Industrial injury prevention user data optimization system based on machine learning

Abstract

The invention discloses an industrial injury prevention user data optimization system based on machine learning, and relates to the technical field of industrial injury prevention, the industrial injury prevention user data optimization system comprises an industrial injury prevention training optimization system, a training enhancement module and a secondary evaluation module, the industrial injury prevention training optimization system comprises a teaching training management end, a central server and a cloud classroom user client, the central server is electrically connected with a user training module and a training management module, the user training module is electrically connected with a data recording module, and the data recording module is electrically connected with a data processing module. According to the industrial injury prevention user data optimization system based on machine learning, a training enhancement module is used for further performing industrial injury prevention training enhancement according to a user training comprehensive evaluation result, and a training optimization module can be used for performing intelligent training optimization on the basis of machine learning so as to realize optimization of industrial injury prevention user data. And the data quality of the industrial injury prevention training result of the user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of work injury prevention, and in particular to a user data optimization system for work injury prevention based on machine learning. Background Art

[0002] In recent years, work-related accidents have occurred frequently, sudden illnesses at work are increasingly occurring at a younger age, and the lack of prevention concepts and shortcomings in training and publicity have buried safety hazards from the source. Work-related injury prevention training, as an important part of enterprise safety management, is of great significance for protecting the lives of employees and reducing economic losses of enterprises.

[0003] The existing user data optimization system for work injury prevention generally adopts the same set of work injury prevention training models for enterprise users. However, due to the different work injury prevention situations in different positions and the different understandings of work injury prevention by users in the same position, different safety hazards are prone to occur, and the best work injury prevention training cannot be effectively carried out. Therefore, it is not conducive to the realization of work injury prevention user data optimization. To this end, we propose a work injury prevention user data optimization system based on machine learning. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a user data optimization system for work injury prevention based on machine learning, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a user data optimization system for work injury prevention based on machine learning, including a work injury prevention training optimization system, a training enhancement module and a secondary evaluation module, the work injury prevention training optimization system includes a teaching and training management terminal, a central server and a cloud classroom user client, the central server is electrically connected to a user training module and a training management module, and the user training module is electrically connected to a data recording module, the data recording module is electrically connected to a data processing module, and the data processing module is electrically connected to a training report generation module, the training report generation module is electrically connected to a file analysis module, and the file analysis module is electrically connected to a comprehensive evaluation module, the comprehensive evaluation module is electrically connected to a training enhancement module, and the training enhancement module includes a machine learning module, a sample training module and a training optimization module, the machine learning module is electrically connected to a sample training module, and the sample training module is electrically connected to a training optimization module, the training optimization module is electrically connected to a secondary evaluation module, and the secondary evaluation module is electrically connected to a comparative analysis module.

[0006] Furthermore, the teaching and training management terminal includes a training video management module and an examination management module, which are used for lecturers to upload and classify training videos and manage examination questions.

[0007] Furthermore, the cloud classroom user client includes a user information entry module and a work injury prevention training module, which are used to enter user information data and conduct work injury prevention training for users.

[0008] Furthermore, the user training module includes an online sign-in module, a video learning module, a question-answering and examination module, and a course feedback module, and the online sign-in module, video learning module, question-answering and examination module, and course feedback module are all connected in parallel.

[0009] Furthermore, the data recording module is used to record user training data, the data processing module is used to analyze and process the user training data, and the training report generating module is used to automatically generate a user training report file according to the user training data processing result.

[0010] Furthermore, the file analysis module is used to analyze the user training report files, the comprehensive evaluation module is used to comprehensively evaluate the user's work injury prevention training results based on the file analysis results, and the training reinforcement module is used to further strengthen the work injury prevention training according to the user training comprehensive evaluation results.

[0011] Furthermore, the machine learning module models the course matching model for user training through machine learning, and then the sample training module uses historical user data as the data set of the training model for sample training, and then through learning and analyzing the training samples, it extracts the characteristics and patterns of user training and obtains the best course matching model, and then the training optimization module is used for intelligent training optimization based on machine learning.

