A method for automatically generating and intelligently checking and correcting law enforcement documents
By introducing intelligent semantic template matching, entity and rule hybrid verification, contextual reasoning error correction, and human-machine collaborative review mechanism, the problem of insufficient accuracy in the generation and verification of law enforcement documents has been solved, realizing efficient and intelligent automatic document generation and verification, adapting to diverse case types, and improving document compliance and review efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 广西信安锐达科技有限公司
- Filing Date
- 2026-04-29
- Publication Date
- 2026-07-21
AI Technical Summary
The existing automatic generation and verification process for law enforcement documents suffers from insufficient accuracy, limited error detection and correction capabilities, incomplete verification of legal application and factual logic, and a lack of continuous learning and human-machine collaboration mechanisms, leading to document defects and increased case-handling risks.
By employing transformative semantic template matching, entity and rule hybrid verification, contextual reasoning error correction, human-machine collaborative review, and continuous learning mechanisms, we can achieve intelligent generation, verification, and optimization of law enforcement documents. These mechanisms include generating semantic templates, entity and rule hybrid verification, contextual reasoning error correction, human-machine collaborative review, and continuous learning.
It improves the quality and efficiency of law enforcement document generation and review, enhances the accuracy and compliance of documents, strengthens the transparency and traceability of the review process, adapts to diverse case types and document formats, and has the ability to intelligently correct complex and difficult issues.
Smart Images
Figure CN122431950A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of intelligent text processing and natural language processing technology, and more specifically relates to a method for automatically generating, intelligently verifying and correcting errors in law enforcement documents. Background Technology
[0002] With the continuous advancement of social governance and the rule of law, law enforcement agencies generate a large number of enforcement documents during the handling of various administrative and judicial cases, such as administrative penalty decisions, case filing notices, and case transfer letters. These documents serve as legal evidence and basis for case handling, and their accuracy, standardization, and legality have a significant impact on case outcomes, the protection of citizens' rights, and public credibility. However, the traditional process of drafting, verifying, and reviewing enforcement documents relies heavily on manual labor, which is not only labor-intensive and inefficient but also prone to errors due to differences in personnel expertise or negligence, such as writing mistakes, improper application of law, or logical contradictions. This can lead to document defects, increased case-handling risks, and even legal disputes and administrative reviews.
[0003] In recent years, with the development of technologies such as artificial intelligence, natural language processing, and knowledge graphs, the automatic generation and intelligent verification of law enforcement documents using intelligent algorithms has become a hot application area. Existing document automation technologies mostly focus on basic aspects such as automatic template filling, keyword recognition, and basic error prompts, making it difficult to conduct in-depth analysis and intelligent error correction of the legal entities, factual information, logical relationships, and application of legal provisions within the documents. Faced with increasingly complex and diverse case types and constantly changing laws and regulations, relying solely on fixed templates and rules is no longer sufficient to fully address actual business needs. Furthermore, the lack of effective utilization of case history, contextual semantics, and human-computer interaction feedback also limits the adaptability and intelligence level of existing systems.
[0004] Therefore, there is an urgent need for a novel approach that combines deep semantic understanding, template adaptation, hybrid multi-layer verification, and continuous learning mechanisms to comprehensively upgrade the processes of law enforcement document generation, preliminary verification, intelligent reasoning and error correction, risk warning, and human-computer interactive review, thereby improving the accuracy, compliance, and efficiency of automated document processing and meeting the high standards of modern law enforcement agencies for standardized law enforcement and risk prevention. Summary of the Invention
[0005] This invention aims to address the technical problems existing in the automatic generation and verification of law enforcement documents, such as insufficient accuracy, limited error detection and correction capabilities, incomplete verification of legal application and factual logic, and lack of continuous learning and human-machine collaboration mechanisms. It achieves type identification of law enforcement document content, automatic review of legal elements, logical consistency verification, contextual semantic reasoning error correction, intelligent risk warning, and adaptive optimization, thereby improving the generation quality, review efficiency, and compliance of law enforcement documents.
