Online visa method

Abstract

The invention relates to the technical field of data processing, and discloses an online visa method, which comprises the following steps that: S1, a user registers in a visa system and submits basic identity information; s2, the system generates a virtual applicant model of the user through a digital twinning technology, and the model simulates a visa application process of the user in real time; s3, based on the virtual applicant model, feeding back whether information and materials submitted by the user are complete and meet requirements in real time through a simulation algorithm, and dynamically adjusting application steps according to a simulation result; and S4, the system applies an identity verification and authority verification model simulation algorithm. Through consistency check of a dual identity verification mechanism and a virtual applicant model and an online visa method, the security of identity verification is improved, the authenticity of user identity is ensured, and in combination with an asymmetric encryption algorithm and a Hash technology, the security of data in the transmission and storage process is ensured, and the user experience is improved. And the risks of identity fraud and information tampering are effectively prevented.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, in particular to an online visa method. Background Art

[0002] With the development of globalization, the demand for cross-border travel, work and study is increasing year by year. The requirements of various countries for visa application processing are becoming increasingly complex. The traditional visa application process usually involves multiple steps, including material submission, identity verification, fee payment and information review, etc., which are inefficient, time-consuming and prone to errors. In addition, with the increase in personal information security risks in the network environment, identity verification and data transmission security in the visa application process have become important issues. The existing online visa system has the following main deficiencies:

[0003] The existing online visa system's authentication methods are mostly single username and password login or verification mechanisms based on a small amount of information, which are easily attacked by hackers through phishing, brute force cracking, etc., leading to identity fraud and data leakage;

[0004] In the existing visa system, visa information is usually stored in a centralized database, which is at risk of being tampered with or forged. In addition, the verification of visa information must be conducted by a specialized agency, and there is a lack of an open and transparent verification mechanism, which makes it impossible for third-party agencies or relevant personnel to independently verify the authenticity of the visa;

[0005] During the visa application process, applicants need to upload files in multiple formats. The existing system has poor compatibility and processing capabilities for document formats, and the document processing process is cumbersome, requiring applicants to manually adjust the format and compress files, which can easily lead to file upload failures, thereby causing delays and errors in visa applications.

[0006] Therefore, those skilled in the art provide an online visa method to solve the problems raised in the above background technology. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides an online visa method to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an online visa method, comprising the following steps: S1, a user registers in a visa system and submits basic identity information;

[0009] S2. The system generates a virtual applicant model of the user through digital twin technology, which simulates the user's visa application process in real time;

[0010] S3. Based on the virtual applicant model, the simulation algorithm provides real-time feedback on whether the information and materials submitted by the user are complete and meet the requirements, and dynamically adjusts the application steps according to the simulation results;

[0011] S4, system application identity authentication and permission verification model simulation algorithm;

[0012] S5. Use the formula F(t) = α1X1(t) + α2X2(t) + ... + α n X n (t) dynamically evaluates the user identity risk, where F(t) is the risk factor, X i (t) is the identity information submitted by the user at different time points, α i is the information weight coefficient;

[0013] S6. Based on the authority verification model, the system dynamically adjusts the user's visa application authority to determine the verification requirements of different users;

[0014] S7. The user uploads the required materials, and the system pre-screens them using a virtual applicant model, automatically checking the file format, size, and content accuracy;

[0015] S8. The system determines whether to trigger additional verification steps or material supplementary submission prompts based on the risk assessment results;

[0016] S9. After the user pays the visa fee, the system verifies the compliance of the user's identity and payment information again through the simulation model;

[0017] S10. Finally, the system generates a digital visa or notifies the user of the visa application result.

[0018] Preferably, the virtual applicant model is updated based on real-time data submitted by the user, predicts potential errors or delays by simulating the user's application process, and provides corresponding solutions;

[0019] The prediction model uses a machine learning algorithm to calculate potential errors and delays in the application process in real time and provide solutions based on the calculation results. The algorithm formula is as follows:

[0020] Assume that the real-time data submitted by the user at time t is X(t) = [x1(t), x2(t), ..., x n (t)], where x i (t) represents the i-th data submitted at time t, and the real-time update model is expressed by the following formula: X′(t)=X(t)+ΔX(t), where ΔX(t) represents the data correction amount generated by the system based on the correction of the submitted data or the system feedback;

[0021] Assume that the probability of error E(t) is expressed as: Where W is the weight vector, X′(t) is the data vector submitted and updated at time, and b is the bias term;

[0022] Assume the probability of delay D(t) is: Among them, γ is the weight vector related to delay, T(t) is the time function of application processing, λ is the time decay coefficient, and β is the bias term;

[0023] If the probability of error E(t) or delay D(t) exceeds the set threshold, the system generates a corresponding solution S(t). The solution is generated by the following optimization formula:

[0024] Among them, S(t) represents the solution generated at time t, C(s) is the cost function of the solution, P(E(t)) and P(D(t)) are the predicted probabilities of errors and delays, and α and β are the corresponding adjustment coefficients.

[0025] Preferably, the identity authentication model includes a distributed identity authentication algorithm based on blockchain technology to ensure that the identity information submitted by the user cannot be tampered with, so as to calculate and verify the hash value through a formula, the formula is as follows:

[0026] P(x)=H(A||x),

[0027] Among them, H is the hash function, A is the user's private identity information, and x is the dynamically submitted data.

