Dynamic vocational ability and post matching education evaluation method based on knowledge graph

Abstract

The invention discloses a knowledge graph-based dynamic vocational ability and post matching education evaluation method. The method comprises the steps of S1, constructing a vocational ability knowledge graph; s2, constructing a post demand knowledge graph; s3, designing and training a double-layer generative adversarial network model; s4, matching the vocational ability enhancement scheme and the post demand change generated by the double-layer generative adversarial network model with the existing vocational ability knowledge graph and post demand knowledge graph; s5, updating the vocational ability knowledge graph and the post demand knowledge graph in real time based on the dynamic change of the market demand and the development condition of the individual vocational ability, generating a new matching relationship by using a double-layer generative adversarial network model, and continuously optimizing a matching result; and S6, according to the optimized matching result, generating an education evaluation report and an occupational planning suggestion. According to the invention, the dynamic adaptability and the matching accuracy of vocational ability and post matching are remarkably improved, and accurate docking between job seekers and post requirements is realized.

Description

Technical Field

[0001] The present invention relates to the field of educational assessment technology, and in particular to an educational assessment method for dynamic professional ability and job matching based on a knowledge graph. Background Art

[0002] In the existing technology, the matching of professional ability and position mainly relies on traditional evaluation methods and static matching algorithms. Traditional methods usually determine the individual's professional ability by analyzing static data such as resumes, skill test results and interview feedback, and make job recommendations based on preset matching rules or historical data. However, this method has gradually revealed many shortcomings when responding to rapidly changing market demands and the dynamic improvement of individual professional ability.

[0003] First, the professional ability assessment in existing technologies is often based on fixed standards and rules, which cannot reflect the dynamic changes of individual professional abilities in a timely manner. As a result, in the career planning process, the individual's actual ability and potential are difficult to be fully reflected, thus affecting the accuracy and personalization of matching. At the same time, traditional matching algorithms are usually based on static rules, which make it difficult to adjust matching strategies in real time according to changes in market demand. Static matching methods often cause job recommendations to lag behind market demand and cannot meet the rapidly changing talent needs of enterprises.

[0004] Secondly, existing technologies lack an effective linkage mechanism between vocational education assessment and job requirements. The training content of vocational education is usually difficult to reflect the latest market needs, resulting in a disconnect between education outcomes and the employment market. In addition, existing career assessment methods and job matching systems are usually independent of each other, and education assessment results have not been effectively applied to the job matching process. There is a lack of a closed-loop feedback system to guide individual career development paths, which reduces the pertinence and actual effectiveness of education and training.

[0005] In addition, the existing professional competency and job matching technologies have obvious deficiencies in processing dynamic data and real-time optimization. With the development of the market environment and technology, companies' requirements for job skills and market trends are constantly changing, and existing matching technologies are often unable to update matching rules and algorithms in a timely manner, resulting in a lag in job matching results, which in turn affects the company's employment efficiency and individual career development.

[0006] To sum up, the existing technologies have the following main shortcomings: first, professional ability assessment and job matching rely on static data and cannot dynamically reflect the changes and development of individual professional abilities; second, there is a lack of effective linkage between vocational education and job requirements, and the results of education assessment cannot be directly used for job matching; finally, the existing technologies are insufficient in processing dynamic data and updating matching strategies in real time, making it difficult to cope with rapidly changing market demands and job requirements. These problems significantly limit the accuracy and real-time nature of professional ability and job matching, and cannot meet the needs of modern enterprises and individuals for efficient matching. Summary of the invention

[0007] One purpose of the present invention is to propose an educational assessment method for dynamic professional ability and job matching based on knowledge graph. The present invention significantly improves the dynamic adaptability and matching accuracy of professional ability and job matching, and realizes the precise connection between job seekers and job requirements.

[0008] According to an embodiment of the present invention, a method for evaluating education for dynamic professional competence and job matching based on a knowledge graph comprises the following steps:

[0009] S1. Collect and organize individual education background, professional experience, skill certificates and project experience data, and structure them into a professional competence knowledge graph to represent the individual's professional competence characteristics and associations;

[0010] S2. Collect the skill requirements, industry standards and market trend data of enterprise positions, structure them into a job demand knowledge graph to represent job characteristics and requirements, and dynamically adjust the job demand knowledge graph according to the update cycle of market data;

[0011] S3. Design and train a two-layer generative adversarial network model. The first layer of the generative adversarial network is used to generate possible professional ability enhancement plans, and the second layer of the generative adversarial network is used to simulate the impact of changes in market demand on job requirements;

[0012] S4. By matching the occupational capability enhancement scheme and job requirement changes generated by the two-layer generative adversarial network model with the existing occupational capability knowledge graph and job requirement knowledge graph, the matching algorithm is optimized using the potential matching relationship generated by the two-layer generative adversarial network model.

[0013] S5. Based on the dynamic changes in market demand and the development of individual professional capabilities, the professional capability knowledge graph and job requirement knowledge graph are updated in real time, and a two-layer generative adversarial network model is used to generate new matching relationships and continuously optimize matching results;

[0014] S6. Generate education assessment reports and career planning suggestions based on the optimized matching results, and provide feedback to educational institutions and individuals to guide them in making adjustments between professional ability improvement and job requirements, ultimately forming a closed-loop vocational education and job matching system.

