CN120146801B - Training management system and method for adaptive learning path based on artificial intelligence - Google Patents
Training management system and method for adaptive learning path based on artificial intelligenceInfo
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Abstract
本发明公开了基于人工智能的适应性学习路径的培训管理系统及其方法，属于人工智能技术领域，本发明包括如下步骤：从数据库中获取各课程的课程信息，分析各课程的关键特征；对待管理对象划分到不同的对象集合中；对各待管理对象的学习课程进行筛选，形成技能等级的关联课程，分析技能等级的关联特征；标记出调整对象，记录调整对象的目标技能等级，分析调整对象的已具备特征和缺失特征，结合数据库中各课程的关键特征，筛选出培训课程集合；根据调整对象的培训课程集合，分析调整对象的最佳学习路径；本发明实现了课程与技能等级的精准匹配，克服了现有技术中培训路径不明确的问题，提高了资源管理的管理效率和培训体系的适应性。
The present invention discloses a training management system and method for an adaptive learning path based on artificial intelligence, which belongs to the field of artificial intelligence technology. The present invention comprises the following steps: obtaining course information of each course from a database and analyzing the key features of each course; dividing the objects to be managed into different object sets; screening the learning courses of each object to be managed, forming associated courses of skill levels, and analyzing the associated features of the skill levels; marking the adjustment objects, recording the target skill levels of the adjustment objects, analyzing the existing features and missing features of the adjustment objects, and screening out a training course set based on the key features of each course in the database; and analyzing the optimal learning path of the adjustment objects based on the training course set of the adjustment objects. The present invention realizes the precise matching of courses and skill levels, overcomes the problem of unclear training paths in the prior art, and improves the management efficiency of resource management and the adaptability of the training system.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to an adaptive learning path training management system and method based on artificial intelligence.
Background
With the development of the current society, the development of the technology level is improved, the development of the information technology is greatly changed, and the changes have great influence on the life of people and the production of society, especially in the industries of business fields, medical fields, manufacturing fields, financial fields and the like, and also have great influence on the storage of resource data. The artificial intelligent management resource data can better know management objects, discover talents and meet object requirements, so that a more efficient resource management system is constructed for enterprises;
However, when a person wants to improve the skill level of the person, the existing management system cannot form a clear training path to carry out planned learning training on the person to be improved, so that the person to be improved can quickly master the knowledge required by the target skill level, and the efficiency of resource management is reduced;
Thus, there is an urgent need for an artificial intelligence based adaptive learning path training management system to solve the above problems.
Disclosure of Invention
The invention aims to provide an artificial intelligence based training management system for adaptive learning paths, so as to solve the problems in the background art.
In order to solve the technical problems, the invention provides the following technical scheme:
the training management method of the adaptive learning path based on artificial intelligence comprises the following steps:
S1, acquiring course information of each course from a database, analyzing key characteristics of each course and storing the key characteristics in the database;
S2, acquiring learning records of all objects to be managed in the object set, screening learning courses of all objects to be managed to form associated courses of skill levels;
S3, marking an adjustment object, recording the target skill level of the adjustment object, analyzing the existing characteristics of the adjustment object according to the learning record of the adjustment object, analyzing the missing characteristics of the adjustment object according to the target skill level of the adjustment object, and screening a training course set by combining the key characteristics of each course in a database;
S4, analyzing the optimal learning path of the adjustment object according to the training course set of the adjustment object and combining all learning records in the database, and forming a recommended course list to push to the adjustment object.
According to the above technical solution, the step S1 includes the following steps:
S1-1, course information comprises course introduction and course content, keyword extraction is carried out on the course information of each course by using a natural language processing technology, and keywords corresponding to each course are used as key features of the corresponding course;
S1-2, the skill level comprises a primary object set, a middle object set and a high object set;
Dividing the object to be managed with the primary skill level into a primary object set according to the skill level of the object to be managed; dividing the object to be managed with the skill level of the middle level into a middle level object set;
The method comprises the steps of extracting course keywords through a natural language processing technology, ensuring accuracy of course feature extraction, improving accuracy of subsequent matching, classifying objects to be managed through skill levels, ensuring that people with different skill levels can form a reasonable learning set, and providing basic data for subsequent learning path planning.
