Training management system and method based on adaptive learning path of artificial intelligence

Abstract

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

[0001] The present invention belongs to the field of artificial intelligence technology, and specifically relates to a training management system and method based on an adaptive learning path of artificial intelligence. Background Art

[0002] With the development of today's society and the advancement of science and technology, information technology has undergone tremendous changes. These changes have had a significant impact on people's lives and social production, especially in the business, medical, manufacturing, and financial sectors. They have also had a significant impact on the storage of resource data. Artificial intelligence management of resource data can better understand the management objects, discover talents, and meet the needs of the objects, thereby building a more efficient resource management system for enterprises.

[0003] However, existing management systems only store and retrieve resource data. When personnel want to improve their skill levels, the existing management system cannot form a clear training path to provide planned learning and training for personnel to quickly master the knowledge required to reach the target skill level, reducing resource management efficiency.

[0004] Therefore, people urgently need a training management system based on artificial intelligence and adaptive learning paths to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a training management system based on artificial intelligence adaptive learning path to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A training management method based on an adaptive learning path of artificial intelligence, the method comprising the following steps:

[0008] S1. Obtain course information of each course from the database, analyze the key features of each course and store them in the database; obtain the skill level of the object to be managed from the database and divide the objects to be managed into different object sets;

[0009] S2. Obtaining learning records of each to-be-managed object in the object set, screening the learning courses of each to-be-managed object to form associated courses of skill levels; analyzing associated features of the skill levels based on key features corresponding to the associated courses of the skill levels;

[0010] S3. Mark the adjustment target and record the target skill level of the adjustment target; analyze the characteristics of the adjustment target based on the adjustment target's learning record, and analyze the missing characteristics of the adjustment target based on the target skill level of the adjustment target. Combined with the key characteristics of each course in the database, a set of training courses is selected;

[0011] S4. Based on the training course set of the adjustment object and combined with all learning records in the database, the optimal learning path of the adjustment object is analyzed, and a recommended course schedule is formed and pushed to the adjustment object.

[0012] According to the above technical solution, step S1 includes the following:

[0013] S1-1. The course information includes a course introduction and course content. Natural language processing technology is used to extract keywords from the course information of each course, and the keywords corresponding to each course are used as key features of the corresponding course. The key features of all courses in the database are extracted and stored in the database.

[0014] S1-2, the skill levels include elementary, intermediate and advanced; the object sets include elementary object sets, intermediate object sets and advanced object sets;

[0015] According to the skill level of the objects to be managed, objects with elementary skill levels are divided into elementary object sets; objects with intermediate skill levels are divided into intermediate object sets; objects with advanced skill levels are divided into advanced object sets;

[0016] Course keywords are extracted through natural language processing technology to ensure the accuracy of course feature extraction and improve the accuracy of subsequent matching; objects to be managed are classified by skill level to ensure that people with different skill levels can form a reasonable learning set, providing basic data for subsequent learning path planning.

[0017] According to the above technical solution, step S2 includes the following:

[0018] S2-1. Each time the managed subject conducts a learning activity, a learning record is generated; the learning record includes the course, course completion degree, and test scores;

[0019] S2-2. Extract all learning records of each object to be managed in a certain object set, set a first threshold and a second threshold, and filter out learning records with a course completion degree greater than or equal to the first threshold and a test score greater than or equal to the second threshold;

[0020] The courses corresponding to all the filtered learning records are used as the associated courses for the skill level corresponding to the object set; based on each object set, the associated courses for each skill level are obtained;

[0021] S2-3. Based on the associated courses for a certain skill level, statistics are collected on the key features corresponding to the associated courses for that skill level, the number of occurrences of each key feature is counted, and the key features are sorted from largest to smallest based on the number of occurrences of each key feature; the sorted key features are used as the associated features for that skill level, and the sequence numbers of each associated feature are recorded;

[0022] By screening learning records, we ensure that the courses included in the analysis have been effectively learned, thus guaranteeing the reliability of the data; by sorting key features, we clarify the core skill requirements of different skill levels and provide data support for learning path recommendations.

