Natural language-based visa material compliance verification method, apparatus and device, and medium

Abstract

The invention relates to a natural language-based visa material compliance verification method, apparatus and device, and a medium. The method comprises the steps of performing optical identification on a visa material image to extract a key field to generate a to-be-verified data set; analyzing the annotation entity through lexical and syntactic analysis and analyzing subjective statement emotional tendency to generate semantic annotation data; constructing an information association map, and comparing the consistency of the multi-material entities to generate a contradiction report; performing authenticity scoring and risk matching based on the credibility model and a historical case library to generate credibility analysis; according to the threshold value, the compliance / risk materials are automatically separated, and a targeted complement list is generated. The method solves the technical defects that in the prior art, multi-material entity contradictions cannot be captured, subjective statement quantitative evaluation is lacked, risk evaluation fragmentation is achieved, and the complement process is low in efficiency, automatic material contradiction recognition, accurate credibility quantification, risk systematization evaluation and intelligent complement guiding are achieved, and the visa examination processing period is greatly shortened.

Description

Technical Field

[0001] This invention relates to the field of text verification technology, specifically to a method, apparatus, equipment, and medium for verifying the compliance of visa materials based on natural language. Background Technology

[0002] In the context of increasingly frequent cross-border travel and globalization, the compliance verification of visa application materials has become a crucial aspect of international immigration management. Visa material compliance verification methods refer to the systematic process of verifying the authenticity and logical consistency of documents submitted by applicants, such as identity documents, financial documents, and travel arrangements, aiming to identify risk factors such as false materials and contradictory statements. These methods not only require verifying the authenticity of individual documents but also comprehensively analyzing the logical connections between multiple documents, such as the matching degree between bank statements and income certificates, and the reasonableness of travel arrangements and purposes. With the continuous growth in visa applications, traditional manual review methods are struggling to cope with the massive volume of materials, necessitating the introduction of intelligent verification technology to improve review efficiency and accuracy.

[0003] However, existing technical solutions have significant drawbacks: First, they cannot effectively capture inconsistencies in the description of the same entity across different documents, such as the difference between the birth date on a passport and the age on an academic certificate; second, they lack a quantitative evaluation mechanism for subjective statements, making it difficult to identify logical conflicts between itinerary arrangements and travel purposes; third, risk assessment relies on isolated case judgments and fails to systematically integrate historical visa refusal experience; and finally, they cannot automatically generate targeted supplementary requirements after identifying document defects, leading to repeated rejections. These problems result in low review efficiency and a high error rate, requiring visa applicants to undergo multiple document corrections, significantly extending the visa processing cycle. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a method, apparatus, device and medium for visa material compliance verification based on natural language that can automatically identify material inconsistencies, quantitatively assess credibility and accurately generate a supplementary document list.

[0005] The objective of this invention is achieved through the following solution:

[0006] In a first aspect, the present invention provides a method for verifying the compliance of visa application materials based on natural language, comprising the following steps:

[0007] S1: Perform optical character recognition and field extraction on the images of visa application materials submitted by the applicant, extract key fields such as name, ID number, education information and bank balance, extract key data fields from unstructured images, and generate a visa dataset to be inspected.

[0008] S2: Perform lexical and syntactic parsing on the visa dataset to be inspected, label the entities of names, places, times and amounts, and perform sentiment analysis on subjective statements containing travel purpose and itinerary to generate a semantically labeled dataset;

[0009] S3: Based on the visa dataset to be inspected and the semantic annotation dataset, construct an information association map between materials, compare the consistency of the description of the same entity in different documents, and generate a consistency check report;

[0010] S4: Based on the pre-trained credibility assessment model and historical rejection case library, the consistency check report is processed, the authenticity of each information point is scored, and risk labels are matched with the historical rejection case library to generate a credibility analysis report.

[0011] S5: Determine whether visa materials are compliant based on the credibility analysis report, match the scoring results with the preset compliance threshold, supplement and sort out the materials for fields or logical contradictions that are below the threshold, and generate a visa verification report that includes the verification results and a list of supplementary materials.

[0012] In one embodiment, S1 of the natural language-based visa material compliance verification method provided by the present invention specifically includes the following steps:

[0013] S11: Perform image prediction on the visa application materials submitted by the applicant, locate text regions using edge detection algorithms, segment tables and paragraph blocks, and generate a verification document image;

[0014] S12: Extract key fields from the verification document image, including name, ID number, education information, and bank balance, and generate a verification image dataset containing the verification fields;

[0015] S13: Perform data augmentation on the verification image dataset, perform super-resolution reconstruction on the blurred text region, extract the keyword field of the blurred text, and generate the visa dataset to be inspected.

[0016] In one embodiment, S2 of the natural language-based visa material compliance verification method provided by the present invention specifically includes the following steps:

[0017] S21: Based on a bidirectional long short-term memory network, perform lexical parsing on the visa dataset to be inspected, annotate the entities of person name, place name, time and amount, and generate entity annotation results;

[0018] S22: Perform syntactic parsing on the entity annotation results, analyze the sentence structure of the visa materials, extract the subject-verb relationship between the purpose of travel and the itinerary, and generate semantic parsing results;

[0019] S23: Perform sentiment analysis on the semantic parsing results: Calculate the sentiment polarity value of the subjective statement text through a preset sentiment polarity calculation model, mark suspicious sentiment tendencies, and generate a semantically labeled data set. The semantically labeled data set is used to indicate the potential risk level of the applicant's statement.

[0020] In one embodiment, step S21 of the natural language-based visa material compliance verification method provided by the present invention specifically includes the following steps:

[0021] S211: Perform context feature extraction processing on the visa dataset to be inspected, capture the contextual dependencies of words, and generate context feature vectors;

[0022] S212: Perform entity boundary recognition processing on the context feature vector, determine the starting position and category probability of the entity, and generate entity boundary annotations;

[0023] S213: Perform entity type annotation processing on entity boundary annotations, integrate continuous boundary annotations to form complete entities and mark their categories, and generate entity annotation results.

[0024] In one embodiment, S3 of the natural language-based visa material compliance verification method provided by the present invention specifically includes the following steps:

[0025] S31: Based on the visa dataset to be inspected and the semantically labeled dataset, perform association graph construction processing to create a graph structure with entities as nodes and logical relationships as edges, and generate an information association graph.

[0026] S32: Based on the information association graph, perform entity consistency comparison processing on the visa dataset to be inspected and the semantic annotation dataset, detect the numerical differences of the same entity in different documents, and generate a set of difference points;

[0027] S33: Perform logical verification on the set of discrepancies, apply predicate logic rules to detect time sequence contradictions and abnormal fund flow, and generate a consistency check report.

[0028] In one embodiment, S4 of the natural language-based visa material compliance verification method provided by the present invention specifically includes the following steps:

[0029] S41: Based on a pre-trained credibility assessment model, the consistency check report is processed to perform authenticity scoring, analyze the logical consistency of information points and calculate credibility scores to generate a scoring dataset.

[0030] S42: Perform risk label matching on the scoring dataset, retrieve similar risk patterns from the historical rejection case database and associate them with risk levels to generate a risk label set;

[0031] S43: Integrate the scoring dataset and risk label set, combine the credibility score and risk level to calculate the comprehensive credibility score, and generate a credibility analysis report.

[0032] In one embodiment, S5 of the natural language-based visa material compliance verification method provided by the present invention specifically includes the following steps:

[0033] S51: Perform risk separation processing on the credibility analysis report, identify information points with scores below the preset compliance threshold and high-risk labels, separate low-risk labels and identify information points with scores above the preset compliance threshold, and generate risk material set and compliance material set;

[0034] S52: Perform supplementary material generation processing on the risk material set, create a standardized material requirement list for missing proofs or contradictory explanations, and generate a supplementary material list;

[0035] S53: The results of the assessment of the compliant materials set, the results of the assessment of the risky materials set, and the supplementary materials list are reported and integrated. The compliant materials are marked as passed, the risky materials are marked as failed, and the supplementary materials requirements are integrated to generate a visa verification report. The visa verification report is used to indicate the materials that the applicant needs to supplement and the compliance assessment status of each material.

