CN117593159A - Training management system and method - Google Patents
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Abstract
The invention provides a training management system and a training management method, which relate to the technical field of training management and comprise the following steps: the user management module is used for collecting and managing the identity information of the target user and training process data; the user classification module is used for classifying and marking the users; the state auditing module is used for constructing a state auditing model to audit the marked training state of the target user to obtain an auditing result; the report generation module is used for combining and analyzing the training process data with the auditing result to generate a training report; and an adjustment module: the method is used for generating a training investigation report for the analysis of the training report so as to adjust a preset training plan. Classifying the users by collecting and managing training process data of the users; establishing a state auditing model to obtain the marked training result of the user and evaluate the training result to generate a training report; and generating a training investigation report by combining the training report with analysis to effectively adjust the corresponding preset training plan so as to improve the training efficiency and effectiveness of the user.
Description
Technical Field
The invention relates to the technical field of training management, in particular to a training management system and method.
Background
The society is developed gradually, and most enterprises train staff for enhancing the competitive power and the cohesive force of the enterprises, improve the working quality and improve the creativity. For individuals, the society has great professional mobility and strong work competitiveness, and people usually choose to participate in training to improve own knowledge and skills in order to enhance employment ability and professional stability.
The rapid development of the Internet promotes the popularization and development of training, but at the same time, training generation data are scattered, and user training performance is not clear enough and can not be effectively adjusted to a preset training plan in time. In this context, it is particularly important how to quickly and effectively analyze the training results of the user by using the data generated in the training process, so as to adjust the training plan to improve the training management efficiency.
Accordingly, the present invention provides a training management system and method.
Disclosure of Invention
The invention provides a training management system and a training management method, which are used for classifying users by collecting and managing training process data of the users; establishing a state auditing model to obtain the marked training result of the user and evaluate the training result to generate a training report; and generating a training investigation report by combining the training report with analysis to effectively adjust the corresponding preset training plan so as to improve the training efficiency and effectiveness of the user.
The invention provides a training management system, comprising:
and a user management module: the system comprises a user interface, a training program, a user interface and a user interface, wherein the user interface is used for acquiring and managing identity information of a target user and training process data;
and a user classification module: the training process data are used for classifying and marking the target users after being analyzed;
a state auditing module: the method comprises the steps of extracting historical training process data of a plurality of historical users from a training database, constructing a state auditing model, and auditing the current training state of the target users after classification marking to obtain auditing results;
and a report generation module: the method comprises the steps of combining and analyzing training process data of each target user with auditing results to generate a training report;
and an adjustment module: the method is used for comprehensively analyzing the training report to generate a training investigation report so as to adjust a preset training plan.
Preferably, the user management module includes:
registration unit: when the target user sends a user identity registration request to the background management system by utilizing a user operation interface constructed at the user end, the background management system checks the identity input information of the target user after receiving the identity registration request of the target user;
after the verification is successful, setting corresponding user permission for the target user according to the identity input information;
an encryption unit: encrypting the identity input information of the target user and the corresponding user authority information, and uploading the encrypted identity input information and the corresponding user authority information to a user information storage library;
a login unit: when the target user sends a user identity login request to the background management system by utilizing the user operation interface, carrying out identity authentication on the target user based on the identity information in the user information storage library;
after the successful authentication of the target user identity is confirmed, the login is completed, login information is regenerated and transmitted to a learning supervision unit;
learning supervision unit: the method comprises the steps of monitoring training learning operation marks of a target user, and generating training learning progress data of the target user by combining login information after finishing training learning of the target user;
feedback management unit: the method is used for obtaining the grading grade of the training process after each training and learning of the target user is finished, and integrating the grading grade with the training and learning progress data to serve as training process data.
