An intelligent generation system for online courses of protective skills
The intelligent generation system solves the problems of rigid and untargeted protective skills training, enabling personalized course generation and cyclical reinforcement of incorrect answers, thereby improving training effectiveness and responsiveness.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- THE 900TH HOSPITAL OF THE CHINESE PEOPLES LIBERATION ARMY JOINT LOGISTICS SUPPORT FORCE
- Filing Date
- 2026-04-24
- Publication Date
- 2026-05-29
AI Technical Summary
Existing protective skills training courses are standardized, lack specificity, employ a single training method, are difficult to form a closed loop, trainees forget skills quickly, course production efficiency is low, and they cannot respond quickly to updates to the protective guidelines for emerging infectious diseases.
Design an intelligent online course generation system for protective skills, including a demand collection module, a test point tag generation module, an intelligent course generation module, a learning execution module, and a closed-loop learning module for incorrect questions, to realize personalized course generation and cyclical reinforcement learning of incorrect questions.
It enables the generation of personalized courses based on the needs of different positions, ensuring that trainees fully master protective skills, improving training effectiveness, and reducing the risk of infection caused by improper operation.
Smart Images
Figure CN122115170A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of interdisciplinary technology of intelligent education and public health, and in particular to an intelligent generation system for online courses on protective skills. Background Technology
[0003] Personal protective equipment (PPE) is the last line of defense protecting healthcare workers from exposure to pathogens, and its correct use is directly related to their safety and the effectiveness of epidemic prevention and control. However, domestic and international experience shows that the qualification rate of PPE use among healthcare workers is generally low. Therefore, increasing training in protective technology is necessary to improve the safety of healthcare workers. Current protective technology training has the following shortcomings: 1. The training courses are standardized, without different levels or specializations, lacking specificity and failing to meet the needs of personnel in different positions such as hospitals and grassroots units; 2. The training methods are limited, mostly relying on face-to-face lectures or video instruction, making it difficult to provide focused and intensive training on high-risk and error-prone aspects such as PPE donning and doffing. 3. The training effect is difficult to form a closed loop. Trainees' wrong questions and incorrect operations cannot be consolidated in a timely and targeted manner, resulting in rapid forgetting of skills and non-standard practice after short-term training. 4. Course production relies on manual labor, which is inefficient and makes it difficult to quickly respond to updates to the prevention and control standards for emerging infectious diseases and the emergency training needs of public health emergencies.
[0004] Therefore, designing an intelligent online course generation system for protective skills that can solve the above-mentioned technical problems is a technical issue that needs to be addressed. Summary of the Invention
[0005] To address the aforementioned problems, the present invention aims to provide an intelligent online course generation system for protective skills, thereby resolving the issues of existing protective training courses being fixed, lacking specificity, and having limited effectiveness.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: including a demand acquisition module, a test point tag generation module, a course intelligent generation module, a learning execution module, and a closed-loop learning module for incorrect questions; The requirement acquisition module is used to obtain the training requirement parameters set by the user and convert the training requirement parameters into a standardized set of requirement tags. The test point tag generation module is used to pre-store the smallest granularity test point units of all protective skills, and each test point unit is bound to a corresponding multi-dimensional attribute tag. The intelligent course generation module is used to match the demand tag set with the attribute tags in the test point tag library, automatically filter and combine them to generate a complete personalized course containing learning content, practice content and assessment content. The learning execution module is used to push the personalized courses to students and record students' learning progress, practice results and assessment scores in real time. The closed-loop learning module for incorrect questions is used to identify incorrect questions and incorrect operation steps generated by students in practice and assessment, extract the corresponding test point unit tags, automatically reinsert the test point units that have not been mastered into the learning process of the current course, and cyclically push the learning, practice and retesting of the test point until the test point unit is mastered. The system determines that the course ends only when all test point units are mastered.
[0007] Furthermore, the training demand parameters acquired by the demand acquisition module include the type of training target, protection level, application scenario, total learning time, assessment type, and key reinforcement content.
[0008] Furthermore, the test point tag generation module includes multi-dimensional attribute tags such as: knowledge point tags, operation step tags, difficulty tags, applicable scope tags, assessment attribute tags, and error-prone point tags.
[0009] Furthermore, the learning content generated by the intelligent course generation module includes: theoretical lessons, illustrated explanations, standardized operation videos, and standardized process documents corresponding to the matching tags.
[0010] Furthermore, the practice content generated by the intelligent course generation module includes: step-by-step breakdown practice questions, operation simulation questions, and key step reinforcement training questions corresponding to the matching tags.
[0011] Furthermore, the assessment content generated by the intelligent course generation module includes: a theoretical question bank corresponding to the matching tags, a practical operation scoring sheet, and an assessment process guide.
