Cognitive evaluation system and method based on enterprise training

Abstract

The invention discloses a cognitive evaluation system and method based on enterprise training, and the system comprises a training unit which is used for displaying the content of a corresponding training course through a display screen, and enabling employees to carry out the interaction and feedback through an interaction interface; the data acquisition unit is used for acquiring physiological data and behavior data of the employees in the training process in real time through a plurality of sensor modules arranged in the working environment of the employees; the analysis unit is used for analyzing the physiological data and the behavior data to obtain a corresponding cognitive analysis report, and the cognitive analysis report comprises the cognitive ability state, the training effect and the health condition of the employee; and the feedback and adjustment unit is used for dynamically adjusting the training course content or progress based on the cognitive analysis report and assisting an enterprise in performing performance evaluation and management on the employees. The learning condition and the health condition of the employees are comprehensively known, data support is provided for performance evaluation and talent management, and enterprises are helped to carry out personnel management and development planning more scientifically.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise training, and in particular to a cognitive assessment system and method based on enterprise training. Background Art

[0002] With the rapid development of the global economy and increasingly fierce competition, workplace stress has become one of the main challenges faced by modern employees. The high-intensity work pace, complex interpersonal relationships and ever-changing work environment have made employees' cognitive function and mental health problems increasingly prominent. These problems not only affect employees' personal happiness, but also directly affect work efficiency and the overall performance of the company.

[0003] Currently, there are a variety of cognitive health management tools on the market, such as mental health assessment software, stress test applications, and cognitive ability training games. At the same time, many companies have also implemented various training systems to improve the professional skills and comprehensive quality of employees. These trainings usually include online courses, offline seminars, team building activities, etc., covering all levels from basic skills to advanced management.

[0004] However, most of these cognitive health management tools and corporate training systems are independent of each other and lack effective integration. Specifically: different systems collect and store data separately, resulting in the inability to share and comprehensively analyze information, making it difficult to form a comprehensive understanding of employees' cognitive health and training status; due to the lack of a unified management platform, companies need to invest more resources to maintain multiple systems, including human, material and financial resources; administrators need to switch between multiple platforms and cannot monitor and evaluate employees' cognitive health status and training effects in real time, affecting the timeliness and effectiveness of decision-making; existing training systems often lack consideration of employees' individual cognitive abilities and mental health conditions, and cannot provide targeted training programs; existing tools make it difficult to timely detect cognitive decline or mental health risks that employees may face, and cannot provide early intervention.

[0005] Therefore, there is an urgent need for a cognitive assessment system and method based on enterprise training. Summary of the invention

[0006] The present invention provides a cognitive assessment system and method based on enterprise training to solve the above problems existing in the prior art.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] The cognitive assessment system based on enterprise training is characterized by including:

[0009] The training unit is used to display the corresponding training course content through the display screen, and employees interact and give feedback through the interactive interface;

[0010] A data collection unit is used to collect the physiological and behavioral data of employees during the training process in real time through multiple sensor modules deployed in the employees' working environment;

[0011] An analysis unit, used to analyze physiological data and behavioral data to obtain a corresponding cognitive analysis report, wherein the cognitive analysis report includes the employee's cognitive ability status, training effect, and health status;

[0012] The feedback and adjustment unit is used to dynamically adjust the content or progress of training courses based on cognitive analysis reports, assisting enterprises in performance evaluation and management of employees.

[0013] The data acquisition unit includes:

[0014] A physiological data collection module is used to collect the physiological data of employees through a physiological data sensor module, and the physiological data includes heart rate, blood pressure and skin electrical response;

[0015] The behavior data collection module is used to collect employee behavior data through behavior data sensors. The behavior data includes participation, interaction frequency, response time, employee movements, postures and expressions.

[0016] The analysis units include:

[0017] The cognitive assessment module is used to assess employees’ attention, memory, and logical thinking abilities based on their behavioral data, thereby deriving their cognitive status;

[0018] The training effect analysis module is used to analyze the learning efficiency of employees at a specific training stage and determine the effectiveness of the training based on their cognitive ability status;

[0019] Health status analysis module, which is used to evaluate employees' health status based on their physiological data, including cardiovascular health, stress and fatigue;

[0020] The data evaluation module is used to comprehensively analyze employees' cognitive evaluation results, training effects and health status to generate an overall employee evaluation report.

[0021] Among them, the feedback and adjustment unit includes:

[0022] The health adjustment module is used to provide employees with regular cognitive health assessments and feedback through an interactive interface to help them improve their work status;

[0023] The performance management module is used to match the overall performance evaluation results with the company's internal performance standards and determine the employee's performance evaluation level.

[0024] The corresponding training course content is displayed on the display screen, including:

[0025] The control display shows the initial training course content and outputs the initial interactive test from the first course module;

[0026] Determine the employee's response results regarding the current interactive test, including correct response results and incorrect response results;

[0027] Determine and output a plurality of new interactive tests, and determine a response result for each new interactive test until the number of consecutive new incorrect response results reaches a preset number threshold, or the current interactive test is determined to be the last test;

[0028] Controlling the display screen to display another set of training course content for the employee, returning to execute the initial interactive test output from the first course module until the continuous number of new incorrect response results reaches a preset number threshold, or the current interactive test is determined to be the last test;

[0029] Generate a list based on each reaction result and output it;

[0030] Determine employee responses to current interactive testing, including the following steps:

[0031] Get the employee's response audio and reaction time for the current interactive test. The reaction time is the time between the output of the current interactive test and the acquisition of the response.

[0032] Determine the response result of the current interactive test based on the response audio, the response result including a correct response result and an incorrect response result;

[0033] If the response result is a correct response result, determining a first score of the correct response result based on the response time and the response audio;

[0034] If the reaction result is an erroneous reaction result, the second score of the erroneous reaction result is determined to be 0.

[0035] Among them, the cognitive ability status of employees is obtained, including:

[0036] Extract employee behavioral data at key points in the training course; insert short cognitive tests, including memory tasks and attention tests, at key points in the training course, including the beginning, middle and end of the course, and record employee performance data in these tests;

[0037] Obtain a preset cognitive ability assessment model, and match the behavioral data with the assessment criteria of attention, memory, and logical thinking ability in the preset cognitive ability assessment model; if the match is met, use the matched behavioral data as valid cognitive behavioral data, and at the same time, obtain the cognitive ability indicators corresponding to the matched cognitive ability assessment criteria, and associate them with the employees;

[0038] Obtain the historical training records corresponding to the effective cognitive behavior data, and at the same time, obtain the specific time when the effective cognitive behavior data was generated;

[0039] Determine from the historical training database a plurality of relevant training behavior data corresponding to the historical training records and generated within a preset time period before and after the generation time;

[0040] Input all relevant training behavior data into the preset AI cognitive ability impact analysis model, obtain at least one cognitive ability status indicator output by the impact analysis model, and at the same time, accumulate and calculate the cognitive ability indicators associated with the employees to obtain the comprehensive cognitive ability status;

[0041] If the comprehensive cognitive ability status is lower than the preset first cognitive ability status threshold, the employee's cognitive ability status assessment fails;

[0042] Otherwise, a preset employee performance record library is obtained, and a plurality of performance records corresponding to the employee are determined from the employee performance record library;

[0043] Input all performance records into the preset cognitive ability importance analysis model to obtain the cognitive ability importance output by the importance analysis model;

[0044] Obtaining a second cognitive ability state threshold corresponding to the cognitive ability importance;

[0045] If the overall cognitive ability status is below the second cognitive ability status threshold, the employee’s cognitive ability status assessment has failed;

[0046] Otherwise, the employee's cognitive ability status is assessed as passed.