According to the above technical solution, the step S2 includes the following steps:
S2-1, generating a learning record each time a subject to be managed performs a learning activity, wherein the learning record comprises courses, course completion degrees and test achievements;
S2-2, extracting all learning records of all objects to be managed in a certain object set, setting a first threshold and a second threshold, and screening out learning records with course completion degree larger than or equal to the first threshold and test score larger than or equal to the second threshold;
The corresponding courses of all the selected learning records are used as the associated courses of the skill levels corresponding to the object set;
s2-3, counting key features corresponding to the associated courses of a certain skill level according to the associated courses of the skill level, counting the occurrence times of the key features, and sorting the key features according to the occurrence times of the key features from large to small;
The learning record is screened to ensure that courses which are included into analysis are effectively learned and the reliability of data is ensured, and the core skill demands of different skill grades are defined through the ordering of key features to provide data support for learning path recommendation.
According to the above technical solution, the step S3 includes the following steps:
S3-1, when a certain object to be managed sends out a request for skill level adjustment, marking the object to be managed as an adjustment object;
s3-2, screening out learning records with the course completion degree larger than or equal to a first threshold value and the test result larger than or equal to a second threshold value according to all learning records of the adjustment object, extracting courses corresponding to all the screened learning records, marking the selected courses as the completion courses of the adjustment object, and counting all key features as the existing features of the adjustment object by combining key features corresponding to all the completion courses;
s3-3, extracting associated features corresponding to the target skill level from a database according to the target skill level of the adjustment object, comparing the associated features with the existing features of the adjustment object, and screening out associated features which are not included in the existing features of the adjustment object in the associated features corresponding to the target skill level;
re-ordering the selected associated features from front to back according to the sequence numbers of the associated features, taking the re-ordered associated features as the missing features of the adjustment object and recording the sequence numbers of the missing features;
S3-4, screening from all courses of a database, and if the key characteristics of a certain course comprise any missing characteristics of an adjustment object, distributing the course to a training course set;
After the adjustment object is identified, the recommendation of the learning path is ensured to be targeted and personalized, the skill shortboards of the personnel are accurately positioned by comparing the characteristic of the adjustment object with the characteristic of the target skill level, the learning content is optimized, the screened training courses are accurately matched with the missing characteristic, the pertinence of training is ensured, and the learning efficiency is improved.
According to the above technical solution, the step S4 includes the following steps:
S4-1, extracting courses corresponding to all learning records from a database, and counting the learned times of each course; according to the sequence numbers of the missing features, sequentially screening courses which correspond to each missing feature and have the largest learned times from the training course set;
When courses with the most learned times corresponding to different missing features are the same course, deleting the missing features with the sequence numbers behind and the sequence numbers thereof;
S4-2, sorting the screened courses from first to last according to serial numbers corresponding to the screened missing features, taking the sorted courses as an optimal learning path, forming a recommended course list and pushing the recommended course list to an adjustment object;
The high-frequency learning courses are screened, so that the recommended courses are guaranteed to have higher practicability and acceptance, the learning paths are more reasonable through optimizing and sorting, the learning efficiency of an adjustment object is improved, personalized course schedules are pushed, the learning process of personnel is simplified, and the training effect is improved.