[0028] Preferably, the permission verification model generates a permission adjustment strategy based on the user's nationality, visa type and application history, and dynamically matches the verification steps that the user needs to perform through the rule engine. The following is an explanation of the algorithm formula:

[0029] Set the user's permission state vector to P(u) = [p1, p2, ..., p n ], where p i Indicates the i-th verification step that the user needs to perform. The system generates a permission allocation vector based on the user's nationality, visa type, and application history;

[0030] The authority allocation model is expressed by weighted summation as:

[0031] P(u)=W N N(u)+W V ·V(u)+W H H(u), where W N , W V , W H is a pre-trained weight matrix, N(u) nationality vector, V(u) visa type vector, H(u) application history vector;

[0032] Based on the user's permission state vector P(u), the system needs to decide whether to increase or decrease the user's verification steps. Assuming the permission adjustment strategy is A(u), it is calculated by the following formula:

[0033] Among them, P(u) is the total score calculated by the rule engine, T A It is the dynamic threshold of the system, indicating whether to adjust the verification step.

[0034] Preferably, the multi-layer identity authentication includes using multi-factor identity authentication technology to comprehensively use biometric information, passwords and one-time passwords to authenticate users, and the algorithm steps are as follows:

[0035] Set the factors of multi-factor authentication: biometric information verification result vector B(u), password verification result P w (u), the one-time password verification result O(u), and the final decision of identity authentication V(u) are calculated by weighted combination of the following verification factors:

[0036] V(u)=w b B(u)+w p P w (u)+w o O(u), where w b 、w p 、w o is the weight coefficient;

[0037] Biometric information verification can be further refined into the following formula, the similarity between the user's fingerprint features and the reference fingerprint features stored in the system:

[0038] Compare the fingerprint features submitted by the user with the stored reference features to generate a matching score;

[0039] In multi-layer identity authentication, the system should perform dynamic risk assessment based on environmental factors and user behavior, and set the risk assessment value R(u): R(u) = λ1T(u) + λ2L(u) + λ3D(u), where T(u) is time correlation, L(u) is geographic location correlation, D(u) is device correlation, and λ1, λ2, and λ3 are risk weights;

[0040] Based on the risk assessment value R(u), the system will dynamically adjust the threshold value T of identity authentication. V (u), if the risk assessment value is high, it means that the difficulty of passing the verification is increased, and the formula for adjusting the threshold is:

[0041] T V (u) = T0 + γR(u), where T0 is the verification threshold and γ is the dynamic adjustment coefficient;

[0042] The final authentication result F(u) is calculated based on the total authentication score V(u) and the dynamic threshold T V (u) Compare to determine whether the user is authenticated:

[0043]

[0044] If V(u)≥T V (u), the verification is successful;

[0045] If V(u) <T V (u), verification fails.

[0046] Preferably, the user material uploading step includes automatically converting files of different formats into a standard format required by the system through an automated document format conversion tool of the system;

[0047] For documents of different formats, the parsed content includes text, images, tables, and metadata. The content parsing formula is based on the document format and is expressed as:

[0048]

[0049] Among them, T i (D(u)) is the i-th paragraph of text extracted from the document, I i (D(u)) is the i-th image extracted, G i (D(u)) is the i-th table or chart extracted;

[0050] To ensure that the size and quality of the document meet the system requirements, the converted document needs to be compressed and optimized. The formula for defining compression optimization is:

[0051]

[0052] Among them, O(D f (u)) is the optimized document size, T i is the compression ratio of the i-th paragraph of text in the document, γ T is the text compression factor, I i is the compression ratio of the i-th image, γ I is the image compression factor, G i is the compression ratio of the ith table, γ G is the table compression factor.

[0053] Preferably, the material pre-examination step automatically analyzes the text content of the uploaded file through a natural language processing algorithm, and the steps of the natural language processing algorithm are as follows:

[0054] The uploaded text needs to be preprocessed. Suppose the uploaded text is T(u), and the result after preprocessing is T′(u). The formula of the preprocessing step is as follows:

[0055] T'(u)=Tokenize(T(u))-StopWords(T(u))-Punctuation(T(u)),

[0056] Among them, Tokenize(T(u)) means tokenizing the text T(u) into a series of words, StopWords(T(u)) means removing stop words, and Punctuation(T(u)) means removing invalid characters such as punctuation marks.

[0057] Convert the text content into a machine-understandable vector and use TF-IDF for feature extraction. The formula is as follows: TF-IDF(t, d, D) = TF(t, d) × IDF(t, D), where t is a specific word, d is a document, and D is a document collection. is the frequency of word t in document d, is the inverse document frequency of word t in the document collection;

[0058] Use the word embedding model to map the words in the text to a high-dimensional vector space. Let the vocabulary w i The word embedding vector is v(w i ), the overall embedding vector of the text T′(u) is:

[0059] Among them, v(w i ) is the word w i The word embedding vector of |T′(u)| is the number of words in the processed text;

[0060] Cosine similarity is used to measure the similarity between the text uploaded by the user and the standard template. Let the feature vector of the text uploaded by the user be V(T′(u)) and the feature vector of the system preset template be V(T m ), the similarity calculation formula is:

[0061]

[0062] Among them, V(T′(u))·V(T m ) represents the dot product between vectors;

[0063] ||V(T′(u))|| and ||V(T m )|| is the modulus of the vector.

