An immersive simulated interview system based on VR and AI and its operation method

Abstract

This invention discloses an immersive simulated interview system based on VR and AI and its operation method, belonging to the fields of virtual reality technology and artificial intelligence application technology. This invention can effectively solve the problem of the single and fixed nature of traditional simulated interview environments. Through scene parameter synthesis algorithms and multi-level scene dynamic loading technology, it achieves precise adaptation between virtual scenes and job requirements and user characteristics, restores the interview atmosphere and pressure of different positions, and provides users with a highly immersive experience, making up for the shortcomings of insufficient immersion in offline and simple online forms. At the same time, this invention breaks through the limitations of traditional subjective and singular assessments, integrates multimodal data such as voice, action, and physiological signals, and constructs a multi-dimensional standardized assessment system through a distributed AI analysis engine and a career matching quantification algorithm, which greatly improves the objectivity and accuracy of assessment results and avoids the bias of relying on human experience or single text analysis.

Description

Technical Field

[0001] This invention relates to the fields of virtual reality technology and artificial intelligence application technology, specifically to an immersive simulated interview system based on VR and AI and its operation method. Background Technology

[0002] In today's increasingly competitive job market, interview training has become a crucial step for job seekers to enhance their competitiveness, especially for university students who lack practical experience; effective mock interview training is indispensable. However, the current interview training field still faces many unresolved technical bottlenecks and application limitations:

[0003] Traditional mock interviews often rely on offline scenarios or simple online video formats. Not only is the environment monotonous and rigid, failing to realistically recreate the interview atmosphere and pressure of different positions (such as finance, technology, and business), making it difficult for users to have an immersive experience, but they also suffer from imperfect evaluation systems. Evaluation results often depend on the subjective experience and judgment of instructors or are analyzed based on a single data dimension such as text answers, lacking standardized and quantitative multi-dimensional evaluation indicators, resulting in insufficient objectivity and accuracy of evaluation results.

[0004] Meanwhile, the existing training process suffers from weak data recording and retrospective capabilities. Key information such as users' interview performance and interaction details are difficult to retain completely, failing to provide continuous data support for subsequent targeted improvements. Although some online interview assistance tools have emerged in the market, most only cover video recording, simple playback, or basic text feedback, lacking in-depth intelligent analysis capabilities.

[0005] In terms of technology integration and application, current VR technology applications in interview scenarios are mostly limited to static scene display, unable to dynamically adapt to job requirements and user characteristics; AI technology, on the other hand, focuses primarily on text content analysis, failing to achieve deep integration with multimodal data such as voice, motion, and physiological signals. The combination of the two is largely superficial, not yet forming a complete technological closed loop encompassing "identity verification - scene adaptation - multimodal interaction - intelligent assessment - feedback recommendation - monitoring and archiving."

[0006] Crucially, existing technologies cannot achieve an integrated solution that enables dynamic adaptation across multiple scenarios, natural multimodal interaction (including emotional state responses), multidimensional quantitative assessment, personalized feedback and recommendation, and stable system operation. They lack verification mechanisms that ensure identity security and personalized adaptation across terminals, interactive solutions that dynamically adjust the interview process based on user states (such as anxiety levels), a multimodal data intelligent analysis system based on a distributed architecture, and a closed-loop feedback mechanism that connects personalized learning resources after assessment. Furthermore, they exhibit significant shortcomings in end-to-end system monitoring, anomaly handling, and secure data archiving.

[0007] To address this, an immersive simulated interview system based on VR and AI and its operation method are proposed. Summary of the Invention

[0008] The purpose of this invention is to provide an immersive simulated interview system based on VR and AI and its operation method, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: an immersive simulated interview system based on VR and AI, including an identity verification module, a scene adaptation module, a multimodal interaction module, a distributed AI analysis module, a reporting and recommendation module, and a monitoring and archiving module. The identity verification module is used to perform cross-terminal collaborative verification through a token generation algorithm and to construct a personalized interview profile.

[0010] The scene adaptation module is used to dynamically generate a multi-level virtual environment based on job requirements and user feature vectors using a scene parameter synthesis algorithm.

[0011] The multimodal interaction module is used to collect user voice, action and physiological data, and calculate the tension index to drive the virtual interviewer to conduct real-time interaction and feedback.

[0012] The distributed AI analysis module is used to fuse multimodal data and calculate the job matching degree through a distributed architecture;

[0013] The reporting and recommendation module is used to generate structured reports and push personalized learning resources;

[0014] The monitoring and archiving module is used for end-to-end status monitoring and intelligent degradation of anomalies to ensure data security and stable system operation.

[0015] As a further preferred embodiment of this technical solution: the authentication module employs a one-way hash digest algorithm to fuse user identity identifiers. Current system timestamp and random salt value Generate a time-sensitive secure reservation code The specific algorithm is as follows:

[0016] (1)

[0017] In its formula (1), A one-way hash digest algorithm is used, where || represents the concatenation of data bits.

[0018] As a further preferred embodiment of this technical solution: the scene adaptation module divides the virtual scene into a basic environment layer, a scene feature layer, and an interactive element layer, and adopts asynchronous loading technology and a detail level control strategy;

[0019] The scene adaptation module runs a scene parameter synthesis algorithm, which generates the final scene rendering configuration vector through weighted superposition operations. The specific algorithm is as follows:

[0020] (2)

[0021] In its formula (2), This is the default base rendering parameter vector. For job feature vectors, For user feature vectors, For the parameter correction function of the vector mapping, and This represents the environmental atmosphere weighting coefficient.

[0022] As a further preferred embodiment of this technical solution: the multimodal interaction module collects head posture, voice signals, and physiological characteristics through multimodal sensors, and runs a tension index calculation algorithm to quantify the user's state. The specific algorithm is as follows:

[0023] (3)

[0024] In its formula (3), for The tension index at any moment, for Time before Head pose data vector within the time window, This represents the average head pose of the user in their baseline state. This represents variance calculation. for The fundamental frequency feature vector of the speech signal at time t. For the fundamental frequency fluctuation sensitivity coefficient, for to The vector of the change in the handle's position over time. The L2 norm of the handle position change. The maximum permissible speed of the handle movement. These are the modal weighting coefficients.

[0025] As a further preferred embodiment of this technical solution: the distributed AI analysis module, through a client-server collaborative concurrency strategy, utilizes a distributed computing cluster to execute the job matching degree calculation algorithm in parallel. The specific algorithm is as follows:

[0026] (4)

[0027] In its formula (4), For career matching, For user capability vectors, As a vector of job requirements, The dot product of the user's ability vector and the job requirement vector. It is the product of the magnitudes of two vectors. and Users and positions respectively in the first Dimensional capability value The system outputs the normalized matching degree result based on the weight coefficient of this dimension.

