A knowledge classification-based training subject management method and system

Abstract

The application relates to the technical field of education training, and discloses a training subject management method and system based on knowledge classification. The system comprises a knowledge classification unit, a knowledge evaluation unit and a matching management unit. Through unique algorithms and technologies, knowledge is accurately divided into three types, namely, basic knowledge, advanced knowledge and application knowledge. For example, basic knowledge is compared by using a keyword library, advanced knowledge is constructed to calculate a correlation degree, and application knowledge is analyzed in combination with actual cases, thereby laying a solid foundation for subsequent training. A standardized evaluation process is designed for different types of knowledge, and the knowledge level of a trainer can be comprehensively understood. Different recommendation strategies are formulated according to the level of each knowledge type of the trainer, for example, excellent basic knowledge is focused on advanced and application knowledge, and large projects are recommended for excellent application knowledge, so that the learning needs of different trainers can be met, the problem of mismatched learning content is avoided, the training effect and efficiency are improved, and the knowledge and application ability of the trainer is more efficiently improved.

Description

Technical Field

[0001] This invention relates to the field of education and training technology, specifically to a method and system for managing training subjects based on knowledge classification. Background Technology

[0002] In today's highly competitive and rapidly evolving knowledge landscape, various training and learning activities are crucial for the development of individuals and organizations. Whether it's vocational skills training, academic education, or in-house corporate training, the challenge lies in effectively managing training programs to enhance training outcomes and learning efficiency.

[0003] Traditional training subject management methods are often rather crude, lacking in-depth analysis and reasonable classification of knowledge content. In terms of knowledge classification, it is usually simply divided according to disciplines or course modules, failing to accurately identify different levels of knowledge such as basic, advanced, and applied knowledge. This results in an unclear and incomplete knowledge system. For example, in some vocational skills training, for complex professional knowledge, there is no clear distinction between which are the basic concepts, theories, and operational procedures that beginners must master, which are advanced knowledge that builds upon the basics, and which are applied knowledge closely integrated with actual work scenarios. This makes the arrangement of training content lack systematicity and logic, easily causing confusion for trainees and making it difficult for them to master knowledge step by step.

[0004] Therefore, there is an urgent need for a training subject management method and system based on knowledge classification to improve the scientific nature, relevance, and effectiveness of training subject management. Summary of the Invention

[0005] The purpose of this invention is to provide a training subject management method and system based on knowledge classification, which solves the technical problems mentioned in the background art.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A training subject management method based on knowledge classification includes:

[0008] Step 1: Knowledge Classification: Classify the knowledge content in the texts related to all training subjects into basic knowledge, advanced knowledge, and applied knowledge.

[0009] The second step is knowledge assessment: Based on basic knowledge, advanced knowledge, and applied knowledge, the trainee's knowledge level is assessed to determine the trainee's knowledge level in basic knowledge, advanced knowledge, and applied knowledge respectively.

[0010] The third step, matching management, involves matching and recommending knowledge content corresponding to the training subjects based on the trainees' knowledge levels in basic, advanced, and applied knowledge, and in conjunction with pre-set knowledge content recommendation rules.

[0011] As a further aspect of the present invention, the classification process is as follows:

[0012] Step F1, Basic Knowledge Classification:

[0013] Step F1.1: Extract the basic concepts, basic theories, and basic operating procedures from all the knowledge content in the relevant texts of all training subjects;

[0014] Step F1.2: Establish a basic knowledge keyword database and use a text matching algorithm to compare the extracted knowledge content with the keyword database.

[0015] If the keyword database contains relevant knowledge content, then the relevant knowledge content will be classified as basic knowledge.

[0016] Step F1.3: Combine all the basic knowledge into a basic knowledge set;

[0017] Step F2, Advanced Knowledge Classification:

[0018] Step F2.1: Select a knowledge content, extract the key concepts and relational statements in the knowledge content through natural language processing technology, and then construct a knowledge graph by using the key concepts as nodes and the relational statements as edges between nodes.

[0019] Step F2.2: Mark this knowledge content as E∈{e j}, j = 1, 2, ..., v, where v represents the number of key concepts in the knowledge content;

[0020] At the same time, the set of basic knowledge is labeled as B∈{b i}, i = 1, 2, ..., n, where n represents the total number of basic knowledge items in the basic knowledge set, b i This represents the i-th basic knowledge item in the set of basic knowledge.

