An employee training system based on the Internet of Things

Abstract

The present invention discloses an employee training system based on the Internet of Things, comprising a registration and login unit, a data collection unit, a data storage unit, an analysis and customization unit, a progress tracking unit, an assessment and evaluation unit and a course improvement unit. By setting the analysis and customization unit, the actual training needs of the employees are automatically analyzed to obtain keywords matching the training content of the employees, and suitable planned training content is formulated according to the keywords and sent to the progress tracking unit for follow-up reminder and record, so as to ensure the orderly completion of the training tasks throughout the whole process, so that the training content can be personalized and customized to meet the actual needs of different employees, avoiding the phenomenon of "one size fits all", thereby improving the accuracy of training and the enthusiasm of employees to participate; by setting the assessment and evaluation unit, the training effects of the employees are assessed from different dimensions, which increases the objectivity and effectiveness of the assessment, and avoids the problem that the training assessment becomes a formality and cannot produce actual application effects.

Description

Technical Field

[0001] The invention relates to a training system, in particular to an employee training system based on the Internet of Things. Background Art

[0002] During the operation and development of an enterprise, since new personnel need to learn and new business modules need to be developed and mastered, employees need to be trained regularly or irregularly.

[0003] The existing training model is too programmed. Employees’ needs and learning abilities are different, and their learning time is not fixed. This makes it difficult for traditional training arrangements and training results to be effectively implemented and highlighted. In order to adapt to a more personalized and efficient training model, we provide an employee training system based on the Internet of Things. Summary of the invention

[0004] The purpose of the present invention is to provide an employee training system based on the Internet of Things.

[0005] The technical problems solved by the present invention are:

[0006] (1) How to automatically analyze the actual training needs of employees by setting up an analysis customization unit, obtain keywords that match the employee's training content, and formulate appropriate planned training content based on the keywords and send it to the progress tracking unit for follow-up reminders and records, thereby ensuring the orderly completion of the training task throughout the process, solving the problem in the existing technology that it is impossible to provide targeted training according to the employee's own needs;

[0007] (2) How to set up an assessment unit to review the key points and assess the content of the training for employees who have completed their training tasks, and set up two forms of discussion: theoretical knowledge and case analysis, to assess the training effects of employees from different dimensions, thereby increasing the objectivity and effectiveness of the assessment. At the same time, through discussion and mutual evaluation, employees are given the opportunity to learn from each other and improve, thus solving the problem in the existing technology that training assessment is superficial and cannot produce actual application effects.

[0008] The present invention can be implemented by the following technical solutions: an employee training system based on the Internet of Things, including a registration and login unit, a data collection unit, a data storage unit, an analysis and customization unit, a progress tracking unit, an assessment and evaluation unit, and a course improvement unit;

[0009] The registration and login unit is used for employees to register their personal information and log into the system. The personal information includes the employee's name, employee ID, department, functional position and length of service. The registration and login unit transmits the personal information to the data storage unit for storage;

[0010] The analysis and customization unit is used to analyze the employee's personal information, task data, and learning intention data, so as to obtain keywords that match the employee's training content, and formulate appropriate training content according to the keywords;

[0011] The assessment and evaluation unit is used to assess and evaluate employees participating in the training. The assessment is divided into theoretical knowledge assessment and case analysis and discussion, and employees who fail the assessment will be reassigned tasks.

[0012] A further technical improvement of the present invention is that the data storage unit pre-stores the task type of each job in the company and the standard processing time required for a single task of the corresponding task type, and the data storage unit also stores the skill training materials required for each functional position.

[0013] A further technical improvement of the present invention is that task data and learning intention data are collected by a data collection unit, the collected task data are the task data of employees in the previous month, and the learning intention data include the job skill keywords and the intended learning degree that the employees intend to learn.

[0014] A further technical improvement of the present invention is that the planned training content formulated by the analysis and customization unit includes skill training materials corresponding to the job skill keywords and fixed training content required for a fixed number of training times per quarter.

