CN113554316A - Staff training system based on Internet of things - Google Patents
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Abstract
The invention discloses an employee training system based on the Internet of things, which comprises a registration login unit, a data collection unit, a data storage unit, an analysis customization unit, a progress follow-up unit, an examination evaluation unit and a course improvement unit, wherein the analysis customization unit is arranged to automatically analyze the actual training requirements of employees to obtain keywords matched with the training contents of the employees, and the appropriate planned training contents are formulated according to the keywords and sent to the progress follow-up unit for follow-up reminding and recording; by arranging the assessment evaluation unit, the training effect of the staff is assessed from different dimensions, so that the objectivity and effectiveness of assessment are improved, and the problem that the training assessment is in a form and cannot generate the actual application effect is solved.
Description
Technical Field
The invention relates to a training system, in particular to an employee training system based on the Internet of things.
Background
In the operation and development process of enterprises, staff training is required regularly or irregularly because new personnel need to learn and new business modules need to be developed and skilled.
The existing training mode is excessively stylized, the requirement positioning and learning capabilities of staff are different, and the learning time of the staff is not fixed, so that the traditional training arrangement and the training effect are difficult to effectively implement and highlight, and in order to adapt to the more personalized and efficient training mode, the staff training system based on the Internet of things is provided.
Disclosure of Invention
The invention aims to provide an employee training system based on the Internet of things.
The technical problem solved by the invention is as follows:
(1) how to automatically analyze the actual training requirements of the staff by setting an analysis customizing unit to obtain keywords matched with the training contents of the staff, and making proper planned training contents according to the keywords and sending the proper planned training contents to a progress follow-up unit for follow-up reminding and recording, so that the ordered completion of training tasks is ensured in the whole process, and the problem that the training cannot be purposefully carried out according to the self requirements of the staff in the prior art is solved;
(2) according to the method, the assessment evaluation unit is arranged, the staff with the training task finished are subjected to key point review and content assessment, two forms of theoretical knowledge and case analysis discussion are arranged, the training effect of the staff is assessed from different dimensions, the objectivity and effectiveness of assessment are increased, the staff are given the opportunity of mutual learning and progress through discussion and mutual assessment, and the problem that the training assessment in the prior art is in a flowing form and cannot generate the actual application effect is solved.
The invention can be realized by the following technical scheme: an employee training system based on the Internet of things comprises a registration login unit, a data collection unit, a data storage unit, an analysis customization unit, a progress follow-up unit, an examination evaluation unit and a course improvement unit;
the registration login unit is used for the staff to perform personal information registration and system login, the personal information comprises staff names, the staff I D, affiliated departments, functional posts and post working ages, and the registration login unit transmits the personal information to the data storage unit for storage;
the analysis customizing unit is used for analyzing the personal information, the task data and the learning intention data of the staff so as to obtain keywords matched with the training content of the staff, and making proper plan training content according to the keywords;
the assessment evaluation unit is used for assessing and evaluating the staff participating in the training, the assessment is divided into theoretical knowledge assessment and case analysis discussion, and tasks are redistributed to the staff with unqualified assessment.
The invention has further technical improvements that: the data storage unit is pre-stored with the task type of each work in the company and the standard processing time required by the single task corresponding to the task type, and the data storage unit is also stored with skill training data required by each functional post.
The invention has further technical improvements that: the task data and the learning intention data are collected by the data collection unit, the collected task data are task data of the staff in the previous month, and the learning intention data comprise position skill keywords and intention learning degree of the staff intention learning.
The invention has further technical improvements that: the planned training content formulated by the analysis and customization unit comprises skill training data corresponding to the post skill keywords and fixed training content required by the fixed training times in seasons.
The invention has further technical improvements that: the fixed training times of the quarter are determined by the working age of the post in the personal information, and the fixed training times of the quarter and the working age of the post are changed in the opposite direction.
The invention has further technical improvements that: and the planned training content is sent to a progress follow-up unit, the progress follow-up unit carries out priority calibration on the training content in the progress follow-up unit, automatic task distribution is carried out according to the calibrated priority, the training frequency is determined after distribution, and training content nodes to be completed every week are set.
The invention has further technical improvements that: the progress follow-up unit carries out weekly follow-up and reminding on the distributed training tasks, a timer is arranged, the times of progress abnormity are recorded, and meanwhile message pushing reminding is carried out, so that corresponding training staff can learn the training content in time according to a plan.