[0036] Secondly, the present invention provides a visa material compliance verification device based on natural language, which is configured with the following modules:

[0037] The visa material extraction unit is used to perform optical character recognition and field extraction on the images of visa materials submitted by the applicant, extract key fields such as name, ID number, education information and bank balance, extract key data fields from unstructured images, and generate a visa dataset to be inspected.

[0038] The semantic annotation unit is used to perform lexical and syntactic parsing on the visa dataset to be examined, annotate entities such as names, places, times and amounts, and perform sentiment analysis on subjective statements containing travel purposes and itineraries to generate a semantically annotated dataset.

[0039] The consistency check unit is used to construct an information association graph between materials based on the visa dataset to be checked and the semantic annotation dataset, compare the consistency of the description of the same entity in different documents, and generate a consistency check report.

[0040] The credibility analysis unit is used to process the consistency check report based on the pre-trained credibility assessment model and the historical rejection case library, score the authenticity of each information point, and match risk tags with the historical rejection case library to generate a credibility analysis report.

[0041] The visa verification unit is used to determine whether visa materials are compliant based on the credibility analysis report. It matches the scoring results with preset compliance thresholds, and supplements and sorts out materials for fields or logical inconsistencies that are below the threshold, generating a visa verification report that includes the verification results and a list of supplementary materials.

[0042] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the above-mentioned natural language-based visa material compliance verification methods.

[0043] Fourthly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-mentioned natural language-based visa material compliance verification methods.

[0044] In summary, the natural language-based visa material compliance verification method provided by this invention achieves multi-dimensional technological breakthroughs: Through optical character recognition and structured field extraction technologies, it can accurately capture key data fields from non-standardized image materials, solving the problem of missed key information in traditional manual data entry; with the help of lexical analysis and sentiment analysis technologies, it can achieve quantitative evaluation of subjective statements, effectively identifying logical contradictions between itinerary arrangements and travel purposes; based on information association graph construction technology, it can systematically capture differences in entity descriptions among multiple materials, achieving in-depth verification of cross-document data contradictions such as passport information and academic certificates; through a risk assessment mechanism that integrates a pre-trained credibility model and a historical visa refusal case database, it can achieve systematic integration of fragmented risk factors, significantly improving the accuracy of identifying fraudulent materials; finally, relying on threshold determination and intelligent supplementary document generation technologies, it can automatically separate compliant and risky materials and generate a targeted supplementary list to achieve closed-loop optimization of the review process.

[0045] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0046] Figure 1 A flowchart illustrating a natural language-based visa material compliance verification method provided for embodiments of this application;

[0047] Figure 2 A schematic diagram illustrating the process of generating a semantically labeled data set provided in an embodiment of this application;

[0048] Figure 3 A schematic diagram of the process for generating a credibility analysis report provided in an embodiment of this application;

[0049] Figure 4This is a schematic diagram of a natural language-based visa material compliance verification device provided in another embodiment of this application. Detailed Implementation

[0050] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred 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 provide a thorough and complete understanding of the disclosure of the invention.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0052] In one embodiment, such as Figure 1 As shown, a natural language-based method for verifying the compliance of visa application materials is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0053] S1: Perform optical character recognition and field extraction on the images of the visa application materials submitted by the applicant, extract key fields such as name, ID number, education information and bank balance, extract key data fields from unstructured images, and generate a visa dataset to be inspected.

[0054] Specifically, the system first receives various visa application materials submitted by applicants online or offline. These materials may include a variety of documents such as ID cards, passports, academic certificates, bank statements, and travel itinerary confirmations, with diverse formats and complex content. The system utilizes Optical Character Recognition (OCR) technology, trained based on deep learning algorithms, which can accurately recognize text in images. Before text recognition, the system performs a series of preprocessing operations on the image, such as converting the image to grayscale to reduce interference from color information; binarizing the image using an adaptive threshold segmentation method to clearly distinguish the text from the background and enhance text contrast; and performing layout analysis to automatically identify text areas, table areas, image areas, etc., in the image to determine the text layout for more accurate text extraction.

[0055] After image preprocessing, the system uses an OCR model to scan and recognize the text content line by line. This OCR model has been trained on a large amount of visa material image data with different styles, fonts, and languages, and can accurately recognize text information in various complex scenarios, including key fields such as name, ID number, educational background, and bank balance. The system accurately extracts the required name field from the recognized text content according to predefined field rules and semantic understanding algorithms, ensuring that the extracted name conforms to common name format specifications. For ID numbers, the system verifies and extracts them according to standard ID number encoding rules, ensuring that the extracted ID number format is correct and logical. When extracting educational background information, the system can automatically identify key elements such as school name, major, educational level, and graduation date in different educational certificate formats and integrate them into a complete educational background information field. For bank balance information, the system accurately locates the balance number portion in bank statements and, combined with contextual semantic understanding, accurately extracts key data such as the current account balance.

[0056] The extracted key field data is automatically integrated by the system and generated into a visa application dataset according to a preset data structure and format. This dataset stores key information from various visa application materials submitted by applicants in a structured manner, providing standardized data input for subsequent verification processes.

[0057] S2: Perform lexical and syntactic parsing on the visa dataset to be inspected, label the entities of names, places, times and amounts, and perform sentiment analysis on subjective statements containing travel purposes and itineraries to generate a semantically labeled dataset.

[0058] Specifically, the system uses a predefined vocabulary rule base and part-of-speech tagging model to decompose the text into independent lexical units and tag each lexical unit with its part of speech, such as noun, verb, adjective, etc. It also identifies the basic morphological features of the words, laying the foundation for subsequent syntactic analysis. Syntactic parsing, based on predefined syntactic rules and dependency relation models, deeply analyzes the grammatical structure and logical relationships between lexical units, constructing a syntactic tree of the text, thus clearly revealing the text's structural framework and semantic logic.

[0059] Meanwhile, the system utilizes named entity recognition technology, combined with pre-defined entity annotation rules and machine learning models, to accurately identify and annotate key entity information such as names, locations, times, and amounts from the parsed text. It generates unique tags for each entity, clearly defining its specific location and content within the text. For subjective statements containing travel purpose and itinerary arrangements, the system activates a sentiment analysis algorithm. This algorithm, trained on a large sentiment-annotated corpus, accurately assesses the sentiment conveyed by the text. Through sentiment analysis of subjective statements, the system can determine whether there are unreasonable, contradictory, or exaggerated sentiment expressions. For example, whether the description of the itinerary deviates significantly from common reasonable expectations, or whether the explanation of the travel purpose contains unrealistic exaggerations. The system comprehensively integrates the annotated entity information, syntactic structure, and sentiment analysis results to form a rich and rigorously structured semantically annotated dataset, generating unique tags for each entity, clearly defining its specific location and content within the text. This dataset not only includes the lexical and syntactic information of the text, but also incorporates semantic features at the entity and sentiment levels, generating unique tags for entities such as names, places, times, and amounts, clarifying their specific location and content in the text.

[0060] S3: Based on the visa dataset to be inspected and the semantically labeled dataset, construct an information association graph between materials, compare the consistency of the description of the same entity in different documents, and generate a consistency check report.

[0061] Specifically, the system utilizes graph database and knowledge graph construction technologies to build an information relationship graph among materials. Different visa application materials are treated as nodes in the graph, encompassing various types such as identity documents, financial documents, educational certificates, and travel itineraries. Each node contains corresponding material information and key fields. The system analyzes multi-dimensional features such as homology, logical relevance, and semantic similarity between entities to uncover the relationships between materials, connecting these relationships as edges in the graph. For example, the system can identify whether the name in the identity document is from the same source as the name in the educational certificate, whether the transaction records in bank statements are consistent with the amount range in the income certificate, or whether the destination in the travel itinerary is logically related to the visa application country in the passport, thus constructing a comprehensive and complex information relationship network.