[0012] Furthermore, the training optimization module includes a feature extraction module, a weak correlation module and a training matching module, the feature extraction module is electrically connected to the weak correlation module, and the weak correlation module is electrically connected to the training matching module.

[0013] Furthermore, the feature extraction module is used to extract the user's workstation behavior characteristics based on user information based on comprehensive training evaluation; the weak association module is used to extract the work injury prevention weak module associated with the user's position based on user characteristics; the training matching module is used to adjust the course matching degree based on user weak association and according to the results of user course feedback, and perform targeted course matching.

[0014] Furthermore, the secondary evaluation module is used to conduct a secondary evaluation of the training results after the training is strengthened, and the comparative analysis module is used to conduct a comprehensive comparison between the secondary evaluation and the initial evaluation to detect the effect of the user's work injury prevention training and ensure the effectiveness of the work injury prevention training.

[0015] The present invention provides a user data optimization system for preventing work-related injuries based on machine learning, which has the following beneficial effects:

[0016] 1. The user data optimization system for work injury prevention based on machine learning is equipped with a user training module. The cloud classroom user client enables users to learn work injury prevention knowledge or participate in training anytime and anywhere; users participate in online sign-in, learning, and examinations through the online sign-in module, video learning module, and question-answering examination module of the user training module, forming attendance data and examination data, which is conducive to the automatic statistics and archiving of the system in the later stage, and the generation of training files, which is convenient for managers to manage and summarize. At the same time, surveys are conducted through the course feedback module to facilitate the retention of satisfaction survey data and feedback on users' preferences for training video instructors, which makes up for the defects of traditional offline teaching that are difficult to uniformly manage and retain data.

[0017] 2. The user data optimization system for work injury prevention based on machine learning is provided with a comprehensive evaluation module. The data recording module is used to record user training data, the data processing module is used to analyze and process user training data, and the training report generation module is used to automatically generate the user's training report file according to the user training data processing results; the file analysis module is used to analyze the user training report file, and the comprehensive evaluation module is used to comprehensively evaluate the user's work injury prevention training results based on the file analysis results.

[0018] 3. The user data optimization system for work injury prevention based on machine learning is provided with a training reinforcement module, which is used to further strengthen the work injury prevention training according to the comprehensive evaluation results of user training. The machine learning module can model the course selection matching model for user training by machine learning, and then the sample training module can use the historical user data as the data set of the training model for sample training, and then through the study and analysis of the training samples, the characteristics and patterns of user training can be extracted, and the optimal course selection matching model can be obtained.

[0019] 4. The user data optimization system for work injury prevention based on machine learning is provided with a training optimization module, which can be used for intelligent training optimization based on machine learning. The feature extraction module is used to extract the user's work station behavior characteristics according to user information based on comprehensive training evaluation. The weak association module is used to extract the weak modules for work injury prevention associated with the user's position according to user characteristics. The training matching module is used to adjust the course matching degree based on the user's weak association and according to the results of user course feedback, and perform targeted course matching.

[0020] 5. The machine learning-based work injury prevention user data optimization system is equipped with a secondary evaluation module. The secondary evaluation module process is consistent with the comprehensive evaluation module. The secondary evaluation module is used to conduct a secondary evaluation of the training results after the training is strengthened. The comparative analysis module is used to conduct a comprehensive comparison between the secondary evaluation and the initial evaluation, detect the effect of the user's work injury prevention training, ensure the effectiveness of the work injury prevention training, and then realize the optimization of the user data of work injury prevention and improve the data quality of the user's work injury prevention training results. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of the system framework of the user data optimization system for preventing work-related injuries based on machine learning of the present invention;

[0022] Figure 2 It is a system flow diagram of the user data optimization system for preventing work-related injuries based on machine learning of the present invention;

[0023] Figure 3 A schematic diagram of a user training module of a user data optimization system for work-related injury prevention based on machine learning according to the present invention;

[0024] Figure 4 A schematic diagram of a training enhancement module of a user data optimization system for preventing work-related injuries based on machine learning according to the present invention;

[0025] Figure 5 This is a schematic diagram of a training optimization module of the user data optimization system for work injury prevention based on machine learning in the present invention.