[0028] As a further preferred embodiment of this technical solution: the report and recommendation module uses a template engine to map AI analysis results to configurable templates, ensures the logical consistency of the report through an automated verification mechanism and a two-level review process, and retrieves resources from the library based on user capability deficiency tags, generating a Top-N personalized learning recommendation list sorted by relevance.

[0029] As a further preferred embodiment of this technical solution: the monitoring and archiving module adopts an event-driven architecture for full-link monitoring, executes intelligent degradation strategies for scenarios of abnormal analysis services or network interruptions, prevents the main thread from being blocked through asynchronous message queues and rate limiting mechanisms, and automatically archives interactive data and evaluation results to cloud storage.

[0030] A method for operating an immersive simulated interview system based on VR and AI includes the following steps:

[0031] S1. Cross-terminal authentication and personalized facial profile construction: Perform cross-terminal authentication, parse data packets to construct user feature vectors and initialize personalized facial profiles;

[0032] S2, Job-Scenario Dynamic Adaptation and Multi-Level Virtual Environment Generation: Run the scene parameter synthesis algorithm to dynamically load multi-level virtual scenes and ensure an immersive experience through performance optimization strategies;

[0033] S3, Multimodal Interaction and Interview Process Execution: Collect multimodal interaction data, calculate the tension index, dynamically schedule the interview process, and drive real-time feedback from the virtual interviewer;

[0034] S4. Multimodal Data Acquisition and Distributed AI Analysis: Multimodal data is analyzed by a distributed AI engine to calculate the job matching degree between users and positions;

[0035] S5. Dynamic Report Generation and Personalized Recommendations: Generate structured reports and push personalized learning resources based on skill gaps;

[0036] S6. Interview process archiving and distributed status monitoring: Archive interview data and ensure stable system operation through full-link monitoring and intelligent degradation mechanisms.

[0037] As a further preferred embodiment of this technical solution: In S1, the specific operating steps are as follows:

[0038] A1. Generation of secure reservation code;

[0039] A2. Dual verification and consistency determination;

[0040] A3. Data mapping and personalized map construction;

[0041] In A1, the system executes a token generation algorithm on the server side, which integrates the user's identity identifier with spatiotemporal information and random entropy value to generate a reservation code with anti-counterfeiting features.

[0042] In A2, when a terminal initiates a login request, the system executes a consistency check logic based on hash comparison to achieve collaborative verification between the mobile terminal and the VR terminal.

[0043] In A3, after successful verification, the system parses and persists the received data packets to build a personalized facet spectrum that supports subsequent AI operation;

[0044] In S2, a multi-level dynamic adaptation architecture is constructed to achieve precise matching between virtual interview scenarios and job requirements and user characteristics. The system adopts a scene parameter synthesis algorithm to transform abstract feature labels into specific rendering parameters. An immersive environment is built through a three-level resource loading mechanism, and asynchronous loading technology is combined to optimize the scene delivery process, ensuring a balance between visual performance and runtime performance. The specific operation steps are as follows:

[0045] B1. Scene parameter synthesis algorithm;

[0046] B2. Dynamic loading of the three-level scene architecture;

[0047] B3. Performance optimization and incremental delivery strategy;

[0048] In B1, the system uses a scene parameter synthesis algorithm based on job requirement vectors and user feature vectors to dynamically generate the final scene rendering configuration vector.

[0049] In B2, to ensure loading efficiency and immersion, the system adopts a layered rendering strategy, dynamically constructing the virtual scene by dividing it into a basic environment layer, a scene feature layer, and an interactive element layer.

[0050] In B3, the system implements intelligent optimization strategies to address the limitations of VR device computing power and rendering load, ensuring the smoothness and immersiveness of scene delivery;

[0051] In S3, a multimodal interaction framework is constructed to enable real-time interaction between users and virtual interviewers, dynamically execute the interview process, and synchronously collect interaction data. The system uses a tension index calculation algorithm to quantify user status and combines it with a dynamic process scheduling mechanism to adjust the interview pace, ensuring immersive interaction and process adaptability. The specific operation steps are as follows:

[0052] C1. Multimodal interaction and tension calculation framework;

[0053] C2. Real-time interaction and feedback mechanism;

[0054] C3. The interview process is executed dynamically.

[0055] C4. Data Acquisition and Synchronization;

[0056] In C1, the system collects user interaction data through multimodal sensors, and after preprocessing and feature extraction, it integrates and analyzes the user's state to drive the response of the virtual interviewer.

[0057] In C2, the system responds to user interactions in real time based on multimodal analysis results, and enhances the immersive experience of the interaction through the voice, actions, and facial expressions of the virtual interviewer.

[0058] In C3, the system dynamically adjusts the interview process based on the user's status and interview progress to ensure the adaptability of the process and the user experience.

[0059] In C4, the system synchronously collects multimodal data during the interaction process to ensure the integrity and real-time nature of the data, providing support for subsequent analysis;

[0060] As a further preferred embodiment of this technical solution: In S4, a multimodal data acquisition framework is constructed to achieve complete capture and synchronization of interactive data. Combined with a distributed AI analysis engine, the job matching degree between users and positions is quantified, providing data support for interview result evaluation. The system uses a job matching degree calculation algorithm to process user ability characteristics, and improves analysis efficiency through a distributed computing architecture to ensure the accuracy and real-time nature of the results. The specific operation steps are as follows:

[0061] D1. Multimodal data acquisition and preprocessing;

[0062] D2. Occupation matching degree calculation algorithm;

[0063] D3, Distributed AI Analysis Process;

[0064] D4. Results Feedback and Optimization;

[0065] In D1, the system comprehensively collects user data during the interview process through multimodal sensors and interactive devices, and after standardized preprocessing, provides structured input for AI analysis;

[0066] In D2, the system runs a job matching algorithm based on the collected user ability characteristics to quantify the degree of fit between users and job requirements.

[0067] In D3, the system adopts a distributed computing architecture, which distributes the job matching calculation task to multiple computing nodes to improve analysis efficiency and scalability;

[0068] In D4, the system provides users with a visual representation of the job matching results and iteratively optimizes the algorithm based on user feedback.

[0069] In S5, a configurable report generation system and personalized recommendation mechanism are built to achieve structured presentation and targeted feedback of interview results, connecting with the preceding assessment process to form a complete technical chain. The specific operation steps are as follows:

[0070] E1. Report template construction and content filling;

[0071] E2. Report quality verification;

[0072] E3. Personalized resource recommendations;

[0073] E4. Report Output and Storage;

[0074] In E1, the system adopts a template engine-based dynamic rendering mechanism to realize the visualization of analysis results and meet the reporting needs in different scenarios;

[0075] In E2, the system introduces an automated verification mechanism to ensure the logical consistency of the generated content and the accuracy of the data, thus maintaining the credibility of the report;

[0076] At E3, based on a precise profile of user capabilities, the system provides a closed-loop learning resource recommendation system from "discovering problems" to "solving problems".

