A remote inspection method based on electronic certificates and related equipment

Abstract

The present invention provides a remote inspection method based on electronic certificates and related equipment, which are applied in the field of data processing technology. The present application obtains encrypted electronic certificate information to be identified, biometric information of a target user, and access information of a target application; processes the biometric information of the target user to generate identity attribute information of the target user; processes the encrypted electronic certificate information to be identified based on preset electronic certificate decryption information to generate decrypted electronic certificate information to be identified; processes an initial certificate recognition model based on a training data set to generate a target certificate recognition model, an initial electronic certificate identity recognition result, access permission information of a target application, and access intention information of a target user to generate a target electronic certificate recognition result.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a remote verification method based on electronic certificates and related equipment. Background Art

[0002] Certificates are proof of identity for individuals or organizations, and are the basis for the normal conduct of various social and economic activities. Nowadays, fake certificates are rampant in society, which seriously affects social order, and some have caused adverse social impacts and even brought great economic losses. Therefore, the anti-counterfeiting and authentication of certificates is necessary for the normal conduct of social activities and is the basis for ensuring the validity of certificates.

[0003] Biometric recognition technology is a discipline in computer science that uses biological characteristics to identify people and perform access control. Biometric recognition technology is widely used and plays an important role in the field of computer applications. In computer security, biometric recognition is an important means of authentication, and biometrics are widely used in the field of security and crime prevention, as well as in the field of national security and public security. The more mature and widely used biometric recognition technologies are mainly fingerprints, irises, faces, ears, palm prints, palm veins, etc. In addition, in recent years, there have been breakthroughs in research on voice recognition, brain wave recognition, and DNA extraction from saliva, which are expected to enter the commercial stage.

[0004] However, current identification technology is also difficult to identify the identity of the counterfeiter, and it is impossible to determine whether the environment in which the user is currently performing identity identification is in a safe state. How to solve the above problems is a technical problem that technical personnel in this field urgently need to solve.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] The purpose of this application is to provide a remote verification method based on electronic certificates and related equipment, which at least overcomes the problems existing in the prior art to a certain extent. When a user wants to access an application, he needs to complete identity authentication first. At this time, the biometrics of the party and the electronic certificate information to be identified are collected first to achieve the verification of the person and the certificate. If the verification result shows that the party can access the current application, the frequency, time period and facial emotion of the party accessing the application are judged, thereby generating the result of identity authentication.

[0007] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present invention.

[0008] According to one aspect of the present application, a remote inspection method based on electronic certificates is provided, comprising: obtaining encrypted electronic certificate information to be identified, biometric information of a target user, and access information of a target application, wherein the access information of the target application includes the access frequency of the target application, the access time period of the target application, and the access purpose information of the target user; processing the biometric information of the target user to generate identity attribute information of the target user, wherein the identity attribute information of the target user includes the identity information of the target user, the emotional information of the target user, and preset electronic certificate decryption information; processing the encrypted electronic certificate information to be identified based on the preset electronic certificate decryption information to generate decrypted electronic certificate information to be identified; obtaining an initial certificate recognition model that matches the identity attribute information of the target user and the access information of the target application, and a training data set that matches the initial certificate recognition model, Among them, the training data set includes other user data that matches the target user's emotional information and access purpose information; the initial certificate recognition model is processed based on the training data set to generate a target certificate recognition model; the decrypted electronic certificate information to be recognized and the identity information of the target user are processed based on the target certificate recognition model to generate an initial electronic certificate identity recognition result; the access frequency of the target application and the access time period of the target application are processed based on the target certificate recognition model to generate access permission information of the target application; the access purpose information of the target user and the emotional information of the target user are processed based on the target certificate recognition model to generate access intention information of the target user; the initial electronic certificate identity recognition result, the access permission information of the target application and the access intention information of the target user are processed based on the target certificate recognition model to generate a target electronic certificate recognition result.

[0009] In one embodiment of the present application, the biometric information of the target user is processed to generate the identity attribute information of the target user, including: processing the biometric information of the target user to generate initial facial image information and the identity information of the target user, wherein the initial facial image information is the facial information of the target user within a preset time period; processing the initial facial image information based on preset processing rules to generate the expression feature influencing factors of the target user; processing the expression feature influencing factors of the target user based on a preset emotion recognition model to generate the target facial image information, wherein the target facial image information includes the expression change information of the target user; processing the target facial image information to generate the emotion information of the target user; processing the identity information of the target user to obtain the preset electronic certificate decryption information; generating the identity attribute information of the target user based on the identity information of the target user, the emotion information of the target user and the preset electronic certificate decryption information.

[0010] In one embodiment of the present application, the initial document recognition model is processed based on the training data set to generate a target document recognition model, including: processing the training data set to generate expression feature influencing factors, wherein the expression feature influencing factors are generated based on facial image information of other users; obtaining a preset expression mapping table, wherein the preset expression mapping table is expression information generated based on different users for different scenarios; processing the expression feature influencing factors and the preset expression mapping table to generate an original feature library; processing the original feature library to generate several feature data sets, and generating a training set and a verification set based on the several feature data sets; processing the initial document recognition model based on the training set and the verification set to generate a target document recognition model.

[0011] In one embodiment of the present application, the initial document recognition model is processed based on the training set and the validation set to generate a target document recognition model, including: obtaining any number of data features in the feature data set; generating adjacent features based on the distance between the any number of data features and other numbers of data features of the same category, wherein the adjacent features include a preset number of the any number of data features; determining a sampling ratio based on the number of each data feature in the training set; determining a sampling ratio based on the sampling ratio; sampling the adjacent features based on the sampling ratio to generate a preset number of sampling features; generating multiple data groups based on any data feature and each sampling feature, wherein each data group includes a preset number of data samples, wherein each data group includes a preset number of data samples, wherein at least one data sample includes identification information; training the initial document recognition model based on the data samples in the multiple data groups to generate a trained document recognition model; processing the trained document recognition model based on the test set to generate a test result; if the data sample containing the identification information in the test result indicates that the electronic passport is in an abnormal state, the trained document recognition model is used as the target document recognition model.

