A visa application method

Abstract

The present invention relates to the field of data processing technology, and in particular to a visa processing method. The present invention adopts digital twin technology to create an identity model based on dynamic data for each applicant, and the model features include the applicant's biometric features, as well as their daily behavior patterns, social network activities, and historical travel record data features; thereby performing a more comprehensive and dynamic identity verification of the applicant, not limited to data at a single point in time; during the visa application process, the digital twin model captures and analyzes the applicant's behavior data in real time, and matches it with the established model. If abnormal behavior is found (such as the behavior pattern is inconsistent with the history), a prompt is automatically issued, requiring further verification or rejecting the application. At the same time, multi-dimensional verification is adopted, in addition to static biometric features, to reduce errors caused by human subjective judgment, thereby greatly improving the security and reliability of visa applications.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a visa application method. Background Art

[0002] The current visa application process mainly relies on static identity verification methods, such as passports, photos, fingerprints and other biometric features, as well as various paper or electronic materials submitted by applicants. Although biometric features such as passports, photos and fingerprints have anti-counterfeiting measures, they can still be forged or misused. This is an unavoidable risk, so an additional manual review process is required.

[0003] However, static identity authentication methods cannot capture the dynamic behavioral characteristics of applicants, such as daily activity patterns, interaction habits, etc., making it difficult to fully assess the applicant's true identity and behavioral credibility. In addition, in the current visa application process, applicants need to repeatedly submit similar materials when applying for visas to multiple countries, and visa agencies in different countries independently review these materials, which increases the time cost. At the same time, there is a lack of unified review standards and communication mechanisms between visa agencies in various countries, resulting in duplication and inconsistency in material review. Applicants may face the risk of visa rejection due to additional requirements of certain countries.

[0004] Regarding identity authentication security issues, the current solutions are usually to improve the accuracy of identity authentication by strengthening the anti-counterfeiting technology of documents, adding interview links, introducing biometric recognition, etc. However, no matter how advanced the anti-counterfeiting technology is, there is always the possibility of being cracked, resulting in the inability to fully guarantee the security of identity authentication. The interview link relies too much on the subjective judgment of the visa officer, which is prone to lead to incorrect identity assessment due to personal bias or cultural differences. Biometric recognition technology is still based on static features and cannot cope with complex situations of identity fraud. In response to the cumbersome material review problem, visa agencies in various countries usually use bilateral agreements or regional cooperation to simplify some visa processes, such as the EU's Schengen visa system. However, this cooperation mechanism is limited to specific regions or specific countries. For visa applicants from various countries, the coverage is limited, and even within regional cooperation, countries still retain independent review rights, resulting in incomplete uniformity of application standards. Applicants still need to deal with different requirements of different countries. Therefore, a visa processing method is urgently needed to solve such problems. Summary of the invention

[0005] To this end, the present invention provides a visa processing method to overcome the problem that static identity authentication means in the prior art cannot effectively guarantee the identity authentication security of visa processing and the visa agencies of various countries have cumbersome material review when applying for visas in multiple countries.

[0006] To achieve the above object, the present invention provides a visa application method, comprising:

[0007] Step S1, visa application identity model construction, at the initial stage of visa application, a digital twin identity model is created for each applicant;

[0008] Step S2, multi-dimensional dynamic identity verification, real-time behavior monitoring and verification. During the application process, the digital twin model constructed in step S1 monitors the applicant's behavior data in real time and compares it with the existing identity model; if there is a significant difference between the current behavior (such as capital flow, geographic location, etc.) and the historical behavior pattern in the model, an alarm is automatically triggered and further verification is required; the multi-dimensional dynamic verification mechanism is not limited to data at a single point in time, and can more comprehensively evaluate the applicant's true identity and behavior credibility, reduce errors in human subjective judgment, and improve the security and accuracy of visa applications;

[0009] Step S3, cross-border information sharing of digital archives, building digital archives including comprehensive verification data, historical visa records, travel history, and financial status information;

[0010] Step S4, visa result feedback: after the visa review is completed, the applicant will be notified of the result in real time through multiple channels (email, SMS, app); for applicants who fail, the reason for rejection will be provided.