[0026] In the figure: 1. Work injury prevention training optimization system; 2. Lecture and training management terminal; 3. Central server; 4. Cloud classroom user client; 5. User training module; 501. Online check-in module; 502. Video learning module; 503. Question answering and examination module; 504. Course feedback module; 6. Data recording module; 7. Training management module; 8. Data processing module; 9. Training report generation module; 10. File analysis module; 11. Comprehensive evaluation module; 12. Training enhancement module; 13. Machine learning module; 14. Sample training module; 15. Training optimization module; 1501. Feature extraction module; 1502. Weak association module; 1503. Training matching module; 16. Secondary evaluation module; 17. Comparative analysis module. DETAILED DESCRIPTION

[0027] See also Figure 1-Figure 3The present invention provides a technical solution: a user data optimization system for industrial injury prevention based on machine learning, including an industrial injury prevention training optimization system 1, a training reinforcement module 12 and a secondary evaluation module 16, the industrial injury prevention training optimization system 1 includes a teaching and training management terminal 2, a central server 3 and a cloud classroom user client 4, the central server 3 is electrically connected with a user training module 5 and a training management module 7, the user training module 5 includes an online sign-in module 501, a video learning module 502, a question answering and examination module 503 and a course feedback module 504, the online sign-in module 501, the video learning module 502, the question answering and examination module 503 and the course feedback module 504 are all connected in parallel;

[0028] The specific operations are as follows: the teaching and training management terminal 2 includes a training video management module and an examination management module, which are used for lecturers to upload and classify training videos and manage examination questions; the cloud classroom user client 4 includes a user information entry module and an occupational injury prevention training module, which are used to enter user information data and conduct user occupational injury prevention training, so that users can learn occupational injury prevention knowledge or participate in training anytime and anywhere; users participate in online sign-in, learning, and examinations through the online sign-in module 501, video learning module 502, and answering examination module 503 of the user training module 5, forming attendance data and examination data, which is conducive to the subsequent automatic statistics and archiving of the system, generating training files, and facilitating managers to manage and summarize. At the same time, surveys are conducted through the course feedback module 504 to facilitate the retention of satisfaction survey data and feedback on users' preferences for training video lecturers, which makes up for the defects of traditional offline teaching that are difficult to uniformly manage and retain data.

[0029] See also Figure 1 , the user training module 5 is electrically connected to the data recording module 6, the data recording module 6 is electrically connected to the data processing module 8, and the data processing module 8 is electrically connected to the training report generating module 9, the training report generating module 9 is electrically connected to the file analyzing module 10, and the file analyzing module 10 is electrically connected to the comprehensive evaluation module 11;

[0030] The specific operations are as follows: the data recording module 6 is used to record the user training data; the data processing module 8 is used to analyze and process the user training data; the training report generation module 9 is used to automatically generate the user's training report file according to the user training data processing results; the file analysis module 10 is used to analyze the user training report file; the comprehensive evaluation module 11 is used to conduct a comprehensive evaluation of the user's work injury prevention training results based on the file analysis results.

[0031] See also Figure 1 and Figure 4The comprehensive evaluation module 11 is electrically connected to the training enhancement module 12, and the training enhancement module 12 includes a machine learning module 13, a sample training module 14 and a training optimization module 15, the machine learning module 13 is electrically connected to the sample training module 14, and the sample training module 14 is electrically connected to the training optimization module 15, the training optimization module 15 is electrically connected to the secondary evaluation module 16, and the secondary evaluation module 16 is electrically connected to the comparative analysis module 17;

[0032] The specific operations are as follows: the training reinforcement module 12 is used to further strengthen the work injury prevention training according to the comprehensive evaluation results of the user training, wherein the machine learning module 13 can model the course matching model of the user training by means of machine learning, and then the sample training module 14 can use the historical user data as the data set of the training model for sample training, and then through the learning and analysis of the training samples, extract the characteristics and patterns of the user training, and obtain the best course matching model, and then the training optimization module 15 can be used for intelligent training optimization based on machine learning; the secondary evaluation module 16 is used for secondary evaluation of the training results after the training is strengthened, and the comparative analysis module 17 is used for comprehensive comparison of the secondary evaluation and the initial evaluation to detect the effect of the user's work injury prevention training and ensure the effectiveness of the work injury prevention training.

