Employee cultivation system based on user portraits
Through the employee training system based on user portraits, the problems of personalization and incomplete data in traditional training management have been solved, dynamic adjustment of training plans and transparent management of the learning process have been achieved, and training efficiency and corporate talent team building have been improved.
Patent Information
- Application Number
- CN202510677781.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-23
- Publication Date
- 2025-09-05
AI Technical Summary
Traditional corporate employee training management relies on manual operations, lacks personalization, has scattered course resources, makes learning progress difficult to track, and has incomplete assessment data, resulting in delayed evaluation of training effectiveness and making it difficult to identify high-potential talents and formulate scientific promotion strategies.
The employee training system based on user portraits formulates personalized training plans through the plan generation module, dynamically adjusts the training plans through the supervision module, generates visual reports through the evaluation module, and establishes digital archives through the storage module, thus realizing the digitization of the entire training process and the visualization of the growth path.
It improves training efficiency, reduces management costs, realizes dynamic adjustment of training plans and transparent management of learning process, and promotes the construction of corporate talent echelon.
Smart Images
Figure CN120598737A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of employee training, and in particular to an employee training system based on user portraits. Background Art
[0002] Traditional enterprise employee training management relies heavily on manual processes, resulting in crude and unpersonalized training plans. Course resources are fragmented, making it difficult to track employee learning progress. Incomplete assessment data and feedback records result in delayed evaluation of training effectiveness. Furthermore, unclear career paths for employees prevent managers from quickly identifying high-potential talent and developing effective promotion and rotation strategies. Paper-based records are prone to loss, difficult to access, and inefficient cross-departmental collaboration.
[0003] There is an urgent need for a systematic solution to digitize the entire training process and visualize the growth path, so as to provide precise support for corporate talent development. Summary of the Invention
[0004] The purpose of the present invention is to formulate personalized training plans based on employee job requirements and career development goals, automatically allocate course resources and push them to employee terminals; employees complete course learning through mobile terminals or PC terminals, and the system collects learning progress, assessment results and feedback data in real time; after the training is completed, the system automatically generates a multi-dimensional training report, including skill mastery, growth trajectory and areas for improvement; after the report is reviewed by the direct superior, it is bound to the employee's career file and archived. Managers can use the system to view the team's training coverage, results analysis and talent reserve status in real time. It realizes the dynamic adjustment of training plans, transparent management of the learning process, and closed-loop tracking of growth paths, significantly improving training efficiency, reducing management costs, and helping enterprises build talent echelons.
[0005] To achieve the above objectives, the present invention provides an employee training system based on user portraits, comprising: Plan generation module, used to generate corresponding training plans based on employee user profiles; The plan supervision module is used to obtain the supervision data of employees during the execution of the training plan and generate employee behavior models based on the employee supervision data; It is also used to adjust training programs based on employees’ behavioral models; Evaluation module, used to generate visual reports based on the training results of employees; It is also used to include secondary adjustments to the training program based on visualization; Storage module: used to create corresponding employee digital files based on employee user portraits.
[0006] In some embodiments of the present invention, the plan generation module is further configured to: Develop multiple training routes based on employee job requirements and career development goals; Divide each training circuit into stages to generate multiple training sub-stages; Generate corresponding employee profiles based on the employee's personal attribute data type; Obtain the personal attribute data value of the employee corresponding to the current employee portrait to generate the employee plan reference value; Obtain the training sub-stages corresponding to historical employee plan reference values to generate employee training samples; Generate a corresponding training plan based on the user profile of the current employees and employee training samples; The generated training plan is sent to the corresponding employees for confirmation and employee confirmation information is generated; Combine employee confirmation information and training plan to send corresponding training plan courses to employees.
