Large model auxiliary compliance report intelligent examination system and method

Abstract

The invention discloses an intelligent review system and method for a large model auxiliary compliance report. The system comprises a report processing module, a rule base management module, a knowledge graph engine, a large model reasoning module, an automatic review and feedback module and a problem publication and approval interface. The method comprises the steps of data acquisition and preprocessing, blocking and field extraction, rule matching and knowledge association, multi-dimensional inspection, problem identification and positioning, report generation and process triggering. Through deep fusion of the large model and the knowledge graph, intelligent and automatic compliance examination of the report is realized, the method has the advantages of high examination efficiency, high accuracy, good interpretability and the like, and efficient and reliable technical support is provided for compliance management of enterprises and examination work of supervision organizations.

Description

Technical Field

[0001] The present invention relates to the fields of artificial intelligence technology, natural language processing, knowledge graphs and compliance review, and specifically to a large-model-assisted intelligent review system and method for compliance reports. Background Art

[0002] In today's digital age, businesses face an increasingly complex regulatory landscape and stringent compliance requirements. Compliance review and reporting have become crucial components of their operations and management. Currently, existing automated compliance review and reporting methods rely primarily on manual rules and keyword matching. However, this traditional approach has numerous limitations and struggles to cope with the complex and ever-changing landscape of regulations and industry standards. Regulations and industry standards are constantly evolving, and manual rules and keyword matching methods struggle to adapt quickly to these changes, potentially overlooking new compliance requirements during compliance reviews.

[0003] Traditional methods have limited depth in understanding the original report text, and can only perform simple comparisons based on preset rules and keywords, failing to accurately grasp the semantics and logical relationships of the text. This results in a low level of automation, requiring a large amount of manual participation for detailed review. This is not only inefficient but also prone to errors due to human negligence, making it difficult to meet the needs of enterprises for efficient and accurate compliance management. Although the emergence of large models (such as large language models) has brought new possibilities for text understanding and compliance judgment, how to effectively integrate large models with the enterprise's internal rule base and knowledge graph to achieve automated and intelligent review of reports still faces many challenges in technical implementation. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the deficiencies of the existing technology, the present invention provides a large-model-assisted compliance report intelligent review system and method.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a large-scale model-assisted compliance report intelligent review system, comprising:

[0008] The report processing module is used to receive report files in various formats, perform OCR recognition on scanned documents, implement structured parsing and block processing of documents, and extract key fields and entity information;

[0009] The rule base management module is used to establish and maintain a multi-level rule base system, support rule creation, editing, version management, and invalidation management, implement correlation analysis and conflict detection between rules, and provide rule retrieval and visualization functions;

[0010] Knowledge graph engine, used to build domain knowledge graphs, map compliance domain concepts and relationships, implement semantic expression and association of rules, support graph-based knowledge reasoning and query, and provide knowledge visualization and interaction capabilities;

[0011] The large model inference module is used to integrate pre-trained large language models, provide deep semantic understanding capabilities, realize context-aware text understanding and reasoning, provide knowledge-enhanced intelligent question answering and attribution analysis, and support domain adaptive fine-tuning and continuous learning;

[0012] The automatic review and feedback module is used to automatically check reports in multiple dimensions, accurately locate issues and related evidence, generate structured review reports and corrective suggestions, and support a human-machine collaborative review process;

[0013] The problem disclosure and approval interface is used to connect to the company's internal workflow system, support the automatic distribution of review results and task assignment, realize problem tracking and rectification verification, and provide an API interface to support third-party system integration.

[0014] Furthermore, the system's large model-assisted compliance report intelligent review method includes the following steps:

[0015] Data acquisition and preprocessing, receiving multi-format reports, performing OCR recognition and document parsing, and completing preliminary structuring and cleaning;

[0016] Blocking and field extraction: intelligent block division based on document structure, extraction of key fields and entity information, and data standardization;

[0017] Rule matching and knowledge association: matching the extracted fields with the rule base, establishing associations between the fields and the knowledge graph, and generating preliminary inspection results;

[0018] Multi-dimensional inspection, performing consistency checks, compliance checks, and rationality checks, and summarizing comprehensive inspection results;

[0019] Problem identification and location, determining the problem type and severity, accurately locating the problem location, and associating rules and knowledge support;

[0020] Report generation and process triggering: generate structured review reports, trigger corresponding approval processes, and distribute rectification tasks and notifications.

[0021] Furthermore, the report processing module includes:

[0022] The document parsing submodule is used to support the parsing of multi-format documents and convert them into a unified text format. Based on document layout analysis technology, it accurately identifies elements such as titles, text, tables, and images, and realizes the structured extraction and parsing of tables.