[0064] Preferably, the payment verification step ensures the security of the payment transaction through a double identity authentication mechanism of the payment gateway, and at the same time, checks the consistency of the payment information with the user identity through a virtual applicant model;

[0065] Two-factor authentication combines password authentication and OTP authentication mechanisms. Set the user's payment password to P w (u), the one-time password is O(u), both return verification success or failure, the double verification result is V auth (u) is expressed by the following formula: V auth (u) = w1P w (u)+w2O(u),

[0066] Among them, w1 and w2 are weight parameters;

[0067] Ensure that double verification is passed and set the threshold T auth , if V auth (u)≥T auth , then the double authentication is passed, where T auth is the threshold for passing double verification;

[0068] In the virtual applicant model, the goal of the consistency check is to compare whether the payment information submitted by the user matches the information in the virtual applicant model, and the degree of match is measured by cosine similarity: Among them, V s (u)·P pay (u) is the vector dot product of the virtual applicant model information and the payment information, ∥V s (u)∥ and ∥P pay (u)∥ is the vector model of the virtual applicant model information and payment information.

[0069] If the similarity S cons (u) exceeds a certain threshold T cons , then the payment information is considered to be consistent with the user information in the virtual applicant model: S cons (u)≥T cons , where T cons is the threshold for consistency check to pass;

[0070] The final payment verification decision combines dual identity verification and virtual applicant model consistency check. Let the total result of payment verification be V pay (u), and is comprehensively judged by the following formula:

[0071] V pay (u) = αV auth (u)+βS cons (u), where α and β are adjustment coefficients, V auth (u) is the result of two-factor authentication, Scons (u) is the result of the consistency check of the virtual applicant model;

[0072] If V pay (u)≥T pay , the payment verification is successful, otherwise the payment fails.

[0073] The present invention provides an online visa method, which has the following beneficial effects:

[0074] 1. The present invention improves the security of identity authentication through the consistency check of the double identity authentication mechanism and the virtual applicant model, and ensures the authenticity of the user's identity. In addition, by combining asymmetric encryption algorithms and hashing technologies, the security of data during transmission and storage is guaranteed, effectively preventing the risks of identity fraud and information tampering.

[0075] 2. The present invention ensures that the visa information cannot be tampered with by storing the hash value of the digital visa on the blockchain and provides a way for public verification. The distributed consensus mechanism of the blockchain allows any third-party organization to verify the authenticity of the visa, thereby improving the transparency and trust of the visa processing process.

[0076] 3. The present invention can automatically identify and convert various document formats uploaded by users through automated document format conversion tools, ensure that the files meet system requirements, compress and optimize the documents, ensure the quality of uploaded files, and improve system processing efficiency. The flexibility of automated document processing reduces the need for manual intervention and reduces application delays caused by document format errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0078] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0079] The present invention is described in detail below in conjunction with the accompanying drawings:

[0080] Example:

[0081] Please refer to the attached Figure 1 The embodiment of the present invention provides an online visa method, comprising the following steps:

[0082] S1. The user registers in the visa system and submits basic identity information;

[0083] S2. The system generates a virtual applicant model of the user through digital twin technology, which simulates the user's visa application process in real time;

[0084] S3. Based on the virtual applicant model, the simulation algorithm provides real-time feedback on whether the information and materials submitted by the user are complete and meet the requirements, and dynamically adjusts the application steps according to the simulation results;

[0085] S4, system application identity authentication and permission verification model simulation algorithm;

[0086] S5. Use the formula F(t) = α1X1(t) + α2X2(t) + ... + α n X n (t) dynamically evaluates the user identity risk, where F(t) is the risk factor, X i (t) is the identity information submitted by the user at different time points, α i is the information weight coefficient;

[0087] S6. Based on the authority verification model, the system dynamically adjusts the user's visa application authority to determine the verification requirements of different users;

[0088] S7. The user uploads the required materials, and the system pre-screens them using a virtual applicant model, automatically checking the file format, size, and content accuracy;

[0089] S8. The system determines whether to trigger additional verification steps or material supplementary submission prompts based on the risk assessment results;

[0090] S9. After the user pays the visa fee, the system verifies the compliance of the user's identity and payment information again through the simulation model;

[0091] S10. Finally, the system generates a digital visa or notifies the user of the visa application result.