[0007] In some embodiments of the present invention, the generating of employee plan reference values further includes: The employee's personal attribute data value set is A, A={a1,a2…ai…an}; Where ai is the attribute value corresponding to the i-th personal attribute type, and n represents the total number of current personal attribute types; Generate employee plan reference value B based on the current employee's personal attribute data value set;
[0008] Among them, wi is the weight of the attribute value corresponding to the i-th personal attribute type, si is the fixed coefficient of the attribute value corresponding to the i-th personal attribute type, and si is used to convert ai into a value between 0 and 1.
[0009] In some embodiments of the present invention, the plan supervision module is further configured to: Obtaining data on the usage of employee training program courses; Categorize usage data to generate supervisory data on employees during the execution of training programs; Build employee behavior models based on their historical supervisory data; c, c={c1,c2…ci…cm}; Where ci is the reference value of the i-th type of historical regulatory data, and m represents the total number of historical regulatory data types; The employee's behavioral reference value m1 is:
[0010] Among them, wi is the weight of the reference value of the i-th historical regulatory data, ki is the fixed coefficient of the reference value of the i-th historical regulatory data, and ki is used to convert ci into a value between 0 and 1; Set multiple employee behavior thresholds based on the employee's behavior reference value m1; Generate a first impact value d (d1, d2, d3, d4) of the employee plan reference value B based on the employee behavior threshold; The employee training plan is modified based on the first impact value d and the employee plan reference value B.
[0011] In some embodiments of the present invention, the modifying of the employee training plan in combination with the first impact value d and the employee plan reference value B further includes: Determine the current first impact value d based on the employee behavior threshold interval in which the employee behavior reference value m1 is located; Combining the current first impact value d and the employee plan reference value B to generate a first revised value B1 of the employee plan reference value B; B1=B+d; A revised employee training plan is generated by combining the first correction value B1 and the employee training sample.
[0012] In some embodiments of the present invention, the evaluation module is further configured to: The training results of employees obtained include: skill test scores, instructor evaluations, and employee feedback information; Combine the skill test results and instructor evaluation to generate the training completion degree m2; m2=r1*e1+r2*e2; Among them, r1 is the proportional coefficient of skill test scores, r2 is the proportional coefficient of tutor evaluation, e1 is the reference value of skill test scores, and e2 is the reference value of tutor evaluation; Generate a list of improvement suggestions based on employee feedback; Generate visual reports combining training completion and improvement suggestion lists; Make secondary adjustments to the training plan based on the visual report.
[0013] In some embodiments of the present invention, generating a list of improvement suggestions based on employee feedback information further includes: Insert multiple comment nodes into employee training program courses and obtain employee feedback information based on the comment nodes; The employee feedback information includes the praise rating and the corresponding employee comment content; Categorize employee feedback information based on the degree of favorable comments and generate multiple levels of employee feedback information; And perform semantic analysis on employee comments of corresponding levels to generate the first comment set; The first review set includes several standard review contents; Compare the standard comments and employee comments in the employee feedback information of the current level in sequence to generate corresponding similarity reference values; Compare the similarity reference value with the similarity preset value, and count the number of employee comments whose similarity reference value is greater than the similarity preset value; The proportion of comments on each standard in employee feedback that generated the current rating; Generate a list of improvement suggestions by integrating the content of each standard comment and the corresponding ratio in the feedback information of employees of all levels; In some embodiments of the present invention, the improvement suggestion list further includes: The content of each standard comment in the feedback information of employees of all levels and the proportion of corresponding standard comments; A hyperlink is set for each standard review content, and all employee review contents corresponding to the current standard review content can be viewed based on the hyperlink.
[0014] In some embodiments of the present invention, when generating a visual report based on the training completion rate and the list of improvement suggestions, the process further includes: Get the training completion degree m2 of all employees corresponding to the current training plan and a list of improvement suggestions; Sort the list of recommendations by the percentage of comments on each criterion from largest to smallest. Generate visual reports including: the top t standard comments, the average training completion rate of all employees, and employee information list The employee information list includes: the employee information list is connected with the employee's personal training completion degree and personal improvement suggestion list.