Preferably, the user classification module includes:
an evaluation unit: the training learning progress data normalization processing and analysis are used for obtaining an evaluation variable after normalization processing and analysis are carried out on training learning progress data in training process data in a preset time period of a user;
calculating a first learning progress evaluation index of the user in a preset time period based on the evaluation variable;
wherein, the calculation formula of the first learning progress evaluation index is as follows:
wherein Y is expressed as a first learning progress evaluation index; />Expressed as evaluation variables, are attendance frequency, learning progress and learning time ratio, respectively, wherein +.>；/>The weight of the evaluation variable aiming at the first learning progress evaluation index is represented as attendance frequency weight, learning progress weight and learning time duty ratio weight in sequence; />The training environment is expressed as an influence factor of training environment on learning progress;
classification unit: the training program classifying method comprises the steps of taking training projects as initial classifying standards, classifying users consistent with the training projects, and obtaining a first classifying result;
taking the first learning progress evaluation index as a reclassification standard, and respectively dividing the users in each first classification result according to the fact that the first learning progress evaluation index is lower than a preset low evaluation threshold value, higher than a preset high evaluation threshold value, higher than the preset low evaluation threshold value and lower than the preset high evaluation threshold value to obtain a second classification result;
a marking unit: for marking all users in the second classification result with the first learning progress evaluation index lower than the preset low evaluation threshold as low active;
marking all users in the second classification result with the first learning progress evaluation index higher than the preset low evaluation threshold and lower than the preset high evaluation threshold as being generally active;
and marking all users in the second classification result with the first learning progress evaluation index higher than the preset high evaluation threshold as high activity.
Preferably, the status auditing module includes:
a data processing unit: the system comprises a training database, a data processing module and a data processing module, wherein the training database is used for extracting historical result evaluation data in historical training process data of a plurality of historical users and carrying out data preprocessing on the historical result evaluation data in the historical training process data of the plurality of historical users;
screening the preprocessed historical result evaluation data by using a training target in a preset training plan to obtain first evaluation data;
the construction unit: dividing the first evaluation data into 10 samples equally, and taking each sample as a test sample and the rest 9 samples as training samples;
determining historical state auditing grades of the training samples and the test samples;
training a Bayesian algorithm based on the training sample and the corresponding historical state auditing level to obtain a state evaluation model;
an evaluation unit: performing performance evaluation on the state evaluation model, and if the performance evaluation reaches a preset standard, determining the state evaluation model as a state auditing model;
an auditing unit: the method comprises the steps of preprocessing result evaluation data in training process data of a target user after classification marking, and inputting the result evaluation data into a state auditing model for state auditing to obtain state auditing grade probability;
and according to the state auditing grade probability, selecting the state auditing grade with the maximum probability as an auditing result of the current training state of the target user.
Preferably, the evaluation unit includes:
an index calculation block: respectively inputting 10 test samples into the state evaluation model for testing to obtain corresponding test state indexes;
average value calculation block: average value calculation is carried out on the obtained test state indexes to obtain target indexes;
if the target index is larger than a preset performance threshold, judging that the performance evaluation of the state evaluation model reaches a preset standard, and determining that the state evaluation model is a state auditing model;
otherwise, the preprocessed result evaluation data is subjected to evaluation data rescreening so as to reconstruct a state auditing model.
Preferably, the report generating module includes:
a data analysis unit: the method comprises the steps of designing a table structure according to data characteristics of training projects, learning progress, learning time duty ratio, user scores, user active marks, user identities and auditing results to obtain report design requirements;
report generation unit: and after the initial report is created based on the report design requirement, inputting training process data and auditing results of the target user into the initial report, generating a training report, and storing the training report into a training database.
Preferably, the adjusting module includes:
report generation unit: the method comprises the steps of integrating training reports with the same training projects to obtain a first report group;
report analysis unit: analyzing the first report group to obtain the average learning progress, average score, activity and average state auditing result of the investigation analysis data user;
analyzing according to a preset training assessment index system based on the investigation analysis data to obtain user execution indexes and project reasonable indexes;
combining the user execution index, the project reasonable index and the investigation analysis data to generate a training investigation report of the training project corresponding to each first report group;
an adjusting unit: when the user execution index and the project reasonable index in the training investigation report are both in the preset standard range, the preset training program of the training project corresponding to the training investigation report is not required to be adjusted;
when the project reasonable index in the training investigation report is not in the preset standard range, the investigation analysis data is combined to integrally adjust a preset training plan of the training project corresponding to the training investigation report;
when the user execution index in the training investigation report is not in the preset standard range, training and learning are carried out on the target user design personal training plan under the training item corresponding to the training investigation report by combining the investigation analysis data.