[0012] In one embodiment of the present application, the access frequency of the target application and the access time period of the target application are processed based on the target certificate recognition model to generate access permission information for the target application, including: obtaining historical access information of the target user for the target application; processing the historical access information of the target application based on the target certificate recognition model to generate access preference information of the target user, wherein the access preference information of the target user includes the access information of the target user within the historical time period; processing the access frequency of the target application and the access time period of the target application based on the access preference information of the target user to generate access permission information for the target application.

[0013] In one embodiment of the present application, the target user's access purpose information and the target user's emotional information are processed based on the target certificate recognition model to generate the target user's access intention information, including: processing the target user's access purpose information based on the target certificate recognition model to generate attribute information of the content to be accessed by the target user; processing the attribute information of the content to be accessed by the target user based on the target user's emotional information to generate a service access matching degree, wherein the service access matching degree is used to characterize the matching relationship between the target user's emotional state and the service to be accessed; processing the service access matching degree to obtain the target user's access intention information.

[0014] In one embodiment of the present application, the initial electronic certificate identity recognition result, the access permission information of the target application, and the access intention information of the target user are processed based on the target certificate recognition model to generate a target electronic certificate recognition result, including: processing the access permission information of the target application and the access intention information of the target user based on the target certificate recognition model to generate access result information of the target application; processing the initial electronic certificate identity recognition result based on the access result information of the target application to generate a target electronic certificate recognition result.

[0015] Another aspect of the present application is a remote inspection device based on electronic certificates, characterized in that it includes: an acquisition module, used to acquire encrypted electronic certificate information to be identified, biometric information of a target user, and access information of a target application, wherein the access information of the target application includes the access frequency of the target application, the access time period of the target application, and the access purpose information of the target user; acquiring an initial certificate recognition model that matches the identity attribute information of the target user and the access information of the target application, and a training data set that matches the initial certificate recognition model, wherein the training data set includes other user data that matches the emotional information and access purpose information of the target user; a processing module, used to process the biometric information of the target user to generate the identity attribute information of the target user, wherein the identity attribute information of the target user includes the identity information of the target user, the emotional information of the target user, and preset electronic certificate decryption information; based on the preset The electronic certificate decryption information processes the encrypted electronic certificate information to be identified to generate decrypted electronic certificate information to be identified; processes the initial certificate identification model based on the training data set to generate a target certificate identification model; processes the decrypted electronic certificate information to be identified and the identity information of the target user based on the target certificate identification model to generate an initial electronic certificate identity identification result; processes the access frequency of the target application and the access time period of the target application based on the target certificate identification model to generate access permission information of the target application; processes the access purpose information of the target user and the emotional information of the target user based on the target certificate identification model to generate access intention information of the target user; processes the initial electronic certificate identity identification result, the access permission information of the target application and the access intention information of the target user based on the target certificate identification model to generate a target electronic certificate identification result.

[0016] According to another aspect of the present application, an electronic device is characterized in that it includes: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the above-mentioned remote verification method based on electronic certificates by executing the executable instructions.

[0017] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a second processor, the above-mentioned remote verification method based on electronic certificates is implemented.

[0018] According to another aspect of the present application, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a third processor, the above-mentioned remote verification method based on electronic certificates is implemented.

[0019] The present application provides a remote inspection method based on electronic certificates and related equipment, wherein a server obtains encrypted electronic certificate information to be identified, biometric information of a target user, and access information of a target application, wherein the access information of the target application includes the access frequency of the target application, the access time period of the target application, and the access purpose information of the target user; processes the biometric information of the target user to generate the identity attribute information of the target user, wherein the identity attribute information of the target user includes the identity information of the target user, the emotional information of the target user, and preset electronic certificate decryption information; processes the encrypted electronic certificate information to be identified based on the preset electronic certificate decryption information to generate the decrypted electronic certificate information to be identified; obtains an initial certificate recognition model that matches the identity attribute information of the target user and the access information of the target application, and a template that matches the initial certificate recognition model The training data set includes other user data that matches the target user's emotional information and access purpose information; the initial certificate recognition model is processed based on the training data set to generate a target certificate recognition model; the decrypted electronic certificate information to be recognized and the identity information of the target user are processed based on the target certificate recognition model to generate an initial electronic certificate identity recognition result; the access frequency of the target application and the access time period of the target application are processed based on the target certificate recognition model to generate the access permission information of the target application; the access purpose information of the target user and the emotional information of the target user are processed based on the target certificate recognition model to generate the access intention information of the target user; the initial electronic certificate identity recognition result, the access permission information of the target application and the access intention information of the target user are processed based on the target certificate recognition model to generate the target electronic certificate recognition result. When a user wants to access an application, he needs to complete identity authentication first. At this time, the biometric features (such as facial images, fingerprints) of the person concerned and the electronic certificate information to be recognized are first collected, and the data is sent to the server to realize the verification of the person and the certificate to ensure the authenticity of the verification. If the verification result shows that the party can access the current application, the frequency and time period of the party's access to the application and the party's facial emotions are judged to determine whether the party logged into the current application voluntarily or was coerced, thereby generating the final electronic certificate recognition result, that is, the electronic certificate and the party's biometric features match and are consistent, but if the party is under coercion or emergency at the time, identity authentication is also refused.

[0020] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a remote verification method based on an electronic certificate provided by an embodiment of the present application is shown;

[0022] Figure 2 A schematic diagram of the structure of a remote inspection device based on an electronic certificate provided in an embodiment of the present application is shown;

[0023] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application is shown;

[0024] Figure 4 A schematic diagram of a storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0026] Combine the following Figure 1 To describe the remote inspection method based on electronic certificates according to the exemplary implementation of the present application. It should be noted that the following application scenarios are only shown to facilitate understanding of the spirit and principles of the present application, and the implementation of the present application is not limited in this regard. On the contrary, the implementation of the present application can be applied to any applicable scenario.