[0015] In some embodiments of the present invention, the storage module is further configured to: Establish corresponding employee digital files based on employee user portraits; Employee digital files include: employee training results, employee feedback information, employee potential rankings, and employee skills matrix; Save employee digital files based on timeline; and building an employee network to connect members of the current employee's group; Switching employee digital profiles through the employee relationship network; Compared with the prior art, the employee training system based on user portraits provided by the embodiment of the present invention has the following advantages: The plan generation module generates training plans based on employee profiles. By comprehensively considering factors such as employee job requirements, career development goals, and personal attribute data types and values, it can customize highly personalized training plans for each employee.
[0016] This module can develop multiple training routes based on employee job requirements and career development goals, and divide the training into multiple sub-stages. This makes the employee training path clearer and more organized, and can gradually improve the employee's capabilities in different aspects and meet the employee's long-term career development needs.
[0017] A behavioral threshold is set based on the employee's behavioral reference value m1, and the first impact value d is generated according to different threshold intervals to modify the employee's training plan. The dynamic adjustment mechanism can adapt to the employee's actual performance during the training process in a timely manner, ensuring that the training plan always fits the employee's learning progress and needs.
[0018] The evaluation module generates visual reports based on employee training results and makes secondary adjustments to the training plan. This comprehensive assessment of training effectiveness and optimization of the training plan based on a list of improvement suggestions ensures continuous improvement and enhances overall training quality.
[0019] Saving employee digital files based on a timeline can fully record the employee's growth trajectory during the training process and provide comprehensive data support for their career development.
[0020] Generating a list of improvement suggestions can deeply explore useful information in employee feedback, which not only provides a basis for adjusting the training plan, but also helps to improve employee satisfaction with training because their feedback is effectively taken into account.
[0021] Visual reports can intuitively display training results and employee status, making it easier for managers to conduct comprehensive evaluations and make decisions.
[0022] The storage module builds an employee relationship network, connecting members of the employee's group to help promote collaboration and communication among employees. Employees can switch through the relationship network to view the digital files of other members, share training experiences and knowledge, and improve the overall capabilities and collaboration efficiency of the team. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] Figure 1 This is a structural diagram of an employee training system based on user portraits provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0024] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following embodiments are used to illustrate the present invention but are not intended to limit the scope of the present invention.
[0025] In the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.
[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.
[0027] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.
[0028] Example 1: The embodiment of the present invention provides an employee training system based on user portraits, such as Figure 1 Shown, including: Plan generation module, used to generate corresponding training plans based on employee user profiles; The plan supervision module is used to obtain the supervision data of employees during the execution of the training plan and generate employee behavior models based on the employee supervision data; It is also used to adjust training programs based on employees’ behavioral models; Evaluation module, used to generate visual reports based on the training results of employees; It is also used to include secondary adjustments to the training program based on visualization; Storage module: used to create corresponding employee digital files based on employee user portraits.
[0029] Embodiment 2: The plan generation module is further used for: Develop multiple training routes based on employee job requirements and career development goals; Divide each training circuit into stages to generate multiple training sub-stages; Generate corresponding employee profiles based on the employee's personal attribute data type; Obtain the personal attribute data value of the employee corresponding to the current employee portrait to generate the employee plan reference value; Obtain the training sub-stages corresponding to historical employee plan reference values to generate employee training samples; Generate a corresponding training plan based on the user profile of the current employees and employee training samples; The generated training plan is sent to the corresponding employees for confirmation and employee confirmation information is generated; Combine employee confirmation information and training plan to send corresponding training plan courses to employees.
[0030] Embodiment 3: When generating employee plan reference values, the method further includes: The employee's personal attribute data value set is A, A={a1,a2…ai…an}; Where ai is the attribute value corresponding to the i-th personal attribute type, and n represents the total number of current personal attribute types; Generate employee plan reference value B based on the current employee's personal attribute data value set;
[0031] Among them, wi is the weight of the attribute value corresponding to the i-th personal attribute type, si is the fixed coefficient of the attribute value corresponding to the i-th personal attribute type, and si is used to convert ai into a value between 0 and 1.