[0011] Furthermore, in step S1, a digital twin identity model is constructed based on the static biometrics and dynamic behavioral characteristics of the applicant; the data in the model is collected through sensors, network activities and historical records, and is continuously updated during the application process;

[0012] Static biometric features include the applicant’s fingerprints and photos, while dynamic behavioral features include the applicant’s daily activity patterns, social network activities, and historical travel records.

[0013] Furthermore, in step S1, digital twin technology is used to create an identity model based on dynamic data for each applicant:

[0014] Collect static and dynamic data related to applicants from multiple data sources, including:

[0015] Static biometric data: fingerprint data B f , iris image B i , facial recognition data B m ;

[0016] Dynamic behavior data: location history L(t), capital flow record F(t), social network activity S(t), travel history T h ;

[0017] Construct feature vectors and map static and dynamic data into high-dimensional feature vectors. Static feature vector B: B = [B f ,Bi ,B m ], dynamic feature vector D(t): D(t) = [L(t), F(t), S(t), T h ].

[0018] Furthermore, in step S1, the static features and dynamic features are combined to form a time-varying multi-dimensional identity vector model:

[0019] I(t)=α·B+β·D(t), where I(t) represents the comprehensive identity vector of the applicant at time t, α and β are weight factors that reflect the relative importance of static and dynamic features. It is automatically adjusted through the historical data regression analysis algorithm to capture and evaluate the identity changes of the applicant in real time, and combine static and dynamic features to ensure the reliability of identity authentication.

[0020] Furthermore, in step S2, based on the digital twin model constructed in step S1, real-time multi-dimensional behavior monitoring and verification are performed:

[0021] Compare the real-time data with the historical behavior data, and extract the feature vector from the collected real-time data, which is expressed as:

[0022] D ratm (t)=[F(t),L(t),S(t),A(t)], where D ratm (t) represents the real-time dynamic behavior feature vector at time t, F(t) represents the capital flow vector at time t, reflecting the applicant's financial behavior; L(t) represents the geographic location vector at time t, recording the applicant's location information; S(t) represents the social network activity vector at time t, capturing the applicant's online behavior; A(t) represents the comprehensive behavior pattern at time t;

[0023] Adaptive filters are used to process behavioral data and predict future behavioral patterns: in It represents the prediction of the behavior pattern at the future time t+1, W(t) represents the adaptive weight matrix, which is updated in real time to capture the changes in the behavior pattern, and b(t) represents the bias vector.

[0024] Furthermore, in step S2, the adaptive update rule of the digital twin model is: Where η represents the learning rate parameter, which controls the speed of updating the weight matrix. represents the model's predicted value for the current behavior, (·) T Represents a transpose operation;

[0025] Compare real-time behavior with historical behavior patterns, calculate errors, and determine whether there is abnormal behavior:

[0026] Where Δ(t) represents the behavior error, which is the difference between the current real-time behavior and the historical behavior prediction. represents the predicted behavior vector calculated based on historical data;

[0027] If the error Δ(t) exceeds the preset threshold δ alert , the alarm will be triggered automatically.

[0028] Furthermore, the multi-dimensional dynamic identity authentication in step S2 further includes:

[0029] Employ multi-scale spatiotemporal analysis, including behavioral patterns across time scales and geographic ranges:

[0030] Where D mlicl (t) represents the behavior feature vector under multi-scale analysis, t represents time, ω k represents the weight factor at different scales, K represents the total number of scales, including different time windows or geographical ranges, and D k (t) represents the behavioral feature vector at the kth spatiotemporal scale, which represents the features extracted from the behavioral data of the applicant at time t based on the kth scale;

[0031] After detecting abnormal behavior, an alert is triggered and further verification is required;

[0032] At the same time, the detected abnormal behavior is fed back into the digital twin model.

[0033] Furthermore, in step S3, the digital file is stored in an encrypted form in a globally shared digital twin network; the applicant only needs to submit materials at the time of the initial application, and the file content will be automatically updated.

