Occupational ability evaluation system based on skill map

Abstract

The present application provides a professional ability evaluation system based on a skill map, relates to the technical field of human resources and the application field of knowledge maps, and comprises an evaluation main system, which comprises a skill star map generation module, a professional track simulation module, a skill challenge module, a skill certification chain module, a future skill navigation module, a skill coordination ecological module, a skill evolution tree module, a professional story generation module, a skill energy pool module, a professional time capsule module and a control flow management module. The present application can collect user professional experience, learning records and test data in time by constructing a dynamic skill star map and combining 5G network real-time data transmission, and can effectively overcome the problems of strong subjectivity, low efficiency and inability to accurately quantify ability of traditional manual evaluation and online testing by dynamically adjusting node weights through a semantic analysis algorithm and a weighted scoring algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of human resources and knowledge graph, and particularly to a professional competence evaluation system based on a skill graph. BACKGROUND

[0002] In the traditional process of professional competence evaluation, it often relies on manual questionnaires, interviews or experience judgments, etc. These methods have strong subjectivity, low efficiency and cannot accurately quantify the ability. With the development of artificial intelligence, big data and other technologies, data-driven professional competence evaluation has gradually become a trend. As an extension of knowledge graph, skill graph can describe and visualize the relationship between professional skills and knowledge points through graph structure, providing a new technical foundation for individual competence evaluation.

[0003] Currently, professional competence evaluation mainly relies on the following technical methods:

[0004] Manual evaluation: Traditional evaluation methods include determining individual skill levels and professional potential through questionnaire surveys, interviews or experience judgments of human resources departments. Although manual evaluation can reflect the user's ability to a certain extent, this method has limited coverage, long time-consuming, poor real-time performance and other problems, which is difficult to adapt to the rapid changes of modern job market.

[0005] Online testing and certificate authentication: Some platforms evaluate user skills through online testing or professional certificates (such as Coursera certificates), but these methods are usually static, lack dynamic correlation analysis of skills and industry trends, and are difficult to provide personalized career development recommendations, and the applicability of the evaluation results is limited.

[0006] Data analysis tools: Some enterprises use data analysis tools (such as HR management systems) combined with resume parsing technology for preliminary evaluation. This method uses natural language processing technology to extract skill information, which has a certain objectivity. However, it is limited by data quality and algorithm complexity, and it is difficult to fully reflect the relevance and future development potential of skills, and lacks a real-time feedback mechanism.

[0007] Although the existing professional competence evaluation technology has improved the efficiency and accuracy of evaluation to a certain extent, there are still many deficiencies:

[0008] Problem one, traditional manual evaluation and online testing are time-consuming and have limited coverage, which cannot realize real-time monitoring of individual professional competence. This leads to the difficulty of discovering potential skill gaps in a timely manner, affecting the timeliness of career planning.

[0009] Problem two, existing data analysis-based evaluation methods can extract skill information, but they are limited to static data and single indicators, making it difficult to dynamically adjust skill weights to adapt to industry trend changes. In addition, the certification results are mostly paper certificates or simple digital records, lacking credibility and tamper resistance, making it difficult to gain widespread recognition in the competitive job market.

[0010] Problem three, existing evaluation methods mostly rely on regular tests or periodic data updates, lacking continuous and real-time monitoring capabilities. This non-real-time nature leads to individuals not being able to obtain accurate feedback in the early stages of career transition or skill improvement, missing the best opportunity for career development.

[0011] Therefore, a skill map-based professional competence evaluation system is needed to solve the above problems. SUMMARY

[0012] To overcome the shortcomings of the prior art, the present application provides a skill map-based professional competence evaluation system to solve the problems in the above background art.

[0013] To achieve the above purpose, the present application is implemented by the following technical solutions: a skill map-based professional competence evaluation system, comprising an evaluation main system, the evaluation main system comprising a skill star map generation module, a professional trajectory simulation module, a skill challenge module, a skill certification chain module, a future skill navigation module, a skill collaboration ecology module, a skill evolution tree module, a professional story generation module, a skill energy pool module, a professional time capsule module, and a control flow management module.