[0092] Benefit of S1: The system can start further processing based on the user's identity to ensure that the application process is targeted from the beginning; Benefit of S2: It can significantly reduce application errors and help users understand the status and requirements of each step, improving the transparency of the application process and user experience; Benefit of S3: It avoids delays and errors in manual inspections in the traditional application process, and users can make corrections in time, reducing application returns and delays caused by unqualified materials, greatly improving application efficiency; Benefit of S4: It ensures that the identity authentication requirements of different users vary from person to person, provides a personalized verification process, effectively improves the security of the system, and prevents identity fraud; Benefit of S5: It helps identify potential high-risk applicants, ensuring that high-risk users undergo a strict verification process, while low-risk users can enjoy a more simplified process, improving the security and efficiency of the system; Benefit of S6: The system can provide different types of Providing different verification levels for visa applicants can ensure high security standards, simplify the operating steps for low-risk applicants, and improve user experience; the benefit of S7 is that it reduces the burden and error rate of manual review, ensures the quality of uploaded materials, and avoids delays; the benefit of S8 is that the system can flexibly respond to applicants with different risk levels, increase the security level for high-risk applicants, and thus improve the security and compliance of the visa process; the benefit of S9 is that it can prevent payment fraud or identity theft, protect the security of user payment information, and ensure the transparency of the payment process; the benefit of S10 is that it reduces the time cost and transportation risk of traditional paper visas. Through digital visas, users can perform subsequent operations more conveniently, and the visa information can be ensured to be authentic through technologies such as encryption and blockchain. For failed applications, the system can promptly notify users and provide follow-up guidance to improve user experience.

[0093] The virtual applicant model is updated based on real-time data submitted by users, predicting potential errors or delays by simulating the user's application process and providing corresponding solutions;

[0094] The predictive model uses a machine learning algorithm to calculate potential errors and delays in the application process in real time and provide solutions based on the calculation results. The algorithm formula is as follows:

[0095] Assume that the real-time data submitted by the user at time t is X(t) = [x1(t), x2(t), ..., x n (t)], where x i (t) represents the i-th data submitted at time t, and the real-time update model is expressed by the following formula: X′(t)=X(t)+ΔX(t), where ΔX(t) represents the data correction amount generated by the system based on the correction of the submitted data or the system feedback;

[0096] Assume that the probability of error E(t) is expressed as: Where W is the weight vector, X′(t) is the data vector submitted and updated at time, and b is the bias term;

[0097] Assume the probability of delay D(t) is: Among them, γ is the weight vector related to delay, T(t) is the time function of application processing, λ is the time decay coefficient, and β is the bias term;

[0098] If the probability of error E(t) or delay D(t) exceeds the set threshold, the system generates a corresponding solution S(t). The solution is generated by the following optimization formula:

[0099] Among them, S(t) represents the solution generated at time t, C(s) is the cost function of the solution, P(E(t)) and P(D(t)) are the predicted probabilities of errors and delays, and α and β are the corresponding adjustment coefficients.

[0100] The formula system can dynamically update the data submitted by the user to ensure that the application data obtained by the system is real-time and accurate, and can be corrected according to the system feedback. This step avoids unnecessary delays or errors and provides a high-quality data foundation for subsequent processing;

[0101] Through the probability of error and delay, the system can predict potential errors or processing delays based on the user's submission data, historical behavior and time factors. Therefore, the system can identify problems in time and take measures in advance to prevent problems from expanding and ensure the smooth progress of the application process;

[0102] When potential errors or delays are predicted, the system automatically generates the best solution through an optimization formula that considers how to solve the problem, optimizes costs, and ensures that the problem is solved efficiently while minimizing resource consumption;

[0103] Through a complete algorithm, the online visa system has intelligent error detection and delay prediction functions, can dynamically adjust the data submitted by users, prevent potential problems in advance, and provide automated, low-cost solutions when necessary. The process improves the degree of automation in the application process, significantly reduces errors or delays caused by human intervention, ensures that users can complete visa applications quickly and accurately, and ultimately improves the system's security, efficiency and user experience.

[0104] The authentication model includes a distributed authentication algorithm based on blockchain technology to ensure that the identity information submitted by the user cannot be tampered with, and the hash value is calculated and verified by the formula as follows:

[0105] P(x)=H(A||x),

[0106] Among them, H is the hash function, A is the user's private identity information, and x is the dynamically submitted data.

[0107] By using the user's private identity information A as the basis for hash value calculation, it is ensured that each user's identity data is unique and cannot be tampered with. Since the hash function is irreversible, any tampering with the identity information will cause the hash value to change. The system can detect anomalies in time to ensure the integrity of identity authentication.

[0108] The dynamically submitted data x ensures the uniqueness of the hash value during each verification. Even if the private identity information is the same, the hash value of each verification will be different according to the changes in the dynamic data, ensuring the real-time and security of the verification. Through the dynamic mechanism, the system effectively prevents common security threats such as replay attacks.

[0109] Use blockchain technology to record hash values, so that the hash values ​​in the identity authentication process are distributed and stored on the blockchain, ensuring that the verification data cannot be tampered with. By using the transparency and distributed characteristics of blockchain, the identity information verification process has decentralized security guarantees.

[0110] Since hash operations are fast, the verification process only requires comparing whether the generated hash values ​​are consistent, avoiding the complex verification steps in traditional identity authentication systems. Through distributed blockchain technology, the storage and reading of verification information are very efficient.

