Identity intelligent identification analysis and safety protection system for fire control identification examination

Abstract

The invention belongs to the field of data processing, and particularly relates to an intelligent identity recognition analysis and safety protection system for a fire-fighting authentication test. The method comprises the following steps: acquiring real-time monitoring image data of a real-time monitoring object by an access main body; inputting the real-time monitoring image into the identity encryption model, and generating a real-time login request adaptive to the encryption channel; sending a real-time login request through the encryption channel; the application trust center intelligently analyzes the received real-time login request by adopting an identity decryption model to obtain real-time identity information; performing monitoring behavior analysis on the real-time identity information to obtain an examination behavior; performing security risk assessment on the real-time identity information by adopting a security protection model to obtain security protection assessment associated with the real-time monitoring object; and marking corresponding examination behaviors and safety protection evaluation for the real-time monitoring object. According to the system, identity recognition, behavior analysis and safety protection evaluation are carried out in the fire-fighting authentication examination process, and the examination invigilation efficiency and the examination room safety are improved.

Description

Technical Field

[0001] The present application belongs to the field of data processing, and in particular, relates to an intelligent identity recognition analysis and safety protection system for fire identification examinations. Background Art

[0002] The fire appraisal examination is an examination for fire industry practitioners, which aims to evaluate their level of fire safety knowledge, skills and emergency response capabilities. The fire appraisal examination usually includes two aspects: theoretical knowledge assessment and practical operation skills assessment. The examination content covers knowledge and skills in fire prevention and control, emergency evacuation, fire fighting, etc. Whether the fire appraisal examination is passed or not is directly related to the ability and responsibility of practitioners in fire safety work, so it has important practical significance.

[0003] During the examination, the possibility of various abnormalities and security issues needs to be fully taken into account, as they may seriously threaten the fairness and security of the examination. For example, cheating may occur through plagiarism, use of communication equipment, etc., which will directly affect the fairness of the examination and damage the rights and interests of normal candidates. For example, monitoring equipment or monitoring systems may not work properly due to technical failures and cannot monitor the examination process in real time, which will increase the difficulty of discovering abnormal situations during the examination.

[0004] Therefore, in order to ensure the smooth progress and security of the examination process, it is urgent to establish a real-time monitoring system to solve at least one of the above technical problems. Summary of the invention

[0005] The present application provides an intelligent identification analysis and safety protection system for fire identification examinations, which is used to identify, analyze and evaluate the safety of real-time monitoring objects during fire identification examinations, which helps to improve the safety, fairness and accuracy of the examination process, improve the efficiency of examination supervision and the safety of examination rooms. The system can monitor abnormal situations during the examination in real time, and can identify and take necessary measures to respond in a timely manner to ensure that the fairness and safety of the examination are not threatened.

[0006] In a first aspect, the present application provides a system for intelligent identification analysis and security protection of fire identification examinations, the system comprising an access subject and an application trust center, an encrypted channel being provided between the access subject and the application trust center; wherein:

[0007] The access subject is used to collect real-time monitoring image data of the real-time monitoring object; input the real-time monitoring image into the identity encryption model to generate a real-time login request adapted to the encryption channel; wherein the real-time login request at least includes: user identity information, authentication credentials, and behavior image information that have been processed through multiple encryptions; and send the real-time login request to the application trust center through the encryption channel;

[0008] An application trust center is used to use an identity decryption model to perform intelligent analysis on received real-time login requests to obtain real-time identity information of the real-time monitored object; the real-time identity information includes at least: user identity information, authentication credentials, and behavior image information obtained after multiple decryption processes; monitoring behavior analysis is performed on the real-time identity information to obtain the examination behavior of the real-time monitored object; a security protection model is used to perform a security risk assessment on the real-time identity information to obtain a security protection assessment associated with the real-time monitored object; corresponding examination behaviors and security protection assessments are marked for the real-time monitored object to complete real-time monitoring of the real-time monitored object.

[0009] In a second aspect, an embodiment of the present application provides a method for intelligent identification analysis and security protection of fire identification examination identity, which is applied to an access subject in a fire identification examination identity intelligent identification analysis and security protection system, wherein the system also includes an application trust center, and an encrypted channel is set between the access subject and the application trust center; the method includes:

[0010] Collect real-time monitoring image data of real-time monitoring objects;

[0011] Input the real-time monitoring image into the identity encryption model to generate a real-time login request adapted to the encryption channel; wherein the real-time login request at least includes: user identity information, authentication credentials, and behavior image information that have been processed through multiple encryptions;

[0012] The real-time login request is sent to the application trust center through an encrypted channel, so that the application trust center can perform monitoring behavior analysis on the real-time identity information to obtain the examination behavior of the real-time monitoring object, and use a security protection model to perform a security risk assessment on the real-time identity information to obtain a security protection assessment associated with the real-time monitoring object, thereby marking the corresponding examination behavior and security protection assessment for the real-time monitoring object to complete real-time monitoring of the real-time monitoring object.

[0013] In a third aspect, an embodiment of the present application provides a method for intelligent identification analysis and security protection of fire identification examination identity, which is applied to an application trust center in a fire identification examination identity intelligent identification analysis and security protection system, wherein the system also includes an access subject, and an encrypted channel is set between the access subject and the application trust center; the method includes:

[0014] Receive a real-time login request from a real-time monitored object through an encrypted channel; the real-time login request includes at least: user identity information, authentication credentials, and behavior image information that have been processed through multiple encryptions; the real-time login request generates request data adapted to the encrypted channel through an identity encryption model;

[0015] An identity decryption model is used to intelligently analyze the received real-time login request to obtain the real-time identity information of the real-time monitored object; the real-time identity information at least includes: user identity information, authentication credentials, and behavior image information obtained through multiple decryption processes;

[0016] Performing monitoring behavior analysis on the real-time identity information to obtain the examination behavior of the real-time monitored object;

[0017] Using a security protection model, a security risk assessment is performed on the real-time identity information to obtain a security protection assessment associated with the real-time monitoring object;

[0018] The corresponding examination behaviors and security protection assessments are marked for the real-time monitoring objects to complete the real-time monitoring of the real-time monitoring objects.

[0019] In a fourth aspect, an embodiment of the present application provides an electronic device, the electronic device comprising:

[0020] at least one processor, memory, and input-output unit;

[0021] Among them, the memory is used to store computer programs, and the processor is used to call the computer programs stored in the memory to execute the first aspect of the intelligent identity recognition analysis and safety protection system for fire identification examinations.

[0022] In a fifth aspect, a computer-readable storage medium is provided, which includes instructions, which, when executed on a computer, enable the computer to execute the first aspect of the intelligent identity recognition analysis and safety protection system for fire identification examinations.