[0062] In constructing the information association graph, the system comprehensively utilizes various data comparison algorithms and technologies to ensure the accuracy of the comparison results. First, the system performs a basic comparison of the representations of the same entities in different documents, such as directly comparing the literal similarity of the text content. For entities like names and place names, the system uses string matching algorithms to calculate the similarity score of the entity names. If the score is lower than a preset similarity threshold, an inconsistency in representation is initially determined. Simultaneously, the system considers differences in entity format and language habits. For example, for different date formats (such as "YYYY-MM-DD" and "DD / MM / YYYY"), the system performs format conversion before comparison to avoid misjudgments due to formatting issues. Furthermore, the system incorporates semantic understanding technology to perform deep semantic analysis of entity representations. For instance, for monetary expressions like "two thousand yuan" and "2,000 yuan," the system can identify their semantic equivalence, thus avoiding incorrect judgments of inconsistency due to different representation formats. After completing the consistency comparison, the system will record in detail the differences in the description of each entity with inconsistencies in different documents, the specific location of the document, the type of material involved, and generate a consistency check report.

[0063] This report not only includes detailed information on inconsistent entities but also presents an information relationship map between materials, intuitively demonstrating the connections and distribution of inconsistencies between different materials. It generates unique markers for entities such as names, places, times, and amounts, clearly defining their specific location and content within the text. This report provides crucial evidence and clues for subsequent risk identification and assessment, helping to gain a deeper understanding of potential problems and risks in visa application materials.

[0064] S4: Based on a pre-trained credibility assessment model and a historical rejection case library, the consistency check report is processed, each information point is scored for authenticity, and risk labels are matched with the historical rejection case library to generate a credibility analysis report.

[0065] Specifically, upon receiving the consistency check report, the system invokes a pre-trained credibility assessment model. This model, meticulously trained using deep learning algorithms and machine learning techniques based on massive amounts of historical visa application data and known real-world cases, possesses a powerful ability to assess the authenticity of visa application information. The system inputs each information point from the consistency check report into the credibility assessment model. The model, using its complex internal assessment algorithm and refined weight allocation mechanism, performs in-depth analysis and calculation on each information point, thereby deriving an authenticity score. This score quantitatively and intuitively reflects the credibility of the information point; for example, a higher score indicates that the information point is closer to the truth, while a lower score suggests the possibility of falsification or inaccuracy.

[0066] At the same time, the system accesses a historical visa refusal case database, a vast knowledge base that collects and organizes past visa application cases that were refused for various reasons. Each case has undergone detailed analysis and organization, including the specific reasons for the refusal, the risk information involved, and the problems in the materials. Furthermore, it has been precisely classified and labeled according to different risk types, such as the risk of falsified materials, unreasonable itinerary, and inconsistent financial status.

[0067] The system performs a deep matching comparison between the consistency check report and a historical refusal case database. Using text similarity calculation algorithms, pattern matching techniques, and semantic analysis methods, it identifies refusal cases with inconsistencies or anomalous information points similar to those in the current report. The system carefully analyzes these matched cases, assigning corresponding risk tags to each information point in the current visa application materials based on the refusal reason and risk type. Examples include "High Risk of Document Forgery," "Medium Risk of Unreasonable Itinerary," and "Low Risk of Inconsistent Financial Status." Unique markers are generated for entities such as names, locations, dates, and amounts, clearly indicating their specific location and content within the text. After completing the authenticity scoring and risk tag matching for each information point, the system integrates these results and generates a detailed and well-organized credibility analysis report according to a preset report format and logical structure.

[0068] This report not only includes scores and risk tags for each information point, but also provides a comprehensive assessment of the overall credibility of visa application materials. It generates unique markers for entities such as names, locations, dates, and amounts, clearly defining their specific location and content within the text. Furthermore, the report offers targeted risk warnings and suggestions, enabling visa review personnel or systems to more comprehensively and accurately understand the credibility of visa application materials. This provides a scientific and reliable basis for the final judgment of the compliance of visa application materials.

[0069] S5: Determine whether visa materials are compliant based on the credibility analysis report, match the scoring results with the preset compliance threshold, supplement and sort out the materials for fields or logical contradictions that are below the threshold, and generate a visa verification report that includes the verification results and a list of supplementary materials.

[0070] Specifically, the system operates on a pre-defined compliance threshold system. This system was developed and refined based on years of visa review experience and industry standards, taking into account the importance and risk characteristics of different types of information points. The threshold system sets compliance judgment standards for information points such as identity information, financial information, educational information, and travel information, as well as various risk labels such as "risk of document falsification" and "risk of unreasonable travel." For example, the authenticity score of identity information must reach X points or higher, the reasonableness score of travel information must reach Y points or higher, and the completeness score of financial information must reach Z points or higher. The system precisely matches and compares the score of each information point in the credibility analysis report with the corresponding compliance threshold. By comparing the relationship between the score of each information point and the threshold, the system determines whether the information point meets the compliance requirements.

[0071] For information points with scores below the threshold, the system identifies them as fields with compliance risks and records in detail their specific location in the visa application materials, the types of materials involved, the reasons for non-compliance, and the corresponding risk tags and scores. Simultaneously, for cases with logical contradictions, the system uses logical reasoning algorithms and semantic analysis technology to conduct in-depth analysis and clarify the specific links and causes of the contradictions. For example, the system analyzes the logical connection between the destination and the purpose of travel in the itinerary document to determine if there are any unreasonable aspects; or it analyzes the matching degree between the source of funds and the expenditure plan in the financial documentation to identify potential financial risks.

[0072] Simultaneously, the system summarizes and organizes the verification results of all visa application materials, generating a visa verification report that includes the verification results and a list of required supplementary materials. This report is presented in a clear and standardized format. First, it clearly indicates the overall compliance assessment result of the visa application materials, such as "partially compliant, supplementary materials required" or "temporarily non-compliant, materials need to be resubmitted," etc. Next, it details each non-compliant field and its corresponding compliance assessment basis, including credibility score, the degree to which it falls below the threshold, and related risk labels. Finally, it includes a complete list of required supplementary materials, with each item in the list explained in detail to ensure that the applicant clearly understands the specific content and requirements for supplementary materials. The report generation process strictly follows the preset template and format specifications, ensuring the accuracy and completeness of the information and providing clear guidance and basis for the subsequent processing of the visa application.

[0073] In summary, the natural language-based visa material compliance verification method provided by this invention achieves multi-dimensional technological breakthroughs: Through optical character recognition and structured field extraction technologies, it can accurately capture key data fields from non-standardized image materials, solving the problem of missed key information in traditional manual data entry; with the help of lexical analysis and sentiment analysis technologies, it can achieve quantitative evaluation of subjective statements, effectively identifying logical contradictions between itinerary arrangements and travel purposes; based on information association graph construction technology, it can systematically capture differences in entity descriptions among multiple materials, achieving in-depth verification of cross-document data contradictions such as passport information and academic certificates; through a risk assessment mechanism that integrates a pre-trained credibility model and a historical visa refusal case database, it can achieve systematic integration of fragmented risk factors, significantly improving the accuracy of identifying fraudulent materials; finally, relying on threshold determination and intelligent supplementary document generation technologies, it can automatically separate compliant and risky materials and generate a targeted supplementary list to achieve closed-loop optimization of the review process.

[0074] The verification method provided in this application can achieve the core effects of eliminating blind spots in the consistency verification of multi-source materials, breaking through the bottleneck of quantitative assessment of subjective statements, integrating fragmented risk assessment experience, and building an automated supplementary document guidance system. It fundamentally solves systemic defects in visa review such as material inconsistencies and omissions, high reliance on manual review, and inefficient supplementary document process, and provides intelligent verification support for the entire chain of immigration management.