The training management system based on the adaptive learning path of the artificial intelligence comprises a course analysis module, a course association module, an adjustment module and a path optimization module;
The course analysis module is used for extracting course keywords through natural language processing, acquiring key features of the courses, storing the key features in a database, classifying the key features according to skill levels, providing basic data for learning path planning, the course association module is used for screening courses with standard learning completion degree and test performance according to learning records of objects to be managed, extracting association features of all skill levels through statistics of the key features of the courses, establishing association relations between the skill levels and the courses, the adjustment module is used for identifying skill level adjustment objects, analyzing the learning records of the skill level adjustment objects, extracting the mastered key features, comparing the mastered key features with the association features of target skill levels, screening missing features, matching the courses meeting requirements, generating a training course set, and the path optimization module is used for counting the learned times of all the courses, screening optimal courses based on the missing features, forming personalized learning paths after sequencing and pushing the personalized learning paths to the adjustment objects.
According to the technical scheme, the course analysis module comprises a course information unit and an object classification unit;
The system comprises a course information unit, an object classification unit and a user management unit, wherein the course information unit is used for extracting course introduction and course content from a database, extracting keywords by using a natural language processing technology to form key characteristics of courses and storing the key characteristics into the database, and the object classification unit is used for classifying object sets according to skill levels and providing customized training schemes for objects to be managed with different skill levels.
According to the technical scheme, the course association module comprises a record screening unit and a feature analysis unit;
The record screening unit is used for extracting all learning records of the objects to be managed, screening out learning records meeting requirements based on the threshold of course completion degree and test result, and determining associated courses of all skill levels, and the feature analysis unit is used for counting the occurrence times of key features in the associated courses of all skill levels, sequencing the key features, extracting required features and obtaining associated features of the skill levels and the courses.
According to the technical scheme, the adjusting module comprises an object identification unit and a difference analysis unit;
The object recognition unit is used for marking the person as an adjustment object, recording the target skill level of the person and extracting all learning records of the person when the object to be managed submits a skill level adjustment request according to the requirement of the person to be managed, preparing for the recommendation of a follow-up learning path, and the difference analysis unit is used for comparing the key characteristics of the adjustment object with the associated characteristics of the target skill level, screening out missing characteristics, and screening out matched training courses from the database based on the missing characteristics.
According to the technical scheme, the path optimization module comprises a course screening unit and a path generating unit;
the course screening unit is used for counting the learned times of all courses and screening training courses meeting requirements based on the priority of missing features, and the path generating unit is used for optimizing and sequencing according to the screened training courses to form an optimal learning path and pushing the optimal learning path to an adjustment object.
Compared with the prior art, the invention has the following beneficial effects:
The method comprises the steps of extracting key characteristics of courses through a natural language processing technology, carrying out intelligent classification by combining skill levels of objects to be managed, realizing accurate matching of the courses and the skill levels, improving scientificity and accuracy of learning path planning, constructing a correlation model of the skill levels and the courses based on historical learning records of the objects to be managed, dynamically optimizing a skill level training system by utilizing key characteristic statistical analysis, improving pertinence and effectiveness of training, and secondly, identifying differences between existing characteristics of the objects to be adjusted and target skill level requirements through intelligent difference analysis, automatically screening complementary training courses, forming personalized learning paths, enabling the objects to be adjusted to quickly learn the skill levels of mastering the skills, shortening learning period, and furthermore, optimizing training course sequencing by combining historical learning data, further improving training quality, overcoming the problem that training paths are ambiguous in the prior art, and improving management efficiency of resource management and adaptability of the training system.
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 flow diagram of a training management method for an adaptive learning path based on artificial intelligence in accordance with the present invention;
FIG. 2 is a schematic diagram of the architecture of the artificial intelligence based adaptive learning path training management system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by a visitor of ordinary skill in the art without making any inventive effort, are intended to be within the scope of the present invention, based on the embodiments of the present invention.