[0023] The technical solution provided by the embodiment of the present application provides a system for intelligent identification, analysis and security protection of identity in fire identification examinations. The system includes an access subject and an application trust center, and an encrypted channel is set between the access subject and the application trust center. Among them, the access subject is used to collect real-time monitoring image data of the real-time monitoring object; input the real-time monitoring image into the identity encryption model to generate a real-time login request adapted to the encrypted channel. The real-time login request includes at least: user identity information, authentication credentials, and behavior image information that have been processed by multiple encryptions; the real-time login request is sent to the application trust center through an encrypted channel. Therefore, by collecting and analyzing the real-time monitoring image data of the monitored object in real time, and applying the identity encryption model and decryption model to process the identity information, it is possible to realize intelligent identification and verification of the identity of the real-time monitored object, and ensure the accuracy and real-time nature of the identity of the monitored object. The application trust center is used to use the identity decryption model to perform intelligent analysis on the received real-time login request to obtain the real-time identity information of the real-time monitoring object; the real-time identity information at least includes: user identity information, authentication credentials, and behavior image information obtained through multiple decryption processes; the real-time identity information is subjected to monitoring behavior analysis to obtain the examination behavior of the real-time monitoring object; thus, the application trust center uses the identity decryption model to perform intelligent analysis on the real-time login request to obtain the identity information and examination behavior information of the real-time monitoring object. This analysis can help identify abnormal behavior or cheating and ensure the fairness and accuracy of the examination. The security protection model is used to perform security risk assessment on the real-time identity information to obtain the security protection assessment associated with the real-time monitoring object; thus, by applying the security protection model to perform security risk assessment on the real-time identity information, potential security threats and risks can be discovered in time, which helps to provide effective security protection measures for the monitoring object and ensure the security of the examination process. The corresponding examination behavior and security protection assessment are marked for the real-time monitoring object to complete the real-time monitoring of the real-time monitoring object. In this way, the corresponding examination behavior and security protection assessment are marked for the real-time monitoring object to achieve real-time monitoring of the monitoring object. This helps to discover and record important events in time and provide a basis for subsequent processing and decision-making.

[0024] The technical solution of this application performs identity recognition, behavior analysis and safety protection assessment on real-time monitored objects during the fire identification examination, which helps to improve the security, fairness and accuracy of the examination process, improve the efficiency of examination supervision and the safety of the examination room. The system can monitor abnormal situations during the examination in real time, and can identify and take necessary measures to respond in a timely manner to ensure that the fairness and security of the examination are not threatened. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0026] Figure 1 This is a structural diagram of an intelligent identification analysis and safety protection system for fire identification examinations according to an embodiment of the present application;

[0027] Figure 2 It is a schematic diagram of the login interface of the fire identification test in the embodiment of the present application;

[0028] Figure 3 It is a schematic diagram of the test answering interface of the fire protection appraisal test in the embodiment of the present application;

[0029] Figure 4 It is a flow chart of a method for intelligent identification analysis and safety protection of fire identification examination in an embodiment of the present application;

[0030] Figure 5 It is a flow chart of another method for intelligent identification analysis and safety protection of fire identification examination in an embodiment of the present application;

[0031] Figure 6 It is a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0034] The fire appraisal examination is an examination for fire industry practitioners, which aims to evaluate their level of fire safety knowledge, skills and emergency response capabilities. Passing the fire appraisal examination can prove that they have the necessary ability and qualifications to engage in firefighting work. These examinations usually include two aspects: theoretical knowledge assessment and practical operation skills assessment. The examination content covers knowledge and skills in fire prevention and control, emergency evacuation, fire fighting, etc. Whether the fire appraisal examination is passed or not is directly related to the ability and responsibility of practitioners in fire safety work, so it has important practical significance.

[0035] During the examination, the possibility of various abnormalities and security issues needs to be fully taken into account, as they may seriously threaten the fairness and security of the examination. For example, cheating may occur through plagiarism, use of communication equipment, etc., which will directly affect the fairness of the examination and damage the rights and interests of normal candidates. For example, monitoring equipment or monitoring systems may not work properly due to technical failures and cannot monitor the examination process in real time, which will increase the difficulty of discovering abnormal situations during the examination.

[0036] In addition, there may be security vulnerabilities. If hacked, the test information may be leaked or tampered with, thus endangering the security and reliability of the test. Identity verification issues are also a potential risk. If the identity verification is wrong or used fraudulently, the accuracy of the candidate's identity may be affected, thereby affecting the authenticity of the test results.

[0037] Therefore, in order to ensure the smooth progress and security of the examination process, it is urgent to establish a real-time monitoring system to solve at least one of the above technical problems.

[0038] The embodiment of the present application provides an intelligent identification analysis and safety protection system for fire protection examination. The system includes an access subject and an application trust center, and an encrypted channel is set between the access subject and the application trust center.

[0039] Among them, the access subject is used to collect real-time monitoring image data of the real-time monitoring object; input the real-time monitoring image into the identity encryption model to generate a real-time login request adapted to the encryption channel. The real-time login request at least includes: user identity information, authentication credentials, and behavior image information that have been processed by multiple encryptions; the real-time login request is sent to the application trust center through the encrypted channel. Therefore, by collecting and analyzing the real-time monitoring image data of the monitoring object in real time, and applying the identity encryption model and decryption model to process the identity information, it is possible to realize intelligent identification and verification of the identity of the real-time monitoring object, and ensure the accuracy and real-time nature of the identity of the monitoring object.

[0040] The application trust center is used to use the identity decryption model to perform intelligent analysis on the received real-time login request to obtain the real-time identity information of the real-time monitoring object; the real-time identity information at least includes: user identity information, authentication credentials, and behavior image information obtained through multiple decryption processes; the real-time identity information is subjected to monitoring behavior analysis to obtain the examination behavior of the real-time monitoring object; thus, the application trust center uses the identity decryption model to perform intelligent analysis on the real-time login request to obtain the identity information and examination behavior information of the real-time monitoring object. This analysis can help identify abnormal behavior or cheating and ensure the fairness and accuracy of the examination. The security protection model is used to perform security risk assessment on the real-time identity information to obtain the security protection assessment associated with the real-time monitoring object; thus, by applying the security protection model to perform security risk assessment on the real-time identity information, potential security threats and risks can be discovered in time, which helps to provide effective security protection measures for the monitoring object and ensure the security of the examination process. The corresponding examination behavior and security protection assessment are marked for the real-time monitoring object to complete the real-time monitoring of the real-time monitoring object. In this way, the corresponding examination behavior and security protection assessment are marked for the real-time monitoring object to achieve real-time monitoring of the monitoring object. This helps to discover and record important events in time and provide a basis for subsequent processing and decision-making. Figure 2 It is a schematic diagram of the login interface of the fire protection appraisal test in the embodiment of the present application. The user can enter the account number and password in the login interface to log in.

[0041] Used in the fire identification examination identity intelligent recognition analysis and safety protection system, it can identify, analyze behavior and evaluate safety protection of real-time monitoring objects during the fire identification examination, which will help improve the safety, fairness and accuracy of the examination process, improve the efficiency of examination supervision and the safety of the examination room. The system can monitor abnormal situations in the examination process in real time, and can identify and take necessary measures to respond in time to ensure that the fairness and safety of the examination are not threatened.

[0042] The intelligent identification analysis and safety protection scheme for fire identification examination provided in the embodiment of the present application can also be executed by an electronic device, which can be a server, a server cluster, or a cloud server. The electronic device can also be a terminal device such as a mobile phone, a computer, a tablet computer, a wearable device, or a special device (such as a special terminal device with an intelligent identification analysis and safety protection system for fire identification examination). These electronic devices can also be equipped with the chips introduced in the above embodiments. Alternatively, these electronic devices can also be installed with a service program for executing the intelligent identification analysis and safety protection scheme for fire identification examination.

[0043] Figure 1A schematic diagram of an intelligent identification analysis and safety protection system for fire identification examination provided in an embodiment of the present application, such as Figure 1 As shown, the system includes an access subject and an application trust center.

[0044] It is worth noting that an encrypted channel is set up between the access subject and the application trust center.

