Identity authentication method

Abstract

The present application relates to an identity authentication method. The method comprises: receiving an identity authentication request, wherein the identity authentication request comprises an identity identifier of a target user; displaying an identity authentication question, wherein the identity authentication question is a question corresponding to identity authentication of the target user and generated by a questioning agent on the basis of the identity authentication request and user information in an integrated dynamic knowledge base; receiving a user answer fed back by the target user; and displaying an identity authentication result of the target user, wherein the identity authentication result is generated by evaluating the accuracy of the user answer by means of an evaluation agent.

Description

Identity authentication methods

[0001] Cross-referencing

[0002] This application claims priority to Chinese Patent Application No. 202510115492.7, filed on January 24, 2025, entitled "Method for Identity Authentication", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of identity security authentication technology, and in particular to an identity authentication method. Background Technology

[0004] Identity authentication, as a crucial component of information security, primarily aims to protect the authenticity of user identities, thereby safeguarding systems from unauthorized access. Common authentication methods include static passwords, two-factor authentication, and biometric recognition.

[0005] However, all of the above-mentioned identity authentication schemes require the collection and storage of raw information, which is a centralized source of information. During identity authentication, they rely on obtaining raw information from a centralized information source and comparing it for authentication. This centralized authentication method is not only vulnerable to attacks or data leaks, but also, once the centralized information source is destroyed, all authentication mechanisms that rely on that information source will face verification security risks. Summary of the Invention

[0006] On one hand, an identity authentication method is provided, the method comprising: receiving an identity authentication request, the identity authentication request containing an identity identifier of a target user; displaying an identity authentication question, wherein the identity authentication question is a question corresponding to the identity authentication of the target user generated by a questioning agent based on user information in a joint dynamic knowledge base, based on the identity authentication request; receiving a user answer from the target user; and displaying the identity authentication result of the target user, wherein the identity authentication result is generated by an evaluation agent after evaluating the accuracy of the user answer.

[0007] On the other hand, an identity authentication method is provided, the method comprising: receiving an identity authentication request sent by a target user device; the identity authentication request containing an identity identifier of the target user; generating an identity authentication question corresponding to the target user based on the identity authentication request by a questioning agent; and sending the identity authentication question to the target user device; the questioning agent is used to generate the identity authentication question based on user information in a joint dynamic knowledge base; and, upon receiving a user answer from the target user device, evaluating the accuracy of the user answer by an evaluation agent to generate an identity authentication result for the target user.

[0008] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor to implement the above-described authentication method.

[0009] On the other hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, the computer program being loaded and executed by a processor to implement the above-described authentication method.

[0010] On the other hand, a computer program product is provided, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to execute to implement the authentication method provided in the various optional implementations described above.

[0011] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0012] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0013] Figure 1 shows a flowchart of an identity authentication method provided in an exemplary embodiment of this application;

[0014] Figure 2 shows a flowchart of an identity authentication method provided by another exemplary embodiment of this application;

[0015] Figure 3 shows a schematic diagram of an identity authentication system provided in an exemplary embodiment of this application;

[0016] Figure 4 shows a flowchart of an identity authentication method provided in yet another exemplary embodiment of this application;

[0017] Figure 5 shows a schematic diagram of the operation of an identity authentication system provided in an exemplary embodiment of this application;

[0018] Figure 6 illustrates an interactive schematic diagram of an identity authentication method provided in an exemplary embodiment of this application;

[0019] Figure 7 shows a structural block diagram of a computer device illustrated in an exemplary embodiment of this application;

[0020] Figure 8 shows a structural block diagram of another computer device illustrated in an exemplary embodiment of this application. Detailed Implementation

[0021] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods consistent with some aspects of this application as detailed in the appended claims.

[0022] This application provides an identity authentication method based on a question-and-answer-based identity authentication mechanism that integrates a multi-agent system (MAS) and an integrated dynamic knowledge base (IDKB), thereby reducing reliance on centralized information sources and improving the security of identity authentication.

[0023] The authentication method provided in this application involves the interaction between a user device and an authentication system. On the target user device side, Figure 1 shows a flowchart of an exemplary embodiment of the authentication method provided in this application. The method can be executed by the target user device, such as a display device. As shown in Figure 1, the method includes the following steps.

[0024] Step 110: Receive an identity authentication request, which contains the identity identifier of the target user.

[0025] The authentication request can be initiated by the target user to request identity verification. For example, it can be sent by the user in response to authentication triggered by the target user's device, such as during a transaction or information query. Upon receiving the authentication request, the target user's device initiates the authentication process to verify the user's identity. This identity identifier is a unique identifier for the target user within the system. For example, it can be a username, ID card number, mobile phone number, etc., and this application does not impose any restrictions on this.

[0026] Step 120: Display the authentication question, which is an authentication question generated by the questioning agent based on user information in the joint dynamic knowledge base, corresponding to the target user's authentication, based on the authentication request.

[0027] A Questioning Agent (QA) is an intelligent device deployed in an identity authentication system that has the ability to intelligently generate questions. It can provide personalized identity authentication questions for target users based on the identity authentication request sent by the target user's terminal and the user information in a federated dynamic knowledge base.

[0028] A joint dynamic knowledge base is a collection of databases that store various types of user-related information through multiple knowledge bases. Illustratively, the user information in this joint dynamic knowledge base may include, but is not limited to, dynamic information such as basic user information, user behavior habits, and historical operation information. The user information in this joint dynamic database can be continuously updated as users interact with the system, providing more comprehensive and accurate information to support the generation of identity authentication questions.

[0029] After receiving an authentication request, the target user device sends the authentication request to the questioning agent. The questioning agent receives the authentication request and retrieves user information related to the target user from the linked dynamic knowledge base based on the identity identifier carried in the authentication request. Based on this information, it generates a personalized authentication question for the target user. Then, the questioning agent sends the authentication question to the target user device so that the target user device can display the authentication question to the target user through the display interface so that the user can respond.

[0030] In this process, the questioning agent can generate one or more authentication questions in each round. The number of authentication questions generated by the questioning agent in each round can be set based on actual needs, and this application does not impose any restrictions on this.

[0031] Step 130: Receive user responses from the target user.

[0032] After obtaining the authentication question through the target user device, the target user can provide the answer to the authentication question. In some possible implementations, the display interface of the target user device can display the authentication question and the corresponding answer feedback component, such as a text box or an answer area marked by a specific symbol. The target user can provide feedback on the answer feedback component by text input or by voice input. This application does not impose any restrictions on this.

[0033] Step 140: Display the identity authentication result of the target user, wherein the identity authentication result is generated by evaluating the accuracy of the user's answer by the evaluation agent.

[0034] After receiving the user's response, the target user device sends the response to the evaluation agent, which then performs an accuracy assessment based on the received response to determine the target user's authentication result.

[0035] Among them, the Evaluation Agent (EA) is an intelligent device deployed in the identity authentication system that can analyze and judge user answers. It can compare the received user answers with the correct image in the joint dynamic knowledge base, and use pre-trained or set algorithms and logic to evaluate the accuracy of the user answers, thereby generating the final identity authentication result.

[0036] After determining the authentication result of the target user, the evaluation agent feeds back the authentication result to the target user device, so that the target user device can display the authentication result to the target user through the display interface; wherein, the authentication result can be successful authentication or authentication failure.

[0037] In summary, the identity authentication method provided in this application, on the target user device side, after the target user terminal receives an identity authentication request containing the target user's identity identifier, displays an identity authentication question corresponding to the target user, generated and fed back by the questioning agent based on user information in the joint dynamic knowledge base and the identity authentication request; after receiving the user's answer, it displays the identity authentication result generated by the evaluation agent after evaluating the accuracy of the user's answer. Through the above method, question-and-answer style identity authentication can be performed using the questioning agent, the evaluation agent, and the joint dynamic knowledge base, thereby achieving personalized identity authentication for each user, reducing reliance on a centralized information source, and improving the security of identity authentication; furthermore, since the user information in the joint dynamic knowledge base is updated in real time, the timeliness and relevance of the identity authentication questions can be guaranteed, thereby further improving the reliability of identity authentication.