[0015] Optionally, S1 includes the following sub-steps:

[0016] S11. Obtain the individual's educational background data, which includes academic qualifications, majors studied, graduate schools, and educational achievement information, and convert the educational background data into an educational background vector E i ;

[0017] S12. Obtain individual professional experience data, which includes work experience, job responsibilities and industry experience information, and convert the professional experience data into a professional experience vector W i ;

[0018] S13. Obtain individual skill certificate data, which includes technical qualification certificates, professional certifications, and language proficiency certificate information, and convert the skill certificate data into a skill certificate vector C i ;

[0019] S14. Obtain individual project experience data, which includes project name, project role and project result information, and convert the project experience data into a project experience vector P. i ;

[0020] S15, the educational background vector E i , professional experience vector W i , Skill Certificate Vector C i and the project experience vector P i Merge to generate the individual's comprehensive professional ability vector A i :

[0021] A i =α·E i +β·W i +γ·C i +δ·P i ;

[0022] Among them, α, β, Y, and δ are the weight coefficients of the corresponding vectors, representing the weights of the influence of different professional ability characteristics on comprehensive professional ability;

[0023] S16, based on comprehensive professional ability vector A iBased on the results, the knowledge graph technology is used to structure the individual's educational background, professional experience, skill certificates and project experience information, generate the individual's professional ability knowledge graph nodes, and establish the association relationship between the nodes to represent the individual's professional ability characteristics and the various dimensions of information associated with them.

[0024] Optionally, the S16 includes the following sub-steps:

[0025] S161, the individual's comprehensive professional ability vector A i Each data feature in is decomposed into independent knowledge graph nodes, generating a node set N i ;

[0026] S162. Assign a unique identifier N to each node i，j , where N represents the knowledge graph node, i represents the individual identifier, and j represents the node in the comprehensive professional ability vector A i The relationship between nodes in the feature dimension is described as follows:

[0027]

[0028] Among them, φ is a nonlinear activation function used to transform the comprehensive professional ability vector A i Through the feature weight matrix W j Projected into the node space, λ m is the correlation coefficient between the features, η i，j，m Represents the interaction effect between different features and is used to adjust the uniqueness and discrimination of nodes;

[0029] S163. Construct edge set R based on the association relationship between nodes i，j，k , where R i，j，k Represents node N i，j With node N i，k The relationship between:

[0030]

[0031] Among them, ψ is an association mapping function, sim(N i，j , N i，k ) represents the similarity measure between nodes, ζ j，k，p is a parameter to adjust the similarity measure, Δ j，k，p Represents the difference measure of a specific feature dimension, ω t is the weight parameter, v i，j，k，t is a multidimensional interaction term used to correct the association relationship;

[0032] S164, analyze the potential association relationship between each node, and adjust the edge set R according to the reasoning result i，j，k The weight value ri，j，k :

[0033]

[0034] Among them, ξ is the function used to update the weight value, κ v Indicates the influence factor of the relationship between different nodes on the association weight during the reasoning process, μ y is the edge adjustment parameter, X i，j，k，y It is the multi-dimensional potential correlation relationship discovered in the reasoning process;

[0035] S165. Generate individual professional competence knowledge graph G i =(N i , R i ), where N i Represents the set of all individual professional competence knowledge graph nodes, R i Represents the set of all associations between nodes. The generation of the professional competence knowledge graph is expressed as:

[0036]

[0037] Among them, θ a，c is the adjustment parameter in the generation of professional competence knowledge graph, Ω i，a，c It is a high-dimensional relational item used to enhance the structure of the professional competence knowledge graph.

[0038] Optionally, S2 includes the following sub-steps:

[0039] S21. Obtain the skill requirement data of the enterprise positions. The skill requirement data includes the technical capabilities, professional knowledge and soft skills required for the positions, and convert the skill requirement data into a skill demand vector Q j ;

[0040] S22. Obtain industry standard data, which includes industry certification standards, industry specifications and best practices, and convert the industry standard data into industry standard vector S j ;

[0041] S23, obtaining market trend data, the market trend data including market demand, technology development trend and economic environment change, and converting the market trend data into a market trend vector T j ;

[0042] S24. Transform the skill demand vector Q j , industry standard vector S j and the market trend vector T j Merge to generate job demand vector D j :

[0043] D j =ρ·Qj +σ·S j +τ·T j ;

[0044] Among them, ρ, σ and τ are the weight coefficients of skill requirements, industry standards and market trends, which are used to adjust the impact of each vector on job demand;

[0045] S25. Use knowledge graph technology to transform the job demand vector D j The structured representation is a job requirement knowledge graph node, generating a job requirement node set M j , where each node represents a specific job feature and generates a job requirement knowledge graph G j :

[0046] G j =(M j , R j );

[0047] Among them, M j represents the set of job demand nodes, R j Represents the association relationship between nodes, which is constructed by constructing the edge set R j，p，q，r accomplish:

[0048]

[0049] Among them, sim(M j，p , M j，q , M j，r ) represents the similarity measure ζ between nodes p，q，r，u To adjust the parameters of the similarity measure, Δ p，q，r，u Represents the difference measure of a specific feature dimension, ω w is the weight parameter, v j，p，q，r，w To correct the multidimensional interaction terms of the association relationship;

[0050] S26. Dynamically adjust the job demand knowledge graph G according to the update cycle of market data j , update the node set M j and the edge set R j The weight value r in j，p，q，r .