[0045] In the embodiment of the present application, an encrypted channel refers to a channel that uses encryption technology to encrypt and protect data during the communication process to ensure the security and confidentiality of data transmission. Setting an encrypted channel between the access subject and the application trust center can effectively prevent data from being stolen, tampered with or monitored by unauthorized third parties during the transmission process, and ensure the security of information transmission between the communicating parties. The encrypted channel usually includes the following important components: encryption algorithm, the encryption channel uses an encryption algorithm to encrypt the data, and common encryption algorithms include symmetric encryption algorithms (such as AES) and asymmetric encryption algorithms (such as RSA). Among them, the symmetric encryption algorithm is used to protect the encryption speed requirements during the transmission process of data, and the asymmetric encryption algorithm is used in the key negotiation stage before data transmission. Digital certificates are used to prove the identity of the communicating parties and ensure the integrity of the data. Digital certificates usually contain information such as public keys and digital signatures, and can be issued and verified by a certificate authority (CA). Key exchange protocol, used to securely negotiate session keys between the communicating parties, common key exchange protocols include Diffie-Hellman key exchange protocol, etc., to ensure that the communicating parties can securely generate shared keys for encrypted communication processes. Data integrity protection module, the encrypted channel must also ensure that the transmitted data is not tampered with during the transmission process, and usually a message authentication code (MAC) or digital signature is used to protect the data integrity.

[0046] By setting up an encrypted channel, the communication between the access subject and the application trust center can be fully protected to ensure that the data will not be stolen or tampered with during the transmission process, thereby ensuring the security and privacy of data transmission.

[0047] It is understandable that in the embodiments of the present application, further optionally, intelligent encryption protocol selection is achieved through deep alternating learning technology. That is, the most appropriate encryption algorithm and key exchange protocol are dynamically selected according to the environment and needs of the communicating parties to improve communication efficiency and security.

[0048] The use of deep alternating learning technology to achieve intelligent encryption protocol selection is a new way to dynamically select the most appropriate encryption algorithm and key exchange protocol according to the environment and needs of both parties in communication, thereby improving communication efficiency and security. The specific steps are as follows: First, the system collects and preprocesses the environmental information and communication needs of both parties in communication, including network conditions, communication equipment information, security policy requirements, etc. Based on the collected data, a deep learning model is designed, which can learn the best encryption protocol selection strategy for both parties in communication from the environment and demand information. Deep alternating learning is an alternating optimization strategy. The system first initializes the encryption algorithm and key exchange protocol through an alternating learning algorithm. Then, in each round of iteration, according to the current environmental information and needs, the system selects the best encryption protocol and updates the deep learning model parameters according to the selection results. The system uses the collected data to train and optimize the deep learning model, continuously improving the model's ability to understand the communication environment and needs, so as to achieve more accurate encryption protocol selection. After training, the system applies the learned encryption protocol selection strategy to actual communication. During the communication process, the system dynamically adjusts the encryption protocol according to real-time environmental changes and communication needs to ensure the security and efficiency of communication. Through deep alternating learning technology, the system can continuously learn and optimize the encryption protocol selection strategy from the communication process, achieving smarter and more adaptable encrypted communication, thereby improving communication efficiency and security.

[0049] Multi-level encryption protection uses multi-level encryption protection measures, including encryption of transmitted data, data integrity verification, identity authentication, etc., to build a more complex and robust encryption channel structure and improve the security of data transmission. See below for an introduction to multiple encryption processing and related encryption and decryption models, which will not be expanded here.

[0050] The following introduces the module functions in the system with specific examples.

[0051] The access subject is used to collect real-time monitoring image data of the real-time monitoring object; input the real-time monitoring image into the identity encryption model to generate a real-time login request adapted to the encryption channel; and send the real-time login request to the application trust center through the encryption channel.

[0052] In an embodiment of the present application, the real-time login request adapted to the encrypted channel includes at least: user identity information, authentication credentials, and behavioral image information that have been subjected to multiple encryption processes.

[0053] In applications that implement end-to-end encryption protection, user identity information is the information provided by the user when logging in to verify the identity, such as user name, mobile phone number, email address, etc. In order to protect the security of user identity information, this information is usually processed by multiple encryptions, including symmetric encryption and asymmetric encryption, to ensure that the information is not stolen or tampered with during transmission. Authentication credentials are passwords, PIN codes, fingerprint information, etc. required for user login, which are used to verify the user's identity. In applications that implement end-to-end encryption protection, authentication credentials are also encrypted to ensure the security of credentials during transmission. Behavioral image information refers to the behavioral characteristics of users when logging in, such as keystroke speed, mouse movement trajectory, etc., which are used to assist in the verification of user identity. This information may also be encrypted to ensure that the user's behavioral information is not maliciously stolen or tampered with.

[0054] In real-time login requests, the above information is transmitted through an end-to-end encrypted channel after multiple encryption, which can ensure that the user's login process is effectively encrypted and protected during transmission, and ensure the security of the user's identity information and authentication credentials. This method of combining multiple encryption and end-to-end encryption technology can effectively prevent data leakage and unauthorized access, and improve the security of applications and the reliability of user authentication.

[0055] For example, suppose there is a smart home security monitoring system, which includes a camera for real-time monitoring of the home environment. The access subject here refers to the camera in the smart home security monitoring system, which is used to collect real-time monitoring image data of the real-time monitoring object. In this example, the identity encryption model refers to a model for identifying and encrypting user identity information. This model may include technologies such as face recognition and voiceprint recognition to confirm the identity of the user. Once the identity encryption model confirms the identity of the user, the system will generate a real-time login request adapted to the encrypted channel. This request includes user identity information, authentication credentials and behavioral image information that have been processed by multiple encryptions. The system sends the real-time login request to the application trust center through an encrypted channel. This encrypted channel can use security protocols such as SSL / TLS to protect the security of data transmission. The application trust center is the authentication center of the smart home security monitoring system, responsible for verifying the identity of the user and processing the login request. Once the real-time login request is received, the application trust center will perform decryption processing and verify whether the user's identity is legal.

[0056] In summary, by inputting real-time monitoring image data into the identity encryption model and sending real-time login requests to the application trust center through an encrypted channel, it can be ensured that the communication process of the monitoring system is end-to-end encrypted, thereby effectively protecting the privacy and security of users.

[0057] The application trust center is used to use an identity decryption model to perform intelligent analysis on the received real-time login requests to obtain the real-time identity information of the real-time monitored object; perform monitoring behavior analysis on the real-time identity information to obtain the examination behavior of the real-time monitored object; use a security protection model to perform security risk assessment on the real-time identity information to obtain a security protection assessment associated with the real-time monitored object; mark the corresponding examination behavior and security protection assessment for the real-time monitored object to complete real-time monitoring of the real-time monitored object.

[0058] In the embodiment of the present application, the real-time identity information at least includes: user identity information, authentication credentials, and behavior image information obtained through multiple decryption processes.

[0059] In this embodiment, real-time identity information refers to the user identity information, authentication credentials and behavior image information obtained after multiple decryption processing of the data contained in the real-time login request of the real-time monitored object. Similar to the above, user identity information, the user identity information obtained after decryption processing refers to the information used to identify and verify the user identity contained in the login request, such as the user's name, ID number, registered mobile phone number, etc. After decryption, this information can be used to identify the true identity of the current monitored object. Authentication credentials, the authentication credentials after decryption processing are credentials used to verify the user's identity, such as the user's password, PIN code, fingerprint information, etc. Through the authentication credentials, the system can confirm whether the identity of the current monitored object is legal. Behavior image information, behavioral image information refers to the behavioral feature data provided by the monitored object in the login request, such as keystroke speed, mouse movement trajectory, etc. After decryption, this information can be used to analyze the behavior patterns and habits of the monitored object.

[0060] These real-time identity information are extracted from real-time login requests through decryption processing. After intelligent analysis, monitoring behavior analysis and security risk assessment, the real-time identity information, test behavior and security protection assessment of the real-time monitoring object can be obtained. By marking the corresponding behavior and security assessment for the monitoring object, the application trust center can monitor the monitoring object in real time to ensure the security and reliability of the system. The comprehensive analysis and evaluation of this information can help the application trust center to promptly detect abnormal behavior and security risks, and take appropriate measures to deal with them. Figure 3 As shown, this is a schematic diagram of the test answering interface of the fire appraisal test in the embodiment of the present application. The behavior of the candidates will be monitored during the process of answering the test answering interface.