[0038] Corresponding to the target terminal side, on the identity authentication system side, Figure 2 shows a flowchart of an identity authentication method provided by another exemplary embodiment of this application. The method can be executed by the identity authentication system. As shown in Figure 2, the method includes the following steps.

[0039] Step 210: Receive an authentication request sent by the target user equipment; the authentication request contains the identity identifier of the target user.

[0040] In this embodiment, the identity authentication system includes a multi-agent system (MAS) and a joint dynamic knowledge base. The multi-agent system includes a questioning agent and an evaluation agent. When a target user triggers an identity authentication operation on the target user's device, the target user's device sends an identity authentication request carrying the target user's identity identifier to the identity authentication system. Optionally, the identity authentication system can receive the identity authentication request through the questioning agent and extract the identity identifier from the identity authentication request to prepare for subsequent operations.

[0041] Step 220: Based on the identity authentication request, generate an identity authentication question corresponding to the target user through a questioning agent, and send the identity authentication question to the target user's device; the questioning agent is used to generate the identity authentication question based on user information in a joint dynamic knowledge base.

[0042] After receiving an authentication request containing an identity identifier, the intelligent agent retrieves various information related to the target user from the joint dynamic knowledge base, using the identity identifier as an index. Based on preset question generation rules and algorithms, and combined with the retrieved user information, the agent generates a personalized authentication question tailored to the target user. For example, if the joint dynamic knowledge base records the location of the target user's most recent login, the agent might generate the authentication question, "Where was your most recent login location?" After generating the authentication question, it is sent back to the target user's device so that the target user can view and answer it.

[0043] In some possible implementations, the authentication question generated by the questioning agent can be a short answer question, or in other possible implementations, the authentication question generated by the questioning agent can be a multiple-choice question, such as "Which of the following locations was your most recent login location?" with multiple options for the user to choose from; this application does not restrict the type of authentication question.

[0044] Step 230: Upon receiving the user's response from the target user's device, the accuracy of the user's response is evaluated by the evaluation agent, and the target user's identity authentication result is generated.

[0045] After receiving the authentication question, the target user device presents it to the target user through a display interface. The target user then provides their response based on their actual situation. The target user device then forwards the response to the evaluation agent in the authentication system. Upon receiving the response, the evaluation agent uses the target user's identifier as an index to retrieve the correct answer corresponding to the question from a joint dynamic knowledge base. It then uses a pre-defined evaluation algorithm to compare and analyze the user's response with the correct answer to determine its accuracy. For example, it uses string matching and semantic analysis to determine whether the user's response matches the correct answer or is within an acceptable margin of error. Based on the evaluation results, the target user's authentication result is generated.

[0046] After generating the authentication result for the target user, the evaluation agent sends the authentication result back to the target user's device. In turn, after receiving the authentication result, the target user's device displays the authentication result to the target user on the device interface, such as displaying a pop-up message box showing "Authentication successful" or "Authentication failed" to inform the target user of the final result of this authentication.

[0047] Furthermore, the identity authentication system can perform corresponding follow-up operations based on the identity authentication result of the target user. For example, if the authentication is successful, the target user can be allowed to access specific resources or functions; if the authentication fails, relevant information can be recorded, and the number of further attempts can be limited, etc.

[0048] Before a user initiates an authentication request, the authentication system needs to prepare the joint dynamic knowledge base and fine-tune the questioning agent and the evaluation agent. During the fine-tuning process, the questioning agent can learn how to generate highly customized, non-repetitive authentication questions that are closely related to the user's real-time state based on data from the joint dynamic knowledge base through a large corpus of real-world scenarios, and dynamically adjust the questioning strategy in different scenarios. The evaluation agent learns how to semantically understand user answers and match them based on keywords through training on a semantic analysis corpus.

[0049] Through the aforementioned fine-tuning training, the questioning agent and the evaluation agent can learn capabilities in four dimensions: planning, reflection, memory, and tool use. In terms of planning, the questioning agent and the evaluation agent's planning is based on their thought chain ability. When generating a question, the questioning agent plans the entire thought process of the question, including the source of knowledge, associative hints, and tool usage. Indicatively, the thought chain for posing a question, based on the questioning agent's planning ability, could be: "The questioning agent breaks down the large question posed to the user into multiple sub-questions: 1. Have you asked the user any questions before? How effective were the answers? 2. From which knowledge base should knowledge be collected for the current question? 3. Based on the collected knowledge, how to ask a reasonable question? Should it be asked from a single knowledge source or after associating knowledge? 4. How to use a reasonable tone when asking the user this question?" The questioning agent connects these sub-questions into a complete thought chain, and in the process of implementing this thought chain... The system continuously utilizes its own reflection, memory, and tool capabilities, through the coordinated use of three tools—knowledge collection, knowledge association, and question formulation—to ultimately present users with highly customized, non-repetitive authentication questions closely related to their real-time status. It should be noted that the thought process for posing these questions can vary depending on adjustments during the fine-tuning process and different configurations; this application does not impose any restrictions on this. After receiving the user's answer, the evaluation agent also analyzes the answer based on the thought process, planning the entire evaluation logic, including question classification, semantic analysis, and tool usage. Illustratively, the thought process by which the evaluation agent evaluates the user's answer based on its planning capabilities could be: The evaluation agent breaks down the large question of evaluating the user's answer into multiple sub-questions: 1. To which category of knowledge base does this question belong? 2. Should questions from this knowledge base be evaluated using character verification or semantic similarity verification? 3. Does the semantic similarity verification exceed the threshold for correct judgment? 4.How to output evaluation results to users using appropriate tone? The evaluation agent links these sub-questions into a complete thought chain, continuously utilizing its reflection, memory, and tool capabilities during the implementation of this chain. Through the cooperation of three types of tools—question classification, semantic similarity verification, and character verification—it ultimately judges the correctness of the answer by combining string matching and semantic understanding and analysis, thereby completing identity authentication. Similarly, the thought chain for evaluating user answers can vary based on adjustments during the fine-tuning process and different configurations; this application does not impose any restrictions on this. Regarding reflection, both the questioning agent and the evaluation agent continuously reflect during the authentication process. The questioning agent can optimize subsequent questions based on the effectiveness of previously generated questions, avoiding repetitive questions and ensuring the uniqueness of each authentication. The evaluation agent, combining context and answer content, analyzes whether further verification questions are needed, or whether authentication is required. Through the reflection function, it dynamically adjusts the entire authentication process, improving the system's intelligence. Regarding memory, both the questioning agent and the evaluation agent possess memory capabilities. Yes, both the questioning and evaluation agents can store and retrieve relevant information during the authentication process. The questioning agent remembers previously asked questions and user answers when posing a question, ensuring that subsequent questions are not repetitive or irrelevant. The evaluation agent's memory function is reflected in the tracking and analysis of user answers. It not only remembers the user's answers in the current session but also retrieves past authentication records as a reference for analyzing the user's current answers, helping to reduce the system's reliance on chance factors and improve authentication accuracy. Regarding tool usage, both the questioning and evaluation agents possess tool usage capabilities. The questioning agent's toolkit can include three categories of tools: knowledge collection, knowledge association, and question formulation. These tools are flexibly invoked to generate questions. The evaluation agent's toolkit includes three categories of tools: question classification, semantic similarity verification, and character verification. These tools are flexibly invoked to evaluate the rationality and correctness of user answers, ensuring the rigor of authentication. The capabilities of the questioning and evaluation agents in the four dimensions of planning, reflection, memory, and tool usage will be explained through subsequent examples.

[0050] In summary, the identity authentication method provided in this application, on the identity authentication system side, after receiving an identity authentication request containing the target user's identity identifier sent by the target user device, generates an identity authentication question corresponding to the target user based on the user information in the joint dynamic knowledge base and the identity authentication request through a questioning agent and sends it to the target user device; upon receiving the user's answer from the target user device, an evaluation agent evaluates the accuracy of the user's answer and generates an identity authentication result. Through the above method, question-and-answer style identity authentication can be performed using a questioning agent, an evaluation agent, and a joint dynamic knowledge base, thereby achieving personalized identity authentication for each user, reducing reliance on a centralized information source, and improving the security of identity authentication; furthermore, since the user information in the joint dynamic knowledge base is updated in real time, the timeliness and relevance of the identity authentication questions can be guaranteed, thereby further improving the reliability of identity authentication.

