Pre-post vocational ability assessment method and training recommendation system

Abstract

The invention discloses a pre-job vocational ability assessment training recommendation system, which comprises a multi-source data acquisition module, an ability assessment engine, an intelligent recommendation module and a closed-loop verification module, and relates to the technical field of job seeker employment ability assessment and training recommendation. According to the system, dynamic acquisition of user capability data is realized through the multi-source data acquisition module, the limitation of traditional static evaluation is broken through, the evaluation result is more comprehensive and accurate, meanwhile, the introduction of a capability evaluation engine sets a clear capability threshold value, training recommendation can be automatically triggered, and the training efficiency is improved. According to the method and the system, a personalized training scheme is provided for the user with the value lower than the threshold value, the vocational ability of the user is effectively improved, the structural failure phenomenon is effectively relieved, the configuration and utilization of training resources are optimized, and the method and the system play a positive role in promoting the healthy development of talent training and employment markets.

Description

Technical Field

[0001] This invention relates to the field of job seeker employability assessment and training recommendation technology, specifically a pre-employment vocational ability assessment method and training recommendation system. Background Technology

[0002] In recent years, with the popularization of higher education in my country, the number of college and vocational school students has increased year by year, making employment a focus of social attention. The employment of college and vocational school students not only relates to their personal development but also affects national economic and social stability. They face numerous challenges, the most prominent being the mismatch between their employability and job requirements. Many college and vocational school students, lacking an accurate assessment of their own professional abilities, often struggle to find positions that match their skills. Therefore, pre-employment professional ability assessment is crucial.

[0003] Currently, most pre-employment vocational competency assessment tools on the market use static questionnaires for evaluation. While this method can provide a preliminary understanding of an individual's career inclinations and skill levels, the assessment results are often disconnected from subsequent learning, recruitment, and selection processes, lacking effective connection and integration. Specifically, existing vocational assessments cannot quantify and analyze identified individual skill gaps to accurately recommend corresponding training courses, nor can they effectively match training effectiveness with specific job requirements. Furthermore, they lack dynamic tracking and continuous optimization of individual career development trajectories. This disconnect leads to a series of problems, such as the difficulty in alleviating structural unemployment, the inability to rationally allocate and efficiently utilize training resources, and ultimately, the negative impact on talent development and the healthy development of the job market. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a pre-employment vocational competency assessment method and training recommendation system, which solves the problem that existing pre-employment vocational competency assessment tools often fail to effectively connect and integrate assessment results with subsequent course learning, recruitment and selection processes.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a pre-employment vocational competency assessment and training recommendation system, comprising a multi-source data acquisition module, a competency assessment engine, an intelligent recommendation module, and a closed-loop verification module; The multi-source data acquisition module dynamically collects users' academic data through the university's academic affairs system API, synchronizes practical data through the project management system, reads skill certificates through OCR recognition and manual review, generates workplace scenario simulation data through game theory models, and uses AI-assisted questionnaires and anonymous evaluations from corporate mentors / alumni. The capability assessment engine constructs user capability profiles based on the multi-source data acquisition module, sets a capability threshold of 60 points, and automatically triggers the capability recommendation module to provide training recommendations for those who score below the threshold. The intelligent recommendation module divides the recommendation strategy into four quadrants based on the evaluation results: high suitability zone, potential reserve zone, emergency supplementation zone, and exploration and observation zone, and forms a knowledge graph, a capability graph, and a job graph. The closed-loop verification module tracks user skill improvement data after training, and those who meet the standards are pushed to the recruitment pool of partner companies, forming a "assessment-training-employment" business closed loop.

[0006] Preferably, the academic data includes core course grades, practical achievements, and graduation project grades; the practical data includes project role, output quality, and team contribution; and the skill credentials include professional level certificates, computer proficiency certificates, and competition certificates.

[0007] Preferably, the training recommendations include internal referrals to top companies and pre-employment training through the certification fast track, weekend intensive classes organized by the pre-exam cram school, and 1-on-1 tutoring.