[0061] It is worth understanding that the real-time identity information is real-time because it is extracted and decrypted from the real-time login request of the real-time monitored object. This means that every time the monitored object logs in, the system will obtain the latest identity information, authentication credentials and behavior image information, and update and evaluate the identity, behavior and security status of the monitored object in real time through intelligent analysis, monitoring behavior analysis and security risk assessment. Therefore, the real-time nature of real-time identity information is reflected in that it is dynamically obtained and updated based on the latest behavior and status of the monitored object.

[0062] The technical effect of this system can be specifically illustrated by a practical scenario:

[0063] Suppose a fire training center uses such a system to monitor the behavior and safety of trainees at the fire identification examination site. When trainees enter the examination site, surveillance cameras will capture their behavior and generate real-time login requests. These requests will be sent to the application trust center through an encrypted channel.

[0064] First, the application trust center uses the identity decryption model to perform intelligent analysis on the real-time login request received, and extracts the student's real-time identity information, such as name, student number, etc. This information is updated in real time to reflect the student's dynamic behavior and location in the examination venue. Then, by monitoring the real-time identity information for behavioral analysis, the system can understand the student's behavior pattern in the examination venue, such as the time they entered the examination room, the duration, etc. If the system detects abnormal behavior, such as a student trying to cheat during the examination, the system will issue an alarm and record the event.

[0065] It is understandable that abnormal behavior and cheating behavior are detected by an exemplary method listed below. The system first collects the identity information of the students in real time through the identity decryption model, including name, student number, entry time, etc. This information will be used to build a behavioral baseline model for the students. The system monitors the behavior of the students, including the time of entering and leaving the examination room, the time spent in the examination room, the movement trajectory, etc. By comparing with the normal behavior pattern, the system can establish the normal behavior pattern of the students. The system will detect whether the student's behavior deviates from the normal pattern. For example, if a student frequently leaves his seat or frequently talks to others during the exam, this may be marked as abnormal behavior. If the system monitors that the student's real-time location deviates from the examination area, or detects that the student stays in a non-permitted area for too long, it may also be marked as abnormal behavior.

[0066] By deeply analyzing the behavior patterns of students, the system can identify some typical cheating behaviors, such as frequent mobile phone use, frequent interactions with other candidates, and frequent leaving seats. These behaviors are usually marked as potential cheating behaviors. The system identifies abnormal behaviors based on known cheating behavior patterns (for example, frequent mobile phone use during the exam period). If behaviors matching these patterns are detected, the system will issue an alarm. The system can also determine whether there is cheating by cross-verifying with other data (such as surveillance camera data, environmental data, etc.). For example, if the system detects that a student has visited multiple non-permitted areas in a short period of time, this may be a sign of cheating. When the system detects abnormal behavior or cheating, it will trigger an alarm mechanism to notify the invigilator or administrator in a timely manner. The system will record the details of all abnormal behaviors and cheating events, including time, location, behavior type, etc., for subsequent review and processing. Through the above steps, the application trust center can intelligently analyze real-time login requests, detect abnormal behaviors, and determine whether there is cheating, thereby ensuring the fairness and security of the exam.

[0067] Next, the system uses a security protection model to conduct a security risk assessment on the real-time identity information to assess the risks associated with the student's exam security. For example, the system can check whether the student's identity information matches the registration information and whether there is any unauthorized behavior. Finally, based on the results of intelligent analysis and security assessment, the system will mark the corresponding exam behavior and security protection assessment for each student. For example, if the student's behavior complies with the exam regulations and the security risk assessment is low risk, the system will mark it as compliant behavior. On the contrary, if the student's behavior is abnormal and there is a security risk, the system will mark it as abnormal behavior and take corresponding security measures. In this way, the application trust center can monitor the student's behavior and safety in the fire identification examination venue in real time, and promptly detect and respond to potential exam cheating, thereby ensuring the fairness and reliability of the exam.

[0068] The system effectively extracts, identifies and protects the identity information of the monitored object in real-time monitoring scenarios to deal with cheating. The system uses the identity decryption model to extract the identity information of the students (such as name, student number). By comparing this information with the registration information, it ensures that the identity of the students is authentic and registered. Optionally, it can also combine biometric technologies such as facial recognition and fingerprint recognition to further confirm the identity of the students and prevent identity fraud. The system tracks the dynamic behavior of students in the examination room in real time, including the time of entering the examination room, movement trajectory, etc., to ensure information consistency and authenticity and reliability of the identity. The system establishes baseline data of normal behavior patterns, such as the behavior patterns of students during the examination process, common movement patterns in the examination room, etc. By monitoring the behavior of students in real time, such as the frequency of leaving seats, frequent interactions with others, and the use of unauthorized devices, the system detects whether there are abnormal situations that do not match the normal behavior patterns. The system identifies potential signs of cheating by analyzing the behavior patterns of students, such as abnormal behavior patterns and similarities with known cheating behaviors. Combined with identity information and behavior patterns, the security risks of students are assessed, such as whether there is unauthorized access behavior and whether there is behavior with a higher risk level. Based on the results of real-time monitoring and security risk assessment, the system determines whether the student's behavior complies with the examination regulations. If the behavior is normal and the risk assessment is low, it is marked as compliant. If the system detects abnormal behavior and the security risk is high, it is marked as abnormal behavior, records the specific abnormal situation, and triggers an alarm. For students marked as abnormal behavior, the system automatically implements corresponding security measures, such as notifying the invigilator, temporarily prohibiting entry into certain areas, etc. The system provides real-time feedback to help invigilators quickly deal with abnormal situations, such as arranging someone to check students with suspicious behavior. The system uses an identity encryption model to protect students' personal information and ensure data security during transmission and storage. All storage involving identity information and monitoring data adopts high-standard security measures to prevent data leakage or unauthorized access. The system complies with relevant privacy protection laws and regulations, such as GDPR or China's Personal Information Protection Law (PIPL), to ensure the legal and compliant use of personal information. During processing and analysis, the data is anonymized as much as possible to protect the privacy of students.

[0069] Through these measures, the Application Trust Center can effectively extract, identify and protect students' identity information in real-time monitoring scenarios, and effectively prevent cheating in exams through intelligent analysis and security assessment, thereby ensuring the fairness and reliability of the exams.

[0070] As an optional embodiment, it is assumed that the identity encryption model includes at least a feature extraction layer, an identity recognition layer, an identity authentication layer, a behavior image construction layer, and multiple encryption layers.

[0071] Based on the above structure, when the access subject inputs the real-time monitoring image into the identity encryption model and generates a real-time login request adapted to the encryption channel, it is specifically used for:

[0072] Extracting identity feature information of the real-time monitoring object from the real-time monitoring image through the feature extraction layer; the identity feature information at least includes: face feature information, body posture feature information, and action feature information;

[0073] Through the identity recognition layer, the identity feature information is processed for identity matching to obtain the user identity information corresponding to the real-time monitoring object;

[0074] Through the identity authentication layer, the identity feature information is authenticated and identified to obtain the authentication credential information corresponding to the real-time monitoring object;

[0075] Through the behavior image construction layer, the identity feature information is processed through behavior construction to obtain behavior image information corresponding to the real-time monitoring object;

[0076] Through multiple encryption layers, the user identity information, authentication credentials, and behavior image information corresponding to the real-time monitoring object are multiple encrypted to construct the real-time login request.

