Interview practice system based on AI technology and digital virtualization

Abstract

The invention provides an interview practice system based on an AI (artificial intelligence) technology and digital virtualization, which comprises a post information asset database, an interview information acquisition unit, a character association unit, a face data capture unit, an information summarization evaluation unit and a contact guidance unit, and is characterized in that the post information asset database captures post classification in any recruitment software; the post information asset database is used for classifying posts, establishing corresponding post demand information according to the post classifications, pre-inputting basic information of the posts in the post classifications of the post information asset database, regularly performing data capture within a set range according to the post demand information, comparing and matching the captured information with the basic information, and updating the captured information and the basic information; the interview information acquisition unit is provided with an interview process, and whole-course audio and video data acquisition is carried out on interview personnel through the interview information acquisition unit; and the character association unit calls the audio data in the interview information acquisition unit and carries out character processing on the audio data in the interview information acquisition unit.

Description

Technical Field

[0001] The present invention belongs to the field of AI assistance, and in particular relates to an interview practice system based on AI technology and digital virtuality. Background Art

[0002] In today's competitive job market, job seekers need to constantly improve their interview skills in order to stand out from the crowd of candidates. Traditional interview training methods usually rely on face-to-face simulation exercises and interpersonal interactions, but this method has many limitations. First, traditional interview training often requires guidance from professional interview coaches or consultants, and such resources are not always easy to obtain in the market. Many job seekers may not be able to effectively improve their interview skills due to the lack of professional coaching. In addition, face-to-face mock interviews are usually limited by time and location, which may make job seekers face many inconveniences when arranging exercises and unable to make full use of fragmented time for practice. This method has poor flexibility and adaptability and is difficult to meet the personalized needs of different job seekers. Secondly, traditional interview training often lacks a real-time feedback mechanism. In a mock interview, job seekers may get advice and feedback from the coach, but this feedback is often based on the coach's subjective judgment and it is difficult to provide systematic, data-driven analysis results. This makes it difficult for job seekers to identify their strengths and weaknesses and unable to make targeted improvements. Furthermore, traditional interview training methods usually cannot simulate a real interview environment. Job seekers may face psychological factors such as tension and pressure in actual interviews, and these factors are often difficult to reproduce in traditional simulations, resulting in job seekers being unable to effectively adapt to the real interview situation during practice. Finally, with the development of technology, many job seekers have begun to seek ways to learn and improve themselves online, but traditional interview training often fails to make full use of modern technology and cannot provide a flexible online learning platform and rich resource support.

[0003] An interview practice system based on AI technology and digital virtualization has emerged to address the shortcomings of the above-mentioned traditional interview training methods. The system uses artificial intelligence technology to intelligently generate and analyze interview questions, and can customize personalized interview questions based on the applicant's resume, industry background and target position. This highly targeted design allows job seekers to practice more effectively and improve the pertinence and effectiveness of interview preparation. In addition, through digital virtual technology, job seekers can conduct simulated interviews at any place and at any time, breaking the time and space limitations of traditional interview training. This flexibility not only improves the convenience of practice, but also allows job seekers to practice multiple times before the actual interview to enhance their self-confidence and adaptability. At the same time, the system can also provide data-driven feedback by recording and analyzing the performance of job seekers in real time, helping job seekers identify their strengths and weaknesses and guiding them to make targeted improvements. This data-based feedback mechanism greatly improves the scientificity and effectiveness of interview training. More importantly, AI technology can simulate the real interview environment, including the interviewer's questioning method, tone, emotions, etc., to help job seekers adapt to the pressure and tension in a real interview. This immersive learning experience can effectively improve the psychological quality and coping ability of job seekers, laying a solid foundation for their success in actual interviews. In summary, the interview practice system based on AI technology and digital virtuality provides job seekers with an efficient, flexible and intelligent interview training method, which greatly improves the quality and effectiveness of interview preparation and promotes the career development process of job seekers. Summary of the invention

[0004] The present invention proposes an interview practice system based on AI technology and digital virtuality, which solves the problems of incomplete information acquisition, incomplete data recording of the interview process and untimely feedback in traditional interview preparation. By integrating job information, audio and video data processing and real-time feedback, the efficiency and pertinence of interview preparation are improved.