The invention provides a training management method, which comprises the following steps:
step 1: acquiring and managing identity information of a target user and training process data;
step 2: after analyzing the training process data, classifying and marking the target users;
step 3: extracting historical training process data of a plurality of historical users from a training database, constructing a state auditing model, and auditing the current training state of the target users after classification marking to obtain auditing results;
step 4: combining and analyzing the training process data of each target user with the auditing result to generate a current training report;
step 5: and comprehensively analyzing the current training report to generate a training investigation report so as to make timely adjustment on a preset training plan.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a training management system in an embodiment of the present invention;
FIG. 2 is a flow chart of a training management method in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
An embodiment of the present invention provides a training management system, as shown in fig. 1, including:
and a user management module: the system comprises a user interface, a training program, a user interface and a user interface, wherein the user interface is used for acquiring and managing identity information of a target user and training process data;
and a user classification module: the training process data are used for classifying and marking the target users after being analyzed;
a state auditing module: the method comprises the steps of extracting historical training process data of a plurality of historical users from a training database, constructing a state auditing model, and auditing the current training state of the target users after classification marking to obtain auditing results;
and a report generation module: the method comprises the steps of combining and analyzing training process data of each target user with auditing results to generate a training report;
and an adjustment module: the method is used for comprehensively analyzing the training report to generate a training investigation report so as to adjust a preset training plan.
In this embodiment, the target user refers to a user who needs training; the identity information comprises name, gender, mobile phone number and initial training requirement; the training process data comprises training learning progress data and grading grades of the user on training, wherein the training learning progress data comprises training projects, project learning progress, attendance data, result evaluation data and learning time.
In this embodiment, the classification marking refers to classifying and actively marking the user by using the training items of the user and a first learning progress evaluation index as classification criteria, where the first learning progress evaluation index is calculated by using an evaluation variable obtained by screening from training learning progress data, and is used for evaluating training learning enthusiasm of the user, and the evaluation variable includes attendance frequency, learning progress and learning time duty ratio.
In the embodiment, the training database mainly comprises a historical user, training process data of the historical user, a historical state auditing grade and a historical training report; historical users refer to users who have training records in a training database; the historical training process data refers to training process data of a historical user, and consists of historical training learning progress data and historical grading grades; the state auditing model is a model obtained by using samples extracted from a training database, introducing a Bayesian algorithm to perform model training and performance evaluation, and is used for obtaining different auditing grade probabilities of the current training state of the target user, wherein the auditing grade comprises four types of difference, general, good and excellent.
In this embodiment, the current training status audit refers to an assessment of the training outcome status of the target user; the auditing result refers to the state auditing grade with highest probability; the training report is a report generated based on training process data and auditing results of the target user; the training investigation report refers to a deep training data research file obtained by integrating the training report with the same training project and then analyzing the content of the training report by combining a preset training examination index system; the preset training program is preset and comprises a training target, a training duration, a training task and the like.
The beneficial effects of the technical scheme are as follows: classifying the users by collecting and managing training process data of the users; establishing a state auditing model to obtain the marked training result of the user and evaluate the training result to generate a training report; and generating a training investigation report by combining the training report with analysis to effectively adjust the corresponding preset training plan so as to improve the training efficiency and effectiveness of the user.
The embodiment of the invention provides a training management system, wherein the user management module comprises:
registration unit: when the target user sends a user identity registration request to the background management system by utilizing a user operation interface constructed at the user end, the background management system checks the identity input information of the target user after receiving the identity registration request of the target user;
after the verification is successful, setting corresponding user permission for the target user according to the identity input information;
an encryption unit: encrypting the identity input information of the target user and the corresponding user authority information, and uploading the encrypted identity input information and the corresponding user authority information to a user information storage library;
a login unit: when the target user sends a user identity login request to the background management system by utilizing the user operation interface, carrying out identity authentication on the target user based on the identity information in the user information storage library;
after the successful authentication of the target user identity is confirmed, the login is completed, login information is regenerated and transmitted to a learning supervision unit;
learning supervision unit: the method comprises the steps of monitoring training learning operation marks of a target user, and generating training learning progress data of the target user by combining login information after finishing training learning of the target user;
feedback management unit: the method is used for obtaining the grading grade of the training process after each training and learning of the target user is finished, and integrating the grading grade with the training and learning progress data to serve as training process data.
In this embodiment, the target user refers to a user who needs training; the user end refers to a computer connected to a network, receives control and management of a network server, and can share network resources for a target user; the user operation interface is used for user identity registration and user login system; the background management system is used for processing user identity registration, auditing and login requests; the identity input information includes name, gender, mobile phone number and initial training requirements.