[0047] Among them, assessing the health status of employees includes:

[0048] Obtain employees' historical work data, including work hours, workload, task type, and task frequency;

[0049] Build a health status assessment model based on physiological and work data, including cardiovascular health, stress level and fatigue;

[0050] Generate an individual employee health status assessment result set based on the employee's historical physiological data and combined with the health status assessment model;

[0051] Determine health risk point data based on the assessment result set and preset health assessment thresholds;

[0052] Obtain real-time monitoring results of employees’ physiological data and compare and analyze them with the assessment result set to identify health risk points;

[0053] Based on the health risk point data, adjust the employees' work task arrangements and generate individualized health intervention recommendations to improve the lower limit of their health status.

[0054] Among them, cognitive assessment methods based on corporate training include:

[0055] S101: The corresponding training course content is displayed on the display screen, and employees interact and give feedback through the interactive interface;

[0056] S102: collecting physiological data and behavioral data of employees during training in real time through multiple sensor modules deployed in the employees' working environment;

[0057] S103: Analyze the physiological data and behavioral data to obtain a corresponding cognitive analysis report, wherein the cognitive analysis report includes the employee's cognitive ability status, training effect, and health status;

[0058] S104: Based on cognitive analysis reports, dynamically adjust the content or progress of training courses to assist enterprises in performance evaluation and management of employees.

[0059] Among them, real-time collection of employees’ physiological and behavioral data during training includes:

[0060] Collect employees' physiological data through physiological data sensor modules, including heart rate, blood pressure and skin electrical response;

[0061] Employees’ behavioral data are collected through behavioral data sensors, and the behavioral data includes employee movements, postures, and expressions.

[0062] Among them, obtaining the corresponding cognitive analysis report includes:

[0063] Based on the employee's behavioral data, the employee's attention, memory and logical thinking ability are evaluated to obtain the employee's cognitive ability status;

[0064] Analyze employees’ learning efficiency at a specific training stage based on their cognitive ability status and determine the effectiveness of the training;

[0065] Assess employee health status based on their physiological data, including cardiovascular health, stress and fatigue;

[0066] Comprehensively analyze employees' cognitive assessment results, training effects and health status to generate an overall employee assessment report.

[0067] Compared with the prior art, the present invention has the following advantages:

[0068] A cognitive assessment system based on enterprise training includes: a training unit, which is used to display the corresponding training course content through a display screen, and employees interact and provide feedback through an interactive interface; a data collection unit, which is used to collect the physiological data and behavioral data of employees in the training process in real time through multiple sensor modules arranged in the employee's work environment; an analysis unit, which is used to analyze the physiological data and behavioral data to obtain the corresponding cognitive analysis report, wherein the cognitive analysis report includes the employee's cognitive ability status, training effect and health status; a feedback and adjustment unit, which is used to dynamically adjust the training course content or progress based on the cognitive analysis report, and assist the enterprise in performance evaluation and management of employees. It can fully understand the learning status and health status of employees, provide data support for performance evaluation and talent management, and help enterprises to conduct personnel management and development planning more scientifically.

[0069] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the present invention.

[0070] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0072] Figure 1 is a structural diagram of a cognitive assessment system based on enterprise training in an embodiment of the present invention;

[0073] Figure 2 A structural diagram of a data acquisition unit in an embodiment of the present invention;

[0074] Figure 3 4 is a structural diagram of an analysis unit in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0076] The embodiment of the present invention provides a cognitive assessment system based on enterprise training, including:

[0077] The training unit is used to display the corresponding training course content through the display screen, and employees interact and give feedback through the interactive interface;

[0078] A data collection unit is used to collect the physiological and behavioral data of employees during the training process in real time through multiple sensor modules deployed in the employees' working environment;

[0079] An analysis unit, used to analyze physiological data and behavioral data to obtain a corresponding cognitive analysis report, wherein the cognitive analysis report includes the employee's cognitive ability status, training effect, and health status;

[0080] The feedback and adjustment unit is used to dynamically adjust the content or progress of training courses based on cognitive analysis reports, assisting enterprises in performance evaluation and management of employees.

[0081] The working principle of the above technical solution is as follows: Training unit: The enterprise sets up a special training classroom for employees, which is equipped with a large-screen display and interactive devices (such as touch screens, gesture recognition devices, etc.). After entering the classroom, employees log in to the system through their personal accounts. The system will automatically match the corresponding training courses according to the employees' positions and ability levels. Employees can view course content, participate in tests and simulation training through the interactive interface. Data collection unit: Multiple sensor modules are deployed in the training classroom and the employees' working environment. These sensors include physiological sensors (such as heart rate, skin electrodermal activity, eye trackers, etc.) and behavioral sensors (such as cameras, motion capture devices, etc.). When employees are training, the sensors will collect employees' physiological data (such as heart rate, stress level) and behavioral data (such as concentration, action reaction speed) in real time. Analysis unit: The collected data will be transmitted to the central data processing system. The system will analyze the physiological and behavioral data of employees through built-in algorithms. For example, the system can analyze whether employees are focused when watching training videos, whether the reaction time in simulated training meets the standards, etc. Finally, the system will generate a detailed cognitive analysis report, which includes the employee's cognitive ability status (such as learning concentration, comprehension ability), training effect (such as knowledge mastery, skill improvement level) and health status (such as stress index, fatigue level). Feedback and adjustment unit: Based on the analysis report, the system will automatically adjust the training content and progress. If the report shows that the employee's comprehension ability in a certain module is weak, the system will arrange more related exercises or adjust the explanation method. If the employee's health is not good (such as excessive fatigue), the system will recommend suspending training or adjusting the training intensity. In addition, management can evaluate the performance of employees based on these reports and use them as a reference in the annual assessment.

[0082] The beneficial effects of the above technical solutions are as follows: the system can dynamically adjust the training content and methods by real-time collection and analysis of employees' cognitive status and training effects, ensuring that each employee can learn in the most suitable way and greatly improving training efficiency; traditional training models are often one-size-fits-all, while the intelligent training system can provide personalized training plans based on the different ability levels and health conditions of employees, ensuring that each employee can get the most suitable learning experience; by monitoring employees' physiological data, such as stress index and fatigue level, the system can promptly remind employees to adjust their work and rest schedules to avoid work mistakes caused by excessive fatigue, thereby improving employees' health awareness and work safety; management can use the cognitive analysis reports generated by the system to fully understand employees' learning status and health status, provide data support for performance evaluation and talent management, and help companies to conduct personnel management and development planning more scientifically; by improving training efficiency, reducing training time, and reducing employee health risks, the company's training costs can be significantly reduced in the long run.

[0083] In another embodiment, the data acquisition unit comprises:

[0084] A physiological data collection module is used to collect the physiological data of employees through a physiological data sensor module, and the physiological data includes heart rate, blood pressure and skin electrical response;

[0085] The behavior data collection module is used to collect employee behavior data through behavior data sensors. The behavior data includes participation, interaction frequency, response time, employee movements, postures and expressions.

[0086] Among them, the behavioral data of employees is collected through behavioral data sensors, including:

[0087] Obtain a preset behavioral data sensor library, and determine from the sensor library multiple sensor types for collecting employee behavioral data, the behavioral data including participation, interaction frequency, response time, employee movements, postures, and expressions;

[0088] Use camera sensors to capture employee engagement during training or task execution, including whether employees stay focused, actively participate in discussions or ask questions;

[0089] The interaction frequency of employees is obtained through the interaction recording system. The interaction frequency is evaluated by recording the number and frequency of interactions between employees and the system, colleagues or superiors to judge the initiative and collaboration ability of employees;

[0090] After the task is arranged, the response time of the employees is obtained. The response time is measured by recording the time interval from when the employees receive the instruction to when they take action.