[0032] Example 4: The plan supervision module is further used to: Obtaining data on the usage of employee training program courses; Categorize usage data to generate supervisory data on employees during the execution of training programs; Build employee behavior models based on their historical supervisory data; c, c={c1,c2…ci…cm}; Where ci is the reference value of the i-th type of historical regulatory data, and m represents the total number of historical regulatory data types; The employee's behavioral reference value m1 is:
[0033] Among them, wi is the weight of the reference value of the i-th historical regulatory data, ki is the fixed coefficient of the reference value of the i-th historical regulatory data, and ki is used to convert ci into a value between 0 and 1; Set multiple employee behavior thresholds based on the employee's behavior reference value m1; Generate a first impact value d (d1, d2, d3, d4) of the employee plan reference value B based on the employee behavior threshold; The employee training plan is modified based on the first impact value d and the employee plan reference value B.
[0034] In this embodiment, this usage data provides a direct basis for understanding employee engagement with training programs. It may include information such as the number of times an employee visits a course, the duration of each visit, the progress achieved in completing each module within the course, and the frequency of interactive actions within the course (such as asking questions, answering questions, and participating in discussions). For example, an online training course system might record data such as the time employees log into the course platform and the length of time they spend in different sections.
[0035] Categorization is used to better analyze employee training behaviors from different dimensions. Data can be categorized based on its nature, such as grouping data related to course learning progress into one category and data related to learning interactions into another. For example, data related to course module completion progress and study duration would fall under the category of learning progress monitoring, while data related to the number of questions asked and the quality of answers would fall under the category of learning interaction monitoring.
[0036] Set multiple employee behavior thresholds based on the employee's behavior reference value m1 These behavioral thresholds categorize employee behavior into different levels or grades. For example, you can set three behavioral thresholds to categorize employee behavior into three levels: excellent, good, and qualified. If the value of m1 is above a certain threshold, the employee is in the excellent level; if the value of m1 is between the other two thresholds, the employee is in the good or qualified level.
[0037] Generate the first impact value d (d1, d2, d3, d4) of the employee plan reference value B based on the employee behavior threshold Different behavioral threshold ranges correspond to different first impact values d. For example, when an employee is in the excellent behavioral threshold range, d1 may be a small positive number, indicating that only minor adjustments to the employee's planned reference value B are required. However, when an employee is in the qualified behavioral threshold range, d3 may be a large negative number, indicating that a larger adjustment to B is required to adapt to the employee's actual situation.
[0038] Embodiment 5: When the employee training plan is modified by combining the first impact value d and the employee plan reference value B, the method further includes: Determine the current first impact value d based on the employee behavior threshold interval in which the employee behavior reference value m1 is located; Combining the current first impact value d and the employee plan reference value B to generate a first revised value B1 of the employee plan reference value B; B1=B+d; A revised employee training plan is generated by combining the first correction value B1 and the employee training sample.
[0039] In this embodiment, the first impact value d is determined based on behavioral threshold intervals. The employee behavioral threshold intervals serve as a standard for categorizing employee performance. By setting multiple threshold intervals, such as excellent, good, qualified, and unqualified, an employee's performance during training can be more accurately assessed. For example, a behavioral reference value m1, derived from a combination of factors such as an employee's participation in a training course, assignment completion, and ability to collaborate with others, can be compared with the set threshold intervals to determine the employee's performance level.
[0040] Different behavioral threshold intervals correspond to different first impact values d. When an employee's behavioral reference value m1 falls within a specific threshold interval, the corresponding first impact value d is determined. For example, if the employee's behavioral reference value m1 is in the excellent threshold interval, the first impact value d may be a small positive number, indicating that a minor adjustment to the employee's planned reference value B is sufficient. If it is in the unsatisfactory threshold interval, d may be a large negative number, indicating that a significant adjustment to the employee's planned reference value B is required.