[0111] The permission verification model generates permission adjustment strategies based on the user's nationality, visa type, and application history, and dynamically matches the verification steps that the user needs to perform through the rule engine. The following is an explanation of the algorithm formula:

[0112] Set the user's permission state vector to P(u) = [p1, p2, ..., p n ], where p i Indicates the i-th verification step that the user needs to perform. The system generates a permission allocation vector based on the user's nationality, visa type, and application history;

[0113] The authority allocation model is expressed by weighted summation as:

[0114] P(u)=W N N(u)+W V ·V(u)+W H H(u), where W N , W V , W H is a pre-trained weight matrix, N(u) nationality vector, V(u) visa type vector, H(u) application history vector;

[0115] Based on the user's permission state vector P(u), the system needs to decide whether to increase or decrease the user's verification steps. Assuming the permission adjustment strategy is A(u), it is calculated by the following formula:

[0116] Among them, P(u) is the total score calculated by the rule engine, T A It is the dynamic threshold of the system, indicating whether to adjust the verification step.

[0117] The system generates personalized verification steps based on nationality, visa type, and application history. Through the product operation of weight matrix and vector, the system can flexibly allocate permission vectors to different users. Its automated allocation strategy improves the accuracy and pertinence of the system and avoids a "one-size-fits-all" verification method.

[0118] After generating the user's permission state vector, the system uses the rule engine to calculate the total score, and decides whether to adjust the user's verification steps based on the comparison between the score and the dynamic threshold. This dynamic adjustment mechanism ensures the flexibility of the verification process. When dealing with high-risk users, the system can automatically increase the verification steps to enhance security, while low-risk users enjoy a simplified process;

[0119] Through the integration of the above steps, the system can achieve a balance between security and efficiency. When users submit visa applications, the system will dynamically adjust according to their risk level to ensure that high-risk applicants undergo more stringent verification, while low-risk applicants can simplify the process, making the visa application process more efficient.

[0120] Multi-layer authentication includes multi-factor authentication technology that uses a combination of biometric information, passwords, and one-time passwords to authenticate users. The algorithm steps are as follows:

[0121] Set the factors of multi-factor authentication: biometric information verification result vector B(u), password verification result P w (u), the one-time password verification result O(u), and the final decision of identity authentication V(u) are calculated by weighted combination of the following verification factors:

[0122] V(u)=w b B(u)+w p P w (u)+w o O(u), where w b 、w p 、w o is the weight coefficient;

[0123] Biometric information verification can be further refined into the following formula, the similarity between the user's fingerprint features and the reference fingerprint features stored in the system:

[0124] Compare the fingerprint features submitted by the user with the stored reference features to generate a matching score;

[0125] In multi-layer identity authentication, the system should perform dynamic risk assessment based on environmental factors and user behavior, and set the risk assessment value R(u): R(u) = λ1T(u) + λ2L(u) + λ3D(u), where T(u) is time correlation, L(u) is geographic location correlation, D(u) is device correlation, and λ1, λ2, and λ3 are risk weights;

[0126] Based on the risk assessment value R(u), the system will dynamically adjust the threshold value T of identity authentication. V (u), if the risk assessment value is high, it means that the difficulty of passing the verification is increased, and the formula for adjusting the threshold is:

[0127] T V (u) = T0 + γR(u), where T0 is the verification threshold and γ is the dynamic adjustment coefficient;

[0128] The final authentication result F(u) is calculated based on the total authentication score V(u) and the dynamic threshold T V (u) Compare to determine whether the user is authenticated:

[0129]

[0130] If V(u)≥T V (u), the verification is successful;

[0131] If V(u) <T V (u), verification fails.

[0132] The multi-layer identity authentication algorithm organically combines biometrics, passwords and one-time password verification to form a multi-factor identity authentication mechanism, and ensures the flexibility and accuracy of verification through weighted combination. Biometric information ensures the authenticity of user identity through precise similarity calculation. The risk assessment mechanism dynamically analyzes the user's behavior and environment to ensure that the system automatically adjusts the strength of verification under different risk conditions. Finally, the user's verification result is obtained by comparing with the dynamic threshold. The system can dynamically adjust the verification process according to the user's actual situation to improve the security, flexibility and user experience of the verification process.

[0133] The user material uploading step includes using the system's automated document format conversion tool to automatically convert files in different formats into the standard format required by the system;

[0134] For documents of different formats, the parsed content includes text, images, tables, and metadata. The content parsing formula is based on the document format and is expressed as:

[0135]

[0136] Among them, T i(D(u)) is the i-th paragraph of text extracted from the document, I i (D(u)) is the i-th image extracted, G i (D(u)) is the i-th table or chart extracted;

[0137] To ensure that the size and quality of the document meet the system requirements, the converted document needs to be compressed and optimized. The formula for defining compression optimization is:

[0138]

[0139] Among them, O(D f (u)) is the optimized document size, T i is the compression ratio of the i-th paragraph of text in the document, γ T is the text compression factor, I i is the compression ratio of the i-th image, γ I is the image compression factor, G i is the compression ratio of the ith table, γ G is the table compression factor.