[0077] In E4, the system ensures data traceability and convenient access for users through multi-channel distribution and long-term cloud storage;

[0078] In S6, a full data archiving system and a high-availability monitoring mechanism are established to ensure the asset-based storage of interview data and the stability of system operation, and intelligent fault tolerance strategies are used to deal with various abnormal scenarios.

[0079] Compared with the prior art, the beneficial effects of the present invention are:

[0080] 1. This invention effectively solves the problem of the single and fixed nature of traditional simulated interview environments. Through scene parameter synthesis algorithms and multi-level scene dynamic loading technology, it achieves precise adaptation between virtual scenes and job requirements and user characteristics, restores the interview atmosphere and pressure of different positions, and provides users with a highly immersive experience, making up for the shortcomings of insufficient immersion in offline and simple online forms. At the same time, this invention breaks through the limitations of the subjective and singular nature of traditional assessments, integrates multimodal data such as voice, action, and physiological signals, and constructs a multi-dimensional standardized assessment system through a distributed AI analysis engine and a career matching quantification algorithm, which greatly improves the objectivity and accuracy of assessment results and avoids the bias of relying on human experience or single text analysis.

[0081] 2. This invention improves data recording and backtracking capabilities. Through full-link data collection, cloud archiving, and version index management, it fully retains key information such as interview interaction details and evaluation results, providing continuous data support for users' subsequent targeted improvements. This solves the problem of incomplete data retention in existing technologies. Furthermore, this invention achieves a complete technical closed loop of deep integration of VR and AI technologies, connecting the entire process of "identity verification - scene adaptation - multimodal interaction - intelligent evaluation - feedback recommendation - monitoring and archiving". This avoids superficial overlap between the two technologies, forming an integrated solution and improving the overall practicality and technological advancement of the system.

[0082] 3. This invention constructs a cross-terminal security verification and personalized adaptation mechanism. It generates a time-sensitive and secure appointment code through a one-way hash digest algorithm and combines it with a personalized interview spectrum to achieve a customized interview process of "one person, one policy". This ensures identity security while improving process adaptability. Furthermore, based on the calculation of tension index and dynamic process scheduling, this invention achieves real-time adaptation between the interview process and the user's state, alleviates user tension, optimizes the interview experience, and solves the problem that existing technologies cannot respond to the user's emotional state.

[0083] 4. This invention forms an "assessment-improvement" closed-loop feedback mechanism, presenting analysis results through structured reports and pushing personalized learning resources based on capability gap tags, helping users accurately identify their shortcomings and improve efficiently. This compensates for the lack of in-depth feedback and targeted guidance in existing tools. At the same time, this invention strengthens system stability and data security by using full-link monitoring, intelligent degradation, asynchronous message queues, and rate limiting mechanisms to deal with service anomalies, network interruptions, and other scenarios, ensuring the continuous and stable operation of the system. Furthermore, cloud archiving and encrypted storage ensure data security and traceability, addressing the shortcomings of existing technologies in anomaly handling and data security. Attached Figure Description

[0084] Figure 1 This is a schematic diagram of the architecture of an immersive simulated interview system based on VR and AI according to the present invention.

[0085] Figure 2This is a flowchart illustrating the operation method of an immersive simulated interview system based on VR and AI according to the present invention.

[0086] Figure 3 This is a flowchart of the operation method of S1 in the operation method of the VR and AI-based immersive simulated interview system of the present invention.

[0087] Figure 4 This is a flowchart of the operation method of S2 in the operation method of the VR and AI-based immersive simulated interview system of the present invention;

[0088] Figure 5 This is a flowchart of the operation method of S3 in the operation method of an immersive simulated interview system based on VR and AI according to the present invention.

[0089] Figure 6 This is a flowchart of the operation method of S4 in the operation method of an immersive simulated interview system based on VR and AI according to the present invention.

[0090] Figure 7 This is a flowchart of step S5 in the operation method of an immersive simulated interview system based on VR and AI according to the present invention. Detailed Implementation

[0091] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0092] Example

[0093] Please see Figures 1-7 The present invention provides a technical solution: an immersive simulated interview system based on VR and AI, including an identity verification module, a scene adaptation module, a multimodal interaction module, a distributed AI analysis module, a reporting and recommendation module, and a monitoring and archiving module. The identity verification module is used to perform cross-terminal collaborative verification through a token generation algorithm and to build a personalized interview profile.

[0094] The scene adaptation module is used to dynamically generate multi-level virtual environments based on job requirements and user feature vectors using a scene parameter synthesis algorithm.

[0095] The multimodal interaction module is used to collect user voice, action and physiological data, and calculate the tension index to drive the virtual interviewer to conduct real-time interaction and feedback;

[0096] The distributed AI analysis module is used to fuse multimodal data and calculate career matching degree through a distributed architecture;

[0097] The reporting and recommendation module is used to generate structured reports and push personalized learning resources;

[0098] The monitoring and archiving module is used for end-to-end status monitoring and intelligent degradation of anomalies to ensure data security and stable system operation.

[0099] In this embodiment, specifically: the authentication module uses a one-way hash digest algorithm to fuse user identity identifiers. Current system timestamp and random salt value Generate a time-sensitive secure reservation code The specific algorithm is as follows:

[0100] (1)

[0101] In its formula (1), The system employs a one-way hash digest algorithm, where || represents data bit concatenation. It achieves dual verification through format validation and network hash comparison, and maps the user feature vector to the global runtime state to initialize the personalized face view spectrum. At the same time, the verified reservation code is set as the global authentication credential for the current session.

[0102] In this embodiment, specifically: the scene adaptation module divides the virtual scene into a basic environment layer, a scene feature layer, and an interactive element layer, and adopts asynchronous loading technology and a level of detail (LOD) strategy;

[0103] The scene adaptation module runs a scene parameter synthesis algorithm, which generates the final scene rendering configuration vector through weighted superposition operations. The specific algorithm is as follows:

[0104] (2)

[0105] In its formula (2), This is the default base rendering parameter vector (including base lighting model, acoustic reverberation parameters, base spatial model, etc.). For job feature vectors, For user feature vectors, For the parameter correction function of the vector mapping, and The environmental atmosphere weighting coefficient is used to balance the dominance of job characteristics and user characteristics in a scenario. The system determines this based on... The specific parameter values ​​(such as light intensity, reverberation time, and texture resolution) drive the rendering engine to adjust lighting, materials, and layout in real time.

[0106] in, This refers to the parameter correction amount for mapping the job feature vector (such as the cool-toned lighting effect parameter corresponding to the financial job). The parameter correction for mapping user feature vectors (such as the decoration density parameter corresponding to extroversion).