[0077] Specifically, the structure and functions of this identity encryption model can be described as follows:

[0078] The feature extraction layer extracts the identity feature information of the monitored object from the real-time monitoring image, including facial feature information, body posture feature information, and action feature information. The function of this layer is to extract the identity features of the monitored object from the monitoring image through image processing and feature extraction algorithms, providing input data for subsequent identity recognition and authentication.

[0079] The identity recognition layer performs identity matching on the extracted identity feature information to determine the user identity information corresponding to the monitored object. The function of this layer is to compare the extracted identity features with the known user identity information to determine the identity of the monitored object, such as confirming which employee or trainee it is.

[0080] The identity feature information is authenticated and identified through the identity authentication layer to obtain the authentication credentials corresponding to the monitored object. The function of this layer is to verify whether the monitored object has a legitimate identity authentication credential, such as swiping a card, entering a password, etc., to ensure that they have the right to enter the examination room or office.

[0081] Through the behavior image construction layer, the identity feature information is processed through behavior construction to obtain the behavior image information corresponding to the monitored object. The function of this layer is to associate the extracted identity features with the behavior of the monitored object and generate the corresponding behavior image for subsequent behavior analysis and security assessment.

[0082] Through multiple encryption layers, the user identity information, authentication credentials and behavior image information of the monitored object are encrypted multiple times to construct a real-time login request. The function of this layer is to protect the privacy information of the monitored object and ensure the security and confidentiality of the real-time login request during transmission.

[0083] In this way, the entire identity encryption model, through the synergy of these layers, can effectively extract, identify and protect the identity information of the monitored object in the real-time monitoring scenario, and generate real-time login requests suitable for the encrypted channel to ensure the security and reliability of information transmission.

[0084] In the above embodiment, the identity feature information is subjected to identity matching processing by the identity recognition layer to obtain the user identity information corresponding to the real-time monitoring object, which is expressed as the following formula:

[0085]

[0086] Wherein, F represents the identity feature matrix obtained by extracting the identity feature information through a multi-scale convolutional network, u i represents the i-th identity feature vector in the identity feature information, W represents a learnable weight parameter matrix, W is used to adjust the importance of the identity feature matrix F at different scales, b i represents the learnable bias term, b i Represents the adjustment of the i-th identity feature vector u i Similarity(F,u i ) represents the identity feature matrix F and the i-th identity feature vector u i The similarity between i represents the vector norm. i That is to say, F·W·u i and the norm of the i-th identity feature vector ui.

[0087] In the above embodiment, the relationship between the corresponding user identity information and the similarity can be understood by the following explanation: the identity feature matrix is ​​extracted by a multi-scale convolutional network and contains identity features at different scales. The identity feature vector is a vector of specific identity feature information used for matching. The learnable weight parameter matrix and the bias term are used to adjust the importance of the identity feature matrix at different scales and the deviation of the similarity calculation. The similarity is obtained by calculating the similarity between the identity feature matrix and the identity feature vector (for example, using a vector inner product or a certain distance metric). The vector norm represents the length or size of each vector. In the above formula, the calculated similarity is quantified by matching the identity feature matrix and the identity feature vector. This similarity represents the degree of matching between the identity feature matrix and the identity feature vector. The corresponding user identity information refers to the specific user identity obtained by comparison during the identity matching process. In other words, the similarity obtained by calculation can be used to determine which identity feature vector (i.e., a certain user) is most matched with the current identity feature matrix. Therefore, the result of the similarity calculation can be used to infer the corresponding user identity information. In summary, the similarity itself is not user identity information, but it is the basis for inferring or determining the corresponding user identity information. By calculating the similarity, we can find the identity feature vector that best matches the identity feature matrix, thereby obtaining the user's identity information.

[0088] In the above embodiment, the identity feature information is authenticated and identified by the identity authentication layer to obtain the authentication credential information corresponding to the real-time monitoring object, which is expressed as the following formula:

[0089]

[0090] Wherein, F represents the identity feature matrix obtained by extracting the identity feature information through a multi-scale convolutional network, u i represents the i-th identity feature vector in the identity feature information, W represents a learnable weight parameter matrix, W is used to adjust the importance of the identity feature matrix F at different scales, b i represents the learnable bias term, b i Represents the adjustment of the i-th identity feature vector u i The similarity of Ath(F,u i ) represents the identity feature matrix F and the i-th identity feature vector u i The similarity between i represents the vector norm, represents the conversion function that converts similarity into authentication probability.

[0091] This formula compares the feature vector of the real-time monitoring object with the identity feature information of the registered user one by one, calculates the similarity, and converts the similarity into authentication probability through an activation function.

[0092] In this case, there is a conversion process between authentication credential information and authentication probability. Authentication credential information may be a set of user feature information, such as ID number, fingerprint, facial features, etc. When the system needs to authenticate the user's identity, it will calculate the similarity between the feature information provided by the user and the identity feature information already in the system. The authentication probability calculated in the formula is obtained by processing the similarity through an activation function. This authentication probability is a quantitative representation of the degree of matching between the feature information provided by the user and the feature information known in the system. Therefore, authentication credential information refers to the identity feature information provided by the user, and authentication probability refers to the confidence of the authenticated user's identity calculated by the system based on these feature information. In other words, when the authentication probability reaches a certain threshold, the system considers the submitted authentication credential information to be valid, and the user is authenticated. In this case, the authentication probability reflects a dynamic authentication state, which is used to determine whether the user's identity is authenticated. In short, the authentication credential information is used to determine whether the user is authenticated through the calculation of the authentication probability.

[0093] Further optionally, when the identity feature information is processed through behavior construction through the behavior image construction layer to obtain behavior image information corresponding to the real-time monitoring object, it is specifically used for:

[0094] The posture feature information, movement feature information, expression feature information, and behavior habit information of the real-time monitoring object are extracted from the identity feature information; the posture feature information, movement feature information, expression feature information, and behavior habit information of the real-time monitoring object are interwoven and projected from multiple dimensions into a high-dimensional coding space to obtain real-time coding features of the real-time monitoring object; the real-time coding features of the real-time monitoring object are dynamically sampled to obtain initial behavior image information of the real-time monitoring object; key local areas in the initial behavior image information of the real-time monitoring object are selected, and image enhancement processing is performed on the key local areas to obtain the behavior image information of the real-time monitoring object.

[0095] This step describes the specific function of the behavior image construction layer when further processing identity feature information: First, the posture feature information (such as standing, sitting, etc.), action feature information (such as walking, raising hands, etc.), expression feature information (such as smile, eyes, etc.) and behavior habit information (such as activity frequency, stay time, etc.) of the real-time monitoring object are extracted from the identity feature information. These features reflect the state and behavior of the monitored object in the examination or work scene. Then, the extracted feature information is interwoven from multiple dimensions into the high-dimensional coding space. This process can be understood as taking multiple feature information into consideration and representing them as high-dimensional coding vectors to more comprehensively describe the state and behavior of the real-time monitoring object. Next, the coding features of the real-time monitoring object are dynamically sampled to capture the changes in its real-time state. In this way, the coding features of the real-time monitoring object at different time points can be obtained, making the behavior image information more dynamic and time-series. Furthermore, key local areas, such as facial expression areas, hand movement areas, etc., are selected from the initial behavior image information of the real-time monitoring object, and image enhancement processing is performed on these key local areas. This includes enhancing the contrast, clarity, etc. of the image to highlight key information, such as facial expression details or hand movement details, thereby obtaining more accurate and specific behavioral image information.