[0140] Through the content parsing formula, the system can automatically extract various contents from the documents uploaded by users, including text, images and tables. Its automated parsing method improves the efficiency of document processing, ensures that different elements in the document can be further processed and converted, simplifies the user's operation process, reduces the necessity of manual intervention, ensures that the system can automatically identify and process files of different formats, improves user experience and system efficiency, and by accurately extracting key information from documents, the system lays the foundation for subsequent compression and format conversion;

[0141] The compression optimization formula ensures that the size of the converted document after compression meets the system requirements. At the same time, the clarity and integrity of the content are guaranteed. The system performs differentiated processing on different types of content to ensure the optimal compression method for each element. Its refined compression processing mechanism prevents submission failures during document upload due to excessive file size or poor quality, while improving file storage and transmission efficiency.

[0142] By integrating content parsing and compression optimization functions, the system can automatically process documents uploaded by users, ensuring that each element in the document is accurately parsed and reasonably compressed. The automated document processing process significantly reduces the need for manual intervention and avoids application delays or failures due to incompatible formats, large files or missing content.

[0143] The material pre-review step automatically analyzes the text content of the uploaded file through a natural language processing algorithm. The steps of the natural language processing algorithm are as follows:

[0144] The uploaded text needs to be preprocessed. Suppose the uploaded text is T(u), and the result after preprocessing is T′(u). The formula of the preprocessing step is as follows:

[0145] T'(u)=Tokenize(T(u))-StopWords(T(u))-Punctuation(T(u)),

[0146] Among them, Tokenize(T(u)) means tokenizing the text T(u) into a series of words, StopWords(T(u)) means removing stop words, and Punctuation(T(u)) means removing invalid characters such as punctuation marks.

[0147] Convert the text content into a machine-understandable vector and use TF-IDF for feature extraction. The formula is as follows: TF-IDF(t, d, D) = TF(t, d) × IDF(t, D), where t is a specific word, d is a document, and D is a document collection. is the frequency of word t in document d, is the inverse document frequency of word t in the document collection;

[0148] Use the word embedding model to map the words in the text to a high-dimensional vector space. Let the vocabulary w i The word embedding vector is v(w i ), the overall embedding vector of the text T′(u) is:

[0149] Among them, v(w i ) is the word w i The word embedding vector of |T′(u)| is the number of words in the processed text;

[0150] Cosine similarity is used to measure the similarity between the text uploaded by the user and the standard template. Let the feature vector of the text uploaded by the user be V(T′(u)) and the feature vector of the system preset template be V(T m ), the similarity calculation formula is:

[0151]

[0152] Among them, V(T′(u))·V(T m ) represents the dot product between vectors;

[0153] ||V(T′(u))|| and ||V(T m )|| is the modulus of the vector.

[0154] During the material pre-review process, the system first pre-processes the uploaded text to remove useless information and decompose the text into meaningful word sequences. This process simplifies the subsequent feature extraction and analysis, ensuring that the system only processes valid text content.

[0155] The system uses TF-IDF and word embedding models to extract features and represent semantics of preprocessed text. TF-IDF extracts important word features in the text, while the word embedding model further maps words to a high-dimensional vector space to generate text embedding vectors. The system can capture the surface features of words and understand the semantic relationship of the text.

[0156] Finally, the system calculates the similarity between the text uploaded by the user and the standard template through the cosine similarity formula. By comparing the feature vectors of the text, the system can quantitatively analyze the similarity. This step allows the system to automatically determine whether the text materials submitted by the user meet the visa application requirements, greatly improving the review efficiency.

[0157] The payment verification step ensures the security of payment transactions through the double authentication mechanism of the payment gateway. At the same time, the consistency of payment information and user identity is checked through the virtual applicant model.

[0158] Two-factor authentication combines password authentication and OTP authentication mechanisms. Set the user's payment password to P w (u), the one-time password is O(u), both return verification success or failure, the double verification result is V auth (u) is expressed by the following formula: V auth (u) = w1P w (u)+w2O(u),

[0159] Among them, w1 and w2 are weight parameters;

[0160] Ensure that double verification is passed and set the threshold T auth , if V auth (u)≥T auth , then the double authentication is passed, where T auth is the threshold for passing double verification;

[0161] In the virtual applicant model, the goal of the consistency check is to compare whether the payment information submitted by the user matches the information in the virtual applicant model, and the degree of match is measured by cosine similarity: Among them, V s (u)·P pay (u) is the vector dot product of the virtual applicant model information and the payment information, ∥V s (u)∥ and ∥P pay (u)∥ is the vector model of the virtual applicant model information and payment information.

[0162] If the similarity S cons (u) exceeds a certain threshold T cons , then the payment information is considered to be consistent with the user information in the virtual applicant model: S cons (u)≥T cons , where T cons is the threshold for consistency check to pass;

[0163] The final payment verification decision combines dual identity verification and virtual applicant model consistency check. Let the total result of payment verification be V pay (u), and is comprehensively judged by the following formula:

[0164] V pay (u) = αV auth (u)+βS cons (u), where α and β are adjustment coefficients, V auth (u) is the result of two-factor authentication, S cons (u) is the result of the consistency check of the virtual applicant model;

[0165] If V pay (u)≥T pay , the payment verification is successful, otherwise the payment fails.

[0166] Two-factor authentication improves the security of identity authentication by combining the payment password and one-time password. It uses a formula to weight the combination of password and OTP verification results, and sets a threshold to ensure the strength of identity authentication. This two-factor authentication mechanism balances ease of operation and security while protecting user payment security.