[0021] Step F2.3: Count the number of times key concepts and basic knowledge points appear together in various sentences within the relevant text, and label them as N(e). j b i );

[0022] Simultaneously, the total number of times key concepts in the statistical knowledge content appear in various sentences within the relevant text is calculated and denoted as N(b). i )

[0023] Step F2.4, Pass: Calculate the correlation R(e) between key concepts and basic knowledge in the knowledge content. j b i );

[0024] Step F2.5: Determine the correlation R(e) between key concepts and basic knowledge in the knowledge content. j b i The correlation coefficient is compared with the preset correlation threshold RY.

[0025] When R(e) j b i If the value of the extracted knowledge content is greater than or equal to RY, then the key concepts in the extracted knowledge content are classified as advanced knowledge; otherwise, they are not considered advanced knowledge.

[0026] Step F2.6: Then, all advanced knowledge is combined into an advanced knowledge set;

[0027] Step F3, Applying Knowledge Classification:

[0028] Step F3.1: Collect practical application cases related to the training subjects and store them in a pre-established case database;

[0029] Step F3.2: Use semantic analysis matching to match each knowledge content with actual application cases in the case database;

[0030] Step F3.3: Count the frequency of each knowledge content and each practical application case in the case database, and label it as K(P). t A g );

[0031] Step F3.4, through Calculate the application frequency F(Pt) of the t-th knowledge content;

[0032] Step F3.5: Calculate the application frequency F(P) of each knowledge content. t The frequency of application is compared with the preset application frequency threshold FY:

[0033] When F(P) t If )≥FY, then the extracted knowledge content is determined to be applied knowledge; otherwise, it is not applied knowledge.

[0034] Step F3.6: Combine all application knowledge into an application knowledge set.

[0035] As a further aspect of the present invention, the matching method in step F3.2 is as follows:

[0036] Semantic analysis tools in natural language processing are used to semantically encode knowledge content and practical application cases, transforming text into semantic vectors that computers can understand.

[0037] The similarity between the semantic vector of knowledge content and the semantic vector of practical application cases is calculated using the cosine similarity formula.

[0038] When the similarity exceeds the preset similarity threshold, the knowledge content is considered to appear in the actual application case.

[0039] As a further aspect of the present invention: the cosine similarity formula is as follows:

[0040]

[0041] In the formula, Sim(P) t A g P represents the similarity between the semantic vector of knowledge content and the semantic vector of practical application cases. t Let A be a semantic vector of the knowledge content, t = 1, 2, ..., r, where r represents the number of knowledge contents in the relevant text. g Let g be the semantic vector of the actual application case, where g = 1, 2, ..., c, and c represents the number of actual application cases in the case database.

[0042] As a further aspect of the present invention, the knowledge level assessment method is as follows:

[0043] Select basic knowledge points for knowledge level assessment;

[0044] Step G1.1: Based on the established set of basic knowledge, design a test question bank covering all basic knowledge; randomly select a specified number of test questions from the test question bank to form a test paper, and have the trainee complete the answer within a specified time.

[0045] Step G1.2: Score the trainee's test paper according to the standard answers corresponding to the test questions, obtain the trainee's score and mark it as DF1, and extract the total score of the test paper and mark it as ZF1.

[0046] Step G1.3, Pass: Calculate the basic knowledge level score SP1;

[0047] Step G1.4: Based on the pre-set rating rules, divide the trainees' basic knowledge level into different levels;

[0048] The assessment methods for advanced knowledge and applied knowledge are the same as those for basic knowledge.

[0049] As a further aspect of the present invention, the rating rules are as follows:

[0050] SP1≥80 indicates an excellent level of basic knowledge; SP2≥80 indicates an excellent level of advanced knowledge; SP3≥80 indicates an excellent level of applied knowledge.

[0051] 60≤SP1<80 indicates that the trainee's basic knowledge level is good; 60≤SP2<80 indicates that the trainee's advanced knowledge level is good; 60≤SP3<80 indicates that the trainee's applied knowledge level is good.

[0052] 40≤SP1<60 indicates that the trainee's basic knowledge level is intermediate; 40≤SP2<60 indicates that the trainee's advanced knowledge level is intermediate; 40≤SP3<60 indicates that the trainee's applied knowledge level is intermediate.

[0053] SP1 < 40 indicates that the trainee's basic knowledge level is poor; SP2 < 40 indicates that the trainee's advanced knowledge level is poor; SP3 < 40 indicates that the trainee's applied knowledge level is poor.

[0054] As a further aspect of this invention, the knowledge content recommendation rules are as follows:

[0055] Step P1, Knowledge Level Based on Basic Knowledge:

[0056] If the trainee's basic knowledge level is excellent, then advanced and applied knowledge will be recommended to the trainee at a ratio of 1:4.

[0057] If the trainee's basic knowledge level is good, then basic knowledge and advanced knowledge will be recommended to the trainee in a ratio of 3:7.