[0006] To achieve the above objectives, the present invention employs the following technical solution: the method comprises: Generate semantic templates and automatically identify the case type, applicable legal provisions, and content elements based on basic case information using the Transformative Semantic Template Matching Algorithm (ASTM). The system combines entity and rule verification, automatically extracting key legal entities, spatiotemporal information, and party relationships from documents. Based on the legal database and business process rules, it then performs preliminary verification of the document's logic, format, and legal elements. Contextual reasoning error correction applies a contextual reasoning error correction algorithm (CRC) to the contextual semantic reasoning of suspicious content for detected suspected errors or inconsistencies. Human-machine collaborative review pushes automatically checked and corrected documents to business personnel, highlighting differences and providing risk warnings; Continuous learning and application scenario expansion: By automatically collecting feedback data after review, we can discover new cases and error patterns, and flexibly expand rules, templates and reasoning patterns.
[0007] In one approach, generating a semantic template includes: performing structured text processing on basic case information, using a deep semantic understanding model to convert the case text into a semantic vector, and introducing a discriminative classification network to determine the case type. After determining the case type, cases that are semantically similar to the current case are extracted from the historical case database, and a candidate template set is formed by judging the similarity. A transformative semantic template matching algorithm is used to represent candidate templates as a set of fields. Fields are automatically adjusted through field fit scores, and highly fit fields are aggregated to form the final document template.
[0008] In one solution, the entity and rule hybrid verification includes: after the document is automatically generated, a hybrid verification is adopted, which integrates the rule engine and the entity recognition algorithm to process the document text and automatically extract key legal entities, spatiotemporal information and party relationships; Each entity is associated with a legal database and preset business rules, and the legality of attributes and relationships between entities is verified through symbolic reasoning and graph traversal algorithms. At the same time, the rule engine performs structured and process-oriented rule validation on the documents to determine whether fields are missing, whether the main body is complete, and whether the format is standardized, and stores all validation results in a structured manner.
[0009] In one approach, the context-based reasoning error correction includes: for suspected errors or inconsistencies detected in the preceding verification, employing a context-based reasoning error correction algorithm to accurately locate the suspected error fields and extract their relevant contextual information, including: The case content is analyzed using a combination of factual descriptions, evidence, subjects, and other elements, along with information from historical case documents and judicial case databases. Through semantic embedding models and differential analysis, the case content is subjected to vertical and horizontal semantic reasoning. Based on the differences in fields in the reference cases and the logic of typical legal provisions in the judgment, intelligent correction suggestions are automatically generated.
[0010] In one approach, the human-machine collaborative review process includes: pushing the documents that have been automatically verified and intelligently corrected to the work interface of law enforcement personnel through a business management platform; using a difference detection algorithm to highlight the differences between the original documents and the suggested modification results in a dual-document comparison method; and using a risk labeling model to generate warnings for suspected high-risk content.
[0011] In one approach, the continuous learning and application scenario expansion includes: automatically collecting feedback data after human-machine review, including human judgment results, modification suggestions, actual corrections and new case examples, and encoding all feedback events into structured records; A scenario-based self-expanding learning mechanism is adopted to perform unsupervised clustering on the feedback data, summarize the high-frequency scenarios that are not covered by existing rules, and label the scenarios at the cluster boundary to extract differential elements and error patterns. We utilize incremental rule mining algorithms and content similarity-based template adaptation mechanisms to dynamically and incrementally optimize and expand the rule base, template base, and inference model. Through incremental training strategies, we continuously improve the model's adaptability to new scenarios.
[0012] In one approach, generating a semantic template includes: further adjusting the field order and introducing necessary related fields through a structural optimization algorithm.
[0013] In one approach, the human-machine collaborative review process includes: law enforcement officers can adopt, reject, or manually revise suggestions through an interactive interface; each operation is recorded as an operation event and the reason for modification is indicated; and a context explanation module is invoked to generate a detailed explanation of the reasons for the suggestion.