[0107] In this embodiment, specifically: the multimodal interaction module collects head posture, voice signals, and physiological characteristics through multimodal sensors, and runs a tension index calculation algorithm to quantify the user's state. The specific algorithm is as follows:

[0108] (3)

[0109] In its formula (3), for The tension index at any moment, for Time before Head pose data vector within the time window, This represents the average head pose of the user in their baseline state. This represents variance calculation. for The fundamental frequency feature vector of the speech signal at time t. For the fundamental frequency fluctuation sensitivity coefficient, for to The vector of the change in the handle's position over time. The L2 norm of the handle position change. The maximum permissible speed of the handle movement. These are modal weighting coefficients used to balance the contribution of each mode in the stress calculation. The system is based on a delay compensation algorithm and an adaptive feedback intensity adjustment mechanism, according to... The value dynamically adjusts the pace of the interview process and the feedback from the virtual interviewer;

[0110] Among them, the system is based on The value (such as the normalized result from 0 to 1) is used to determine the user's current state (e.g., below 0.2 indicates relaxation, and above 0.6 indicates high tension).

[0111] In this embodiment, specifically: the distributed AI analysis module, through a client-server collaborative concurrency strategy, utilizes a distributed computing cluster to execute the job matching degree calculation algorithm in parallel. The specific algorithm is as follows:

[0112] (4)

[0113] In its formula (4), For career matching, The user capability vector includes dimensions such as professional skills, communication skills, and stress resistance. This is a job requirement vector (containing the core competency indicators required for the job). It is the dot product of the user's ability vector and the job requirement vector (measures the consistency of direction). It is the product of the magnitudes of two vectors (normalized). and Users and positions respectively in the first Dimensional capability value The system outputs the normalized matching degree result based on the weight coefficients for this dimension.

[0114] in, This is a weighted correction term for the capability gaps in each dimension.

[0115] In this embodiment, specifically: the report and recommendation module uses a template engine to map AI analysis results to configurable templates, ensures the logical consistency of the report through an automated verification mechanism and a two-level review process, and retrieves resources from the library based on user capability deficiency tags, generating a Top-N personalized learning recommendation list sorted by relevance.

[0116] In this embodiment, specifically: the monitoring and archiving module adopts an event-driven architecture for full-link monitoring, executes intelligent degradation strategies for scenarios of analysis service anomalies or network interruptions, prevents the main thread from being blocked through asynchronous message queues and rate limiting mechanisms, and automatically archives interactive data and evaluation results to cloud storage.

[0117] A method for operating an immersive simulated interview system based on VR and AI includes the following steps:

[0118] S1. Cross-terminal authentication and personalized facial profile construction: Perform cross-terminal authentication, parse data packets to construct user feature vectors and initialize personalized facial profiles;

[0119] S2, Job-Scenario Dynamic Adaptation and Multi-Level Virtual Environment Generation: Run the scene parameter synthesis algorithm to dynamically load multi-level virtual scenes and ensure an immersive experience through performance optimization strategies;

[0120] S3, Multimodal Interaction and Interview Process Execution: Collect multimodal interaction data, calculate the tension index, dynamically schedule the interview process, and drive real-time feedback from the virtual interviewer;

[0121] S4. Multimodal Data Acquisition and Distributed AI Analysis: Multimodal data is analyzed by a distributed AI engine to calculate the job matching degree between users and positions;

[0122] S5. Dynamic Report Generation and Personalized Recommendations: Generate structured reports and push personalized learning resources based on skill gaps;

[0123] S6. Interview process archiving and distributed status monitoring: Archive interview data and ensure stable system operation through full-link monitoring and intelligent degradation mechanisms.

[0124] In this embodiment, specifically: in S1, the specific operating steps are as follows:

[0125] A1. Generation of secure reservation code;

[0126] A2. Dual verification and consistency determination;

[0127] A3. Data mapping and personalized map construction;

[0128] In A1, the system executes a token generation algorithm on the server side, which integrates the user's identity identifier with spatiotemporal information and random entropy value to generate a reservation code with anti-counterfeiting features. The algorithm uses timestamps to give the reservation code a time validity window, ensuring the dynamic security of the reservation code in the public interview booth environment and preventing historical reservation codes from being reused.

[0129] In A2, when a terminal initiates a login request, the system executes a consistency check logic based on hash comparison to achieve collaborative verification between the mobile terminal and the VR terminal. The verification content includes:

[0130] Format validation: The system first checks the compliance of the entered reservation code to ensure that it conforms to the preset encoding specifications (such as character length and character set range) and filters out illegal input;

[0131] Network hash comparison: The system uploads the user's input identity identifier and reservation code to the server. The server retrieves the database, extracts the original reservation code corresponding to the user, and performs a consistency comparison. If the comparison results are consistent and the user status and reservation time window are valid, the verification is deemed successful and a standardized user data packet is sent out. Otherwise, the verification is deemed unsuccessful and an error status code is returned.

[0132] In A3, after successful verification, the system parses and persists the received data packets to build a personalized facet spectrum that supports subsequent AI operation;

[0133] Among them, structured mapping: the system parses and maps received standardized text format data (such as JSON) into user feature vectors in memory through a data serialization interface. This vector contains the user's global attributes. ,Right now It covers dimensions such as basic information, historical capability data, and scenario preferences;

[0134] Global state persistence: The system persists user feature vectors Assign the value to the system's global runtime state so that it is used throughout the entire interview process. At the same time, set the verified appointment code as the global authentication credential for the current session, which will be used for identity verification in subsequent operations such as report uploading and file archiving.

[0135] Graph initialization: based on already assigned user feature vectors System initialization personalized surface view spectrum The graph is used as the input index benchmark for the scenario adaptation algorithm (S2) and career matching algorithm (S4) in subsequent steps to realize the intelligent interview process of "one person, one policy".

[0136] In S2, a multi-level dynamic adaptation architecture is constructed to achieve precise matching between virtual interview scenarios and job requirements and user characteristics. The system adopts a scene parameter synthesis algorithm to transform abstract feature labels into specific rendering parameters. An immersive environment is built through a three-level resource loading mechanism, and asynchronous loading technology is combined to optimize the scene delivery process, ensuring a balance between visual performance and runtime performance. The specific operation steps are as follows:

[0137] B1. Scene parameter synthesis algorithm;

[0138] B2. Dynamic loading of the three-level scene architecture;

[0139] B3. Performance optimization and incremental delivery strategy;

[0140] In B1, the system uses a scene parameter synthesis algorithm based on job requirement vectors and user feature vectors to dynamically generate the final scene rendering configuration vector.

[0141] In B2, to ensure loading efficiency and immersion, the system adopts a layered rendering strategy, dynamically constructing the virtual scene by dividing it into a basic environment layer, a scene feature layer, and an interactive element layer.

[0142] Among them, the basic environment layer: the system loads a general physical space model and global environment parameters, combines a real-time lighting engine to simulate the natural lighting environment, and constructs a basic sound field that conforms to acoustic characteristics;

[0143] Scene feature layer: generated by algorithm Configure parameters, and the system dynamically and differentiates the call to industry identifiers, scene decorations and functional areas (such as injecting serious business elements into the financial position). This layer of resources adopts asynchronous loading technology to complete the rapid switching of scene elements without interrupting the main thread rendering.