[0091] Use motion capture equipment to monitor employees' working postures and obtain their motion and posture data to determine whether they have used the correct motions to complete their work tasks;

[0092] The facial recognition system and camera sensors are used to obtain employees’ facial expression data, which is used to analyze employees’ emotional changes in different tasks or training situations.

[0093] Obtain a preset behavior data feature library, and match and analyze the collected employee behavior data with the standard features in the behavior data feature library;

[0094] If the match is met, obtain the evaluation value corresponding to the matching behavioral characteristics and associate it with the employee's behavioral performance;

[0095] The evaluation values ​​associated with the employees are accumulated and calculated to obtain a comprehensive evaluation score;

[0096] If the comprehensive evaluation score is greater than or equal to the preset evaluation threshold, the employee's performance in training or task execution is determined to be positive;

[0097] Otherwise, obtain improvement suggestions corresponding to the comprehensive evaluation score and generate corresponding training or guidance plans.

[0098] When all the behavioral data that need to be improved are identified, the remaining behavioral data are used as the final evaluation data;

[0099] All final evaluation data are input into the preset cognitive evaluation model to obtain the final evaluation result output by the cognitive evaluation model.

[0100] The working principle of the above technical solution is as follows: Physiological data acquisition module. During the training, each employee wore a smart bracelet with multiple built-in physiological data sensors. These sensors can continuously collect employees' heart rate, blood pressure and skin galvanic response. Heart rate: The system monitors the employee's heart rate in real time through the photoelectric volume pulse wave sensor in the bracelet. If the employee's heart rate is abnormal (such as too fast or too slow), the system will automatically record and issue a health reminder. Blood pressure: The micro blood pressure sensor in the bracelet can measure the employee's blood pressure and determine whether the employee is in a stressful working state. When the blood pressure rises to a certain threshold, the system will prompt the employee to stop working and take a rest. Skin galvanic response: The skin galvanic response sensor measures the changes in the conductivity of the employee's skin to assess their emotional state, such as anxiety, tension or relaxation. These data can reflect the psychological state of employees when facing pressure and help adjust their work arrangements or rest time.

[0101] Multiple cameras and motion capture devices are also deployed to monitor employee behavior in real time. The system collects data on employee participation, interaction frequency, response time, movements, postures, and expressions.

[0102] Participation: The camera captures whether the employee is involved in the training or task execution process, such as whether they stay focused, whether they actively participate in discussions or ask questions. Interaction frequency: By recording the number and frequency of interactions between employees and the system, colleagues or superiors, the system can evaluate the employee's initiative and collaboration ability. Response time: After the task is arranged, the system will record the employee's response time from receiving the instruction to taking action. If the response time is too long, it may indicate that the employee is tired or inefficient. Movement and posture: Motion capture equipment monitors the employee's working posture to determine whether the employee uses the correct movement to complete the work (such as whether the posture when carrying goods meets safety regulations) to avoid occupational diseases or injuries caused by improper posture. Expression: Through the facial recognition system, the camera can capture the changes in employees' expressions, identify stress, tension, fatigue or other emotions, and then judge the employee's mental state.

[0103] All of this data will be aggregated into the system, and after data analysis, a report on the employee's physiological status and behavioral performance will be generated. Managers can make corresponding decisions about employee health and performance based on these reports.

[0104] The beneficial effects of the above technical solution are as follows: by real-time monitoring of heart rate, blood pressure and skin galvanic response, enterprises can timely discover health risks of employees. For example, if the system finds that the heart rate and blood pressure of employees are too high, the system will remind employees to rest to avoid health problems caused by overwork or stress. This continuous health monitoring helps reduce the risk of occupational diseases of employees and promote the long-term health of employees; the collection and analysis of behavioral data can help enterprises optimize the work process of employees. For example, the system can identify employees with slow response or low efficiency by monitoring the response time and interaction frequency of employees, and provide additional training or support to these employees in a targeted manner, thereby improving overall work efficiency; by monitoring the movements and postures of employees, the system can timely discover work movements that do not meet the standards, prevent employees from causing work injuries due to improper postures when carrying goods or performing other operations, and employees can adjust their movements according to the feedback of the system, thereby reducing the risk of occupational diseases caused by muscle strain or long-term poor posture; the comprehensive analysis report generated by the system provides management with a large amount of valuable data, which not only reflects the health status of employees, but also reveals their work attitude and performance. Managers can make more accurate decisions based on these data, such as optimizing work allocation, adjusting work rhythm, and even conducting performance evaluation and reward and punishment measures. This data-based management method can significantly improve employee management efficiency; by analyzing employees' galvanic skin response, facial expressions and behavioral data, the system can help companies understand the emotional state of employees. If it is found that employees are under long-term stress or anxiety, the company can provide psychological support or adjust work intensity in a timely manner, thereby creating a healthier working environment and reducing psychological problems caused by work pressure.

[0105] In another embodiment, the analyzing unit comprises:

[0106] The cognitive assessment module is used to assess employees’ attention, memory, and logical thinking abilities based on their behavioral data, thereby deriving their cognitive status;

[0107] The training effect analysis module is used to analyze the learning efficiency of employees at a specific training stage and determine the effectiveness of the training based on their cognitive ability status;

[0108] Health status analysis module, which is used to evaluate employees' health status based on their physiological data, including cardiovascular health, stress and fatigue;

[0109] The data evaluation module is used to comprehensively analyze employees' cognitive evaluation results, training effects and health status to generate an overall employee evaluation report.

[0110] The working principle of the above technical solution is as follows: In daily work, the behavior data of employees are recorded by the behavior data collection module. These data include employee participation, response time, movements and expressions. The cognitive assessment module uses these data to assess employees' attention, memory and logical thinking ability.

[0111] Attention: By analyzing the employee's concentration level during task execution (such as long-term concentration, frequent attention shifts), the system can evaluate the employee's attention level. Memory: The system evaluates the employee's memory through the employee's performance in repeated tasks (such as memory and application of previous tasks). Logical thinking ability: By analyzing the employee's steps and decision-making process when solving problems, the system evaluates their logical thinking ability.

[0112] After an employee attends a training course, the system will analyze the employee's learning efficiency in the training based on the cognitive ability status provided by the cognitive assessment module. Learning efficiency: By comparing the changes in employees' cognitive abilities before and after training, the system can judge the effectiveness of the training. For example, if an employee's logical thinking ability improves significantly after training, it means that the training content is effective in improving this ability. Training effectiveness: The system combines employee participation and feedback to evaluate the overall effect of the training course and provide data support for future training plans.

[0113] The physiological data collection devices worn by employees continuously monitor their heart rate, blood pressure and galvanic skin response. The health status analysis module uses this data to evaluate the health status of employees; through long-term monitoring of heart rate and blood pressure data, the system evaluates the cardiovascular health status of employees and identifies potential health risks; through galvanic skin response and heart rate variability analysis, the system can evaluate employees' stress levels and fatigue, helping companies make adjustments in work arrangements; the data evaluation module conducts a comprehensive analysis of cognitive assessment results, training effects and health status data to generate an overall employee assessment report; the system integrates data from each module to provide a comprehensive assessment of employees' cognitive abilities, training effects and health status; the generated report provides management with detailed employee status information to help them make more accurate management decisions.