[0027] In one implementation, the present application also proposes a remote verification method based on electronic certificates and related equipment. Figure 1 The flowchart of a remote verification method based on an electronic certificate according to an embodiment of the present application is schematically shown. Figure 1 As shown, the method is applied to a server, comprising:

[0028] S101, obtaining the encrypted electronic certificate information to be identified, the biometric information of the target user and the access information of the target application.

[0029] In one implementation, encrypted electronic certificate information is obtained from a terminal device (such as a smartphone APP), which may include the user's personal identity information and certificate data; biometric information of the target user, such as facial images and fingerprints, is collected and used for identity authentication and certificate recognition.

[0030] The access information of the target application includes the access frequency of the target application, the access time period of the target application and the access purpose information of the target user. For example, the application to be accessed is a government software program such as "Minzhengtong" or "Suishenban", which involves various service contents.

[0031] Analyze the user's access frequency to determine the user's usage habits and preferences, determine the time period when the user accesses the application in order to provide time-related services and support, understand the purpose of the user's access to the application, such as handling business, querying information or using specific services, and match the corresponding service content provided by the target application based on the user's access purpose information.

[0032] S102: Process the biometric information of the target user to generate identity attribute information of the target user.

[0033] In one embodiment, the biometric information of the target user (such as the target user's facial information or fingerprint information, etc.) is processed to generate initial facial image information and the target user's identity information, wherein the initial facial image information is the facial information of the target user within a preset time period, and the facial image data of the same person collected within a specific time period are used to create a facial template.

[0034] The initial facial image information is processed based on preset processing rules to generate the influencing factors of the target user's expression characteristics, extract key features in the image, such as changes in facial action units (AU), and then used for subsequent micro-expression recognition. The influencing factors of the target user's expression characteristics are processed based on a preset emotion recognition model to generate target facial image information, wherein the preset emotion recognition model uses deep learning technology, especially convolutional neural networks (CNNs), to recognize micro-expressions. These networks can learn and extract complex features from a large amount of facial expression data. The real-time captured facial image is input into a well-trained model, which will analyze the image and predict the emotional state. By continuously monitoring the slight changes in facial expressions, the dynamic change process of the user's emotions can be tracked. The target facial image information includes the expression change information of the target user, and the target facial image information is processed to generate the target user's emotional information, including outputting the results of the emotional change, which may include the type and intensity of the emotion.

[0035] Specifically, the collected images are preprocessed, including denoising, contrast adjustment, normalization, etc., to improve the image quality. A facial detection algorithm is used to determine the position of the face in the image and locate the key points of the face, such as the eyes, nose, mouth, etc. This solution does not limit the specific facial detection algorithm, and the applicant can choose according to actual needs. Among them, the facial detection algorithm includes but is not limited to the histogram of oriented gradients (HOG), local binary pattern (LBP), and Haar cascade classifier. Facial action units are the basic components of facial expressions. By analyzing the changes in these action units, different micro-expressions can be identified. For example, AU1 indicates the lifting of the inner side of the eyebrows, AU12 indicates the closure of the lips, etc., and key features in the image are extracted, such as changes in facial action units, changes in facial contours, etc. According to the extracted features, a machine learning algorithm is used to classify the expressions and identify different emotions or psychological states.

[0036] The target user's identity information is processed to obtain preset electronic certificate decryption information. The target user's real identity is obtained by processing the target user's identity information, and the preset electronic certificate decryption information used to decrypt the encrypted electronic certificate information to be identified is obtained. The decryption information is set based on the user's personal habits or preset encryption and decryption rules. The purpose is to decrypt the encrypted electronic certificate information to be identified that matches the target user's identity, and generate identity attribute information unique to the target user based on the target user's identity information, the target user's emotional information and the preset electronic certificate decryption information.

[0037] S103, processing the encrypted electronic certificate information to be identified based on the preset electronic certificate decryption information to generate decrypted electronic certificate information to be identified.

[0038] In one implementation, the decrypted electronic certificate to be identified is not necessarily the real electronic certificate information. If the decrypted information does not match, it means that the electronic certificate may not match the target user. For example, the decrypted information may be garbled. If the decrypted information is correct, the decrypted electronic certificate information to be identified will be obtained. At this time, the obtained electronic certificate information to be identified may match the target user, or may not match the target user (maybe the password settings are just the same).

[0039] S104, obtaining an initial certificate recognition model that matches the identity attribute information of the target user and the access information of the target application and a training data set that matches the initial certificate recognition model.

[0040] In one implementation, the training data set includes other user data that matches the target user's emotion information and access purpose information.

[0041] The initial document recognition model is to identify the various emotions that different users may have when accessing different application services, and to determine what the user's true thoughts are under various emotions, so as to build a corresponding database to facilitate subsequent judgment of the target user's behavior. The initial document recognition model is improved by obtaining a corresponding training data set, wherein the training data set includes other user data that matches the target user's emotional information and access purpose information.

[0042] S105: Process the initial document recognition model based on the training data set to generate a target document recognition model.

[0043] In one implementation, the training data set is processed to generate expression feature influencing factors, wherein the expression feature influencing factors are generated based on the facial image information of other users. Specifically, the data set is annotated with expressions, i.e., one or more expression category labels are assigned to each image, and expression features are extracted from the aligned facial images. This includes geometric features (such as the shape and position of eyes and mouth), texture features (such as skin texture information), optical flow features, etc., and algorithms (such as networks based on deep learning) are used to locate key points on the face, such as the corners of the eyes and the corners of the mouth. The changes in these key points are crucial for expression recognition. A preset expression mapping table is obtained, wherein the preset expression mapping table is based on the expression information generated by different users for different scenes, i.e., it contains the expressions that different users may generate for different scenes, and then compared with the target user, so as to obtain which expressions are reasonable and which expressions are unreasonable for the target user under the specific access purpose.