[0058] When the trainee's basic knowledge level is intermediate, basic knowledge and advanced knowledge are recommended to the trainee at a ratio of 4:1.

[0059] If the trainee's basic knowledge level is poor, then basic knowledge content will be recommended to the trainee.

[0060] Step P2, Knowledge Level Based on Advanced Knowledge:

[0061] When a trainee’s advanced knowledge level is excellent, advanced knowledge and applied knowledge will be recommended to the trainee at a ratio of 4:1.

[0062] If the trainee's advanced knowledge level is good, then advanced knowledge and applied knowledge will be recommended to the trainee at a ratio of 1:1.

[0063] When the trainee's advanced knowledge level is intermediate, basic knowledge and advanced knowledge are recommended to the trainee in a ratio of 2:3.

[0064] If the trainee's advanced knowledge level is poor, then basic knowledge and advanced knowledge will be recommended to the trainee at a ratio of 4:1.

[0065] Step P3, Knowledge Level Based on Application Knowledge:

[0066] If a trainee's advanced knowledge level is excellent, then the trainee is recommended to participate in large-scale practical application projects.

[0067] When a trainee’s advanced knowledge level is good, it is recommended that the trainee participate in small-scale practical application projects, and at the same time, the knowledge content corresponding to the application knowledge level is recommended to the trainee.

[0068] When the trainee's advanced knowledge level is intermediate, the knowledge content corresponding to the application knowledge level will be recommended to the trainee, and the trainee will be recommended to participate in the practical courses corresponding to the application projects.

[0069] If the trainee's advanced knowledge level is poor, then the basic knowledge, advanced knowledge, and applied knowledge will be provided to the trainee in a recommended ratio of 5:3:2.

[0070] A training subject management system based on knowledge classification, implemented through a training subject management method based on knowledge classification, comprising:

[0071] The knowledge classification unit is used to classify the knowledge content in the texts related to all training subjects into basic knowledge, advanced knowledge, and applied knowledge.

[0072] The knowledge assessment unit is used to assess the trainee's knowledge level based on basic knowledge, advanced knowledge, and applied knowledge, and to determine the trainee's knowledge level in basic knowledge, advanced knowledge, and applied knowledge, respectively.

[0073] The matching management unit is used to match and recommend knowledge content corresponding to training subjects based on the trainee's knowledge level in basic knowledge, advanced knowledge, and applied knowledge.

[0074] The beneficial effects of this invention are:

[0075] This invention categorizes knowledge content in training subject-related texts into basic knowledge, advanced knowledge, and applied knowledge through a detailed and systematic classification process. The basic knowledge classification utilizes a keyword database and text matching algorithms to accurately extract fundamental concepts, theories, and operational procedures. The advanced knowledge classification employs knowledge graphs and correlation calculations to deeply analyze the relationship between knowledge content and basic knowledge, effectively distinguishing advanced knowledge. The applied knowledge classification combines practical application cases and semantic analysis matching methods, determining applied knowledge based on application frequency, making the knowledge classification more aligned with actual training needs and providing a solid foundation for subsequent training.

[0076] This invention designs scientific assessment methods for basic knowledge, advanced knowledge, and applied knowledge. By designing a test question bank covering all knowledge types, randomly selecting test questions to form test papers, and having participants answer within a specified time and receive scores based on standard answers, the invention calculates knowledge level scores using a specific formula and finally categorizes participants into levels according to preset rating rules. This comprehensive and accurate assessment of trainees' levels at different knowledge levels provides a basis for personalized training.

[0077] This invention employs a detailed matching management strategy based on trainees' varying levels of basic, advanced, and applied knowledge. For trainees with different levels of basic knowledge, different knowledge content is recommended in varying proportions; for trainees with different levels of advanced knowledge, corresponding knowledge content is recommended based on their level differences; and for trainees with different levels of applied knowledge, not only is knowledge content recommended, but also participation in practical application projects or courses of varying scales is suggested according to their level. This personalized matching management better meets trainees' learning needs, improves training effectiveness and efficiency, and achieves individualized instruction.

[0078] This invention, through scientific knowledge classification, accurate knowledge level assessment, and personalized matching management, enables trainees to obtain training content that is appropriate to their knowledge level, avoiding situations where the learning content is too difficult or too easy. This helps trainees improve their knowledge level and application ability more efficiently, thereby improving the overall training effect and efficiency, and enhancing the training quality.

[0079] This invention collects real-world application cases during the knowledge classification process and uses semantic analysis matching to match knowledge content with cases. In the matching management, participants are recommended to participate in real-world application projects based on their level of applied knowledge. This ensures that the training content is closely integrated with real-world application scenarios, enhances trainees' ability to apply knowledge in practice, and cultivates talents who better meet practical needs. Attached Figure Description

[0080] The invention will now be further described with reference to the accompanying drawings.