[0041] The employee training sample is derived from the training sub-phases corresponding to the historical employee plan reference values. It incorporates data on past training successes and failures. Combining the first revision value, B1, with the employee training sample ensures that the revised training plan takes into account both the employee's current performance (reflected by B1) and historical training experience (reflected by the employee training sample).
[0042] For example, employee training samples might reveal that for employees with similar B1 values, additional practical training content or extended theoretical learning time is needed in a certain training sub-phase. When generating a revised employee training plan, adjustments to the training content, duration, and methods will be made based on these experiences. For example, increasing the duration of practical courses for specific skills or adjusting the ratio of theoretical to practical courses will be made to better suit the employee's actual situation and improve training effectiveness.
[0043] Example 6: The evaluation module is further used for: The training results of employees obtained include: skill test scores, instructor evaluations, and employee feedback information; Combine the skill test results and instructor evaluation to generate the training completion degree m2; m2=r1*e1+r2*e2; Among them, r1 is the proportional coefficient of skill test scores, r2 is the proportional coefficient of tutor evaluation, e1 is the reference value of skill test scores, and e2 is the reference value of tutor evaluation; Generate a list of improvement suggestions based on employee feedback; Generate visual reports combining training completion and improvement suggestion lists; Make secondary adjustments to the training plan based on the visual report.
[0044] In this embodiment, the skill test score is a quantitative assessment of the skills acquired by the employee during the training process. It directly reflects the employee's mastery of the knowledge and skills covered in the training. For example, in a programming training program, the skill test score may include evaluation results on the accuracy and efficiency of coding, as well as the ability to apply specific algorithms.
[0045] A mentor's evaluation is a subjective assessment of an employee's overall performance during training. The mentor considers factors such as the employee's learning attitude, teamwork skills, and understanding and application of knowledge. For example, a mentor might observe an employee's active participation in a group project, their communication and collaboration with team members, and provide an evaluation based on these observations.
[0046] Employee feedback is the employee's subjective experience and opinions about the training process. This includes their views on the training content, training methods, and training schedule. For example, an employee might report that a certain knowledge point in the training course was too brief, or that the training schedule conflicted with their work tasks.
[0047] The proportionality coefficients r1 and r2 reflect the relative importance of skill test scores and instructor evaluations in assessing training completion. The determination of these two coefficients depends on the objectives and nature of the training. For example, for training primarily focused on skill improvement, such as technical training, the proportionality coefficient r1 for skill test scores might be set higher, such as r1 = 0.7, indicating that skill test scores account for 70% of the training completion assessment. For soft skills training, such as leadership training, instructor evaluations may be more important, and r2 might be set to 0.6, indicating that instructor evaluations account for 60%.
[0048] The skill test score reference value, e1, is the standardized value of the skill test score. For example, if the full score on the skill test is 100 and an employee actually scores 80, e1 is calculated after certain conversions (such as adjustments based on score distribution). The mentor evaluation reference value, e2, converts the mentor's subjective evaluation into a quantifiable value. For example, mentor evaluations are categorized into four levels: excellent, good, acceptable, and unacceptable. These can each correspond to a different value, such as 90 for excellent (e2 = 90) and 80 for good.
[0049] First, categorize employee feedback by topic (e.g., training content, training methods, training time), or by nature (positive, negative). Then, conduct in-depth analysis of each feedback category. For example, for feedback on training content, analyze which knowledge points employees generally find difficult to understand or require further explanation.
[0050] By analyzing common and critical issues in employee feedback, we can identify a list of improvement suggestions. For example, if multiple employees report that there are too few hands-on sessions in a training course, "Increase hands-on sessions" will be added to the list. Suggestions can also be ranked based on frequency and severity of feedback to prioritize key areas for improvement.