[0044] The factors affecting expression features and the preset expression mapping table are processed to generate an original feature library, the original feature library is processed to generate several feature data sets, training sets and verification sets are generated based on the several feature data sets, and the initial document recognition model is processed based on the training sets and verification sets to generate a target document recognition model. The process of processing the factors affecting expression features and generating document recognition models involves multiple steps, including data preprocessing, feature extraction, data set construction, model training and verification, etc. The collected factors affecting expression features are normalized and standardized to eliminate the deviation of data from different sources, an existing preset expression mapping table is created or used, and expression features are associated with specific expression categories. An original feature library containing all possible expression features is generated by combining the factors affecting expression features and the preset expression mapping table, and feature selection and dimensionality reduction are performed on the original feature library to extract the most informative features. Several feature data sets are extracted or constructed from the original feature library, each of which contains feature vectors for training and testing.

[0045] The feature data set is divided into a training set and a validation set, usually randomly allocated in a certain ratio (such as 80% training set, 20% validation set). The initial document recognition model is trained using the training set, and the model parameters are iteratively optimized. The model performance is evaluated on the validation set, and the model structure or parameters are adjusted to improve accuracy and generalization ability. Based on the performance on the validation set, the model is further optimized using techniques such as cross-validation and hyperparameter tuning. After the training and validation process is completed, an optimized target document recognition model is obtained, which can be used for actual document recognition tasks. A comprehensive evaluation of the target document recognition model is performed, including indicators such as accuracy, recall, and F1 score.

[0046] In another embodiment, adjacent features are generated based on the distance between any number of data features and other number of data features of the same category, wherein the adjacent features include a preset number of any number of data features; in the field of data analysis and machine learning, generating adjacent features based on the distance between data features is a feature engineering method that aims to discover the relationship and pattern between features. Specifically, a certain number of data features are selected from the feature data set, which can reflect the use of electronic certificates, and the distance between any data feature and other data features of the same category is calculated using a distance metric (such as Euclidean distance, Manhattan distance or cosine similarity). According to the calculated distance, other data features closest to any data feature are determined, and these features are considered to be adjacent. A preset number is set, that is, the number of data features that are expected to be included in the adjacent feature set. For each data feature, the preset number of data features closest to it is selected to form an adjacent feature set, and the adjacent feature set is analyzed to identify the potential relationships and patterns between the features, which may be crucial to understanding the use of electronic certificates, and the importance of each data feature in the adjacent feature set is evaluated to determine which features are most critical to predicting the target variable. That is, which places may users go to in which time periods, and whether there is a sequence of appearance between different places, etc.

[0047] If there are too many features in the adjacent feature set, it may be necessary to apply dimensionality reduction techniques (such as principal component analysis PCA) to reduce the number of features while retaining important information. The generated adjacent feature set is used to train the machine learning model to predict or classify the use of electronic certificates, evaluate the performance of the model, such as accuracy, recall rate, and F1 score, and optimize the model as needed. It may be necessary to adjust the preset number and distance measurement method multiple times to find the optimal feature set. This method can better understand the complexity of the use of electronic certificates and improve the predictive ability of the early warning model. This is very important for the security management and risk assessment of electronic certificates. At the same time, this also helps to improve the model's ability to identify abnormal behaviors and ensure the security and reliability of the electronic certificate system.

[0048] Based on the number of data features in the training set, a sampling ratio is determined; based on the sampling ratio, a sampling ratio is determined; based on the sampling ratio, adjacent features are sampled to generate a preset number of sampling features; based on any data feature and each sampling feature, multiple data groups are generated, wherein each data group contains a preset number of data samples, wherein each data group contains a preset number of data samples, wherein at least one data sample includes identification information.

[0049] In data analysis and machine learning, the sampling ratio and sampling rate are determined based on the number of data features in the training set. By counting the number of each data feature in the training set, the distribution of different features can be understood. The sampling ratio is determined based on the number of data features and the size of the data set. The sampling ratio can help us decide how many samples to extract from each feature. According to the sampling ratio, the specific sampling ratio can be calculated. The sampling ratio is the ratio of the actual number of samples to the total number. The adjacent features are sampled using the sampling ratio to generate a preset number of sampled features. This process ensures the representativeness and diversity of the samples. Through the sampling process, a set of sampled features is generated, which will be used for subsequent data analysis and model training. Multiple data sets are constructed based on any data feature and each sampled feature. Each data set contains a preset number of data samples. Ensure that the samples in each data set are diverse and contain at least one sample with identification information, which helps the model learn the feature performance in different situations. Use these data sets for model training to evaluate the contribution of different features to the prediction target. Use the sampled feature set to train the machine learning model and evaluate the performance of the model through the validation set. According to the results of model training and validation, the sampling ratio and rate may need to be adjusted to optimize the prediction ability of the model. With this approach, large-scale data sets can be processed efficiently while maintaining data quality and model generalization capabilities, helping to identify potential security risks and abnormal behaviors.

[0050] S106: Process the decrypted electronic certificate information to be identified and the identity information of the target user based on the target certificate identification model to generate an initial electronic certificate identity identification result.

[0051] In one implementation, necessary preprocessing is performed on the electronic certificate information and user identity information, including format conversion, normalization, noise removal, etc., and features are extracted from the electronic certificate information using a target certificate recognition model. These features may include text information, image features, security features, etc. The extracted certificate features are matched with the target user's identity information, which may include comparison of fields such as name, date of birth, and certificate number.