[0012] Furthermore, the closed-loop learning module for incorrect answers specifically includes the following steps: Step S1: Collect records of incorrect answers and incorrect operations from trainees during practice and assessment; Step S2: Associate the unique test point unit tag corresponding to the erroneous content; Step S3: Mark the test point unit as unmastered and automatically insert it into the next learning node of the current course; Step S4: Push the reinforcement learning materials, special practice questions and retest questions for the test point unit in sequence; Step S5: If the retest result meets the standard, mark the test point unit as mastered and remove it from the learning list; if it does not meet the standard, repeat steps S3-S4 until it meets the standard.
[0013] Furthermore, the intelligent course generation module supports adding, removing, replacing, and sorting test point units in the course in real time based on user-adjusted requirements parameters, and dynamically updating learning, practice, and assessment content.
[0014] Furthermore, the test point tag generation module supports adding, modifying, and deleting test point units and their corresponding attribute tags, enabling continuous updates and maintenance of course content.
[0015] The present invention has the following beneficial effects: 1-This invention, through standardized demand collection and tagging conversion, allows the system to automatically filter and combine test point units based on parameters such as training target type, protection level, application scenario, learning duration, and assessment requirements, generating personalized courses that adapt to different needs and meet the training needs of health security personnel in different positions.
[0016] 2. The system of this invention automatically identifies wrong questions and incorrect operations in students' practice and assessment, extracts the corresponding test point units and re-inserts them into the learning process, and pushes reinforcement learning materials and retest content in a loop until all test points are met, ensuring that students fully master the protection skills, significantly improving the training effect and reducing the risk of infection caused by non-standard operation. Attached Figure Description
[0017] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation
[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: See Figure 1 As shown, this method includes a demand collection module, a test point tag generation module, a course intelligent generation module, a learning execution module, and a closed-loop learning module for incorrect questions; The requirement acquisition module is used to obtain training requirement parameters set by the user and convert these parameters into a standardized requirement tag set. Specifically, the requirement acquisition module provides a visual web interactive interface for training administrators to input training requirement parameters through drop-down selection, single selection, and multiple selection. The module has a built-in standardized tag mapping table that automatically converts the natural language parameters input by the user into a key-value pair format requirement tag set, which serves as the sole input basis for subsequent course generation. In this embodiment, the training requirement parameters obtained by the requirement acquisition module include: training target type: optional values are "hospital health personnel", "grassroots health personnel", and "emergency rescue team members"; protection level: optional values are "Level 1 protection", "Level 2 protection", and "Level 3 protection"; application scenario: optional values are "fever clinic", "makeshift hospital", "nucleic acid sampling point", "patient transfer", and "biological laboratory"; total learning time: optional values are "15-minute crash course", "1-hour standard course", and "4-hour intensive course"; assessment type: optional values are "theoretical assessment only", "practical assessment only", and "theoretical and practical assessment"; key reinforcement content: optional values are "PPE removal procedure", "hand hygiene", "contamination emergency treatment", and "fluorescent contamination prevention and control".
[0019] The test point tag generation module is used to pre-store the smallest granularity test point units of all protection skills, and each test point unit is bound to a corresponding multi-dimensional attribute tag; in this embodiment, each test point unit is the smallest teaching unit that cannot be further divided.
[0020] The intelligent course generation module is used to match the demand tag set with the attribute tags in the test point tag library, automatically filter and combine them to generate a complete personalized course containing learning content, practice content and assessment content. The learning execution module is used to push the personalized courses to students and record students' learning progress, practice results and assessment scores in real time. The closed-loop learning module for incorrect questions is used to identify incorrect questions and incorrect operation steps generated by students in practice and assessment, extract the corresponding test point unit tags, automatically reinsert the test point units that have not been mastered into the learning process of the current course, and cyclically push the learning, practice and retesting of the test point until the test point unit is mastered. The system determines that the course ends only when all test point units are mastered.
[0021] Furthermore, the training demand parameters acquired by the demand acquisition module include the type of training target, protection level, application scenario, total learning time, assessment type, and key reinforcement content.
[0022] Furthermore, the test point tag generation module includes multi-dimensional attribute tags such as: knowledge point tags, operation step tags, difficulty tags, applicable scope tags, assessment attribute tags, and error-prone point tags.
[0023] Furthermore, the learning content generated by the intelligent course generation module includes: theoretical lessons, illustrated explanations, standardized operation videos, and standardized process documents corresponding to the matching tags.
[0024] Furthermore, the practice content generated by the intelligent course generation module includes: step-by-step breakdown practice questions, operation simulation questions, and key step reinforcement training questions corresponding to the matching tags.
[0025] Furthermore, the assessment content generated by the intelligent course generation module includes: a theoretical question bank corresponding to the matching tags, a practical operation scoring sheet, and an assessment process guide.