[0023] According to the above technical solution, step S3 includes the following:

[0024] S3-1. When a subject to be managed issues a request for skill level adjustment, the subject to be managed is marked as an adjustment subject; the target skill level of the adjustment subject is collected and stored in a database, and all learning records of the adjustment subject are extracted from the database;

[0025] S3-2. Based on all learning records of the adjustment subject, select learning records with course completion greater than or equal to a first threshold and test scores greater than or equal to a second threshold; extract the courses corresponding to all the selected learning records and mark them as completed courses of the adjustment subject; combine the key features corresponding to each completed course, and calculate all the key features as the acquired features of the adjustment subject;

[0026] S3-3. Extracting, from the database, related features corresponding to the target skill level of the adjustment subject, and comparing them with the already possessed features of the adjustment subject, thereby screening out related features of the target skill level that are not included in the already possessed features of the adjustment subject;

[0027] Reorder the selected related features from front to back according to the sequence number of each related feature, use the reordered related features as the missing features of the adjustment object and record the sequence number of the missing features;

[0028] S3-4. Screen all courses in the database. If the key characteristics of a course include any missing characteristics of the adjustment object, assign the course to the training course set.

[0029] After identifying the adjustment object, ensure that the recommended learning path is targeted and personalized; by comparing the adjustment object's existing characteristics with the target skill level characteristics, accurately locate the personnel's skill shortcomings and optimize the learning content; the selected training courses accurately match the missing characteristics to ensure the targeted training and improve learning efficiency.

[0030] According to the above technical solution, step S4 includes the following:

[0031] S4-1. Extract all courses corresponding to learning records from the database and count the number of times each course has been learned. According to the sequence number of the missing feature, select the course with the most learning times corresponding to each missing feature in the training course set.

[0032] When the course with the most learning times corresponding to different missing features is the same course, the missing features with the later serial numbers and their serial numbers will be deleted;

[0033] S4-2. Sort the selected courses from the earliest to the latest according to the serial numbers corresponding to the missing features after screening, and use the sorted courses as the best learning path, and form a recommended course schedule and push it to the adjustment object;

[0034] By screening high-frequency learning courses, we ensure that the recommended courses are highly practical and recognized; by optimizing the sorting, we make the learning path more reasonable and improve the learning efficiency of the adjusted objects; by pushing personalized course schedules, we simplify the learning process of personnel and improve the training effect.

[0035] A training management system based on artificial intelligence adaptive learning paths, which includes a course analysis module, a course association module, an adjustment module, and a path optimization module;

[0036] The course analysis module is used to extract course keywords through natural language processing, obtain the key features of the courses and store them in the database, and classify them by skill level to provide basic data for learning path planning; the course association module is used to screen courses with satisfactory learning completion and test scores based on the learning records of the objects to be managed, extract the associated features of each skill level by counting the key features of the courses, and establish an associated relationship between skill levels and courses; the adjustment module is used to identify the objects of skill level adjustment, analyze their learning records, extract the key features that have been mastered, and compare them with the associated features of the target skill level, screen for missing features, and match training courses that meet the requirements to generate a training course set; the path optimization module is used to count the number of times all courses have been studied, and screen the best courses based on missing features, form a personalized learning path after sorting, and push it to the adjustment object.

[0037] According to the above technical solution, the course analysis module includes a course information unit and an object classification unit;

[0038] The course information unit is used to extract course introductions and course content from the database, and use natural language processing technology to extract keywords to form key features of the course and store them in the database; the object classification unit is used to divide the object set according to skill level and provide customized training plans for objects to be managed with different skill levels.

[0039] According to the above technical solution, the course association module includes a record screening unit and a feature analysis unit;

[0040] The record screening unit is used to extract all learning records of the object to be managed, and screen out learning records that meet the requirements based on the thresholds of course completion and test scores, and determine the associated courses for each skill level; the feature analysis unit is used to count the number of occurrences of key features in the associated courses for each skill level, sort the key features, extract the required features, and obtain the associated features between skill levels and courses.

[0041] According to the above technical solution, the adjustment module includes an object recognition unit and a difference analysis unit;

[0042] The object identification unit is used to mark the person as an adjustment object, record their target skill level, and extract all their learning records when the object to be managed submits a request for skill level adjustment according to their own needs, in preparation for subsequent learning path recommendations; the difference analysis unit is used to compare the key characteristics already possessed by the adjustment object with the associated characteristics of the target skill level, screen out missing characteristics, and screen matching training courses from the database based on the missing characteristics.