[0014] Beneficial effects of this invention: This invention achieves intelligent assistance and quality assurance for the entire process of law enforcement document processing by introducing transformative semantic template matching, entity and rule hybrid verification, contextual reasoning error correction, human-machine collaborative review, and continuous learning optimization mechanisms. Compared with existing technologies, this invention can automatically adapt to diverse case types and document formats, accurately identify and extract key legal elements in documents, and promptly detect potential errors and defects in factual descriptions, legal application, spatiotemporal information, and subject relationships.
[0015] By leveraging contextual semantic reasoning and referencing historical cases, this invention possesses intelligent correction capabilities for complex and difficult issues, effectively improving the accuracy and depth of document verification. Furthermore, the high-level risk highlighting and deep collaboration between human and machine review not only enhances law enforcement efficiency but also strengthens the transparency and traceability of the review process. Continuous learning and application scenario expansion mechanisms constantly optimize the algorithm and rule base, enabling the system's capabilities to dynamically evolve and rapidly adapt to new case requirements. Attached Figure Description
[0016] Figure 1 This is a flowchart of the method of the present invention; Figure 2 A flowchart for generating semantic templates for this invention. Detailed Implementation To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
[0017] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.
[0018] like Figure 1 As shown, a method for automatically generating and intelligently verifying and correcting law enforcement documents includes the following steps: Step 1: Generate semantic templates like Figure 2 As shown, based on basic case information, the Transformational Semantic Template Matching (ASTM) algorithm automatically identifies the case type, applicable legal provisions, and content elements. This algorithm, combined with historical case data and a deep semantic understanding model, dynamically adjusts and optimizes the field templates used when generating documents, ensuring that each document's structure is not only rigorous but also flexible enough to adapt to changing case circumstances, thus laying the foundation for subsequent intelligent verification and error correction.
[0019] S101. Semantic Feature Extraction and Case Type Determination First, the original case information (such as the cause of action, factual description, and participating parties) undergoes structured text processing. A pre-trained deep semantic understanding model (BERT) is used to embed the case text, transforming the text into semantic vectors. Where t is the case text, This is the mapping function from text to semantic vectors. Next, a discriminative classification network is introduced, which, through maximum likelihood discrimination, outputs the case's position in the defined category space. Type probability within: Ultimately Output the most likely case category for subsequent template selection.
[0020] S102, Historical Case Migration and Template Matching After determining the case type, the algorithm retrieves data from the historical case database. Cases semantically similar to the current case are extracted. The semantic vector of each case is determined using a cosine similarity metric. Historical Cases Distance: Based on the maximum similarity, select the top K templates. This forms a candidate template set.
[0021] S103, Transformative Template Adaptive Generation A transformative semantic template matching algorithm (ASTM) is introduced. The algorithm represents each candidate template as a set of fields. Each field All come with generation rules Through dynamic adaptive adjustment: First, specific elements in the case are used to match fields, such as facts, applicable legal provisions, and the parties involved, to introduce field adaptation scoring. in As weight, This is a score from a human-based knowledge base regarding the suitability of fields. The algorithm automatically removes fields with low suitability scores and aggregates fields with high suitability scores to form the final document template. ,for Threshold.
[0022] S104, Template Generation and Structure Optimization Once the document template is generated, it will be further optimized using a structural optimization algorithm to adjust the field order and introduce necessary related fields (such as the logical relationship between case facts and legal provisions) to improve the accuracy of subsequent intelligent verification. This optimization step can be modeled as a structured template sorting problem, using heuristic sorting or dynamic programming schemes to arrange the fields, so that the final generated document template is both standardized and highly adaptable and verifiable.
[0023] Step 2: Mixed Entity and Rule Validation After the documents are automatically generated, a hybrid verification method is adopted, which integrates a rule engine and an entity recognition algorithm. First, key legal entities, spatiotemporal information, and relationships between parties are automatically extracted from the documents. Then, based on the legal database and business process rules, the logic, format, and legal elements of the document content are initially verified. For example, it is determined whether the cause of action and the facts are logically consistent and whether the chronological order is reasonable. This step provides data support for subsequent personalized intelligent error correction.