[0005] The technical solution of the present invention is implemented as follows: an interview practice system based on AI technology and digital virtuality, including a job information asset database, an interview information collection unit, a text association unit, a face data capture unit, an information summary evaluation unit and a contact guidance unit. The job information asset database captures the job classification in any recruitment software, and establishes corresponding job requirement information according to the job classification. The basic information of the job is pre-entered in the job classification of the job information asset database, and data is captured regularly within a set range according to the job requirement information, and the captured information is compared and matched with the basic information before updating; the interview information collection unit is provided with an interview process, and the interview information collection unit collects the entire audio and video data of the interviewee; the text association unit calls the audio data in the interview information collection unit, and The audio data is processed into text, the processed text is associated with the basic information in the job information asset database, the associated matching data is obtained, and the associated matching data is imported into the information summary and evaluation unit; the face data capture unit calls the video data in the interview information collection unit, and after determining the face position in the video image, records the facial expression information, and imports the expression information into the information summary and evaluation unit; the information summary and evaluation unit forms the interview comprehensive table information according to the feedback information of the text association unit and the face data capture unit, and establishes a score range, and sets corresponding job profile supplementary data in each score range, the contact guidance unit calls the job profile supplementary data in the corresponding score range, and while displaying the job profile supplementary data, sends the job profile supplementary information to the mobile terminal pre-entered by the user.

[0006] Compared with existing technologies, this interview practice system based on AI technology and digital virtuality has many significant differences and innovations. Current interview simulation systems often rely on fixed interview question banks and lack targeted personalized customization. The problem that job seekers usually face when preparing for interviews is that they cannot obtain specific questions and requirements that match their target positions. However, this system uses the job information asset database to capture job classification information in the recruitment software and establish corresponding job requirement information based on job requirements. This dynamic capture and update mechanism enables the system to always maintain consistency with market demand, ensuring that the content practiced by job seekers is consistent with actual job requirements, greatly improving the pertinence and effectiveness of interview preparation. The introduction of the interview information collection unit can collect audio and video data throughout the interview process, providing rich original data support for subsequent analysis.

[0007] Existing systems often lack real-time records of the interview process and cannot provide effective feedback to job seekers. This system captures the performance of job seekers through audio and video recordings, thereby providing a data basis for improvement. Furthermore, the design of the text association unit enables audio data to be converted into text and associated with basic information in the job information asset database. This text processing not only facilitates analysis, but also allows more accurate feedback to be obtained through data matching. This data-driven analysis method is not common in traditional interview systems and often relies on subjective judgment, lacking scientific basis and objectivity.

[0008] The introduction of the facial data capture unit enables the system to monitor the facial expression information of job seekers in real time, helping to evaluate their emotional state and psychological changes during the interview process. Traditional systems are often unable to effectively capture the non-verbal signals of job seekers, and this system provides job seekers with a more comprehensive performance evaluation through the analysis of video data. The establishment of the information summary evaluation unit enables the system to form comprehensive interview evaluation information based on text association and facial data feedback, and establish a score range. This design makes the evaluation of the interview process more systematic and data-based, and can provide job seekers with clear improvement directions and suggestions.

[0009] As a preferred implementation, the basic information of the positions in the position information asset database is divided into position professional information, position general information and basic general information. Values ​​are assigned to the position professional information, position general information and basic general information respectively according to the position classification. When associating with the text in the text association unit, the keywords in the basic information of the position are extracted, and words similar to the keywords are formed into phrases through the AI ​​algorithm for synchronous assignment. The length of the keywords and the assignment score are adjusted according to the characteristics of the position professional information, position general information and basic general information.