[0052] The target certificate recognition model is used to calculate the similarity or distance measurement between the electronic certificate features and the user identity information. The preset threshold or confidence level is used to determine whether the electronic certificate information matches the target user. The initial electronic certificate identity recognition result is generated based on the matching result. This is usually a match or mismatch judgment, that is, whether the electronic certificate information can match the target user. The recognition result is post-processed, such as outlier detection, logical verification, etc., to improve the accuracy of recognition.

[0053] S107: Process the access frequency of the target application and the access time period of the target application based on the target certificate recognition model to generate access permission information of the target application.

[0054] In one implementation, historical access information of a target user for a target application is obtained, and the historical access information of the target application is processed based on a target certificate recognition model to generate access preference information of the target user, wherein the access preference information of the target user includes the access information of the target user within a historical time period, and the access frequency of the target application and the access time period of the target application are processed based on the access preference information of the target user to generate access permission information of the target application.

[0055] That is, by judging the content accessed by the target user for the target application in the past period of time and the time of access, the target user's access preference is generated (based on this, it is judged whether the target user has the authority to access the target application, for example, during what time period it can be used for free and during what time period it requires paid use, or in other words, during a specific time period the target application is in maintenance, but the target user uses the target application frequently during that time period). Some target applications may also involve the need to activate membership, or to access them through a network proxy. Based on the above content, it is judged whether the target user has the authority to access the target application.

[0056] S108, processing the target user's access purpose information and the target user's emotional information based on the target certificate recognition model to generate the target user's access intention information.

[0057] In one implementation, the target user's access purpose information is processed based on the target certificate recognition model to generate the attribute information of the target user's to-be-accessed content, that is, to determine what the target user wants to access. For example, if the target user wants to check the balance, this involves information about the amount, or if the target user wants to check personal clock-in records or work materials, this involves work information. The attribute information of the target user's to-be-accessed content is processed based on the target user's emotional information to generate a business access matching degree, wherein the business access matching degree is used to characterize the matching relationship between the target user's emotional state and the business to be accessed; the business access matching degree is processed to obtain the target user's access intention information. That is, to analyze whether there is a logical matching relationship between the user's emotional state and the purpose of the visit. For example, if the user is nervous and the purpose of the visit is to handle urgent business, this may indicate a match. If the user's emotional state does not match the purpose of the visit, such as the user showing abnormal nervousness when performing routine inquiries, this may indicate a problem.

[0058] S109: Process the initial electronic certificate identity recognition result, the access permission information of the target application and the access intention information of the target user based on the target certificate recognition model to generate a target electronic certificate recognition result.

[0059] In one implementation, the target application's access permission information and the target user's access intention information are processed based on the target certificate recognition model to generate the target application's access result information, and the initial electronic certificate identity recognition result is processed based on the target application's access result information to generate the target electronic certificate recognition result. That is, the electronic certificate and the party's biometric features match and are consistent, but if the party is under duress or emergency at the time, identity authentication is also rejected.

[0060] This application processes the access permission information of the target application and the access intention information of the target user based on the target certificate recognition model to generate the access result information of the target application, and further affects the process of electronic certificate recognition results, which requires comprehensive consideration of security, legality and the user's true intentions. Obtain the target user's access intention under specific circumstances, which may include the user's expression of intention under duress or emergency; use the target certificate recognition model to process the electronic certificate information, verify the validity of the certificate, and match it with the target user's identity information; perform biometric verification, such as fingerprint, facial recognition or iris scanning, to ensure that the electronic certificate is consistent with the biometrics of the person concerned; analyze the access permission information of the target application, determine the user's access permission status within a specific time period, analyze whether the user really wants to perform identity authentication based on the collected access intention information, implement an emergency detection mechanism to identify whether the user is in a state of duress or emergency; comprehensively integrate the certificate recognition results, biometric verification results, access permission information and access intention information to generate access result information for the target application; evaluate the access result information to determine whether the user is allowed to access the target application; if the user is in duress or emergency, even if the certificate and biometric verification are passed, the identity authentication should be rejected, and appropriate security measures should be taken; based on the access result information, generate the final electronic certificate recognition result, and promptly notify the user of the access result, including the reason for the success or failure of the authentication.

[0061] The application obtains the encrypted electronic certificate information to be identified, the biometric information of the target user and the access information of the target application by the server, wherein the access information of the target application includes the access frequency of the target application, the access time period of the target application and the access purpose information of the target user; processes the biometric information of the target user to generate initial facial image information and the identity information of the target user, wherein the initial facial image information is the facial information of the target user within a preset time period; processes the initial facial image information based on preset processing rules to generate the influencing factors of the expression characteristics of the target user; processes the influencing factors of the expression characteristics of the target user based on a preset emotion recognition model to generate the target facial image information, wherein the target facial image information includes the expression change information of the target user; processes the target facial image information to generate the emotional information of the target user; processes the identity information of the target user to obtain the preset electronic certificate decryption information; generates the identity attribute information of the target user based on the identity information of the target user, the emotional information of the target user and the preset electronic certificate decryption information, wherein the identity attribute information of the target user includes the identity information of the target user, the emotional information of the target user and the preset electronic certificate decryption information.

[0062] Process the encrypted electronic certificate information to be identified based on the preset electronic certificate decryption information to generate the decrypted electronic certificate information to be identified; obtain an initial certificate recognition model that matches the identity attribute information of the target user and the access information of the target application and a training data set that matches the initial certificate recognition model, wherein the training data set includes other user data that matches the emotional information and access purpose information of the target user; process the training data set to generate expression feature influencing factors, wherein the expression feature influencing factors are generated based on the facial image information of other users; obtain a preset expression mapping table, wherein the preset expression mapping table is the expression information generated based on different users for different scenarios; process the expression feature influencing factors and the preset expression mapping table to generate an original feature library; process the original feature library to generate several feature data sets, and generate a training set and a verification set based on the several feature data sets; obtain any number of data features in the feature data set; based on any The method comprises the following steps: generating adjacent features based on the distance between a number of data features and other numbers of data features of the same category, wherein the adjacent features include a preset number of any number of data features; determining a sampling ratio based on the number of each data feature in the training set; determining a sampling ratio based on the sampling ratio; sampling adjacent features based on the sampling ratio to generate a preset number of sampling features; generating multiple data groups based on any data feature and each sampling feature, wherein each data group includes a preset number of data samples, wherein each data group includes a preset number of data samples, wherein at least one data sample includes identification information; training an initial document recognition model based on the data samples in the multiple data groups to generate a trained document recognition model; processing the trained document recognition model based on the test set to generate a test result; if the data sample containing the identification information in the test result indicates that the electronic passport is in an abnormal state, the trained document recognition model is used as the target document recognition model.