[0024] First, a deep learning-based Named Entity Recognition (NER) algorithm is used to process the document text and automatically extract a set of key legal entities. This includes elements such as names of people, places, case numbers, causes of action, legal provisions, time, and amount. The NER model based on BiLSTM-CRF processes the input sequence... Generate label sequence The following probability modeling is used: ,in This represents the hidden representation of the i-th word using BiLSTM. For the recognition of multiple entities and complex relationships, it can be further extended to joint extraction, utilizing attention mechanisms or graph neural networks to obtain more refined entity boundaries and semantic associations between entities.
[0025] After accurate entity extraction, each entity is associated and mapped with the regulatory database and preset business rules. The regulatory database can be represented as a set of several triples, i.e. , representing entities and There are rule relationships between them. (Applicable, Covering, Conflicting, or Sequence). The legality of attributes and relationships between entities is verified through symbolic reasoning and graph traversal algorithms. This applies to the time series involved in the case facts. According to the constraints: This allows for quick assessment of whether the timeline aligns with the logic of the case. For example, by searching the legal database, the semantic fit between cause of action (a) and applicable legal provision (L) can be calculated. ,in and These are the semantic vectors for the cause of action and the legal provision, respectively. If the fit is lower than a preset threshold... If so, it indicates that the cause of action does not match the legal provisions.
[0026] At the same time, the rules engine performs structured and procedural rule checks on the documents: such as whether fields are missing, whether the body is complete, and whether the format is standardized. Define a set of rules. For each rule ,like If the required information is not found, a warning will be triggered. For example, if a certain case type requires information such as the age of the parties involved and the amount to be executed, a missing information warning will be given directly if the corresponding entity is not identified or filled in. All verification results will be stored in a structured manner, laying a precise data support foundation for subsequent context-based error correction and human interaction. The entire process integrates cutting-edge natural language processing methods and legal rule symbolic reasoning, achieving comprehensive automatic verification and initial risk screening of entities, time sequence, elements, and document logic.
[0027] Step 3: Contextual Reasoning and Intelligent Error Correction For detected suspected errors or inconsistencies, a context-based error correction algorithm (CRC) is applied. This algorithm not only examines the matching of individual content with the template, but also performs contextual semantic reasoning on suspicious content by comparing it vertically with other documents in the case and horizontally with a judicial case database. For example, when problems such as abnormal amounts involved or mismatched applicable laws occur, it can provide automated correction suggestions based on similar case scenarios, demonstrating intelligence and targeting in error correction recommendations.
[0028] For suspected errors or inconsistencies detected in the preliminary verification, a context-based error correction (CRC) algorithm is used for deep automatic correction. First, the suspected erroneous fields are precisely located, and their relevant context is extracted, namely the related factual descriptions, evidence, subjects, and other elements in the case documents, while also incorporating information from the case's historical documents and the judicial case database.
[0029] This process transforms instantiated content into vector representations through a semantic embedding model. For example, for the field f to be corrected, its context C is represented as a semantic vector. The semantic vector of the field itself The algorithm then calculates the fit between the two. Determine the validity of the field.
[0030] In the vertical reasoning section, historical documents of the current case or similar cases are traversed, and the process is modeled as a sequential decision-making process. For the detection of the amount involved, all monetary-related fields in the case are collected. It also calculates its statistical characteristics, such as mean and range, and combines them with conventional judgment values from the domain knowledge base. Determine the amount to be corrected. Is this an outlier? ,in This is a threshold; if an anomaly is detected, a revision suggestion will be recommended.
[0031] During the horizontal comparison process, from the case library Search for cases semantically similar to the current case using cosine similarity. The algorithm selects highly relevant reference cases and then performs a difference analysis between the relevant fields in these cases and those in the current case. If a mismatch is detected, such as inconsistencies in applicable law, it references typical legal provisions and judgment logic from the cases and automatically generates correction suggestions through contextual reasoning. The core of the algorithm employs a Transformer inference model with an attention mechanism to handle suspicious scenarios. Compared with the reference case scenario Calculate the relevance weight We use weighted aggregation of reference scenarios to generate targeted error correction and completion answers.