In this embodiment, the user rights refer to the access rights and resources that the user is authorized to access; the user information storage library consists of identity input information encrypted by the target user; identity authentication refers to the identity confirmation process of a logged-in user in a training management system; the login information comprises login time, login ip and login user identity information.
In the embodiment, the training learning operation trace refers to a training project browsing trace, a project adding and exiting trace and corresponding data acquisition after a user successfully logs in; the training learning progress data is composed of training projects, project learning progress, attendance data, result evaluation data and learning time data, wherein the training projects are obtained by integrating and analyzing training learning operation marks and login information; the grading grade is used for representing feedback evaluation of training data and training courses after each training of the target user is finished, and the grade is divided into one grade to three grades; training process data the training process data is composed of training learning progress data and a grade of the user's score for training.
The beneficial effects of the technical scheme are as follows: checking the registered new user identity, setting authority and encrypting and storing the identity; and generating login information, supervised training learning operation traces and grading grade after user training are finished for generating training process data for login user identity authentication, so that effective acquisition and management of user identity information and training process data are realized, and a foundation is laid for subsequent data calculation.
The embodiment of the invention provides a training management system, wherein the user classification module comprises:
an evaluation unit: the training learning progress data normalization processing and analysis are used for obtaining an evaluation variable after normalization processing and analysis are carried out on training learning progress data in training process data in a preset time period of a user;
calculating a first learning progress evaluation index of the user in a preset time period based on the evaluation variable;
wherein, the calculation formula of the first learning progress evaluation index is as follows:
wherein Y is expressed as a first learning progress evaluation index; />Expressed as evaluation variables, are attendance frequency, learning progress and learning time ratio, respectively, wherein +.>；/>The weight of the evaluation variable aiming at the first learning progress evaluation index is represented as attendance frequency weight, learning progress weight and learning time duty ratio weight in sequence; />The training environment is expressed as an influence factor of training environment on learning progress;
classification unit: the training program classifying method comprises the steps of taking training projects as initial classifying standards, classifying users consistent with the training projects, and obtaining a first classifying result;
taking the first learning progress evaluation index as a reclassification standard, and respectively dividing the users in each first classification result according to the fact that the first learning progress evaluation index is lower than a preset low evaluation threshold value, higher than a preset high evaluation threshold value, higher than the preset low evaluation threshold value and lower than the preset high evaluation threshold value to obtain a second classification result;
a marking unit: for marking all users in the second classification result with the first learning progress evaluation index lower than the preset low evaluation threshold as low active;
marking all users in the second classification result with the first learning progress evaluation index higher than the preset low evaluation threshold and lower than the preset high evaluation threshold as being generally active;
and marking all users in the second classification result with the first learning progress evaluation index higher than the preset high evaluation threshold as high activity.
In this embodiment, the preset time period is set in advance; the training process data comprises training learning progress data and a grading grade of a user for training, wherein the training learning progress data comprises training projects, project learning progress, attendance and learning time; the evaluation variables comprise attendance frequency, learning progress and learning time duty ratio; the first learning progress evaluation index is calculated by using evaluation variables obtained by screening from training learning progress data and is used for evaluating the training learning enthusiasm of the user.
In this embodiment, the primary classification criterion refers to a primary classification specification of the target user; the first classification result is a combination obtained by primarily dividing the user according to the training items as the classification standard; reclassifying criteria; means a provision for reclassifying the first classification result; the preset low evaluation threshold is set in advance; the preset high evaluation threshold is set in advance.
The beneficial effects of the technical scheme are as follows: the training progress data is screened to obtain the evaluation variables to obtain the first learning progress evaluation index, and the training items are taken as classification standards together to carry out classification marking on the users, so that effective management of the users is facilitated.
The embodiment of the invention provides a training management system, wherein the state auditing module comprises:
a data processing unit: the system comprises a training database, a data processing module and a data processing module, wherein the training database is used for extracting historical result evaluation data in historical training process data of a plurality of historical users and carrying out data preprocessing on the historical result evaluation data in the historical training process data of the plurality of historical users;
screening the preprocessed historical result evaluation data by using a training target in a preset training plan to obtain first evaluation data;
the construction unit: dividing the first evaluation data into 10 samples equally, and taking each sample as a test sample and the rest 9 samples as training samples;
determining historical state auditing grades of the training samples and the test samples;
training a Bayesian algorithm based on the training sample and the corresponding historical state auditing level to obtain a state evaluation model;
an evaluation unit: performing performance evaluation on the state evaluation model, and if the performance evaluation reaches a preset standard, determining the state evaluation model as a state auditing model;
an auditing unit: the method comprises the steps of preprocessing result evaluation data in training process data of a target user after classification marking, and inputting the result evaluation data into a state auditing model for state auditing to obtain state auditing grade probability;
and according to the state auditing grade probability, selecting the state auditing grade with the maximum probability as an auditing result of the current training state of the target user.