[0033] See also Figure 5 The training optimization module 15 includes a feature extraction module 1501, a weak correlation module 1502 and a training matching module 1503, the feature extraction module 1501 is electrically connected to the weak correlation module 1502, and the weak correlation module 1502 is electrically connected to the training matching module 1503;

[0034] The specific operations are as follows: the feature extraction module 1501 is used to extract the user's workstation behavior characteristics based on user information based on comprehensive training evaluation; the weak association module 1502 is used to extract the work injury prevention weak module associated with the user's position based on user characteristics; the training matching module 1503 is used to adjust the course matching degree based on the user's weak association and according to the results of user course feedback, and perform targeted course matching.

[0035] In summary, please refer to Figure 1-Figure 5, when using the work injury prevention user data optimization system based on machine learning, first the teaching and training management terminal 2 can upload and classify training videos and manage examination questions, and then the work injury prevention user can log in to the cloud classroom user client 4 and enter information and conduct work injury prevention training. The cloud classroom user client 4 allows users to learn work injury prevention knowledge or participate in training anytime and anywhere. During use, users participate in online sign-in, learning, and examinations through the online sign-in module 501, video learning module 502, and answering examination module 503 of the user training module 5 to form attendance data and examination data. At the same time, surveys are conducted through the course feedback module 504 to retain satisfaction survey data and feedback users' preferences for training video instructors, which makes up for the defects of traditional offline teaching that are difficult to uniformly manage and difficult to retain data;

[0036] Then, the data recording module 6 is used to record the user training data, and then the data processing module 8 is used to analyze and process the user training data. Then, the training report generation module 9 can automatically generate the user's training report file according to the user training data processing result. Then, the file analysis module 10 can analyze the user training report file, so that the comprehensive evaluation module 11 can comprehensively evaluate the user's work injury prevention training results based on the file analysis results. Then, the training reinforcement module 12 further strengthens the work injury prevention training according to the comprehensive evaluation results of the user training. At this time, the machine learning module 13 can model the course selection matching model of the user training by machine learning. Then, the sample training module 14 can use the historical user data as the data set of the training model for sample training, and then extract the characteristics and patterns of the user training through learning and analysis of the training samples, and obtain the best course selection matching model;

[0037] Then the training optimization module 15 can be used for intelligent training optimization based on machine learning, wherein the feature extraction module 1501 can extract the user's workstation behavior characteristics based on the user information based on the comprehensive training evaluation, and then the weak association module 1502 is used to extract the work injury prevention weak modules associated with the user's position based on the user characteristics, and then the training matching module 1503 is used to adjust the course matching degree based on the user's weak association and the results of the user's course feedback, and perform targeted course matching, giving priority to matching the work injury prevention weak knowledge courses associated with the user's position, and matching the lecturer courses with a high degree of synergy with it, thereby realizing the optimization of the work injury prevention user data and improving the data quality of the user's work injury prevention training results;

[0038] After the training is strengthened, a secondary evaluation of the training results is carried out through the secondary evaluation module 16. The process of the secondary evaluation module 16 is consistent with that of the comprehensive evaluation module 11. Then, a comprehensive comparison between the secondary evaluation and the initial evaluation is carried out through the comparative analysis module 17 to detect the effect of the user's work injury prevention training and ensure the effectiveness of the work injury prevention training. If the effect of the enhanced training for work injury prevention does not reach the threshold, the training reinforcement module 12 is used to conduct another training reinforcement. In this way, the entire use process of the work injury prevention user data optimization system based on machine learning is completed.