[0096] The above steps involve extracting and processing multiple features from the identity feature information of the real-time monitoring object and generating behavioral image information in a high-dimensional coding space. Specifically, information such as posture, action, expression, and behavioral habits are extracted from the identity feature information of the real-time monitoring object. These features represent the state and behavior of the monitored object in a specific scenario. The extracted feature information (such as posture, action, etc.) is comprehensively considered and projected into a high-dimensional coding space. This process fuses features of multiple dimensions into a high-dimensional vector, aiming to more comprehensively describe the state and behavior of the object. This high-dimensional encoding can capture the complex relationships and interactions between features. Dynamic sampling of the coding features is to capture the temporal changes in the state of the object. Feature extraction is performed on the object at different time points to generate a series of coding features. These time points can be evenly spaced or adjusted according to the changes in the object's behavior. The coding features at these time points are serialized to form time series data. In this way, the system can observe and analyze the dynamic changes in the state of the object. Key areas (such as facial expressions, hand movements, etc.) are selected from the behavioral image information, which are particularly important for behavioral analysis. Furthermore, these key local areas are enhanced, including improving contrast, clarity, etc., so as to more accurately extract and analyze detail information. This helps to identify small changes and features, thereby improving the accuracy of the analysis of behavioral images. Through sampling techniques (such as sliding windows, time steps, etc.), the encoding features of the object are extracted at different time points. In this way, a time series can be obtained, and the encoding features at each time point reflect the state of the object at that time point. These time series data can be used to analyze behavioral patterns, detect anomalies, etc. In the high-dimensional encoding space, each encoding vector contains a comprehensive expression of multiple feature information. Through the time series analysis of these vectors, the system can identify the trends and patterns of the object's behavior. The enhancement processing makes the information extracted from the image clearer and more significant, which helps to finely analyze specific behaviors (such as expression changes, hand movements). In summary, through dynamic sampling, the system can capture the state changes of the real-time monitoring object at different time points, and encode these changes into high-dimensional vectors to form complete behavioral image information. Through the analysis of these encoding vectors, the system can more accurately understand and describe the dynamic behavior of the real-time monitoring object.

[0097] Through these steps, the behavior image construction layer can further transform the identity feature information of the real-time monitored object into behavior image information that is temporal, dynamic, and information-rich, providing a more accurate and comprehensive data foundation for subsequent behavior analysis and security assessment.

[0098] Further optionally, the multiple encryption processing includes at least one or more of data encryption, tag encryption, time stamp mechanism, data integrity check, anti-tampering processing, and anti-counterfeiting processing. Further optionally, the processing flow parameters in the multiple encryption processing are associated with the encryption channel.

[0099] These multiple encryption processes can be combined with the characteristics of the encryption channel to ensure the secure transmission and integrity of data. For example, data encryption is performed on sensitive data in real-time login requests (such as user identity information, authentication credentials, and behavioral image information). The encryption algorithm can select a suitable encryption algorithm and key length according to the requirements of the encryption channel, such as using the AES algorithm to encrypt the data. Tag information is added to the real-time login request to identify the type or source of the data. These tag information are encrypted to ensure that only authorized recipients can decrypt and identify these tag information to prevent information leakage or tampering. Timestamp information is added to the real-time login request to record the time when the data is generated or transmitted. The timestamp information can be signed and encrypted by an encryption algorithm to ensure the timeliness and integrity of the data, and to prevent replay attacks or tampering with the time sequence of the data. The data in the real-time login request is integrity checked using a hash algorithm to generate data digests or signature information. These digests or signature information can be encrypted by an encryption algorithm to ensure that the data has not been tampered with or damaged during transmission. Anti-tampering tags are added to the real-time login request to detect whether the data has been tampered with. Technologies such as digital signatures or message authentication codes (MAC) can be used to authenticate and verify data to ensure the integrity and credibility of data during transmission. Technologies such as identity authentication mechanisms or digital certificates can be used to authenticate and authorize the sender of real-time login requests. This ensures that the source of real-time login requests is legitimate and credible, preventing forgery and malicious attacks.

[0100] The comprehensive use of these multiple encryption processing technologies can effectively protect the data security in real-time login requests and ensure the security and reliability of data transmission on encrypted channels.

[0101] In practical applications, it is further optional to use adaptive security policies to dynamically adjust the security policies of encrypted channels according to the communication environment and security risks, including the selection of encryption algorithms, adjustment of key lengths, changes in identity authentication methods, etc., to cope with security threats in different scenarios.

[0102] Specifically, adaptive security policy is an intelligent security management method that dynamically adjusts the security policy of the encrypted channel by monitoring the communication environment and evaluating security risks to cope with security threats in different scenarios. The specific implementation method is as follows: The system obtains current security intelligence by monitoring the communication environment, including network status, device information, access mode, etc., and evaluating security risks, including possible attack types and attack intensity. Based on the results of environmental monitoring and risk assessment, the system dynamically adjusts the security policy of the encrypted channel. This includes selecting the most appropriate encryption algorithm and key length, and adjusting the identity authentication method. According to the evaluation results, the system can select a stronger or lighter encryption algorithm to adapt to the current security threats. For example, a more complex symmetric encryption algorithm may be selected in a high-risk environment, while a lighter algorithm may be selected to improve efficiency in a low-risk environment. According to the results of environmental monitoring, the system can dynamically adjust the key length to enable it to resist the current possible attacks. For example, in a high-risk environment, the key length is increased to improve security, while in a low-risk environment, the key length can be reduced to improve efficiency. The system can select different identity authentication methods according to actual conditions. For example, two-factor authentication or multi-factor authentication can be used in high-risk environments, while single-factor authentication can be used in low-risk environments to improve convenience.

[0103] Through adaptive security strategies, the system can dynamically adjust the security strategies of encrypted channels according to actual conditions to respond to security threats in different scenarios and ensure the security and efficiency of communications.

[0104] In addition, end-to-end encryption protection can be added to ensure end-to-end encryption protection between the communicating parties, that is, encryption is performed throughout the entire process of data transmission to avoid security vulnerabilities in any link of the communication link.

[0105] Exemplarily, end-to-end encryption protection is an important security measure that aims to ensure end-to-end encryption protection between the two communicating parties, thereby avoiding security vulnerabilities in any link of data transmission. The specific steps to implement end-to-end encryption protection are as follows: Before the communication begins, the sender encrypts the data content to be transmitted. This can be achieved using a symmetric encryption algorithm or an asymmetric encryption algorithm to ensure that the data is not stolen or tampered with during transmission. The encrypted data is transmitted to the receiver through a secure channel. Typically, these channels use security protocols (such as SSL / TLS) to protect confidentiality and integrity during data transmission. After receiving the encrypted data, the receiver uses the corresponding key and decryption algorithm to decrypt the data and restore the original data content. This process ensures the confidentiality and integrity of the data during transmission. After the data transmission is completed, the receiver can perform end-to-end verification on the received data to ensure the integrity and authenticity of the data. This can be achieved through methods such as digital signatures or message authentication codes. Through end-to-end encryption protection, the communicating parties can ensure that their communication content is effectively encrypted and protected throughout the transmission process, thereby avoiding the possibility of man-in-the-middle attacks or other security threats.

[0106] As an optional embodiment, it is assumed that the identity decryption model at least includes: a subject identification layer, a multiple decryption layer, and an identification layer. Based on the above structure, when the application trust center uses the identity decryption model to perform intelligent analysis on the received real-time login request to obtain the real-time identity information of the real-time monitoring object, it is specifically used to: extract the access subject information in the real-time login request through the subject identification layer, and identify the type of the corresponding access subject; call the target decryption module corresponding to the type of the access subject through the multiple decryption layers, and perform multi-layer real-time decryption on the real-time login request to obtain the identity information to be processed; perform intelligent identification processing on the identity information to be processed through the identification layer to obtain the real-time identity information.