In the embodiment, the training database mainly comprises a user name, training process data, a state auditing grade and a training report; historical users refer to users who have training records in a training database; the historical training process data refers to training process data of a historical user, and consists of historical training learning progress data and historical grading grades; the historical result evaluation data comprises operation data and assessment data; the preset training program is preset and comprises a training target, a training duration, a training task and the like.
In this embodiment, the first evaluation data is data obtained by screening the preprocessed historical result evaluation data by using a training target; historical state audit levels include bad, general, good, and excellent four; the state evaluation model is obtained by using samples extracted from a training database, and introducing a Bayesian algorithm to perform model training; the preset standard is set in advance; the state auditing model is a state evaluation model with performance evaluation reaching standards and is used for acquiring different auditing grade probabilities of the current training state of the target user.
In this embodiment, for example, there is result evaluation data X1 after preprocessing of the target user A1, and the data X1 is input into the state audit model to obtain probabilities of four states of poor, general, good and excellent audit levels, wherein the audit level has the highest probability of being excellent, and at this time, the excellent is taken as the audit result of the current training state of the target user A1.
The beneficial effects of the technical scheme are as follows: the historical result data extracted from the training database is screened to obtain first evaluation data serving as a sample, a Bayesian algorithm training and verification model is introduced to obtain a state auditing model, so that the state auditing of the user is carried out, and the auditing grade of the current training state is obtained, and the current training effect of the target user is effectively obtained.
The embodiment of the invention provides a training management system, wherein the evaluation unit comprises:
an index calculation block: respectively inputting 10 test samples into the state evaluation model for testing to obtain corresponding test state indexes;
average value calculation block: average value calculation is carried out on the obtained test state indexes to obtain target indexes;
if the target index is larger than a preset performance threshold, judging that the performance evaluation of the state evaluation model reaches a preset standard, and determining that the state evaluation model is a state auditing model;
otherwise, the preprocessed result evaluation data is subjected to evaluation data rescreening so as to reconstruct a state auditing model.
In the embodiment, the state evaluation model is obtained by using samples extracted from a training database, introducing a Bayesian algorithm to perform model training; the test state index is obtained by inputting a test sample into a state evaluation model for testing; the target index is the average of all test state indexes; the preset performance threshold is set in advance and is used for judging the performance condition of the model; the state auditing model is a state evaluation model with performance evaluation reaching standards and is used for acquiring different auditing grade probabilities of the current training state of the target user; the result evaluation data mainly refers to job data and assessment data.
The beneficial effects of the technical scheme are as follows: and evaluating the performance of the model by acquiring the average value of test state indexes obtained by inputting different test samples into the model test, so as to ensure the auditing accuracy of the state auditing model.
The embodiment of the invention provides a training management system, wherein the report generation module comprises:
a data analysis unit: the method comprises the steps of designing a table structure according to data characteristics of training projects, learning progress, learning time duty ratio, user scores, user active marks, user identities and auditing results to obtain report design requirements;
report generation unit: and after the initial report is created based on the report design requirement, inputting training process data and auditing results of the target user into the initial report, generating a training report, and storing the training report into a training database.
In this embodiment, report design requirements are determined based on data characteristics of training items, learning progress, learning time duty, user scores, user activity markers, user identities, and auditing results; the initial report is a report created according to the report design requirement; the training report is a report generated after the training process data and the auditing result of the target user are input into the initial report and is used for recording the training data of the user; the training database mainly comprises a user name, training process data, a state auditing grade and a training report.
The beneficial effects of the technical scheme are as follows: the training process data and the auditing result are imported into the initial report designed according to the report design requirement determined according to the data characteristics to generate the training report, so that the training progress of the current training project of the user can be effectively reflected in a straight line.