[0039] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A user data optimization system for work injury prevention based on machine learning, characterized in that: The invention comprises an occupational injury prevention training optimization system (1), a training reinforcement module (12) and a secondary evaluation module (16); the occupational injury prevention training optimization system (1) comprises a teaching and training management terminal (2), a central server (3) and a cloud classroom user client (4); the central server (3) is electrically connected to a user training module (5) and a training management module (7); the user training module (5) is electrically connected to a data recording module (6); the data recording module (6) is electrically connected to a data processing module (8); the data processing module (8) is electrically connected to a training report generation module (9); the training report generation module (9) is electrically connected to an archive The file analysis module (10) is electrically connected to a comprehensive evaluation module (11), the comprehensive evaluation module (11) is electrically connected to a training enhancement module (12), and the training enhancement module (12) includes a machine learning module (13), a sample training module (14) and a training optimization module (15), the machine learning module (13) is electrically connected to a sample training module (14), and the sample training module (14) is electrically connected to a training optimization module (15), the training optimization module (15) is electrically connected to a secondary evaluation module (16), and the secondary evaluation module (16) is electrically connected to a comparative analysis module (17).

2. The user data optimization system for preventing work-related injuries based on machine learning according to claim 1, characterized in that: The teaching and training management terminal (2) comprises a training video management module and an examination management module, which are used for lecturers to upload and classify training videos and manage examination questions.

3. The user data optimization system for preventing work-related injuries based on machine learning according to claim 1, characterized in that: The cloud classroom user client (4) comprises a user information input module and a work injury prevention training module, which are used to input user information data and conduct work injury prevention training for users.

4. The user data optimization system for preventing work-related injuries based on machine learning according to claim 1, characterized in that: The user training module (5) comprises an online sign-in module (501), a video learning module (502), a question-answering and examination module (503) and a course feedback module (504), and the online sign-in module (501), the video learning module (502), the question-answering and examination module (503) and the course feedback module (504) are all connected in parallel.

5. The user data optimization system for preventing work-related injuries based on machine learning according to claim 1, characterized in that: The data recording module (6) is used to record user training data, the data processing module (8) is used to analyze and process the user training data, and the training report generating module (9) is used to automatically generate a user training report file based on the user training data processing result.

6. The user data optimization system for preventing work-related injuries based on machine learning according to claim 1, characterized in that: The file analysis module (10) is used to analyze the user training report file, the comprehensive evaluation module (11) is used to comprehensively evaluate the user's work injury prevention training results based on the file analysis results, and the training reinforcement module (12) is used to further strengthen the work injury prevention training based on the user training comprehensive evaluation results.

7. The user data optimization system for preventing work-related injuries based on machine learning according to claim 1, characterized in that: The machine learning module (13) models the course selection matching model for user training through machine learning, and then the sample training module (14) uses the historical user data as the data set of the training model for sample training, and then extracts the characteristics and patterns of user training through learning and analysis of the training samples, and obtains the best course selection matching model, and then the training optimization module (15) is used for intelligent training optimization based on machine learning.

8. The user data optimization system for preventing work-related injuries based on machine learning according to claim 1, characterized in that: The training optimization module (15) comprises a feature extraction module (1501), a weak correlation module (1502) and a training matching module (1503), wherein the feature extraction module (1501) is electrically connected to the weak correlation module (1502), and the weak correlation module (1502) is electrically connected to the training matching module (1503).

9. The user data optimization system for preventing work-related injuries based on machine learning according to claim 8, characterized in that: The feature extraction module (1501) is used to extract the user's workstation behavior characteristics based on user information based on comprehensive training evaluation. The weak association module (1502) is used to extract the work injury prevention weak module associated with the user's position based on user characteristics. The training matching module (1503) is used to adjust the course matching degree based on the user's weak association and according to the results of user course feedback, and perform targeted course matching.

10. The user data optimization system for preventing work-related injuries based on machine learning according to claim 1, characterized in that: The secondary evaluation module (16) is used to perform a secondary evaluation of the training results after the training is strengthened, and the comparative analysis module (17) is used to perform a comprehensive comparison between the secondary evaluation and the initial evaluation to detect the effect of the user's work injury prevention training and ensure the effectiveness of the work injury prevention training.

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