructing supervision fine-tuning corpus based on system compliance check and early warning business needs; Based on the system metadata database and the supervision fine-tuning corpus, data preparation is performed, an initial model is constructed and trained, the system compliance checking, system question answering, system writing and revision warning task capabilities are improved, and the trained system large model is formed and version management is performed; Analyze the change factors of system documents and policies and regulations, determine the warning type, build the warning key data based on the system metadata database, establish the system revision warning model for different warning types, and trigger the warning reminder according to the output result of the warning model; Based on the system large model, compare and analyze the differences of systems with the same theme, check the consistency of upper and lower systems, and check the legal risks of enterprise system clauses to generate a compliance check report; Receive natural language questions, match and search the question content with the system metadata database, generate answers through the system large model, and provide system traceability information; Generate a system outline according to the system revision needs, generate chapter summaries and clause content based on the system outline, and support real-time compliance checking and clause recommendation.

2. The system compliance alerting method of claim 1, wherein, Collecting system document data for standardized processing and structured analysis, extracting system basic information, chapters and clauses, constructing system metadata database, vectorizing system content to form system vector library, and constructing supervision fine-tuning corpus based on system compliance check and early warning business needs, further comprising: Collecting system documents in multiple electronic document formats, performing text recognition on scanned documents, and performing format analysis on electronic documents to generate standardized text data; Structurally process the standardized text, identify the hierarchical structure of the system document, establish the logical relationship between chapters, and generate a structured document; Extract system basic information from the structured document, establish the mapping relationship between chapters and clauses, and construct a system metadata database containing multiple levels of structure; Use vectorization algorithms to semantically encode system content, create multi-granularity vector indexes, and form a system vector library.

3. The method of claim 1, wherein, Based on the system metadata database and the supervision fine-tuning corpus, data preparation is performed, an initial model is constructed and trained, the system compliance checking, system question answering, system writing and revision warning task capabilities are improved, and the trained system large model is formed and version management is performed, further comprising: Performing deduplication and format checking on the data in the system metadata database, and performing sample balancing on the supervision fine-tuning corpus, and labeling system difference checking, consistency checking, and compliance checking task labels; Configure the large model training parameters, load the pre-trained model, and initialize the model parameters; Use parameter optimization methods to adapt to the system domain tasks by updating part of the model parameters; Save the model version during training and establish a version management mechanism.

4. The method of claim 1, wherein, Analyze the change factors of system documents and policies and regulations, determine the early warning types, build the early warning key data based on the system meta database, establish the system revision early warning model for different early warning types, trigger the early warning reminder according to the output results of the early warning model, and further include: Monitor the release version and revision history of system documents, track the promulgation, revision and abolition of relevant laws and regulations, and establish a time-triggered early warning detection mechanism; Calculate the cosine similarity between system theme vectors, build a system correlation graph, calculate the semantic similarity between clause vectors and regulation vectors, and establish the mapping relationship between system and regulation; Establish a time early warning model based on the revision cycle, a content early warning model based on the theme similarity, a dependency early warning model based on the reference relationship, a compliance early warning model based on the regulation change, and a patrol early warning model based on the regular inspection; Set the early warning level and trigger threshold, and generate and distribute early warning reminders when detecting system overage without revision, theme-related system lag, reference system update, relevant regulation change or compliance inspection anomaly.

5. The method of claim 1, wherein, Based on the system large model, compare and analyze the differences between systems with the same theme, check the consistency of the upper and lower systems, and check the legal risks of the enterprise system clauses, and generate a compliance inspection report, which further includes: Extract the chapter structure and clause content of the system with the same theme, perform semantic comparison and analysis item by item, identify content expression differences, specification requirement differences, and scope of application differences, and generate a difference comparison table and difference analysis explanation; Compare the corresponding clause content of the upper and lower systems item by item, detect requirement conflicts, inconsistent standards, and execution deviation problems, identify the location of conflicting clauses, analyze the conflict reasons and provide solutions; Match and compare the system clause content with the latest legal regulation clauses, identify clauses that violate mandatory provisions, do not conform to legal requirements, or have legal risks; Integrate the difference analysis results, consistency check results and compliance check results to generate a comprehensive compliance inspection report containing problem details, risk level, legal basis and improvement suggestions.

6. The method of claim 1, wherein, Receive natural language questions, match the question content with the system meta database, generate answers through the system large model, and provide system traceability information, which further includes: Perform word segmentation and semantic analysis on the user input natural language question, and convert it to a question vector representation; Retrieval the system clauses with the highest similarity to the question vector in the system vector library, and set a similarity threshold to filter candidate answers; Use the reordering model to reorder the relevance of the candidate answers, considering the semantic relevance, clause authority and timeliness factors, and select the optimal answer fragment; Combine the optimal answer fragment with the user question to build a prompt text, input the system large model to generate a structured answer, and associate the answer with the corresponding system name, chapter number and specific clause content.

7. The method of claim 1, wherein, Generate a system outline based on the system revision needs, generate chapter summaries and clause content based on the system outline, and support real-time compliance checking and clause recommendation, which further includes: Analyze the user input system compilation requirements, including system title, scope of application, and specification object, and generate a system outline framework containing chapter, section and clause three-level structure; According to the chapter title in the system outline, the corresponding chapter summary content is generated, including chapter purpose, scope of application, basic principles; Based on the chapter summary content, generate specific clause text item by item, support step-by-step generation according to chapter order, including definition clause, specification clause, responsibility clause, penalty clause; Provide rewriting, continuation, expansion, and contraction text editing functions for the generated clauses, real-time compliance check of the generated content with existing systems, and recommendation of related clause templates based on clause vector similarity.

8. A system compliance alert system, characterized by, It includes: The data analysis module is configured to collect system file data for standardized processing and structured analysis, extract system basic information, chapters and clauses, build a system metadata database, vectorize system content, form a system vector library, and build a supervision fine-tuning corpus based on system compliance check and early warning business needs; The system large model training module is configured to prepare data based on the system metadata database and the supervision fine-tuning corpus, build an initial model and train it to improve the system compliance checking, system question answering, system writing and revision warning task capabilities, form the trained system large model and manage the version; The system revision warning module is configured to analyze the change factors of system files and policies and regulations, determine the warning type, build the warning key data based on the system metadata database, establish the system revision warning model for different warning types, and trigger the warning reminder according to the output result of the warning model; The compliance check report generation module is configured to compare and analyze the same theme systems based on the system large model, check the consistency of the upper and lower systems, and check the legal risks of the enterprise system clauses, and generate a compliance check report; The answer generation module is configured to receive natural language questions, match and retrieve the question content with the system metadata database, generate answers through the system large model, and provide system traceability information; The system auxiliary writing module is configured to generate a system outline according to the system revision requirements, generate chapter summaries and clause contents based on the system outline, and support real-time compliance checking and clause recommendation.

9. An electronic device, comprising: It includes: A processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program executable by an electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps of the method of any one of claims 1 to 7.

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