[0063] Based on the target certificate recognition model, the decrypted electronic certificate information to be identified and the identity information of the target user are processed to generate an initial electronic certificate identity recognition result; the historical access information of the target user to the target application is obtained; based on the target certificate recognition model, the historical access information of the target application is processed to generate the access preference information of the target user, wherein the access preference information of the target user includes the access information of the target user in the historical time period; based on the access preference information of the target user, the access frequency of the target application and the access time period of the target application are processed to generate the access permission information of the target application; based on the target certificate recognition model, the access purpose of the target user is processed; The information is processed to generate the attribute information of the target user's to-be-accessed content; the attribute information of the target user's to-be-accessed content is processed based on the target user's emotional information to generate the service access matching degree, wherein the service access matching degree is used to characterize the matching relationship between the target user's emotional state and the service to be accessed; the service access matching degree is processed to obtain the target user's access intention information; the access permission information of the target application and the access intention information of the target user are processed based on the target certificate recognition model to generate the access result information of the target application; the initial electronic certificate identification result is processed based on the access result information of the target application to generate the target electronic certificate identification result. When a user wants to access an application, he needs to complete the identity authentication first. At this time, the biometric features of the person (such as facial images, fingerprints) and the electronic certificate information to be identified are first collected, and the data is sent to the server to realize the verification of the person and the certificate to ensure the authenticity of the verification. If the verification result shows that the party can access the current application, the frequency and time period of the party's access to the application and the party's facial emotions are judged to determine whether the party logged into the current application voluntarily or was coerced, thereby generating the final electronic certificate recognition result, that is, the electronic certificate and the party's biometric features match and are consistent, but if the party is under coercion or emergency at the time, identity authentication is also refused.

[0064] In one embodiment, if Figure 2 As shown, the present application also provides a remote inspection device based on electronic certificates, including:

[0065] The acquisition module 201 is used to acquire the encrypted electronic certificate information to be identified, the biometric information of the target user and the access information of the target application, wherein the access information of the target application includes the access frequency of the target application, the access time period of the target application and the access purpose information of the target user; acquire an initial certificate recognition model that matches the identity attribute information of the target user and the access information of the target application and a training data set that matches the initial certificate recognition model, wherein the training data set includes other user data that matches the emotional information and access purpose information of the target user;

[0066] The processing module 202 is used to process the biometric information of the target user to generate the identity attribute information of the target user, wherein the identity attribute information of the target user includes the identity information of the target user, the emotional information of the target user and the preset electronic certificate decryption information; based on the preset electronic certificate decryption information, the encrypted electronic certificate information to be identified is processed to generate the decrypted electronic certificate information to be identified; based on the training data set, the initial certificate recognition model is processed to generate the target certificate recognition model; based on the target certificate recognition model, the decrypted electronic certificate information to be identified and the identity information of the target user are processed to generate the initial electronic certificate identity recognition result; based on the target certificate recognition model, the access frequency of the target application and the access time period of the target application are processed to generate the access permission information of the target application; based on the target certificate recognition model, the access purpose information of the target user and the emotional information of the target user are processed to generate the access intention information of the target user; based on the target certificate recognition model, the initial electronic certificate identity recognition result, the access permission information of the target application and the access intention information of the target user are processed to generate the target electronic certificate recognition result.

[0067] In another embodiment of the present application, the processing module 202 is configured to process the biometric information of the target user to generate identity attribute information of the target user, including:

[0068] Processing the biometric information of the target user to generate initial facial image information and identity information of the target user, wherein the initial facial image information is facial information of the target user within a preset time period;

[0069] Processing the initial facial image information based on preset processing rules to generate expression feature influencing factors of the target user;

[0070] Processing the influencing factors of the target user's expression characteristics based on a preset emotion recognition model to generate target face image information, wherein the target face image information includes expression change information of the target user;

[0071] Processing the target face image information to generate emotion information of the target user;

[0072] Processing the identity information of the target user to obtain preset electronic certificate decryption information;

[0073] The identity attribute information of the target user is generated based on the identity information of the target user, the emotional information of the target user and the preset electronic certificate decryption information.

[0074] In another embodiment of the present application, the processing module 202 is configured to process the initial document recognition model based on the training data set to generate a target document recognition model, including:

[0075] Processing the training data set to generate expression feature influencing factors, wherein the expression feature influencing factors are generated based on facial image information of other users;

[0076] Obtaining a preset expression mapping table, wherein the preset expression mapping table is based on expression information generated by different users for different scenarios;

[0077] Processing the expression feature influencing factors and the preset expression mapping table to generate an original feature library;

[0078] The original feature library is processed to generate several feature data sets,

[0079] Generate training sets and validation sets based on several feature data sets;

[0080] The initial document recognition model is processed based on the training set and the verification set to generate a target document recognition model.