[0081] Figure 1This is a system block diagram of a training subject management system based on knowledge classification according to the present invention.

[0082] Figure 2 This is a flowchart illustrating a training subject management method based on knowledge classification according to the present invention. Detailed Implementation

[0083] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0084] Example 1

[0085] Please see Figure 1 and Figure 2 As shown, this invention is a training subject management method based on knowledge classification, comprising:

[0086] Step 1: Knowledge Classification

[0087] The knowledge content in the texts related to all training subjects is classified into basic knowledge, advanced knowledge, and applied knowledge.

[0088] The classification and processing methods are as follows:

[0089] Step F1, Basic Knowledge Classification:

[0090] Step F1.1: Extract the basic concepts, basic theories, and basic operating procedures from all the knowledge content in the relevant texts of all training subjects;

[0091] For example, in language training subjects, letters, phonetics, basic vocabulary, and simple grammar rules fall under the category of basic knowledge;

[0092] Step F1.2: Establish a basic knowledge keyword database and use a text matching algorithm to compare the extracted knowledge content with the keyword database.

[0093] The text matching algorithm is existing technology, so it will not be described in detail.

[0094] If the keyword database contains relevant knowledge content, then the relevant knowledge content will be classified as basic knowledge.

[0095] Step F1.3: Combine all the basic knowledge into a basic knowledge set;

[0096] In this embodiment, the basic knowledge set is also manually reviewed and confirmed by professionals. The method is as follows: professionals judge the knowledge content in the basic knowledge set one by one according to the essential attributes of the relevant knowledge content and its fundamental position in the training system, and finally determine the basic knowledge set.

[0097] Step F2, Advanced Knowledge Classification:

[0098] Step F2.1: Select a knowledge content, extract the key concepts and relational statements in the knowledge content through natural language processing technology, and then construct a knowledge graph by using the key concepts as nodes and the relational statements as edges between nodes.

[0099] Natural language processing technology is existing technology, so it will not be elaborated on here.

[0100] In this embodiment, the key concepts in the knowledge content are extracted as follows: the knowledge content is segmented into words, and the frequency of each word obtained from the segmentation is counted; then, nouns and noun phrases with a frequency higher than a preset frequency threshold are defined as key concepts.

[0101] Step F2.2: Mark this knowledge content as E∈{e j}, j = 1, 2, ..., v, where v represents the number of key concepts in the knowledge content;

[0102] At the same time, the set of basic knowledge is labeled as B∈{b i}, i = 1, 2, ..., n, where n represents the total number of basic knowledge items in the basic knowledge set, b i This represents the i-th basic knowledge item in the set of basic knowledge.

[0103] Step F2.3: Count the number of times key concepts and basic knowledge points appear together in various sentences within the relevant text, and label them as N(e). j b i );

[0104] Simultaneously, the total number of times key concepts in the statistical knowledge content appear in various sentences within the relevant text is calculated and denoted as N(b). i )

[0105] Step F2.4, Pass: Calculate the correlation R(e) between key concepts and basic knowledge in the knowledge content. j b i );

[0106] Step F2.5: Determine the correlation R(e) between key concepts and basic knowledge in the knowledge content. j b iThe correlation coefficient is compared with the preset correlation threshold RY.

[0107] When R(e) j b i If )≥RY, then the key concepts in the extracted knowledge content are classified as advanced knowledge.

[0108] When R(e) j b i If ) < RY, then the key concepts in the extracted knowledge content will not be judged as advanced knowledge;

[0109] Step F2.6: Then, all advanced knowledge is combined into an advanced knowledge set;

[0110] In this embodiment, the basic knowledge set is also manually reviewed and confirmed by professionals. The method is as follows: professionals judge the knowledge content in the basic knowledge set one by one according to the difficulty level of the relevant knowledge content and its role in the training and advancement process, and finally determine the basic knowledge set.

[0111] Step F3, Applying Knowledge Classification:

[0112] Step F3.1: Collect practical application cases related to the training subjects and store them in a pre-established case database;

[0113] Step F3.2: Use semantic analysis matching to match each knowledge content with actual application cases in the case database. The matching method is as follows:

[0114] Semantic analysis tools in natural language processing are used to semantically encode knowledge content and practical application cases, transforming text into semantic vectors that computers can understand.

[0115] The similarity between the semantic vector of knowledge content and the semantic vector of practical application cases is calculated using the cosine similarity formula.

[0116] When the similarity exceeds the preset similarity threshold, the knowledge content is considered to appear in a real-world application case.