[0043] According to the above technical solution, the path optimization module includes a course screening unit and a path generation unit;

[0044] The course screening unit is used to count the number of times all courses have been studied, and based on the priority of missing features, screen training courses that meet the needs; the path generation unit is used to optimize the sorting of the screened training courses, form the best learning path, and push it to the adjustment object.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention extracts key features of courses through natural language processing technology, and intelligently classifies them in combination with the skill levels of the objects to be managed, thereby achieving accurate matching of courses and skill levels, and improving the scientificity and accuracy of learning path planning; at the same time, the present invention constructs a correlation model between skill levels and courses based on the historical learning records of the objects to be managed, and dynamically optimizes the skill level training system by using key feature statistical analysis, thereby improving the pertinence and effectiveness of training; secondly, the present invention identifies the gap between the existing characteristics of the adjustment object and the target skill level requirements through intelligent difference analysis, automatically screens supplementary training courses, and forms a personalized learning path, so that the adjustment object can quickly learn and master the skill level and shorten the learning cycle; in addition, the present invention combines historical learning data to optimize the sequencing of training courses, further improves the quality of training, 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0048] Figure 1 It is a flow chart of the training management method of the adaptive learning path based on artificial intelligence of the present invention;

[0049] Figure 2 It is a structural diagram of the training management system of the adaptive learning path based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without making any creative effort shall fall within the scope of protection of the present invention.

[0051] See also Figure 1 , the present invention provides a technical solution:

[0052] A training management method based on an adaptive learning path of artificial intelligence, the method comprising the following steps:

[0053] S1. Obtain course information of each course from the database, analyze the key features of each course and store them in the database; obtain the skill level of the object to be managed from the database and divide the objects to be managed into different object sets;

[0054] According to the above technical solution, step S1 includes the following:

[0055] S1-1. The course information includes a course introduction and course content. Natural language processing technology is used to extract keywords from the course information of each course, and the keywords corresponding to each course are used as key features of the corresponding course. The key features of all courses in the database are extracted and stored in the database.

[0056] S1-2, the skill levels include elementary, intermediate and advanced; the object sets include elementary object sets, intermediate object sets and advanced object sets;

[0057] According to the skill level of the objects to be managed, objects with elementary skill levels are divided into elementary object sets; objects with intermediate skill levels are divided into intermediate object sets; objects with advanced skill levels are divided into advanced object sets;

[0058] Course keywords are extracted through natural language processing technology to ensure the accuracy of course feature extraction and improve the accuracy of subsequent matching; objects to be managed are classified by skill level to ensure that people with different skill levels can form a reasonable learning set, providing basic data for subsequent learning path planning.

[0059] S2. Obtaining learning records of each to-be-managed object in the object set, screening the learning courses of each to-be-managed object to form associated courses of skill levels; analyzing associated features of the skill levels based on key features corresponding to the associated courses of the skill levels;

[0060] According to the above technical solution, step S2 includes the following:

[0061] S2-1. Each time the managed subject conducts a learning activity, a learning record is generated; the learning record includes the course, course completion degree, and test scores;

[0062] S2-2. Extract all learning records of each object to be managed in a certain object set, set a first threshold and a second threshold, and filter out learning records with a course completion degree greater than or equal to the first threshold and a test score greater than or equal to the second threshold;

[0063] The courses corresponding to all the filtered learning records are used as the associated courses for the skill level corresponding to the object set; based on each object set, the associated courses for each skill level are obtained;

[0064] S2-3. Based on the associated courses for a certain skill level, statistics are collected on the key features corresponding to the associated courses for that skill level, the number of occurrences of each key feature is counted, and the key features are sorted from largest to smallest based on the number of occurrences of each key feature; the sorted key features are used as the associated features for that skill level, and the sequence numbers of each associated feature are recorded;

[0065] By screening learning records, we ensure that the courses included in the analysis have been effectively learned, thus guaranteeing the reliability of the data; by sorting key features, we clarify the core skill requirements of different skill levels and provide data support for learning path recommendations.