Referring to fig. 1, the present invention provides the following technical solutions:
the training management method of the adaptive learning path based on artificial intelligence comprises the following steps:
S1, acquiring course information of each course from a database, analyzing key characteristics of each course and storing the key characteristics in the database;
According to the above technical solution, the step S1 includes the following steps:
S1-1, course information comprises course introduction and course content, keyword extraction is carried out on the course information of each course by using a natural language processing technology, and keywords corresponding to each course are used as key features of the corresponding course;
S1-2, the skill level comprises a primary object set, a middle object set and a high object set;
Dividing the object to be managed with the primary skill level into a primary object set according to the skill level of the object to be managed; dividing the object to be managed with the skill level of the middle level into a middle level object set;
The method comprises the steps of extracting course keywords through a natural language processing technology, ensuring accuracy of course feature extraction, improving accuracy of subsequent matching, classifying objects to be managed through skill levels, ensuring that people with different skill levels can form a reasonable learning set, and providing basic data for subsequent learning path planning.
S2, acquiring learning records of all objects to be managed in the object set, screening learning courses of all objects to be managed to form associated courses of skill levels;
according to the above technical solution, the step S2 includes the following steps:
S2-1, generating a learning record each time a subject to be managed performs a learning activity, wherein the learning record comprises courses, course completion degrees and test achievements;
S2-2, extracting all learning records of all objects to be managed in a certain object set, setting a first threshold and a second threshold, and screening out learning records with course completion degree larger than or equal to the first threshold and test score larger than or equal to the second threshold;
The corresponding courses of all the selected learning records are used as the associated courses of the skill levels corresponding to the object set;
s2-3, counting key features corresponding to the associated courses of a certain skill level according to the associated courses of the skill level, counting the occurrence times of the key features, and sorting the key features according to the occurrence times of the key features from large to small;
The learning record is screened to ensure that courses which are included into analysis are effectively learned and the reliability of data is ensured, and the core skill demands of different skill grades are defined through the ordering of key features to provide data support for learning path recommendation.
S3, marking an adjustment object, recording the target skill level of the adjustment object, analyzing the existing characteristics of the adjustment object according to the learning record of the adjustment object, analyzing the missing characteristics of the adjustment object according to the target skill level of the adjustment object, and screening a training course set by combining the key characteristics of each course in a database;
according to the above technical solution, the step S3 includes the following steps:
S3-1, when a certain object to be managed sends out a request for skill level adjustment, marking the object to be managed as an adjustment object;
s3-2, screening out learning records with the course completion degree larger than or equal to a first threshold value and the test result larger than or equal to a second threshold value according to all learning records of the adjustment object, extracting courses corresponding to all the screened learning records, marking the selected courses as the completion courses of the adjustment object, and counting all key features as the existing features of the adjustment object by combining key features corresponding to all the completion courses;
s3-3, extracting associated features corresponding to the target skill level from a database according to the target skill level of the adjustment object, comparing the associated features with the existing features of the adjustment object, and screening out associated features which are not included in the existing features of the adjustment object in the associated features corresponding to the target skill level;
re-ordering the selected associated features from front to back according to the sequence numbers of the associated features, taking the re-ordered associated features as the missing features of the adjustment object and recording the sequence numbers of the missing features;
S3-4, screening from all courses of a database, and if the key characteristics of a certain course comprise any missing characteristics of an adjustment object, distributing the course to a training course set;
After the adjustment object is identified, the recommendation of the learning path is ensured to be targeted and personalized, the skill shortboards of the personnel are accurately positioned by comparing the characteristic of the adjustment object with the characteristic of the target skill level, the learning content is optimized, the screened training courses are accurately matched with the missing characteristic, the pertinence of training is ensured, and the learning efficiency is improved.