[0144] Interactive element layer: Generates interactive virtual props that are strongly associated with the job type, such as professional documents or design tools. The system uses a physics engine to configure mass parameters, friction coefficients and operation feedback logic for the props, and supports users to trigger grabbing, operation and other behaviors through interactive devices.

[0145] In B3, the system implements intelligent optimization strategies to address the limitations of VR device computing power and rendering load, ensuring the smoothness and immersiveness of scene delivery;

[0146] Among them, Level of Detail (LOD) control: The system dynamically adjusts the scene model precision and texture resolution based on the user's virtual viewing distance and the current system frame rate load. When the rendering pressure is detected to be increasing, the number of faces of the model in the distant scene is automatically reduced.

[0147] Asynchronous progressive delivery: The system adopts a streaming loading mechanism of "waiting area - interview room". When the user enters the interview waiting area, the system prioritizes loading high-precision resources and AI models in the core interaction area. During the waiting period, non-core area resources are silently loaded in the background.

[0148] Resource pre-caching and unloading: Based on the user's historical operation path, the system predicts the resources that may be loaded and pre-caches them. At the same time, for scenario resources that are outside the scope of interaction and do not need to be used again, real-time unloading operations are performed.

[0149] In S3, a multimodal interaction framework is constructed to enable real-time interaction between users and virtual interviewers, dynamically execute the interview process, and synchronously collect interaction data. The system uses a tension index calculation algorithm to quantify user status and combines it with a dynamic process scheduling mechanism to adjust the interview pace, ensuring immersive interaction and process adaptability. The specific operation steps are as follows:

[0150] C1. Multimodal interaction and tension calculation framework;

[0151] C2. Real-time interaction and feedback mechanism;

[0152] C3. The interview process is executed dynamically.

[0153] C4. Data Acquisition and Synchronization;

[0154] In C1, the system collects user interaction data through multimodal sensors, and after preprocessing and feature extraction, it integrates and analyzes the user's state to drive the response of the virtual interviewer.

[0155] The data acquisition layer includes: the system collects voice signals through a microphone, captures the user's head posture and limb movements through the built-in sensors of the VR device, and collects physiological signals (such as heart rate and skin conductance) through wearable devices.

[0156] Preprocessing and feature extraction: After noise reduction and framing, the speech signal is used to extract acoustic features (such as Mel frequency cepstral coefficients MFCC). After filtering and denoising, the action data is used to extract posture features (such as head offset and limb movement amplitude). After filtering, the physiological signal is used to extract time domain features (such as heart rate variability HRV).

[0157] In C2, the system responds to user interactions in real time based on multimodal analysis results, and enhances the immersive experience of the interaction through the voice, actions, and facial expressions of the virtual interviewer.

[0158] Among them, voice interaction: The system uses a speech recognition engine (such as ASR) to convert the user's voice into text, and combines natural language processing (NLP) technology to understand the semantics, triggering the virtual interviewer's preset responses (such as asking questions, follow-up questions, and encouragement). The voice response realizes the sense of direction and distance through spatial audio technology, simulating real dialogue scenarios.

[0159] Action and facial expression interaction: The system uses motion capture technology to recognize the user's body movements (such as gestures and nodding) and triggers corresponding actions from the virtual interviewer (such as shaking hands and nodding in response). It also uses facial expression recognition technology to capture the user's emotional changes and drive the virtual interviewer to adjust their facial expressions (such as smiling and frowning) to achieve emotional synchronization.

[0160] Real-time feedback optimization: The system employs a latency compensation algorithm to compensate for the latency of multimodal data acquisition and processing in real time, ensuring timely feedback (e.g., voice response latency does not exceed 200ms). Simultaneously, for streaming data (SSE) returned by the AI ​​server, the system incorporates an incremental data packet splicing mechanism. To address potential data fragmentation or truncation during network transmission, the system identifies data stream prefixes, caches incomplete data fragments, and dynamically reassembles them with subsequent data packets. This ensures real-time and accurate parsing of incomplete JSON data streams in the main thread, achieving a smooth typewriter effect. Furthermore, through adaptive feedback intensity adjustment, the system adjusts the feedback amplitude according to the user's level of tension (e.g., reducing follow-up questions when tense and increasing interaction when relaxed).

[0161] In C3, the system dynamically adjusts the interview process based on the user's status and interview progress to ensure the adaptability of the process and the user experience.

[0162] The process scheduling logic includes: the system presets a basic interview process (such as self-introduction, professional questions, and situational simulation), and adjusts the process based on the tension index. Dynamically adjust process nodes, for example, when When the stress level is >0.6 (high tension), the system automatically inserts questions to ease the tension (such as "Please briefly introduce your hobbies"); when the user's answer is too brief, the system triggers follow-up questions (such as "Could you explain the key steps of this project in detail?").

[0163] Process state management: The system uses a state machine model to manage the interview process. Each process node includes a trigger condition, an execution action, and a transition condition. For example, the trigger condition for the self-introduction node (state 1) is "the user completes voice input", the execution action is "the virtual interviewer plays a welcome message", and the transition condition is "the user ends the self-introduction", transitioning to the professional question node (state 2).

[0164] Dynamic difficulty adjustment: The system adjusts the difficulty of subsequent questions based on the quality of the user's answers (such as accuracy and completeness). For example, if the user answers professional questions correctly in a row, the system increases the difficulty of the questions (such as from basic concepts to complex applications). If the user answers incorrectly multiple times, the system decreases the difficulty (such as from application questions to conceptual questions) to avoid the user feeling frustrated.

[0165] In C4, the system synchronously collects multimodal data during the interaction process to ensure the integrity and real-time nature of the data, providing support for subsequent analysis;

[0166] Among them, the data acquisition strategy is as follows: The system adopts a segmented acquisition and caching mechanism, which collects interactive data in time slices (such as every 5 seconds as a segment) and caches it in local memory to avoid data loss due to network fluctuations.

[0167] Data synchronization mechanism: The system synchronizes collected data to the backend server in real time via the WebSocket protocol, employing an incremental synchronization algorithm to reduce data transmission volume (e.g., synchronizing only changed data fragments). Synchronized data includes: user interaction feature vectors, tension index, etc. Process node status, virtual interviewer response records, etc.;

[0168] Data integrity guarantee: The system uses a checksum mechanism to verify the integrity of synchronized data. If data corruption is detected, it will automatically request retransmission. At the same time, local cached data will be automatically cleared after successful synchronization to avoid storage space occupation.