The embodiment of the invention provides a training management system, wherein the adjusting module comprises:
report generation unit: the method comprises the steps of integrating training reports with the same training projects to obtain a first report group;
report analysis unit: analyzing the first report group to obtain the average learning progress, average score, activity and average state auditing result of the investigation analysis data user;
analyzing according to a preset training assessment index system based on the investigation analysis data to obtain user execution indexes and project reasonable indexes;
combining the user execution index, the project reasonable index and the investigation analysis data to generate a training investigation report of the training project corresponding to each first report group;
an adjusting unit: when the user execution index and the project reasonable index in the training investigation report are both in the preset standard range, the preset training program of the training project corresponding to the training investigation report is not required to be adjusted;
when the project reasonable index in the training investigation report is not in the preset standard range, the investigation analysis data is combined to integrally adjust a preset training plan of the training project corresponding to the training investigation report;
when the user execution index in the training investigation report is not in the preset standard range, training and learning are carried out on the target user design personal training plan under the training item corresponding to the training investigation report by combining the investigation analysis data.
In this embodiment, the first report group refers to a training report set with the same training items; the investigation analysis data comprise average learning progress, average score, liveness and average state auditing results of the users, and are integration of training performance data of all users under the same training item; the preset training assessment index system is set in advance and is used for acquiring user execution indexes and project reasonable indexes based on investigation analysis data, wherein the user execution indexes are used for representing training liveness and participation of all users under the same training project, and the project reasonable indexes are used for representing objectivity and effectiveness of a preset training program corresponding to the training project.
In the embodiment, the training investigation report refers to a deep training data research file obtained by analyzing the content of the training report by combining a preset training assessment index system after integrating the training reports with the same training project; the preset standard range is preset in advance; the personal training program refers to a retraining program for personal situations for all users under the training program corresponding to the user execution index that is not within the preset standard range.
The beneficial effects of the technical scheme are as follows: the investigation analysis data is obtained by integrating and analyzing all the training reports of the same training project, and the training investigation report is generated based on a preset training examination index system to adjust the corresponding preset training plan so as to improve training efficiency and flexibility.
The invention provides a training management method, as shown in fig. 2, comprising the following steps:
step 1: acquiring and managing identity information of a target user and training process data;
step 2: after analyzing the training process data, classifying and marking the target users;
step 3: extracting historical training process data of a plurality of historical users from a training database, constructing a state auditing model, and auditing the current training state of the target users after classification marking to obtain auditing results;
step 4: combining and analyzing the training process data of each target user with the auditing result to generate a current training report;
step 5: and comprehensively analyzing the current training report to generate a training investigation report so as to make timely adjustment on a preset training plan.
The beneficial effects of the technical scheme are as follows: classifying the users by collecting and managing training process data of the users; establishing a state auditing model to obtain the marked training result of the user and evaluate the training result to generate a training report; and generating a training investigation report by combining the training report with analysis to effectively adjust the corresponding preset training plan so as to improve the training efficiency and effectiveness of the user.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. A training management system, comprising:
and a user management module: the system comprises a user interface, a training program, a user interface and a user interface, wherein the user interface is used for acquiring and managing identity information of a target user and training process data;
and a user classification module: the training process data are used for classifying and marking the target users after being analyzed;
a state auditing module: the method comprises the steps of extracting historical training process data of a plurality of historical users from a training database, constructing a state auditing model, and auditing the current training state of the target users after classification marking to obtain auditing results;
and a report generation module: the method comprises the steps of combining and analyzing training process data of each target user with auditing results to generate a training report;
and an adjustment module: the method is used for comprehensively analyzing the training report to generate a training investigation report so as to adjust a preset training plan.
2. The training management system of claim 1, wherein the user management module comprises:
registration unit: when the target user sends a user identity registration request to the background management system by utilizing a user operation interface constructed at the user end, the background management system checks the identity input information of the target user after receiving the identity registration request of the target user;
after the verification is successful, setting corresponding user permission for the target user according to the identity input information;
an encryption unit: encrypting the identity input information of the target user and the corresponding user authority information, and uploading the encrypted identity input information and the corresponding user authority information to a user information storage library;
a login unit: when the target user sends a user identity login request to the background management system by utilizing the user operation interface, carrying out identity authentication on the target user based on the identity information in the user information storage library;
after the successful authentication of the target user identity is confirmed, the login is completed, login information is regenerated and transmitted to a learning supervision unit;
learning supervision unit: the method comprises the steps of monitoring training learning operation marks of a target user, and generating training learning progress data of the target user by combining login information after finishing training learning of the target user;
feedback management unit: the method is used for obtaining the grading grade of the training process after each training and learning of the target user is finished, and integrating the grading grade with the training and learning progress data to serve as training process data.