The invention has further technical improvements that: and the examination evaluation unit automatically generates a training thinking guide graph after the training task is finished, so that the staff can fill the training thinking guide graph, the integration capability of the staff to the whole training venation is improved, and when the total score is calculated, the filling accuracy of the training thinking guide graph and the times of progress abnormity are substituted for comprehensive calculation, and a result is output.
The invention has further technical improvements that: the system also comprises a course perfecting unit, wherein the course perfecting unit is used for integrating problems and corresponding measures encountered in actual work into a training case by staff, setting keywords for the training case, and transmitting the training case to a corresponding classification of the data storage unit after the training case is audited by a system manager, so that training contents can be updated and supplemented in a targeted manner, and materials are provided for subsequent training.
Compared with the prior art, the invention has the following beneficial effects:
1. the personal information is registered and uploaded through a registration unit and stored, a data collection unit obtains task data of the previous month and learning intention data of staff and stores the task data, an analysis customization unit analyzes the personal information, the task data and the learning intention data of the staff to obtain keywords matching training contents of the staff, proper plan training contents are made according to the keywords, the plan training contents are sent to a progress following unit, the progress following unit sorts the obtained skill training data, the priority of the skill training data corresponding to the task type to be promoted is calibrated to be the highest priority of fixed training contents required by the seasonal fixed training times and the lowest priority of the skill training data corresponding to the learning intention data, and automatic task distribution is carried out according to the priority, the completion condition of the staff is followed and reminded, the actual training requirements of the staff are automatically analyzed by arranging the analysis customizing unit to obtain keywords matched with the training content of the staff, the proper plan training content is made according to the keywords and sent to the progress following unit for follow-up reminding and recording, the ordered completion of the training tasks is guaranteed in the whole process, the training content can adapt to the actual requirements of different staff for personalized customization, the phenomenon of 'one-time cutting' is avoided, and the training accuracy and the participation enthusiasm of the staff are improved.
2. After the training task is completed, the examination and evaluation unit automatically generates a training thought chart to enable staff to fill, so that the integration capability of the staff to the whole training venation is improved, when total score calculation is carried out, the filling accuracy of the training thought chart and the times of progress abnormity is carried out, comprehensive calculation is carried out, results are output, meanwhile, the staff can integrate problems and corresponding measures encountered in actual work into training cases through the course perfecting unit, keywords are set for the training cases, the training cases are audited by system managers and then transmitted to corresponding classifications of the data storage unit, so that training contents can be updated and supplemented in a targeted mode, materials are provided for subsequent training, the examination and evaluation unit is arranged to carry out key point review and content examination on the staff with the training task completed, and two modes of theoretical knowledge and case analysis discussion are set, the training effect of the staff is checked from different dimensions, the objectivity and the effectiveness of the check are improved, meanwhile, the opportunity of mutual learning and progress of the staff is given through discussion and mutual evaluation, and the problem that the training check in the prior art is in a flowing form and cannot generate the actual application effect is solved.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic block diagram of the system of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects according to the present invention will be given with reference to the accompanying drawings and preferred embodiments.
