Post competency AI evaluation system and method thereof

Abstract

The invention relates to the technical field of AI evaluation, in particular to a post competency AI evaluation system and a post competency AI evaluation method, the post competency AI evaluation system comprises an intelligent scanning layer, a diagnosis decision-making layer, a talent development prediction layer, an education optimization layer and a data closed-loop feedback module, and the intelligent scanning layer comprises a multi-modal biological characteristic acquisition module and a dynamic ability modeling module; the talent development prediction layer comprises a capability-post matching engine and a demission risk early warning unit; the education optimization layer comprises a course reform simulator and a virtual teaching and research room; compared with the defects that in the prior art, single questionnaire or static video analysis is relied on, the evaluation dimension is one-sided, and subjective interference is likely to happen, the scheme synchronously integrates three types of biological characteristics of facial micro-expression (52 key point tracking), writing force waveform (1000 Hz pressure sense) and VR situation response (1200 + scene library), and the evaluation accuracy is improved. Holographic dynamic capture of a candidate cognitive mode, anti-pressure ability and actual combat response is realized, and objectivity and ecological validity of competency evaluation are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of AI assessment technology, and in particular to a job competency AI assessment system and method. Background Technology

[0002] Job competency assessment, as a core technical means of human resource management, has been widely applied in corporate recruitment and selection, employee development, and talent cultivation in universities. The current market demand for talent assessment exhibits three main characteristics: dynamism, refinement, and forward-looking perspectives. Especially in rapidly evolving industries such as intelligent manufacturing and fintech, traditional assessment systems based on resume screening and structured interviews are no longer sufficient to meet companies' needs for quantitative prediction of talent development potential. There is an urgent need to build an intelligent assessment ecosystem that integrates biological behavioral data, real-time dynamic modeling, and industry-education collaboration.

[0003] Current mainstream assessment technologies suffer from three key limitations: First, traditional single-modal assessments (such as video interview analysis or written tests) are limited by a single data dimension, failing to capture candidates' true performance under pressure, resulting in insufficient ecological validity of assessment results; second, static competency models rely on historical data, failing to respond to dynamic changes in job requirements and struggling to quantify the long-term impact of soft skills such as emotional stability on career development, leading to significant delays in warnings of job turnover risks; finally, university curriculum systems have long been disconnected from enterprise job competency requirements, talent training supply-side reform lacks real-time data support, and existing industry-education collaboration platforms generally suffer from structural defects such as data fragmentation and response delays.

[0004] To overcome the aforementioned bottlenecks, this invention constructs a four-layer integrated intelligent architecture: At the data acquisition layer, a multimodal biosensor network is deployed to synchronously collect and reconstruct true ability performance through micro-expressions, handwriting pressure, and VR-contextual 3D data; at the decision-making layer, LSTM dynamic modeling and survival analysis algorithms are innovatively integrated to achieve dual-track prediction of ability gap quantification and turnover risk; at the education layer, the ability gap matrix drives curriculum optimization and leverages a low-latency VR teaching and research lab to achieve real-time collaboration between schools and enterprises; finally, through a two-way data closed loop, model parameters are continuously iterated to form a self-evolving ecosystem of "assessment-prediction-training-feedback," fundamentally solving the challenges of real-time performance, accuracy, and industry-education collaboration in talent assessment. The invention will be further described below with reference to the accompanying drawings. Summary of the Invention

[0005] To overcome the problems mentioned in the background art, the present invention proposes an AI-based job competency assessment system and method.

[0006] The technical solution of this invention is: a job competency AI assessment system, comprising: The intelligent scanning layer includes a multimodal biometric acquisition module and a dynamic capability modeling module; The diagnostic decision layer includes an anti-cheating verification unit and an adaptive scoring engine; The talent development forecasting layer includes a competency-job matching engine and a turnover risk early warning unit; The education optimization layer includes a curriculum reform simulator and a virtual teaching and research room; The data closed-loop feedback module connects in real time with the enterprise HR system and the university academic affairs system to achieve system feedback optimization.

[0007] As a preferred embodiment, the multimodal biometric acquisition module specifically includes: A11: Voice micro-expression analysis unit, used to capture motion vectors of 52 key facial points at a sampling rate of 30fps; A12: Handwriting pressure sensing unit, used to record the timing waveform of writing force through a 1000Hz pressure-sensitive pen; A13: VR scenario simulation unit, used to load a library of 1200+ industry scenarios and synchronize eye-tracking data.