3. The training management system of claim 1, wherein the user classification module comprises:
an evaluation unit: the training learning progress data normalization processing and analysis are used for obtaining an evaluation variable after normalization processing and analysis are carried out on training learning progress data in training process data in a preset time period of a user;
calculating a first learning progress evaluation index of the user in a preset time period based on the evaluation variable;
wherein, the calculation formula of the first learning progress evaluation index is as follows:
wherein Y is expressed as a first learning progress evaluation index; />Expressed as evaluation variables, are attendance frequency, learning progress and learning time ratio, respectively, wherein +.>；/>The weight of the evaluation variable aiming at the first learning progress evaluation index is represented as attendance frequency weight, learning progress weight and learning time duty ratio weight in sequence; />The training environment is expressed as an influence factor of training environment on learning progress;
classification unit: the training program classifying method comprises the steps of taking training projects as initial classifying standards, classifying users consistent with the training projects, and obtaining a first classifying result;
taking the first learning progress evaluation index as a reclassification standard, and respectively dividing the users in each first classification result according to the fact that the first learning progress evaluation index is lower than a preset low evaluation threshold value, higher than a preset high evaluation threshold value, higher than the preset low evaluation threshold value and lower than the preset high evaluation threshold value to obtain a second classification result;
a marking unit: for marking all users in the second classification result with the first learning progress evaluation index lower than the preset low evaluation threshold as low active;
marking all users in the second classification result with the first learning progress evaluation index higher than the preset low evaluation threshold and lower than the preset high evaluation threshold as being generally active;
and marking all users in the second classification result with the first learning progress evaluation index higher than the preset high evaluation threshold as high activity.
4. The training management system of claim 1, wherein the status auditing module comprises:
a data processing unit: the system comprises a training database, a data processing module and a data processing module, wherein the training database is used for extracting historical result evaluation data in historical training process data of a plurality of historical users and carrying out data preprocessing on the historical result evaluation data in the historical training process data of the plurality of historical users;
screening the preprocessed historical result evaluation data by using a training target in a preset training plan to obtain first evaluation data;
the construction unit: dividing the first evaluation data into 10 samples equally, and taking each sample as a test sample and the rest 9 samples as training samples;
determining historical state auditing grades of the training samples and the test samples;
training a Bayesian algorithm based on the training sample and the corresponding historical state auditing level to obtain a state evaluation model;
an evaluation unit: performing performance evaluation on the state evaluation model, and if the performance evaluation reaches a preset standard, determining the state evaluation model as a state auditing model;
an auditing unit: the method comprises the steps of preprocessing result evaluation data in training process data of a target user after classification marking, and inputting the result evaluation data into a state auditing model for state auditing to obtain state auditing grade probability;
and according to the state auditing grade probability, selecting the state auditing grade with the maximum probability as an auditing result of the current training state of the target user.
5. The training management system according to claim 4, wherein the evaluation unit comprises:
an index calculation block: respectively inputting 10 test samples into the state evaluation model for testing to obtain corresponding test state indexes;
average value calculation block: average value calculation is carried out on the obtained test state indexes to obtain target indexes;
if the target index is larger than a preset performance threshold, judging that the performance evaluation of the state evaluation model reaches a preset standard, and determining that the state evaluation model is a state auditing model;
otherwise, the preprocessed result evaluation data is subjected to evaluation data rescreening so as to reconstruct a state auditing model.
6. The training management system of claim 1, wherein the report generation module comprises:
a data analysis unit: the method comprises the steps of designing a table structure according to data characteristics of training projects, learning progress, learning time duty ratio, user scores, user active marks, user identities and auditing results to obtain report design requirements;
report generation unit: and after the initial report is created based on the report design requirement, inputting training process data and auditing results of the target user into the initial report, generating a training report, and storing the training report into a training database.