[0010] As a preferred implementation, the basic general information establishes a corresponding virtual interview scene according to the job type, and after calling the data in the job professional information and the job general information, the interviewer's question information is pre-entered, a virtual interviewer is generated through AI, and the virtual interviewer is entered into the virtual interview scene, and the user enters the virtual interview scene for an interview.

[0011] As a preferred implementation, the interview information collection unit sets up the interview process according to the job classification and establishes a communication node before the interview. The interviewer enters the virtual interview meeting room through the channel node, and the interview information collection unit collects real-time audio and video data in the virtual interview meeting room.

[0012] As a preferred embodiment, after the text association unit processes the audio data into text, it will associate the acquired text information with the basic information of the position in the position classification in advance, and then perform approximate phrase matching based on the text information and the basic information to obtain the overlap data of the text information and the basic information, and generate associated matching data based on the overlap.

[0013] As a preferred embodiment, the facial data capture unit recognizes the face in the video image, and uses the center point of the face as the center of the dot matrix to perform dot matrix analysis on the recognized facial image, performs expression analysis on the facial information, determines auxiliary information by analyzing the expression, scores the data of the auxiliary information, and imports the corresponding score data into the information summary and evaluation unit.

[0014] After adopting the above technical solution, the beneficial effect of the present invention is as follows: the system ensures that the content of job seekers' practice is always consistent with market demand by dynamically capturing job information, thereby improving the effectiveness of interview preparation. Job seekers can obtain questions and requirements that are highly relevant to their target positions, reducing the risk of interview failure due to insufficient preparation. Secondly, through the collection of audio and video data throughout the process, the system provides job seekers with a real interview experience, allowing them to practice repeatedly in a simulated environment to improve their self-confidence and adaptability. This immersive learning experience is significantly helpful for improving the psychological quality of job seekers and can effectively relieve their nervousness in actual interviews.

[0015] The combination of text association and facial data capture enables the system to provide more comprehensive feedback. Job seekers can not only understand their performance in professional knowledge, but also recognize their emotional management through facial expression analysis, which is crucial to comprehensively improving their interview capabilities. The design of the information summary evaluation unit makes the interview evaluation more systematic and scientific. Job seekers can adjust their preparation strategies based on specific feedback and improve their shortcomings in a targeted manner, thereby improving their performance in actual interviews. All of this makes the system play an important supporting role in the career development of job seekers and promotes the overall improvement of job seekers' interview skills. This interview practice system based on AI technology and digital virtuality provides job seekers with an efficient, scientific and personalized interview training method, greatly improving their competitiveness in the highly competitive job market. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 It is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example:

[0020] like Figure 1 As shown, an interview practice system based on AI technology and digital virtuality includes a job information asset database, an interview information collection unit, a text association unit, a face data capture unit, an information summary evaluation unit and a contact guidance unit. The job information asset database captures the job classification in any recruitment software, and establishes corresponding job demand information according to the job classification. The basic information of the job is pre-entered in the job classification of the job information asset database, and data is captured regularly within a set range according to the job demand information, and the captured information is compared and matched with the basic information before updating; the interview information collection unit is provided with an interview process, and the interview information collection unit collects the entire audio and video data of the interviewee; the text association unit calls the audio data in the interview information collection unit, and the audio data therein is converted into the audio data The face data is processed into text, the processed text is associated with the basic information in the job information asset database, the associated matching data is obtained, and the associated matching data is imported into the information summary and evaluation unit; the face data capture unit calls the video data in the interview information acquisition unit, and after determining the face position in the video image, records the facial expression information, and imports the expression information into the information summary and evaluation unit; the information summary and evaluation unit forms the interview comprehensive table information according to the feedback information of the text association unit and the face data capture unit, and establishes a score range, and sets corresponding job profile supplementary data in each score range, the contact guidance unit calls the job profile supplementary data in the corresponding score range, and while displaying the job profile supplementary data, sends the job profile supplementary information to the mobile terminal pre-entered by the user.