Referring to fig. 1, an employee training system based on the internet of things comprises a registration login unit, a data collection unit, a data storage unit, an analysis customization unit, a progress follow-up unit, an examination evaluation unit and a course improvement unit;
the registration login unit is used for the staff to perform personal information registration and system login, the personal information comprises staff names, the staff I D, affiliated departments, functional posts and post working ages, and the registration login unit transmits the personal information to the data storage unit for storage;
the data storage unit is prestored with task types of each work in a company and standard processing time required by a single task corresponding to the task types, the standard processing time is the average time for all staff executing related tasks in the company to process the corresponding tasks according to big data analysis, skill training data required by each functional post is also stored in the data storage unit, and the skill training data are classified according to different post skill keywords and are stored in different storage areas of the data storage unit;
the data collection unit is used for acquiring task data corresponding to the previous month of the staff, the task data comprises task type data, single-type task quantity and single-type processing duration data, meanwhile, the data collection unit is also used for collecting learning intention data of the staff, the learning intention data represents position skill keywords and intention learning degree corresponding to intention learning of the staff, the intention learning degree is divided into 'understanding', 'familiar' and 'proficiency', and the data collection unit transmits the acquired data to the data storage unit for storage;
the analysis customizing unit is used for analyzing the personal information, the task data and the learning intention data of the staff so as to obtain keywords matched with the training content of the staff, and making proper plan training content according to the keywords;
the specific steps for matching and planning training content are as follows:
step S1: extracting personal information, task data and learning intention data from a data storage unit, determining the fixed training times of a quarter according to the job age of a post in the personal information, and presetting a job age-training time corresponding form by an analysis and customization unit, wherein the higher the job age of the post, the less the fixed training times of the quarter, the lower the job age of the post and the higher the fixed training times of the quarter;
step S2: marking the task type data as A type, B type and C type … …, respectively carrying out ratio operation on the single type processing time length and the single type task quantity of the corresponding task type according to different task types to obtain the single task processing time length corresponding to the task type data, and extracting the standard processing time length required by the single task of the task type from the data storage unit;
step S3: calculating the difference between the single task processing time length and the standard processing time length, performing de-symbolization processing to obtain a difference result, comparing the obtained difference result with a preset time length deviation threshold, when the difference result is within the preset time length deviation threshold value, the processing capacity of the task of the corresponding staff is judged to reach the average level, when the difference result is out of the preset time length deviation threshold value and the single task processing time length is less than the standard processing time length, the processing capability of the task of the corresponding staff is judged to exceed the average level, when the difference result is out of the preset time length deviation threshold value and the single task processing time length is longer than the standard processing time length, judging that the processing capacity of the task of the corresponding staff is lower than the average level, training the processing capacity of the task type of the staff is needed, recording the data of the task type, and marking the data as the type of the task to be promoted;
step S4: and extracting post skill keywords in the learning intention data, transmitting the post skill keywords or the type of the task to be promoted to a data storage unit for query and matching, thereby obtaining skill training data corresponding to the post skill keywords, and transmitting the skill training data and fixed training contents required by the fixed training times of the quarter to a progress follow-up unit.
The progress follow-up unit sorts the acquired skill training data, the priority of the skill training data corresponding to the type of the task to be promoted is calibrated to be the highest, the priority of the fixed training content required by the fixed training times in the quarter and the priority of the skill training data corresponding to the learning intention data are the lowest, and automatic task distribution is carried out according to the priorities, such as: the method comprises the steps of determining the weekly training frequency of skill training data corresponding to the type of a task to be promoted, setting specific time points on a mobile phone terminal by staff, setting training content nodes needing to be completed every week, carrying out training reminding on the staff at set time points through the mobile phone terminal, judging progress abnormity when the staff do not complete the training frequency in the week and reach the training content nodes, starting a counter, recording the times of progress abnormity, carrying out message pushing reminding, and enabling the progress follow-up mode of other training contents to be the same as the above mode.
The assessment evaluation unit performs assessment evaluation on a training task when the training task is finished, and the assessment evaluation unit comprises the following specific steps:
step SS 1: after the training task is finished, automatically generating a training thinking guide picture according to chapter contents of training data, and erasing all character contents to enable staff to fill the character contents;
step SS 2: the staff make examination time appointment through the mobile phone terminal, the examination and evaluation unit uniformly schedules the staff who finish the same training content in one week, the examination time is determined, and the corresponding staff is informed through the mobile phone terminal;
step SS 3: performing theoretical knowledge assessment and case analysis discussion on the employees participating in assessment, performing mutual assessment on the employees participating in assessment in the case analysis discussion link, and inputting mutual assessment results through a mobile phone terminal;
step SS 4: substituting the times of theoretical knowledge assessment results, mutual assessment results, training thinking guide graph filling accuracy and progress abnormity into a calculation formula to obtain a total score, wherein the calculation formula is as follows: the total score is (theoretical knowledge assessment score a + mutual assessment score b) training thinking guide graph filling accuracy rate-the number of times of progress abnormality occurrence 5, wherein a represents a theoretical knowledge assessment proportion coefficient, b represents a case analysis discussion proportion coefficient, a and b are preset values, a + b is 1, the number of times of progress abnormality occurrence 5 represents that the progress abnormality occurs once, namely 5 scores are deducted from the score;
step SS 5: and comparing the total score with a preset qualification score line, judging that the training assessment is qualified when the total score is larger than the qualification score line, otherwise judging that the training assessment is unqualified, replying the training content by the staff with unqualified training assessment, sending a relearning instruction to the progress follow-up unit, and replying corresponding task allocation by the progress follow-up unit.