[0066] S3. Mark the adjustment target and record the target skill level of the adjustment target; analyze the characteristics of the adjustment target based on the adjustment target's learning record, and analyze the missing characteristics of the adjustment target based on the target skill level of the adjustment target. Combined with the key characteristics of each course in the database, a set of training courses is selected;

[0067] According to the above technical solution, step S3 includes the following:

[0068] S3-1. When a subject to be managed issues a request for skill level adjustment, the subject to be managed is marked as an adjustment subject; the target skill level of the adjustment subject is collected and stored in a database, and all learning records of the adjustment subject are extracted from the database;

[0069] S3-2. Based on all learning records of the adjustment subject, select learning records with course completion greater than or equal to a first threshold and test scores greater than or equal to a second threshold; extract the courses corresponding to all the selected learning records and mark them as completed courses of the adjustment subject; combine the key features corresponding to each completed course, and calculate all the key features as the acquired features of the adjustment subject;

[0070] S3-3. Extracting, from the database, related features corresponding to the target skill level of the adjustment subject, and comparing them with the already possessed features of the adjustment subject, thereby screening out related features of the target skill level that are not included in the already possessed features of the adjustment subject;

[0071] Reorder the selected related features from front to back according to the sequence number of each related feature, use the reordered related features as the missing features of the adjustment object and record the sequence number of the missing features;

[0072] S3-4. Screen all courses in the database. If the key characteristics of a course include any missing characteristics of the adjustment object, assign the course to the training course set.

[0073] After identifying the adjustment object, ensure that the recommended learning path is targeted and personalized; by comparing the adjustment object's existing characteristics with the target skill level characteristics, accurately locate the personnel's skill shortcomings and optimize the learning content; the selected training courses accurately match the missing characteristics to ensure the targeted training and improve learning efficiency.

[0074] S4. Analyze the optimal learning path for the adjustment target based on the training course set of the adjustment target and all learning records in the database, and create a recommended course schedule that is pushed to the adjustment target;

[0075] According to the above technical solution, step S4 includes the following:

[0076] S4-1. Extract all courses corresponding to learning records from the database and count the number of times each course has been learned. According to the sequence number of the missing feature, select the course with the most learning times corresponding to each missing feature in the training course set.

[0077] When the course with the most learning times corresponding to different missing features is the same course, the missing features with the later serial numbers and their serial numbers will be deleted;

[0078] S4-2. Sort the selected courses from the earliest to the latest according to the serial numbers corresponding to the missing features after screening, and use the sorted courses as the best learning path, and form a recommended course schedule and push it to the adjustment object;

[0079] For example:

[0080] The training course collection includes Introduction to Machine Learning, Basics of Deep Learning, and Basics of Reinforcement Learning. The key features of Introduction to Machine Learning are machine learning and deep learning, the key features of Basics of Deep Learning are deep learning and model training, and the key features of Basics of Reinforcement Learning are reinforcement learning and policy gradients. Furthermore, Introduction to Machine Learning has been studied 500 times, Basics of Deep Learning has been studied 400 times, and Basics of Reinforcement Learning has been studied 300 times.

[0081] The missing features of an adjustment object A are deep learning, reinforcement learning, and policy gradient; the sequence number of deep learning is 01, the sequence number of reinforcement learning is 02, and the sequence number of policy gradient is 03;

[0082] The courses that correspond to each missing feature and have been learned the most times are selected from the training course set in turn. The courses are: Deep Learning 01, which corresponds to the introduction to machine learning; Reinforcement Learning 02, which corresponds to the foundation of reinforcement learning; Policy Gradient 03, which corresponds to the foundation of reinforcement learning;

[0083] The courses corresponding to Reinforcement Learning 02 and Policy Gradient 03 are the same course, both of which are reinforcement learning foundations. At this time, the sequence number of Policy Gradient 03 is after the sequence number of Reinforcement Learning 02, so Policy Gradient 03 is deleted.

[0084] The filtered courses are sorted from first to last according to the serial numbers corresponding to the missing features after screening, and the serial number of Introduction to Machine Learning is 11, and the serial number of Basics of Reinforcement Learning is 12.

[0085] By screening high-frequency learning courses, we ensure that the recommended courses are highly practical and recognized; by optimizing the sorting, we make the learning path more reasonable and improve the learning efficiency of the adjusted objects; by pushing personalized course schedules, we simplify the learning process of personnel and improve the training effect.

[0086] See also Figure 2 , a training management system based on adaptive learning paths based on artificial intelligence, which includes a course analysis module, a course association module, an adjustment module and a path optimization module;

[0087] The course analysis module is used to extract course keywords through natural language processing, obtain the key features of the courses and store them in the database, and classify them by skill level to provide basic data for learning path planning; the course association module is used to screen courses with satisfactory learning completion and test scores based on the learning records of the objects to be managed, extract the associated features of each skill level by counting the key features of the courses, and establish an associated relationship between skill levels and courses; the adjustment module is used to identify the objects of skill level adjustment, analyze their learning records, extract the key features that have been mastered, and compare them with the associated features of the target skill level, screen for missing features, and match training courses that meet the requirements to generate a training course set; the path optimization module is used to count the number of times all courses have been studied, and screen the best courses based on missing features, form a personalized learning path after sorting, and push it to the adjustment object.