[0051] Optionally, S3 includes the following sub-steps:

[0052] S31. Design and initialize the first-layer generative adversarial network to generate a professional ability enhancement plan. The first-layer generative adversarial network consists of the first-layer generator G1(z1) and the first-layer discriminator D1(A i ), where z1 is the input noise vector, and the occupational ability enhancement solution vector generated by the first layer generator G1(z1) is expressed as The first layer discriminator D1 (A i ) is used to distinguish the generated occupational capability enhancement scheme from the original occupational capability vector A i ;

[0053] S32. Design and initialize the second-layer generative adversarial network to simulate the impact of changes in market demand on job requirements. The second-layer generative adversarial network consists of the second-layer generator G2(z2) and the second-layer discriminator D2(D j ), where z2 is the input noise vector, and the job demand change vector generated by the second-layer generator G2(z2) is expressed as The second layer discriminator D2 (D j ) is used to distinguish the generated job demand changes from the original job demand vector D j ;

[0054] S33, jointly train the first-layer generative adversarial network and the second-layer generative adversarial network, adopt an adversarial training mechanism, optimize the first-layer generator G1 and the second-layer generator G2, and the objective function of the joint training is expressed as:

[0055]

[0056] S34. Enhance the professional ability of the first layer of the trained generative adversarial network and professional competence knowledge graph G i Match, analyze and optimize strategies to improve individual professional capabilities;

[0057] S35. Change the job requirements generated by the second layer of the trained generative adversarial network and job requirements knowledge graph G j Conduct matching, evaluate the impact of changes in market demand on job requirements, and optimize job matching strategies;

[0058] S36. Based on the matching results of the first-layer generative adversarial network and the second-layer generative adversarial network, optimize the matching algorithm between professional ability and job requirements and adjust the matching parameters Make the matching function To achieve the best:

[0059]

[0060] in, It is a matching function, which is used to evaluate the matching degree between the occupational capability enhancement plan and the changes in job requirements.

[0061] Optionally, S6 includes the following sub-steps:

[0062] S61. Based on the matching results between professional ability and job requirements, calculate the matching score between individual professional ability and target job requirements, with the score ranging from 0 to 1, where 1 indicates a perfect match and 0 indicates a mismatch;

[0063] S62. Generate an education assessment report based on the matching score. The content of the education assessment report includes the strengths and weaknesses of the individual's current professional ability, the specific requirements of the target position, and the professional skills areas that need to be improved;

[0064] S63. Combine the analysis results in the education evaluation report with the professional competence knowledge map G i Compare and identify the professional capabilities that need to be further improved, and generate individual career planning suggestions i , career planning suggestions include recommended courses to study, required skill certificates to obtain, and recommended practical projects to participate in:

[0065]

[0066] in, is the target professional ability characteristic value, is the characteristic value of the current professional ability, w k is the weight coefficient of each professional ability characteristic in career planning;

[0067] S64. Provide career planning advice i Feedback is provided to educational institutions and individuals to guide them to make corresponding adjustments in the process of improving their professional capabilities. Educational institutions adjust their curriculum according to the suggestions, and individuals choose learning paths and practical activities based on the suggestions.

[0068] S65. Dynamically adjust the professional competence knowledge graph G based on feedback from educational institutions and individuals i and job requirements knowledge graph G j , and recalculate the matching score;

[0069] S66. Through multiple feedback and optimization iterations, a closed-loop vocational education and job matching system will eventually be formed. The system will provide the best career planning suggestions and job matching solutions in real time based on changes in market demand and the development of individual professional capabilities.

[0070] The beneficial effects of the present invention are:

[0071] (1) The present invention achieves an accurate characterization of the correlation between job seekers' abilities and job requirements by constructing a professional ability knowledge graph and a job requirement knowledge graph. Different from the traditional static matching method, the system can dynamically adjust the node and edge weights in the knowledge graph according to the changes in market demand and the development of individual professional abilities. Through the application of a two-layer generative adversarial network model, the system can generate professional ability enhancement plans and simulate changes in market demand, thereby achieving adaptive optimization. This not only improves the real-time and accuracy of matching, but also effectively responds to rapidly changing market demands, making the matching results more personalized and forward-looking.

[0072] (2) The present invention predicts and captures the potential changing trend of job requirements through a two-layer generative adversarial network model, especially through the market demand change simulation function of the second-layer generative adversarial network, and combines the career capability enhancement plan generated by the first-layer generative adversarial network to discover potential matching relationships. The system can predict the rise of certain emerging technologies, thereby suggesting job seekers to acquire relevant skills in advance to enhance their market competitiveness. Through the discovery of potential matching relationships, the system can not only perform effective matching in the current market environment, but also prepare for future job requirements, thereby improving the foresight and accuracy of talent allocation.