[0015] A further technical improvement of the present invention is that the quarterly fixed training frequency is determined by the job length of service in the personal information, and the quarterly fixed training frequency changes in the opposite direction to the job length of service.

[0016] A further technical improvement of the present invention is that the planned training content is sent to the progress tracking unit, which prioritizes the training content therein and automatically allocates tasks according to the prioritized levels. After the allocation, the training frequency is determined and the training content nodes to be completed each week are set.

[0017] A further technical improvement of the present invention is that the progress tracking unit follows up and reminds the assigned training tasks every week, sets a timer to record the number of abnormal progress, and pushes messages to remind, so that the corresponding training employees can learn the training content in time according to the plan.

[0018] A further technical improvement of the present invention is that the assessment and evaluation unit automatically generates a training mind map after the training task is completed, allowing employees to fill it out, thereby improving employees' ability to integrate the overall training context. When calculating the total score, the accuracy of filling in the training mind map and the number of abnormal progress occurrences are substituted for comprehensive calculation, and the results are output.

[0019] A further technical improvement of the present invention is that the system also includes a course improvement unit, which is used for employees to integrate problems and coping measures encountered in actual work into training cases, and set keywords for the training cases. After the training cases are reviewed by system administrators, they are transmitted to the corresponding categories of the data storage unit, so that the training content can be updated and supplemented in a targeted manner, providing materials for subsequent training.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. The registration and login unit will register and upload personal information and store it. The data collection unit will obtain and store the task data of the previous month and the learning intention data of the employees. The analysis and customization unit will analyze the personal information, task data and learning intention data of the employees to obtain keywords that match the training content of the employees, and formulate suitable planned training content based on the keywords. After sending the planned training content to the progress tracking unit, the progress tracking unit will sort and sort the obtained skill training materials, and mark the priority of the skill training materials corresponding to the task type to be improved as the highest and the fixed training content with the quarterly fixed training number requirements. The priority is second, and the skill training materials corresponding to the learning intention data have the lowest priority. Automatic task allocation is carried out according to the priority, and the employee's completion status is followed up and reminded. By setting up the analysis customization unit, the employee's actual training needs are automatically analyzed, and the keywords matching the employee's training content are obtained. According to the keywords, suitable planned training content is formulated and sent to the progress tracking unit for follow-up reminders and records, ensuring the orderly completion of the training tasks throughout the process, so that the training content can be personalized to meet the actual needs of different employees, avoiding the "one size fits all" phenomenon, thereby improving the accuracy of training and the enthusiasm of employees to participate.

[0022] 2. After the training task is completed, the assessment and evaluation unit automatically generates a training mind map for employees to fill in, so as to improve their ability to integrate the overall training context. When calculating the total score, the training mind map filling accuracy and the number of abnormal progress are substituted for comprehensive calculation and the result is output. At the same time, employees can integrate the problems and countermeasures encountered in actual work into training cases through the course improvement unit, and set keywords for the training cases. After the training cases are reviewed by the system administrator, they are transferred to the corresponding categories of the data storage unit, so that the training content can be updated and supplemented in a targeted manner, providing materials for subsequent training. By setting up the assessment and evaluation unit, the key points and content assessment are reviewed for employees who have completed the training task, and two forms of theoretical knowledge and case analysis discussion are set up to assess the training effect of employees from different dimensions, which increases the objectivity and effectiveness of the assessment. At the same time, through discussion and mutual evaluation, employees are given the opportunity to learn from each other and make progress, which solves the problem that the training assessment in the existing technology is superficial and cannot produce actual application effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0024] Figure 1 It is a system principle block diagram of the present invention. DETAILED DESCRIPTION

[0025] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0026] See also Figure 1 As shown, an employee training system based on the Internet of Things includes a registration and login unit, a data collection unit, a data storage unit, an analysis and customization unit, a progress tracking unit, an assessment and evaluation unit, and a course improvement unit;