[0107] In the fire identification test scenario, the trust center uses the identity decryption model to perform intelligent analysis on the received real-time login request to obtain the real-time identity information of the real-time monitoring object. The specific applications are as follows:

[0108] Through the subject identification layer, in the fire identification test scenario, the subject identification layer first extracts the access subject information in the real-time login request and identifies the type of the corresponding access subject. For example, the access subject may be a candidate taking the fire identification test, a fire test administrator or an invigilator, etc.

[0109] Through multiple decryption layers, according to the type of access subject, the multiple decryption layers call the corresponding target decryption module to perform multi-layer real-time decryption on the real-time login request. In this scenario, different access subjects may use different identity authentication methods or credentials. For example, candidates may use ID card information or examination admission ticket number, and administrators may use special identity authentication credentials.

[0110] Through the recognition layer, the decrypted identity information to be processed is intelligently recognized and processed by the recognition layer to obtain real-time identity information. In the fire identification test scenario, the recognition layer will identify the candidate's identity information based on the decrypted data, such as confirming the candidate's name, ID number, and test session.

[0111] In this way, through the extraction and identification of the subject identification layer, the decryption processing of the multiple decryption layers, and the intelligent identification of the identification layer, the trust center can accurately obtain the real-time identity information of the candidates participating in the fire identification examination. This is crucial for examination management and monitoring, and can ensure the security and credibility of the examination.

[0112] As an optional embodiment, the security protection model is a framework or method for evaluating and managing system security risks. In this scenario, the security protection model can be used to evaluate the security risks associated with real-time identity information to ensure the security and credibility of the monitored object. Based on this, when the application trust center uses the security protection model to perform a security risk assessment on the real-time identity information to obtain a security protection assessment associated with the real-time monitored object, the following steps may be included:

[0113] First, identify security risks associated with real-time identity information, such as identity information leakage, identity information tampering, identity information forgery, etc. Then, evaluate the likelihood and impact of the identified security risks to determine which risks are the most urgent and serious. Next, take necessary security control measures to reduce or eliminate the identified security risks, such as strengthening encryption protection of identity information, implementing access control policies, strengthening monitoring and auditing, etc. Finally, continuously monitor the changes and evolution of security risks, and adjust security control measures in a timely manner to respond to new threats and risks.

[0114] In the context of an application trust center, the security protection model can ensure the security and integrity of the identity information of the monitored object during transmission and processing by identifying, evaluating, controlling and monitoring risks of real-time identity information.

[0115] In an embodiment of the present application, a system for intelligent identification analysis and safety protection for fire identification examinations is provided. The system performs identity recognition, behavior analysis and safety protection assessment on real-time monitored objects during the fire identification examination, which helps to improve the security, fairness and accuracy of the examination process, improve the efficiency of examination supervision and the safety of the examination room. The system can monitor abnormal situations during the examination in real time, and can identify and take necessary measures to respond in a timely manner to ensure that the fairness and security of the examination are not threatened.

[0116] In another embodiment of the present application, a method for intelligent identification analysis and security protection of fire identification examination identity is also provided. The method is applied to the access subject in the fire identification examination identity intelligent identification analysis and security protection system, and the system also includes an application trust center, and an encrypted channel is set between the access subject and the application trust center, see Figure 4 Said method comprises the following steps:

[0117] 201, collecting real-time monitoring image data of a real-time monitoring object;

[0118] 202, inputting the real-time monitoring image into the identity encryption model to generate a real-time login request adapted to the encryption channel; wherein the real-time login request at least includes: user identity information, authentication credentials, and behavior image information that have been processed through multiple encryptions;

[0119] 203. Send the real-time login request to the application trust center through an encrypted channel, so that the application trust center can perform monitoring behavior analysis on the real-time identity information to obtain the examination behavior of the real-time monitoring object, and use a security protection model to perform a security risk assessment on the real-time identity information to obtain a security protection assessment associated with the real-time monitoring object, thereby marking the corresponding examination behavior and security protection assessment for the real-time monitoring object to complete real-time monitoring of the real-time monitoring object.

[0120] It is worth noting that the access subject is also used to perform other functions in the above system, which will not be elaborated here.

[0121] In the embodiment of the present application, by accessing the subject, the real-time monitoring object in the fire identification examination process is identified, the behavior is analyzed, and the safety protection is evaluated, which helps to improve the safety, fairness and accuracy of the examination process, improve the efficiency of examination supervision and the safety of the examination room. The system can monitor abnormal situations in the examination process in real time, and can identify and take necessary measures to respond in time to ensure that the fairness and safety of the examination are not threatened.

[0122] In another embodiment of the present application, a method for intelligent identification analysis and security protection of fire identification examination identity is also provided. The method is applied to the application trust center of the fire identification examination identity intelligent identification analysis and security protection system, and the system also includes an access subject, and an encrypted channel is set between the access subject and the application trust center, see Figure 5 Said method comprises the following steps:

[0123] 301, receiving a real-time login request of a real-time monitored object through an encrypted channel; the real-time login request at least includes: user identity information, authentication credentials, and behavior image information that have been subjected to multiple encryption processes; the real-time login request is request data adapted to the encrypted channel generated by an identity encryption model;

[0124] 302, using the identity decryption model to intelligently analyze the received real-time login request to obtain the real-time identity information of the real-time monitored object; the real-time identity information at least includes: user identity information, authentication credentials, and behavior image information obtained through multiple decryption processes;

[0125] 303, performing monitoring behavior analysis on the real-time identity information to obtain the test behavior of the real-time monitored object;

[0126] 304, using a security protection model to perform a security risk assessment on the real-time identity information to obtain a security protection assessment associated with the real-time monitoring object;

[0127] 305, marking the corresponding examination behavior and security protection assessment for the real-time monitoring object to complete the real-time monitoring of the real-time monitoring object.

[0128] It is worth mentioning that the application trust center is also used to perform other functions in the above system, which will not be elaborated here.

[0129] In the embodiment of the present application, by applying the trust center, the real-time monitoring objects in the fire identification examination process are identified, behavior analyzed, and safety protection evaluated, which helps to improve the security, fairness, and accuracy of the examination process, and improve the efficiency of examination invigilation and the safety of the examination room. The system can monitor abnormal situations in the examination process in real time, and can identify and take necessary measures to respond in a timely manner to ensure that the fairness and security of the examination are not threatened.

[0130] In another embodiment of the present application, an electronic device is provided, including: a processor, a communication interface, a memory, and a communication bus, wherein: Figure 6 As shown, the processor, the communication interface, and the memory communicate with each other via a communication bus;

[0131] Memory, used to store computer programs;

[0132] The processor is used to implement the intelligent identification analysis and safety protection system for fire identification examination as described in the method embodiment when executing the program stored in the memory.

[0133] The communication bus 1140 mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, etc.

[0134] The embodiment of the present application provides a system for constructing a low-energy consumption computing unit for intelligent identity recognition analysis and safety protection in fire identification examinations.

[0135] For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0136] The communication interface 1120 is used for communication between the above electronic device and other devices.

[0137] The memory 1130 may include a random access memory (RAM) or a non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0138] The above-mentioned processor 1110 can be a general-purpose processor, including an artificial intelligence processor, a graphics processing unit (GPU), an artificial intelligence processor card (Machine Learning Unit, MLU), 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 field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0139] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed, can implement each step that can be executed by an electronic device in the above method embodiment.