[0117] The cosine similarity formula is:

[0118] In the formula, Sim(P) t A g P represents the similarity between the semantic vector of knowledge content and the semantic vector of practical application cases. t Let A be a semantic vector of the knowledge content, t = 1, 2, ..., r, where r represents the number of knowledge contents in the relevant text. g For the semantic vector of actual application cases, g = 1, 2, ..., c, where c represents the number of actual application cases in the case database;

[0119] Step F3.3: Count the frequency of each knowledge content and each practical application case in the case database, and label it as K(P). t A g );

[0120] Step F3.4, through Calculate the application frequency F(Pt) of the t-th knowledge content;

[0121] Step F3.5: Calculate the application frequency F(P) of each knowledge content. t The frequency of application is compared with the preset application frequency threshold FY:

[0122] When F(P) t If )≥FY, then the extracted knowledge content is determined to be applied knowledge;

[0123] When F(P) t If ) < FY, then the extracted knowledge content will not be judged as applied knowledge;

[0124] Step F3.6: Combine all application knowledge into an application knowledge set;

[0125] In this embodiment, the application knowledge set is also manually reviewed and confirmed by professionals. The method is as follows: professionals judge the knowledge content in the application knowledge set one by one according to the relevant knowledge content, and finally determine the application knowledge set.

[0126] Step Two: Knowledge Assessment

[0127] The trainees' knowledge level is assessed based on basic knowledge, advanced knowledge, and applied knowledge to determine their respective knowledge level grades in these three areas.

[0128] The knowledge level assessment methods are as follows:

[0129] Step G1, Basic Knowledge Level Assessment:

[0130] Step G1.1: Based on the established set of basic knowledge, design a test question bank covering all basic knowledge; randomly select a specified number of test questions from the test question bank to form a test paper, and have the trainee complete the answer within a specified time.

[0131] Step G1.2: Score the trainee's test paper according to the standard answers corresponding to the test questions, obtain the trainee's score and mark it as DF1, and extract the total score of the test paper and mark it as ZF1.

[0132] Step G1.3, Pass: Calculate the basic knowledge level score SP1;

[0133] Step G1.4: Based on the pre-set rating rules, divide the trainees' basic knowledge level into different levels;

[0134] The rating rules are as follows:

[0135] If SP1≥80, the trainee's basic knowledge level is judged to be excellent;

[0136] If 60≤SP1<80, then the trainee's basic knowledge level is judged to be good;

[0137] If 40≤SP1<60, then the trainee's basic knowledge level is judged to be intermediate.

[0138] If SP1 < 40, the trainee's basic knowledge level is judged to be poor.

[0139] Step G2, Advanced Knowledge Level Assessment:

[0140] Step G2.1: Based on the established set of advanced knowledge, design a test question bank covering each advanced knowledge level; randomly select a specified number of test questions from the test question bank to form a test paper, and have the trainee complete the answer within a specified time.

[0141] Step G2.2: Score the trainee's test paper according to the standard answers corresponding to the test questions, obtain the trainee's score and mark it as DF2, and extract the total score of the test paper and mark it as ZF2.

[0142] Step G2.3, Pass: Calculate the advanced knowledge level score SP2;

[0143] Step G2.4: Based on the pre-set rating rules, divide the trainees' advanced knowledge level into different levels;

[0144] The rating rules are as follows:

[0145] If SP2 ≥ 80, the trainee's advanced knowledge level is judged to be excellent;

[0146] If 60≤SP2<80, then the trainee's advanced knowledge level is judged to be good;

[0147] If 40≤SP2<60, then the trainee's advanced knowledge level is judged to be intermediate.

[0148] If SP2 < 40, the trainee's advanced knowledge level is judged to be poor;

[0149] Step G3, Application of Knowledge Level Assessment:

[0150] Step G3.1: Based on the established set of application knowledge, design a test question bank covering all application knowledge; randomly select a specified number of test questions from the test question bank to form a test paper, and have the trainee complete the answer within a specified time.

[0151] Step G3.2: Score the trainee's test paper according to the standard answers corresponding to the test questions, obtain the trainee's score and mark it as DF3, and extract the total score of the test paper and mark it as ZF3;

[0152] Step G3.3, Pass: Calculate the application knowledge level score SP3;

[0153] Step G3.4: Based on the pre-set rating rules, divide the trainees' applied knowledge level into different levels;

[0154] The rating rules are as follows:

[0155] If SP3 ≥ 80, the trainee's applied knowledge level is judged to be excellent;

[0156] If 60≤SP3<80, the trainee's applied knowledge level is judged to be good;

[0157] If 40≤SP3<60, then the trainee's applied knowledge level is judged to be intermediate.