[0032] Step 4: Human-Machine Collaborative Review Documents that have been automatically checked and corrected are pushed to business personnel, supplemented by intelligent difference highlighting and risk warning mechanisms. Law enforcement officers can view the marked suggestions and reasons for modification, and make targeted secondary adjustments through the interactive interface. All manual judgment results are fed back into the model learning module, enabling the error correction algorithm to self-evolve and improve.
[0033] First, the automatically verified and intelligently corrected law enforcement documents are pushed to the dedicated work interface of law enforcement officers through the business management platform. Based on the difference detection algorithm, a dual-document comparison method is used to compare the original documents with the suggested revisions, and quantitative difference measurement is applied. ,in This is an indicator function, where n is the total number of fields, visually displaying the different highlighted parts. Simultaneously, using a risk labeling model, warnings are automatically generated for potentially high-risk content, for example, through a comprehensive anomaly score. ,in Score for content consistency. For difference weights, For reference, the risk exposure of the case study The weighted coefficients are used to filter out important risk points and highlight them.
[0034] Law enforcement officers can choose to "adopt," "reject," or "manually revise" suggestions, and each interaction is recorded as an operation event. It will prompt the reason for the modification, invoke the context explanation module, and generate a detailed explanation of the suggested reasons based on the natural language generation model. ,in Provide field context to help people understand the reasoning logic and improve decision-making efficiency.
[0035] Each manual judgment and modification is fed into the model learning module in real time to update the discrimination weights and inference rules of the error correction algorithm. An online learning strategy is employed, accumulating human feedback. Adjusting model parameters based on incremental optimization algorithms, such as adaptive gradient updates. ,in For model parameters, For learning rate, This is a loss function based on human feedback. In this way, by continuously absorbing experience from human-machine collaboration, the accuracy and professionalism of the verification suggestions are improved, enabling the error correction algorithm to self-evolve and enhance its capabilities.
[0036] Step 5: Continuous Learning and Expansion of Application Scenarios By automating the collection of feedback data after review, new cases and error patterns are dynamically identified, and rules, templates, and reasoning models are flexibly expanded. This ensures long-term adaptability to changes in law enforcement practice and continuously improves the comprehensiveness of intelligent verification and error correction.
[0037] The system automatically collects feedback data after each human-machine review, including human judgment results, modification suggestions, actual corrections, and novel case examples. All feedback events are encoded into structured records. , of which Case scene characteristics, For feedback fields, This is a manual operation type. The core algorithm adopts a scenario self-expanding learning mechanism (SEL), which aims to automatically discover the characteristics and error types of newly emerging cases using feedback information, and to dynamically and incrementally optimize the rule engine, template library, and inference model.
[0038] First, scene features are learned inductively using an unsupervised clustering algorithm. A vector set of all feedback scenes is defined. K-means clustering method was used. The clustering process calculates cluster centers and boundaries to identify clusters of scenes that occur frequently but are not covered by rules. The clustering formula is as follows: ,in The center of the k-th class is defined. Typical cluster boundary scenarios are manually or automatically labeled to extract their dissimilar features and error patterns.
[0039] Secondly, an incremental rule mining algorithm is employed. For novel error scenarios, their features are extracted and compared with the existing rule base. If a rule is found to be missing during the comparison, a temporary rule is generated online. Add to the rule base: This process can employ the Apriori algorithm based on association rules to statistically analyze frequent itemsets between feedback scenarios: if If the condition is met, then a new rule will be output; where ,for Threshold.