[0051] Figure 3 shows a schematic diagram of an identity authentication system provided by an exemplary embodiment of this application. As shown in Figure 3, the identity authentication system includes a user interface layer 310, a business logic layer 320, a data storage layer 330, and a management backend layer 340.

[0052] The user interface layer may include an authentication request component and an authentication result component. The authentication request component is used to receive the authentication request sent by the user device and pass the authentication request to the questioning agent of the business logic layer. The authentication result component is used to receive the authentication result fed back by the evaluation agent and send the authentication result to the user device.

[0053] In some possible implementations, the user equipment can be part of the identity authentication system, serving as the user interface layer of the identity authentication system to receive identity authentication requests and provide feedback on identity authentication results.

[0054] In some other possible implementations, the user device is independent of the authentication system. For example, the user device can be located at the user's end (such as a home, public place, or other location where the user actually operates), while the authentication system is located in a place with powerful computing and storage capabilities, such as a cloud server or data center.

[0055] The business logic layer corresponds to a multi-agent system, including a questioning agent and an evaluation agent, which are responsible for generating authentication questions and evaluating user answers, respectively.

[0056] The questioning agent may include a first planning component, a first information retrieval component, a question construction component, a first reflection component, and a first memory component. The first planning component plans the question generation logic, including the question's source, type, and difficulty level. The first information retrieval component retrieves relevant information from a joint dynamic knowledge base to provide data support for question generation. The question construction component generates customized, non-repetitive authentication questions related to the user's real-time state based on information provided by the retrieval component. The first reflection component optimizes subsequent questions based on the user's responses to previous questions, avoiding poor accuracy due to question type or domain. The first memory component stores constructed questions to prevent repetitive question generation, ensuring the uniqueness of each authentication process. It can also store historical question-and-answer information to provide a reference for personalized user preferences in question construction.

[0057] The evaluation agent may include a second planning component, a second information retrieval component, an accuracy judgment component, a second reflection component, and a second memory component. The second planning component plans the evaluation logic for user answers, including the selection of evaluation methods and the setting of evaluation criteria. The second information retrieval component retrieves reference answers to the current question from a joint dynamic knowledge base, providing data support for user answer evaluation. The accuracy judgment component judges the accuracy of user answers based on the determined evaluation method and reference information. The second reflection component determines whether further verification questions or authentication is required; the reflection component allows for dynamic adjustment of the authentication process for different users, improving the system's intelligence. The second memory component stores and retrieves users' historical question-and-answer information, providing a reference for personalized user preferences when evaluating the current user's answer. This serves as a reference for evaluating the current user's answer, improving the accuracy of identity authentication.

[0058] The data storage layer corresponds to a joint dynamic knowledge base, which may include a Basic Information Knowledge Base (BIKB), a Behavioral Knowledge Base (BKB), and a Supplementary Knowledge Base (SKB) to provide data support, ensure the personalization and real-time nature of questions, and the accuracy and real-time nature of user answer evaluation. The basic knowledge base stores static basic information, such as a user's relatively fixed information like their registered email address, phone number, and birthday. This base includes an information update component, allowing users to update their static basic information. The behavioral knowledge base records and analyzes dynamic behavioral information, including but not limited to recently participated projects, recently visited web pages, meeting topics, frequently used contacts, and location information. This base may include a data analysis component to periodically analyze user behavior models, extract behavioral features and activity trajectories, and enrich and update the background and content of questions. The supplementary knowledge base accepts personalized information uploaded by users, such as favorite books, movie summaries, or life experiences. This base may include a data management component, allowing users to manage and edit their personalized information.

[0059] The management backend layer provides a management platform for users with administrative privileges (i.e., administrators). This platform allows administrators to receive system management commands, including knowledge base management, agent training, and real-time monitoring, ensuring the system's correct operation and data accuracy. Knowledge base management includes configuration management and data auditing. Configuration management allows administrators to configure and manage various knowledge bases within the federated dynamic knowledge base, ensuring the accuracy and timeliness of information. Data auditing allows administrators to review user-uploaded data, ensuring its quality and compliance. Agent training allows administrators to fine-tune and train individual agents to improve their performance in different scenarios. Real-time monitoring allows administrators to monitor the operational status of each agent, ensuring efficient operation and timely problem identification and resolution.

[0060] Based on the above-mentioned identity authentication system, Figure 4 shows a flowchart of an identity authentication method provided in another exemplary embodiment of this application. The method can be executed by the target user device in interaction with the identity authentication system. As shown in Figure 4, the method includes the following steps.

[0061] Step 401: The target user equipment receives an authentication request, which contains the target user's authentication identifier.

[0062] The target user device receives an authentication request initiated by the user.

[0063] Step 402: The target user equipment sends an authentication request to the authentication system, and the authentication system receives the authentication request sent by the target user equipment.

[0064] Furthermore, the target user device sends an authentication request to the questioning agent in the authentication system, and the corresponding questioning agent receives the authentication request.

[0065] Step 403: Based on the identity authentication request, the identity authentication system generates an identity authentication question corresponding to the target user through a questioning agent; the questioning agent is used to generate identity authentication questions based on user information in a joint dynamic knowledge base.

[0066] A joint dynamic knowledge base is a collection of multiple knowledge bases. Each knowledge base can store user information in different dimensions. In some possible implementations, the joint dynamic knowledge base includes at least one of the following: a basic knowledge base, a behavioral knowledge base, and a supplementary knowledge base; the basic knowledge base stores the user's static basic information, the behavioral knowledge base stores the user's dynamic behavioral information, and the supplementary knowledge base stores the user's personalized user information uploaded by the user.

[0067] In some possible implementations, the process by which the identity authentication system generates authentication questions corresponding to the target user through a questioning agent can be implemented as follows:

[0068] Based on the target user's identity identifier contained in the identity authentication request, the questioning agent retrieves information from the joint dynamic knowledge base to obtain the target user's user information.

[0069] Based on the question building element, an identity authentication question corresponding to the target user is constructed. The question building element includes the target user's user information and a first preset prompt word.

[0070] When the questioning agent retrieves information from a joint dynamic knowledge base based on the target user's identity, in some possible implementations, the questioning agent can sequentially retrieve information from each knowledge base according to a pre-set information retrieval order, extracting user information corresponding to the target user. For example, if the information retrieval order is from the basic knowledge base, the behavioral knowledge base, to the supplementary knowledge base, and the number of identity authentication questions generated in a single authentication is three, the questioning agent can sequentially extract one or more user information items from each of the basic knowledge base, the behavioral knowledge base, and the supplementary knowledge base. Alternatively, in other possible implementations, the questioning agent can randomly determine the database for information retrieval to extract the target user's user information.

[0071] It should be noted that the questioning agent can obtain a set of user information of the target user during information retrieval, and then extract user information from the set of user information to construct corresponding identity authentication questions based on the number of identity authentication questions; or, the questioning agent can obtain a corresponding number of user information from the joint dynamic knowledge base during information retrieval based on the number of identity authentication questions, in order to construct corresponding identity authentication questions.

[0072] When constructing an authentication question based on user information and a first preset prompt word, the questioning agent can generate a question construction prompt based on the acquired user information and the first preset prompt word, and input the question construction prompt into a large language model for processing to obtain the authentication question; illustratively, the question construction prompt can be "Please construct an authentication prompt question based on the following user information"; different types of authentication questions can be generated based on different first preset prompt words, which can be keywords or phrases pre-set by relevant personnel to guide the questioning agent in constructing the authentication question.

[0073] In some possible implementations, the questioning agent includes a first short-term memory component; this first short-term memory component records the authentication questions that have been constructed in the current authentication process.