[0075] In one embodiment, S1 of the natural language-based visa material compliance verification method provided by the present invention specifically includes the following steps:

[0076] S11: Perform image prediction on the visa application materials submitted by the applicant, locate text regions using edge detection algorithms, segment tables and paragraph blocks, and generate a verification document image.

[0077] Specifically, the system employs edge detection algorithms to analyze images and accurately locate text regions. Edge detection algorithms effectively distinguish text from background by identifying abrupt changes in pixel intensity within the image, thus determining the specific location of the text. The system performs a comprehensive scan of the image, identifying the text portions and differentiating them from other elements. After locating the text regions, the system further processes them, segmenting them into table and paragraph blocks. This process involves in-depth analysis of the text layout. The system identifies table borders, cell structure, and paragraph start and end markers to divide complex text content into multiple logical units. For example, the system can identify rows and columns in a table and extract the text content of each cell individually; for paragraph text, the system segments based on features such as line spacing and text alignment, ensuring that each paragraph block can be processed independently. Through this refined segmentation operation, the system generates a verification document image that highlights the text regions, providing clear and accurate input for subsequent key field extraction operations.

[0078] S12: Extract key fields from the verification document image, including name, ID number, education information, and bank balance, and generate a verification image dataset containing the verification fields.

[0079] Specifically, the system relies on pre-defined rules and pattern matching technology to identify and extract key fields such as name, ID number, educational background, and bank balance. The system accurately locates these key fields by analyzing the text's format, keywords, and semantic information. For example, the system identifies the name field based on common name formats; identifies the ID number field based on the fixed number of digits and encoding rules; extracts educational background information based on common formats and keywords in educational certificates; and extracts the bank balance field based on the numerical format and organization identifier of the bank balance.

[0080] Preferably, the system can use Optical Character Recognition (OCR) technology to convert the text in the image into editable text, and then scan and analyze the text line by line. During the extraction process, the system also performs data verification to ensure that the extracted fields conform to the corresponding format and logical specifications. For example, the system will verify whether the number of digits in the ID number is correct and whether the date format conforms to the standard, to ensure the accuracy of the extracted data. Finally, the system integrates the extracted key fields to generate a verification image dataset containing verification fields. This dataset contains all the key information, providing a foundation for subsequent data processing and analysis.

[0081] S13: Perform data augmentation on the verification image dataset, perform super-resolution reconstruction on the blurred text region, extract the keyword field of the blurred text, and generate the visa dataset to be inspected.

[0082] Specifically, super-resolution reconstruction technology generates higher-resolution images by analyzing the features of blurred images and combining them with deep learning algorithms. The system first identifies and locates blurred text regions, then uses algorithms to analyze and process these regions at the pixel level. By increasing the image's pixel density, sharpening edges, and enhancing contrast, the system can generate clearer text images. After completing super-resolution reconstruction, the system performs key field extraction on the enhanced image again, ensuring that all keyword fields of the blurred text can be accurately identified and extracted. The system improves the accuracy of blurred text recognition by using an improved optical character recognition (OCR) algorithm, combined with contextual analysis and a language model.

[0083] For example, the system can infer possible words or phrases based on the context of the text, or use language models to evaluate the reasonableness of the recognition results, thereby correcting recognition errors. These keyword fields, combined with the previously extracted clear text fields, form a complete dataset of visa applications to be checked. This dataset not only contains accurate information on all key fields, but also improves the quality and completeness of the data through data augmentation, providing high-quality data support for subsequent visa document verification processes and ensuring the efficiency and accuracy of the entire verification process.

[0084] In one embodiment, such as Figure 2 As shown, S2 of the visa material compliance verification method based on natural language provided by the present invention specifically includes the following steps:

[0085] S21: Based on a bidirectional long short-term memory network, perform lexical parsing on the visa dataset to be inspected, annotate the entities of person name, place name, time and amount, and generate entity annotation results.

[0086] Specifically, upon receiving the visa dataset to be inspected, the system invokes a Bidirectional Long Short-Term Memory Network (BiLSTM) for lexical parsing. BiLSTM is a powerful sequence processing model that can simultaneously utilize forward and backward information of a sequence, thereby providing accurate context-sensitive representations of each word in the text. The system inputs the text content from the visa dataset sentence by sentence into the BiLSTM network. The network vectorizes each word and captures the long and short-term dependencies between words through the hidden layer's state update mechanism.

[0087] After encoding the entire text sequence, the system uses a pre-trained Named Entity Recognition (NER) component to process the encoded feature vector, identifying and labeling key entities such as names, locations, times, and amounts in the text. The NER component, trained on a large amount of labeled data, can accurately identify different types of entities and assign corresponding labels to each entity. The system integrates this labeled entity information to generate entity labeling results, creating unique tags for entities such as names, locations, times, and amounts, clearly indicating their specific location and content in the text. Preferably, the entity labeling results are generated through the following steps:

[0088] S211: Perform context feature extraction processing on the visa dataset to be inspected, capture the contextual dependencies of words, and generate context feature vectors.

[0089] Specifically, the system processes the text using a BiLSTM network. The BiLSTM network can simultaneously consider the contextual information surrounding each word, effectively capturing long-distance dependencies. The system segments the text content of the visa dataset into words and inputs it into the BiLSTM network. The embedding layer inside the network first converts each word into a fixed-length vector representation, which reflects the semantic and grammatical features of the word. Subsequently, the vectors are processed through forward and backward Long Short-Term Memory (LSTM) network layers. The forward layer processes words sequentially from the beginning to the end of the text, capturing the features of words in the forward order context; the backward layer processes words from the end to the beginning, capturing the features of words in the reverse order context. In this way, each word, after processing, obtains a feature representation that integrates its surrounding context information.

[0090] During feature extraction, the system utilizes pre-trained word vector models, such as Word2Vec or GloVe, which provide semantic features of words. Simultaneously, the system incorporates character-level embeddings to capture morphological features of words. Through this multi-layered feature extraction approach, the system can generate richer contextual feature vectors.

[0091] Finally, the system integrates the context feature vectors of each word to form a context feature vector set. This set is stored in matrix form, where each row represents the context feature vector of a word. These feature vectors not only contain the semantic information of the word itself, but also incorporate the semantic dependencies in its context, providing strong semantic support for subsequent entity boundary recognition processing.

[0092] S212: Perform entity boundary recognition processing on the context feature vector to determine the starting position and category probability of the entity and generate entity boundary annotations.

[0093] Specifically, the system employs a deep learning-based sequence labeling model to perform entity boundary recognition. A Conditional Random Field (CRF) layer is integrated into the model to optimize the labeling results for the entire sequence. The model first takes the contextual feature vector as input and transforms and abstracts the features through multiple neural network layers, thereby capturing the sequence information and contextual patterns contained within the feature vector. In this process, the system utilizes a predefined set of entity category labels, which cover various key entities that may appear in visa application materials, such as names, locations, times, and amounts.

[0094] For each word's feature vector, the model outputs a probability distribution representing the probability that the word belongs to the starting position or an internal position of each predefined entity category. For example, the model predicts that the word "Zhang San" has a 0.95 probability of belonging to the starting position (B-PERSON) of a person entity, while the probability of belonging to other categories or non-entity entities is relatively low. By learning from a large amount of labeled data, the system can accurately identify these probability distribution patterns.

[0095] To improve the accuracy of entity boundary recognition, the system employs a cross-entropy loss function during training. This allows the model to effectively adjust its parameters to minimize the difference between the predicted probability distribution and the true label distribution. Simultaneously, the system introduces label smoothing techniques to prevent the model from overfitting to noise in the training data, thereby improving its generalization ability in practical applications.

[0096] When determining the starting position of entity boundaries, the system considers not only the probability prediction of individual words but also the prediction results of adjacent words, as well as predefined entity boundary rules. For example, if multiple consecutive words are predicted to belong to the same entity category's internal location (I-category), the system integrates them into a potential entity fragment and further verifies its integrity. Finally, the system integrates the entity boundary recognition results into an entity boundary annotation dataset. This dataset stores the entity boundary information for each word in a structured manner, including the starting position and category probability, providing detailed boundary information for subsequent entity type annotation processing.