[0081] In another embodiment of the present application, the processing module 202 is configured to process the initial document recognition model based on the training set and the verification set to generate a target document recognition model, including:

[0082] Obtaining any number of data features in the feature data set;

[0083] Generate adjacent features based on the distance between the any number of data features and other numbers of data features of the same category, wherein the adjacent features include a preset number of the any number of data features;

[0084] Determining a sampling ratio based on the number of each data feature in the training set;

[0085] Based on the sampling ratio, determining a sampling ratio;

[0086] Sampling the adjacent features based on the sampling ratio to generate a preset number of sampling features;

[0087] Based on any data feature and each sampling feature, generating a plurality of data groups, wherein each data group comprises a preset number of data samples, wherein each data group comprises a preset number of data samples, wherein at least one data sample comprises identification information;

[0088] Training the initial document recognition model based on the data samples in the plurality of data groups to generate a trained document recognition model;

[0089] Processing the trained document recognition model based on the test set to generate a test result;

[0090] If the data sample containing identification information in the test result indicates that the electronic passport is in an abnormal state, the trained document recognition model is used as the target document recognition model.

[0091] In another embodiment of the present application, the processing module 202 is configured to process the access frequency of the target application and the access time period of the target application based on the target certificate recognition model to generate access permission information of the target application, including:

[0092] Obtain the target user's historical access information for the target application;

[0093] Processing the historical access information of the target application based on the target certificate recognition model to generate access preference information of the target user, wherein the access preference information of the target user includes the access information of the target user in a historical time period;

[0094] The access frequency of the target application and the access time period of the target application are processed based on the access preference information of the target user to generate access permission information of the target application.

[0095] In another embodiment of the present application, the processing module 202 is configured to process the target user's access purpose information and the target user's emotional information based on the target certificate recognition model to generate the target user's access intention information, including:

[0096] Processing the target user's access purpose information based on the target certificate recognition model to generate attribute information of the content to be accessed by the target user;

[0097] Processing the attribute information of the content to be accessed by the target user based on the emotional information of the target user to generate a service access matching degree, wherein the service access matching degree is used to characterize the matching relationship between the emotional state of the target user and the service to be accessed;

[0098] The service access matching degree is processed to obtain the access intention information of the target user.

[0099] In another embodiment of the present application, the processing module 202 is configured to process the initial electronic certificate identity recognition result, the access permission information of the target application and the access intention information of the target user based on the target certificate recognition model to generate a target electronic certificate recognition result, including:

[0100] Processing the access permission information of the target application and the access intention information of the target user based on the target certificate recognition model to generate access result information of the target application;

[0101] The initial electronic certificate identity recognition result is processed based on the access result information of the target application to generate a target electronic certificate recognition result.

[0102] The present application embodiment provides an electronic device, such as Figure 3 As shown, the electronic device 3 includes a first processor 300, a memory 301, a bus 302 and a communication interface 303. The first processor 300, the communication interface 303 and the memory 301 are connected via the bus 302. The memory 301 stores a computer program that can be run on the first processor 300. When the first processor 300 runs the computer program, it executes the remote verification method based on electronic certificates provided in any of the aforementioned embodiments of the present application.

[0103] The memory 301 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 303 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0104] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs, and the first processor 300 executes the programs after receiving the execution instruction. The remote inspection method based on electronic certificates disclosed in any implementation of the aforementioned embodiment of the present application may be applied to the first processor 300, or implemented by the first processor 300.

[0105] The first processor 300 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the first processor 300. The above-mentioned first processor 300 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor to be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the first processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0106] The electronic device provided by the above-mentioned embodiments of the present application and the remote verification method based on electronic certificates provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0107] The present application embodiment provides a computer-readable storage medium, such as Figure 4 As shown, the computer-readable storage medium 401 stores a computer program, and when the computer program is read and executed by the second processor 402, the aforementioned remote verification method based on electronic certificates is implemented.

[0108] The technical solution of the embodiment of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be an air conditioner, a refrigeration device, a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the method described in the embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0109] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the remote verification method based on electronic certificates provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0110] An embodiment of the present application provides a computer program product, including a computer program, wherein the computer program is executed by a third processor to implement the method described above.

[0111] The computer program product provided by the above-mentioned embodiments of the present application and the remote verification method based on electronic certificates provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0112] It should be noted that, in this application, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or still includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0113] Each embodiment in this application is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for evaluating the remote inspection method based on electronic certificates, electronic devices, electronic devices, and readable storage medium embodiments, since they are basically similar to the above-mentioned remote inspection method based on electronic certificates, the description is relatively simple, and the relevant parts can be referred to the partial description of the above-mentioned remote inspection method based on electronic certificates.

[0114] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application shall be subject to the scope defined by the claims.

Claims

1. A remote inspection method based on electronic certificates, characterized in that: include: Obtaining the encrypted electronic certificate information to be identified, the biometric information of the target user, and the access information of the target application, wherein the access information of the target application includes the access frequency of the target application, the access time period of the target application, and the access purpose information of the target user; Processing the biometric information of the target user to generate identity attribute information of the target user, wherein the identity attribute information of the target user includes identity information of the target user, emotional information of the target user and preset electronic certificate decryption information; Processing the encrypted electronic certificate information to be identified based on the preset electronic certificate decryption information to generate decrypted electronic certificate information to be identified; Acquire an initial document recognition model that matches the identity attribute information of the target user and the access information of the target application and a training data set that matches the initial document recognition model, wherein the training data set includes other user data that matches the emotional information and access purpose information of the target user; Processing the initial document recognition model based on the training data set to generate a target document recognition model; Processing the decrypted electronic certificate information to be identified and the identity information of the target user based on the target certificate identification model to generate an initial electronic certificate identity identification result; Processing the access frequency of the target application and the access time period of the target application based on the target certificate recognition model to generate access permission information of the target application; Processing the target user's access purpose information and the target user's emotional information based on the target certificate recognition model to generate the target user's access intention information; Processing the initial electronic certificate identity recognition result, the access permission information of the target application and the access intention information of the target user based on the target certificate recognition model to generate a target electronic certificate recognition result; The target user's access purpose information and the target user's emotional information are processed based on the target certificate recognition model to generate the target user's access intention information, including: processing the target user's access purpose information based on the target certificate recognition model to generate attribute information of the content to be accessed by the target user; processing the attribute information of the content to be accessed by the target user based on the target user's emotional information to generate a service access matching degree, wherein the service access matching degree is used to characterize the matching relationship between the target user's emotional state and the service to be accessed; processing the service access matching degree to analyze whether there is a logical matching relationship between the user's emotional state and the purpose of access, and generating the target user's access intention information.