[0034] Furthermore, in step S3, the digital archives cross-border information sharing method is:

[0035] Data related to the applicant is obtained from multiple sources and fused and feature extracted. The data includes: static biometric data (the model generated by step S1), dynamic behavioral feature data (behavioral data updated in real time), historical visa records V h (t), that is, the applicant’s historical visa data and travel history T at time t h (t), that is, the applicant’s travel history and financial status at time t s (t), i.e., the financial data of the applicant at time t;

[0036] The fusion is expressed as a high-dimensional feature vector X(t);

[0037] The state space model SSM is used to describe the data evolution process, and the archive state vector is defined as Where Z(t) represents the archive state vector at time t, that is, the current state of the digital archive, and A represents the state transfer matrix, which describes the evolution of the archive over time. represents the input matrix, describing the impact of new data on the archive state, w(t) represents the process noise, representing random disturbance;

[0038] The Kalman filter is used to update the content of the digital archive in real time to dynamically reflect the applicant's information:

[0039] Z(t+1)=Z(t)+K t ·(X ma (t)-H·Z(t))

[0040] , where K t represents the Kalman gain, which controls the rate at which the archive content is updated, X ma (t) represents the latest feature vector collected at time t, H represents the observation matrix, which maps the state space to the observation space;

[0041] Kalman gain K t The update rule is: K t =P(t)·H T ·(H·P(t)·H T +R) 1 , where P(t) is the covariance matrix of the state estimate, R is the covariance matrix of the observation noise, and H T is the transpose of the observation matrix.

[0042] Furthermore, in step S3, the generated digital archive is stored in an encrypted form in the globally shared digital twin network:

[0043] Introducing Multi-Party Computation (MPC) technology, countries can collaborate when sharing and accessing archival data without leaking data privacy;

[0044] Use homomorphic encryption technology to process and calculate data in an encrypted state, and store and share archive data in a homomorphic encrypted form;

[0045] Blockchain technology is used for distributed storage and management of archival data, and each update is recorded in the distributed ledger;

[0046] When there is a need to share or verify archival data, visa agencies in various countries authorize access to digital archives;

[0047] Visa agencies in various countries can share applicants' information by authorizing access to the digital file, thus avoiding repeated submission of documents. Agencies in all visa application countries can access and verify the information in the file in real time, thus achieving cross-border synchronous review. This improves the approval speed, ensures the consistency of review information between countries, and reduces the risk of visa rejection due to additional requirements of different countries.

[0048] Through the digital twin network, visa agencies in various countries test the impact of different review standards on applicants in a simulation environment, thereby continuously adjusting and optimizing the approval process; collaborative simulation can not only improve the approval efficiency among countries, but also better coordinate approval standards and improve the efficiency and consistency of the entire visa process.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention uses digital twin technology to create an identity model based on dynamic data for each applicant. The model features include the applicant's biometrics, as well as their daily behavior patterns, social network activities, and historical travel record data features; thereby providing a more comprehensive and dynamic identity verification of the applicant, not limited to data at a single point in time.

[0051] In the present invention, during the visa application process, the digital twin model captures and analyzes the applicant's behavioral data in real time and matches it with the established model. If abnormal behavior is found (such as behavioral patterns that are inconsistent with history), a prompt is automatically issued to require further verification or reject the application.

[0052] The present invention adopts multi-dimensional verification, which, in addition to static biometric features, additionally verifies the applicant's capital flow and multi-dimensional data of geographical location history; greatly improves the reliability of identity authentication; through digital twin identity authentication simulation, the visa application process can dynamically and real-time verify the applicant's identity, enhance the ability to prevent forgery and fraudulent use, and at the same time reduce errors caused by human subjective judgment, greatly improving the security and reliability of visa applications.

[0053] The present invention adopts digital twin technology to establish a unified digital file of applicants; the file contains the applicant's identity authentication data, historical visa records, travel history, and financial status; the applicant's data is encrypted and stored in a digital twin network shared by various countries, allowing visa agencies in various countries to access applicant data information through authorization, thereby avoiding applicants from submitting materials repeatedly.