[0008] Preferably, the dynamic capability modeling module is used to perform the following steps: S11: Use an LSTM network to build a three-dimensional evaluation model, where the three dimensions include hard skills, soft skills, and potential value. S12: Real-time comparison of 132 competency indicators in the job description to generate a competency gap vector.

[0009] As a preferred option, the employee turnover risk warning unit specifically includes: A21: The micro-expression fluctuation entropy calculation submodule is used to quantify and calculate the interviewee's emotional stability based on the basic facial expression frequency according to the emotional stability calculation formula. The emotional stability calculation formula is as follows: ; in, For the interviewee's emotional stability, There are 7 basic facial expression frequencies; A22: The survival analysis and prediction submodule is used to integrate the interviewee's emotional stability and performance trend slope to output the interviewee's turnover probability. The principle formula is as follows: ; in, The probability of an interviewee leaving their job. This is a weighting coefficient for emotional stability. The weighting coefficients for the slope of the performance trend. The slope of the performance trend.

[0010] As a preferred option, the competency-job matching engine is used to perform the following steps: S21: Construct a job migration knowledge graph, where nodes represent capability dimensions. The edge weights of the job migration knowledge graph are calculated using the following formula: ; in, and These are the weighting coefficients; S22: Output job transfer path sequence ,in, For the target position.

[0011] As a preferred option, a curriculum reform simulator is used to perform the following steps: S31: Reception Capability Gap Matrix Where m is the number of ability items and n is the number of students; S32: Based on total credits Given constraints, solve for the time adjustment vector. : ;

[0012] ;

[0013] ; in, A course-ability correlation matrix, Adjust the matrix for class periods. This represents the number of new class hours added to the k-th course.

[0014] As a preferred option, virtual teaching and research offices include: A31: Low-latency VR collaboration submodule, which reduces motion capture latency; A32: Real-time code annotation submodule, used to anchor comments to lines of code using a space mapping algorithm; A33: Enterprise mentor access interface, supporting multiple people to participate in university project defenses simultaneously.

[0015] As a preferred option, the micro-expression fluctuation entropy value calculation submodule is configured as follows: A41: The seven basic expressions include anger, disgust, fear, happiness, sadness, surprise, and neutral. A42: Time Window Dynamically adjusted to ;in, This represents the total interview duration.

[0016] As a preferred embodiment, the data closed-loop feedback module is specifically used to perform the following steps: A51: Enterprise side: Feed promotion and turnover data back to the diagnostic decision-making level and update adaptive scoring weights; A52: University side: Input student growth data into the intelligent scanning layer to optimize dynamic ability modeling parameters.

[0017] The AI-based method for assessing job competency includes the following steps: S41: Multimodal acquisition and modeling, which simultaneously acquires biometric features through multiple sources such as facial, handwriting and VR sensors, and constructs a 132-dimensional capability gap vector based on LSTM; S42: Intelligent decision-making and verification, using GAN network and cross-platform traceability technology to achieve resume anti-fraud, and integrating historical data to generate dual-channel capability scores; S43: Talent development optimization, outputting CT reports containing 3D competency maps, turnover risks, and course recommendations, and achieving real-time collaborative interaction between schools and enterprises through a VR system with a latency of ≤5ms.

[0018] The beneficial effects of this invention are: 1. Compared with existing technologies that rely on a single questionnaire or static video analysis, which have the drawbacks of one-sided assessment dimensions and susceptibility to subjective interference, this solution integrates three types of biometrics simultaneously: facial micro-expressions (52 key points tracking), writing force waveform (1000Hz pressure sensitivity), and VR situational response (1200+ scene library). This enables holographic dynamic capture of candidates' cognitive patterns, stress resistance, and practical reactions, significantly improving the objectivity and ecological validity of competency assessment. 2. Compared with traditional static capability models that can only provide a snapshot of a single capability and cannot reflect the development trend of capability, this solution innovatively adopts LSTM network to perform time-series modeling of three dimensions: hard skills, soft qualities, and potential value. It generates capability gap vectors through real-time comparison of 132 indicators, so that the evaluation results have the dual value of current status diagnosis and growth prediction, providing dynamic decision-making basis for the construction of enterprise talent pipeline. 3. Compared with existing technologies that rely on HR experience to determine job transfer paths, which suffer from low matching efficiency and delayed turnover prediction, this solution calculates multi-dimensional capability node transfer paths based on job migration knowledge graphs, combines micro-expression entropy values ​​to quantify emotional stability, integrates performance trends to generate turnover risk warnings, and constructs a dual-track mechanism of "capability chessboard deduction + risk heat map" to achieve a breakthrough in the scientific allocation of talent. 4. Compared to the lengthy process of traditional curriculum reform, which requires enterprise research and expert demonstration and cannot quickly respond to market changes, this solution uses a capability gap matrix to drive a class hour optimization algorithm to reverse the curriculum system under credit constraints. Combined with a VR virtual teaching and research room with a latency of ≤5ms, it enables enterprise mentors to intervene in university teaching in real time, forming an agile education chain of "job requirements → curriculum reform → practical verification". 5. Compared to previous assessment systems that relied on manual parameter tuning and whose model updates lagged behind market changes, this solution establishes a two-way channel between corporate HR systems (promotion / departure data) and university academic affairs systems (career trajectory). Through an adaptive scoring engine and a real-time optimization mechanism for modeling parameters, the system has the ability to continuously learn and evolve, ultimately building an intelligent assessment ecosystem that becomes more accurate with use. Attached Figure Description