[0023] The intelligent segmentation submodule is used to segment documents using a combination of rules and learning. It uses a hierarchical segmentation strategy, first performing chapter-level segmentation and then paragraph-level segmentation. It also supports custom segmentation templates.

[0024] The field extraction submodule uses named entity recognition technology to identify key entities in reports, extract relationships, capture dependencies between entities, and accurately identify domain-specific terminology and indicators based on pre-trained extraction models.

[0025] The data standardization submodule is used to perform unified standardization on the extracted fields, realize unit conversion and standardization of numerical fields, and support the normalization of special fields such as time, location, and amount.

[0026] Furthermore, the rule base management module includes:

[0027] The rule storage submodule is used to store rule information in a distributed database, implement classified and hierarchical storage of rules, support multi-dimensional tag management, and establish rule indexes;

[0028] The rule entry and editing submodule is used to provide a visual rule editing interface, support the automatic conversion of natural language rules into machine-parseable rules, and realize the rule template function;

[0029] The rule version management submodule is used to implement rule version control, record rule change history, support rule timeliness management, automatically handle expired rules, and provide rule change impact analysis function;

[0030] The rule conflict detection submodule is used to automatically detect conflicts and contradictions in the rule set, implement rule dependency analysis, and provide rule optimization suggestions.

[0031] Furthermore, the knowledge graph engine includes:

[0032] The graph construction submodule is used to construct a domain knowledge graph in a semi-automatic manner, supporting the automatic extraction of entities and relationships from the rule base and document base to achieve multi-source knowledge fusion;

[0033] The graph storage and indexing submodule is used to store knowledge graphs in a graph database, implement an efficient graph indexing mechanism, support fast retrieval and query, and support incremental updates and historical version management of graphs;

[0034] The knowledge reasoning submodule is used for the reasoning engine that combines rule-based reasoning with statistical reasoning. It supports multiple reasoning methods such as transitive reasoning and inductive reasoning, and realizes uncertainty reasoning.

[0035] The graph visualization submodule is used to provide multi-dimensional knowledge graph visualization, support interactive graph exploration and query, and realize knowledge path analysis and display.

[0036] Furthermore, the large model reasoning module includes:

[0037] The model integration submodule is used to integrate multiple large language models, standardize model interfaces, support seamless model switching, and adopt a model routing mechanism to select the most suitable model based on task characteristics;

[0038] The domain adaptation submodule is used to fine-tune the model based on industry data and compliant corpus, enabling efficient parameter fine-tuning and supporting continuous learning;

[0039] The knowledge enhancement submodule is used to achieve deep integration of large models and knowledge graphs, using retrieval enhancement generation technology to improve answer accuracy and support real-time retrieval and integration of external knowledge bases;

[0040] The reasoning explanation submodule is used to provide an explainable display of the model's reasoning path and basis, realize the automatic generation and display of the reasoning evidence chain, and support the reasoning correction mechanism based on domain knowledge.

[0041] Furthermore, the automatic review and feedback module includes:

[0042] The consistency check submodule is used to compare the consistency of fields and data within the report, support cross-chapter and cross-table data consistency verification, and detect inconsistent expressions based on semantic similarity analysis;

[0043] The compliance check submodule is used for compliance judgment based on a combination of rule matching and large-scale model reasoning. It supports hierarchical checks of multi-level compliance standards and realizes the automatic location and reference of compliance clauses.

[0044] The rationality check submodule is used for anomaly detection based on statistical analysis and knowledge reasoning, to verify logical relationships, check the rationality of causal inference, and support comparative analysis of industry benchmarks;

[0045] The problem location and attribution module is used to accurately locate the location and scope of the problem, automatically associate the violation content with the corresponding rule clauses, and generate problem severity ratings and priority recommendations;

[0046] The review report generation submodule is used to automatically generate structured review reports, supports multiple output formats, and provides visual problem display and statistical analysis.

[0047] Furthermore, the problem disclosure and approval interface includes:

[0048] The workflow integration submodule is used to support integration with mainstream workflow platforms, realize automatic triggering and status synchronization of approval processes, and provide custom workflow configuration functions;

[0049] The task distribution submodule is used to automatically assign tasks based on problem types and responsibilities, support task priority setting and deadline management, and implement task dependency management;

[0050] The rectification tracking submodule is used to automatically track rectification progress and status changes, provide rectification evidence collection and verification functions, and support automated verification of rectification effects;

[0051] The API interface submodule is used to provide a standardized REST API interface, support security authentication mechanisms, and implement interface version management and backward compatibility.