[0074] In this context, when the querying agent performs information retrieval, it can refer to the identity authentication questions already constructed in the current authentication process recorded in the first short-term memory component. On the one hand, it can determine the question-and-answer context of the current authentication process based on the constructed identity authentication questions to retrieve user information that matches the current question-and-answer context. On the other hand, it can filter user information that is different from the constructed identity authentication questions to ensure the non-repetition of subsequently constructed identity authentication questions. This process can be implemented as follows: determining the question-and-answer context of the current authentication process based on the identity authentication questions already constructed in the current authentication process recorded in the first short-term memory component; and retrieving user information of the target user from the joint dynamic knowledge base based on the target user's identity identifier and the question-and-answer context of the current authentication process.

[0075] The question-and-answer context refers to the contextual environment of the current authentication process, which reflects the logical relationship between the constructed identity authentication questions. The questioning agent can extract the contextual environment of the current authentication process, i.e., the question-and-answer context, by analyzing factors such as the topic, type, and information domain of the questions recorded in the first short-term memory component. For example, if the recorded questions involve the user's historical transaction information, the questioning agent will determine that the current question-and-answer context is related to the user's transactions. When performing information retrieval, it can further extract transaction-related information, such as the location and time of the transaction in previous questions, and new identity authentication questions can be constructed based on the name of the goods in the transaction, etc.

[0076] When constructing authentication questions, the questioning agent can also combine the question-and-answer context with the previously constructed authentication questions recorded in the first short-term memory component to construct subsequent authentication questions. In other words, the question construction elements can also include the question-and-answer context of the current authentication process, and the authentication questions do not exist in the first short-term memory component. This process can be implemented as follows: construct authentication questions based on the target user's user information, the question-and-answer context of the current authentication process, and the first preset prompt words; these authentication questions do not exist in the first short-term memory component.

[0077] The above construction method ensures that the newly constructed identity authentication question not only conforms to the current question-and-answer context, but is also different from the existing questions in the first short-term memory component. Indicatively, this question-and-answer context can serve as an index for obtaining the first preset prompt words, thereby filtering out the first preset prompt words that conform to the current context, and then constructing an identity authentication question that conforms to the current context.

[0078] In some possible implementations, the questioning agent includes a first long-term memory component; the first long-term memory component stores the user's historical question and answer information.

[0079] In this context, when the questioning agent performs information retrieval, it can refer to the historical answer information stored in the first long-term memory component to determine the user's personalized preferences, thereby filtering out user information that matches the user's personalized preferences and constructing personalized questions for that user. This process can be implemented as follows: determining the target user's personalized preferences based on the target user's historical question and answer information stored in the first long-term memory component; and retrieving user information from the joint dynamic knowledge base based on the target user's identity and personalized preferences.

[0080] Personalized preferences refer to the unique preferences and tendencies of users in answering questions, using systems, or processing information, which are summarized by analyzing users' historical question and answer information. For example, users may be more inclined to answer questions about their own profession, or they may be more adept at using specific dates as answers.

[0081] In some possible implementations, when determining the personalized preferences of a target user, the questioning agent can evaluate personalized preferences based on the duration and accuracy of the user's responses in historical question-and-answer information. For example, by analyzing historical answer information, it can be determined that the user can answer questions about their own professional experience accurately and quickly, but responds slowly and with low accuracy when answering questions involving information such as home address. This indicates that the user's preference is for questions related to their own professional experience. Therefore, when extracting user information, user information related to the user's professional experience can be extracted first.

[0082] When constructing authentication questions, the intelligent agent can also build subsequent authentication questions based on personalized information. In other words, the question construction elements can also include the target user's personalized preferences. This process can be implemented as follows:

[0083] The intelligent agent constructs identity authentication questions based on the target user's user information, personalized preferences, and a first preset prompt.

[0084] The above construction method ensures that the newly constructed identity authentication questions conform to the user's personalized preferences, making it easier for the user to understand and answer the questions. For example, the user's personalized preferences can serve as an index for obtaining the first preset prompt words, thereby filtering out the first preset prompt words that match the user's personalized preferences. For instance, if the user's personalized preferences indicate that they are good at answering professional knowledge questions, then more professional first preset prompt words can be selected, making the generated identity authentication questions conform to the question-and-answer format of the professional field; if the user's personalized preferences indicate that they are good at general daily communication, then more everyday first preset prompt words can be selected, making the generated identity authentication questions more easily understood.

[0085] It should be noted that the above-mentioned method of information retrieval and / or question construction by the questioning agent based on information stored in the first short-term memory component and the method of information retrieval and / or question construction based on information stored in the first long-term memory component can be applied separately or in combination. This application does not impose any restrictions on this. That is to say, the identity authentication question is a question corresponding to the identity authentication of the target user, constructed by the questioning agent based on at least one question construction element based on the identity authentication request. The question construction element includes the user information of the target user obtained from the joint dynamic knowledge base, the question-and-answer context of the current authentication process, the personalized preferences of the target user, and the first preset prompt words.

[0086] During the process of the intelligent agent constructing authentication questions, the target user's terminal can display question construction progress information on its screen. This progress information indicates the progress of the intelligent agent in constructing the authentication questions.

[0087] In some possible implementations, the problem construction progress information can be displayed in the form of problem generation process information. This problem generation process information is used to instruct the questioning agent on the various problem generation processes for constructing the identity authentication problem. The problem generation process includes: a user information retrieval process and a problem construction process. The user information retrieval process information, which instructs the user information retrieval process, can include the knowledge base for obtaining user information, the type of information obtained, etc. For example, the user information retrieval process information could be "obtain information from the behavioral knowledge base" or "obtain information from the supplementary knowledge base." The problem construction process information, which instructs the problem construction process, could be "problem construction in progress," etc. The above information content is only illustrative; different information content may be displayed depending on the actual situation of the questioning agent constructing the identity authentication problem or different pre-set parameters. This application does not impose any restrictions on this.

[0088] In other possible implementations, the issue creation progress information can be displayed in at least one form, either as an image or as text. For example, the text progress information could be displayed as "Issue creation progress: x%", where x is between 0 and 100, and 100% indicates that the issue creation is complete. Alternatively, the image information could be displayed as a progress bar, and when the progress bar is full, the issue creation is complete.

[0089] It should be noted that the display formats of the above-mentioned problem construction progress information can be used in combination or separately, and this application does not impose any restrictions on this.

[0090] Step 404: The identity authentication system sends the identity authentication question to the target user device, and the target user device receives the identity authentication question accordingly.

[0091] Step 405: The target user device displays an authentication problem.

[0092] Step 406: The target user equipment receives the user's response from the target user.

[0093] Step 407: The target user device sends the user's response to the authentication system; correspondingly, the authentication system receives the user's response from the target user device.

[0094] Step 408: The identity authentication system evaluates the accuracy of the user's answer by assessing the intelligent agent and generates the identity authentication result for the target user.

[0095] In some possible implementations, the authentication system determines a user's authentication result by the user's answer to an authentication question. In this case, the evaluation result of the user's answer is the user's authentication result. If the evaluation result of the user's answer is correct, the user's authentication result is successful; if the evaluation result of the user's answer is incorrect, the user's authentication result is unsuccessful.

[0096] In other possible implementations, the evaluation agent can maintain a corresponding identity authentication score for each user. This score has an initial value, which serves as a benchmark at the start of the identity authentication process. This initial value can be set based on actual needs. Based on the accuracy of the user's answer, the evaluation system updates the identity authentication score (including adding or subtracting points). When the identity authentication score reaches the threshold corresponding to successful authentication, or the threshold corresponding to authentication failure, the current identity authentication process is considered complete, and the target user's identity authentication result is obtained. Therefore, the accuracy evaluation process of the identity authentication system through the evaluation agent can be implemented as follows: the evaluation agent evaluates the accuracy of the user's answer, obtaining the corresponding evaluation result; based on the evaluation result, the identity authentication score is updated; this identity authentication score has a preset initial value; if the identity authentication score is higher than a first threshold, the target user's identity authentication result is determined to be successful; if the identity authentication score is lower than a second threshold, the target user's identity authentication result is determined to be unsuccessful, where the second threshold is less than the first threshold.