[0021] The core of the system is the job information asset database. This database builds a comprehensive job demand information base by capturing job classifications in any recruitment software. The job information asset database not only includes basic information of various jobs, but also regularly captures data within a set range to ensure the timeliness and accuracy of the information. By comparing and matching the captured information with the basic information, job information can be updated in a timely manner to ensure that users obtain the latest career information when using the system. This dynamic update method enables the system to adapt to the rapidly changing job market and improves the efficiency of users' job search preparation.

[0022] Next, the interview information collection unit sets up a complete interview process, through which the audio and video data of the interviewer are collected throughout the process. The complete recording of audio and video data not only provides rich information for subsequent analysis, but also helps users review their performance and identify areas for improvement. Compared with traditional interview training methods, this full recording method allows users to have a deeper understanding of their interview performance, so as to make targeted improvements.

[0023] In terms of data processing, the text association unit converts the audio data into text by calling the audio data in the interview information collection unit. The key to this process is to convert the voice information in the interview into text for subsequent analysis. The processed text information is associated with the basic information in the job information asset database to obtain associated matching data. This information association can help users understand the degree of fit between the content they mentioned in the interview and the job requirements, thereby providing data support for their interview preparation.

[0024] The facial data capture unit identifies the interviewee's face position from the video data and records their facial expressions. This process provides the system with an additional dimension of emotion and attitude by analyzing the interviewee's facial expressions in real time. The recording of facial expression information can help evaluate the interviewee's performance during the interview, such as nervousness, relaxation, confidence and other emotional states, which is extremely important for the comprehensive evaluation of their interview ability.

[0025] The information summary and evaluation unit is the data analysis core of the entire system. It forms an interview summary table based on the feedback information from the text association unit and the face data capture unit. This summary table not only summarizes the performance data of the interviewee, but also establishes a score range, and sets corresponding job information supplementary data for each score range. In this way, the system can provide users with personalized feedback and suggestions to help users make better preparations for future interviews.

[0026] The contact guidance unit displays the information to the user by calling the supplementary data of the job information in the corresponding score range, and sends the supplementary information of the job information to the mobile terminal pre-entered by the user. This design enables the user to view the job information related to his / her interview performance on the mobile terminal at any time after completing the interview practice, which enhances the user's learning and application ability.

[0027] The setup and operation process of this system has multiple advantages: the dynamically updated job information asset database ensures that users obtain the latest career information, improving the targeted job search; secondly, comprehensive audio and video data collection and analysis enable users to have a deeper understanding of their interview performance, promoting the improvement of their personal career development; the combination of text association and expression analysis provides multi-dimensional feedback, providing users with a more comprehensive evaluation; and through mobile information push, users can flexibly obtain career development resources, enhancing the convenience and effectiveness of learning.

[0028] The basic information of the positions in the position information asset database is divided into position professional information, position general information and basic general information. Values ​​are assigned to the position professional information, position general information and basic general information respectively according to the position classification. When associating with the text in the text association unit, the keywords in the basic information of the position are extracted, and the words similar to the keywords are formed into phrases through the AI ​​algorithm for synchronous assignment. In addition, the length of the keywords and the assignment score are adjusted according to the characteristics of the position professional information, position general information and basic general information.

[0029] In the job information asset database, the basic information of the job is subdivided into job professional information, job general information and basic general information, which shows significant innovation and systematicness compared with existing technologies. Traditional job information management usually integrates information into a unified category, lacks detailed division of different information types, and leads to insufficient flexibility in data retrieval and application. By clearly classifying job information, this method can more accurately meet the needs of different users and improve the availability and pertinence of data.

[0030] The classification of job professional information, job general information and basic general information makes the structure of information management clearer. This structured design allows the system to locate specific information types more effectively when processing and querying. For example, job professional information can cover the skills and knowledge required for a specific occupation, while job general information can include work environment, responsibilities, etc., and basic general information provides more extensive background data. This segmentation can help users find the information they need more quickly, especially when performing job matching and talent screening, which can significantly improve efficiency.