[0073] (3) The present invention combines education assessment with job matching process to form a closed-loop feedback mechanism. Through the comprehensive application of knowledge graph and generative adversarial network model, the system can not only generate matching results, but also generate detailed education assessment reports and career planning suggestions based on matching analysis and market demand changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] 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:

[0075] Figure 1 This is a flow chart of an educational assessment method for dynamic professional ability and job matching based on knowledge graph proposed by the present invention. DETAILED DESCRIPTION

[0076] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0077] refer to Figure 1 , an education assessment method for dynamic professional ability and job matching based on knowledge graph, including the following steps:

[0078] S1. Collect and organize individual education background, professional experience, skill certificates and project experience data, and structure them into a professional competence knowledge graph to represent the individual's professional competence characteristics and associations;

[0079] S2. Collect the skill requirements, industry standards and market trend data of enterprise positions, structure them into a job demand knowledge graph to represent job characteristics and requirements, and dynamically adjust the job demand knowledge graph according to the update cycle of market data;

[0080] S3. Design and train a two-layer generative adversarial network model. The first layer of the generative adversarial network is used to generate possible professional ability enhancement plans, and the second layer of the generative adversarial network is used to simulate the impact of changes in market demand on job requirements;

[0081] S4. By matching the occupational capability enhancement scheme and job requirement changes generated by the two-layer generative adversarial network model with the existing occupational capability knowledge graph and job requirement knowledge graph, the matching algorithm is optimized using the potential matching relationship generated by the two-layer generative adversarial network model.

[0082] S5. Based on the dynamic changes in market demand and the development of individual professional capabilities, the professional capability knowledge graph and job requirement knowledge graph are updated in real time, and a two-layer generative adversarial network model is used to generate new matching relationships and continuously optimize matching results;

[0083] S6. Generate education assessment reports and career planning suggestions based on the optimized matching results, and provide feedback to educational institutions and individuals to guide them in making adjustments between professional ability improvement and job requirements, ultimately forming a closed-loop vocational education and job matching system.

[0084] In this implementation, S1 includes the following sub-steps:

[0085] S11. Obtain the individual's educational background data, which includes academic qualifications, majors studied, graduate schools, and educational achievement information, and convert the educational background data into an educational background vector E i ;

[0086] S12. Obtain individual professional experience data, which includes work experience, job responsibilities and industry experience information, and convert the professional experience data into a professional experience vector W i ;

[0087] S13. Obtain individual skill certificate data, which includes technical qualification certificates, professional certifications, and language proficiency certificate information, and convert the skill certificate data into a skill certificate vector C i ;

[0088] S14. Obtain individual project experience data, which includes project name, project role and project result information, and convert the project experience data into a project experience vector P. i ;

[0089] S15. Convert the education background vector E i , professional experience vector W i , Skill Certificate Vector C i and the project experience vector P i Merge to generate the individual's comprehensive professional ability vector A i :

[0090] A i =α·E i +β·W i +γ·C i +δ·P i ;

[0091] Among them, α, β, Y, and δ are the weight coefficients of the corresponding vectors, representing the weights of the influence of different professional ability characteristics on comprehensive professional ability;

[0092] S16, based on comprehensive professional ability vector A i Based on the results, the knowledge graph technology is used to structure the individual's educational background, professional experience, skill certificates and project experience information, generate the individual's professional ability knowledge graph nodes, and establish the association relationship between the nodes to represent the individual's professional ability characteristics and the various dimensions of information associated with them.

[0093] In this implementation, S16 includes the following sub-steps:

[0094] S161, the individual's comprehensive professional ability vector A i Each data feature in is decomposed into independent knowledge graph nodes, generating a node set N i ;

[0095] S162. Assign a unique identifier N to each node i，j , where N represents the knowledge graph node, i represents the individual identifier, and j represents the node in the comprehensive professional ability vector A i The relationship between nodes in the feature dimension is described as follows:

[0096]

[0097] Among them, φ is a nonlinear activation function used to transform the comprehensive professional ability vector A i Through the feature weight matrix W j Projected into the node space, λ m is the correlation coefficient between the features, η i，j，mRepresents the interaction effect between different features and is used to adjust the uniqueness and discrimination of nodes;

[0098] S163. Construct edge set R based on the association relationship between nodes i，j，k , where R i，j，k Represents node N i，j With node N i，k The relationship between:

[0099]

[0100] Among them, ψ is an association mapping function, sim(N i，j , N i，k ) represents the similarity measure between nodes, ζ j，k，p is a parameter to adjust the similarity measure, Δ j，k，p Represents the difference measure of a specific feature dimension, ω t is the weight parameter, V i，j，k，t is a multidimensional interaction term used to correct the association relationship;

[0101] S164, analyze the potential association relationship between each node, and adjust the edge set R according to the reasoning result i，j，k The weight value r i，j，k :

[0102]

[0103] Among them, ξ is the function used to update the weight value, Kv represents the influence factor of the relationship between different nodes on the association weight during the reasoning process, μ y is the edge adjustment parameter, χ i，j，k，y It is the multi-dimensional potential correlation relationship discovered in the reasoning process;

[0104] S165. Generate individual professional competence knowledge graph G i =(N i , R i ), where N i Represents the set of all individual professional competence knowledge graph nodes, R i Represents the set of all associations between nodes. The generation of the professional competence knowledge graph is expressed as:

[0105]

[0106] Among them, θ a，c is the adjustment parameter in the generation of professional competence knowledge graph, Ω i，a，c It is a high-dimensional relational item used to enhance the structure of the professional competence knowledge graph.