[0097] The process of evaluating the accuracy of a user's answer by an evaluation agent and obtaining the evaluation result corresponding to the user's answer can be implemented as follows: the evaluation agent performs information retrieval in a joint dynamic knowledge base based on the identity authentication question and the identity identifier of the target user to obtain the identity reference information for the identity authentication question; the evaluation agent evaluates the accuracy of the user's answer based on the identity reference information to obtain the evaluation result corresponding to the user's answer.

[0098] In some possible implementations, the evaluation methods for assessing the accuracy of the evaluation agent can include character verification and semantic similarity verification. Each evaluation method can correspond to a knowledge base. For example, for a basic knowledge base, where user information is relatively fixed, character verification is suitable. For a behavioral knowledge base, where user information is more flexible, semantic similarity verification is suitable. It should be noted that, based on different actual needs, the correspondence between different types of knowledge bases and evaluation methods can be set differently. The same type of knowledge base can correspond to one or more verification methods. This application does not impose any restrictions on this. Based on this, the process of the evaluation agent assessing accuracy can be implemented as follows: The evaluation agent determines the evaluation method based on the knowledge base type to which its identity reference information belongs. The evaluation method includes at least one of the following: character verification and semantic similarity verification. Based on the answer evaluation elements, the accuracy of the user's answer is assessed according to the evaluation method to obtain the evaluation result corresponding to the user's answer. The answer evaluation elements include identity reference information and a second preset prompt word.

[0099] The second preset prompt can be a keyword or phrase pre-set by relevant personnel, used to guide the evaluation agent to evaluate the accuracy of the user's answer based on the identity reference information and according to a determined evaluation method; optionally, the evaluation agent can construct evaluation prompt information based on the above content, and conduct accuracy evaluation based on the evaluation prompt information. For example, the constructed evaluation prompt information can be "Please evaluate the accuracy of the user's answer based on the question-and-answer context and the reference identity information using semantic similarity verification method," etc. This application does not impose any restrictions on this.

[0100] When evaluating the accuracy of user responses using a character-based verification method, the evaluation agent can check the format of the user's response. If the format is incorrect, the evaluation result is that the user's response is incorrect. If the format is correct, the identity reference information can be compared character by character with the user's response. If all characters are the same, the evaluation result is that the user's response is correct; if one character is incorrect, the evaluation result is that the user's response is incorrect. For example, when verifying a mobile phone number, the agent first verifies whether the user's response is in 11-digit format. If the format is incorrect, if the format is correct, the agent further compares the characters in the identity reference information with the characters in the user's response character by character. If all characters match, the user's response is correct; otherwise, the response is incorrect.

[0101] When evaluating the accuracy of user answers using semantic similarity verification, the evaluation intelligent system can calculate the similarity (e.g., cosine similarity) between the identity reference information and the user answer. If the similarity is greater than a preset similarity threshold, the evaluation agent determines that the user answer is semantically consistent with the identity reference information and confirms that the user answer is correct. If the similarity is less than the preset similarity threshold, the evaluation agent determines that the user answer is semantically inconsistent with the identity reference information and confirms that the user answer is incorrect.

[0102] In some possible implementations, the evaluation agent can also maintain a question difficulty assessment rule. This rule can be used to evaluate the relative difficulty of each authentication question. Different difficulty levels correspond to different weights, and the relationship between the difficulty and weight can be set by relevant personnel; it can be a positive or negative correlation. In this case, when updating the authentication score, the weight corresponding to the difficulty of the authentication question can be referenced. For each authentication question, a base score is added. If the evaluation result indicates a correct answer, the base score plus the weight of the authentication question is added to the current authentication score. If the evaluation result indicates a failed answer, the base score plus the weight of the authentication question is subtracted from the current authentication score. For example, if the base score for each authentication question is 10 points, the current authentication score is 50 points, and the weight of the current authentication question is 1, then if the user answers correctly, the updated authentication score is 60 points; if the user answers incorrectly, the updated authentication score is 40 points.

[0103] If the updated identity verification score is higher than the first scoring threshold, the user is determined to have passed identity verification. For example, assuming the first scoring threshold is set at 80 points, if the user's identity verification score reaches or exceeds 80 points after answering multiple identity verification questions, the user is determined to have passed identity verification. If the updated identity verification score is lower than the second scoring threshold, the user is determined to have failed identity verification. For example, assuming the second scoring threshold is set at 30 points, if the user's identity verification score reaches or falls below 30 points after answering multiple identity verification questions, the user is determined to have failed identity verification.

[0104] It should be noted that the first and second scoring thresholds can be flexibly adjusted according to different scenarios or security strategies. For example, in some high-risk scenarios, higher first and second scoring thresholds can be set to increase the difficulty of identity authentication; in low-risk scenarios, lower first and second scoring thresholds can be set to reduce the difficulty of identity authentication and improve authentication efficiency.

[0105] If the identity authentication score does not reach the threshold for successful authentication, nor the threshold for authentication failure, the evaluation agent cannot determine the current user's identity authentication result and determines that the current identity authentication process is not yet complete. The process of re-authenticating the user involves reconstructing the identity authentication question and evaluating the user's responses based on feedback. This process can be implemented as follows:

[0106] If the identity authentication score is greater than the second scoring threshold but less than the first scoring threshold, the agent will reconstruct the identity authentication question corresponding to the target user.

[0107] As an illustration, if a user's identity verification score is 60 after answering multiple authentication questions, which is neither the first nor the second threshold, the evaluation agent cannot determine the current user's identity assessment result. The evaluation agent can send a question reconstruction instruction to the questioning agent to instruct it to reconstruct the authentication questions corresponding to the user.

[0108] After entering the identity authentication process again, to avoid the problem of low user response accuracy due to the difficulty in understanding the constructed identity authentication question, the questioning agent can calculate the correlation value between the historical identity authentication questions in the current authentication process and the corresponding user answers when reconstructing the identity authentication question corresponding to the target user. Based on the correlation value between the historical identity authentication questions in the current authentication process and the corresponding user answers, the questioning agent reconstructs the identity authentication question corresponding to the target user. The elements of the reconstructed identity authentication question include at least one of the following: the type of updated user information, the knowledge base to which the updated user information belongs, and the updated first preset prompt word.

[0109] In some possible implementations, the questioning agent can calculate relevance values ​​from multiple dimensions, such as text similarity calculation and topic association analysis. Specifically, when calculating text similarity, a similarity algorithm can be used to convert historical authentication questions and corresponding user answers into vector form. Based on word vectors, the similarity value between vectors is calculated to determine the semantic similarity of the text. In topic association analysis, the questioning agent can introduce a topic model from natural language processing to determine the topic category of each question and answer, thereby determining the similarity value between them. When calculating multi-dimensional relevance, the similarity value between the authentication question and the corresponding user answer can be determined by comprehensively considering the similarity values ​​calculated from multiple dimensions. For example, the weighted sum of the similarity values ​​from multiple dimensions can be used to determine the final similarity value. Optionally, the aforementioned historical authentication questions can be authentication questions generated during the current authentication process, or they can be historical authentication questions within a time period from the current target time during the current authentication process. The time range for obtaining historical authentication questions can be set based on actual needs, and this application does not impose any restrictions on this.

[0110] In some possible implementations, after obtaining the similarity between historical identity authentication questions and corresponding user answers, the questioning agent can select historical identity authentication questions and user answers with high similarity values ​​for analysis to determine the questioning methods, question-answering domains, etc. that are easy for users to understand. In this way, when constructing identity authentication questions in the future, identity authentication questions corresponding to the questioning methods and / or question-answering domains can be constructed.

[0111] In other possible implementations, the process of reconstructing the identity authentication question for the target user based on the relevance value between the historical identity authentication questions and corresponding user answers in the current authentication process can be implemented as follows: A comprehensive relevance value is determined based on the relevance value between the historical identity authentication questions and corresponding user answers in the current authentication process; this comprehensive relevance value is obtained by integrating various relevance values ​​and is used to comprehensively reflect the overall closeness of the correlation between historical identity authentication questions and user answers in the authentication process; if the comprehensive relevance value is lower than the relevance threshold, the elements for reconstructing the identity authentication question are updated; and the questioning agent reconstructs the identity authentication question for the target user based on the updated elements.