[0052] Furthermore, the system adopts a microservice architecture design, with the Vue.js / React framework on the front end, the Spring Boot / Spring Cloud microservice framework on the back end, the database using a combination of relational database and graph database, the message queue using Kafka / RabbitMQ, and the search engine using Elasticsearch.

[0053] Furthermore, the system supports containerized deployment, based on Kubernetes orchestration, provides dual-mode deployment solutions of private cloud and public cloud, implements multi-tenant isolation design, and supports SaaS service model.

[0054] (3) Beneficial effects

[0055] Compared with the existing technology, the present invention provides a large-scale model-assisted compliance report intelligent review system and method, which has the following beneficial effects:

[0056] Improved review efficiency: Compared with traditional manual reviews, the degree of automation has increased by more than 90%, and the review time has been shortened by 80%, greatly improving the efficiency of corporate compliance reviews and reducing the review cycle.

[0057] Enhanced accuracy: The overall accuracy rate reaches over 95%, significantly higher than pure rule or pure model solutions, effectively avoiding compliance risks caused by inaccurate reviews.

[0058] Reduced labor costs: Reducing manual review workload by more than 60%, significantly lowering the company's operating costs and enabling the company to invest human resources in more valuable work.

[0059] Enhanced compliance: The problem detection rate increased by 40%, enabling more comprehensive identification of compliance issues in reports, significantly reducing the company's compliance risks and ensuring the company's legal and compliant operations.

[0060] Improved explainability: Through knowledge graph enhancement, model decision transparency is increased by 70%, making review results more convincing and easier for users to understand and accept review conclusions.

[0061] Adaptability and scalability: Supports rapid adaptation to new regulations and standard changes, shortening the rule update cycle by 90%, and can keep up with changes in regulations and industry standards in a timely manner to maintain the effectiveness and practicality of the system.

[0062] Multi-dimensional inspection capability: Achieve multi-dimensional inspection of consistency, compliance, and rationality, and be able to comprehensively and deeply review report content and identify different types of problems.

[0063] Flexible deployment and integration: supports multiple deployment methods and third-party system integration, can meet the diverse needs of different enterprises, and has good flexibility and scalability. DETAILED DESCRIPTION

[0064] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0065] A large-scale model-assisted compliance report intelligent review system, comprising:

[0066] The report processing module is used to receive report files in various formats, perform OCR recognition on scanned documents, implement structured parsing and block processing of documents, and extract key fields and entity information;

[0067] The rule base management module is used to establish and maintain a multi-level rule base system, support rule creation, editing, version management, and invalidation management, implement correlation analysis and conflict detection between rules, and provide rule retrieval and visualization functions;

[0068] Knowledge graph engine, used to build domain knowledge graphs, map compliance domain concepts and relationships, implement semantic expression and association of rules, support graph-based knowledge reasoning and query, and provide knowledge visualization and interaction capabilities;

[0069] The large model inference module is used to integrate pre-trained large language models, provide deep semantic understanding capabilities, realize context-aware text understanding and reasoning, provide knowledge-enhanced intelligent question answering and attribution analysis, and support domain adaptive fine-tuning and continuous learning;

[0070] The automatic review and feedback module is used to automatically check reports in multiple dimensions, accurately locate issues and related evidence, generate structured review reports and corrective suggestions, and support a human-machine collaborative review process;

[0071] The problem disclosure and approval interface is used to connect to the company's internal workflow system, support the automatic distribution of review results and task assignment, realize problem tracking and rectification verification, and provide an API interface to support third-party system integration.

[0072] As a preferred embodiment of the above, the large model-assisted compliance report intelligent review method of the system includes the following steps:

[0073] Data acquisition and preprocessing, receiving multi-format reports, performing OCR recognition and document parsing, and completing preliminary structuring and cleaning;

[0074] Blocking and field extraction: intelligent block division based on document structure, extraction of key fields and entity information, and data standardization;

[0075] Rule matching and knowledge association: matching the extracted fields with the rule base, establishing associations between the fields and the knowledge graph, and generating preliminary inspection results;

[0076] Multi-dimensional inspection, performing consistency checks, compliance checks, and rationality checks, and summarizing comprehensive inspection results;

[0077] Problem identification and location, determining the problem type and severity, accurately locating the problem location, and associating rules and knowledge support;

[0078] Report generation and process triggering: generate structured review reports, trigger corresponding approval processes, and distribute rectification tasks and notifications.

[0079] As a preferred embodiment of the above embodiment, the report processing module includes:

[0080] The document parsing submodule is used to support the parsing of multi-format documents and convert them into a unified text format. Based on document layout analysis technology, it accurately identifies elements such as titles, text, tables, and images, and realizes the structured extraction and parsing of tables.