[0107] In this implementation, S2 includes the following sub-steps:

[0108] S21. Obtain the skill requirement data of the enterprise positions. The skill requirement data includes the technical capabilities, professional knowledge and soft skills required for the positions, and convert the skill requirement data into a skill demand vector Q j ;

[0109] S22. Obtain industry standard data, which includes industry certification standards, industry specifications and best practices, and convert the industry standard data into industry standard vector S j ;

[0110] S23, obtain market trend data, which includes market demand, technology development trends and economic environment changes, and convert the market trend data into a market trend vector T j ;

[0111] S24. Transform the skill demand vector Q j , industry standard vector S j and the market trend vector T j Merge to generate job demand vector D j :

[0112] D j =ρ·Q j +σ·S j +T·T j ;

[0113] Among them, ρ, σ and τ are the weight coefficients of skill requirements, industry standards and market trends, which are used to adjust the impact of each vector on job demand;

[0114] S25. Use knowledge graph technology to transform the job demand vector D j The structured representation is a job requirement knowledge graph node, generating a job requirement node set M j , where each node represents a specific job feature and generates a job requirement knowledge graph G j :

[0115] G j =(M j , R j );

[0116] Among them, M j represents the set of job demand nodes, R j Represents the association relationship between nodes, which is constructed by constructing the edge set R j，p，q，r accomplish:

[0117]

[0118] Among them, sim(M j，p , M j，q , Mj，r ) represents the similarity measure ζ between nodes p，q，r，u To adjust the parameters of the similarity measure, Δ p，q，r，u Represents the difference measure of a specific feature dimension, ω w is the weight parameter, v j，p，q，r，w To correct the multidimensional interaction terms of the association relationship;

[0119] S26. Dynamically adjust the job demand knowledge graph G according to the update cycle of market data j , update the node set M j and the edge set R j The weight value r in j，p，q，r .

[0120] In this implementation, S3 includes the following sub-steps:

[0121] S31. Design and initialize the first-layer generative adversarial network to generate a professional ability enhancement plan. The first-layer generative adversarial network consists of the first-layer generator G1(z1) and the first-layer discriminator D1(A i ), where z1 is the input noise vector, and the occupational ability enhancement solution vector generated by the first layer generator G1(z1) is expressed as The first layer discriminator D1 (A i ) is used to distinguish the generated occupational capability enhancement scheme from the original occupational capability vector A i ;

[0122] S32, design and initialize the second-layer generative adversarial network to simulate the impact of changes in market demand on job requirements. The second-layer generative adversarial network consists of the second-layer generator G2(z2) and the second-layer discriminator D2(D j ), where z2 is the input noise vector, and the job demand change vector generated by the second layer generator G2z2 is expressed as The second layer discriminator D2D j Used to distinguish the generated job demand changes from the original job demand vector D j ;

[0123] S33, jointly train the first-layer generative adversarial network and the second-layer generative adversarial network, adopt an adversarial training mechanism, optimize the first-layer generator G1 and the second-layer generator G2, and the objective function of the joint training is expressed as:

[0124]

[0125] S34. Enhance the professional ability of the first layer of the trained generative adversarial network and professional competence knowledge graph G i Match, analyze and optimize strategies to improve individual professional capabilities;

[0126] S35. Change the job requirements generated by the second layer of the trained generative adversarial network and job requirements knowledge graph G j Conduct matching, evaluate the impact of changes in market demand on job requirements, and optimize job matching strategies;

[0127] S36. Based on the matching results of the first-layer generative adversarial network and the second-layer generative adversarial network, optimize the matching algorithm between professional ability and job requirements and adjust the matching parameters Make the matching function To achieve the best:

[0128]

[0129] in, It is a matching function, which is used to evaluate the matching degree between the occupational capability enhancement plan and the changes in job requirements.

[0130] In this implementation, S6 includes the following sub-steps:

[0131] S61. Based on the matching results between professional ability and job requirements, calculate the matching score between individual professional ability and target job requirements, with the score ranging from 0 to 1, where 1 indicates a perfect match and 0 indicates a mismatch;

[0132] S62. Generate an education assessment report based on the matching score. The content of the education assessment report includes the strengths and weaknesses of the individual's current professional ability, the specific requirements of the target position, and the professional skills areas that need to be improved;

[0133] S63. Combine the analysis results in the education evaluation report with the professional competence knowledge map G i Compare and identify the professional capabilities that need to be further improved, and generate individual career planning suggestions i , career planning suggestions include recommended courses to study, required skill certificates to obtain, and recommended practical projects to participate in:

[0134]

[0135] in, is the target professional ability characteristic value, is the characteristic value of the current professional ability, w k is the weight coefficient of each professional ability characteristic in career planning;

[0136] S64. Provide career planning advice i Feedback is provided to educational institutions and individuals to guide them to make corresponding adjustments in the process of improving their professional capabilities. Educational institutions adjust their curriculum according to the suggestions, and individuals choose learning paths and practical activities based on the suggestions.