[0114] The beneficial effects of the above technical solution are as follows: through the cognitive assessment module, enterprises can identify employees' cognitive shortcomings and provide targeted training and support. This personalized intervention helps to improve employees' overall cognitive ability, work efficiency and innovation ability. The training effect analysis module helps enterprises evaluate the effectiveness of training courses. By analyzing employees' performance and cognitive ability changes in training, enterprises can optimize training content and methods to ensure efficient use of training resources. The real-time health monitoring data provided by the health status analysis module helps enterprises identify employees' health risks in a timely manner. Enterprises can adjust work arrangements based on these data, provide health support, reduce employees' health risks, and improve employees' job satisfaction and loyalty. The overall assessment report generated by the data evaluation module provides management with comprehensive employee status information. This comprehensive analysis helps enterprises make more accurate decisions in employee management, such as employee promotion, job transfer and performance evaluation. By fully understanding employees' cognitive ability, training effect and health status, enterprises can allocate human resources more effectively, improve the overall performance and innovation ability of the team, and thus enhance the market competitiveness of enterprises.

[0115] In another embodiment, the feedback and adjustment unit comprises:

[0116] The health adjustment module is used to provide employees with regular cognitive health assessments and feedback through an interactive interface to help them improve their work status;

[0117] The performance management module is used to match the overall performance evaluation results with the company's internal performance standards and determine the employee's performance evaluation level.

[0118] Among them, determining the employee's performance evaluation level includes:

[0119] Compare the employee's overall performance with the performance standards set within the company;

[0120] When an employee's overall performance is higher than the corresponding performance standard, the employee's performance is judged to be excellent, and the employee's specific performance in each performance dimension is obtained. The performance data of the relevant dimensions are merged to obtain the comprehensive performance indicators of each dimension;

[0121] Compare the comprehensive performance indicators of the employee in each dimension to determine the employee's performance distribution ratio. Based on the performance distribution ratio and internal enterprise data, adjust the employee's current performance evaluation level to generate a real-time performance evaluation strategy.

[0122] Among them, overall performance includes completion rate, quality indicators, efficiency indicators and innovation capabilities;

[0123] When an employee's overall performance is less than or equal to the corresponding performance standard, the employee's performance is considered normal;

[0124] Among them, based on the performance distribution ratio and internal enterprise data, the current performance evaluation level of the employee is adjusted to generate a real-time performance evaluation strategy, including:

[0125] Based on the performance distribution ratio, determine the relatively concentrated dimensions and secondary concentrated dimensions of performance;

[0126] Obtain the specific indicators of the employee in the first performance dimension and the second performance dimension, and predict the completion time of the employee in the first performance dimension and the second performance dimension respectively according to the average completion speed of each performance dimension;

[0127] Obtaining a first growth rate of the performance indicator corresponding to the relatively concentrated dimension, and determining the performance growth amount of the employee in the first performance dimension based on the first growth rate, combined with the weights of each specific indicator corresponding to the relatively concentrated dimension, the waiting time of the first performance dimension, and the preset completion speed corresponding to the current cycle;

[0128] According to the first performance increase, the first completion time, the first performance flashing time and the start-up error of the adjacent performance cycles, the first performance evaluation level extension time corresponding to the relatively concentrated dimension is obtained;

[0129] At the same time, the second growth rate of the performance indicator corresponding to the secondary concentrated dimension is obtained, and based on the second growth rate, combined with the weights of each specific indicator corresponding to the secondary concentrated dimension, the waiting time of the second performance dimension and the preset completion speed corresponding to the current cycle, the performance growth amount of the employee in the second performance dimension is determined;

[0130] According to the second performance increase, the second completion time, the second performance flashing time and the start-up error of the adjacent performance cycles, the second performance evaluation level extension time corresponding to the secondary concentration dimension is obtained;

[0131] The evaluation time corresponding to the current performance evaluation level of the employee is adjusted based on the first performance evaluation level extension time and the second performance evaluation level extension time to generate a real-time performance evaluation strategy.

[0132] The working principle of the above technical solution is: the health adjustment module provides employees with regular cognitive health assessment and feedback through a friendly interactive interface, helping employees understand and improve their work status. The operation process is as follows:

[0133] Regular assessment: The system reminds employees to conduct cognitive health assessments through an interactive interface at regular intervals, including cognitive health indicators such as stress levels, fatigue, and attention.

[0134] Feedback provision: Based on the evaluation results, the system generates a detailed feedback report for employees, including the current cognitive health status and potential problems. If it is found that the employee is under too much stress or feels very tired, the system will prompt the employee to take a break, adjust the work rhythm, etc.

[0135] Personalized suggestions: Based on the nature of the employee's work and health status, the system provides personalized improvement suggestions, such as short meditation, physical exercise, or adjusting work schedules. Employees can use this module to regularly track their own improvements.

[0136] For example, if an employee finds that his stress level is high after completing a regular assessment, the health adjustment module will recommend that he meditate for a few short periods of time in the next few days or appropriately reduce the arrangement of high-intensity tasks. The system may also continue to track changes in his health status during the next assessment and adjust the recommendations.

[0137] The performance management module helps determine the employee's performance evaluation level by matching the employee's overall performance evaluation results with the company's internal performance standards. The operation process is as follows:

[0138] Data collection: The performance management module first collects key performance data (KPIs) of each department and employee, such as task completion volume, quality, and time management ability.

[0139] Performance matching: The system compares the overall performance of employees with the performance standards set by the company. These standards may include the expected completion rate and quality indicators of employees at different levels.

[0140] Evaluation grade determination: The system automatically generates an employee's performance evaluation grade, such as "excellent", "qualified" or "needs improvement", based on the match between the employee's performance and the performance standards.

[0141] Personalized feedback: The system also provides personalized feedback to each employee, pointing out areas where they excel and areas that need improvement, helping employees to clarify the direction of improvement in future work.

[0142] For example, a salesperson's KPI shows that he has achieved 120% of his annual performance target and has high customer satisfaction. The performance management module compares this data with the company's "excellent" standard, determines that the employee's performance level is "excellent", and generates reward and promotion recommendations for him.

[0143] Among them, completion rate: the proportion of employees completing scheduled tasks (for example, Task A is planned to be completed 100%, and actually completed 90%). Quality indicators: the quality of tasks or work output (for example, product defect rate or report accuracy). Efficiency indicators: work output per unit time (for example, work-hour output or response time). Innovation ability: the number and influence of new ideas, new methods or optimization suggestions proposed by employees. These data will be compared with the performance standards set by the company. If the overall performance of an employee is higher than the standard, the employee is considered to have excellent performance and enters the further analysis stage.

[0144] If an employee's performance exceeds the standard, we will collect the employee's specific performance data in various performance dimensions (such as the completion rate, quality, efficiency, etc. mentioned above). These data will be combined into comprehensive performance indicators. For example: combine the employee's completion rate, quality, efficiency and other dimensions to obtain a comprehensive score for each dimension. By comparing the comprehensive indicators of each dimension, the system can determine the performance distribution ratio of the employee in each dimension. Simply put, the performance distribution ratio reflects in which aspects the employee performs well and in which aspects he needs to improve.

[0145] Based on the employee's performance distribution ratio and the company's internal data, the system will make real-time adjustments to the employee's current performance evaluation level. Specifically: Relatively concentrated dimensions: refers to the two performance dimensions in which the employee performs the best. For example, if an employee performs particularly well in efficiency and quality, these two dimensions are considered "concentrated dimensions." Sub-concentrated dimensions: refers to the dimension in which the employee performs relatively moderately, which is innovation ability. Based on the performance of these dimensions, the system will predict the employee's future performance. For example: Predicted completion time: Based on historical data, the system can predict the employee's task completion time in each dimension. For example, if an employee is good at the "efficiency" dimension, the system will predict how much work he can complete in the next task cycle based on his average completion speed.

[0146] Performance growth rate: The growth rate of each dimension refers to the speed at which an employee's performance improves over a period of time. For example, if an employee's completion rate has increased from 80% to 90% in the past month, his growth rate is 10%. Extension time: If an employee performs well in a certain dimension, the system will calculate the extension time of the employee's evaluation level based on factors such as the predicted growth rate, waiting time (idle time before the task starts), and preset completion speed. This means that excellent performance in a certain dimension may keep the employee's performance evaluation level at a higher level for a longer time. Assuming that an employee's performance in the efficiency dimension is very outstanding and the system predicts that his performance growth will be large, the system will extend the effective time of his performance evaluation level so that he can still maintain a high performance level in the next cycle.