[0097] S213: Perform entity type annotation processing on entity boundary annotations, integrate continuous boundary annotations to form complete entities and mark their categories, and generate entity annotation results.

[0098] Specifically, the system first scans the entity boundary annotation dataset to identify consecutive boundary annotation sequences. For example, the system detects consecutive "B-PERSON" (starting position of a person's name entity) and "I-PERSON" (internal position of a person's name entity) annotations and integrates them into a complete person's name entity. During the integration process, the system utilizes a predefined entity type rule base, which contains naming conventions and common patterns for various types of entities. For example, person names typically consist of two or three Chinese characters, and place names may include administrative region names such as provinces, cities, and districts. The system verifies whether the integrated entity conforms to these rules to ensure the integrity and consistency of the entities.

[0099] For each integrated entity, the system labels its category, such as name, place, time, amount, etc. Category labeling is based on the category probability of the entity boundaries and predefined category mapping rules. For example, a sequence of entities starting with "B-LOC" and ending with "I-LOC" will be labeled as a place name entity. The system also considers the contextual information of the entity's appearance in this process to further improve the accuracy of category labeling. For example, if a time entity appears in the context of "planned to go to...", the system will combine this contextual information to confirm the relevance of the time entity to the travel arrangements.

[0100] In addition, the system utilizes external knowledge bases, such as place name databases and organization name databases, to verify and supplement the identified entities. For example, for entities tagged as place names, the system queries the place name database to verify their existence and whether they conform to the standard place name format. For potential place names that are not explicitly listed but conform to the naming pattern, the system makes inferences and tags them based on a probability model.

[0101] Finally, the system integrates the entity type annotation results into an entity annotation result dataset. This dataset stores each entity and its corresponding category information in a structured manner, providing detailed entity information for subsequent syntactic parsing and semantic analysis. The system also performs quality evaluation on the entity annotation results, calculating metrics such as precision, recall, and F1 score between the annotated results and the validation dataset to ensure the reliability and accuracy of the annotation results.

[0102] S22: Perform syntactic parsing on the entity annotation results, analyze the sentence structure of the visa materials, extract the subject-verb relationship between the purpose of travel and the itinerary, and generate semantic parsing results.

[0103] Specifically, the system performs syntactic parsing based on entity annotation results, analyzing the grammatical structure of sentences in visa application materials and clarifying the dependency relationships and syntactic roles between words. The system employs a dependency parser based on a transfer neural network to further analyze the entity-annotated text. By identifying dependency relationships between words, the dependency parser constructs a dependency tree for the sentence, thus clearly revealing the sentence's structural framework and semantic logic.

[0104] During syntactic parsing, the system pays special attention to sentences related to travel purpose and itinerary arrangements. By analyzing the subject-verb relationships in these sentences, it extracts the core semantic information of travel purpose and itinerary arrangements. The system identifies the subject and predicate of the travel purpose, as well as the key actions and entities involved in the itinerary arrangements. Through this syntactic analysis, the system can gain a deeper understanding of the logical relationships and semantic content stated in visa application materials, generating unique tags for entities such as names, places, times, and amounts, clarifying their specific location and content in the text.

[0105] S23: Perform sentiment analysis on the semantic parsing results: Calculate the sentiment polarity value of the subjective statement text through a preset sentiment polarity calculation model, mark suspicious sentiment tendencies, and generate a semantically labeled data set. The semantically labeled data set is used to indicate the potential risk level of the applicant's statement.

[0106] Specifically, the system utilizes a pre-defined sentiment polarity calculation model, which is based on a deep learning architecture and trained on a large amount of sentiment-annotated text data, effectively capturing the sentiment features in the text.

[0107] The system first segments the subjective statements in the semantic parsing results into individual sentiment analysis units. Then, it extracts sentiment features from the words in each unit. The system locates the words in a pre-trained sentiment lexicon, which stores a large number of words and their corresponding sentiment polarity and intensity values. Simultaneously, the system analyzes the contextual collocation patterns of the words; for example, the appearance of negation words reverses the sentiment polarity of words, while degree adverbs strengthen or weaken sentiment intensity. Next, the system inputs the extracted sentiment features into a sentiment polarity calculation model. The model processes the features through multiple neural network layers, including embedding layers, recurrent layers, and fully connected layers. The embedding layer converts the sentiment features of words into vector representations; the recurrent layer captures the sentiment change trends in the text sequence; and the fully connected layer finally outputs the sentiment polarity value of the text unit, which is typically represented as a continuous numerical value, such as between -1 and 1, with negative values ​​representing negative sentiment, positive values ​​representing positive sentiment, and 0 representing neutral sentiment.

[0108] The system labels suspicious sentiment based on the magnitude of sentiment polarity values. By setting a sentiment intensity threshold, if the sentiment polarity value of a text unit exceeds the normal range, it is marked as suspicious. For example, in visa application materials, if the applicant's description of their travel purpose uses excessively exaggerated positive sentiment vocabulary, the system may mark that part as suspicious because it may not match the true purpose of travel. Finally, the system integrates the sentiment analysis results into a semantically labeled dataset. This dataset not only contains the sentiment polarity values ​​and suspicious sentiment markers of the text but also retains the semantic and syntactic structural information of the original text. The semantically labeled dataset is used to indicate the potential risk level of the applicant's statement, providing a basis for subsequent risk assessment and decision-making, ensuring that the entire processing flow comprehensively and accurately reflects the semantic and sentiment characteristics of visa application materials.

[0109] The aforementioned natural language-based visa application compliance verification method utilizes a bidirectional long short-term memory network to achieve context-sensitive entity annotation, accurately identifying key entities such as names and place names, thus resolving the omission issue in complex texts encountered by traditional rule-based matching. Combined with deep subject-verb extraction through syntactic parsing, it systematically captures the logical connection between travel purpose and itinerary, enabling structured verification of itinerary rationality. Furthermore, based on a sentiment polarity model, it quantitatively analyzes subjective statements, overcoming the limitations of human experience and achieving objective risk assessment of sentiment tendencies. This method achieves three key technical effects: first, it eliminates the fragmentation of entity recognition in cross-document scenarios, ensuring no key information is missed; second, it deconstructs logical contradictions in complex sentences, addressing the blind spots of traditional review regarding implicit conflicts; and third, it transforms ambiguous sentiment expressions into quantifiable risk indicators, overcoming the bottleneck of subjective text assessment lacking objective basis and providing semantic-level standardized support for visa application credibility determination.

[0110] In one embodiment, S3 of the natural language-based visa material compliance verification method provided by the present invention specifically includes the following steps:

[0111] S31: Based on the visa dataset to be inspected and the semantically labeled dataset, perform association graph construction processing to create a graph structure with entities as nodes and logical relationships as edges, and generate an information association graph.

[0112] Specifically, the system identifies each entity in the dataset as a node in a graph structure. These entities include key information such as names, locations, times, and amounts. Using deep learning and natural language processing techniques, the system analyzes the logical relationships between entities and connects these relationships as edges to the corresponding nodes, creating a graph structure with entities as nodes and logical relationships as edges. The system utilizes graph database technology to store these nodes and edges, forming an information association graph. During the construction process, the system considers various relationships between entities, such as belonging, association, and inclusion, to ensure that the graph comprehensively reflects the information associations in the visa application materials. The system uses complex algorithms and rules to analyze and verify the relationships between entities, ensuring the accuracy and completeness of the information association graph.

[0113] S32: Based on the information association graph, perform entity consistency comparison processing on the visa dataset to be inspected and the semantic annotation dataset, detect the numerical differences of the same entity in different documents, and generate a set of difference points.