[0169] In this embodiment, specifically: In S4, a multimodal data acquisition framework is constructed to achieve complete capture and synchronization of interactive data. Combined with a distributed AI analysis engine, the job matching degree between users and positions is quantified, providing data support for interview result evaluation. The system uses a job matching degree calculation algorithm to process user ability characteristics, and improves analysis efficiency through a distributed computing architecture to ensure the accuracy and real-time nature of the results. The specific operation steps are as follows:

[0170] D1. Multimodal data acquisition and preprocessing;

[0171] D2. Occupation matching degree calculation algorithm;

[0172] D3, Distributed AI Analysis Process;

[0173] D4. Results Feedback and Optimization;

[0174] In D1, the system comprehensively collects user data during the interview process through multimodal sensors and interactive devices, and after standardized preprocessing, provides structured input for AI analysis;

[0175] Among them, the data acquisition layer: the system synchronously collects voice signals (user's answer content), motion data (head posture, limb movements), physiological signals (heart rate, skin conductance response), interactive text (semantic content of the answer) and process node data (interview progress, question type).

[0176] Preprocessing and feature extraction: After noise reduction and framing, the speech signal is processed to extract acoustic features (such as Mel frequency cepstral coefficients MFCC). After filtering and denoising, the action data is processed to extract posture features (such as head offset and limb movement amplitude). After filtering, the physiological signal is processed to extract time-domain features (such as heart rate variability HRV). After word segmentation and stop word removal, the interactive text is processed to extract semantic feature vectors through a pre-trained language model (such as BERT).

[0177] Data synchronization mechanism: The system uses the WebSocket protocol to synchronize preprocessed data to the backend distributed storage cluster (such as HDFS) in real time, and uses an incremental synchronization algorithm to reduce the amount of data transmission (such as synchronizing only the changed data fragments) to ensure data integrity and consistency.

[0178] In D2, the system runs a job matching algorithm based on the collected user ability characteristics to quantify the degree of fit between users and job requirements.

[0179] The operational logic is as follows: The system first extracts user capability features from the interactive text (such as generating responses through LLM analysis). Then retrieve from the job database Next, the vector dot product and the product of the magnitudes are calculated to obtain the directional consistency score. Finally, the impact of the gap is adjusted by a weighted correction term, and the normalized career matching degree is output. (Range 0 to 1, 1 indicates a perfect match);

[0180] In D3, the system adopts a distributed computing architecture, which distributes the job matching calculation task to multiple computing nodes to improve analysis efficiency and scalability;

[0181] Task allocation: The backend scheduler divides the preprocessed data into shards and distributes them to multiple nodes in the distributed computing cluster (such as a Spark cluster), with each node responsible for processing a portion of the data.

[0182] Parallel computing: Each node executes the career matching degree calculation algorithm in parallel. The MapReduce framework is used to realize the parallel calculation of vector operations and weighted correction terms, which greatly shortens the analysis time. At the same time, the system adopts a client-server collaborative concurrency strategy. After the interview, the client immediately initiates concurrent independent scoring requests for each interaction segment, which works in conjunction with the parallel processing of the backend distributed cluster. The above strategy eliminates the time overhead of serial waiting and significantly shortens the overall time from the end of the interview to the generation of the report.

[0183] Results aggregation: The master node collects the calculation results from each node, performs aggregation and normalization processing, and generates the final career matching report (including user strengths, skills to be improved, job suitability suggestions, etc.).

[0184] In D4, the system provides users with a visual representation of the job matching results and iteratively optimizes the algorithm based on user feedback.

[0185] Among them, visual feedback: a matching report is displayed through a UI interface (such as a radar chart showing the matching degree of each dimension, and textual explanations of advantages and disadvantages) to help users intuitively understand the interview results;

[0186] Algorithm optimization: The system employs a reinforcement learning mechanism to adjust weight coefficients based on user feedback (such as the level of satisfaction with the report). (For example, by increasing the weight of capability dimensions that users consider important) to improve the accuracy of matching degree calculation;

[0187] In S5, a configurable report generation system and personalized recommendation mechanism are built to achieve structured presentation and targeted feedback of interview results, connecting with the preceding assessment process to form a complete technical chain. The specific operation steps are as follows:

[0188] E1. Report template construction and content filling;

[0189] E2. Report quality verification;

[0190] E3. Personalized resource recommendations;

[0191] E4. Report Output and Storage;

[0192] In E1, the system adopts a template engine-based dynamic rendering mechanism to realize the visualization of analysis results and meet the reporting needs in different scenarios;

[0193] The modular template system includes a configurable template library with core modules such as personal profile, holographic interview scenario review, interactive answer details, in-depth analysis of career matching, multi-dimensional assessment of personality traits, and ability improvement path planning. The templates support modular combination and can be flexibly adjusted in structure and emphasis according to the interview type (such as technical interview or HR interview).

[0194] Data standardization binding: Define a standardized data exchange format (such as JSON Schema), map the AI ​​analysis results (vector scores, tags, text summaries) output by S4 to template placeholders, decouple data and view through the template engine, support dynamic generation of PDF, HTML and other formats, and use responsive layout technology to ensure consistent display and easy viewing across multiple terminals such as PC and mobile.

[0195] In E2, the system introduces an automated verification mechanism to ensure the logical consistency of the generated content and the accuracy of the data, thus maintaining the credibility of the report;

[0196] Among them, logical consistency verification: The system's built-in rule engine performs a full-link logical scan on the generated report. The verification content includes: semantic consistency between the scoring range and the evaluation text (such as "high score" corresponding to "excellent" evaluation), visual consistency between the matching degree conclusion and the ability radar chart, and logical self-consistency between personality traits and behavioral descriptions.

[0197] Anomaly Handling and Review Process: For detected logical conflicts or data anomalies (such as extremely high matching degree but low core skill score), the system automatically marks them with an "anomaly" tag and triggers a secondary review process. The review process supports rule-based automatic correction or manual review mechanisms to ensure that the final report delivered to the user has a high degree of credibility.

[0198] At E3, based on a precise profile of user capabilities, the system provides a closed-loop learning resource recommendation system from "discovering problems" to "solving problems".

[0199] Among them, the identification and mapping of weaknesses: the system analyzes the AI ​​analysis results, extracts the user's ability deficiency tags (such as "weak communication skills" and "insufficient algorithm foundation"), and quantifies their priority;

[0200] Resource Retrieval and Sorting: Based on the extracted tags, the system searches in a structured learning resource repository. The system calculates the semantic similarity or tag overlap between the resource tags and the user's shortcomings, and sorts them in descending order of relevance.

[0201] Targeted learning support: Select the top-N high-value resources (such as online courses, practical cases, and simulation exercises), generate a targeted learning improvement list and embed it into the "improvement suggestions" module of the report to provide users with practical career development support;

[0202] In E4, the system ensures data traceability and convenient access for users through multi-channel distribution and long-term cloud storage;

[0203] Among them, multi-channel output and sharing: It supports outputting the report through multiple channels after it is generated, including direct preview within the system, downloading it as an offline file (PDF / Word) and generating an encrypted sharing link. It also integrates a social sharing interface, allowing users to share the report to their tutors or recruitment platforms with one click, thus expanding the application scenarios of the report.