[0040] Meanwhile, the template library and inference model expand their content based on new scenarios. The template adaptation mechanism employs content aggregation and automatic generation based on content similarity, for example, for document templates. If a new cluster of scenes is discovered Calculate similarity If the value is below the threshold, a new template will be automatically generated. The parameters of the inference model are trained using an incremental training strategy, and the scene set is used after each round of feedback learning. Make fine adjustments to the formula. We will continuously improve the model's ability to adapt to new scenarios.
[0041] Example: Taking the standardized preparation of penalty notices for cases of "motor vehicles violating prohibitory signs" by a traffic law enforcement brigade in a certain area as an example, this demonstrates the application process of the intelligent verification and error correction system for automatic generation of law enforcement documents.
[0042] 1. Input of basic case information and generation of semantic templates Law enforcement officers input basic case information into the case processing system, as shown in the table below: The system uses a deep semantic understanding model to vectorize the case information, automatically identifies the case type as "traffic violation - prohibition sign", and retrieves similar cases from the historical case database to form a set of structured candidate templates.
[0043] The candidate template table is as follows: After matching the scores, the system selected T001 as the main template and automatically constructed a preliminary document according to the principles of field association and order optimization.
[0044] 2. Mixed Entity and Rule Validation After the document is automatically generated, it enters the stage of mixed entity and rule verification.
[0045] The system identifies and extracts entities as follows: The system automatically compares data with legal and rule databases to verify the completeness and logical consistency of elements. For example, it might detect that the "signature of law enforcement officer" field in a document is empty, triggering a warning from the rule engine regarding missing fields.
[0046] The results of the automatic structured validation are as follows: 3. Contextual Reasoning Error Correction After automatic verification, the system found that the "law enforcement officer signature" field was missing. It further analyzed the risk of missing information from similar historical cases using a contextual reasoning error correction algorithm.
[0047] Case Comparison Table (Partial): Based on the case content and the high-risk correlation of missing signatures in historical cases, the reasoning module automatically generates a suggestion: "It is recommended to complete the signature of law enforcement officers; otherwise, the validity of the document may be in dispute." 4. Human-machine collaborative review The system automatically sends the physical examination and error correction results to law enforcement officers, using a dual-document comparison and highlighting of differences. The interface is shown below (for example): Law enforcement officers can adopt suggestions, supplement signatures, or make special notes. The system records all manual operation events and prompts the reason for modification, such as "The law enforcement scene was handled by Li Si, and now a supplementary signature is required."
[0048] 5. Continuous learning and scenario expansion All case document verification, review, and feedback data will be automatically archived. Typical event data table (partial): Data analysis using unsupervised clustering revealed a recent increase in missing signatures during on-site law enforcement due to instability in mobile device signature functionality. The system automatically pushed optimization suggestions: adding signature process checks and dynamically optimizing relevant verification rules in the online template library.
[0049] 6. Optimization, iteration, and performance improvement Each round of human-machine collaboration and feedback triggers an incremental learning phase, where the model undergoes incremental training on common error types, missing fields, and document structure adjustment patterns. The template library, rule library, and inference model are thus dynamically optimized, achieving efficient adaptation for various types of traffic case documents.
[0050] In this embodiment, automatic generation of law enforcement documents, hybrid verification, contextual reasoning, human-machine collaboration, and continuous learning are organically integrated to achieve a closed-loop process that is automated, intelligent, and feedback-driven. This significantly improves document compliance, reduces manual verification costs, and adapts to the ever-changing needs of actual law enforcement scenarios.
[0051] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.
[0052] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for automatically generating and intelligently verifying and correcting law enforcement documents, characterized in that: The method includes: Generate semantic templates and automatically identify the case type, applicable legal provisions, and content elements based on basic case information using the Transformative Semantic Template Matching Algorithm (ASTM). The system combines entity and rule verification, automatically extracting key legal entities, spatiotemporal information, and party relationships from documents. Based on the legal database and business process rules, it then performs preliminary verification of the document's logic, format, and legal elements. Contextual reasoning error correction applies a contextual reasoning error correction algorithm (CRC) to the contextual semantic reasoning of suspicious content for detected suspected errors or inconsistencies. Human-machine collaborative review pushes automatically checked and corrected documents to business personnel, highlighting differences and providing risk warnings; Continuous learning and application scenario expansion: By automatically collecting feedback data after review, we can discover new cases and error patterns, and flexibly expand rules, templates and reasoning patterns.