[0054] According to the present invention, during the review process, the visa agency verifies and synchronizes the information in the digital twin file in real time without waiting for the review results of other countries. This not only improves the approval speed but also ensures the consistency of visa review information among countries.

[0055] The present invention utilizes the digital twin network so that countries can pre-test the impact of different review standards on applicants in a simulation environment, and adjust and optimize the approval process accordingly. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The figure is a flow chart of the visa application method of the present invention. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0058] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0059] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0060] Example 1

[0061] See also Figure 1 The present invention provides a visa application method, comprising:

[0062] Step S1, visa application identity model construction, at the initial stage of visa application, a digital twin identity model is created for each applicant;

[0063] In step S1, a digital twin identity model is constructed based on the static biometrics and dynamic behavioral characteristics of the applicant; the data in the model is collected through sensors, network activities and historical records, and is continuously updated during the application process;

[0064] Static biometric features include the applicant’s fingerprints and photos, while dynamic behavioral features include the applicant’s daily activity patterns, social network activities, and historical travel records;

[0065] In step S1, digital twin technology is used to create an identity model based on dynamic data for each applicant:

[0066] Collect static and dynamic data related to applicants from multiple data sources, including:

[0067] Static biometric data: fingerprint data B f , iris image B i , facial recognition data B m ;

[0068] Dynamic behavior data: location history L(t), capital flow record F(t), social network activity S(t), travel history T h ;

[0069] Construct feature vectors and map static and dynamic data into high-dimensional feature vectors. Static feature vector B: B = [B f ,B i ,B m ], dynamic feature vector D(t): D(t) = [L(t), F(t), S(t), T h ];

[0070] In step S1, the static features and dynamic features are combined to form a time-varying multi-dimensional identity vector model:

[0071] I(t) = α·B+β·D(t), where I(t) represents the applicant's comprehensive identity vector at time t, and α and β are weight factors that reflect the relative importance of static and dynamic features. They are automatically adjusted through historical data regression analysis algorithms to capture and evaluate applicants' identity changes in real time, combining static and dynamic features to ensure the reliability of identity verification;

[0072] Specifically, by combining static biometric features (fingerprints, facial recognition) and dynamic behavioral features (geographic location, capital flow, social network activities), the applicant's identity changes can be captured and evaluated in real time. Dynamic modeling not only improves the reliability of identity authentication, but also reduces the risk of identity authentication failure due to data errors at a single point in time.

[0073] Step S2, multi-dimensional dynamic identity verification, real-time behavior monitoring and verification. During the application process, the digital twin model constructed in step S1 monitors the applicant's behavior data in real time and compares it with the existing identity model; if there is a significant difference between the current behavior (such as capital flow, geographic location, etc.) and the historical behavior pattern in the model, an alarm is automatically triggered and further verification is required; the multi-dimensional dynamic verification mechanism is not limited to data at a single point in time, and can more comprehensively evaluate the applicant's true identity and behavior credibility, reduce errors in human subjective judgment, and improve the security and accuracy of visa applications;

[0074] In step S2, based on the digital twin model constructed in step S1, real-time multi-dimensional behavior monitoring and verification are performed:

[0075] Compare the real-time data with the historical behavior data, and extract the feature vector from the collected real-time data, which is expressed as:

[0076] D ratm (t)=[F(t),L(t),S(t),A(t)], where D ratm (t) represents the real-time dynamic behavior feature vector at time t, F(t) represents the capital flow vector at time t, reflecting the applicant's financial behavior; L(t) represents the geographic location vector at time t, recording the applicant's location information; S(t) represents the social network activity vector at time t, capturing the applicant's online behavior; A(t) represents the comprehensive behavior pattern at time t;

[0077] Adaptive filters are used to process behavioral data and predict future behavioral patterns: in It represents the prediction of the behavior pattern at the future time t+1, W(t) represents the adaptive weight matrix, which is updated in real time to capture the changes in the behavior pattern, and b(t) represents the bias vector;