[0114] Specifically, the system detects numerical differences of the same entity across different documents by traversing an information association graph. The system employs a combination of exact and fuzzy matching to compare entity values. Exact matching detects completely identical values, while fuzzy matching handles potential format differences or approximations. The system records all detected numerical differences, generating a set of discrepancy points. This set details the entity type, document, and specific value for each discrepancy point, generating unique markers for entities such as names, locations, times, and amounts, clearly indicating their location and content within the text. The system utilizes efficient algorithms to process large amounts of data rapidly, ensuring the accurate and timely generation of the discrepancy point set.

[0115] S33: Perform logical verification on the set of discrepancies, apply predicate logic rules to detect time sequence contradictions and abnormal fund flow, and generate a consistency check report.

[0116] Specifically, after acquiring the set of discrepancies, the system performs logical verification to detect potential issues such as temporal inconsistencies and abnormal fund flows. The system uses predicate logic rules to reason and verify each discrepancy in the set. For detecting temporal inconsistencies, the system applies time logic rules. For example, the system verifies whether the "planned entry date" in the visa application form is earlier than the "planned departure date," and whether the events in the itinerary are arranged in logical order. The system constructs a time series model, arranging time-related entities in chronological order and checking for inverted time points, unreasonable time intervals, and other issues within the sequence.

[0117] For detecting anomalies in fund flows, the system applies logical rules regarding fund transactions. The system checks whether the transaction amounts and times in bank statements match the balances in the financial statements. For example, the system verifies whether the account balance is sufficient to cover the budgeted expenses during the visa application period, and whether there are any unusual large inflows or outflows of funds within a short period. The system integrates the results of these logical verifications into a consistency check report. The report details the logical verification results for each discrepancy, including whether there are any issues such as contradictory timelines or abnormal fund flows. For detected logical inconsistencies, the system provides a detailed description, including the entities involved, document locations, and specific details of the logical rule violations. Simultaneously, the system assesses the overall consistency of the visa application materials based on the severity of the discrepancies and the scope of the logical contradictions, generating a consistency score. The consistency check report provides objective and comprehensive evidence for subsequent visa application reviews, helping reviewers quickly identify potential risks and improve review efficiency and accuracy.

[0118] In one embodiment, such as Figure 3 As shown, S4 of the visa material compliance verification method based on natural language provided by the present invention specifically includes the following steps:

[0119] S41: Based on a pre-trained credibility assessment model, the consistency check report is processed to perform authenticity scoring, analyze the logical consistency of information points and calculate credibility scores, and generate a scoring dataset.

[0120] Specifically, after obtaining the consistency check report, the system initiates an authenticity scoring process based on a pre-trained credibility assessment model. The credibility assessment model is a neural network architecture built using deep learning technology, trained on large-scale visa application data labeled with authenticity and risk levels. It can accurately analyze the logical consistency of textual information and calculate credibility scores. The system first processes the text content in the consistency check report into sentences and words, decomposing it into multiple information point units. Each information point unit contains a specific entity and its representation in different documents. For example, an information point unit might involve the applicant's date of birth information provided in different materials. The system inputs these information point units one by one into the credibility assessment model. The model contains multiple neural network layers used to extract textual features and capture the logical relationships between information points. The model assesses authenticity by analyzing the textual features of information points, such as word choice, sentence structure, and information completeness, as well as the logical consistency between information points, such as the reasonableness of the time sequence and the matching degree of numerical values. For example, the model checks whether the applicant's itinerary matches their travel purpose and whether their financial proof supports their travel budget. The model ultimately outputs a truthfulness score for each information point, typically ranging from 0 to 1, where 0 represents completely unreliable and 1 represents completely reliable. The system integrates these scores into a scoring dataset, providing a quantitative basis for subsequent risk label matching.

[0121] S42: Perform risk label matching on the scoring dataset, retrieve similar risk patterns from the historical visa refusal case database and associate them with risk levels to generate a risk label set.

[0122] Specifically, the system accesses a historical visa refusal case database, which contains a large amount of historical visa application data, covering various reasons for refusal and risk patterns, such as false documents, unreasonable itineraries, and insufficient funds. The system uses predefined risk pattern matching rules and machine learning algorithms to search the historical refusal case database for risk patterns similar to the current scoring dataset. The system first filters low-scoring information points in the scoring dataset; these low-scoring information points are usually potential risk points, such as inconsistent birth dates or insufficient proof of funds. The system compares these low-scoring information points with risk patterns in the historical refusal case database, finding matching risk patterns by calculating similarity. Similarity calculation is based on the content features of the information points, the context, and the historical performance of the risk patterns. For each matched risk pattern, the system associates it with a corresponding risk level. Risk levels are typically divided into low, medium, and high levels, with specific grading criteria based on statistical analysis of the historical refusal case database and knowledge definitions from domain experts. For example, the risk pattern of insufficient proof of funds might be associated with a medium risk level, while the risk pattern of false documents might be associated with a high risk level. The system integrates the matched risk tags and their corresponding risk levels into a risk tag set, providing a risk-level assessment basis for subsequent comprehensive credibility calculations.

[0123] S43: Integrate the scoring dataset and risk label set, combine the credibility score and risk level to calculate the comprehensive credibility score, and generate a credibility analysis report.

[0124] Specifically, the system employs a weighted fusion algorithm to comprehensively consider both credibility scores and risk levels. The algorithm assigns weights to both the credibility score and risk level based on predefined weight coefficients, determined using statistical analysis of historical data and the experience of domain experts. First, the system normalizes the credibility score of each information point in the scoring dataset to ensure that scores from different ranges can be compared on the same scale. Then, the system converts the risk levels in the risk tag set into numerical form; for example, low risk corresponds to a value of 0.3, medium risk to 0.5, and high risk to 0.7. The system then performs a weighted sum of the normalized credibility score and risk level value for each information point based on the predefined weight coefficients to obtain a comprehensive credibility score. Finally, the system generates a credibility analysis report, which includes detailed information such as the credibility score for each information point, the matched risk tags and their risk levels, and the comprehensive credibility score. The report also provides an overall credibility assessment, helping reviewers quickly understand the authenticity and risk profile of visa application materials. This provides an objective and comprehensive basis for visa application approval decisions, ensuring the efficiency and accuracy of the review process while reducing potential risks.

[0125] The natural language-based visa application compliance verification method provided in this application achieves in-depth analysis of the logical consistency of information points through a pre-trained credibility model, overcoming the limitations of traditional manual verification in identifying implicit contradictions. Combined with a risk label matching mechanism from a historical refusal case database, it systematically integrates fragmented review experience to achieve accurate mapping of risk patterns. The comprehensive calculation model integrating credibility and risk factors overcomes the bias of single-dimensional assessments, achieving a systematic integration of multi-source risk assessment indicators. This method addresses three major technical bottlenecks: first, it eliminates logical blind spots in the assessment of document authenticity, improving the comprehensiveness of contradiction detection; second, it transforms discrete historical cases into a structured risk knowledge base, overcoming barriers to experience transmission; and third, it constructs a quantitatively controllable comprehensive assessment system, resolving the disconnect between subjective judgment and objective facts in traditional review, providing traceable and verifiable assessment basis for visa application compliance decisions.

[0126] In one embodiment, S5 of the natural language-based visa material compliance verification method provided by the present invention specifically includes the following steps:

[0127] S51: Perform risk separation processing on the credibility analysis report, identify information points with scores below the preset compliance threshold and high-risk labels, separate low-risk labels and identify information points with scores above the preset compliance threshold, and generate risk material sets and compliance material sets.

[0128] Specifically, the system reviews each information point in the report one by one, judging its credibility based on a preset compliance threshold. This threshold, determined using historical visa review data and domain expert experience, differentiates the risk level of each information point. By comparing the credibility score of each information point with the compliance threshold, the system accurately identifies those scoring below the threshold. Simultaneously, the system analyzes the tags in the risk tag set, filtering out high-risk tags. The definition of high-risk tags is based on statistical analysis of historical visa refusal cases and is typically related to issues such as false information and significant logical contradictions.