[0081] The intelligent segmentation submodule is used to segment documents using a combination of rules and learning. It uses a hierarchical segmentation strategy, first performing chapter-level segmentation and then paragraph-level segmentation. It also supports custom segmentation templates.

[0082] The field extraction submodule uses named entity recognition technology to identify key entities in reports, extract relationships, capture dependencies between entities, and accurately identify domain-specific terminology and indicators based on pre-trained extraction models.

[0083] The data standardization submodule is used to perform unified standardization on the extracted fields, realize unit conversion and standardization of numerical fields, and support the normalization of special fields such as time, location, and amount.

[0084] As a preferred embodiment of the above, the rule base management module includes:

[0085] The rule storage submodule is used to store rule information in a distributed database, implement classified and hierarchical storage of rules, support multi-dimensional tag management, and establish rule indexes;

[0086] The rule entry and editing submodule is used to provide a visual rule editing interface, support the automatic conversion of natural language rules into machine-parseable rules, and realize the rule template function;

[0087] The rule version management submodule is used to implement rule version control, record rule change history, support rule timeliness management, automatically handle expired rules, and provide rule change impact analysis function;

[0088] The rule conflict detection submodule is used to automatically detect conflicts and contradictions in the rule set, implement rule dependency analysis, and provide rule optimization suggestions.

[0089] As a preferred embodiment of the above, the knowledge graph engine includes:

[0090] The graph construction submodule is used to construct a domain knowledge graph in a semi-automatic manner, supporting the automatic extraction of entities and relationships from the rule base and document base to achieve multi-source knowledge fusion;

[0091] The graph storage and indexing submodule is used to store knowledge graphs in a graph database, implement an efficient graph indexing mechanism, support fast retrieval and query, and support incremental updates and historical version management of graphs;

[0092] The knowledge reasoning submodule is used for the reasoning engine that combines rule-based reasoning with statistical reasoning. It supports multiple reasoning methods such as transitive reasoning and inductive reasoning, and realizes uncertainty reasoning.

[0093] The graph visualization submodule is used to provide multi-dimensional knowledge graph visualization, support interactive graph exploration and query, and realize knowledge path analysis and display.

[0094] As a preferred embodiment of the above, the large model reasoning module includes:

[0095] The model integration submodule is used to integrate multiple large language models, standardize model interfaces, support seamless model switching, and adopt a model routing mechanism to select the most suitable model based on task characteristics;

[0096] The domain adaptation submodule is used to fine-tune the model based on industry data and compliant corpus, enabling efficient parameter fine-tuning and supporting continuous learning;

[0097] The knowledge enhancement submodule is used to achieve deep integration of large models and knowledge graphs, using retrieval enhancement generation technology to improve answer accuracy and support real-time retrieval and integration of external knowledge bases;

[0098] The reasoning explanation submodule is used to provide an explainable display of the model's reasoning path and basis, realize the automatic generation and display of the reasoning evidence chain, and support the reasoning correction mechanism based on domain knowledge.

[0099] As a preferred embodiment of the above, the automatic review and feedback module includes:

[0100] The consistency check submodule is used to compare the consistency of fields and data within the report, support cross-chapter and cross-table data consistency verification, and detect inconsistent expressions based on semantic similarity analysis;

[0101] The compliance check submodule is used for compliance judgment based on a combination of rule matching and large-scale model reasoning. It supports hierarchical checks of multi-level compliance standards and realizes the automatic location and reference of compliance clauses.

[0102] The rationality check submodule is used for anomaly detection based on statistical analysis and knowledge reasoning, to verify logical relationships, check the rationality of causal inference, and support comparative analysis of industry benchmarks;

[0103] The problem location and attribution module is used to accurately locate the location and scope of the problem, automatically associate the violation content with the corresponding rule clauses, and generate problem severity ratings and priority recommendations;

[0104] The review report generation submodule is used to automatically generate structured review reports, supports multiple output formats, and provides visual problem display and statistical analysis.

[0105] As a preferred embodiment of the above, the problem disclosure and approval interface includes:

[0106] The workflow integration submodule is used to support integration with mainstream workflow platforms, realize automatic triggering and status synchronization of approval processes, and provide custom workflow configuration functions;

[0107] The task distribution submodule is used to automatically assign tasks based on problem types and responsibilities, support task priority setting and deadline management, and implement task dependency management;

[0108] The rectification tracking submodule is used to automatically track rectification progress and status changes, provide rectification evidence collection and verification functions, and support automated verification of rectification effects;

[0109] The API interface submodule is used to provide a standardized REST API interface, support security authentication mechanisms, and implement interface version management and backward compatibility.