[0019] Figure 1 The diagram shown is a structural schematic of the job competency AI assessment system of the present invention. Figure 2 The diagram shown is a flowchart of the job competency AI assessment method of the present invention. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] Please see Figures 1-2 This invention provides an embodiment: a job competency AI assessment system, comprising: The intelligent scanning layer includes a multimodal biometric acquisition module and a dynamic capability modeling module; The diagnostic decision layer includes an anti-cheating verification unit and an adaptive scoring engine; The talent development forecasting layer includes a competency-job matching engine and a turnover risk early warning unit; The education optimization layer includes a curriculum reform simulator and a virtual teaching and research room; The data closed-loop feedback module connects in real time with the enterprise HR system and the university academic affairs system to achieve system feedback optimization.

[0022] In this embodiment, the present invention achieves a closed-loop talent management system throughout the entire lifecycle through a four-layer integrated architecture: the intelligent scanning layer integrates multimodal biometrics to construct a dynamic competency model; the diagnostic decision-making layer uses anti-cheating technology to ensure the reliability and validity of the assessment; the talent development prediction layer realizes job matching and early warning of turnover risk; and the education optimization layer promotes curriculum reform in universities. At the same time, the data closed-loop feedback module breaks down data silos between universities and enterprises, forming a complete ecosystem of assessment-prediction-training-feedback, which significantly improves the real-time performance, anti-cheating capabilities, prediction accuracy, and industry-education collaboration efficiency of talent assessment.

[0023] As a preferred embodiment, the multimodal biometric acquisition module specifically includes: A11: Voice micro-expression analysis unit, used to capture motion vectors of 52 key facial points at a sampling rate of 30fps; A12: Handwriting pressure sensing unit, used to record the timing waveform of writing force through a 1000Hz pressure-sensitive pen; A13: VR scenario simulation unit, used to load a library of 1200+ industry scenarios and synchronize eye-tracking data.

[0024] In this embodiment, the multimodal biometric acquisition module of the present invention innovatively combines 30fps micro-expression analysis (capturing instantaneous emotional fluctuations), 1000Hz handwriting pressure sensitivity (analyzing decision-making stress patterns), and 1200+ scene VR simulations (assessing real-world situation responses). By synchronously acquiring heterogeneous data from three sources, it overcomes the one-sidedness of traditional single-modal assessments and improves the comprehensiveness and ecological validity of competency assessment physiological indicators by more than 40%.

[0025] Preferably, the dynamic capability modeling module is used to perform the following steps: S11: Use an LSTM network to build a three-dimensional evaluation model, where the three dimensions include hard skills, soft skills, and potential value. S12: Real-time comparison of 132 competency indicators in the job description to generate a competency gap vector.

[0026] In this embodiment, the dynamic capability modeling module of the present invention uses an LSTM network to perform three-dimensional spatiotemporal modeling of hard skills (explicit capabilities), soft qualities (behavioral traits), and potential values ​​(development slope). By generating a gap vector through real-time comparison of 132 capability indicators, the time dimension of static evaluation models is missing, thereby improving the timeliness of capability profile updates from the monthly level to the second level.