[0204] Cloud backup and full lifecycle management: The system synchronously uploads the generated report files and metadata to the cloud object storage, establishes a version index for user profiles, and adopts a distributed storage strategy to ensure high availability and persistence of data. It also supports users to review and review historical reports at any time, ensuring the integrity and traceability of interview data.

[0205] In S6, by establishing a full data archiving system and a high-availability monitoring mechanism, the asset-based storage of interview data and the stability of system operation are ensured, and various abnormal scenarios are dealt with through intelligent fault tolerance strategies.

[0206] Among them, automated data archiving: relying on the core archiving unit, the system automatically stores interaction data, evaluation results and report files when the interview ends or is interrupted, and updates the user's personalized interview spectrum in sync. The system also uses a cloud backup mechanism to achieve long-term data retention.

[0207] End-to-end status monitoring: Adopting an event-driven architecture to coordinate various functional modules, a real-time monitoring system is built to continuously track device connection, service load and network transmission status, and record detailed anomaly logs and processing trajectories;

[0208] Intelligent degradation and fault tolerance: Different fault tolerance strategies are formulated for different abnormal scenarios. When analyzing service anomalies, backup logic is activated to prioritize the process progress. When the network or device is interrupted, it automatically switches to offline mode and saves progress and data through local caching. After recovery, it can be seamlessly resumed and synchronized.

[0209] Interaction status and resource management: The system has a built-in asynchronous message queue and rate limiting mechanism. By setting the maximum concurrent display threshold, high-frequency system events are queued, buffered, and serialized to prevent the main thread from being blocked due to UI element stacking, thus ensuring smooth interaction and stable system resources.

[0210] Working principle: First, the identity verification module uses a one-way hash digest algorithm to fuse user identity identifier, timestamp, and random salt value to generate a time-sensitive and secure reservation code. Cross-terminal dual verification ensures identity security. At the same time, it parses user data to construct feature vectors and personalized facial profiles, providing a customized foundation for subsequent processes.

[0211] Next, the scene adaptation module generates a rendering configuration vector based on job requirements and user feature vectors through a scene parameter synthesis algorithm. The virtual scene is divided into a basic environment layer, a scene feature layer, and an interactive element layer. Performance optimization strategies such as asynchronous loading and detail level control are adopted to dynamically construct a multi-level virtual environment that fits the characteristics of the job and user preferences, balancing visual immersion and smooth operation.

[0212] During the interview execution phase, the multimodal interaction module collects user voice, head posture, body movements, and physiological data through sensors. After preprocessing and extracting core features, it quantifies the user's real-time state through a tension index calculation algorithm, driving the virtual interviewer to provide real-time feedback through voice, actions, and facial expressions. At the same time, based on the user's state and the quality of their answers, the system adjusts the interview pace, question difficulty, and follow-up questioning strategies through a dynamic process scheduling mechanism to simulate real interview interaction scenarios.

[0213] In the data processing stage, the distributed AI analysis module adopts a client-server collaborative concurrent strategy to distribute multimodal data to a distributed computing cluster for parallel processing. It uses a career matching degree calculation algorithm to quantify the degree of fit between user ability vectors and job requirement vectors, accurately identify user strengths and weaknesses, and ensure analysis efficiency and result accuracy.

[0214] After the interview, the report and recommendation module maps the AI ​​analysis results to configurable templates through the template engine. After automated verification and secondary review, a logically consistent structured report is generated. At the same time, the resource library is searched based on the user's skill gap tags, and personalized learning resources are pushed according to relevance, forming an "assessment-improvement" closed loop.

[0215] Throughout the process, the monitoring and archiving module adopts an event-driven architecture for end-to-end monitoring, executes intelligent degradation strategies for scenarios such as service anomalies and network interruptions, and prevents the main thread from being blocked through asynchronous message queues and rate limiting mechanisms. At the same time, interactive data and evaluation results are automatically archived to cloud storage to ensure data security, system stability and traceability.

[0216] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An immersive simulated interview system based on VR and AI, comprising an identity verification module, a scenario adaptation module, a multimodal interaction module, a distributed AI analysis module, a reporting and recommendation module, and a monitoring and archiving module, characterized in that: The identity verification module is used to perform cross-terminal collaborative verification through a token generation algorithm and to construct a personalized facial profile. The scene adaptation module is used to dynamically generate a multi-level virtual environment based on job requirements and user feature vectors using a scene parameter synthesis algorithm. The multimodal interaction module is used to collect user voice, action and physiological data, and calculate the tension index to drive the virtual interviewer to conduct real-time interaction and feedback. The distributed AI analysis module is used to fuse multimodal data and calculate the job matching degree through a distributed architecture; The reporting and recommendation module is used to generate structured reports and push personalized learning resources; The monitoring and archiving module is used for end-to-end status monitoring and intelligent degradation of anomalies to ensure data security and stable system operation.

2. The immersive simulated interview system based on VR and AI and its operation method according to claim 1, characterized in that: The authentication module uses a one-way hash digest algorithm to fuse user identity identifiers. Current system timestamp and random salt value Generate a time-sensitive secure reservation code The specific algorithm is as follows: （1） In its formula (1), A one-way hash digest algorithm is used, where || represents the concatenation of data bits.

3. The immersive simulated interview system based on VR and AI and its operation method according to claim 1, characterized in that: The scene adaptation module divides the virtual scene into a basic environment layer, a scene feature layer, and an interactive element layer, and adopts asynchronous loading technology and a level of detail control strategy. The scene adaptation module runs a scene parameter synthesis algorithm, which generates the final scene rendering configuration vector through weighted superposition operations. The specific algorithm is as follows: （2） In its formula (2), This is the default base rendering parameter vector. For job feature vectors, For user feature vectors, For the parameter correction function of the vector mapping, and This represents the environmental atmosphere weighting coefficient.

4. The immersive simulated interview system based on VR and AI and its operation method according to claim 1, characterized in that: The multimodal interaction module collects head posture, voice signals, and physiological characteristics through multimodal sensors, and runs a tension index calculation algorithm to quantify the user's state. The specific algorithm is as follows: （3） In its formula (3), for The tension index at any moment, for Time before Head pose data vector within the time window, This represents the average head pose of the user in their baseline state. This represents variance calculation. for The fundamental frequency feature vector of the speech signal at time t. For the fundamental frequency fluctuation sensitivity coefficient, for to The vector of the change in the handle's position over time. The L2 norm of the handle position change. The maximum permissible speed of the handle movement. These are the modal weighting coefficients.

5. The immersive simulated interview system based on VR and AI and its operation method according to claim 1, characterized in that: The distributed AI analysis module utilizes a client-server collaborative concurrency strategy to execute the job matching degree calculation algorithm in parallel using a distributed computing cluster. The specific algorithm is as follows: （4） In its formula (4), For career matching, For user capability vectors, As a vector of job requirements, The dot product of the user's ability vector and the job requirement vector. It is the product of the magnitudes of two vectors. and Users and positions respectively in the first Dimensional capability value The system outputs the normalized matching degree result based on the weight coefficient of this dimension.