2. The method for automatic generation, intelligent verification, and error correction of law enforcement documents according to claim 1, characterized in that: The generation of semantic templates includes: performing structured text processing on basic case information, using a deep semantic understanding model to convert the case text into semantic vectors, and introducing a discriminative classification network to determine the case type; After determining the case type, cases that are semantically similar to the current case are extracted from the historical case database, and a candidate template set is formed by judging the similarity. A transformative semantic template matching algorithm is used to represent candidate templates as a set of fields. Fields are automatically adjusted through field fit scores, and highly fit fields are aggregated to form the final document template.
3. The method for automatic generation, intelligent verification, and error correction of law enforcement documents according to claim 1, characterized in that: The aforementioned entity and rule hybrid verification includes: after the document is automatically generated, a hybrid verification method is adopted, which integrates the rule engine and the entity recognition algorithm to process the document text and automatically extract key legal entities, spatiotemporal information and party relationships; Each entity is associated with a legal database and preset business rules, and the legality of attributes and relationships between entities is verified through symbolic reasoning and graph traversal algorithms. At the same time, the rule engine performs structured and process-oriented rule validation on the documents to determine whether fields are missing, whether the main body is complete, and whether the format is standardized, and stores all validation results in a structured manner.
4. The method for automatic generation, intelligent verification, and error correction of law enforcement documents according to claim 1, characterized in that: The aforementioned context-based reasoning error correction includes: for suspected errors or inconsistencies detected in the preceding verification, employing a context-based reasoning error correction algorithm to accurately locate the suspected error fields and extract their relevant contextual information, including: The case content is analyzed using a combination of factual descriptions, evidence, subjects, and other elements, along with information from historical case documents and judicial case databases. Through semantic embedding models and differential analysis, the case content is subjected to vertical and horizontal semantic reasoning. Based on the differences in fields in the reference cases and the logic of typical legal provisions in the judgment, intelligent correction suggestions are automatically generated.
5. The method for automatic generation, intelligent verification, and error correction of law enforcement documents according to claim 1, characterized in that: The aforementioned human-machine collaborative review includes: pushing the documents obtained after automatic verification and intelligent error correction to the work interface of law enforcement personnel through the business management platform; using a difference detection algorithm to highlight the differences between the original documents and the suggested modification results in a dual-document comparison method; and using a risk labeling model to generate warnings for suspected high-risk content.
6. The method for automatic generation, intelligent verification, and error correction of law enforcement documents according to claim 1, characterized in that: The aforementioned continuous learning and application scenario expansion includes: automatically collecting feedback data after human-machine review, including human judgment results, modification suggestions, actual correction content and new case examples, and encoding all feedback events into structured records; A scenario-based self-expanding learning mechanism is adopted to perform unsupervised clustering on the feedback data, summarize the high-frequency scenarios that are not covered by existing rules, and label the scenarios at the cluster boundary to extract differential elements and error patterns. We utilize incremental rule mining algorithms and content similarity-based template adaptation mechanisms to dynamically and incrementally optimize and expand the rule base, template base, and inference model. Through incremental training strategies, we continuously improve the model's adaptability to new scenarios.
7. The method for automatic generation, intelligent verification, and error correction of law enforcement documents according to claim 2, characterized in that: The process of generating a semantic template includes: further adjusting the field order and introducing necessary related fields through a structural optimization algorithm.
8. The method for automatic generation, intelligent verification, and error correction of law enforcement documents according to claim 5, characterized in that: The aforementioned human-machine collaborative review includes: law enforcement officers can adopt, reject, or manually revise suggestions through an interactive interface; each operation is recorded as an operation event and the reason for modification is prompted; and the context explanation module is called to generate a specific explanation of the reason for the suggestion.