[0129] The system integrates the materials corresponding to these potential risk points and high-risk labels to generate a risk material set. This risk material set details the location, content, score, and corresponding risk label for each risk information point, providing a basis for subsequent risk assessment and supplementary material requirement analysis. For information points with scores higher than a preset compliance threshold and their corresponding low-risk labels, the system separates them and integrates them to generate a compliance material set. This compliance material set also contains detailed information about these information points, providing support for the compliance section of the final visa verification report. During processing, the system ensures that each information point is accurately categorized to avoid omissions or misjudgments, thereby guaranteeing the accuracy and reliability of risk separation processing.

[0130] S52: Perform supplementary material generation processing on the risk material set, create a standardized material requirement list for missing proofs or contradictory explanations, and generate a supplementary material list.

[0131] Specifically, the system conducts a detailed analysis of each risk information point in the risk material set, identifying supplementary supporting documents or inconsistencies requiring further explanation. Based on a predefined visa review rule base and supplementary material guidelines, the system creates specific supplementary material requirements for each risk information point. For example, if a risk information point involves insufficient proof of funds, the system will generate requirements for supplementary bank statements, proof of the source of funds, etc., according to the requirements in the rule base. The creation of the supplementary material list follows a standardized format to ensure the completeness and accuracy of the information. The system also optimizes the supplementary material list, removing duplicate or unnecessary requirements to ensure the list is concise and practical. During the generation of the supplementary material list, the system references common supplementary material cases from historical visa approvals and combines them with the specific circumstances of the current application to generate a targeted and feasible supplementary material list. Finally, the system generates a supplementary material list that details the materials the applicant needs to supplement and their specific requirements, providing clear guidance so that applicants can quickly and accurately prepare and submit the required supplementary materials, improving the efficiency and success rate of visa applications.

[0132] S53: The results of the assessment of the compliant materials set, the results of the assessment of the risky materials set, and the supplementary materials list are reported and integrated. The compliant materials are marked as passed, the risky materials are marked as failed, and the supplementary materials requirements are integrated to generate a visa verification report. The visa verification report is used to indicate the materials that the applicant needs to supplement and the compliance assessment status of each material.

[0133] Specifically, the system integrates the assessment results of the compliant materials set, the risk materials set, and the supplementary materials list. Following a preset report format and logical structure, the system integrates the information from the compliant materials set into the compliance section of the visa verification report, clearly marking these materials as approved. Simultaneously, the system integrates the information from the risk materials set into the risk section of the report, marking these materials as rejected and detailing the reasons for rejection, such as the credibility score of the information points being lower than the compliance threshold, logical contradictions, or risk patterns similar to historical visa refusal cases. The system also integrates the supplementary materials list into the report, clearly listing the materials the applicant needs to supplement and their specific requirements, ensuring the applicant clearly understands the necessary actions. The visa verification report is generated following strict format specifications and content completeness requirements, ensuring the report accurately and comprehensively reflects the compliance assessment status and supplementary needs of the visa application materials. The final visa verification report generated by the system provides crucial decision support for the subsequent processing of visa applications, helping reviewers make efficient and accurate approval decisions, while also providing clear guidance to applicants to supplement the required materials in a timely manner, improving the success rate of visa applications. The entire report integration and processing process is completed automatically by the system, ensuring the accuracy and consistency of information and providing strong support for the standardization and normalization of the visa application process.

[0134] Preferably, such as Figure 4 As shown, the present invention provides a visa material compliance verification device 600 based on natural language, which is configured with the following modules:

[0135] The visa material extraction unit 610 is used to perform optical character recognition and field extraction on the images of visa materials submitted by the applicant, extract key fields such as name, ID number, education information and bank balance, extract key data fields from unstructured images, and generate a visa dataset to be inspected.

[0136] Semantic annotation unit 620 is used to perform lexical and syntactic parsing on the visa dataset to be examined, annotate names, place names, time and amount entities, and perform sentiment analysis on subjective statements containing travel purpose and itinerary, generating a semantically annotated dataset.

[0137] The consistency check unit 630 is used to construct an information association graph between materials based on the visa dataset to be checked and the semantic annotation dataset, compare the consistency of the description of the same entity in different documents, and generate a consistency check report.

[0138] The credibility analysis unit 640 is used to process the consistency check report based on the pre-trained credibility assessment model and the historical rejection case library, score the authenticity of each information point, and match risk tags with the historical rejection case library to generate a credibility analysis report.

[0139] The visa verification unit 650 is used to determine whether visa materials are compliant based on the credibility analysis report. It matches the scoring results with preset compliance thresholds, and supplements and sorts out materials for fields or logical inconsistencies that are below the threshold, generating a visa verification report that includes the verification results and a list of supplementary materials.

[0140] In summary, the visa material compliance verification device based on natural language provided by this invention achieves multi-dimensional technological breakthroughs: Through optical character recognition and structured field extraction technologies, it can accurately capture key data fields from non-standardized image materials, solving the problem of easily missing key information in traditional manual data entry; Utilizing lexical analysis and sentiment analysis technologies, it can achieve quantitative evaluation of subjective statements, effectively identifying logical contradictions between itinerary arrangements and travel purposes; Based on information association graph construction technology, it can systematically capture differences in entity descriptions among multiple materials, achieving in-depth verification of cross-document data contradictions such as passport information and academic certificates; Through a risk assessment mechanism that integrates a pre-trained credibility model and a historical visa refusal case database, it can achieve systematic integration of fragmented risk factors, significantly improving the accuracy of identifying fraudulent materials; Finally, relying on threshold determination and intelligent supplementary document generation technologies, it can automatically separate compliant and risky materials and generate a targeted supplementary list to achieve closed-loop optimization of the review process.

[0141] Preferably, the visa material retrieval unit 610 provided in this application is configured with the following units:

[0142] The document image preprocessing unit is used to perform image prediction on visa material images, locate text regions through edge detection algorithms, segment tables and paragraph blocks, and generate verification document images.

[0143] The key field extraction unit is used to extract key fields from the verification document image, such as name, ID number, education information and bank balance, and generate a verification image dataset containing the verification fields;

[0144] The data augmentation and reconstruction unit is used to perform data augmentation on the verification image dataset, perform super-resolution reconstruction on blurred text regions and extract key fields to generate a visa dataset to be inspected.

[0145] Preferably, the semantic annotation unit 620 provided in this application is configured with the following units:

[0146] The entity annotation unit is used to perform lexical parsing on the visa dataset to be inspected based on a bidirectional long short-term memory network, annotate entities such as names, places, times and amounts, and generate entity annotation results.

[0147] The semantic parsing unit is used to perform syntactic parsing on entity annotation results, analyze the sentence structure of visa materials, extract the subject-verb relationship of travel purpose and itinerary, and generate semantic parsing results.

[0148] The sentiment analysis unit is used to perform sentiment bias analysis on the semantic parsing results. It calculates the sentiment polarity value of subjective statement text through a preset sentiment polarity calculation model, marks suspicious sentiment biases, and generates a set of semantically labeled data indicating potential risk levels.

[0149] Preferably, the entity annotation unit includes a feature extraction subunit, an entity boundary recognition subunit, and an entity type annotation subunit. The feature extraction subunit is used to extract contextual features from the visa dataset to be inspected, capturing the contextual dependencies of words and generating a contextual feature vector. The entity boundary recognition subunit is used to perform entity boundary recognition processing on the contextual feature vector, determining the entity's starting position and category probability, and generating entity boundary annotations. The entity type annotation subunit is used to perform entity type annotation processing on the entity boundary annotations, integrating continuous boundary annotations to form complete entities and labeling their categories, generating entity annotation results.