[0158] If SP3 < 40, the trainee's applied knowledge level is judged to be poor;

[0159] Step 3, Match Management:

[0160] Based on the trainees' knowledge levels in basic knowledge, advanced knowledge, and applied knowledge, and combined with pre-set knowledge content recommendation rules, the system matches and recommends knowledge content corresponding to the training subjects for the trainees.

[0161] This embodiment uses a knowledge classification unit to meticulously categorize the textual knowledge related to the training subjects. This includes extracting basic concepts to determine foundational knowledge, using knowledge graphs and correlation calculations to determine advanced knowledge, and identifying applied knowledge based on practical application cases and frequency of use. The classification is then reviewed and confirmed by professionals to ensure accuracy. A knowledge assessment unit designs separate test question banks for each of the three knowledge categories to evaluate trainees' knowledge levels and categorize them into different levels, providing a basis for subsequent matching management. The matching management unit then recommends trainees to matching knowledge content based on the assessment results and rules. This overall process helps to manage training subjects more scientifically and rationally, improving training effectiveness.

[0162] Example 2

[0163] Please see Figure 1 and Figure 2As shown, as a second embodiment of the present invention, in specific implementation, the technical solution of this embodiment differs from that of the first embodiment only in that a knowledge content recommendation rule is proposed in this embodiment;

[0164] The rules for recommending knowledge content are as follows:

[0165] Step P1, Knowledge Level Based on Basic Knowledge:

[0166] If the trainee's basic knowledge level is excellent, then the knowledge content corresponding to the advanced knowledge level and the applied knowledge level will be recommended to the trainee.

[0167] The ratio of knowledge content at the advanced knowledge level to that at the applied knowledge level is 1:4.

[0168] Its purpose is to further enhance the trainees' knowledge level and application ability;

[0169] If the trainee's basic knowledge level is good, then the knowledge content corresponding to the basic knowledge level and the advanced knowledge level will be recommended to the trainee.

[0170] The ratio of basic knowledge content to advanced knowledge content is 3:7.

[0171] Its purpose is to consolidate the trainee's basic knowledge and appropriately expand advanced knowledge;

[0172] When the trainee's basic knowledge level is intermediate, the knowledge content corresponding to the basic knowledge level and the advanced knowledge level will be recommended to the trainee.

[0173] The ratio of basic knowledge content to advanced knowledge content is 4:1.

[0174] Its purpose is to help trainees gradually improve their knowledge level;

[0175] If the trainee's basic knowledge level is poor, then basic knowledge content will be recommended to the trainee.

[0176] Its purpose is to ensure that trainees acquire a solid foundation;

[0177] Step P2, Knowledge Level Based on Advanced Knowledge:

[0178] When a trainee’s advanced knowledge level is excellent, the knowledge content corresponding to the advanced knowledge level and the applied knowledge level will be recommended to the trainee.

[0179] The ratio of advanced knowledge level to applied knowledge level knowledge content is 4:1.

[0180] Its purpose is to fully unleash the trainee's potential;

[0181] When the trainee's advanced knowledge level is good, the knowledge content corresponding to the advanced knowledge level and the applied knowledge level will be recommended to the trainee.

[0182] The ratio of advanced knowledge level to applied knowledge level knowledge content is 1:1.

[0183] When the trainee's advanced knowledge level is intermediate, the knowledge content corresponding to the basic knowledge level and the advanced knowledge level will be recommended to the trainee.

[0184] The ratio of basic knowledge content to advanced knowledge content is 2:3.

[0185] Its purpose is to help trainees enhance their understanding and application of advanced knowledge;

[0186] If the trainee's advanced knowledge level is poor, then the knowledge content corresponding to the basic knowledge level and the advanced knowledge level will be recommended to the trainee.

[0187] The ratio of basic knowledge content to advanced knowledge content is 4:1.

[0188] Step P3, Knowledge Level Based on Application Knowledge:

[0189] If a trainee's advanced knowledge level is excellent, then the trainee is recommended to participate in large-scale practical application projects.

[0190] Its purpose is to enable trainees to continuously improve their application and innovation abilities in practice;

[0191] When a trainee’s advanced knowledge level is good, it is recommended that the trainee participate in small-scale practical application projects, and at the same time, the knowledge content corresponding to the application knowledge level is recommended to the trainee.

[0192] Its purpose is to enable trainees to further improve their application skills in practice;

[0193] When the trainee's advanced knowledge level is intermediate, the knowledge content corresponding to the application knowledge level will be recommended to the trainee, and the trainee will be recommended to participate in the practical courses corresponding to the application projects.