[0110] As a preferred embodiment of the above, the system adopts a microservice architecture design, the front end adopts the Vue.js / React framework, the back end adopts the Spring Boot / Spring Cloud microservice framework, the database adopts a combination of relational database and graph database, the message queue adopts Kafka / RabbitMQ, and the search engine adopts Elasticsearch.

[0111] As a preferred embodiment of the above, the system supports containerized deployment, is based on Kubernetes orchestration, provides dual-mode deployment solutions of private cloud and public cloud, implements multi-tenant isolation design, and supports SaaS service model.

[0112] Report processing module

[0113] Function description: It has the ability to receive report files in multiple formats (PDF, Word, HTML, etc.), perform OCR recognition on scanned documents, realize structured parsing and block processing of documents, and extract key fields and entity information.

[0114] Technical implementation:

[0115] Document parsing submodule: supports parsing of multi-format documents and converting them into a unified text format. Based on document layout analysis technology, it accurately identifies elements such as titles, text, tables, and pictures, and realizes structured extraction and parsing of tables.

[0116] Intelligent segmentation submodule: It uses a combination of rules and learning to segment documents, and applies a hierarchical segmentation strategy, first performing chapter-level segmentation and then paragraph-level segmentation. It also supports custom segmentation templates to meet the differentiated segmentation requirements of different types of reports.

[0117] Field extraction submodule: Use named entity recognition technology to identify key entities in the report, such as company names, project numbers, etc., to implement relationship extraction, capture the dependencies between entities, and accurately identify professional terms and indicators in specific fields based on pre-trained extraction models.

[0118] Data standardization submodule: performs unified standardization processing on the extracted fields, realizes unit conversion and standardization of numerical fields, and supports normalization processing of special fields such as time, location, and amount.

[0119] Rule base management module

[0120] Function description: Establish and maintain a multi-level rule base system, support rule creation, editing, version management and invalidation management, implement correlation analysis and conflict detection between rules, and provide rule retrieval and visualization display functions.

[0121] Technical implementation:

[0122] Rule storage submodule: uses a distributed database to store rule information, implements classified and hierarchical storage of rules, supports multi-dimensional tag management, establishes rule indexes, and effectively improves rule retrieval efficiency.

[0123] Rule entry and editing submodule: provides a visual rule editing interface to facilitate users to create and modify rules, supports automatic conversion of natural language rules to machine-parseable rules, implements rule template functions, and improves rule creation efficiency.

[0124] Rule version management submodule: implements rule version control, records rule change history in detail, supports rule timeliness management, automatically handles expired rules, and provides rule change impact analysis function to help users understand the impact of rule changes on the system.

[0125] Rule conflict detection submodule: automatically detects conflicts and contradictions in rule sets, implements rule dependency analysis, provides rule optimization suggestions, and ensures the accuracy and effectiveness of the rule base.

[0126] Knowledge graph engine

[0127] Function description: Build a domain knowledge graph, map compliance domain concepts and relationships, realize the semantic expression and association of rules, support graph-based knowledge reasoning and query, and provide knowledge visualization and interaction functions.

[0128] Technical implementation:

[0129] Graph construction sub-module: uses a semi-automatic method to build a domain knowledge graph, supports automatic extraction of entities and relationships from rule libraries and document libraries, realizes multi-source knowledge fusion, integrates internal and external knowledge resources, and ensures the integrity and accuracy of the knowledge graph.

[0130] Graph storage and indexing submodule: Use graph database to store knowledge graphs, implement efficient graph indexing mechanism, support fast retrieval and query, support incremental update and historical version management of graphs, and ensure the timeliness and traceability of knowledge graphs.

[0131] Knowledge reasoning submodule: An inference engine based on the combination of rule reasoning and statistical reasoning, which supports multiple reasoning methods such as transitive reasoning and inductive reasoning, realizes uncertainty reasoning, can handle fuzzy and incomplete knowledge, and enhance the intelligent reasoning ability of the knowledge graph.

[0132] Graph visualization submodule: provides multi-dimensional knowledge graph visualization, supports interactive graph exploration and query, realizes knowledge path analysis and display, and helps users intuitively understand the structure and association relationships of the knowledge graph.

[0133] Large model inference module

[0134] Function description: Integrates pre-trained large language models, provides deep semantic understanding capabilities, realizes context-aware text understanding and reasoning, provides knowledge-enhanced intelligent question answering and attribution analysis, and supports domain adaptive fine-tuning and continuous learning.

[0135] Technical implementation:

[0136] Model integration submodule: Integrates multiple advanced large language models, such as the GPT series and Claude series, to achieve model interface standardization, support seamless model switching, and adopt a model routing mechanism to select the most suitable model based on task characteristics, thereby improving the system's processing efficiency and accuracy.