[0150] Preferably, the consistency checking unit 630 provided in this application is configured with the following units:

[0151] Information Association Graph Construction Unit: Used to create a graph structure with entities as nodes and logical relationships as edges based on the visa dataset to be inspected and the semantically labeled dataset, and generate an information association graph;

[0152] Entity consistency comparison unit: used to detect numerical differences of the same entity in different documents based on information association graph, and generate a set of difference points;

[0153] Logical verification processing unit: used to apply predicate logic rules to the set of differences, detect time sequence contradictions and abnormal fund flow, and generate a consistency check report.

[0154] Preferably, the credibility analysis unit 640 provided in this application is configured with the following units:

[0155] The authenticity scoring unit is used to perform authenticity scoring on consistency check reports based on a pre-trained credibility assessment model, analyze the logical consistency of information points and calculate credibility scores to generate a scoring dataset.

[0156] The risk label matching unit is used to perform risk label matching processing on the scoring dataset, retrieve similar risk patterns from the historical rejection case database and associate them with risk levels to generate a risk label set;

[0157] The credibility analysis report generation unit is used to integrate and process the scoring dataset and risk label set, combine the credibility score and risk level to calculate the comprehensive credibility score, and generate a credibility analysis report.

[0158] Preferably, the visa verification unit 650 provided in this application is configured with the following units:

[0159] The risk and compliance separation unit is used to perform risk separation processing on the credibility analysis report, identify information points with scores below the preset compliance threshold and high-risk labels, separate low-risk labels and information points with scores above the threshold, and generate risk material sets and compliance material sets.

[0160] The supplementary materials list generation unit is used to analyze the risk materials set, create a standardized materials requirement list for missing proofs or contradictory explanations, and generate a supplementary materials list.

[0161] The visa verification report integration unit is used to integrate the judgment results of the compliant materials set, the judgment results of the risk materials set, and the supplementary materials list. It marks compliant materials as passed, risk materials as failed, integrates the supplementary materials requirements, and generates a visa verification report. The visa verification report is used to indicate the materials that the applicant needs to supplement and the compliance judgment status of each material.

[0162] In one embodiment, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described natural language-based visa material compliance verification method.

[0163] In one embodiment, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described natural language-based visa material compliance verification method.

[0164] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0165] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0166] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for verifying the compliance of visa application materials based on natural language processing, characterized in that, The following steps are involved: S1: Perform optical character recognition and field extraction on the images of visa application materials submitted by the applicant, extract key fields such as name, ID number, education information and bank balance, extract key data fields from unstructured images, and generate a visa application dataset to be inspected. S2: Perform lexical and syntactic parsing on the visa dataset to be inspected, label the entities of person names, place names, time and amount, and perform sentiment analysis on subjective statements containing travel purpose and itinerary to generate a semantically labeled data set; S3: Based on the visa dataset to be inspected and the semantic annotation dataset, construct an information association graph between materials, compare the consistency of the description of the same entity in different documents, and generate a consistency check report; S4: The consistency check report is processed based on the pre-trained credibility assessment model and the historical visa refusal case library. The authenticity score of each information point is calculated, and risk labels are matched with the historical visa refusal case library to generate a credibility analysis report. S5: Determine whether the visa materials are compliant based on the credibility analysis report, match the scoring results with the preset compliance threshold, supplement and sort out the materials for fields or logical contradictions that are below the threshold, and generate a visa verification report containing the verification results and a list of materials that need to be supplemented.

2. The method according to claim 1, characterized in that, S1 includes: S11: Perform image prediction on the visa application materials submitted by the applicant, locate text regions using edge detection algorithms, segment tables and paragraph blocks, and generate a verification document image; S12: Extract key fields from the verification document image, including name, ID number, education information and bank balance, and generate a verification image dataset containing the verification fields; S13: Perform data augmentation processing on the verification image dataset, perform super-resolution reconstruction on the blurred text region, extract the keyword field of the blurred text, and generate the visa dataset to be inspected.

3. The method according to claim 1, characterized in that, S2 includes: S21: Perform lexical parsing on the visa dataset to be inspected based on a bidirectional long short-term memory network, label the entities of person names, place names, time and amount, and generate entity labeling results; S22: Perform syntactic parsing on the entity annotation results, analyze the sentence structure of the visa materials, extract the subject-verb relationship between the purpose of travel and the itinerary, and generate semantic parsing results; S23: Perform sentiment analysis on the semantic parsing results: calculate the sentiment polarity value of the subjective statement text through a preset sentiment polarity calculation model, mark suspicious sentiment tendencies, and generate a semantically labeled data set, which is used to indicate the potential risk level of the applicant's statement.

4. The method according to claim 3, characterized in that, S21 includes: S211: Perform context feature extraction processing on the visa dataset to be inspected, capture the contextual dependencies of words, and generate context feature vectors; S212: Perform entity boundary recognition processing on the context feature vector to determine the entity's starting position and category probability, and generate entity boundary annotations; S213: Perform entity type annotation processing on the entity boundary annotations, integrate continuous boundary annotations to form complete entities and mark their categories, and generate entity annotation results.

5. The method according to claim 1, characterized in that, S3 includes: S31: Based on the visa dataset to be inspected and the semantic annotation dataset, perform association graph construction processing to create a graph structure with entities as nodes and logical relationships as edges, and generate an information association graph. S32: Based on the information association graph, perform entity consistency comparison processing on the visa dataset to be inspected and the semantic annotation dataset, detect the numerical differences of the same entity in different documents, and generate a set of difference points; S33: Perform logical verification on the set of discrepancies, apply predicate logic rules to detect time sequence contradictions and abnormal fund flow, and generate a consistency check report.

6. The method according to claim 1, characterized in that, S4 includes: S41: Based on the pre-trained credibility assessment model, the consistency check report is processed to perform authenticity scoring, analyze the logical consistency of information points and calculate credibility scores to generate a scoring dataset; S42: Perform risk label matching processing on the scoring dataset, retrieve similar risk patterns from the historical visa refusal case database and associate them with risk levels to generate a risk label set; S43: Integrate the scoring dataset and the risk label set, combine the credibility score and risk level to calculate the comprehensive credibility score, and generate a credibility analysis report.

7. The method according to any one of claims 1-6, characterized in that, S5 includes: S51: Perform risk separation processing on the credibility analysis report, identify information points with scores below the preset compliance threshold and high-risk labels, separate low-risk labels and identify information points with scores above the preset compliance threshold, and generate a risk material set and a compliance material set. S52: Perform supplementary material generation processing on the risk material set, create a standardized material requirement list for missing proofs or contradictory interpretations, and generate a supplementary material list; S53: The judgment results of the compliant materials set, the judgment results of the risk materials set, and the supplementary materials list are processed and integrated into a report. The compliant materials are marked as passed, the risk materials are marked as failed, and the supplementary materials requirements are integrated to generate a visa verification report. The visa verification report is used to indicate the materials that the applicant needs to supplement and the compliance judgment status of each material.

8. A visa material compliance verification device based on natural language, characterized in that, The device includes: The visa material extraction unit is used to perform optical character recognition and field extraction on the images of visa materials submitted by the applicant, extract key fields such as name, ID number, education information and bank balance, extract key data fields from unstructured images, and generate a visa dataset to be inspected. The semantic annotation unit is used to perform lexical and syntactic parsing on the visa dataset to be inspected, annotate names, place names, time and amount entities, and perform sentiment analysis on subjective statements containing travel purpose and itinerary, generating a semantically annotated data set. The consistency check unit is used to construct an information association graph between materials based on the visa dataset to be checked and the semantic annotation dataset, compare the consistency of the description of the same entity in different documents, and generate a consistency check report. The credibility analysis unit is used to process the consistency check report based on a pre-trained credibility assessment model and a historical rejection case library, score the authenticity of each information point, and match risk tags with the historical rejection case library to generate a credibility analysis report. The visa verification unit is used to determine whether the visa materials are compliant based on the credibility analysis report, match the scoring results with the preset compliance threshold, supplement and sort out the materials for fields or logical contradictions that are below the threshold, and generate a visa verification report containing the verification results and a list of supplementary materials.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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