Claims

1. A fire identification test identity intelligent recognition analysis and safety protection system, characterized in that: The system includes an access subject and an application trust center, and an encrypted channel is set between the access subject and the application trust center; wherein, The access subject is used to collect real-time monitoring image data of the real-time monitoring object; input the real-time monitoring image into the identity encryption model to generate a real-time login request adapted to the encryption channel; wherein the real-time login request at least includes: user identity information, authentication credentials, and behavior image information that have been processed through multiple encryptions; and send the real-time login request to the application trust center through the encryption channel; An application trust center is used to use an identity decryption model to perform intelligent analysis on received real-time login requests to obtain real-time identity information of the real-time monitored object; the real-time identity information includes at least: user identity information, authentication credentials, and behavior image information obtained after multiple decryption processes; monitoring behavior analysis is performed on the real-time identity information to obtain the examination behavior of the real-time monitored object; a security protection model is used to perform a security risk assessment on the real-time identity information to obtain a security protection assessment associated with the real-time monitored object; corresponding examination behaviors and security protection assessments are marked for the real-time monitored object to complete real-time monitoring of the real-time monitored object.

2. The intelligent identification analysis and safety protection system for fire identification examination according to claim 1 is characterized in that: The identity encryption model at least includes a feature extraction layer, an identity recognition layer, an identity authentication layer, a behavior image construction layer, and a multiple encryption layer; The access subject inputs the real-time monitoring image into the identity encryption model to generate a real-time login request adapted to the encryption channel, specifically for: Extracting identity feature information of the real-time monitoring object from the real-time monitoring image through the feature extraction layer; the identity feature information at least includes: face feature information, body posture feature information, and action feature information; Through the identity recognition layer, the identity feature information is processed for identity matching to obtain the user identity information corresponding to the real-time monitoring object; Through the identity authentication layer, the identity feature information is authenticated and identified to obtain the authentication credential information corresponding to the real-time monitoring object; Through the behavior image construction layer, the identity feature information is processed through behavior construction to obtain behavior image information corresponding to the real-time monitoring object; Through multiple encryption layers, the user identity information, authentication credentials, and behavior image information corresponding to the real-time monitoring object are multiple encrypted to construct the real-time login request.

3. The intelligent identification analysis and safety protection system for fire identification examination according to claim 2 is characterized in that: Through the identity recognition layer, the identity feature information is processed for identity matching to obtain the user identity information corresponding to the real-time monitoring object, which is expressed as the following formula: Wherein, F represents the identity feature matrix obtained by extracting the identity feature information through a multi-scale convolutional network, u i represents the i-th identity feature vector in the identity feature information, W represents a learnable weight parameter matrix, W is used to adjust the importance of the identity feature matrix F at different scales, b i represents the learnable bias term, b i Represents the adjustment of the i-th identity feature vector u i Similarity(F,u i ) represents the identity feature matrix F and the i-th identity feature vector u i The similarity between i Represents the vector norm.

4. The intelligent identification analysis and safety protection system for fire identification examination according to claim 2 is characterized in that: Through the identity authentication layer, the identity feature information is authenticated and identified to obtain the authentication credential information corresponding to the real-time monitoring object, which is expressed as the following formula: Wherein, F represents the identity feature matrix obtained by extracting the identity feature information through a multi-scale convolutional network, u i represents the i-th identity feature vector in the identity feature information, W represents a learnable weight parameter matrix, W is used to adjust the importance of the identity feature matrix F at different scales, b i represents the learnable bias term, b i Represents the adjustment of the i-th identity feature vector u i The similarity of Ath(F,u i ) represents the similarity between the identity feature matrix F and the i-th identity feature vector ui, w i represents the vector norm, represents the conversion function that converts similarity into authentication probability.

5. The intelligent identification analysis and safety protection system for fire identification examination according to claim 2 is characterized in that: When the identity feature information is processed through the behavior image construction layer to obtain the behavior image information corresponding to the real-time monitoring object, it is specifically used for: Extracting posture feature information, movement feature information, expression feature information, and behavior habit information of the real-time monitored object from the identity feature information; The posture feature information, movement feature information, expression feature information, and behavior habit information of the real-time monitoring object are interwoven and projected into a high-dimensional coding space from multiple dimensions to obtain the real-time coding features of the real-time monitoring object; Dynamically sampling the real-time coding features of the real-time monitoring object to obtain initial behavior image information of the real-time monitoring object; A key local area in the initial behavior image information of the real-time monitoring object is selected, and image enhancement processing is performed on the key local area to obtain the behavior image information of the real-time monitoring object.

6. The intelligent identification analysis and safety protection system for fire identification examination according to claim 2 is characterized in that: The multiple encryption processing includes at least one or more of data encryption, tag encryption, time stamp mechanism, data integrity check, anti-tampering processing, and anti-counterfeiting processing; The process flow parameters in the multiple encryption process are associated with the encryption channel.

7. The intelligent identification analysis and safety protection system for fire identification examination according to claim 1 is characterized in that: The identity decryption model at least includes: a subject identification layer, multiple decryption layers, and an identification layer; The application trust center uses the identity decryption model to perform intelligent analysis on the received real-time login request to obtain the real-time identity information of the real-time monitored object, specifically for: Extracting the access subject information in the real-time login request through the subject identification layer and identifying the type of the corresponding access subject; Through multiple decryption layers, the target decryption module corresponding to the type of the access subject is called to perform multi-layer real-time decryption on the real-time login request to obtain the identity information to be processed; Through the recognition layer, the identity information to be processed is subjected to intelligent recognition processing to obtain the real-time identity information.

8. A method for intelligent identification analysis and safety protection of fire identification examination, characterized in that: The method is applied to the access subject in the fire identification test identity intelligent recognition analysis and safety protection system, the system also includes an application trust center, and an encrypted channel is set between the access subject and the application trust center; the method includes: Collect real-time monitoring image data of real-time monitoring objects; Input the real-time monitoring image into the identity encryption model to generate a real-time login request adapted to the encryption channel; wherein the real-time login request at least includes: user identity information, authentication credentials, and behavior image information that have been processed through multiple encryptions; The real-time login request is sent to the application trust center through an encrypted channel, so that the application trust center can perform monitoring behavior analysis on the real-time identity information to obtain the examination behavior of the real-time monitoring object, and use a security protection model to perform a security risk assessment on the real-time identity information to obtain a security protection assessment associated with the real-time monitoring object, thereby marking the corresponding examination behavior and security protection assessment for the real-time monitoring object to complete real-time monitoring of the real-time monitoring object.

9. A method for intelligent identification analysis and safety protection of fire identification examination, characterized in that: The method is applied to an application trust center in a fire identification test identity intelligent recognition analysis and safety protection system, wherein the system also includes an access subject, and an encrypted channel is provided between the access subject and the application trust center; the method includes: Receive a real-time login request from a real-time monitored object through an encrypted channel; the real-time login request includes at least: user identity information, authentication credentials, and behavior image information that have been processed through multiple encryptions; the real-time login request generates request data adapted to the encrypted channel through an identity encryption model; An identity decryption model is used to intelligently analyze the received real-time login request to obtain the real-time identity information of the real-time monitored object; the real-time identity information at least includes: user identity information, authentication credentials, and behavior image information obtained through multiple decryption processes; Performing monitoring behavior analysis on the real-time identity information to obtain the examination behavior of the real-time monitored object; Using a security protection model, a security risk assessment is performed on the real-time identity information to obtain a security protection assessment associated with the real-time monitoring object; The corresponding examination behaviors and security protection assessments are marked for the real-time monitoring objects to complete the real-time monitoring of the real-time monitoring objects.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor, memory, and input-output unit; Wherein, the memory is used to store computer programs, and the processor is used to call the computer programs stored in the memory to execute the intelligent identity recognition analysis and safety protection system for fire identification examinations as described in any one of claims 1 to 7.

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