[0137] Domain Adaptation Submodule: Fine-tune the model based on industry data and compliance corpus to achieve efficient parameter fine-tuning (PEFT), reduce computing resource requirements, support continuous learning, and continuously optimize model performance to better adapt it to the needs of the compliance review field.

[0138] Knowledge enhancement submodule: realizes the deep integration of large models and knowledge graphs, uses retrieval enhancement generation (RAG) technology to improve answer accuracy, supports real-time retrieval and integration of external knowledge bases, and enhances the system's knowledge reserves and application capabilities.

[0139] Reasoning and interpretation submodule: provides an explainable display of the model's reasoning path and basis, realizes the automatic generation and display of the reasoning evidence chain, supports the reasoning correction mechanism based on domain knowledge, and improves the transparency and reliability of model decision-making.

[0140] Automatic review and feedback module

[0141] Functional description: Implement multi-dimensional automatic inspection of reports, accurately locate problems and related evidence, generate structured review reports and rectification suggestions, and support human-machine collaborative review processes.

[0142] Technical implementation:

[0143] Consistency check submodule: realizes consistency comparison between internal fields and data in the report, supports data consistency verification across chapters and tables, and effectively detects inconsistent expressions based on semantic similarity analysis.

[0144] Compliance check submodule: Compliance judgment based on the combination of rule matching and large model reasoning, supports hierarchical inspection of multi-level compliance standards, and realizes automatic positioning and reference of compliance clauses.

[0145] Rationality check submodule: Anomaly detection based on statistical analysis and knowledge reasoning, realizes logical relationship verification, detects the rationality of causal inference, supports industry benchmark comparative analysis, and discovers data that deviates from the normal range.

[0146] Problem location and attribution module: accurately locates the location and scope of the problem, automatically associates the illegal content with the corresponding rule clauses, and generates problem severity ratings and priority recommendations.

[0147] Review report generation submodule: automatically generates structured review reports, supports multiple output formats such as Word, PDF, HTML, etc., provides visual problem display and statistical analysis, and facilitates users to view and understand the review results.

[0148] Problem disclosure and approval interface

[0149] Function description: Connect to the company's internal workflow system, support automatic distribution of review results and task assignment, implement problem tracking and rectification verification, and provide API interface to support third-party system integration.

[0150] Technical implementation:

[0151] Workflow integration submodule: supports integration with mainstream workflow platforms, such as OA systems, to achieve automatic triggering and status synchronization of approval processes, and provides custom workflow configuration functions to meet the business process requirements of different enterprises.

[0152] Task distribution submodule: automatically assigns tasks based on problem type and responsibility division, supports task priority setting and deadline management, implements task dependency management, and ensures a reasonable rectification sequence.

[0153] Correction tracking submodule: automatically tracks the progress and status changes of corrections, provides correction evidence collection and verification functions, supports automated verification of correction effects, and ensures that problems are effectively resolved.

[0154] API interface submodule: provides a standardized REST API interface, supports security authentication mechanisms such as JWT, implements interface version management and backward compatibility, and facilitates seamless integration with other systems.

[0155] Data flow and processing flow

[0156] This system adopts an efficient pipeline processing architecture. The main processing flow is as follows:

[0157] Data acquisition and preprocessing stage: Receive reports in multiple formats, perform OCR recognition and document parsing, complete preliminary structuring and cleaning, and convert the original report data into a format that the system can process.

[0158] Blocking and field extraction stage: Intelligent block division is performed based on the document structure, key fields and entity information are extracted, and data standardization is achieved to prepare for subsequent review work.

[0159] Rule matching and knowledge association stage: Match the extracted fields with the rule base, establish the association between the fields and the knowledge graph, generate preliminary inspection results, and provide basic data for multi-dimensional inspections.

[0160] Multi-dimensional inspection stage: perform consistency checks, compliance checks, and rationality checks, summarize comprehensive inspection results, and comprehensively and in-depth review the report content.

[0161] Problem identification and location stage: Determine the type and severity of the problem, accurately locate the problem, and use association rules and knowledge support to provide a clear direction for problem handling.

[0162] Report generation and process triggering stage: Generate a structured review report, trigger the corresponding approval process, distribute rectification tasks and notifications, and ensure that the review results are effectively processed and fed back.