[0147] Based on the calculation results of the above process, the system will adjust the employee's performance evaluation level by combining the first performance dimension (such as efficiency) and the second performance dimension (such as quality). In this step, the system takes into account the extended time of different dimensions and makes the final adjustment to the employee's current evaluation level.

[0148] Preliminary evaluation of employee training: By analyzing the initial performance of employees in different training modules (such as technical training, management training, innovation capability improvement, etc.), evaluate which aspects have outstanding performance and which aspects need to be improved. This is similar to the "performance distribution ratio" in the system. Real-time adjustment of learning paths: If an employee performs well in technical training but performs averagely in innovation capability improvement, the system will customize a training path for him to enhance his innovation capability based on his training progress and comprehensive score. Predict learning effects: By analyzing each employee's learning speed and results (such as course completion speed, depth of understanding, etc.), the system predicts the employee's final performance in a specific area and adjusts his training plan or learning progress accordingly.

[0149] The beneficial effects of the above technical solution are as follows: the performance management module ensures the fairness and transparency of performance evaluation through an automated evaluation process. Automatically matching employee performance data with corporate standards reduces the deviation of manual evaluation, so that each employee's performance can be evaluated fairly and objectively; employees receive clear feedback and performance levels through the performance management module, and can see their position in the organization. This transparent performance evaluation process motivates employees to work towards clear goals and drives them to continuously improve, thereby improving work motivation and overall team productivity.

[0150] In another embodiment, the corresponding training course content is displayed on a display screen, including:

[0151] The control display shows the initial training course content and outputs the initial interactive test from the first course module;

[0152] Determine the employee's response results regarding the current interactive test, including correct response results and incorrect response results;

[0153] Determine and output a plurality of new interactive tests, and determine a response result for each new interactive test until the number of consecutive new incorrect response results reaches a preset number threshold, or the current interactive test is determined to be the last test;

[0154] Controlling the display screen to display another set of training course content for the employee, returning to execute the initial interactive test output from the first course module until the continuous number of new incorrect response results reaches a preset number threshold, or the current interactive test is determined to be the last test;

[0155] Generate a list based on each reaction result and output it;

[0156] Determine employee responses to current interactive testing, including the following steps:

[0157] Get the employee's response audio and reaction time for the current interactive test. The reaction time is the time between the output of the current interactive test and the acquisition of the response.

[0158] Determine the response result of the current interactive test based on the response audio, the response result including a correct response result and an incorrect response result;

[0159] If the response result is a correct response result, determining a first score of the correct response result based on the response time and the response audio;

[0160] If the reaction result is an erroneous reaction result, the second score of the erroneous reaction result is determined to be 0.

[0161] The working principle of the above technical solution is as follows: Initial training course content display and interactive test output: The system first displays the content of the first course module on the display screen and outputs the initial interactive test. This interactive test may be a multiple-choice question or a question-and-answer question, and the employee needs to answer it by voice.

[0162] For example, during a safety training course, the display screen will show the relevant content of "How to use protective equipment correctly" and then give an interactive test question: "Which protective equipment must be worn when entering a hazardous area?".

[0163] To determine the employee's response results, the system obtains the employee's voice response to the interactive test and records the response time, that is, the time from the question output to the employee's response. The system uses speech recognition technology to analyze the response audio and determine whether the response is correct or incorrect.

[0164] For example, when an employee answers "hard hat" by voice, the system immediately analyzes whether the answer matches the standard answer and calculates the time from when the question is output to when the answer is completed.

[0165] Scores are determined based on the response results. Correct response results: If the system recognizes that the employee's answer is correct, it will calculate a score based on the response time and the accuracy of the response. The shorter the response time, the higher the score may be. For example, if the employee gives the correct answer "hard hat" within 5 seconds, the system will assign the first score based on the time. Incorrect response results: If the employee's answer is incorrect, the system will set the score of the test to 0 as the second score. For example, if the employee answers incorrectly, such as "protective gloves", the system will mark the answer as incorrect and record a score of 0.

[0166] Output and termination conditions of continuous interactive tests. The system will decide whether to continue giving new interactive tests based on the employee's performance until one of the following termination conditions is met: The number of consecutive incorrect answers by the employee reaches a preset threshold (for example, three consecutive errors). All tests in the current course module have been completed. For example, after an employee has given several correct answers, if he or she gives two consecutive incorrect answers, the system will continue testing until the number of errors reaches three, at which point the system will terminate the current round of testing and move on to the next set of training courses.

[0167] Display the next set of training course content. If the test is completed, the system will display another set of training content and output a new interactive test. The reaction process is the same as above until the termination condition is reached again. Generate and output a reaction result list. The system will generate a reaction result list based on the results of each test, including each employee's answer, reaction time, correct or incorrect situation, and score. This list will be automatically exported for management or employees to review.

[0168] The beneficial effects of the above technical solution are: through interactive testing, employees can maintain a high degree of participation in the training process, avoiding purely passive learning. The real-time feedback mechanism of the test enables employees to correct mistakes in time and strengthen their understanding during the learning process, thereby improving the training effect; the system quickly feeds back employees' test results, so that employees can immediately understand where they are deficient. This instant feedback helps them quickly adjust their learning strategies and improve learning effects. Especially in the case of continuous errors, the system can provide prompts in time to prevent employees from training under wrong cognition for a long time.

[0169] In another embodiment, obtaining the cognitive ability status of an employee includes:

[0170] Extract employee behavioral data at key points in the training course; insert short cognitive tests, including memory tasks and attention tests, at key points in the training course, including the beginning, middle and end of the course, and record employee performance data in these tests;

[0171] Obtain a preset cognitive ability assessment model, and match the behavioral data with the assessment criteria of attention, memory, and logical thinking ability in the preset cognitive ability assessment model; if the match is met, use the matched behavioral data as valid cognitive behavioral data, and at the same time, obtain the cognitive ability indicators corresponding to the matched cognitive ability assessment criteria, and associate them with the employees;

[0172] Obtain the historical training records corresponding to the effective cognitive behavior data, and at the same time, obtain the specific time when the effective cognitive behavior data was generated;

[0173] Determine from the historical training database a plurality of relevant training behavior data corresponding to the historical training records and generated within a preset time period before and after the generation time;

[0174] Input all relevant training behavior data into the preset AI cognitive ability impact analysis model, obtain at least one cognitive ability status indicator output by the impact analysis model, and at the same time, accumulate and calculate the cognitive ability indicators associated with the employees to obtain the comprehensive cognitive ability status;

[0175] If the comprehensive cognitive ability status is lower than the preset first cognitive ability status threshold, the employee's cognitive ability status assessment fails;

[0176] Otherwise, a preset employee performance record library is obtained, and a plurality of performance records corresponding to the employee are determined from the employee performance record library;

[0177] Input all performance records into the preset cognitive ability importance analysis model to obtain the cognitive ability importance output by the importance analysis model;

[0178] Obtaining a second cognitive ability state threshold corresponding to the cognitive ability importance;

[0179] If the overall cognitive ability status is below the second cognitive ability status threshold, the employee’s cognitive ability status assessment has failed;

[0180] Otherwise, the employee's cognitive ability status is assessed as passed.

[0181] The working principle of the above technical solution is as follows: Extracting behavioral data at key nodes: In the online training courses provided by the company, multiple key nodes are set, including the beginning, middle and end of the course. These nodes are not only important nodes of learning progress, but also short cognitive tests are inserted, including memory tasks, attention tests, etc. The system automatically records the performance of employees in these tests, including data such as accuracy, reaction time and error type.