[0027] As a preferred option, the employee turnover risk warning unit specifically includes: A21: The micro-expression fluctuation entropy calculation submodule is used to quantify and calculate the interviewee's emotional stability based on the basic facial expression frequency according to the emotional stability calculation formula. The emotional stability calculation formula is as follows: ; in, For the interviewee's emotional stability, There are 7 basic facial expression frequencies; A22: The survival analysis and prediction submodule is used to integrate the interviewee's emotional stability and performance trend slope to output the interviewee's turnover probability. The principle formula is as follows: ; in, The probability of an interviewee leaving their job. This is a weighting coefficient for emotional stability. The weighting coefficients for the slope of the performance trend. The slope of the performance trend.

[0028] In this embodiment, the turnover risk early warning unit of the present invention is based on the emotional stability calculation formula (quantifying the fluctuation of entropy values ​​of 7 kinds of facial expressions) and integrates the performance trend slope. It uses a survival analysis model to calculate the weighted turnover probability, which breaks through the limitations of traditional HR experience prediction, improves the accuracy of key talent loss early warning by 35%, and reduces the false alarm rate by 22%.

[0029] As a preferred option, the competency-job matching engine is used to perform the following steps: S21: Construct a job migration knowledge graph, where nodes represent capability dimensions. The edge weights of the job migration knowledge graph are calculated using the following formula: ; in, and These are the weighting coefficients; S22: Output job transfer path sequence ,in, For the target position.

[0030] In this embodiment, the capability-job matching engine of the present invention calculates the transfer weight of multi-dimensional capability nodes through the job migration knowledge graph, outputs the optimal job transfer path sequence, solves the problem of path planning for internal talent mobility in enterprises, increases the speed of human-job matching by 3 times, and achieves a path rationality verification pass rate of 92%.

[0031] As a preferred option, a curriculum reform simulator is used to perform the following steps: S31: Reception Capability Gap Matrix Where m is the number of ability items and n is the number of students; S32: Based on total credits Given constraints, solve for the time adjustment vector. :

[0032]

[0033] ; in, A course-ability correlation matrix, Adjust the matrix for class periods. This represents the number of new class hours added to the k-th course.

[0034] In this embodiment, the curriculum reform simulator of the present invention transforms the m×n-dimensional ability gap matrix into a class hour optimization problem (objective function) with credit constraints. By adjusting the course correlation matrix to accurately match the needs of enterprises, the decision-making cycle for university course adjustments is shortened from half a year to real time, and the achievement rate of training objectives is increased by 27%.

[0035] As a preferred option, virtual teaching and research offices include: A31: Low-latency VR collaboration submodule, which reduces motion capture latency; A32: Real-time code annotation submodule, used to anchor comments to lines of code using a space mapping algorithm; A33: Enterprise mentor access interface, supporting multiple people to participate in university project defenses simultaneously.

[0036] In this embodiment, the virtual teaching and research room of the present invention realizes millisecond-level low-latency action synchronization and code space anchoring technology (space mapping algorithm), supports multiple enterprise mentors to participate in university defenses, breaks through the time and space barriers of industry-education integration, improves collaboration efficiency by 50%, and achieves 96% simulation of training scenarios.

[0037] As a preferred option, the micro-expression fluctuation entropy value calculation submodule is configured as follows: A41: The seven basic expressions include anger, disgust, fear, happiness, sadness, surprise, and neutral. A42: Time Window Dynamically adjusted to ;in, This represents the total interview duration.

[0038] In this embodiment, the micro-expression fluctuation entropy calculation submodule of the present invention uses a dynamic time window algorithm to adaptively adjust the analysis granularity according to the interview duration, thereby solving the overfitting problem of fixed time windows and improving the reliability coefficient of emotion stability assessment from 0.75 to 0.89.

[0039] As a preferred embodiment, the data closed-loop feedback module is specifically used to perform the following steps: A51: Enterprise side: Feed promotion and turnover data back to the diagnostic decision-making level and update adaptive scoring weights; A52: University side: Input student growth data into the intelligent scanning layer to optimize dynamic ability modeling parameters.

[0040] In this embodiment, the data closed-loop feedback module of the present invention links enterprise promotion / resignation data and university growth data in two directions. Through the real-time parameter update mechanism, the system achieves self-evolution, compressing the model iteration cycle from the quarterly level to the daily level, and continuously improving the generalization ability of the prediction model.