[0167] The core of the consistency check is to quantify the matching degree between the payment information submitted by the user and the payment information stored in the virtual applicant model through the cosine similarity formula. By setting the threshold, the system can flexibly control the strictness of the consistency check to ensure that the user's payment information is authentic and reliable, and avoid information tampering and fraud;

[0168] The overall result of payment verification is jointly determined by the results of dual identity authentication and consistency check of the virtual applicant model. The formula balances the influence of the two by adjusting coefficients α and β. The significance of this step is to combine the matching of user identity authentication and payment information to ensure the security and accuracy of the payment process and prevent a single factor from causing verification failure or success. Finally, the payment verification passes the conditions to ensure the overall security of the payment process.

[0169] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An online visa method, characterized in that: The steps include: S1. The user registers in the visa system and submits basic identity information; S2. The system generates a virtual applicant model of the user through digital twin technology, which simulates the user's visa application process in real time; S3. Based on the virtual applicant model, the simulation algorithm provides real-time feedback on whether the information and materials submitted by the user are complete and meet the requirements, and dynamically adjusts the application steps according to the simulation results; S4, system application identity authentication and permission verification model simulation algorithm; S5. Use the formula F(t) = α1X1(t) + α2X2(t) + ... + α n X n (t) dynamically evaluates the user identity risk, where F(t) is the risk factor, X i (t) is the identity information submitted by the user at different time points, α i is the information weight coefficient; S6. Based on the authority verification model, the system dynamically adjusts the user's visa application authority to determine the verification requirements of different users; S7. The user uploads the required materials, and the system pre-screens them using a virtual applicant model, automatically checking the file format, size, and content accuracy; S8. The system determines whether to trigger additional verification steps or material supplementary submission prompts based on the risk assessment results; S9. After the user pays the visa fee, the system verifies the compliance of the user's identity and payment information again through the simulation model; S10. Finally, the system generates a digital visa or notifies the user of the visa application result.

2. The online visa method according to claim 1, characterized in that: The virtual applicant model is updated based on real-time data submitted by users, predicts potential errors or delays by simulating the user's application process, and provides corresponding solutions; The prediction model uses a machine learning algorithm to calculate potential errors and delays in the application process in real time and provide solutions based on the calculation results. The algorithm formula is as follows: Assume that the real-time data submitted by the user at time t is X(t) = [x1(t), x2(t), ..., x n (t)], where x i (t) represents the i-th data submitted at time t, and the real-time update model is expressed by the following formula: X′(t)=X(t)+ΔX(t), where ΔX(t) represents the data correction amount generated by the system based on the correction of the submitted data or the system feedback; Assume that the probability of error E(t) is expressed as: Where W is the weight vector, X′(t) is the data vector submitted and updated at time, and b is the bias term; Assume the probability of delay D(t) is: Among them, γ is the weight vector related to delay, T(t) is the time function of application processing, λ is the time decay coefficient, and β is the bias term; If the probability of error E(t) or delay D(t) exceeds the set threshold, the system generates a corresponding solution S(t). The solution is generated by the following optimization formula: Among them, S(t) represents the solution generated at time t, C(s) is the cost function of the solution, P(E(t)) and P(D(t)) are the predicted probabilities of errors and delays, and α and β are the corresponding adjustment coefficients.

3. The online visa method according to claim 1, characterized in that: The authentication model includes a distributed authentication algorithm based on blockchain technology to ensure that the identity information submitted by the user cannot be tampered with, and the hash value is calculated and verified by the formula, which is as follows: P(x)=H(A||x), Among them, H is the hash function, A is the user's private identity information, and x is the dynamically submitted data.

4. The online visa method according to claim 1, characterized in that: The permission verification model generates a permission adjustment strategy based on the user's nationality, visa type, and application history, and dynamically matches the verification steps that the user needs to perform through the rule engine. The following is an explanation of the algorithm formula: Set the user's permission state vector to P(u) = [p1, p2, ..., p n ], where p i Indicates the i-th verification step that the user needs to perform. The system generates a permission allocation vector based on the user's nationality, visa type, and application history; The authority allocation model is expressed by weighted summation as: P(u)=W N N(u)+W V ·V(u)+W H H(u), where W N , W V , W H is a pre-trained weight matrix, N(u) nationality vector, V(u) visa type vector, H(u) application history vector; Based on the user's permission state vector P(u), the system needs to decide whether to increase or decrease the user's verification steps. Assuming the permission adjustment strategy is A(u), it is calculated by the following formula: Among them, P(u) is the total score calculated by the rule engine, T A It is the dynamic threshold of the system, indicating whether to adjust the verification step.