[0027] The registration and login unit is used for employees to register their personal information and log into the system. The personal information includes the employee's name, employee ID, department, functional position and length of service. The registration and login unit transmits the personal information to the data storage unit for storage;

[0028] The data storage unit stores in advance the task type of each job in the company and the standard processing time required for a single task of the corresponding task type. The standard processing time is the average processing time of all employees in the company who perform related tasks based on big data analysis. The data storage unit also stores the skill training materials required for each functional position. The skill training materials are classified according to different job skill keywords and stored in different storage areas of the data storage unit.

[0029] The data collection unit is used to obtain the task data of the corresponding employee in the previous month. The task data includes task type data, the number of single-type tasks and single-type processing time data. At the same time, the data collection unit is also used to collect the learning intention data of the employees. The learning intention data represents the job skill keywords and the intended learning degree of the corresponding employees. The intended learning degree is divided into "understanding", "familiarity" and "proficiency". The data collection unit transmits the obtained data to the data storage unit for storage;

[0030] The analysis and customization unit is used to analyze the employee's personal information, task data, and learning intention data, so as to obtain keywords that match the employee's training content, and formulate appropriate training content according to the keywords;

[0031] The specific steps for matching and planning training content are:

[0032] Step S1: extracting personal information, task data and learning intention data from the data storage unit, determining the quarterly fixed training frequency according to the length of service in the personal information, and presetting a corresponding form of length of service-number of trainings in the analysis and customization unit. The higher the length of service, the fewer the quarterly fixed training frequency, and the lower the length of service, the higher the quarterly fixed training frequency;

[0033] Step S2: Mark the task type data as A, B, C, etc., perform ratio calculation on the single-type processing time and the number of single-type tasks of the corresponding task type according to different task types, obtain the single-task processing time corresponding to the task type data, and extract the standard processing time required for a single task of the task type from the data storage unit;

[0034] Step S3: Calculate the difference between the single-task processing time and the standard processing time and perform de-symbolization to obtain the difference result, compare the obtained difference result with the preset time deviation threshold, when the difference result is within the preset time deviation threshold, determine that the processing ability of the corresponding employee for this type of task reaches the average level, when the difference result is outside the preset time deviation threshold and the single-task processing time is less than the standard processing time, determine that the processing ability of the corresponding employee for this type of task exceeds the average level, when the difference result is outside the preset time deviation threshold and the single-task processing time is greater than the standard processing time, determine that the processing ability of the corresponding employee for this type of task is lower than the average level, and training on the processing ability of this type of task is required, and record the data of this type of task and mark it as a task type to be improved;

[0035] Step S4: Extract the job skill keywords in the learning intention data, and transmit the job skill keywords or the task types to be improved to the data storage unit for query matching, so as to obtain the skill training materials corresponding to the job skill keywords, and send them together with the fixed training content required for the quarterly fixed training times to the progress tracking unit.

[0036] The progress tracking unit will organize and sort the obtained skill training materials, mark the skill training materials corresponding to the task type to be improved as the highest priority, the fixed training content required by the quarterly fixed training frequency as the second priority, and the skill training materials corresponding to the learning intention data as the lowest priority, and automatically allocate tasks according to the priority, such as: determining the weekly training frequency of the skill training materials corresponding to the task type to be improved, and the specific time point can be set by the employees themselves on the mobile terminal, and setting the training content nodes that need to be completed each week. The system will remind employees of training at the set time point through the mobile terminal. When the employee fails to complete the training frequency and reaches the training content node that week, it is determined that the progress is abnormal, and the counter is started to record the number of times the progress is abnormal, and a message push reminder is sent. The progress tracking method for other training content is the same as above.