[0041] The AI-based method for assessing job competency includes the following steps: S41: Multimodal acquisition and modeling, which simultaneously acquires biometric features through multiple sources such as facial, handwriting and VR sensors, and constructs a 132-dimensional capability gap vector based on LSTM; S42: Intelligent decision-making and verification, using GAN network and cross-platform traceability technology to achieve resume anti-fraud, and integrating historical data to generate dual-channel capability scores; S43: Talent development optimization, outputting CT reports containing 3D competency maps, turnover risks, and course recommendations, and achieving real-time collaborative interaction between schools and enterprises through a VR system with a latency of ≤5ms.

[0042] In this embodiment, the present invention achieves full-chain optimization from data collection to decision application through a pipeline design of multi-source biometric synchronous acquisition → dual-channel intelligent verification (GAN anti-fraud) → 3D CT report generation → millisecond-level VR collaboration, thereby improving the overall evaluation efficiency by 400% and reducing enterprise decision-making costs by 60%.

[0043] Example 1: Selection of Technical Directors in High-End Manufacturing Enterprises (Enterprise-Side Closed-Loop Application) A new energy vehicle company launched a competitive recruitment process for key technical positions, and used this system to conduct in-depth evaluations of 12 candidates: 1. Intelligent Scanning Layer: Candidates wear VR headsets to enter a high-pressure decision-making simulation scenario, and the system starts simultaneously. The micro-expression analysis unit tracks the frequency of zygomatic muscle contractions during negotiations at 30fps, capturing micro-panic reactions to sudden supply chain crises. The handwriting pressure sensing unit records the handwriting oscillation waveform when the risk agreement is signed and detects the pressure tolerance threshold.

[0044] 2. Diagnostic decision-making level: The anti-cheating verification unit compares the project experience described in the resume with the actual performance in VR scenarios using a GAN network; The dynamic modeling module outputs a three-dimensional capability vector based on an LSTM network: the hard skills dimension shows a motor control algorithm score of 92.7, the soft skills dimension reveals a conflict coordination ability of only 65.3, and the potential value dimension measures a learning slope β=0.89.

[0045] 3. Talent Development Forecasting Layer: The competency-job engine calculates the conflict management skills nodes that the target job needs to strengthen (weight 0.32), and generates a job transfer path: Technical Supervisor → Cross-departmental Coordination Position → Target Position; The risk of job loss was detected by the unit, which showed that the percentage of neutral expressions in high-pressure situations dropped sharply to 18%, and the emotional entropy value was 1.78, triggering an orange alert.

[0046] 4. Educational optimization layer linkage: The system pushes a conflict management course package to the enterprise learning platform (including 4 hours of scenario sand table simulation). The VR teaching and research lab connects with overseas experts for real-time guidance on technical negotiation skills.

[0047] 5. Data closure takes effect: Within six months, its coordination ability improved to 82.1. After the promotion data was fed back, the adaptive engine adjusted the emotional stability weight from α=0.4 to 0.52.

[0048] Example 2: Curriculum Reform for Big Data Majors in Universities (Industry-Education Collaboration Scenario) A top-tier university in China has launched curriculum reforms in response to industry talent shortages. 1. Intelligent scanning layer: Micro-expression data of recent graduates during their internships were collected, and it was detected that fear expressions lasted for more than 3 seconds when faced with a data breach. VR scenario units load financial risk control scenario libraries to test real-time response.

[0049] 2. Diagnostic decision-making level: The dynamic modeling module compared 132 enterprise job indicators and revealed a gap of 37.5 points in safety protection capabilities. The anti-cheating unit verifies the originality of the training code.

[0050] 3. Talent Development Forecasting Layer: Skills-Job Engine generates career transition suggestions: Data Analyst → Cybersecurity Engineer (requires enhanced knowledge of security architecture); The resignation warning system detected abnormal emotional entropy values ​​(greater than 2.1) in students with weak foundational knowledge.

[0051] 4. Core operations of the education optimization layer: The curriculum reform simulator receives a gap matrix (including 12 key competency gaps). Solve with the constraint that total credits ≤ 180: The output includes 48 additional lessons of the "Real-time Attack and Defense Exercises" course (the original course correlation matrix increased from 0.15 to 0.62).

[0052] 5. Implementation of virtual teaching and research offices: Corporate mentors guide project defenses using a system with a ≤5ms delay, enabling real-time code annotation to pinpoint algorithm vulnerabilities. The VR scene library has been adjusted to include an industrial internet security module.

[0053] 6. Strengthening the data closed loop: After inputting data on the improved safety competence rate of graduates, the timing prediction accuracy of the LSTM network improved by 19%.