[0026] Furthermore, the closed-loop learning module for incorrect answers specifically includes the following steps: Step S1: Collect records of incorrect answers and incorrect operations from trainees during practice and assessment; Step S2: Associate the unique test point unit tag corresponding to the erroneous content; Step S3: Mark the test point unit as unmastered and automatically insert it into the next learning node of the current course; Step S4: Push the reinforcement learning materials, special practice questions and retest questions for the test point unit in sequence; Step S5: If the retest result meets the standard, mark the test point unit as mastered and remove it from the learning list; if it does not meet the standard, repeat steps S3-S4 until it meets the standard.
[0027] Furthermore, the intelligent course generation module supports adding, removing, replacing, and sorting test point units in the course in real time based on user-adjusted requirements parameters, and dynamically updating learning, practice, and assessment content.
[0028] Furthermore, the test point tag generation module supports adding, modifying, and deleting test point units and their corresponding attribute tags, enabling continuous updates and maintenance of course content.
[0029] The workflow is roughly as follows: Training administrators select training requirement parameters on the requirements collection interface, and the system generates a corresponding set of requirement tags. The intelligent course generation module, based on the requirement tag set, selects core test point units from the test point tag generation module and automatically combines them to generate personalized courses. Trainees log into the system and learn, practice, and assess according to the course sequence. If a trainee makes an operational error during the practical assessment, the system records the error and extracts the corresponding test point tag. The error-correction closed-loop learning module marks these two test points as unmastered, automatically inserts them at the end of the course, and pushes reinforcement learning materials and specialized practice questions. Once trainees complete reinforcement learning and pass the retest, the system marks the two test points as mastered. When all test points are met, the system determines the course has ended.
[0030] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0031] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0032] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0033] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0034] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.
Claims
1. A smart online course generation system for protective skills, characterized in that: It includes a demand collection module, a test point tag generation module, a course intelligent generation module, a learning execution module, and a closed-loop learning module for incorrect questions; The requirement acquisition module is used to obtain the training requirement parameters set by the user and convert the training requirement parameters into a standardized set of requirement tags. The test point tag generation module is used to pre-store the smallest granularity test point units of all protective skills, and each test point unit is bound to a corresponding multi-dimensional attribute tag. The intelligent course generation module is used to match the demand tag set with the attribute tags in the test point tag library, automatically filter and combine them to generate a complete personalized course containing learning content, practice content and assessment content. The learning execution module is used to push the personalized courses to students and record students' learning progress, practice results and assessment scores in real time. The closed-loop learning module for incorrect questions is used to identify incorrect questions and incorrect operation steps generated by students in practice and assessment, extract the corresponding test point unit tags, automatically reinsert the test point units that have not been mastered into the learning process of the current course, and cyclically push the learning, practice and retesting of the test point until the test point unit is mastered. The system determines that the course ends only when all test point units are mastered.
2. The intelligent generation system for online courses on protective skills according to claim 1, characterized in that: The training demand parameters acquired by the demand acquisition module include the type of trainees, protection level, application scenario, total learning time, assessment type, and key reinforcement content.
3. The intelligent generation system for online courses on protective skills according to claim 1, characterized in that: The test point tag generation module includes multi-dimensional attribute tags such as: knowledge point tags, operation step tags, difficulty tags, applicable scope tags, assessment attribute tags, and common mistake tags.
4. The intelligent generation system for online courses on protective skills according to claim 1, characterized in that: The learning content generated by the intelligent course generation module includes: theoretical lessons, illustrated explanations, standardized operation videos, and standardized process documents corresponding to matching tags.
5. The intelligent generation system for online courses on protective skills according to claim 1, characterized in that: The practice content generated by the intelligent course generation module includes: step-by-step breakdown exercises, operation simulation exercises, and key step reinforcement training exercises corresponding to the matching tags.
6. The intelligent generation system for online courses on protective skills according to claim 1, characterized in that: The assessment content generated by the intelligent course generation module includes: a theoretical question bank corresponding to the matching tags, a practical operation scoring sheet, and an assessment process guide.
7. The intelligent generation system for online courses on protective skills according to claim 1, characterized in that: The closed-loop learning module for incorrect answers specifically includes the following steps: Step S1: Collect records of incorrect answers and incorrect operations from trainees during practice and assessment; Step S2: Associate the unique test point unit tag corresponding to the erroneous content; Step S3: Mark the test point unit as unmastered and automatically insert it into the next learning node of the current course; Step S4: Push the reinforcement learning materials, special practice questions and retest questions for the test point unit in sequence; Step S5: If the retest result meets the standard, mark the test point unit as mastered and remove it from the learning list; if it does not meet the standard, repeat steps S3-S4 until it meets the standard.
8. The intelligent generation system for online courses on protective skills according to claim 1, characterized in that: The intelligent course generation module supports adding, removing, replacing, and sorting test point units in the course in real time based on user-adjusted requirements parameters, and dynamically updating learning, practice, and assessment content.
9. The intelligent generation system for online courses on protective skills according to claim 1, characterized in that: The test point tag generation module supports adding, modifying, and deleting test point units and their corresponding attribute tags, enabling continuous updates and maintenance of course content.