[0078] In step S2, the adaptive update rule of the digital twin model is:

[0079]

[0080] , where η represents the learning rate parameter, which controls the speed of updating the weight matrix. represents the model's predicted value for the current behavior, (·) T Represents a transpose operation;

[0081] Compare real-time behavior with historical behavior patterns, calculate errors, and determine whether there is abnormal behavior:

[0082] Where Δ(t) represents the behavior error, which is the difference between the current real-time behavior and the historical behavior prediction. represents the predicted behavior vector calculated based on historical data;

[0083] If the error Δ(t) exceeds the preset threshold δ alert , the alarm will be triggered automatically;

[0084] The multi-dimensional dynamic identity authentication in step S2 also includes:

[0085] Employ multi-scale spatiotemporal analysis, including behavioral patterns across time scales and geographic ranges:

[0086] Where D mlicl (t) represents the behavior feature vector under multi-scale analysis, t represents time, ω k represents the weight factor at different scales, K represents the total number of scales, including different time windows or geographical ranges, and D k (t) represents the behavioral feature vector at the kth spatiotemporal scale, which represents the features extracted from the behavioral data of the applicant at time t based on the kth scale;

[0087] After detecting abnormal behavior, an alert is triggered and further verification is required;

[0088] At the same time, the detected abnormal behavior is fed back into the digital twin model;

[0089] Specifically, the multi-dimensional dynamic identity authentication mechanism significantly improves the ability to identify abnormal behavior. By monitoring the applicant's behavioral data in real time and comparing it with historical data, it can quickly detect abnormal behavior and automatically trigger an alarm. Multi-dimensional dynamic verification does not only rely on static data, but is based on behavioral pattern analysis over multiple time scales and geographic ranges. It can more comprehensively assess the credibility of applicants' behavior, reduce errors caused by human subjective judgment, and improve the security and accuracy of visa applications.

[0090] Step S3, cross-border information sharing of digital archives, building digital archives including comprehensive verification data, historical visa records, travel history, and financial status information;

[0091] In step S3, the digital file is stored in encrypted form in the globally shared digital twin network; applicants only need to submit materials when applying for the first time, and the file content will be automatically updated;

[0092] In step S3, the digital archives cross-border information sharing method is:

[0093] Data related to the applicant is obtained from multiple sources and fused and feature extracted. The data includes: static biometric data (the model generated by step S1), dynamic behavioral feature data (behavioral data updated in real time), historical visa records V h (t), that is, the applicant’s historical visa data and travel history T at time t h (t), that is, the applicant’s travel history and financial status at time t s (t), i.e., the financial data of the applicant at time t;

[0094] The fusion is expressed as a high-dimensional feature vector X(t);

[0095] The state space model SSM is used to describe the data evolution process, and the archive state vector is defined as Where Z(t) represents the archive state vector at time t, that is, the current state of the digital archive, and A represents the state transfer matrix, which describes the evolution of the archive over time. represents the input matrix, describing the impact of new data on the archive state, w(t) represents the process noise, representing random disturbance;

[0096] The Kalman filter is used to update the content of the digital archive in real time to dynamically reflect the applicant's information:

[0097] Z(t+1)=Z(t)+K t ·(X ma (t)-H·Z·(t))

[0098] , where K t represents the Kalman gain, which controls the rate at which the archive content is updated, X ma (t) represents the latest feature vector collected at time t, H represents the observation matrix, which maps the state space to the observation space;

[0099] Kalman gain K t The update rule is: K t =P(t)·H T ·(H·P(t)·H T +R) 1 , where P(t) is the covariance matrix of the state estimate, R is the covariance matrix of the observation noise, and H T is the transpose of the observation matrix;

[0100] In step S3, the generated digital archive is stored in encrypted form in the globally shared digital twin network:

[0101] Introducing Multi-Party Computation (MPC) technology, countries can collaborate when sharing and accessing archival data without leaking data privacy;