The course improvement unit is used for integrating the problems and the corresponding measures encountered in the actual engineering into a training case by the staff, setting keywords for the training case, and transmitting the training case to the corresponding classification of the data storage unit after the training case is audited by the system management staff, so that the practical value and the operability of the training are improved.
When the system is used, firstly, personnel register and upload and store personal information through a register and login unit, a data collection unit acquires and stores task data of the previous month and learning intention data of the personnel, an analysis and customization unit analyzes the personal information, the task data and the learning intention data of the personnel to obtain keywords matched with training contents of the personnel, and prepares proper plan training contents according to the keywords, after the plan training contents are sent to a progress follow-up unit, the progress follow-up unit sorts the obtained skill training data, the priority of the skill training data corresponding to the task type to be promoted is calibrated to be the highest priority of fixed training contents required by fixed training times of seasons and the lowest priority of skill training data corresponding to the learning intention data, and automatic task distribution is carried out according to the priorities, the training data processing method comprises the steps that a training thinking guide graph is automatically generated by an examination and evaluation unit after a training task is completed, the staff are filled, the integration capability of the staff on the whole training venation is improved, the filling accuracy of the training thinking guide graph and the number of times of progress abnormity occurring are substituted for comprehensive calculation when total score calculation is performed, a result is output, meanwhile, the staff can integrate problems and countermeasures encountered in actual work into a training case through a course improvement unit, keywords are set for the training case, the training case is transmitted to a corresponding classification of a data storage unit after being audited by system managers, training contents can be updated and supplemented in a targeted mode, and materials are provided for subsequent training.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. The utility model provides an staff training system based on thing networking which characterized in that, includes analysis customization unit and examination evaluation unit, wherein:
the analysis customizing unit is used for analyzing the personal information, the task data and the learning intention data of the staff so as to obtain keywords matched with the training content of the staff, and making proper plan training content according to the keywords;
the assessment evaluation unit is used for assessing and evaluating the staff participating in the training, the assessment is divided into theoretical knowledge assessment and case analysis discussion, and tasks are redistributed to the staff with unqualified assessment.
2. The staff training system based on the internet of things as claimed in claim 1, wherein personal information of staff is uploaded through a registration login unit and stored in a data storage unit.
3. The system for training employees based on the internet of things as claimed in claim 2, wherein the data storage unit is pre-stored with task types of each job in the company and standard processing time required for a single task corresponding to the task type, and skill training data required for each functional post.
4. The staff training system based on the Internet of things as claimed in claim 1, wherein task data and learning intention data are collected by a data collection unit, the collected task data are task data of staff in the previous month, and the learning intention data comprise position skill keywords and intention learning degree of staff intention learning.
5. The staff training system based on the internet of things as claimed in claim 1, wherein the planned training content formulated by the analysis and customization unit comprises skill training data corresponding to the post skill keyword and fixed training content required by the fixed training times in the quarter.
6. The Internet of things-based employee training system of claim 5, wherein the fixed number of training sessions per quarter is determined by the post age in the personal information, and the fixed number of training sessions per quarter varies in a direction opposite to the post age.
7. The staff training system based on the internet of things as claimed in claim 5, wherein the planned training content is sent to the progress follow-up unit, the progress follow-up unit performs priority calibration on the training content in the progress follow-up unit, automatic task distribution is performed according to the calibrated priority, training frequency determination is performed after distribution, and training content nodes to be completed every week are set.
8. The staff training system based on the Internet of things is characterized in that the progress follow-up unit carries out follow-up and reminding on distributed training tasks every week, a timer is arranged, the number of times of progress abnormity is recorded, and meanwhile message pushing reminding is carried out.
9. The system for training staff based on internet of things as claimed in claim 8, wherein the assessment and evaluation unit automatically generates a training mind map after the training task is completed, allows staff to fill in the training mind map, improves the staff's ability to integrate the whole training vein, and when performing total score calculation, substitutes the filling accuracy of the training mind map and the number of times of progress abnormality occurrence for comprehensive calculation, and outputs the result.
10. The system for training employees based on the internet of things as claimed in claim 1, further comprising a course improvement unit, wherein the course improvement unit is used for integrating the problems and the corresponding measures encountered in the actual work into a training case, setting keywords for the training case, and transmitting the training case to the corresponding classification of the data storage unit after the system manager audits the training case.
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