[0112] The overall relevance value can be the average or median of the relevance values ​​between historical identity authentication questions and corresponding user answers in the current authentication process, or it can be a value calculated in other ways, such as the value obtained by weighted summation through the target rules. If the overall relevance value is lower than the relevance threshold, it indicates that the constructed identity authentication question is ineffective and the question construction elements need to be updated.

[0113] When updating elements, at least one of the following elements can be updated: the type of user information, the knowledge base to which the user information belongs, and the first preset prompt word. When updating the type of user information, information types with high user answer accuracy can be extracted during information retrieval. The user answer accuracy for each information type can be obtained based on historical data statistics. When updating the knowledge base to which the user information belongs, if the overall relevance of user answers to identity authentication questions constructed based on user information extracted from the current knowledge base is low, then another knowledge base is switched for information retrieval. When updating the first preset prompt word, the overall matching degree value corresponding to each first preset prompt word is obtained. If certain prompt words frequently cause users to answer incorrectly or have difficulty understanding, that is, the overall matching degree value corresponding to these prompt words is low, then these prompt words are modified or replaced. For example, if the first preset prompt word "your commonly used contact method" causes ambiguity for users, that is, the relevance between the user's answer and the corresponding identity authentication question is low, it can be changed to "which mobile phone number do you usually use to receive important notifications," etc.

[0114] After updating the question building elements, the questioning agent re-executes the question building process based on the updated question building elements. This process can be referred to in the relevant content of step 403, which will not be repeated here.

[0115] When evaluating a user's answer, the evaluation agent can retrieve information from a joint dynamic knowledge base to obtain the identity reference information of the target user corresponding to the current authentication question. In some possible implementations, the evaluation agent includes a second short-term memory component, which records the user answers received during the current authentication process.

[0116] In this context, when evaluating a user's answer, the evaluating agent can refer to the user answers already received during the current authentication process recorded in the second short-term memory component to determine the question-and-answer context of the current authentication process. This allows for the determination of identity reference information for the authentication question within the current question-and-answer context, thereby improving the accuracy of the evaluation of the user's answer based on this reference information. This process can be implemented as follows: determining the question-and-answer context of the current authentication process based on the user answers already received during the current authentication process recorded in the second short-term memory component; and obtaining identity reference information by the evaluating agent performing information retrieval in the joint dynamic knowledge base based on the authentication question, the target user's identity identifier, and the question-and-answer context of the current authentication process.

[0117] The evaluation agent can analyze factors such as the topic, type, and information domain of user answers recorded in the second short-term memory component to extract the context of the current authentication process, i.e., the question-and-answer context. When obtaining reference information, the agent can use the user's identity identifier as an index to retrieve user information that matches the current question-and-answer context and the identity authentication question from the joint dynamic knowledge base. This information is then used as identity reference information. The accuracy of the user's answer is evaluated by combining the identity reference information with the second preset prompt words. Optionally, the user information that matches the identity authentication question can be user information with a semantic relevance greater than a semantic relevance threshold.

[0118] Furthermore, when conducting accuracy assessment, the assessment agent can also combine the current question-and-answer context to conduct accuracy assessment. That is, the answer assessment element can also include the answer context of the current authentication process. This process can be implemented as follows: the assessment agent assesses the accuracy of the user's answer based on identity reference information, the question-and-answer context of the current authentication process, and the second preset prompt words, and obtains the assessment result corresponding to the user's answer.

[0119] The above evaluation method allows for consideration of the accuracy of user responses within the current question-and-answer context, thus improving the evaluation effect. For example, if a user's response does not match the topic or scenario in the current question-and-answer context, the evaluation agent will still identify it as an incorrect answer.

[0120] In some possible implementations, the evaluation agent includes a second long-term memory component that stores the user's historical question-and-answer information.

[0121] In this context, when evaluating a user's answer, the evaluation agent can refer to the user's historical question-and-answer information stored in the second long-term memory component to determine the user's personalized preferences. This allows for the determination of identity reference information corresponding to the authentication question that matches the user's personalized preferences, thereby improving the accuracy of the evaluation of the user's answer based on this reference information. This process can be implemented as follows: the evaluation agent determines the target user's personalized preferences based on the target user's historical question-and-answer information stored in the second long-term memory component; the evaluation agent then retrieves the identity reference information by performing information retrieval in the joint dynamic knowledge base based on the authentication question, the target user's identity identifier, and the target user's personalized preferences.

[0122] The process of evaluating the agent to determine the personalized preferences of the target user can be referenced from the process of the agent that asks questions to determine the personalized preferences of the target user, and will not be repeated here.

[0123] Furthermore, when conducting accuracy assessments, the assessment agent can also incorporate the user's personalized preferences. In other words, the assessment elements should also include the target user's personalized preferences. This process can be implemented as follows:

[0124] The accuracy of user responses is assessed by evaluating the intelligent agent based on identity reference information, the target user's personalized preferences, second preset prompts, and the evaluation method, thus obtaining the evaluation results.

[0125] The above evaluation method allows for consideration of the impact of users' personalized preferences on their answers during question-and-answer evaluation, thereby improving the accuracy of user responses and enhancing the evaluation effect. For example, if a user's personalized preferences indicate that they prefer to use "terminal" instead of "mobile phone" when answering questions, a correspondence between the two can be established when judging accuracy, thus avoiding misjudgment.

[0126] It should be noted that the aforementioned method of obtaining reference information and evaluating user responses based on information stored in the second short-term memory component can be applied separately or in combination with the method of obtaining reference information and evaluating user responses based on information stored in the second long-term memory component; this application does not impose any restrictions on this. In other words, the identity authentication result is generated by the evaluation agent after performing an accuracy assessment based on at least one response evaluation element. The response evaluation element includes identity reference information for the identity authentication question, the question-and-answer context of the current authentication process, the target user's personalized preferences, and a second preset prompt word.

[0127] During the accuracy assessment of the evaluation agent, the target terminal interface can display evaluation progress information, which is used to indicate the progress of the evaluation agent in generating identity authentication results.

[0128] In some possible implementations, the evaluation progress information can be displayed in the form of evaluation process information, which instructs the evaluation agent on each evaluation process in generating the authentication result. The evaluation process includes a reference information retrieval process and an accuracy evaluation process. Specifically, the identity reference information retrieval process information indicating the reference information retrieval process can be "Reference information being acquired," and the accuracy evaluation process information indicating the accuracy evaluation process can include evaluation method information, such as "Performing answer semantic analysis," or "Answer evaluation," etc. The above information is merely illustrative; different information content may be displayed depending on the actual situation of the evaluation agent's accuracy evaluation or different pre-set parameters. This application does not impose any limitations on this. Furthermore, for the user's answer to the current authentication question, after determining the evaluation result of the user's answer, the evaluation agent can feed back the evaluation result to the target user device, so that the target user device can display the evaluation result of the current user's answer. This evaluation result includes whether the answer is correct or incorrect.

[0129] In other possible implementations, the evaluation progress information can be displayed in at least one form, either as an image or as text. For example, the text progress information could be displayed as “Answer Evaluation Progress: x%”, where x is a value from 0 to 100, and 100% indicates that the answer evaluation is complete; or the image information could be displayed as a progress bar, and when the progress bar is full, it indicates that the answer evaluation is complete.

[0130] It should be noted that the various display formats of the above-mentioned assessment progress information can be used in combination or separately, and this application does not impose any restrictions on this.