[0051] Secondary adjustment of training plan based on visual report: If you find that overall employee completion is low, you may need to re-examine the training plan to determine whether the content is too difficult or the training duration is too short. For example, if the content is too difficult, you can simplify the training or explain it in stages. If the training duration is too short, you can appropriately extend the training period.
[0052] Make targeted adjustments to the suggestions for improvement. For example, if the suggestion is to add practical sessions, then add corresponding practical courses to the training plan. If the suggestion is to improve the training method, consider adopting new teaching methods, such as hybrid online and offline teaching, case studies, etc.
[0053] Example 7: When generating a list of improvement suggestions based on employee feedback information, the method further includes: Insert multiple comment nodes into employee training program courses and obtain employee feedback information based on the comment nodes; The employee feedback information includes the praise rating and the corresponding employee comment content; Categorize employee feedback information based on the degree of favorable comments and generate multiple levels of employee feedback information; And perform semantic analysis on employee comments of corresponding levels to generate the first comment set; The first review set includes several standard review contents; Compare the standard comments and employee comments in the employee feedback information of the current level in sequence to generate corresponding similarity reference values; Compare the similarity reference value with the similarity preset value, and count the number of employee comments whose similarity reference value is greater than the similarity preset value; The proportion of comments on each standard in employee feedback that generated the current rating; Generate a list of improvement suggestions by integrating the content of each standard comment and the corresponding ratio in the feedback information of employees of all levels; In this embodiment, multiple review nodes are inserted into the employee training program course. Employee feedback information is obtained based on the review nodes. This employee feedback information includes a positive rating and the corresponding employee review content. The employee feedback information is categorized based on the positive rating, generating multiple levels of employee feedback information, such as positive, neutral, and negative. Semantic analysis is then performed on the employee reviews of the corresponding levels to generate a first review set, which includes multiple standard review contents. For example, semantic analysis of employee reviews of the positive rating may yield standard review contents such as "the course content is practical" and "the training method is interesting."
[0054] The standard comments and employee comments in the employee feedback for the current level are sequentially compared to generate corresponding similarity reference values. This similarity reference value is then compared with the preset similarity value. The number of employee comments with a similarity reference value greater than the preset similarity value is counted to calculate the proportion of each standard comment in the employee feedback for the current level. For example, for the standard comment "The course content is practical," the number of employee comments with a similarity greater than the preset value is counted, and their proportion in the employee feedback for that level is then calculated.
[0055] A list of improvement suggestions is generated by combining the content and corresponding proportions of each standard comment from all levels of employee feedback. This list can reflect employee satisfaction with various aspects of the training course and key areas for improvement.
[0056] Example 8: The improvement suggestion list also includes: The content of each standard comment in the feedback information of employees of all levels and the proportion of corresponding standard comments; A hyperlink is set for each standard review content, and all employee review contents corresponding to the current standard review content can be viewed based on the hyperlink.
[0057] Example 9: When generating a visualization report based on the training completion rate and the list of improvement suggestions, the method further includes: Get the training completion degree m2 of all employees corresponding to the current training plan and a list of improvement suggestions; Sort the list of recommendations by the percentage of comments on each criterion from largest to smallest. Generate visual reports including: the top t standard comments, the average training completion rate of all employees, and employee information list The employee information list includes: the employee information list is connected with the employee's personal training completion degree and personal improvement suggestion list.
[0058] In this embodiment, a visual report is generated in combination with the training completion and improvement suggestion list. The training completion m2 and improvement suggestion list of all employees corresponding to the current training plan are obtained, and the standard comment content in the suggestion list is sorted from large to small based on the proportion. The generated visual report includes the first t standard comment contents, the average training completion of all employees, and the employee information list, wherein the employee information list includes the employee information list connected with the employee's personal training completion and personal improvement suggestion list. For example, the visual report may first display the first few standard comment contents with the highest proportion, such as "the training time arrangement is unreasonable", etc., followed by the average training completion of all employees, and then the information list of each employee, which includes the employee's personal training completion and his corresponding personal improvement suggestion list.