S4, analyzing the optimal learning path of the adjustment object according to the training course set of the adjustment object and combining all learning records in the database, and forming a recommended course list to push to the adjustment object;
according to the above technical solution, the step S4 includes the following steps:
S4-1, extracting courses corresponding to all learning records from a database, and counting the learned times of each course; according to the sequence numbers of the missing features, sequentially screening courses which correspond to each missing feature and have the largest learned times from the training course set;
When courses with the most learned times corresponding to different missing features are the same course, deleting the missing features with the sequence numbers behind and the sequence numbers thereof;
S4-2, sorting the screened courses from first to last according to serial numbers corresponding to the screened missing features, taking the sorted courses as an optimal learning path, forming a recommended course list and pushing the recommended course list to an adjustment object;
For example:
The training course set comprises a machine learning entrance, a deep learning foundation and a reinforcement learning foundation, wherein key characteristics of the machine learning entrance are machine learning and deep learning, key characteristics of the deep learning foundation are deep learning and model training, key characteristics of the reinforcement learning foundation are reinforcement learning and strategy gradient, the number of times of learning of the machine learning entrance is 500, the number of times of learning of the deep learning foundation is 400 and the number of times of learning of the reinforcement learning foundation is 300;
The missing feature of a certain adjustment object A is deep learning, reinforcement learning and strategy gradient, wherein the serial number of the deep learning is 01, the serial number of the reinforcement learning is 02, and the serial number of the strategy gradient is 03;
Sequentially screening courses which correspond to each missing feature and have the largest learning times from the training course set, wherein the courses corresponding to the deep learning 01 are machine learning entry, the courses corresponding to the reinforcement learning 02 are reinforcement learning basis, and the courses corresponding to the strategy gradient 03 are reinforcement learning basis;
The courses corresponding to the strategy gradients 03 of the reinforcement learning 02 are the same course and are all reinforcement learning bases, and the sequence number of the strategy gradients 03 is deleted after the sequence number of the reinforcement learning 02;
And sequencing the screened courses from first to last according to the sequence numbers corresponding to the screened missing features to obtain the sequence number 11 of the machine learning entry and the sequence number 12 of the reinforcement learning basis.
The high-frequency learning courses are screened, so that the recommended courses are guaranteed to have higher practicability and acceptance, the learning paths are more reasonable through optimizing and sorting, the learning efficiency of an adjustment object is improved, personalized course schedules are pushed, the learning process of personnel is simplified, and the training effect is improved.
Referring to fig. 2, the training management system for adaptive learning path based on artificial intelligence includes a course analysis module, a course association module, an adjustment module and a path optimization module;
The course analysis module is used for extracting course keywords through natural language processing, acquiring key features of the courses, storing the key features in a database, classifying the key features according to skill levels, providing basic data for learning path planning, the course association module is used for screening courses with standard learning completion degree and test performance according to learning records of objects to be managed, extracting association features of all skill levels through statistics of the key features of the courses, establishing association relations between the skill levels and the courses, the adjustment module is used for identifying skill level adjustment objects, analyzing the learning records of the skill level adjustment objects, extracting the mastered key features, comparing the mastered key features with the association features of target skill levels, screening missing features, matching the courses meeting requirements, generating a training course set, and the path optimization module is used for counting the learned times of all the courses, screening optimal courses based on the missing features, forming personalized learning paths after sequencing and pushing the personalized learning paths to the adjustment objects.
According to the technical scheme, the course analysis module comprises a course information unit and an object classification unit;
The system comprises a course information unit, an object classification unit and a user management unit, wherein the course information unit is used for extracting course introduction and course content from a database, extracting keywords by using a natural language processing technology to form key characteristics of courses and storing the key characteristics into the database, and the object classification unit is used for classifying object sets according to skill levels and providing customized training schemes for objects to be managed with different skill levels.
According to the technical scheme, the course association module comprises a record screening unit and a feature analysis unit;
The record screening unit is used for extracting all learning records of the objects to be managed, screening out learning records meeting requirements based on the threshold of course completion degree and test result, and determining associated courses of all skill levels, and the feature analysis unit is used for counting the occurrence times of key features in the associated courses of all skill levels, sequencing the key features, extracting required features and obtaining associated features of the skill levels and the courses.