[0008] Preferably, the knowledge graph breaks down courses into knowledge point granularity and dynamically associates them with enterprise job requirement tags, prioritizing the coverage of knowledge points corresponding to users' skill gaps during recommendations.

[0009] A pre-employment vocational competency assessment method, characterized by the following steps: S1. Dynamically acquire multi-dimensional user capability data through a multi-source data acquisition module; S2. Calculate the overall score using the competency assessment engine. Those scoring below 60 points are marked as training pending activation. S3. Based on knowledge graphs, competency graphs, and job graphs, user weaknesses are linked to course resource libraries to generate a tiered growth plan; S4. After safety training and certification training, those who meet the performance evaluation criteria will have their recruitment channels unlocked by the company.

[0010] Preferably, the calculation of the comprehensive score in S2 incorporates the weight of keyboard operation log analysis to correct the deviation between the user's theoretical score and actual operation.

[0011] Preferably, the tiered growth plan described in S3 includes a "career shadowing program" that involves cross-disciplinary elective courses and following corporate mentors for no less than 8 hours.

[0012] Beneficial effects This invention provides a pre-employment vocational competency assessment method and training recommendation system, which has the following beneficial effects: This system achieves dynamic acquisition of user competency data through a multi-source data acquisition module, breaking the limitations of traditional static assessments and making the assessment results more comprehensive and accurate. Simultaneously, the introduction of a competency assessment engine sets clear competency thresholds, automatically triggering training recommendations and providing personalized training programs for users below the threshold, effectively improving their professional skills. The intelligent recommendation module's four-quadrant recommendation strategy divides users into different regions based on assessment results and forms a knowledge graph, achieving precise matching of course resources with user competency gaps and improving training effectiveness. Furthermore, the closed-loop verification module tracks user competency improvement data after training and pushes qualified individuals to the recruitment pool of partner companies, forming a "assessment-training-employment" business closed loop. This effectively alleviates structural unemployment, optimizes the allocation and utilization of training resources, and plays a positive role in promoting talent cultivation and the healthy development of the employment market. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the pre-employment vocational competency assessment method of the present invention. Detailed Implementation

[0014] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments and 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0015] This invention provides a technical solution: a pre-employment vocational competency assessment and training recommendation system, comprising a multi-source data acquisition module, a competency assessment engine, an intelligent recommendation module, and a closed-loop verification module; The multi-source data acquisition module dynamically collects users' academic data through the university's academic affairs system API, synchronizes practical data through the project management system, reads skill certificates through OCR recognition and manual review, generates workplace scenario simulation data through game theory models, and uses AI-assisted questionnaires and anonymous evaluations from corporate mentors / alumni. The capability assessment engine constructs user capability profiles based on the multi-source data acquisition module, sets a capability threshold of 60 points, and automatically triggers the capability recommendation module to provide training recommendations for those who score below the threshold. The intelligent recommendation module divides the recommendation strategy into four quadrants based on the evaluation results: high suitability zone, potential reserve zone, emergency supplementation zone, and exploration and observation zone, and forms a knowledge graph, a capability graph, and a job graph. The closed-loop verification module tracks user skill improvement data after training, and those who meet the standards are pushed to the recruitment pool of partner companies, forming a "assessment-training-employment" business closed loop.

[0016] In this embodiment, the academic data includes core course grades, practical achievements, and graduation project grades; the practical data includes project role, output quality, and team contribution; and the skill certificates include professional level certificates, computer level certificates, and competition certificates. In the multi-source data collection module, the AI-assisted questionnaire uses natural language processing technology to intelligently analyze the semantic tendencies and ability characteristics in users' answers, ensuring the comprehensiveness and accuracy of data collection; the anonymous evaluations by corporate mentors / alumni ensure the fairness and privacy protection of the evaluation process through encrypted transmission and anonymization.