[0131] Figure 5 illustrates the operation of an identity authentication system provided in an exemplary embodiment of this application. As shown in Figure 5, after receiving an identity authentication request, the evaluation agent first activates the questioning agent 510. Based on the identity authentication request, the questioning agent generates question construction prompts using a joint dynamic knowledge base and preset prompt words. The large language model then processes these prompts to obtain the identity authentication question. Optionally, in the above process, the questioning agent can invoke tools to implement each step, such as using a knowledge collection tool to retrieve information from the joint dynamic knowledge base, using a knowledge association tool to generate question construction prompts, and using a question-asking tool to invoke the large language model, etc. Furthermore, when generating questions, the questioning agent can utilize a first short-term memory component to record the question content and tool usage in the current session, providing a contextual reference for question construction, ensuring the real-time nature and personalization of each question generation, and avoiding duplicate questions in the same session. The first long-term memory component stores historical question-and-answer information, providing a reference for user personalized preferences in question construction. After generating an authentication question, the questioning agent displays it to the user through the target user's device interface. The user can then provide an answer to the evaluation agent 520 based on this authentication question. Upon receiving the answer, the evaluation agent generates evaluation prompts using a joint dynamic knowledge base and preset prompts. A large language model then processes these prompts to obtain the evaluation result corresponding to the user's answer. Optionally, the evaluation agent can utilize tools to implement each step in this process. For example, it can use a question classification tool to determine the type of authentication question, facilitating the retrieval of identity reference information from the joint dynamic knowledge base; it can perform accuracy verification using a semantic similarity verification tool when the verification method is determined to be semantic similarity verification; and it can perform accuracy verification using a character verification tool when the verification method is determined to be character verification. Furthermore, the evaluation agent can use a second short-term memory component to record user answers and tool usage in the current session, providing contextual reference for answer evaluation; it can also use a second long-term memory component to record historical question-and-answer information, providing personalized information preference reference for answer evaluation.

[0132] Step 409: The identity authentication system sends the identity authentication result of the target user to the target user device, and the target user device receives the identity authentication result of the target user.

[0133] When evaluating the agent's identity authentication result based on the identity authentication score, if the user's identity authentication result can be determined after the user answers multiple identity authentication questions, the determined identity authentication result is sent to the target user device; otherwise, identity authentication continues.

[0134] Step 410: The target user device displays the target user's authentication result.

[0135] In summary, the identity authentication method provided in this application, through the interaction between the target user device and the identity authentication system, allows the identity authentication system to generate an identity authentication question corresponding to the target user based on the user information in the joint dynamic knowledge base and the identity authentication request after receiving an identity authentication request containing the target user's identity identifier from the target user device. This question is then sent to the target user device. Upon receiving a user response from the target user device, an evaluation agent assesses the accuracy of the user response and generates an identity authentication result. This method enables question-and-answer-based identity authentication through a question agent, an evaluation agent, and the joint dynamic knowledge base, achieving personalized identity authentication for each user. This reduces reliance on a centralized information source and improves the security of identity authentication. Furthermore, since the user information in the joint dynamic knowledge base is updated in real time, the timeliness and relevance of the identity authentication questions are guaranteed, further enhancing the reliability of identity authentication.

[0136] Furthermore, the questioning agent and the evaluation agent can construct authentication questions and evaluate the accuracy of user answers based on the question-and-answer context and / or the user's personalized preferences. This makes the constructed authentication questions more consistent with user authentication and actual situations, improves the effectiveness of question construction, and enables users to make more comprehensive judgments when evaluating their answers, thereby improving the accuracy of authentication.

[0137] Figure 6 illustrates an interactive schematic diagram of an identity authentication method provided in an exemplary embodiment of this application, involving the interaction between a user device 610, a questioning agent 620, an evaluation agent 630, and a joint dynamic knowledge base 640. As shown in Figure 6, the user sends an identity authentication request through the display interface of the user device; the user device sends the received identity authentication request to the questioning agent, the questioning agent obtains the user's user information from the joint dynamic knowledge base, and correspondingly, the joint dynamic knowledge base returns the user's user information to the questioning agent; the questioning agent generates and sends an identity authentication question to the user device based on the user information, and the user device displays the identity authentication question through the display interface.

[0138] The user provides their answer to the user device based on the authentication information. The user device then submits the answer to the evaluation agent. Upon receiving the answer, the evaluation agent retrieves the corresponding identity reference information from the Joint Dynamic Knowledge Base (JDK). The JDK then provides the authentication reference information to the evaluation agent. The evaluation agent evaluates the user's answer based on the identity reference information and sends the evaluation result back to the user device. The user device displays the evaluation result to the user, indicating whether the answer is correct or not. Furthermore, the evaluation agent determines the user's authentication result based on the evaluation result and sends it back to the user device. The user device displays the authentication result to the user, indicating whether the authentication was successful or failed.

[0139] Since the identity authentication method provided in this application involves the interaction between the user terminal and the identity authentication system, in order to ensure the effective implementation of the identity authentication method, the application environment can have the following conditions when it is applied:

[0140] 1. Regarding network conditions, a stable network connection is essential to ensure real-time communication between user terminals and the identity authentication system, between various agents within the identity authentication system, and between each agent and the joint dynamic knowledge base. This guarantees the timeliness and accuracy of data transmission during the identity authentication process. To ensure user data security, data transmission over the network can employ encrypted transmission methods, such as SSL / TLS encryption, to ensure that data is not eavesdropped on or tampered with during transmission. In addition, network security measures such as firewalls and intrusion detection systems can be deployed to prevent unauthorized access or attacks. To ensure continuous system operation and avoid single points of failure, a high-availability architecture design, such as load balancing and redundant backups, can be adopted. Furthermore, a data backup and recovery mechanism can be established to ensure rapid restoration of normal service in the event of unforeseen circumstances.

[0141] 2. Regarding technical support, since the operation of the multi-intelligence system and the joint dynamic knowledge base requires substantial computing resources, high-performance servers, ample storage space, and an efficient database management system can be used during system deployment to provide sufficient data resources, thereby meeting computing demands and reducing the pressure of high-concurrency access. Furthermore, the joint dynamic knowledge base needs to be flexible and scalable to dynamically update information based on real-time user behavior. Moreover, the joint dynamic knowledge base can employ distributed database technology to support data reading and writing at universities and provide a knowledge base configuration interface for data management and maintenance. Since this embodiment involves the collection and processing of user information, to protect user privacy, an authorization request can be sent to the user before information collection and processing. Upon receiving user authorization, corresponding operations can be performed. In addition, the system can formulate and implement privacy protection strategies to protect the security of user information.

[0142] Figure 7 shows a structural block diagram of a computer device 700 according to an exemplary embodiment of this application. This computer device can be implemented as the questioning agent or evaluation agent described in the above-described scheme of this application. The computer device 700 includes a processor (e.g., a central processing unit (CPU)) 701, a system memory 704 including random access memory (RAM) 702 and read-only memory (ROM) 703, and a system bus 705 connecting the system memory 704 and the processor 701.

[0143] The computer device 700 also includes a large-capacity storage device 706 for storing the operating system 709, application instances 710, and other program modules 711. The system memory 704 and the large-capacity storage device 706 can be collectively referred to as memory.

[0144] According to various embodiments of this application, the computer device 700 can also be connected to a remote computer on a network, such as the Internet. That is, the computer device 700 can be connected to a network 708 via a network interface unit 707 connected to the system bus 705, or it can use the network interface unit 707 to connect to other types of networks or remote computer systems (not shown).

[0145] The memory also stores at least one computer program, which the processor 701 executes to implement all or part of the steps in the authentication methods shown in the above embodiments.

[0146] Figure 8 shows a structural block diagram of another computer device 800 illustrated in an exemplary embodiment of this application. The computer device 800 can be implemented as the target user device described above. For example, the computer device can be an Android terminal device; typically, the computer device 800 includes a processor 801 and a memory 802. The memory 802 may include one or more computer-readable storage media for storing at least one instruction, which is executed by the processor 801 to implement all or part of the steps in the authentication method shown in the method embodiments of this application. In some embodiments, the computer device 800 may also optionally include a peripheral device interface 803 and at least one peripheral device. The processor 801, memory 802, and peripheral device interface 803 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 803 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of a radio frequency circuit 804, a display screen 805, a camera assembly 806, an audio circuit 807, and a power supply 808. In some embodiments, the computer device 800 also includes one or more sensors 809. The one or more sensors 809 include, but are not limited to, an accelerometer 810, a gyroscope 811, a pressure sensor 812, an optical sensor 813, and a proximity sensor 814. Those skilled in the art will understand that the structure shown in FIG8 does not constitute a limitation on the computer device 800, and may include more or fewer components than shown, or combine certain components, or employ different component arrangements.