[0031] When associating with the text in the text association unit, extracting keywords from the basic job information and using AI algorithms to form phrases with similar words for synchronous assignment is an intelligent way of information processing. Traditional information processing methods usually rely on manual annotation or simple text matching, while this AI-based automated processing can reduce human intervention to a greater extent and improve the accuracy and efficiency of keyword matching. The introduction of AI algorithms enables the system to dynamically adapt to new vocabulary and expressions, enhancing the flexibility and intelligence of the system.

[0032] The design of adjusting the length and value scores of keywords based on the characteristics of job professional information, job general information, and basic general information further improves the intelligence level of the system. This function of dynamic adjustment based on information characteristics can optimize the role of keywords in different contexts. For example, the core skills of certain positions may require higher value scores to ensure that they are given priority in the matching process, while some common general terms may only require lower value scores. This flexible value assignment mechanism ensures that the system can maintain efficiency and accuracy when associating and matching information.

[0033] The basic general information establishes a corresponding virtual interview scene according to the job type, and after calling the data in the job professional information and the job general information, the interviewer's question information is pre-entered, a virtual interviewer is generated through AI, and the virtual interviewer is entered into the virtual interview scene, and the user enters the virtual interview scene for an interview.

[0034] The advantages of this method are mainly reflected in the following aspects: information structuring: through classification management, the efficiency of retrieval and utilization of job information is improved; intelligent processing: the introduction of AI algorithms makes keyword extraction and phrase formation more efficient, reduces manual intervention, and improves the accuracy of data processing; dynamic adaptability: the design of adjusting keyword length and assignment score according to characteristics enables the system to flexibly respond to the needs of different positions and enhances the intelligence of information processing; improving matching effect: by optimizing keywords and assignment strategies, the matching accuracy between positions and job seekers is improved, helping companies find suitable talents faster.

[0035] The interview information collection unit sets the interview process according to the job classification and establishes a communication node before the interview. The interviewer enters the virtual interview meeting room through the channel node, and the interview information collection unit collects real-time audio and video data in the virtual interview meeting room.

[0036] Traditional interview processes are often unified and lack targeted design, and cannot be adjusted according to the needs of different positions. This one-size-fits-all interview method may result in candidates being unable to fully demonstrate skills and traits related to the position during the interview, reducing the effectiveness of the interview. By setting up the interview process according to job classification, the system can customize the corresponding questions and evaluation criteria for each position, thereby improving the pertinence and effectiveness of the interview.

[0037] The design of establishing communication nodes is also a key innovation. In traditional interviews, candidates usually need to be interviewed in a fixed location, but this method allows interviews to be conducted in a virtual environment by setting up channel nodes. Interviewers enter the virtual interview meeting room through these communication nodes, which enhances the flexibility and convenience of the interview. This innovation not only provides candidates with more choices, but also breaks geographical restrictions and attracts more outstanding talents to participate in interviews. Especially in the global recruitment environment, this flexibility is particularly important.

[0038] In the virtual interview room, the interview information collection unit collects real-time audio and video data from candidates, further improving the quality and reliability of the interview. Traditional interviews often rely on the subjective judgment of the interviewer and may be affected by personal bias. By collecting real-time audio and video data, the system can record all the details of the interview process and provide an objective basis for subsequent evaluation. In addition, this real-time data collection also provides rich information support for post-interview analysis, making the interview results more transparent and fair.

[0039] The differences from existing technologies are mainly reflected in the following aspects: Personalized interview process: The interview process is set up according to the job classification to ensure that each job can get a tailored interview experience, which improves the relevance and effectiveness of the interview; Flexible interview environment: With the design of virtual meetings, interviews are no longer restricted by physical space, allowing more candidates to participate and increasing the diversity of the candidate pool; Efficient data recording: The collection of real-time audio and video data not only improves the objectivity of the evaluation, but also provides data support for subsequent interview review and analysis; Enhanced transparency: By systematically recording the interview process, the impact of subjective judgment can be reduced and the fairness of the interview results can be improved; Strong adaptability: The system can flexibly adjust the interview structure according to the needs of different positions, thereby improving overall recruitment efficiency.