2. The method according to claim 1, characterized in that The biometric information of the target user is processed to generate identity attribute information of the target user, including: Processing the biometric information of the target user to generate initial facial image information and identity information of the target user, wherein the initial facial image information is facial information of the target user within a preset time period; Processing the initial facial image information based on preset processing rules to generate expression feature influencing factors of the target user; Processing the influencing factors of the target user's expression characteristics based on a preset emotion recognition model to generate target face image information, wherein the target face image information includes expression change information of the target user; Processing the target face image information to generate emotion information of the target user; Processing the identity information of the target user to obtain preset electronic certificate decryption information; The identity attribute information of the target user is generated based on the identity information of the target user, the emotional information of the target user and the preset electronic certificate decryption information.

3. The method according to claim 1, characterized in that Processing the initial document recognition model based on the training data set to generate a target document recognition model includes: Processing the training data set to generate expression feature influencing factors, wherein the expression feature influencing factors are generated based on facial image information of other users; Obtaining a preset expression mapping table, wherein the preset expression mapping table is based on expression information generated by different users for different scenarios; Processing the expression feature influencing factors and the preset expression mapping table to generate an original feature library; The original feature library is processed to generate several feature data sets, Generate training sets and validation sets based on several feature data sets; The initial document recognition model is processed based on the training set and the verification set to generate a target document recognition model.

4. The method according to claim 3, characterized in that The initial document recognition model is processed based on the training set and the verification set to generate a target document recognition model, including: Obtaining any number of data features in the feature data set; Generate adjacent features based on the distance between the any number of data features and other numbers of data features of the same category, wherein the adjacent features include a preset number of the any number of data features; Determining a sampling ratio based on the number of each data feature in the training set; Based on the sampling ratio, determining a sampling ratio; Sampling the adjacent features based on the sampling ratio to generate a preset number of sampling features; Based on any data feature and each sampling feature, generating a plurality of data groups, wherein each data group includes a preset number of data samples, wherein at least one data sample includes identification information; Training the initial document recognition model based on the data samples in the plurality of data groups to generate a trained document recognition model; Processing the trained document recognition model based on the verification set to generate a test result; If the data sample containing identification information in the test result indicates that the electronic passport is in an abnormal state, the trained document recognition model is used as the target document recognition model.

5. The method according to claim 2, characterized in that Processing the access frequency of the target application and the access time period of the target application based on the target certificate recognition model to generate access permission information of the target application, including: Obtain the target user's historical access information for the target application; Processing the historical access information of the target application based on the target certificate recognition model to generate access preference information of the target user, wherein the access preference information of the target user includes the access information of the target user in a historical time period; The access frequency of the target application and the access time period of the target application are processed based on the access preference information of the target user to generate access permission information of the target application.

6. The method according to claim 1, characterized in that The initial electronic certificate identification result, the access permission information of the target application and the access intention information of the target user are processed based on the target certificate identification model to generate a target electronic certificate identification result, including: Processing the access permission information of the target application and the access intention information of the target user based on the target certificate recognition model to generate access result information of the target application; The initial electronic certificate identity recognition result is processed based on the access result information of the target application to generate a target electronic certificate recognition result.

7. A remote inspection device based on electronic certificates, characterized in that: The device comprises: An acquisition module is used to acquire the encrypted electronic certificate information to be identified, the biometric information of the target user, and the access information of the target application, wherein the access information of the target application includes the access frequency of the target application, the access time period of the target application, and the access purpose information of the target user; acquire an initial certificate recognition model that matches the identity attribute information of the target user and the access information of the target application, and a training data set that matches the initial certificate recognition model, wherein the training data set includes other user data that matches the emotional information and access purpose information of the target user; a processing module, configured to process the biometric information of the target user to generate the identity attribute information of the target user, wherein the identity attribute information of the target user includes the identity information of the target user, the emotional information of the target user and the preset electronic certificate decryption information; based on the preset electronic certificate decryption information, the encrypted electronic certificate information to be identified is processed to generate the decrypted electronic certificate information to be identified; based on the training data set, the initial certificate recognition model is processed to generate the target certificate recognition model; based on the target certificate recognition model, the decrypted electronic certificate information to be identified and the identity information of the target user are processed to generate the initial electronic certificate identity recognition result; based on the target certificate recognition model, the access frequency of the target application and the access time period of the target application are processed to generate the access permission information of the target application; based on the target certificate recognition model, the access purpose information of the target user and the emotional information of the target user are processed to generate the access intention information of the target user; based on the target certificate recognition model, the initial electronic certificate identity recognition result, the access permission information of the target application and the access intention information of the target user are processed to generate the target electronic certificate recognition result; The target user's access purpose information and the target user's emotional information are processed based on the target certificate recognition model to generate the target user's access intention information, including: processing the target user's access purpose information based on the target certificate recognition model to generate attribute information of the content to be accessed by the target user; processing the attribute information of the content to be accessed by the target user based on the target user's emotional information to generate a service access matching degree, wherein the service access matching degree is used to characterize the matching relationship between the target user's emotional state and the service to be accessed; processing the service access matching degree to analyze whether there is a logical matching relationship between the user's emotional state and the purpose of access, and generating the target user's access intention information.

8. An electronic device, characterized in that: include: a first processor; and a memory for storing executable instructions of the first processor; Wherein, the first processor is configured to execute the remote verification method based on electronic certificates as described in any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the second processor, the remote verification method based on electronic certificates as described in any one of claims 1 to 6 is implemented.

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