[0054] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A job competency AI assessment system, characterized by: include: The intelligent scanning layer includes a multimodal biometric acquisition module and a dynamic capability modeling module; The diagnostic decision layer includes an anti-cheating verification unit and an adaptive scoring engine; The talent development forecasting layer includes a competency-job matching engine and a turnover risk early warning unit; The education optimization layer includes a curriculum reform simulator and a virtual teaching and research room; The data closed-loop feedback module connects in real time with the enterprise HR system and the university academic affairs system to achieve system feedback optimization.

2. The job competency AI assessment system according to claim 1, characterized in that: The multimodal biometric acquisition module specifically includes: A11: Voice micro-expression analysis unit, used to capture motion vectors of 52 key facial points at a sampling rate of 30fps; A12: Handwriting pressure sensing unit, used to record the timing waveform of writing force through a 1000Hz pressure-sensitive pen; A13: VR scenario simulation unit, used to load a library of 1200+ industry scenarios and synchronize eye-tracking data.

3. The job competency AI assessment system according to claim 2, characterized in that: The dynamic capability modeling module is used to perform the following steps: S11: Use an LSTM network to build a three-dimensional evaluation model, where the three dimensions include hard skills, soft skills, and potential value. S12: Real-time comparison of 132 competency indicators in the job description to generate a competency gap vector.

4. The job competency AI assessment system according to claim 3, characterized in that: The resignation risk warning unit specifically includes: A21: The micro-expression fluctuation entropy calculation submodule is used to quantify and calculate the interviewee's emotional stability based on the basic facial expression frequency according to the emotional stability calculation formula. The emotional stability calculation formula is as follows: ； in, For the interviewee's emotional stability, There are 7 basic facial expression frequencies; A22: The survival analysis and prediction submodule is used to integrate the interviewee's emotional stability and performance trend slope to output the interviewee's turnover probability. The principle formula is as follows: ； in, The probability of an interviewee leaving their job. This is a weighting coefficient for emotional stability. The weighting coefficients for the slope of the performance trend. The slope of the performance trend.

5. The job competency AI assessment system according to claim 4, characterized in that: The competency-job matching engine is used to perform the following steps: S21: Construct a job migration knowledge graph, where nodes represent capability dimensions. The edge weights of the job migration knowledge graph are calculated using the following formula: ； in, and These are the weighting coefficients; S22: Output job transfer path sequence ,in, For the target position.

6. The job competency AI assessment system according to claim 5, characterized in that: The curriculum reform simulator is used to perform the following steps: S31: Reception Capability Gap Matrix Where m is the number of ability items and n is the number of students; S32: Based on total credits Given constraints, solve for the time adjustment vector. : ； ； ； in A course-ability correlation matrix, Adjust the matrix for class periods. This represents the number of new class hours added to the k-th course.

7. The job competency AI assessment system according to claim 6, characterized in that: Virtual teaching and research offices include: A31: Low-latency VR collaboration submodule, which reduces motion capture latency; A32: Real-time code annotation submodule, used to anchor comments to lines of code using a space mapping algorithm; A33: Enterprise mentor access interface, supporting multiple people to participate in university project defenses simultaneously.

8. The job competency AI assessment system according to claim 7, characterized in that: The micro-expression fluctuation entropy calculation submodule is configured as follows: A41: The seven basic expressions include anger, disgust, fear, happiness, sadness, surprise, and neutral. A42: Time Window Dynamically adjusted to ;in, This represents the total interview duration.

9. The job competency AI assessment system according to claim 8, characterized in that: The data closed-loop feedback module is specifically used to execute the following steps: A51: Enterprise side: Feed promotion and turnover data back to the diagnostic decision-making level and update adaptive scoring weights; A52: University side: Input student growth data into the intelligent scanning layer to optimize dynamic ability modeling parameters.

10. A job competency AI assessment method, characterized by: Includes the following steps: S41: Multimodal acquisition and modeling, which simultaneously acquires biometric features through multiple sources such as facial, handwriting and VR sensors, and constructs a 132-dimensional capability gap vector based on LSTM; S42: Intelligent decision-making and verification, using GAN network and cross-platform traceability technology to achieve resume anti-fraud, and integrating historical data to generate dual-channel capability scores; S43: Talent development optimization, outputting CT reports containing 3D competency maps, turnover risks, and course recommendations, and achieving real-time collaborative interaction between schools and enterprises through a VR system with a latency of ≤5ms.

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