[0182] Matching cognitive ability assessment model: The company has preset a cognitive ability assessment model, which assesses employees' attention, memory and logical thinking ability. The system matches the employee's behavioral data at key points of the course with the assessment criteria in the model. If it meets the preset criteria, the behavioral data is considered valid cognitive behavioral data.

[0183] Obtaining historical training records: After an employee's cognitive behavior data is determined to be valid, the system automatically queries the employee's historical training records, such as similar courses attended in the past and their performance. At the same time, the system also records the specific time points when the employee generated these valid cognitive behavior data.

[0184] Analyze relevant training behavior data: The system extracts multiple relevant behavior data generated by the employee in the current and historical training process in a specific time period before and after the effective cognitive behavior moment from the historical training database, and inputs these data into the AI ​​cognitive ability impact analysis model. The model evaluates at least one cognitive ability status indicator based on these behavior data.

[0185] Calculate comprehensive cognitive ability status: The system adds the employee's current cognitive ability status with the cognitive ability status indicators obtained in previous training to calculate a comprehensive cognitive ability status value. If this value is lower than the preset first cognitive ability status threshold, the system will prompt that the employee's cognitive ability status assessment has failed.

[0186] Combined with the performance evaluation model: If the comprehensive cognitive ability status passes the first evaluation, the system will further query the employee's historical performance records and input them into the preset cognitive ability importance analysis model to calculate the impact of each cognitive ability on the employee's work performance, and finally output a cognitive ability importance index.

[0187] Evaluate cognitive ability status: The system obtains the second cognitive ability status threshold corresponding to the cognitive ability importance index. If the comprehensive cognitive ability status is lower than this threshold, the system will consider that the employee's cognitive ability does not meet the job requirements and the evaluation has failed. Otherwise, the employee's cognitive ability status evaluation has passed.

[0188] The beneficial effects of the above technical solution are: by inserting cognitive tests at key training nodes and combining with the preset cognitive ability assessment model, the system can accurately assess employees' attention, memory and logical thinking ability. This helps companies identify which employees have insufficient cognitive ability in specific courses so that personalized training strategies can be adopted; the system not only evaluates cognitive performance in the current training process, but also combines historical training records and data to ensure that employees' cognitive ability development can be dynamically tracked. This can help companies understand employees' learning effects in the long term and optimize training content; by combining cognitive ability status with employees' work performance, the system can better determine the extent to which certain cognitive abilities affect work performance. Companies can formulate reasonable improvement plans for employees based on these analysis results to improve work efficiency; if the system detects that the employee's cognitive ability status is below the threshold, the company can intervene in advance to prevent employees from affecting their work performance due to insufficient cognitive ability. This early warning mechanism can effectively reduce employee errors or inefficiencies at work.

[0189] In another embodiment, assessing the health status of an employee includes:

[0190] Obtain employees' historical work data, including work hours, workload, task type, and task frequency;

[0191] Build a health status assessment model based on physiological and work data, including cardiovascular health, stress level and fatigue;

[0192] Generate an individual employee health status assessment result set based on the employee's historical physiological data and combined with the health status assessment model;

[0193] Determine health risk point data based on the assessment result set and preset health assessment thresholds;

[0194] Obtain real-time monitoring results of employees’ physiological data and compare and analyze them with the assessment result set to identify health risk points;

[0195] Based on the health risk point data, adjust the employees' work task arrangements and generate individualized health intervention recommendations to improve the lower limit of their health status.

[0196] The working principle of the above technical solution is: firstly, in the enterprise management system, the historical work data of employees is collected. Such data usually includes the following:

[0197] Working hours: For example, the number of working hours an employee has worked every day in the past month (for example, 8 hours / day). Workload: Records the intensity of an employee's work every week or every day, which may involve the number of projects handled or the complexity of the tasks. Task type: The nature of different tasks, which may be mental work (such as writing code, writing reports) or physical work (such as equipment operation). Task frequency: Reflects the frequency of tasks, such as how many reports are processed or how many production operations are completed every day. Example: An employee's work data shows that he has worked 50 hours per week in the past three months. His workload is to handle high-intensity tasks, and the weekly task frequency is 50 times.

[0198] Collect employees' physiological data, such as heart rate, blood pressure, sleep duration, and stress level. These data can be obtained through smart bracelets or other physiological monitoring devices. Combined with work data, a health status assessment model is established through machine learning or expert systems. The model mainly evaluates the following health indicators: Cardiovascular health: Evaluate the state of the cardiovascular system based on data such as blood pressure and heart rate. Stress level: Infer the stress level of employees through data such as heart rate changes and skin electrical response. Fatigue: Evaluate the fatigue level of employees by combining sleep duration, rest time, and continuous working time. The physiological data analysis of an employee shows that he works long hours, has high stress, and has a high heart rate. The model evaluates that his cardiovascular health is poor and he feels more fatigued. Generate a health status assessment result set for each employee through the health assessment model. For example, a comprehensive assessment report on an employee's cardiovascular health, stress level, and fatigue is generated based on the data of an employee in the past year. Each assessment result may contain a detailed health status score and be compared with industry standards or the employee's normal status. For example, an employee’s health assessment result showed that his stress level was 85 points (out of a total of 100 points, indicating excessive stress) and his cardiovascular health was 60 points (out of a total of 100 points, indicating poor health).

[0199] According to the preset health assessment thresholds, determine which employees' health conditions are beyond the safe range. For example, if the threshold for stress level is set to 70 points and the threshold for cardiovascular health status is set to 65 points, any employee who exceeds these thresholds may be at health risk. Example: The assessment results of the above employee show that his stress level is 85 points and his cardiovascular health status is 60 points, both of which exceed the health assessment thresholds, and the employee is determined to have health risk points. Continuously obtain the real-time monitoring results of the employee's physiological data and compare them with the historical health assessment results. If the real-time data is found to be abnormal (such as a sudden increase in heart rate, blood pressure, etc.), identify new health risk points in a timely manner. Example: In a real-time monitoring, the employee's heart rate is 20% higher than the average level. Combined with the assessment results, it is believed that the employee is experiencing excessive stress and has cardiovascular health risks. Adjust the work task arrangement of the employee according to the health risk points of the employee. For example, reduce working hours, reduce task load, or change task type (for example, from high-intensity tasks to easy tasks). At the same time, make individualized health intervention suggestions based on health risk points, such as increasing rest time, psychological counseling or exercise suggestions. Example: Based on an employee’s health risk points, it is recommended that he reduce his working hours by 1 hour per day, regularly meditate and relax, and switch to a job with less frequent tasks.

[0200] The beneficial effect of the above technical solution is that by collecting and analyzing employees' work and physiological data, the health status of employees can be continuously monitored. Before problems occur, the system will issue early warnings based on data analysis to help management identify potential health risks in a timely manner and reduce health damage caused by overwork or stress.

[0201] In another embodiment, a cognitive assessment method based on enterprise training includes:

[0202] S101: The corresponding training course content is displayed on the display screen, and employees interact and give feedback through the interactive interface;

[0203] S102: collecting physiological data and behavioral data of employees during training in real time through multiple sensor modules deployed in the employees' working environment;

[0204] S103: Analyze the physiological data and behavioral data to obtain a corresponding cognitive analysis report, wherein the cognitive analysis report includes the employee's cognitive ability status, training effect, and health status;

[0205] S104: Based on cognitive analysis reports, dynamically adjust the content or progress of training courses to assist enterprises in performance evaluation and management of employees.