[0194] Its purpose is to cultivate trainees' awareness and ability to apply their knowledge;

[0195] If the trainee's advanced knowledge level is poor, then the knowledge content corresponding to the basic knowledge level, advanced knowledge level and applied knowledge level will be recommended to the trainee.

[0196] The ratio of knowledge content at the basic knowledge level, advanced knowledge level, and applied knowledge level is 5:3:2.

[0197] This embodiment proposes a comprehensive and targeted set of knowledge content recommendation rules. Based on the trainee's different levels of proficiency in basic, advanced, and applied knowledge, corresponding recommendation schemes are provided. For example, those with excellent basic knowledge will receive more recommendations for advanced and applied knowledge. The recommendation ratio for different levels of knowledge content is clearly defined. Personalized recommendation strategies tailored to different levels of trainees can better meet their learning needs and help them improve their knowledge system and enhance their abilities.

[0198] Example 3

[0199] Please see Figure 1 and Figure 2 As shown, as a third embodiment of the present invention, in specific implementation, compared with embodiments one and two, the technical solution of this embodiment is to combine the solutions of embodiments one and two.

[0200] This embodiment combines the solutions of Embodiment 1 and Embodiment 2, possessing both the advantages of Embodiment 1 in scientifically managing training subjects through knowledge classification and assessment, and the strengths of Embodiment 2 in personalized knowledge content recommendation rules. By combining the advantages of both, it can manage training subjects more comprehensively and effectively, providing trainees with knowledge content recommendations that are more in line with their actual level and needs, further improving the quality and efficiency of training management, and promoting the overall improvement of trainees' knowledge level and application ability.

[0201] A training subject management system based on knowledge classification, implemented through a training subject management method based on knowledge classification, comprising:

[0202] The knowledge classification unit is used to classify the knowledge content in the texts related to all training subjects into basic knowledge, advanced knowledge, and applied knowledge.

[0203] The knowledge assessment unit is used to assess the trainee's knowledge level based on basic knowledge, advanced knowledge, and applied knowledge, and to determine the trainee's knowledge level in basic knowledge, advanced knowledge, and applied knowledge, respectively.

[0204] The matching management unit is used to match and recommend knowledge content corresponding to training subjects based on the trainee's knowledge level in basic knowledge, advanced knowledge, and applied knowledge.

[0205] It should be stated that all data collected in this application was collected with the user's consent and authorization, and the use of the data is legal and compliant, and the use and processing of the data comply with the relevant laws, regulations and standards of the relevant regions.

[0206] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0207] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that cannot be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A training subject management method based on knowledge classification, characterized in that, Includes the following steps: Knowledge Classification: The knowledge content in the texts related to all training subjects is classified into basic knowledge, advanced knowledge, and applied knowledge. Knowledge assessment: The trainee's knowledge level is assessed based on basic knowledge, advanced knowledge, and applied knowledge to determine the trainee's knowledge level in basic knowledge, advanced knowledge, and applied knowledge respectively. Matching Management: Based on the trainee's knowledge level in basic knowledge, advanced knowledge, and applied knowledge, and combined with pre-set knowledge content recommendation rules, the corresponding knowledge content for the training subject is matched and recommended to the trainee. The basic knowledge is categorized and processed as follows: Step F1.1: Extract the basic concepts, basic theories, and basic operating procedures from all the knowledge content in the relevant texts of all training subjects; Step F1.2: Establish a basic knowledge keyword database and use a text matching algorithm to compare the extracted knowledge content with the keyword database. If the keyword database contains relevant knowledge content, then the relevant knowledge content will be classified as basic knowledge. Step F1.3: Combine all the basic knowledge into a basic knowledge set; The classification and processing methods for advanced knowledge are as follows: Step F2.1: Select a knowledge content, extract the key concepts and relational statements in the knowledge content through natural language processing technology, and then construct a knowledge graph by using the key concepts as nodes and the relational statements as edges between nodes. Step F2.2: Mark this knowledge content as E∈{e} j }, j=1, 2, ..., v, where v represents the number of key concepts in the knowledge content; At the same time, the set of basic knowledge is labeled as B∈{b} i }, i = 1, 2, ..., n, where n represents the total number of basic knowledge items in the basic knowledge set, b i This represents the i-th basic knowledge item in the set of basic knowledge. Step F2.3: Count the number of times key concepts and basic knowledge points appear together in various sentences within the relevant text, and label them as N (e^( ...i^i)'))))). j b i ); At the same time, the total number of times key concepts in the knowledge content appear in various sentences within the relevant text is statistically analyzed and denoted as N(b). i ); Step F2.4, through Calculate the correlation R(e) between key concepts and basic knowledge in the knowledge content. j b i ); Step F2.5: Determine the correlation R(e) between key concepts and basic knowledge in the knowledge content. j b i The correlation coefficient is compared with the preset correlation threshold RY. When R(e) j b i If the value of the extracted knowledge content is greater than or equal to RY, then the key concepts in the extracted knowledge content are determined to be advanced knowledge; otherwise, they are not advanced knowledge. Step F2.6: Then, all advanced knowledge is combined into an advanced knowledge set; The classification and processing methods for applied knowledge are as follows: Step F3.1: Collect practical application cases related to the training subjects and store them in a pre-established case database; Step F3.2: Use semantic analysis matching to match each knowledge content with actual application cases in the case database; Step F3.3: For each knowledge content, count the cumulative number of times it appears in each practical application case in the case database, and label it as K (P). t A g ); Step F3.4, through Calculate the application frequency F(P) of the t-th knowledge content. t ); Step F3.5: Calculate the application frequency F(P) of each knowledge content. t The frequency of application (FY) is compared with the preset application frequency threshold FY. When F(P) t If FY ≥ FY, then the extracted knowledge content is determined to be applied knowledge; otherwise, it is not applied knowledge. Step F3.6: Combine all application knowledge into an application knowledge set.