According to the technical scheme, the adjusting module comprises an object identification unit and a difference analysis unit;
The object recognition unit is used for marking the person as an adjustment object, recording the target skill level of the person and extracting all learning records of the person when the object to be managed submits a skill level adjustment request according to the requirement of the person to be managed, preparing for the recommendation of a follow-up learning path, and the difference analysis unit is used for comparing the key characteristics of the adjustment object with the associated characteristics of the target skill level, screening out missing characteristics, and screening out matched training courses from the database based on the missing characteristics.
According to the technical scheme, the path optimization module comprises a course screening unit and a path generating unit;
the course screening unit is used for counting the learned times of all courses and screening training courses meeting requirements based on the priority of missing features, and the path generating unit is used for optimizing and sequencing according to the screened training courses to form an optimal learning path and pushing the optimal learning path to an adjustment object.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the above-mentioned embodiments are merely preferred embodiments of the present invention, and the present invention is not limited to the above-mentioned embodiments, but may be modified or some of the technical features thereof may be replaced by other technical solutions described in the above-mentioned embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The training management method for the adaptive learning path based on the artificial intelligence is characterized by comprising the following steps of:
S1, acquiring course information of each course from a database, analyzing key characteristics of each course and storing the key characteristics in the database;
S2, acquiring learning records of all objects to be managed in the object set, screening learning courses of all objects to be managed to form associated courses of skill levels;
S3, marking an adjustment object, recording the target skill level of the adjustment object, analyzing the existing characteristics of the adjustment object according to the learning record of the adjustment object, analyzing the missing characteristics of the adjustment object according to the target skill level of the adjustment object, and screening a training course set by combining the key characteristics of each course in a database;
s4, analyzing the optimal learning path of the adjustment object according to the training course set of the adjustment object and combining all learning records in the database, and forming a recommended course list to push to the adjustment object;
Step S4 includes the following:
S4-1, extracting courses corresponding to all learning records from a database, and counting the learned times of each course; according to the sequence numbers of the missing features, sequentially screening courses which correspond to each missing feature and have the largest learned times from the training course set;
When courses with the most learned times corresponding to different missing features are the same course, deleting the missing features with the sequence numbers behind and the sequence numbers thereof;
s4-2, sorting the screened courses from first to last according to the serial numbers corresponding to the screened missing features, taking the sorted courses as an optimal learning path, forming a recommended course list and pushing the recommended course list to an adjustment object.
2. The method for training management of adaptive learning path based on artificial intelligence according to claim 1, wherein the step S1 comprises the steps of:
S1-1, course information comprises course introduction and course content, keyword extraction is carried out on the course information of each course by using a natural language processing technology, and keywords corresponding to each course are used as key features of the corresponding course;
S1-2, the skill level comprises a primary object set, a middle object set and a high object set;
According to the skill level of the object to be managed, the object to be managed with the skill level being primary is divided into a primary object set, the object to be managed with the skill level being medium is divided into a medium object set, and the object to be managed with the skill level being high is divided into a high object set.
3. The method for training management of adaptive learning path based on artificial intelligence according to claim 2, wherein the step S2 comprises the steps of:
S2-1, generating a learning record each time a subject to be managed performs a learning activity, wherein the learning record comprises courses, course completion degrees and test achievements;
S2-2, extracting all learning records of all objects to be managed in a certain object set, setting a first threshold and a second threshold, and screening out learning records with course completion degree larger than or equal to the first threshold and test score larger than or equal to the second threshold;
The corresponding courses of all the selected learning records are used as the associated courses of the skill levels corresponding to the object set;
S2-3, counting key features corresponding to the associated courses of a certain skill level according to the associated courses of the skill level, counting the occurrence times of the key features, sorting the key features according to the occurrence times of the key features from large to small, taking the sorted key features as the associated features of the skill level, and recording the sequence numbers of the associated features.