[0163] System deployment and implementation technology

[0164] This system adopts microservice architecture design, which supports flexible deployment and horizontal expansion:

[0165] Technology stack selection: The front-end uses the Vue.js / React framework, based on component-based design, to provide a good user interaction experience; the back-end uses the Spring Boot / Spring Cloud microservice framework to ensure system stability and scalability; the database uses a combination of relational databases (MySQL / PostgreSQL) and graph databases (Neo4j) to meet different data storage requirements; the message queue uses Kafka / RabbitMQ to implement asynchronous processing and improve system processing efficiency; the search engine uses Elasticsearch to provide full-text search capabilities, allowing users to quickly find information.

[0166] Deployment method: Supports containerized deployment and Kubernetes-based orchestration to achieve efficient resource management and scheduling; provides dual-mode deployment solutions for private cloud and public cloud to meet the security and cost requirements of different enterprises; implements multi-tenant isolation design and supports the SaaS service model to improve system resource utilization and service flexibility.

[0167] Extensibility design: Based on interface design principles, it supports dynamic expansion of functional modules, implements plug-in architecture, supports integration of third-party functional components, provides open APIs, supports seamless connection with other systems, and facilitates continuous functional upgrades and expansions of the system.

[0168] Security considerations: Use data transmission encryption technology to ensure the security of information during transmission; implement role-based access control (RBAC) to achieve sophisticated permission management and ensure that users can only access authorized resources; establish a complete audit log to record all operations to facilitate security audits and problem tracing; desensitize data to protect sensitive information from being leaked.

[0169] References to "embodiments" herein mean that the specific features, structures, or characteristics described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the word "embodiment" in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or relevance to other embodiments. In principle, in this application, as long as there are no technical contradictions or conflicts, the various technical features mentioned in the embodiments can be combined in any manner to form a corresponding implementable technical solution.

[0170] Unless otherwise defined, the technical terms used herein have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms herein is only for describing specific embodiments and is not intended to limit this application.

[0171] 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-scale model-assisted compliance report intelligent review system, characterized by: include: The report processing module is used to receive report files in various formats, perform OCR recognition on scanned documents, implement structured parsing and block processing of documents, and extract key fields and entity information; The rule base management module is used to establish and maintain a multi-level rule base system, support rule creation, editing, version management, and invalidation management, implement correlation analysis and conflict detection between rules, and provide rule retrieval and visualization functions; Knowledge graph engine, used to build domain knowledge graphs, map compliance domain concepts and relationships, implement semantic expression and association of rules, support graph-based knowledge reasoning and query, and provide knowledge visualization and interaction capabilities; The large model inference module is used to integrate pre-trained large language models, provide deep semantic understanding capabilities, realize context-aware text understanding and reasoning, provide knowledge-enhanced intelligent question answering and attribution analysis, and support domain adaptive fine-tuning and continuous learning; The automatic review and feedback module is used to automatically check reports in multiple dimensions, accurately locate issues and related evidence, generate structured review reports and corrective suggestions, and support a human-machine collaborative review process; The problem disclosure and approval interface is used to connect to the company's internal workflow system, support the automatic distribution of review results and task assignment, realize problem tracking and rectification verification, and provide an API interface to support third-party system integration.

2. A large model-assisted compliance report intelligent review method based on the system of claim 1, characterized in that: The following steps are involved: Data acquisition and preprocessing, receiving multi-format reports, performing OCR recognition and document parsing, and completing preliminary structuring and cleaning; Blocking and field extraction: intelligent block division based on document structure, extraction of key fields and entity information, and data standardization; Rule matching and knowledge association: matching the extracted fields with the rule base, establishing associations between the fields and the knowledge graph, and generating preliminary inspection results; Multi-dimensional inspection, performing consistency checks, compliance checks, and rationality checks, and summarizing comprehensive inspection results; Problem identification and location, determining the problem type and severity, accurately locating the problem location, and associating rules and knowledge support; Report generation and process triggering: generate structured review reports, trigger corresponding approval processes, and distribute rectification tasks and notifications.

3. The large model-assisted compliance report intelligent review system according to claim 1 is characterized in that: The report processing module includes: The document parsing submodule is used to support the parsing of multi-format documents and convert them into a unified text format. Based on document layout analysis technology, it accurately identifies elements such as titles, text, tables, and images, and realizes the structured extraction and parsing of tables. The intelligent segmentation submodule is used to segment documents using a combination of rules and learning. It uses a hierarchical segmentation strategy, first performing chapter-level segmentation and then paragraph-level segmentation. It also supports custom segmentation templates. The field extraction submodule uses named entity recognition technology to identify key entities in reports, extract relationships, capture dependencies between entities, and accurately identify domain-specific terminology and indicators based on pre-trained extraction models. The data standardization submodule is used to perform unified standardization on the extracted fields, realize unit conversion and standardization of numerical fields, and support the normalization of special fields such as time, location, and amount.