7. The training management system of claim 1, wherein the adjustment module comprises:
report generation unit: the method comprises the steps of integrating training reports with the same training projects to obtain a first report group;
report analysis unit: analyzing the first report group to obtain the average learning progress, average score, activity and average state auditing result of the investigation analysis data user;
analyzing according to a preset training assessment index system based on the investigation analysis data to obtain user execution indexes and project reasonable indexes;
combining the user execution index, the project reasonable index and the investigation analysis data to generate a training investigation report of the training project corresponding to each first report group;
an adjusting unit: when the user execution index and the project reasonable index in the training investigation report are both in the preset standard range, the preset training program of the training project corresponding to the training investigation report is not required to be adjusted;
when the project reasonable index in the training investigation report is not in the preset standard range, the investigation analysis data is combined to integrally adjust a preset training plan of the training project corresponding to the training investigation report;
when the user execution index in the training investigation report is not in the preset standard range, training and learning are carried out on the target user design personal training plan under the training item corresponding to the training investigation report by combining the investigation analysis data.
8. A training management method, comprising:
step 1: acquiring and managing identity information of a target user and training process data;
step 2: after analyzing the training process data, classifying and marking the target users;
step 3: extracting historical training process data of a plurality of historical users from a training database, constructing a state auditing model, and auditing the current training state of the target users after classification marking to obtain auditing results;
step 4: combining and analyzing the training process data of each target user with the auditing result to generate a current training report;
step 5: and comprehensively analyzing the current training report to generate a training investigation report so as to make timely adjustment on a preset training plan.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102081759A (en) * | 2009-11-30 | 2011-06-01 | 鸿富锦精密工业（深圳）有限公司 | Training management system |
| CN107180284A (en) * | 2017-07-07 | 2017-09-19 | 北京航空航天大学 | A kind of SPOC student based on learning behavior feature shows weekly Forecasting Methodology and device |
| CN112581329A (en) * | 2019-09-29 | 2021-03-30 | 中国石油化工股份有限公司 | Personalized learning and intelligent training management system and method |
| CN113435694A (en) * | 2021-05-20 | 2021-09-24 | 江苏谷德运维信息技术有限公司 | Enterprise talent information management system based on big data |
| CN113516572A (en) * | 2021-05-21 | 2021-10-19 | 武汉软艺信息技术有限公司 | Online learning statistics and evaluation system |
| CN114240221A (en) * | 2021-12-21 | 2022-03-25 | 杭州尚悠科技有限公司 | Remote training intelligent management system based on mobile internet |
| US11551321B1 (en) * | 2021-05-25 | 2023-01-10 | Formation Labs Inc. | Dynamic education planning methods and systems |
| CN116777225A (en) * | 2023-08-17 | 2023-09-19 | 清华大学 | Quantification method, device, computer equipment and medium for enterprise security risk level |
| CN117274000A (en) * | 2023-08-31 | 2023-12-22 | 重庆阿克索信息科技有限公司 | Training management system for medicine enterprise |
-
2024
- 2024-01-17 CN CN202410064878.5A patent/CN117593159A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102081759A (en) * | 2009-11-30 | 2011-06-01 | 鸿富锦精密工业（深圳）有限公司 | Training management system |
| CN107180284A (en) * | 2017-07-07 | 2017-09-19 | 北京航空航天大学 | A kind of SPOC student based on learning behavior feature shows weekly Forecasting Methodology and device |
| CN112581329A (en) * | 2019-09-29 | 2021-03-30 | 中国石油化工股份有限公司 | Personalized learning and intelligent training management system and method |
| CN113435694A (en) * | 2021-05-20 | 2021-09-24 | 江苏谷德运维信息技术有限公司 | Enterprise talent information management system based on big data |
| CN113516572A (en) * | 2021-05-21 | 2021-10-19 | 武汉软艺信息技术有限公司 | Online learning statistics and evaluation system |
| US11551321B1 (en) * | 2021-05-25 | 2023-01-10 | Formation Labs Inc. | Dynamic education planning methods and systems |
| CN114240221A (en) * | 2021-12-21 | 2022-03-25 | 杭州尚悠科技有限公司 | Remote training intelligent management system based on mobile internet |
| CN116777225A (en) * | 2023-08-17 | 2023-09-19 | 清华大学 | Quantification method, device, computer equipment and medium for enterprise security risk level |
| CN117274000A (en) * | 2023-08-31 | 2023-12-22 | 重庆阿克索信息科技有限公司 | Training management system for medicine enterprise |
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