[0040] After the text association unit processes the audio data into text, it associates the acquired text information with the basic information of the position pre-entered in the position classification, then performs approximate phrase matching based on the text information and the basic information, obtains the overlap data of the text information and the basic information, and generates associated matching data based on the overlap.

[0041] The text association unit associates the acquired text information with the basic information in the job classification after text processing of the audio data, which shows significant innovation and effectiveness compared with the prior art. Traditional interview data processing methods generally rely on manual recording or basic keyword matching. This method is not only inefficient, but also easily affected by human factors, resulting in omissions and inaccuracies of information. This method can effectively convert audio data into structured text information through automated text processing, thereby improving the speed and accuracy of information processing.

[0042] In the process of associating text information with basic information, the system can use the pre-entered basic information of the position for effective matching. This design ensures that the text information is not just isolated text, but is closely integrated with specific job requirements. By establishing a basic information database in the job classification, the system can conduct targeted analysis based on the characteristics of the position, making information processing more purposeful and accurate.

[0043] The system obtains the overlap data between text information and basic information through approximate phrase matching. The innovation of this process is that it not only relies on exact matching keywords, but also can identify semantically similar words through approximate matching algorithms. This method significantly improves the flexibility and fault tolerance of information matching, allowing the system to better cope with different expressions and wording changes. Compared with traditional strict matching, this approximate matching can more comprehensively capture the abilities and adaptability expressed by candidates, thereby providing more accurate job matching results.

[0044] The design of generating related matching data based on the overlap further enhances the intelligence level of the system. By quantifying the overlap between text information and basic information, the system can generate specific matching results based on actual data, helping users understand whether the abilities demonstrated by candidates during the interview process are consistent with the job requirements. This quantitative analysis method is often overlooked in traditional technologies, resulting in subjectivity and inaccuracy in the evaluation process.

[0045] The differences from the existing technology are mainly reflected in the following aspects: Automated processing: By processing the audio data into text, the need for manual recording is reduced and the efficiency of information processing is improved. Accurate matching: By entering the basic information of the position, the relationship between text information and job requirements is closer, which enhances the targeted nature of the analysis; High flexibility: The introduction of approximate phrase matching improves the system's adaptability to different expressions and reduces matching failures caused by differences in wording; Data-driven decision support: The generation of overlap data provides a quantitative basis for evaluation, which enhances the objectivity and scientific nature of the evaluation process.

[0046] The face data capture unit recognizes the face in the video image, and uses the center point of the face as the center of the dot matrix to perform dot matrix analysis on the recognized face image, performs expression analysis on the face information, determines auxiliary information by analyzing the expression, sorts the data of the auxiliary information by scores, and imports the corresponding score data into the information summary and evaluation unit.

[0047] The unit recognizes faces in video images and uses the center point of the face as the center of the dot matrix for dot matrix analysis. This method improves the accuracy and reliability of face recognition. Traditional face recognition technology usually relies only on simple feature extraction, such as edge detection or skin color segmentation, and often cannot effectively process complex backgrounds or multi-angle face images. The dot matrix analysis method can better locate and identify facial features in more complex environments, thereby improving recognition accuracy.

[0048] The system is not limited to basic face recognition functions, but also introduces the ability to analyze facial expressions. This expansion allows the system to obtain more in-depth facial information. Traditional technologies usually only focus on identity recognition and ignore the emotional information conveyed by facial expressions. Through facial expression analysis, the system can assist in determining the user's emotional state, psychological state or intention, which is of great value in many application scenarios, such as in customer service, mental health monitoring and market research.