2. The training subject management method based on knowledge classification according to claim 1, characterized in that, The matching method in step F3.2 is as follows: Semantic analysis tools in natural language processing are used to semantically encode knowledge content and practical application cases, transforming text into semantic vectors that computers can understand. The similarity between the semantic vector of knowledge content and the semantic vector of practical application cases is calculated using the cosine similarity formula. When the similarity exceeds the preset similarity threshold, the knowledge content is considered to appear in the actual application case.

3. The training subject management method based on knowledge classification according to claim 1, characterized in that, The knowledge level assessment methods are as follows: Select basic knowledge points for knowledge level assessment: Step G1.1: Based on the established set of basic knowledge, design a test question bank that covers all basic knowledge. A test paper is composed of a specified number of test questions randomly selected from the test question bank, and trainees are required to complete the test within a specified time. Step G1.2: Score the trainee's test paper according to the standard answers corresponding to the test questions, obtain the trainee's score and mark it as DF1, and extract the total score of the test paper and mark it as ZF1. Step G1.3, through Calculate the basic knowledge level score SP1; Step G1.4: Based on the pre-set rating rules, divide the trainees' basic knowledge level into different levels.

4. The training subject management method based on knowledge classification according to claim 3, characterized in that, in, The assessment methods for advanced and applied knowledge levels are the same as those for basic knowledge levels.

5. The training subject management method based on knowledge classification according to claim 4, characterized in that, The rating rules are as follows: SP1≥80 indicates an excellent level of basic knowledge; SP2≥80 indicates an excellent level of advanced knowledge; SP3≥80 indicates an excellent level of applied knowledge. 60≤SP1<80 indicates that the trainee's basic knowledge level is good; 60≤SP2<80 indicates that the trainee's advanced knowledge level is good; 60≤SP3<80 indicates that the trainee's applied knowledge level is good; 40≤SP1<60 indicates that the trainee's basic knowledge level is intermediate; 40≤SP2<60 indicates that the trainee's advanced knowledge level is intermediate; 40≤SP3<60 indicates that the trainee's applied knowledge level is intermediate; SP1 < 40 indicates that the trainee's basic knowledge level is poor; SP2 < 40 indicates that the trainee's advanced knowledge level is poor; SP3 < 40 indicates that the trainee's level of applied knowledge is poor.

6. The training subject management method based on knowledge classification according to claim 2, characterized in that, in, The cosine similarity formula is: ; In the formula, Sim(P) t A g P represents the similarity between the semantic vector of knowledge content and the semantic vector of practical application cases. t Let A be a semantic vector of the knowledge content, t = 1, 2, ..., r, where r represents the number of knowledge contents in the relevant text. g For the semantic vector of actual application cases, g = 1, 2, ..., c, where c represents the number of actual application cases in the case database.

7. A training subject management system based on knowledge classification, wherein the system is implemented by the training subject management method based on knowledge classification as described in any one of claims 1-6, characterized in that, The system includes: The knowledge classification unit is used to classify the knowledge content in the texts related to all training subjects into basic knowledge, advanced knowledge, and applied knowledge. The knowledge assessment unit is used to assess the trainee's knowledge level based on basic knowledge, advanced knowledge, and applied knowledge, and to determine the trainee's knowledge level in basic knowledge, advanced knowledge, and applied knowledge, respectively. The matching management unit is used to match and recommend knowledge content corresponding to training subjects based on the trainee's knowledge level in basic knowledge, advanced knowledge, and applied knowledge.

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