[0102] Use homomorphic encryption technology to process and calculate data in an encrypted state, and store and share archive data in a homomorphic encrypted form;

[0103] Blockchain technology is used for distributed storage and management of archival data, and each update is recorded in the distributed ledger;

[0104] When there is a need to share or verify archival data, visa agencies in various countries authorize access to digital archives;

[0105] Visa agencies in various countries can share applicants' information by authorizing access to the digital file, thus avoiding repeated submission of documents. Agencies in all visa application countries can access and verify the information in the file in real time, thus achieving cross-border synchronous review. This improves the approval speed, ensures the consistency of review information between countries, and reduces the risk of visa rejection due to additional requirements of different countries.

[0106] Through the digital twin network, visa agencies in various countries test the impact of different review standards on applicants in a simulation environment, thereby continuously adjusting and optimizing the approval process; collaborative simulation can not only improve the approval efficiency among countries, but also better coordinate approval standards and improve the efficiency and consistency of the entire visa process;

[0107] Specifically, a cross-border information sharing mechanism is adopted to build a digital file containing comprehensive verification data, historical visa records, travel history and financial status information, and encrypt and store it in a globally shared digital twin network. Visa agencies in various countries can access and verify the information in the file in real time, eliminating the cumbersome process of applicants repeatedly submitting materials. Through unified standards and real-time cross-border synchronous review, the approval speed and consistency can be significantly improved, and the risk of visa rejection due to inconsistent requirements of different countries can be reduced.

[0108] Step S4: visa result feedback: after the visa review is completed, the applicant will be notified of the result in real time through multiple channels (email, SMS, app); the reason for rejection will be provided for applicants who fail the review;

[0109] Specifically, homomorphic encryption and blockchain technology provide security for digital archives. Through multi-party secure computing (MPC) and homomorphic encryption, data is prevented from being leaked during processing and sharing. Blockchain technology records every update of archival data in the form of a distributed ledger, which also ensures the data's immutability and transparency.

[0110] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A visa application method, characterized in that: include: Step S1, visa application identity model construction, at the initial stage of visa application, a digital twin identity model is created for each applicant; Step S2: multi-dimensional dynamic identity verification, real-time behavior monitoring and verification. During the application process, the digital twin model constructed in step S1 monitors the applicant's behavior data in real time and compares it with the existing identity model. If there is a significant difference between the current behavior and the historical behavior pattern in the model, an alarm is automatically triggered, requiring further verification. Step S3, cross-border information sharing of digital archives, building digital archives including comprehensive verification data, historical visa records, travel history, and financial status information; Step S4: visa result feedback: after the visa review is completed, the applicant will be notified of the result in real time through multiple channels; the reason for rejection will be provided for applicants who fail the review; In step S1, a digital twin identity model is constructed based on the static biometrics and dynamic behavioral characteristics of the applicant; the data in the model is collected through sensors, network activities and historical records, and is continuously updated during the application process; Static biometric features include the applicant’s fingerprints and photos, while dynamic behavioral features include the applicant’s daily activity patterns, social network activities, and historical travel records; In step S1, digital twin technology is used to create an identity model based on dynamic data for each applicant: Collect static and dynamic data related to applicants from multiple data sources, including: Static biometric data: fingerprint data B f , iris image B i , facial recognition data B m ; Dynamic behavior data: location history L(t), capital flow record F(t), social network activity S(t), travel history T h ; Construct feature vectors and map static and dynamic data into high-dimensional feature vectors. Static feature vector B: B = [B f ,B i ,B m ], dynamic feature vector D(t): D(t) = [L(t), F(t), S(t), T h ]; In step S1, the static features and dynamic features are combined to form a time-varying multidimensional identity vector model: I(t) = α·B + β·D(t), where I(t) represents the applicant's comprehensive identity vector at time t, and α and β are weight factors reflecting the relative importance of static and dynamic characteristics; In step S2, based on the digital twin model constructed in step S1, real-time multi-dimensional behavior monitoring and verification are performed: Compare the real-time data with the historical behavior data, and extract the feature vector from the collected real-time data, which is expressed as: D ratm (t)=[F(t),L(t),S(t),A(t)], where D ratm (t) represents the real-time dynamic behavior feature vector at time t, F(t) represents the capital flow vector at time t, reflecting the applicant's financial behavior; L(t) represents the geographic location vector at time t, recording the applicant's location information; S(t) represents the social network activity vector at time t, capturing the applicant's online behavior; A(t) represents the comprehensive behavior pattern at time t; Adaptive filters are used to process behavioral data and predict future behavioral patterns: in It represents the prediction of the behavior pattern at the future time t+1, W(t) represents the adaptive weight matrix, which is updated in real time to capture the changes in the behavior pattern, and b(t) represents the bias vector; In step S2, the adaptive update rule of the digital twin model is: Where η represents the learning rate parameter, which controls the speed of updating the weight matrix. represents the model's predicted value for the current behavior, (·) T Represents a transpose operation; Compare real-time behavior with historical behavior patterns, calculate errors, and determine whether there is abnormal behavior: Where Δ(t) represents the behavior error, which is the difference between the current real-time behavior and the historical behavior prediction. represents the predicted behavior vector calculated based on historical data; If the error Δ(t) exceeds the preset threshold δ alert , the alarm will be triggered automatically; The multi-dimensional dynamic identity authentication in step S2 also includes: Employ multi-scale spatiotemporal analysis, including behavioral patterns across time scales and geographic ranges: Where D mlicl (t) represents the behavior feature vector under multi-scale analysis, t represents time, ω k represents the weight factor at different scales, K represents the total number of scales, including different time windows or geographical ranges, and D k (t) represents the behavioral feature vector at the kth spatiotemporal scale, which represents the features extracted from the behavioral data of the applicant at time t based on the kth scale; After detecting abnormal behavior, an alert is triggered and further verification is required; At the same time, the detected abnormal behavior is fed back into the digital twin model; In step S3, the digital file is stored in an encrypted form in a globally shared digital twin network; the applicant only needs to submit materials during the initial application, and the file content will be automatically updated.