[0206] The working principle of the above technical solution is as follows: employees participate in training courses through an interactive display screen. This display screen not only displays the training content, but also allows employees to interact and provide feedback. For example, in a sales skills training course, employees can answer questions, participate in simulated sales conversations, and get instant feedback through the touch screen. At the same time, multiple sensor modules are deployed in the employee's work environment, which collect employees' physiological data (such as heart rate, skin galvanic response) and behavioral data (such as eye movement, mouse click frequency) in real time. For example, when employees are watching training videos, eye movement sensors can record their concentration, while heart rate sensors can detect their emotional reactions. After these data are collected, they are processed by the data analysis system to generate a cognitive analysis report. The report describes in detail the employee's cognitive ability status (such as attention level, information processing speed), training effect (such as knowledge mastery, skill improvement) and health status (such as stress level, fatigue level). Based on these analysis reports, the training system can dynamically adjust the course content or progress. For example, if an employee shows a high level of stress in a module, the system may recommend increasing rest time or adjusting the course difficulty. In addition, these data can also be used for corporate performance evaluation and management to help management understand employees' learning progress and health status.

[0207] The beneficial effects of the above technical solutions are: by identifying employees' attention and understanding levels, training content can be communicated more effectively, reducing unnecessary repetition and time waste; real-time monitoring of employees' physiological status helps companies to promptly identify and respond to employees' health problems and promote employees' physical and mental health; through detailed cognitive analysis reports, companies can better conduct performance evaluation and human resource management, and make smarter decisions based on data.

[0208] In another embodiment, the physiological data and behavioral data of employees during training are collected in real time, including:

[0209] Collect employees' physiological data through physiological data sensor modules, including heart rate, blood pressure and skin electrical response;

[0210] Employees’ behavioral data are collected through behavioral data sensors, and the behavioral data includes employee movements, postures, and expressions.

[0211] The working principle of the above technical solution is as follows: During the training, each employee wears a smart bracelet with multiple built-in physiological data sensors. These sensors can continuously collect employees' heart rate, blood pressure and skin galvanic response. Heart rate: The system monitors the employee's heart rate in real time through the photoelectric volume pulse wave sensor in the bracelet. If the employee's heart rate is abnormal (such as too fast or too slow), the system will automatically record and issue a health reminder. Blood pressure: The micro blood pressure sensor in the bracelet can measure the employee's blood pressure and determine whether the employee is in a stressful working state. When the blood pressure rises to a certain threshold, the system will prompt the employee to stop working and take a rest. Skin galvanic response: The skin galvanic response sensor measures the changes in the conductivity of the employee's skin to assess their emotional state, such as anxiety, tension or relaxation. These data can reflect the psychological state of employees when facing pressure and help adjust their work arrangements or rest time.

[0212] Multiple cameras and motion capture devices are also deployed to monitor employee behavior in real time. The system collects data on employee participation, interaction frequency, response time, movements, postures, and expressions.

[0213] Participation: The camera captures whether the employee is involved in the training or task execution process, such as whether they stay focused, whether they actively participate in discussions or ask questions. Interaction frequency: By recording the number and frequency of interactions between employees and the system, colleagues or superiors, the system can evaluate the employee's initiative and collaboration ability. Response time: After the task is arranged, the system will record the employee's response time from receiving the instruction to taking action. If the response time is too long, it may indicate that the employee is tired or inefficient. Movement and posture: Motion capture equipment monitors the employee's working posture to determine whether the employee uses the correct movement to complete the work (such as whether the posture when carrying goods meets safety regulations) to avoid occupational diseases or injuries caused by improper posture. Expression: Through the facial recognition system, the camera can capture the changes in employees' expressions, identify stress, tension, fatigue or other emotions, and then judge the employee's mental state.

[0214] All of this data will be aggregated into the system, and after data analysis, a report on the employee's physiological status and behavioral performance will be generated. Managers can make corresponding decisions about employee health and performance based on these reports.

[0215] The beneficial effects of the above technical solution are as follows: by real-time monitoring of heart rate, blood pressure and skin galvanic response, enterprises can timely discover health risks of employees. For example, if the system finds that the heart rate and blood pressure of employees are too high, the system will remind employees to rest to avoid health problems caused by overwork or stress. This continuous health monitoring helps reduce the risk of occupational diseases of employees and promote the long-term health of employees; the collection and analysis of behavioral data can help enterprises optimize the work process of employees. For example, the system can identify employees with slow response or low efficiency by monitoring the response time and interaction frequency of employees, and provide additional training or support to these employees in a targeted manner, thereby improving overall work efficiency; by monitoring the movements and postures of employees, the system can timely discover work movements that do not meet the standards, prevent employees from causing work injuries due to improper postures when carrying goods or performing other operations, and employees can adjust their movements according to the feedback of the system, thereby reducing the risk of occupational diseases caused by muscle strain or long-term poor posture; the comprehensive analysis report generated by the system provides management with a large amount of valuable data, which not only reflects the health status of employees, but also reveals their work attitude and performance. Managers can make more accurate decisions based on these data, such as optimizing work allocation, adjusting work rhythm, and even conducting performance evaluation and reward and punishment measures. This data-based management approach can significantly improve employee management efficiency.

[0216] In another embodiment, obtaining a corresponding cognitive analysis report includes:

[0217] Based on the employee's behavioral data, the employee's attention, memory and logical thinking ability are evaluated to obtain the employee's cognitive ability status;

[0218] Analyze employees’ learning efficiency at a specific training stage based on their cognitive ability status and determine the effectiveness of the training;

[0219] Assess employee health status based on their physiological data, including cardiovascular health, stress and fatigue;

[0220] Comprehensively analyze employees' cognitive assessment results, training effects and health status to generate an overall employee assessment report.

[0221] The working principle of the above technical solution is as follows: In daily work, the behavior data of employees are recorded by the behavior data collection module. These data include employee participation, response time, movements and expressions. The cognitive assessment module uses these data to assess employees' attention, memory and logical thinking ability.

[0222] Attention: By analyzing the degree of concentration of employees in task execution (such as long periods of concentration, frequent attention shifts), the system can assess the employee's attention level.

[0223] Memory: The system assesses an employee’s memory by how well they perform on repeated tasks, such as remembering and applying previous tasks.

[0224] Logical thinking ability: Systematically evaluate employees' logical thinking ability by analyzing their steps and decision-making process when solving problems.

[0225] After an employee attends a training course, the system will analyze the employee's learning efficiency in the training based on the cognitive ability status provided by the cognitive assessment module. Learning efficiency: By comparing the changes in employees' cognitive abilities before and after training, the system can judge the effectiveness of the training. For example, if an employee's logical thinking ability improves significantly after training, it means that the training content is effective in improving this ability. Training effectiveness: The system combines employee participation and feedback to evaluate the overall effect of the training course and provide data support for future training plans.

[0226] The physiological data collection devices worn by employees continuously monitor their heart rate, blood pressure and galvanic skin response. The health status analysis module uses this data to evaluate the health status of employees; through long-term monitoring of heart rate and blood pressure data, the system evaluates the cardiovascular health status of employees and identifies potential health risks; through galvanic skin response and heart rate variability analysis, the system can evaluate employees' stress levels and fatigue, helping companies make adjustments in work arrangements; the data evaluation module conducts a comprehensive analysis of cognitive assessment results, training effects and health status data to generate an overall employee assessment report; the system integrates data from each module to provide a comprehensive assessment of employees' cognitive abilities, training effects and health status; the generated report provides management with detailed employee status information to help them make more accurate management decisions.