[0137] S65. Dynamically adjust the professional competence knowledge graph G based on feedback from educational institutions and individuals i and job requirements knowledge graph G j , and recalculate the matching score;

[0138] S66. Through multiple feedback and optimization iterations, a closed-loop vocational education and job matching system will eventually be formed. The system will provide the best career planning suggestions and job matching solutions in real time based on changes in market demand and the development of individual professional capabilities.

[0139] Embodiment 1:

[0140] In June 2024, the HR department of a technology company in Beijing is recruiting a senior AI algorithm engineer. In order to cope with the rapid changes in the market and the complexity of job requirements, the company decides to use this invention for experimentation.

[0141] The HR department first extracted detailed information on 10 job applicants from its talent database. The applicants’ backgrounds covered fields related to computer science and artificial intelligence. Each applicant’s educational background, professional experience, skill certificates, and project experience were systematically collected and converted into a professional competency knowledge graph.

[0142] In the embodiment, job applicant Zhang San (No. A001), male, 31 years old, graduated from University A with a master's degree in computer science in 2015. His professional experience includes working as a data scientist in a well-known Internet company, participating in the development of multiple AI projects, and holding TensorFlow advanced development certification. After the system structures the information, it generates a professional ability knowledge graph of Zhang San, which includes multiple nodes, such as an education background node (Master of Computer Science from University A), a professional experience node (5 years of experience as a data scientist), a skill certificate node (TensorFlow advanced development certification), and a project experience node (participation in multiple AI projects).

[0143] At the same time, the demand for enterprise AI algorithm engineer positions has also been structured into a job requirement knowledge graph. This position requires candidates to have professional knowledge of machine learning and deep learning, be able to process large-scale data, and have more than 3 years of relevant work experience. In addition, as the market demand for edge computing technology is growing rapidly, the job requirement map particularly emphasizes familiarity with edge computing technology.

[0144] After the system analyzed Zhang San's professional ability knowledge graph and the AI ​​algorithm engineer job requirement graph, it was found that Zhang San's professional ability score reached 85 points (out of 100 points). The system then used a two-layer generative adversarial network (GAN) model for further optimization. First, the first-layer generative adversarial network generated a professional ability enhancement plan, suggesting that Zhang San could participate in a two-week intensive training in edge computing technology to improve his match. Then, the second-layer generative adversarial network simulated changes in market demand and predicted that the demand for edge computing will continue to increase in the next 6 months. Therefore, HR is advised to give priority to candidates with edge computing skills.

[0145] After the optimized matching algorithm, Zhang San's professional ability score increased to 92 points. The system generated a detailed matching report, suggesting that the HR department invite Zhang San to participate in the next round of interviews and providing Zhang San with specific career development suggestions. The report listed Zhang San's current advantages, including his rich experience in the field of AI algorithms and solid academic background, and also pointed out his room for improvement in edge computing.

[0146] To verify the effectiveness of the system, the HR department decided to use both the traditional recruitment method and the new system for comparison. During the two-month recruitment cycle, the HR department interviewed a total of 20 candidates using the traditional method, of which 5 entered the final interview stage, and only 1 finally passed the probation period. The entire recruitment process took 50 days.

[0147] Through the knowledge graph-based system, the HR department screened 10 high-potential candidates at the same time, 7 of whom entered the final interview stage, and 4 of them finally passed the probation period. The whole process took only 25 days. The matching accuracy provided by the system reached 90%, which is 20% higher than the traditional method. At the same time, through the career development suggestions generated by the system, the 4 employees who passed the probation period performed well in their work, and 2 of them successfully completed edge computing-related projects during the probation period, creating significant economic benefits for the company.

[0148] Table 1 Comparative data of the present invention method and the traditional method in terms of recruitment cycle and job matching

[0149]

[0150] It can be seen from Example 1 that the education assessment method of dynamic professional ability and job matching based on knowledge graph not only significantly shortens the recruitment cycle and improves the accuracy of job matching, but also effectively improves the pass rate and job adaptability of employees during the probation period. The HR department finally decided to fully adopt the system and plans to further promote the application of this technology in future recruitment and career development to better respond to market changes and corporate development needs.

[0151] The present invention realizes the accurate characterization of the correlation between job seekers' abilities and job requirements by constructing a professional ability knowledge graph and a job requirement knowledge graph. Different from the traditional static matching method, the system can dynamically adjust the node and edge weights in the knowledge graph according to the changes in market demand and the development of individual professional abilities. Through the application of a two-layer generative adversarial network model, the system can generate professional ability enhancement plans and simulate changes in market demand, thereby realizing adaptive optimization. It not only improves the real-time and accuracy of matching, but also effectively responds to the rapidly changing market demand, making the matching results more personalized and forward-looking.

[0152] The present invention uses a two-layer generative adversarial network model, especially the market demand change simulation function of the second-layer generative adversarial network, to predict and capture the potential change trend of job requirements, and combines the career ability enhancement plan generated by the first-layer generative adversarial network to explore potential matching relationships. The system can predict the rise of certain emerging technologies, thereby suggesting job seekers to acquire relevant skills in advance and enhance their market competitiveness. Through the exploration of potential matching relationships, the system can not only perform effective matching in the current market environment, but also prepare for future job requirements, thereby improving the foresight and accuracy of talent allocation.