[0017] In this embodiment, the training recommendations include internal referrals to top companies and pre-job training through the certification fast track, weekend intensive classes and 1-on-1 tutoring organized by the exam preparation class; The certification fast track can establish in-depth cooperation with many well-known companies, accurately match internal referral positions based on user ability assessment results, and provide 1-2 weeks of pre-job training to help users quickly adapt to the workplace environment; the pre-exam intensive course offers weekend intensive classes targeting users' weak knowledge points, adopts a small class teaching model to ensure teaching quality, and is equipped with 1-on-1 tutors for personalized guidance to help users improve their core competencies in a short period of time. The internal referral positions, pre-job training and intensive courses adopt a system of advance notification and self-appointment.

[0018] In this embodiment, the knowledge graph is further configured to break down courses into knowledge point granularities and dynamically associate them with enterprise job requirement tags, and prioritize covering knowledge points corresponding to user skill gaps when making recommendations. The knowledge graph continuously optimizes the association model through machine learning algorithms to ensure that the matching degree between knowledge points and enterprise needs is updated in real time with market changes. The system automatically analyzes the recruitment needs of partner companies every quarter and dynamically adjusts the weight of knowledge points. For example, when a technical position adds the requirement of "data analysis tools", the system can automatically increase the priority of relevant course modules.

[0019] A pre-employment vocational competency assessment method, characterized by the following steps: S1. Dynamically acquire multi-dimensional user capability data through a multi-source data acquisition module; S2. Calculate the overall score using the competency assessment engine. Those scoring below 60 points are marked as training pending activation. S3. Based on knowledge graphs, competency graphs, and job graphs, user weaknesses are linked to course resource libraries to generate a tiered growth plan; S4. After safety training and certification training, those who meet the performance improvement data will unlock the company's recruitment channels. When building user competency profiles, the competency assessment engine not only sets a competency threshold of 60 points, but also introduces a dynamic weight adjustment mechanism. Based on industry trends and changes in enterprise needs, it optimizes the assessment criteria in real time to ensure the timeliness and relevance of the assessment results. When those below the threshold trigger the intelligent recommendation module, the system automatically generates a personalized training suggestion report, covering course recommendations, learning path planning, and expected competency improvement goals.

[0020] In this embodiment, the calculation of the comprehensive score in S2 incorporates the weight of keyboard operation log analysis to correct the deviation between the user's theoretical score and actual operation. Keyboard operation log analysis quantifies the match between a user's practical skills and theoretical knowledge by recording their operation trajectory, reaction speed, and error types in a simulated workplace environment. For example, in programming job evaluations, the system can analyze code writing efficiency, debugging frequency, and logical rigor, converting operational data into ability correction coefficients. These coefficients are then weighted with theoretical exam scores to generate a comprehensive score that more closely reflects job requirements, avoiding evaluation biases such as "high scores but low skills" or "strong theory but weak practice."

[0021] This embodiment is further configured such that the tiered growth plan described in S3 includes a “career shadowing plan” of cross-disciplinary elective courses with corporate mentors, with a duration of no less than 8 hours; The "Career Shadowing Program" provides immersive on-the-job learning, allowing users to directly observe the daily work, problem-solving processes, and team collaboration models of corporate mentors. Users also participate in non-core aspects of real-world projects. For example, in software development roles, users can follow their mentors to complete tasks such as writing requirements analysis documents and designing test cases. Mentors provide real-time feedback on operational standards and improvement suggestions. Upon completion of the program, users are required to submit a shadowing log and a results report, which will be graded by the mentor and included in their competency profile, serving as a reference for future recommendations of highly suitable positions.