[0147] In one exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one computer program that is loaded and executed by a processor to implement all or part of the steps in the above-described authentication method. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0148] In one exemplary embodiment, a computer program product is also provided, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform all or part of the steps of the embodiments shown in any of the embodiments of FIG1, FIG2 or FIG4 above.

[0149] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0150] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. An identity authentication method, comprising: Receive an identity authentication request, wherein the identity authentication request contains the identity identifier of the target user; The system displays an identity authentication question, which is a question generated by the questioning agent based on user information in a joint dynamic knowledge base, corresponding to the identity authentication of the target user, based on the identity authentication request. Receive user responses from the target user; The authentication result of the target user is displayed, wherein the authentication result is generated by evaluating the accuracy of the user's answer by an evaluation agent.

2. The method of claim 1, wherein, The joint dynamic knowledge base includes at least one of the following: a basic knowledge base, a behavioral knowledge base, and a supplementary knowledge base; the basic knowledge base stores the user's static basic information, the behavioral knowledge base stores the user's dynamic behavioral information, and the supplementary knowledge base stores the user's personalized user information uploaded by the user.

3. The method of claim 1 or 2, wherein, Before displaying the authentication question, the method further includes: Displays the question construction progress information, which is used to indicate the construction progress of the identity authentication question by the questioning agent.

4. The method of claim 1 or 2, wherein, Before displaying the authentication result of the target user, the method further includes: The evaluation progress information is displayed, which is used to indicate the generation progress of the identity authentication result by the evaluation agent.

5. The method of claim 1, wherein, The identity authentication question is a question corresponding to the identity authentication of the target user, constructed by the questioning agent based on at least one question construction element, based on the identity authentication request. The question construction element includes the user information of the target user obtained from the joint dynamic knowledge base, the question-and-answer context of the current authentication process, the personalized preferences of the target user, and a first preset prompt word.

6. The method of claim 1, wherein, The identity authentication result is generated by the evaluation agent after performing an accuracy evaluation based on at least one answer evaluation element. The answer evaluation element includes the identity reference information of the identity authentication question, the question-and-answer context of the current authentication process, the personalized preferences of the target user, and a second preset prompt word.

7. An authentication method, comprising: Receive authentication requests sent by the target user equipment; The authentication request contains the target user's identity identifier; Based on the identity authentication request, an identity authentication question corresponding to the target user is generated by a questioning agent, and the identity authentication question is sent to the target user's device; The questioning agent is used to generate authentication questions based on user information in a joint dynamic knowledge base; Upon receiving a user response from the target user device, an evaluation agent assesses the accuracy of the user response and generates an authentication result for the target user.

8. The method of claim 7, wherein, The step of generating an authentication question corresponding to the target user through a questioning agent based on the authentication request includes: Based on the identity identifier of the target user contained in the identity authentication request, the questioning agent performs information retrieval in the joint dynamic knowledge base to obtain the user information of the target user; An authentication question corresponding to the target user is constructed based on the question construction element, which includes the target user's user information and a first preset prompt word.

9. The method of claim 8, wherein, The questioning agent includes a first short-term memory component; the first short-term memory component records the identity authentication questions constructed during the current authentication process; The step of retrieving user information of the target user from the joint dynamic knowledge base through the questioning agent includes: Based on the identity authentication questions already constructed in the current authentication process recorded in the first short-term memory component, the question-and-answer context of the current authentication process is determined; Based on the target user's identity and the question-and-answer context of the current authentication process, information is retrieved from the joint dynamic knowledge base to obtain the target user's user information.

10. The method of claim 9, wherein, The question-building element also includes the question-and-answer context of the current authentication process, and the identity authentication question does not exist in the first short-term memory component.

11. The method of claim 8, wherein, The questioning agent includes a first long-term memory component; the first long-term memory component stores the user's historical question and answer information; The step of retrieving user information of the target user from the joint dynamic knowledge base through the questioning agent includes: Based on the historical question-and-answer information of the target user stored in the first long-term memory component, the personalized preferences of the target user are determined; Based on the target user's identity and personalized preferences, information is retrieved from the joint dynamic knowledge base to obtain the target user's user information.

12. The method of claim 11, wherein, The question-building elements also include the personalized preferences of the target user.

13. The method of claim 7, wherein, The step of evaluating the accuracy of the user's answer by an evaluation agent and generating the identity authentication result of the target user includes: The evaluation agent assesses the accuracy of the user's answer to obtain the evaluation result corresponding to the user's answer. The identity authentication score is updated based on the evaluation results corresponding to the user's answer; the identity authentication score has a preset initial value. If the identity authentication score is higher than the first score threshold, the identity authentication result of the target user is determined to be successful. If the identity authentication score is lower than the second score threshold, the identity authentication result of the target user is determined to be authentication failure, where the second score threshold is less than the first score threshold; If the identity authentication score is greater than the second scoring threshold and less than the first scoring threshold, the questioning agent will reconstruct an identity authentication question corresponding to the target user.

14. The method of claim 13, wherein, The step of reconstructing the identity authentication question corresponding to the target user through the questioning agent includes: Calculate the correlation between historical identity authentication questions and corresponding user answers in the current authentication process; The questioning agent reconstructs an identity authentication question for the target user based on the correlation value between the historical identity authentication questions and the corresponding user answers in the current authentication process. The elements of the reconstructed identity authentication question include at least one of the following: the type of updated user information, the knowledge base to which the updated user information belongs, and the updated first preset prompt word.

15. The method of claim 13, wherein, The step of evaluating the accuracy of the user's answer through an evaluation agent to obtain the evaluation result corresponding to the user's answer includes: The evaluation agent retrieves information from the joint dynamic knowledge base based on the identity authentication question and the identity identifier of the target user to obtain identity reference information for the identity authentication question. The evaluation agent assesses the accuracy of the user's answer based on the identity reference information to obtain the evaluation result corresponding to the user's answer.

16. The method of claim 15, wherein, The step of evaluating the accuracy of the user's answer based on the identity reference information by the evaluation agent to obtain the evaluation result corresponding to the user's answer includes: Based on the knowledge base type to which the identity reference information belongs, an evaluation method is determined, and the evaluation method includes at least one of the following: character verification method and semantic similarity verification method; Based on the answer evaluation elements, the accuracy of the user's answer is evaluated according to the evaluation method to obtain the evaluation result corresponding to the user's answer; the answer evaluation elements include the identity reference information and the second preset prompt words.

17. The method of claim 15, wherein, The evaluation agent includes a second short-term memory component; the second short-term memory component records user responses received during the current authentication process; The evaluation agent retrieves identity reference information for the authentication question from the joint dynamic knowledge base based on the authentication question and the target user's identity identifier, including: Based on the user answers received during the current authentication process recorded in the second short-term memory component, the question-and-answer context of the current authentication process is determined; Based on the identity authentication question, the target user's identity identifier, and the question-and-answer context of the current authentication process, information is retrieved from the joint dynamic knowledge base to obtain the identity reference information.

18. The method of claim 17, wherein, The answer evaluation elements also include the question-and-answer context of the current authentication process.

19. The method of claim 15, wherein, The evaluation agent includes a second long-term memory component; the second long-term memory component stores the user's historical question-and-answer information; The evaluation agent retrieves identity reference information for the authentication question from the joint dynamic knowledge base based on the authentication question and the target user's identity identifier, including: The evaluation agent determines the target user's personalized preferences based on the target user's historical question-and-answer information stored in the second long-term memory component. The evaluation agent retrieves the identity reference information from the joint dynamic knowledge base based on the identity authentication question, the target user's identity identifier, and the target user's personalized preferences.

20. The method of claim 19, wherein, The answer evaluation element further comprises a personalization preference of the target user.

Images