[0153] The present invention combines education assessment with job matching process to form a closed-loop feedback mechanism. Through the comprehensive application of knowledge graph and generative adversarial network model, the system can not only generate matching results, but also generate detailed education assessment reports and career planning suggestions based on matching analysis and market demand changes.

[0154] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An education assessment method for dynamic professional ability and job matching based on knowledge graph, characterized in that: The steps include: S1. Collect and organize individual education background, professional experience, skill certificates and project experience data, and structure them into a professional competency knowledge graph; S2. Collect the skill requirements, industry standards and market trend data of enterprise positions and structure them into a job demand knowledge map; S3. Design and train a two-layer generative adversarial network model. The first layer of the generative adversarial network is used to generate professional ability enhancement plans, and the second layer of the generative adversarial network is used to simulate the impact of changes in market demand on job requirements; S4. By matching the occupational capability enhancement scheme and job requirement changes generated by the two-layer generative adversarial network model with the existing occupational capability knowledge graph and job requirement knowledge graph, the matching algorithm is optimized using the potential matching relationship generated by the two-layer generative adversarial network model. S5. Based on the dynamic changes in market demand and the development of individual professional capabilities, the professional capability knowledge graph and job requirement knowledge graph are updated in real time, and a two-layer generative adversarial network model is used to generate new matching relationships and continuously optimize matching results; S6. Generate education assessment reports and career planning suggestions based on the optimized matching results, and provide feedback to educational institutions and individuals to guide them in making adjustments between professional ability improvement and job requirements, ultimately forming a closed-loop vocational education and job matching system.

2. According to claim 1, a method for dynamic professional ability and job matching education assessment based on knowledge graph is characterized in that: The S1 comprises the following sub-steps: S11. Obtain the individual's educational background data, which includes academic qualifications, majors studied, graduate schools, and educational achievement information, and convert the educational background data into an educational background vector E i ; S12. Obtain individual professional experience data, which includes work experience, job responsibilities and industry experience information, and convert the professional experience data into a professional experience vector W i ; S13. Obtain individual skill certificate data, which includes technical qualification certificates, professional certifications, and language proficiency certificate information, and convert the skill certificate data into a skill certificate vector C i ; S14. Obtain individual project experience data, which includes project name, project role and project result information, and convert the project experience data into a project experience vector P. i ; S15, the educational background vector E i , professional experience vector W i , Skill Certificate Vector C i and the project experience vector P i Merge to generate the individual's comprehensive professional ability vector A i : A i =α·E i +β·W i +γ·C i +δ·P i ; Among them, α, β, γ, and δ are the weight coefficients of the corresponding vectors, representing the weights of the influence of different professional ability characteristics on comprehensive professional ability; S16, based on comprehensive professional ability vector A i Based on the results, the knowledge graph technology is used to structure the individual's educational background, professional experience, skill certificates and project experience information, generate the individual's professional ability knowledge graph nodes, and establish the association relationship between the nodes to represent the individual's professional ability characteristics and the various dimensions of information associated with them.

3. According to claim 2, a method for dynamic professional ability and job matching education assessment based on knowledge graph is characterized in that: The S16 comprises the following sub-steps: S161, the individual's comprehensive professional ability vector A i Each data feature in is decomposed into independent knowledge graph nodes, generating a node set N i ; S1 62. Assign a unique identifier N to each node i,j , where N represents the knowledge graph node, i represents the individual identifier, and j represents the node in the comprehensive professional ability vector A i The relationship between nodes in the feature dimension is described as follows: Among them, φ is a nonlinear activation function used to transform the comprehensive professional ability vector A i Through the feature weight matrix W j Projected into the node space, λ m is the correlation coefficient between the features, η i,j,m Represents the interaction effect between different features and is used to adjust the uniqueness and discrimination of nodes; S163. Construct edge set R based on the association relationship between nodes i,j,k , where R i,j,k Represents node N i,j With node N i,k The relationship between: Among them, ψ is an association mapping function, sim(N i,j ,N i,k ) represents the similarity measure between nodes, ζ j,k,p is a parameter to adjust the similarity measure, Δ j,k,p Represents the difference measure of a specific feature dimension, ω t is the weight parameter, ν i,j,k,t is a multidimensional interaction term used to correct the association relationship; S164, analyze the potential association relationship between each node, and adjust the edge set R according to the reasoning result i,j,k The weight value r i,j,k : Among them, ξ is the function used to update the weight value, κ v Indicates the influence factor of the relationship between different nodes on the association weight during the reasoning process, μ y is the edge adjustment parameter, χ i,j,k,y It is the multi-dimensional potential correlation relationship discovered in the reasoning process; S165. Generate individual professional competence knowledge graph G i =(N i ,R i ), where N i Represents the set of all individual professional competence knowledge graph nodes, R i Represents the set of all associations between nodes. The generation of the professional competence knowledge graph is expressed as: Among them, θ a,c is the adjustment parameter in the generation of professional competence knowledge graph, Ω i,a,c It is a high-dimensional relational item used to enhance the structure of the professional competence knowledge graph.