[0227] The beneficial effects of the above technical solutions are as follows: With the cognitive assessment module, enterprises can identify employees' cognitive shortcomings and provide targeted training and support. This personalized intervention helps to improve employees' overall cognitive ability, work efficiency and innovation. The training effect analysis module helps enterprises evaluate the effectiveness of training courses. By analyzing employees' performance and cognitive changes in training, enterprises can optimize training content and methods to ensure efficient use of training resources. The real-time health monitoring data provided by the health status analysis module helps enterprises identify employees' health risks in a timely manner. Enterprises can adjust work arrangements based on these data, provide health support, reduce employees' health risks, and improve employees' job satisfaction and loyalty. The overall assessment report generated by the data evaluation module provides management with comprehensive employee status information. This comprehensive analysis helps enterprises make more accurate decisions on employee management, such as employee promotion, job transfer and performance evaluation.

[0228] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the same technology, the present invention is also intended to include these changes and variations.

Claims

1. A cognitive assessment system based on corporate training, characterized by: include: The training unit is used to display the corresponding training course content through the display screen, and employees interact and give feedback through the interactive interface; A data collection unit is used to collect the physiological and behavioral data of employees during the training process in real time through multiple sensor modules deployed in the employees' working environment; An analysis unit, used to analyze physiological data and behavioral data to obtain a corresponding cognitive analysis report, wherein the cognitive analysis report includes the employee's cognitive ability status, training effect, and health status; The feedback and adjustment unit is used to dynamically adjust the content or progress of training courses based on cognitive analysis reports, assisting enterprises in performance evaluation and management of employees.

2. The cognitive assessment system based on enterprise training according to claim 1, characterized in that: The data acquisition unit includes: A physiological data collection module is used to collect the physiological data of employees through a physiological data sensor module, and the physiological data includes heart rate, blood pressure and skin electrical response; The behavior data collection module is used to collect employee behavior data through behavior data sensors. The behavior data includes participation, interaction frequency, response time, employee movements, postures and expressions.

3. The cognitive assessment system based on enterprise training according to claim 1, characterized in that: The analysis units include: The cognitive assessment module is used to assess employees’ attention, memory, and logical thinking abilities based on their behavioral data, thereby deriving their cognitive status; The training effect analysis module is used to analyze the learning efficiency of employees at a specific training stage and determine the effectiveness of the training based on their cognitive ability status; Health status analysis module, which is used to evaluate employees' health status based on their physiological data, including cardiovascular health, stress and fatigue; The data evaluation module is used to comprehensively analyze employees' cognitive evaluation results, training effects and health status to generate an overall employee evaluation report.

4. The cognitive assessment system based on enterprise training according to claim 1, characterized in that: The feedback and adjustment unit consists of: The health adjustment module is used to provide employees with regular cognitive health assessments and feedback through an interactive interface to help them improve their work status; The performance management module is used to match the overall performance evaluation results with the company's internal performance standards and determine the employee's performance evaluation level.

5. The cognitive assessment system based on enterprise training according to claim 1, characterized in that: The corresponding training course content is displayed on the display screen, including: The control display shows the initial training course content and outputs the initial interactive test from the first course module; Determine the employee's response results regarding the current interactive test, including correct response results and incorrect response results; Determine and output a plurality of new interactive tests, and determine a response result for each new interactive test until the number of consecutive new incorrect response results reaches a preset number threshold, or the current interactive test is determined to be the last test; Controlling the display screen to display another set of training course content for the employee, returning to execute the initial interactive test output from the first course module until the continuous number of new incorrect response results reaches a preset number threshold, or the current interactive test is determined to be the last test; Generate a list based on each reaction result and output it; Determine employee responses to current interactive testing, including the following steps: Get the employee's response audio and reaction time for the current interactive test. The reaction time is the time between the output of the current interactive test and the acquisition of the response. Determine the response result of the current interactive test based on the response audio, the response result including a correct response result and an incorrect response result; If the response result is a correct response result, determining a first score of the correct response result based on the response time and the response audio; If the reaction result is an erroneous reaction result, the second score of the erroneous reaction result is determined to be 0.

6. The cognitive assessment system based on enterprise training according to claim 3, characterized in that: Derive the cognitive status of employees, including: Extract employee behavioral data at key points in the training course; insert short cognitive tests, including memory tasks and attention tests, at key points in the training course, including the beginning, middle and end of the course, and record employee performance data in these tests; Obtain a preset cognitive ability assessment model, and match the behavioral data with the assessment criteria of attention, memory, and logical thinking ability in the preset cognitive ability assessment model; if the match is met, use the matched behavioral data as valid cognitive behavioral data, and at the same time, obtain the cognitive ability indicators corresponding to the matched cognitive ability assessment criteria, and associate them with the employees; Obtain the historical training records corresponding to the effective cognitive behavior data, and at the same time, obtain the specific time when the effective cognitive behavior data was generated; Determine from the historical training database a plurality of relevant training behavior data corresponding to the historical training records and generated within a preset time period before and after the generation time; Input all relevant training behavior data into the preset AI cognitive ability impact analysis model, obtain at least one cognitive ability status indicator output by the impact analysis model, and at the same time, accumulate and calculate the cognitive ability indicators associated with the employees to obtain the comprehensive cognitive ability status; If the comprehensive cognitive ability status is lower than the preset first cognitive ability status threshold, the employee's cognitive ability status assessment fails; Otherwise, a preset employee performance record library is obtained, and a plurality of performance records corresponding to the employee are determined from the employee performance record library; Input all performance records into the preset cognitive ability importance analysis model to obtain the cognitive ability importance output by the importance analysis model; Obtaining a second cognitive ability state threshold corresponding to the cognitive ability importance; If the overall cognitive ability status is below the second cognitive ability status threshold, the employee’s cognitive ability status assessment has failed; Otherwise, the employee's cognitive ability status is assessed as passed.

7. The cognitive assessment system based on enterprise training according to claim 3, characterized in that: Assess employee health status, including: Obtain employees' historical work data, including work hours, workload, task type, and task frequency; Build a health status assessment model based on physiological and work data, including cardiovascular health, stress level and fatigue; Generate an individual employee health status assessment result set based on the employee's historical physiological data and combined with the health status assessment model; Determine health risk point data based on the assessment result set and preset health assessment thresholds; Obtain real-time monitoring results of employees’ physiological data and compare and analyze them with the assessment result set to identify health risk points; Based on the health risk point data, adjust the employees' work task arrangements and generate individualized health intervention recommendations to improve the lower limit of their health status.

8. A cognitive assessment method based on corporate training, characterized in that: include: S101: The corresponding training course content is displayed on the display screen, and employees interact and give feedback through the interactive interface; S102: collecting physiological data and behavioral data of employees during training in real time through multiple sensor modules deployed in the employees' working environment; S103: Analyze the physiological data and behavioral data to obtain a corresponding cognitive analysis report, wherein the cognitive analysis report includes the employee's cognitive ability status, training effect, and health status; S104: Based on cognitive analysis reports, dynamically adjust the content or progress of training courses to assist enterprises in performance evaluation and management of employees.

9. The cognitive assessment method based on enterprise training according to claim 8, characterized in that: Real-time collection of employees’ physiological and behavioral data during training, including: Collect employees' physiological data through physiological data sensor modules, including heart rate, blood pressure and skin electrical response; Employees’ behavioral data are collected through behavioral data sensors, and the behavioral data includes employee movements, postures, and expressions.

10. The cognitive assessment method based on enterprise training according to claim 8, characterized in that: Obtain the corresponding cognitive analysis report, including: Based on the employee's behavioral data, the employee's attention, memory and logical thinking ability are evaluated to obtain the employee's cognitive ability status; Analyze employees’ learning efficiency at a specific training stage based on their cognitive ability status and determine the effectiveness of the training; Assess employee health status based on their physiological data, including cardiovascular health, stress and fatigue; Comprehensively analyze employees' cognitive assessment results, training effects and health status to generate an overall employee assessment report.

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