4. The large model-assisted compliance report intelligent review system according to claim 1 is characterized in that: The rule base management module includes: The rule storage submodule is used to store rule information in a distributed database, implement classified and hierarchical storage of rules, support multi-dimensional tag management, and establish rule indexes; The rule entry and editing submodule is used to provide a visual rule editing interface, support the automatic conversion of natural language rules into machine-parseable rules, and realize the rule template function; The rule version management submodule is used to implement rule version control, record rule change history, support rule timeliness management, automatically handle expired rules, and provide rule change impact analysis function; The rule conflict detection submodule is used to automatically detect conflicts and contradictions in the rule set, implement rule dependency analysis, and provide rule optimization suggestions.

5. The large model-assisted compliance report intelligent review system according to claim 1 is characterized in that: The knowledge graph engine includes: The graph construction submodule is used to construct a domain knowledge graph in a semi-automatic manner, supporting the automatic extraction of entities and relationships from the rule base and document base to achieve multi-source knowledge fusion; The graph storage and indexing submodule is used to store knowledge graphs in a graph database, implement an efficient graph indexing mechanism, support fast retrieval and query, and support incremental updates and historical version management of graphs; The knowledge reasoning submodule is used for the reasoning engine that combines rule-based reasoning with statistical reasoning. It supports multiple reasoning methods such as transitive reasoning and inductive reasoning, and realizes uncertainty reasoning. The graph visualization submodule is used to provide multi-dimensional knowledge graph visualization, support interactive graph exploration and query, and realize knowledge path analysis and display.

6. The large model-assisted compliance report intelligent review system according to claim 1 is characterized in that: The large model reasoning module includes: The model integration submodule is used to integrate multiple large language models, standardize model interfaces, support seamless model switching, and adopt a model routing mechanism to select the most suitable model based on task characteristics; The domain adaptation submodule is used to fine-tune the model based on industry data and compliant corpus, enabling efficient parameter fine-tuning and supporting continuous learning; The knowledge enhancement submodule is used to achieve deep integration of large models and knowledge graphs, using retrieval enhancement generation technology to improve answer accuracy and support real-time retrieval and integration of external knowledge bases; The reasoning explanation submodule is used to provide an explainable display of the model's reasoning path and basis, realize the automatic generation and display of the reasoning evidence chain, and support the reasoning correction mechanism based on domain knowledge.

7. The large model-assisted compliance report intelligent review system according to claim 1 is characterized in that: The automatic review and feedback module includes: The consistency check submodule is used to compare the consistency of fields and data within the report, support cross-chapter and cross-table data consistency verification, and detect inconsistent expressions based on semantic similarity analysis; The compliance check submodule is used for compliance judgment based on a combination of rule matching and large-scale model reasoning. It supports hierarchical checks of multi-level compliance standards and realizes the automatic location and reference of compliance clauses. The rationality check submodule is used for anomaly detection based on statistical analysis and knowledge reasoning, to verify logical relationships, check the rationality of causal inference, and support comparative analysis of industry benchmarks; The problem location and attribution module is used to accurately locate the location and scope of the problem, automatically associate the violation content with the corresponding rule clauses, and generate problem severity ratings and priority recommendations; The review report generation submodule is used to automatically generate structured review reports, supports multiple output formats, and provides visual problem display and statistical analysis.

8. The large model-assisted compliance report intelligent review system according to claim 1 is characterized in that: The problem disclosure and approval interface includes: The workflow integration submodule is used to support integration with mainstream workflow platforms, realize automatic triggering and status synchronization of approval processes, and provide custom workflow configuration functions; The task distribution submodule is used to automatically assign tasks based on problem types and responsibilities, support task priority setting and deadline management, and implement task dependency management; The rectification tracking submodule is used to automatically track rectification progress and status changes, provide rectification evidence collection and verification functions, and support automated verification of rectification effects; The API interface submodule is used to provide a standardized REST API interface, support security authentication mechanisms, and implement interface version management and backward compatibility.

9. The large model-assisted compliance report intelligent review system according to claim 1 is characterized in that: The system adopts a microservice architecture design, with the Vue.js / React framework on the front end and the Spring Boot / Spring Cloud microservice framework on the back end. The database adopts a combination of relational database and graph database, the message queue uses Kafka / RabbitMQ, and the search engine uses Elasticsearch.

10. The large model-assisted compliance report intelligent review system according to claim 1 is characterized in that: The system supports containerized deployment, based on Kubernetes orchestration, provides dual-mode deployment solutions of private cloud and public cloud, implements multi-tenant isolation design, and supports SaaS service model.