6. The immersive simulated interview system based on VR and AI and its operation method according to claim 1, characterized in that: The reporting and recommendation modules use a template engine to map AI analysis results to configurable templates. An automated verification mechanism and a two-level review process ensure the logical consistency of the reports. The modules also search the resource library based on user skill gap tags and generate a Top-N personalized learning recommendation list sorted by relevance.

7. The immersive simulated interview system based on VR and AI and its operation method according to claim 1, characterized in that: The monitoring and archiving module adopts an event-driven architecture for end-to-end monitoring, executes intelligent degradation strategies for scenarios such as analysis service anomalies or network interruptions, prevents the main thread from being blocked through asynchronous message queues and rate limiting mechanisms, and automatically archives interactive data and evaluation results to cloud storage.

8. A method for operating an immersive simulated interview system based on VR and AI, characterized in that, Includes the following steps: S1. Cross-terminal authentication and personalized facial profile construction: Perform cross-terminal authentication, parse data packets to construct user feature vectors and initialize personalized facial profiles; S2, Job-Scenario Dynamic Adaptation and Multi-Level Virtual Environment Generation: Run the scene parameter synthesis algorithm to dynamically load multi-level virtual scenes and ensure an immersive experience through performance optimization strategies; S3, Multimodal Interaction and Interview Process Execution: Collect multimodal interaction data, calculate the tension index, dynamically schedule the interview process, and drive real-time feedback from the virtual interviewer; S4. Multimodal Data Acquisition and Distributed AI Analysis: Multimodal data is analyzed by a distributed AI engine to calculate the job matching degree between users and positions; S5. Dynamic Report Generation and Personalized Recommendations: Generate structured reports and push personalized learning resources based on skill gaps; S6. Interview process archiving and distributed status monitoring: Archive interview data and ensure stable system operation through full-link monitoring and intelligent degradation mechanisms.

9. The method for operating an immersive simulated interview system based on VR and AI according to claim 8, characterized in that: In S1, the specific operating steps are as follows: A1. Generation of secure reservation code; A2. Dual verification and consistency determination; A3. Data mapping and personalized map construction; In A1, the system executes a token generation algorithm on the server side, which integrates the user's identity identifier with spatiotemporal information and random entropy value to generate a reservation code with anti-counterfeiting features. In A2, when a terminal initiates a login request, the system executes a consistency check logic based on hash comparison to achieve collaborative verification between the mobile terminal and the VR terminal. In A3, after successful verification, the system parses and persists the received data packets to build a personalized facet spectrum that supports subsequent AI operation; In S2, a multi-level dynamic adaptation architecture is constructed to achieve precise matching between virtual interview scenarios and job requirements and user characteristics. The system adopts a scene parameter synthesis algorithm to transform abstract feature labels into specific rendering parameters. An immersive environment is built through a three-level resource loading mechanism, and asynchronous loading technology is combined to optimize the scene delivery process, ensuring a balance between visual performance and runtime performance. The specific operation steps are as follows: B1. Scene parameter synthesis algorithm; B2. Dynamic loading of the three-level scene architecture; B3. Performance optimization and incremental delivery strategy; In B1, the system uses a scene parameter synthesis algorithm based on job requirement vectors and user feature vectors to dynamically generate the final scene rendering configuration vector. In B2, to ensure loading efficiency and immersion, the system adopts a layered rendering strategy, dynamically constructing the virtual scene by dividing it into a basic environment layer, a scene feature layer, and an interactive element layer. In B3, the system implements intelligent optimization strategies to address the limitations of VR device computing power and rendering load, ensuring the smoothness and immersiveness of scene delivery; In S3, a multimodal interaction framework is constructed to enable real-time interaction between users and virtual interviewers, dynamically execute the interview process, and synchronously collect interaction data. The system uses a tension index calculation algorithm to quantify user status and combines it with a dynamic process scheduling mechanism to adjust the interview pace, ensuring immersive interaction and process adaptability. The specific operation steps are as follows: C1. Multimodal interaction and tension calculation framework; C2. Real-time interaction and feedback mechanism; C3. The interview process is executed dynamically. C4. Data Acquisition and Synchronization; In C1, the system collects user interaction data through multimodal sensors, and after preprocessing and feature extraction, it integrates and analyzes the user's state to drive the response of the virtual interviewer. In C2, the system responds to user interactions in real time based on multimodal analysis results, and enhances the immersive experience of the interaction through the voice, actions, and facial expressions of the virtual interviewer. In C3, the system dynamically adjusts the interview process based on the user's status and interview progress to ensure the adaptability of the process and the user experience. In C4, the system synchronously collects multimodal data during the interaction process to ensure the integrity and real-time nature of the data, providing support for subsequent analysis.

10. The method for operating an immersive simulated interview system based on VR and AI according to claim 8, characterized in that: In S4, a multimodal data acquisition framework is constructed to achieve complete capture and synchronization of interactive data. Combined with a distributed AI analysis engine, the system quantifies the job matching degree between users and positions, providing data support for interview result evaluation. The system uses a job matching degree calculation algorithm to process user ability characteristics and improves analysis efficiency through a distributed computing architecture, ensuring the accuracy and real-time nature of the results. The specific operation steps are as follows: D1. Multimodal data acquisition and preprocessing; D2. Occupation matching degree calculation algorithm; D3, Distributed AI Analysis Process; D4. Results Feedback and Optimization; In D1, the system comprehensively collects user data during the interview process through multimodal sensors and interactive devices, and after standardized preprocessing, provides structured input for AI analysis; In D2, the system runs a job matching algorithm based on the collected user ability characteristics to quantify the degree of fit between users and job requirements. In D3, the system adopts a distributed computing architecture, which distributes the job matching calculation task to multiple computing nodes to improve analysis efficiency and scalability; In D4, the system provides users with a visual representation of the job matching results and iteratively optimizes the algorithm based on user feedback. In S5, a configurable report generation system and personalized recommendation mechanism are built to achieve structured presentation and targeted feedback of interview results, connecting with the preceding assessment process to form a complete technical chain. The specific operation steps are as follows: E1. Report template construction and content filling; E2. Report quality verification; E3. Personalized resource recommendations; E4. Report Output and Storage; In E1, the system adopts a template engine-based dynamic rendering mechanism to realize the visualization of analysis results and meet the reporting needs in different scenarios; In E2, the system introduces an automated verification mechanism to ensure the logical consistency of the generated content and the accuracy of the data, thus maintaining the credibility of the report; At E3, based on a precise profile of user capabilities, the system provides a closed-loop learning resource recommendation system from "discovering problems" to "solving problems"; In E4, the system ensures data traceability and convenient access for users through multi-channel distribution and long-term cloud storage; In S6, a full data archiving system and a high-availability monitoring mechanism are established to ensure the asset-based storage of interview data and the stability of system operation, and intelligent fault tolerance strategies are used to deal with various abnormal scenarios.

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