[0059] Make secondary adjustments to the training plan based on the visualization report. Based on the content in the visualization report, such as employee groups with low training completion rates and areas of general employee feedback requiring improvement, make targeted adjustments to the training plan, such as adjusting the training content, changing the training method, or rescheduling the training time.
[0060] Embodiment 10: The storage module is further used for: Establish corresponding employee digital files based on employee user portraits; Employee digital files include: employee training results, employee feedback information, employee potential rankings, and employee skills matrix; Save employee digital files based on timeline; and building an employee network to connect members of the current employee's group; Switching employee digital profiles through the employee relationship network; In this embodiment, a corresponding employee digital profile is created based on the employee's user profile. The employee digital profile includes the employee's training results, employee feedback, employee potential ranking, and the employee's skills matrix. For example, an employee's training results can record their performance and completion status in various training courses; employee feedback can store their evaluation and opinions of the training courses; an employee's potential ranking can be determined based on factors such as their performance during training and their rate of progress; and an employee's skills matrix can reflect the various skills they currently possess and their skill level.
[0061] Save employee digital profiles based on a timeline. This records employee training and development progress at different points in time, creating a complete growth trajectory. For example, you can view employee training achievements and skill development progress over the past one, two, or multiple years.
[0062] Build an employee network to connect with members of your current team. Exchange employee digital profiles through the employee network. This facilitates communication and learning among employees. For example, employees can view the digital profiles of other team members to learn about their training experience, skill levels, and other aspects, allowing them to learn from each other.
[0063] Finally, it should be noted that it is apparent that those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, to the extent such modifications and variations fall within the scope of the present invention and its equivalents, the present invention is intended to include such modifications and variations.
[0064] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.
Claims
1. An employee training system based on user portraits, characterized in that: include: Plan generation module, used to generate corresponding training plans based on employee user profiles; The plan supervision module is used to obtain the supervision data of employees during the execution of the training plan and generate employee behavior models based on the employee supervision data; It is also used to adjust training programs based on employees’ behavioral models; Evaluation module, used to generate visual reports based on the training results of employees; It is also used to include secondary adjustments to the training program based on visualization; Storage module: used to create corresponding employee digital files based on employee user portraits.
2. The employee training system based on user portrait according to claim 1, characterized in that: The plan generation module is also used to: Develop multiple training routes based on employee job requirements and career development goals; Divide each training circuit into stages to generate multiple training sub-stages; Generate corresponding employee profiles based on the employee's personal attribute data type; Obtain the personal attribute data value of the employee corresponding to the current employee portrait to generate the employee plan reference value; Obtain the training sub-stages corresponding to historical employee plan reference values to generate employee training samples; Generate a corresponding training plan based on the user profile of the current employees and employee training samples; The generated training plan is sent to the corresponding employees for confirmation and employee confirmation information is generated; Combine employee confirmation information and training plan to send corresponding training plan courses to employees.
3. The employee training system based on user portrait according to claim 2, characterized in that: When generating employee plan reference values, the following is also included: The employee's personal attribute data value set is A, A={a1,a2…ai…an}; Where ai is the attribute value corresponding to the i-th personal attribute type, and n represents the total number of current personal attribute types; Generate employee plan reference value B based on the current employee's personal attribute data value set; Among them, wi is the weight of the attribute value corresponding to the i-th personal attribute type, si is the fixed coefficient of the attribute value corresponding to the i-th personal attribute type, and si is used to convert ai into a value between 0 and 1.