4. The method for training management of adaptive learning path based on artificial intelligence according to claim 3, wherein the step S3 comprises the steps of:
S3-1, when a certain object to be managed sends out a request for skill level adjustment, marking the object to be managed as an adjustment object;
s3-2, screening out learning records with the course completion degree larger than or equal to a first threshold value and the test result larger than or equal to a second threshold value according to all learning records of the adjustment object, extracting courses corresponding to all the screened learning records, marking the selected courses as the completion courses of the adjustment object, and counting all key features as the existing features of the adjustment object by combining key features corresponding to all the completion courses;
s3-3, extracting associated features corresponding to the target skill level from a database according to the target skill level of the adjustment object, comparing the associated features with the existing features of the adjustment object, and screening out associated features which are not included in the existing features of the adjustment object in the associated features corresponding to the target skill level;
re-ordering the selected associated features from front to back according to the sequence numbers of the associated features, taking the re-ordered associated features as the missing features of the adjustment object and recording the sequence numbers of the missing features;
S3-4, screening from all courses in the database, and if the key characteristics of a certain course comprise any missing characteristics of the adjustment object, distributing the course to a training course set.
5. The training management system based on the adaptive learning path of artificial intelligence is used for realizing the training management method of the adaptive learning path based on artificial intelligence according to any one of claims 1-4, and is characterized in that the system comprises a course analysis module, a course association module, an adjustment module and a path optimization module;
The course analysis module is used for extracting course keywords through natural language processing, acquiring key features of the courses, storing the key features in a database, classifying the key features according to skill levels, providing basic data for learning path planning, the course association module is used for screening courses with standard learning completion degree and test performance according to learning records of objects to be managed, extracting association features of all skill levels through statistics of the key features of the courses, establishing association relations between the skill levels and the courses, the adjustment module is used for identifying skill level adjustment objects, analyzing the learning records of the skill level adjustment objects, extracting the mastered key features, comparing the mastered key features with the association features of target skill levels, screening missing features, matching the courses meeting requirements, generating a training course set, and the path optimization module is used for counting the learned times of all the courses, screening optimal courses based on the missing features, forming personalized learning paths after sequencing and pushing the personalized learning paths to the adjustment objects.
6. The artificial intelligence based adaptive learning path training management system of claim 5 wherein the course analysis module includes a course information unit and an object classification unit;
The system comprises a course information unit, an object classification unit and a user management unit, wherein the course information unit is used for extracting course introduction and course content from a database, extracting keywords by using a natural language processing technology to form key characteristics of courses and storing the key characteristics into the database, and the object classification unit is used for classifying object sets according to skill levels and providing customized training schemes for objects to be managed with different skill levels.
7. The training management system for adaptive learning path based on artificial intelligence according to claim 5, wherein the course association module comprises a record screening unit and a feature analysis unit;
The record screening unit is used for extracting all learning records of the objects to be managed, screening out learning records meeting requirements based on the threshold of course completion degree and test result, and determining associated courses of all skill levels, and the feature analysis unit is used for counting the occurrence times of key features in the associated courses of all skill levels, sequencing the key features, extracting required features and obtaining associated features of the skill levels and the courses.
8. The training management system for adaptive learning path based on artificial intelligence according to claim 5, wherein the adjustment module comprises an object recognition unit and a variance analysis unit;
The object recognition unit is used for marking the person as an adjustment object, recording the target skill level of the person and extracting all learning records of the person when the object to be managed submits a skill level adjustment request according to the requirement of the person to be managed, preparing for the recommendation of a follow-up learning path, and the difference analysis unit is used for comparing the key characteristics of the adjustment object with the associated characteristics of the target skill level, screening out missing characteristics, and screening out matched training courses from the database based on the missing characteristics.
9. The training management system for adaptive learning path based on artificial intelligence according to claim 5, wherein the path optimization module comprises a course screening unit and a path generating unit;
the course screening unit is used for counting the learned times of all courses and screening training courses meeting requirements based on the priority of missing features, and the path generating unit is used for optimizing and sequencing according to the screened training courses to form an optimal learning path and pushing the optimal learning path to an adjustment object.
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