[0022] Example: The system can dynamically acquire multi-dimensional user capability data through a multi-source data acquisition module, calculate a comprehensive score using a capability assessment engine, and mark those scoring below 60 points as pending training activation. Based on a knowledge graph, the system associates user weaknesses with a course resource library to generate a tiered growth plan, verifying capability improvement data after training. Those who meet the standards unlock corporate recruitment channels. The calculation of the comprehensive score incorporates the weight of keyboard operation log analysis to correct the discrepancy between the user's theoretical performance and actual operation. The tiered growth plan includes a "career shadow plan" of cross-disciplinary elective courses with corporate mentors, lasting no less than 8 hours. Through the above implementation methods, this system achieves intelligent management of the entire process from data acquisition to job recommendation. Specifically, the multi-source data acquisition module integrates academic, practical, skills, and simulated scenario data to construct a three-dimensional model of user capability profiles, avoiding the limitations of a single assessment dimension. For example, if a computer science user achieves satisfactory grades in core courses but has low project contributions, the system will identify their weaknesses in teamwork through practical data, rather than relying solely on theoretical grades. The dynamic weighting mechanism of the competency assessment engine further enhances the accuracy of the assessment. For instance, when industry demands shift towards big data analysis, the system can automatically increase the weight of relevant courses, aligning assessment results with market trends. The intelligent recommendation module's four-quadrant strategy enables differentiated training paths: users in the high-fitness zone are directly recommended for internal positions, users in the potential reserve zone are given priority to participate in the career shadowing program, and users in the emergency supplementation zone enter weekend intensive classes for targeted improvement. The closed-loop verification module tracks user competency improvement data after training, forming a dynamic feedback loop of "assessment-training-employment." This end-to-end optimization not only shortens the adaptation period for users from campus to workplace, but also reduces recruitment costs for enterprises, achieving a precise match between talent development and market demand.

[0023] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, the phrase "comprising an element defined as..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0024] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A pre-employment vocational competency assessment and training recommendation system, characterized in that, It includes a multi-source data acquisition module, a capability assessment engine, an intelligent recommendation module, and a closed-loop verification module; The multi-source data acquisition module dynamically collects users' academic data through the university's academic affairs system API, synchronizes practical data through the project management system, reads skill certificates through OCR recognition and manual review, generates workplace scenario simulation data through game theory models, and uses AI-assisted questionnaires and anonymous evaluations from corporate mentors / alumni. The capability assessment engine constructs user capability profiles based on the multi-source data acquisition module, sets a capability threshold of 60 points, and automatically triggers the capability recommendation module to provide training recommendations for those who score below the threshold. The intelligent recommendation module divides the recommendation strategy into four quadrants based on the evaluation results: high suitability zone, potential reserve zone, emergency supplementation zone, and exploration and observation zone, and forms a knowledge graph, a capability graph, and a job graph. The closed-loop verification module tracks user skill improvement data after training, and those who meet the standards are pushed to the recruitment pool of partner companies, forming a "assessment-training-employment" business closed loop.

2. The pre-employment vocational competency assessment and training recommendation system according to claim 1, characterized in that, The academic data includes core course grades, practical achievements, and graduation project grades. The practical data includes project roles, output quality, and team contributions. The skills credentials include professional level certificates, computer proficiency certificates, and competition certificates.

3. The pre-employment vocational competency assessment method and training recommendation system according to claim 1, characterized in that, The training recommendations include internal referrals to top companies and pre-employment training through the Certification Express Program, weekend intensive classes organized by the Exam Preparation Class, and 1-on-1 tutoring.

4. The pre-employment vocational competency assessment method and training recommendation system according to claim 1, characterized in that, The knowledge graph breaks down courses into knowledge point granularity and dynamically associates them with enterprise job requirement tags, prioritizing the coverage of knowledge points corresponding to users' skill gaps during recommendations.

5. A method for pre-employment vocational competence assessment, characterized in that, Includes the following steps: S1. Dynamically acquire multi-dimensional user capability data through a multi-source data acquisition module; S2. Calculate the overall score using the competency assessment engine. Those scoring below 60 points are marked as training pending activation. S3. Based on knowledge graphs, competency graphs, and job graphs, user weaknesses are linked to course resource libraries to generate a tiered growth plan; S4. After safety training and certification training, those who meet the performance evaluation criteria will have their recruitment channels unlocked by the company.

6. The pre-employment vocational competency assessment method according to claim 5, characterized in that, The calculation of the comprehensive score described in S2 incorporates keyboard operation log analysis weights to correct the discrepancy between the user's theoretical score and actual operation.

7. The pre-employment vocational competency assessment method according to claim 5, characterized in that, The tiered growth program described in S3 includes a "career shadowing program" that involves cross-disciplinary elective courses and mentorship with corporate mentors, lasting no less than 8 hours.

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