[0088] According to the above technical solution, the course analysis module includes a course information unit and an object classification unit;

[0089] The course information unit is used to extract course introductions and course content from the database, and use natural language processing technology to extract keywords to form key features of the course and store them in the database; the object classification unit is used to divide the object set according to skill level and provide customized training plans for objects to be managed with different skill levels.

[0090] According to the above technical solution, the course association module includes a record screening unit and a feature analysis unit;

[0091] The record screening unit is used to extract all learning records of the object to be managed, and screen out learning records that meet the requirements based on the thresholds of course completion and test scores, and determine the associated courses for each skill level; the feature analysis unit is used to count the number of occurrences of key features in the associated courses for each skill level, sort the key features, extract the required features, and obtain the associated features between skill levels and courses.

[0092] According to the above technical solution, the adjustment module includes an object recognition unit and a difference analysis unit;

[0093] The object identification unit is used to mark the person as an adjustment object, record their target skill level, and extract all their learning records when the object to be managed submits a request for skill level adjustment according to their own needs, in preparation for subsequent learning path recommendations; the difference analysis unit is used to compare the key characteristics already possessed by the adjustment object with the associated characteristics of the target skill level, screen out missing characteristics, and screen matching training courses from the database based on the missing characteristics.

[0094] According to the above technical solution, the path optimization module includes a course screening unit and a path generation unit;

[0095] The course screening unit is used to count the number of times all courses have been studied, and based on the priority of missing features, screen training courses that meet the needs; the path generation unit is used to optimize the sorting of the screened training courses, form the best learning path, and push it to the adjustment object.

[0096] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0097] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A training management method based on an adaptive learning path using artificial intelligence, characterized by: The method comprises the following steps: S1. Obtain course information of each course from the database, analyze the key features of each course and store them in the database; obtain the skill level of the object to be managed from the database and divide the objects to be managed into different object sets; S2. Obtaining learning records of each to-be-managed object in the object set, screening the learning courses of each to-be-managed object to form associated courses of skill levels; analyzing associated features of the skill levels based on key features corresponding to the associated courses of the skill levels; S3. Mark the adjustment target and record the target skill level of the adjustment target; analyze the characteristics of the adjustment target based on the adjustment target's learning record, and analyze the missing characteristics of the adjustment target based on the target skill level of the adjustment target. Combined with the key characteristics of each course in the database, a set of training courses is selected; S4. Analyze the optimal learning path for the adjustment target based on the training course set of the adjustment target and all learning records in the database, and create a recommended course schedule that is pushed to the adjustment target; Step S4 includes the following: S4-1. Extract all courses corresponding to learning records from the database and count the number of times each course has been learned. According to the sequence number of the missing feature, select the course with the most learning times corresponding to each missing feature in the training course set. When the course with the most learning times corresponding to different missing features is the same course, the missing features with the later serial numbers and their serial numbers will be deleted; S4-2. Sort the selected courses from first to last according to the serial numbers corresponding to the missing features after screening, and use the sorted courses as the best learning path, and form a recommended course schedule and push it to the adjustment object.

2. The training management method based on adaptive learning path of artificial intelligence according to claim 1, characterized in that: The step S1 includes the following: S1-1. The course information includes a course introduction and course content. Natural language processing technology is used to extract keywords from the course information of each course, and the keywords corresponding to each course are used as key features of the corresponding course. The key features of all courses in the database are extracted and stored in the database. S1-2, the skill levels include elementary, intermediate and advanced; the object sets include elementary object sets, intermediate object sets and advanced object sets; According to the skill level of the objects to be managed, the objects to be managed with a primary skill level are divided into a primary object set; Classify the objects to be managed with intermediate skill levels into the intermediate object set; Objects to be managed with advanced skill levels are classified into the advanced object set.