4. According to claim 1, a method for dynamic professional ability and job matching education assessment based on knowledge graph is characterized in that: The S2 comprises the following sub-steps: S21. Obtain the skill requirement data of the enterprise positions. The skill requirement data includes the technical capabilities, professional knowledge and soft skills required for the positions, and convert the skill requirement data into a skill demand vector Q j ; S22. Obtain industry standard data, which includes industry certification standards, industry specifications and best practices, and convert the industry standard data into industry standard vector S j ; S23, obtaining market trend data, the market trend data including market demand, technology development trend and economic environment change, and converting the market trend data into a market trend vector T j ; S24. Transform the skill demand vector Q j , industry standard vector S j and the market trend vector T j Merge to generate job demand vector D j : D j =ρ·Q j +σ·S j +τ·T j ; Among them, ρ, σ and τ are the weight coefficients of skill requirements, industry standards and market trends, which are used to adjust the impact of each vector on job demand; S25. Use knowledge graph technology to transform the job demand vector D j The structured representation is a job requirement knowledge graph node, generating a job requirement node set M j , where each node represents a specific job feature and generates a job requirement knowledge graph G j : G j =(M j ,R j ); Among them, M j represents the set of job demand nodes, R j Represents the association relationship between nodes, which is constructed by constructing the edge set R j,p,q,r accomplish: Among them, sim(M j,p ,M j,q ,M j,r ) represents the similarity measure ζ between nodes p,q,r,u To adjust the parameters of the similarity measure, Δ p,q,r,u Represents the difference measure of a specific feature dimension, ω w is the weight parameter, ν j,p,q,r,w To correct the multidimensional interaction terms of the association relationship; S26. Dynamically adjust the job demand knowledge graph G according to the update cycle of market data j , update the node set M j and the edge set R j The weight value r in j,p,q,r .

5. According to claim 1, a method for dynamic professional ability and job matching education assessment based on knowledge graph is characterized in that: The S3 comprises the following sub-steps: S31. Design and initialize the first-layer generative adversarial network to generate a professional ability enhancement plan. The first-layer generative adversarial network consists of the first-layer generator G1(z1) and the first-layer discriminator D1(A i ), where z1 is the input noise vector, and the occupational ability enhancement solution vector generated by the first layer generator G1(z1) is expressed as The first layer discriminator D1 (A i ) is used to distinguish the generated occupational capability enhancement scheme from the original occupational capability vector A i ; S32, design and initialize the second-layer generative adversarial network to simulate the impact of changes in market demand on job requirements. The second-layer generative adversarial network consists of the second-layer generator G2(z2) and the second-layer discriminator D2(D j ), where z2 is the input noise vector, and the job demand change vector generated by the second-layer generator G2(z2) is expressed as The second layer discriminator D2 (D j ) is used to distinguish the generated job demand changes from the original job demand vector D j ; S33, jointly train the first-layer generative adversarial network and the second-layer generative adversarial network, adopt an adversarial training mechanism, optimize the first-layer generator G1 and the second-layer generator G2, and the objective function of the joint training is expressed as: S34. Enhance the professional ability of the first layer of the trained generative adversarial network and professional competence knowledge graph G i Match, analyze and optimize strategies to improve individual professional capabilities; S35. Change the job requirements generated by the second layer of the trained generative adversarial network and job requirements knowledge graph G j Conduct matching, evaluate the impact of changes in market demand on job requirements, and optimize job matching strategies; S36. Based on the matching results of the first-layer generative adversarial network and the second-layer generative adversarial network, optimize the matching algorithm between professional ability and job requirements and adjust the matching parameters Make the matching function To achieve the best: in, It is a matching function, which is used to evaluate the matching degree between the occupational capability enhancement plan and the changes in job requirements.

6. According to claim 1, a method for dynamic professional ability and job matching education assessment based on knowledge graph is characterized in that: The S6 comprises the following sub-steps: S61. Based on the matching results between professional ability and job requirements, calculate the matching score between individual professional ability and target job requirements, with the score ranging from 0 to 1, where 1 indicates a perfect match and 0 indicates a mismatch; S62. Generate an education assessment report based on the matching score. The content of the education assessment report includes the strengths and weaknesses of the individual's current professional ability, the specific requirements of the target position, and the professional skills areas that need to be improved; S63. Combine the analysis results in the education evaluation report with the professional competence knowledge map G i Compare and identify the professional capabilities that need to be further improved, and generate individual career planning suggestions i , career planning suggestions include recommended courses to study, required skill certificates to obtain, and recommended practical projects to participate in: in, is the target professional ability characteristic value, is the characteristic value of the current professional ability, w k is the weight coefficient of each professional ability characteristic in career planning; S64. Provide career planning advice i Feedback is provided to educational institutions and individuals to guide them to make corresponding adjustments in the process of improving their professional capabilities. Educational institutions adjust their curriculum according to the suggestions, and individuals choose their learning paths and practical activities based on the suggestions. S65. Dynamically adjust the professional competence knowledge graph G based on feedback from educational institutions and individuals i and job requirements knowledge graph G j , and recalculate the matching score; S66. Through multiple feedback and optimization iterations, a closed-loop vocational education and job matching system will eventually be formed. The system will provide the best career planning suggestions and job matching solutions in real time based on changes in market demand and the development of individual professional capabilities.

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