2. A visa application method according to claim 1, characterized in that: In step S3, the digital archives cross-border information sharing method is: Acquire applicant-related data from multiple sources and perform fusion and feature extraction. The data includes: static biometric data, dynamic behavioral feature data, historical visa records V h (t), that is, the applicant’s historical visa data and travel history T at time t h (t), that is, the applicant’s travel history and financial status at time t s (t), i.e., the financial data of the applicant at time t; The fusion is expressed as a high-dimensional feature vector X(t); The state space model SSM is used to describe the data evolution process, and the archive state vector is defined as Where Z(t) represents the archive state vector at time t, that is, the current state of the digital archive, and A represents the state transfer matrix, which describes the evolution of the archive over time. represents the input matrix, describing the impact of new data on the archive state, w(t) represents the process noise, and represents random disturbance; The Kalman filter is used to update the content of the digital archive in real time to dynamically reflect the applicant's information: Z(t+1)=Z(t)+K t ·(X ma (t)-H·Z(t)), Where K t represents the Kalman gain, which controls the rate at which the archive content is updated, X ma (t) represents the latest feature vector collected at time t, H represents the observation matrix, which maps the state space to the observation space; Kalman gain K t The update rule is: K t =P(t)·H T ·(H·P(t)·H T +R) 1 , where P(t) is the covariance matrix of the state estimate, R is the covariance matrix of the observation noise, and H T is the transpose of the observation matrix.

3. A visa processing method according to claim 2, characterized in that: In step S3, the generated digital archive is stored in encrypted form in the globally shared digital twin network: Introducing multi-party secure computing technology, countries can collaborate without leaking data privacy when sharing and accessing archival data; Use homomorphic encryption technology to process and calculate data in an encrypted state, and store and share archive data in a homomorphic encrypted form; Blockchain technology is used for distributed storage and management of archival data, and each update is recorded in the distributed ledger; When there is a need to share or verify archival data, visa authorities in various countries access digital archives through authorization.

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