[0049] This unit sorts the auxiliary information obtained by analysis by scores, providing a basis for subsequent information processing and decision-making. Existing technologies often lack such data processing mechanisms and can usually only provide basic recognition results, but cannot further analyze and evaluate the recognized information. By sorting the data by scores, the system can help users quickly identify the most critical information and prioritize high-scoring emotions or intentions, thereby improving the efficiency of information processing.

[0050] The design of importing the corresponding score data into the information summary and evaluation unit improves the overall data utilization efficiency of the system. In the existing technology, data processing is often discrete and lacks systematic integration. However, this system can provide users with more comprehensive and systematic information feedback through centralized summary and evaluation, helping users make more scientific decisions.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An interview practice system based on AI technology and digital virtuality, characterized in that: The system comprises a job information asset database, an interview information collection unit, a text association unit, a face data capture unit, an information summary evaluation unit and a contact guidance unit. The job information asset database captures the job classification in any recruitment software, and establishes corresponding job requirement information according to the job classification. The basic information of the job is pre-entered in the job classification of the job information asset database, and data is captured regularly within a set range according to the job requirement information, and the captured information is updated after being compared and matched with the basic information; the interview information collection unit is provided with an interview process, and the interview information collection unit collects the whole audio and video data of the interviewee; the text association unit calls the audio data in the interview information collection unit, and converts the audio data into text, and converts the processed audio data into text. The text is associated with the basic information in the job information asset database to obtain associated matching data, and the associated matching data is imported into the information summary and evaluation unit; the face data capture unit calls the video data in the interview information collection unit, and after determining the face position in the video image, records the facial expression information, and imports the expression information into the information summary and evaluation unit; the information summary and evaluation unit forms the interview comprehensive table information according to the feedback information of the text association unit and the face data capture unit, and establishes a score interval, and sets corresponding job profile supplementary data in each score interval; the contact guidance unit calls the job profile supplementary data in the corresponding score interval, and while displaying the job profile supplementary data, sends the job profile supplementary information to the mobile terminal pre-entered by the user.

2. An interview practice system based on AI technology and digital virtuality as claimed in claim 1, characterized in that: The basic information of the positions in the position information asset database is divided into position professional information, position general information and basic general information. Values ​​are assigned to the position professional information, position general information and basic general information respectively according to the position classification. When associating with the text in the text association unit, the keywords in the basic information of the position are extracted, and the words similar to the keywords are formed into phrases through the AI ​​algorithm for synchronous assignment. In addition, the length of the keywords and the assignment score are adjusted according to the characteristics of the position professional information, position general information and basic general information.

3. An interview practice system based on AI technology and digital virtuality as claimed in claim 2, characterized in that: The basic general information establishes a corresponding virtual interview scene according to the job type, and after calling the data in the job professional information and the job general information, the interviewer's question information is pre-entered, a virtual interviewer is generated through AI, and the virtual interviewer is entered into the virtual interview scene, and the user enters the virtual interview scene for an interview.

4. The interview practice system based on AI technology and digital virtuality as claimed in claim 1, characterized in that: The interview information collection unit sets the interview process according to the job classification and establishes a communication node before the interview. The interviewer enters the virtual interview meeting room through the channel node, and the interview information collection unit collects real-time audio and video data in the virtual interview meeting room.

5. The interview practice system based on AI technology and digital virtuality as claimed in claim 1, characterized in that: After the text association unit processes the audio data into text, it associates the acquired text information with the basic information of the position pre-entered in the position classification, then performs approximate phrase matching based on the text information and the basic information, obtains the overlap data of the text information and the basic information, and generates associated matching data based on the overlap.

6. The interview practice system based on AI technology and digital virtuality as claimed in claim 1, characterized in that: The face data capture unit recognizes the face in the video image, and uses the center point of the face as the center of the dot matrix to perform dot matrix analysis on the recognized face image, performs expression analysis on the face information, determines auxiliary information by analyzing the expression, sorts the data of the auxiliary information by scores, and imports the corresponding score data into the information summary and evaluation unit.

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