[0037] The assessment and evaluation unit will conduct an assessment and evaluation on a training task at the end of the task. The specific steps are as follows:

[0038] Step SS1: After the training task is completed, a training mind map is automatically generated based on the chapter content of the training materials, and all text content is erased so that employees can fill it in by themselves;

[0039] Step SS2: The employee makes an appointment for the assessment time through the mobile terminal. The assessment and evaluation unit uniformly schedules the employees who have completed the same training content within a week, determines the assessment time, and informs the corresponding employees through the mobile terminal;

[0040] Step SS3: Conduct theoretical knowledge assessment and case analysis discussion for employees participating in the assessment. During the case analysis discussion, employees participating in the assessment will conduct mutual evaluation and input the mutual evaluation results through mobile terminals;

[0041] Step SS4: Substitute the theoretical knowledge assessment score, mutual evaluation score, training mind map filling accuracy rate and the number of abnormal progress into the calculation formula to obtain the total score, which is: Total score = (theoretical knowledge assessment score * a + mutual evaluation score * b) * training mind map filling accuracy rate - number of abnormal progress * 5, where a represents the theoretical knowledge assessment ratio, b represents the case analysis discussion ratio, a and b are both preset values, and a + b = 1, and the number of abnormal progress * 5 means that 5 points will be deducted from the score for each abnormal progress;

[0042] Step SS5: Compare the total score with the preset passing score. When the total score is greater than the passing score, the training assessment is considered to be passed. Otherwise, the training assessment is considered to be unqualified. Employees who fail the training assessment need to re-learn the training content and send a re-learning instruction to the progress tracking unit, which will re-assign the corresponding tasks.

[0043] The course improvement unit is used for employees to integrate the problems and countermeasures encountered in actual projects into training cases, set keywords for the training cases, and transfer the training cases to the corresponding categories of the data storage unit after review by system managers, thereby improving the practical value and operability of the training.

[0044] When the present invention is in use, first, the employee registers and uploads the personal information through the registration and login unit and stores it; the data collection unit obtains and stores the task data of the previous month and the learning intention data of the employee; the analysis and customization unit analyzes the employee's personal information, task data and learning intention data to obtain keywords that match the employee's training content, and formulates suitable planned training content based on the keywords; after sending the planned training content to the progress tracking unit, the progress tracking unit sorts and sorts the obtained skill training materials, and marks the priority of the skill training materials corresponding to the task type to be improved as the highest, the priority of the fixed training content required for the quarterly fixed training number of times as the second, and the priority of the skill training materials corresponding to the learning intention data as the highest. The priority is the lowest, and tasks are automatically assigned according to the priority, and employees are followed up and reminded on their completion status. After the training task is completed, the assessment and evaluation unit automatically generates a training mind map for employees to fill in, thereby improving their ability to integrate the overall training context. When calculating the total score, the accuracy of filling in the training mind map and the number of abnormal progress are substituted for comprehensive calculation, and the results are output. At the same time, employees can integrate the problems and countermeasures encountered in actual work into training cases through the course improvement unit, and set keywords for the training cases. After being reviewed by the system administrator, the training cases are transferred to the corresponding categories of the data storage unit, so that the training content can be updated and supplemented in a targeted manner to provide materials for subsequent training.