3. The training management method based on adaptive learning path of artificial intelligence according to claim 2, characterized in that: The step S2 includes the following: S2-1. Each time the managed subject conducts a learning activity, a learning record is generated; the learning record includes the course, course completion degree, and test scores; S2-2. Extract all learning records of each object to be managed in a certain object set, set a first threshold and a second threshold, and filter out learning records with a course completion degree greater than or equal to the first threshold and a test score greater than or equal to the second threshold; The courses corresponding to all the filtered learning records are used as the associated courses for the skill level corresponding to the object set; based on each object set, the associated courses for each skill level are obtained; S2-3. Based on the associated courses of a certain skill level, statistics are collected on the key features corresponding to the associated courses of the skill level, the number of occurrences of each key feature is counted, and the key features are sorted from largest to smallest according to the number of occurrences of each key feature; The sorted key features are used as the associated features of the skill level and the serial numbers of each associated feature are recorded.

4. The training management method based on adaptive learning path of artificial intelligence according to claim 3, characterized in that: The step S3 includes the following: S3-1. When a subject to be managed issues a request for skill level adjustment, the subject to be managed is marked as an adjustment subject; the target skill level of the adjustment subject is collected and stored in a database, and all learning records of the adjustment subject are extracted from the database; S3-2. Based on all learning records of the adjustment subject, select learning records with course completion greater than or equal to a first threshold and test scores greater than or equal to a second threshold; extract the courses corresponding to all the selected learning records and mark them as completed courses of the adjustment subject; combine the key features corresponding to each completed course, and calculate all the key features as the acquired features of the adjustment subject; S3-3. Extracting, from the database, related features corresponding to the target skill level of the adjustment subject, and comparing them with the already possessed features of the adjustment subject, thereby screening out related features of the target skill level that are not included in the already possessed features of the adjustment subject; Reorder the selected related features from front to back according to the sequence number of each related feature, use the reordered related features as the missing features of the adjustment object and record the sequence number of the missing features; S3-4. Screen all courses in the database. If the key feature of a course contains any missing feature of the adjustment object, assign the course to the training course set.

5. A training management system based on an adaptive learning path of artificial intelligence, for implementing the training management method based on an adaptive learning path of artificial intelligence according to any one of claims 1 to 4, characterized in that: The system includes a course analysis module, a course association module, an adjustment module and a path optimization module; The course analysis module is used to extract course keywords through natural language processing, obtain the key features of the courses and store them in the database, and classify them by skill level to provide basic data for learning path planning; the course association module is used to screen courses with satisfactory learning completion and test scores based on the learning records of the objects to be managed, extract the associated features of each skill level by counting the key features of the courses, and establish an associated relationship between skill levels and courses; the adjustment module is used to identify the objects of skill level adjustment, analyze their learning records, extract the key features that have been mastered, and compare them with the associated features of the target skill level, screen for missing features, and match training courses that meet the requirements to generate a training course set; the path optimization module is used to count the number of times all courses have been studied, and screen the best courses based on missing features, form a personalized learning path after sorting, and push it to the adjustment object.

6. The training management system based on adaptive learning path of artificial intelligence according to claim 5, characterized in that: The course analysis module includes a course information unit and an object classification unit; The course information unit is used to extract course introductions and course content from the database, and use natural language processing technology to extract keywords to form key features of the course and store them in the database; the object classification unit is used to divide the object set according to skill level and provide customized training plans for objects to be managed with different skill levels.

7. The training management system based on adaptive learning path of artificial intelligence according to claim 5, characterized in that: The course association module includes a record screening unit and a feature analysis unit; The record screening unit is used to extract all learning records of the object to be managed, and screen out learning records that meet the requirements based on the thresholds of course completion and test scores, and determine the associated courses for each skill level; The feature analysis unit is used to count the number of occurrences of key features in the associated courses of each skill level, sort the key features, extract the required features, and obtain the associated features between the skill level and the course.

8. The training management system based on adaptive learning path of artificial intelligence according to claim 5, characterized in that: The adjustment module includes an object recognition unit and a difference analysis unit; The object identification unit is used to mark the person as an adjustment object, record their target skill level, and extract all their learning records when the object to be managed submits a request for skill level adjustment according to their own needs, in preparation for subsequent learning path recommendations; the difference analysis unit is used to compare the key characteristics already possessed by the adjustment object with the associated characteristics of the target skill level, screen out missing characteristics, and screen matching training courses from the database based on the missing characteristics.

9. The training management system based on adaptive learning path based on artificial intelligence according to claim 5, characterized in that: The path optimization module includes a course screening unit and a path generation unit; The course screening unit is used to count the number of times all courses have been studied, and based on the priority of missing features, screen training courses that meet the needs; the path generation unit is used to optimize the sorting of the screened training courses, form the best learning path, and push it to the adjustment object.

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