5. The online visa method according to claim 1, characterized in that: The multi-layer identity authentication includes using multi-factor identity authentication technology to comprehensively use biometric information, passwords and one-time passwords to authenticate users. The algorithm steps are as follows: Set the factors of multi-factor authentication: biometric information verification result vector B(u), password verification result P w (u), the one-time password verification result O(u), and the final decision of identity authentication V(u) are calculated by weighted combination of the following verification factors: V(u)=w b B(u)+w p P w (u)+w o O(u), where w b 、w p 、w o is the weight coefficient; Biometric information verification can be further refined into the following formula, the similarity between the user's fingerprint features and the reference fingerprint features stored in the system: Compare the fingerprint features submitted by the user with the stored reference features to generate a matching score; In multi-layer identity authentication, the system should perform dynamic risk assessment based on environmental factors and user behavior, and set the risk assessment value R(u): R(u) = λ1T(u) + λ2L(u) + λ3D(u), where T(u) is time correlation, L(u) is geographic location correlation, D(u) is device correlation, and λ1, λ2, and λ3 are risk weights; Based on the risk assessment value R(u), the system will dynamically adjust the threshold value T of identity authentication. V (u), if the risk assessment value is high, it means that the difficulty of passing the verification is increased, and the formula for adjusting the threshold is: T V (u) = T0 + γR(u), where T0 is the verification threshold and γ is the dynamic adjustment coefficient; The final authentication result F(u) is calculated based on the total authentication score V(u) and the dynamic threshold T V (u) Compare to determine whether the user is authenticated: If V(u)≥T V (u), the verification is successful; If V(u) <T V (u), verification fails.

6. The online visa method according to claim 1, characterized in that: The user material uploading step includes automatically converting files of different formats into the standard format required by the system through the system's automated document format conversion tool; For documents of different formats, the parsed content includes text, images, tables, and metadata. The content parsing formula is based on the document format and is expressed as: Among them, T i (D(u)) is the i-th paragraph of text extracted from the document, I i (D(u)) is the i-th image extracted, G i (D(u)) is the i-th table or chart extracted; To ensure that the size and quality of the document meet the system requirements, the converted document needs to be compressed and optimized. The formula for defining compression optimization is: Among them, O(D f (u)) is the optimized document size, T i is the compression ratio of the i-th paragraph of text in the document, γ T is the text compression factor, I i is the compression ratio of the i-th image, γ I is the image compression factor, G i is the compression ratio of the ith table, γ G is the table compression factor.

7. The online visa method according to claim 1, characterized in that: The material pre-examination step automatically analyzes the text content of the uploaded file through a natural language processing algorithm. The steps of the natural language processing algorithm are as follows: The uploaded text needs to be preprocessed. Suppose the uploaded text is T(u), and the result after preprocessing is T′(u). The formula of the preprocessing step is as follows: T'(u)=Tokenize(T(u))-StopWords(T(u))-Punctuation(T(u)), Among them, Tokenize(T(u)) means tokenizing the text T(u) into a series of words, StopWords(T(u)) means removing stop words, and Punctuation(T(u)) means removing invalid characters such as punctuation marks. Convert the text content into a machine-understandable vector and use TF-IDF for feature extraction. The formula is as follows: TF-IDF(t, d, D) = TF(t, d) × IDF(t, D), where t is a specific word, d is a document, and D is a document collection. is the frequency of word t in document d, is the inverse document frequency of word t in the document collection; Use the word embedding model to map the words in the text to a high-dimensional vector space. Let the vocabulary w i The word embedding vector is v(w i ), the overall embedding vector of the text T′(u) is: Among them, v(w i ) is the word w i The word embedding vector of |T′(u)| is the number of words in the processed text; Cosine similarity is used to measure the similarity between the text uploaded by the user and the standard template. Let the feature vector of the text uploaded by the user be V(T′(u)) and the feature vector of the system preset template be V(T m ), the similarity calculation formula is: Among them, V(T′(u))·V(T m ) represents the dot product between vectors; ||V(T′(u))|| and ||V(T m )|| is the modulus of the vector.

8. The online visa method according to claim 1, characterized in that: The payment verification step ensures the security of the payment transaction through the double authentication mechanism of the payment gateway, and at the same time, checks the consistency of the payment information and the user identity through the virtual applicant model; Two-factor authentication combines password authentication and OTP authentication mechanisms. Set the user's payment password to P w (u), the one-time password is O(u), both return verification success or failure, the double verification result is V auth (u) is expressed by the following formula: V auth (u) = w1P w (u)+w2O(u), Among them, w1 and w2 are weight parameters; Ensure that double verification is passed and set the threshold T auth , if V auth (u)≥T auth , then the double authentication is passed, where T auth is the threshold for passing double verification; In the virtual applicant model, the goal of the consistency check is to compare whether the payment information submitted by the user matches the information in the virtual applicant model, and the degree of match is measured by cosine similarity: Among them, V s (u)·P pay (u) is the vector dot product of the virtual applicant model information and the payment information, ||V s (u)|| and||P pay (u)|| is the vector model of the virtual applicant model information and payment information. If the similarity S cons (u) exceeds a certain threshold T cons , then the payment information is considered to be consistent with the user information in the virtual applicant model: S cons (u)≥T cons , where T cons is the threshold for consistency check to pass; The final payment verification decision combines dual identity verification and virtual applicant model consistency check. Let the total result of payment verification be V pay (u), and is comprehensively judged by the following formula: V pay (u) = αV auth (u)+βS cons (u), where α and β are adjustment coefficients, V auth (u) is the result of two-factor authentication, S cons (u) is the result of the consistency check of the virtual applicant model; If V pay (u)≥T pay , the payment verification is successful, otherwise the payment fails.

Images