[0045] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. An employee training system based on the Internet of Things, characterized in that: It includes analysis and customization unit and assessment and evaluation unit, among which: The analysis and customization unit is used to analyze the employee's personal information, task data, and learning intention data, so as to obtain keywords that match the employee's training content, and formulate appropriate training content according to the keywords; The assessment and evaluation unit is used to assess and evaluate employees who participate in the training. The assessment is divided into theoretical knowledge assessment and case analysis and discussion, and employees who fail the assessment will be reassigned tasks; It also includes a registration and login unit, a data collection unit, a data storage unit and a progress tracking unit; The registration and login unit is used for employees to register their personal information and log into the system. The personal information includes the employee's name, employee ID, department, functional position and length of service. The registration and login unit transmits the personal information to the data storage unit for storage; The data storage unit stores in advance the task type of each job in the company and the standard processing time required for a single task of the corresponding task type. The standard processing time is the average processing time of all employees in the company who perform related tasks based on big data analysis. The data storage unit also stores the skill training materials required for each functional position. The skill training materials are classified according to different job skill keywords and stored in different storage areas of the data storage unit. The data collection unit is used to obtain the task data of the corresponding employee in the previous month. The task data includes task type data, the number of single-type tasks and single-type processing time data. At the same time, the data collection unit is also used to collect the learning intention data of the employees. The learning intention data represents the job skill keywords and the intended learning degree of the corresponding employees. The intended learning degree is divided into "understanding", "familiarity" and "proficiency". The data collection unit transmits the obtained data to the data storage unit for storage; The specific steps for matching and planning training content are: Step S1: extracting personal information, task data and learning intention data from the data storage unit, determining the quarterly fixed training frequency according to the length of service in the personal information, and presetting a corresponding form of length of service-number of trainings in the analysis and customization unit. The higher the length of service, the fewer the quarterly fixed training frequency, and the lower the length of service, the higher the quarterly fixed training frequency; Step S2: performing ratio calculations on the single-type processing time and the number of single-type tasks of the corresponding task type according to different task types, obtaining the single-task processing time corresponding to the task type data, and extracting the standard processing time required for a single task of the task type from the data storage unit; Step S3: Calculate the difference between the single-task processing time and the standard processing time and perform de-symbolization to obtain the difference result, compare the obtained difference result with the preset time deviation threshold, when the difference result is within the preset time deviation threshold, determine that the processing ability of the corresponding employee for this task type reaches the average level, when the difference result is outside the preset time deviation threshold and the single-task processing time is less than the standard processing time, determine that the processing ability of the corresponding employee for this task type exceeds the average level, when the difference result is outside the preset time deviation threshold and the single-task processing time is greater than the standard processing time, determine that the processing ability of the corresponding employee for this task type is lower than the average level, and training on the processing ability of this task type is required, and record the task type data and mark it as a task type to be improved; Step S4: extracting job skill keywords from the learning intention data, transmitting the job skill keywords or the task types to be improved to the data storage unit for query matching, thereby obtaining skill training materials corresponding to the job skill keywords, and sending them together with the fixed training content required for the quarterly fixed training times to the progress tracking unit; The progress tracking unit will organize and sort the obtained skill training materials, mark the priority of the skill training materials corresponding to the task type to be improved as the highest, the priority of the fixed training content required by the quarterly fixed training frequency as the second, and the priority of the skill training materials corresponding to the learning intention data as the lowest, and automatically allocate tasks according to the priority; determine the weekly training frequency of the skill training materials corresponding to the task type to be improved, and the specific time point can be set by the employees themselves on the mobile terminal, set the training content nodes that need to be completed each week, and the system will remind employees of training at the set time point through the mobile terminal. When the employee fails to complete the training frequency and reaches the training content node that week, the progress is judged to be abnormal, and the counter is started to record the number of progress abnormalities and send message push reminders.

2. According to the employee training system based on the Internet of Things in claim 1, it is characterized in that: The progress follow-up unit conducts weekly follow-up and reminders on the assigned training tasks, sets a timer to record the number of abnormal progress and sends message push reminders.

3. The employee training system based on the Internet of Things according to claim 2 is characterized in that: After the training task is completed, the assessment and evaluation unit automatically generates a training mind map for employees to fill in, thereby improving their ability to integrate the overall training context. When calculating the total score, the accuracy of filling in the training mind map and the number of abnormal progress occurrences are substituted for comprehensive calculation and the results are output.

4. The employee training system based on the Internet of Things according to claim 1 is characterized in that: The system also includes a course improvement unit, which is used for employees to integrate problems and coping measures encountered in actual work into training cases, set keywords for the training cases, and transfer the training cases to the corresponding categories of the data storage unit after review by system administrators.

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