4. The employee training system based on user portrait according to claim 3, characterized in that: The plan supervision module is also used to: Obtaining data on the usage of employee training program courses; Categorize usage data to generate supervisory data on employees during the execution of training programs; Build employee behavior models based on their historical supervisory data; c, c={c1,c2…ci…cm}; Where ci is the reference value of the i-th type of historical regulatory data, and m represents the total number of historical regulatory data types; The employee's behavioral reference value m1 is: Among them, wi is the weight of the reference value of the i-th historical regulatory data, ki is the fixed coefficient of the reference value of the i-th historical regulatory data, and ki is used to convert ci into a value between 0 and 1; Set multiple employee behavior thresholds based on the employee's behavior reference value m1; Generate a first impact value d (d1, d2, d3, d4) of the employee plan reference value B based on the employee behavior threshold; The employee training plan is modified based on the first impact value d and the employee plan reference value B.
5. The employee training system based on user portrait according to claim 4, characterized in that: When the employee training plan is modified by combining the first impact value d and the employee plan reference value B, the method further includes: Determine the current first impact value d based on the employee behavior threshold interval in which the employee behavior reference value m1 is located; Combining the current first impact value d and the employee plan reference value B to generate a first revised value B1 of the employee plan reference value B; B1=B+d; A revised employee training plan is generated by combining the first correction value B1 and the employee training sample.
6. The employee training system based on user portrait according to claim 5, characterized in that: The evaluation module is also used to: The training results of employees obtained include: skill test scores, instructor evaluations, and employee feedback information; Combine the skill test results and instructor evaluation to generate the training completion degree m2; m2=r1*e1+r2*e2; Among them, r1 is the proportional coefficient of skill test scores, r2 is the proportional coefficient of tutor evaluation, e1 is the reference value of skill test scores, and e2 is the reference value of tutor evaluation; Generate a list of improvement suggestions based on employee feedback; Generate visual reports combining training completion and improvement suggestion lists; Make secondary adjustments to the training plan based on the visual report.
7. The employee training system based on user portrait according to claim 6, characterized in that: When generating a list of improvement suggestions based on employee feedback information, the following is also included: Insert multiple comment nodes into employee training program courses and obtain employee feedback information based on the comment nodes; The employee feedback information includes the praise rating and the corresponding employee comment content; Categorize employee feedback information based on the degree of favorable comments and generate multiple levels of employee feedback information; And perform semantic analysis on employee comments of corresponding levels to generate the first comment set; The first review set includes several standard review contents; Compare the standard comments and employee comments in the employee feedback information of the current level in sequence to generate corresponding similarity reference values; Compare the similarity reference value with the similarity preset value, and count the number of employee comments whose similarity reference value is greater than the similarity preset value; The proportion of comments on each standard in employee feedback that generated the current rating; A list of improvement suggestions is generated by integrating the content of each standard comment and the corresponding ratio in the feedback information of employees of all levels.
8. The employee training system based on user portrait according to claim 7, characterized in that: The list of improvement suggestions also includes: The content of each standard comment in the feedback information of employees of all levels and the proportion of corresponding standard comments; A hyperlink is set for each standard review content, and all employee review contents corresponding to the current standard review content can be viewed based on the hyperlink.
9. The employee training system based on user portraits according to claim 8, characterized in that: When generating a visual report based on training completion and a list of improvement suggestions, it also includes: Get the training completion degree m2 of all employees corresponding to the current training plan and a list of improvement suggestions; Sort the list of recommendations by the percentage of comments on each criterion from largest to smallest. Generate visual reports including: the top t standard comments, the average training completion rate of all employees, and employee information list The employee information list includes: the employee information list is connected with the employee's personal training completion degree and personal improvement suggestion list.
10. The employee training system based on user portrait according to claim 9, characterized in that: The storage module is also used for: Establish corresponding employee digital files based on employee user portraits; Employee digital files include: employee training results, employee feedback information, employee potential rankings, and employee skills matrix; Save employee digital files based on timeline; and building an employee network to connect members of the current employee's group; Switch employee digital profiles through the employee relationship network.