[0014] The skill star map generation module collects user professional experience, learning records, and test data, and generates dynamic skill star map hybrid data through semantic analysis algorithms. The dynamic skill star map hybrid data is transmitted to the control flow management module in JSON format through a 5G network.

[0015] The control flow management module coordinates the instruction flow between the skill star map generation module, the professional trajectory simulation module, the skill challenge module, the skill certification chain module, the future skill navigation module, the skill collaboration ecology module, the skill evolution tree module, the professional story generation module, the skill energy pool module, and the professional time capsule module, realizes data transmission priority management and feedback cycle optimization of the system, and guarantees the real-time performance and stability of the system.

[0016] The skill star map generation module dynamically adjusts the node weight of the dynamic skill star map mixed data according to the user career goal and industry trend, the node weight is calculated by a weighted scoring algorithm, and the weight value ranges from 0 to 100; the career trajectory simulation module receives the dynamic skill star map mixed data of the skill star map generation module, generates a career path, and predicts a success probability; the skill challenge module receives the dynamic skill star map mixed data of the skill star map generation module, generates an interactive task, and evaluates user performance; the skill certification chain module receives the evaluation data of the skill challenge module, and generates a blockchain-based digital badge; the future skill navigation module receives the dynamic skill star map mixed data of the skill star map generation module and industry trend data, and generates a learning plan; the skill collaboration ecology module receives the dynamic skill star map mixed data of the skill star map generation module, and matches collaborative users; the skill evolution tree module receives the dynamic skill star map mixed data of the skill star map generation module, and generates a tree-shaped growth path; the career story generation module receives the dynamic skill star map mixed data of the skill star map generation module and the evaluation data of the skill challenge module, and generates a career narrative; the skill energy pool module receives the dynamic skill star map mixed data of the skill star map generation module and the evaluation data of the skill challenge module, calculates an energy value, and supports resource exchange;

[0017] The career time capsule module receives the dynamic skill star map mixed data of the skill star map generation module and user goal data, stores and generates a growth report; the operation results of the skill star map generation module, the career trajectory simulation module, the skill challenge module, the skill certification chain module, the future skill navigation module, the skill collaboration ecology module, the skill evolution tree module, the career story generation module, the skill energy pool module, and the career time capsule module are fed back to the user in the form of push notifications through the user interaction terminal, and the user feedback data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module updates the node weight of the dynamic skill star map mixed data according to the user feedback data, forming a closed-loop management.

[0018] Preferably, the skill star map generation module comprises an experience collection unit, a test evaluation unit and an industry trend analysis unit;

[0019] The experience collection unit collects user resumes, project experience and learning records through the user interaction terminal, generates structured career data, and stores the structured career data in JSON format; the test evaluation unit generates skill proficiency data through online testing, the skill proficiency data is calculated by a weighted scoring algorithm, and the score ranges from 0 to 100; the industry trend analysis unit extracts industry skill demand by crawling public recruitment data and social media platform career discussions, and generates demand weight data by using a semantic analysis algorithm, and the weight value ranges from 0 to 100;

[0020] The skill star map generation module integrates structured occupational data, skill proficiency data, and demand weight data to generate dynamic skill star map hybrid data. The dynamic skill star map hybrid data is represented in the form of nodes and lines, where nodes represent skills and lines represent the strength of skill association. The dynamic skill star map hybrid data is transmitted to the control flow management module in JSON format via a 5G network.

[0021] The control flow management module includes an instruction distribution unit, a data transmission monitoring unit, and a feedback loop optimization unit. The instruction distribution unit receives dynamic skill star map hybrid data from the skill star map generation module and distributes it to the career trajectory simulation module, skill challenge module, skill certification chain module, future skill navigation module, skill collaboration ecosystem module, skill evolution tree module, career story generation module, skill energy pool module, and career time capsule module. The career trajectory simulation module, skill challenge module, skill certification chain module, future skill navigation module, skill collaboration ecosystem module, skill evolution tree module, career story generation module, skill energy pool module, and career time capsule module perform path planning, task generation, badge generation, learning recommendation, user matching, growth path generation, career narrative generation, energy calculation, and growth report generation operations based on the received dynamic skill star map hybrid data.

[0022] Preferably, the career trajectory simulation module includes a path planning unit and a potential assessment unit;

[0023] The path planning unit receives dynamic skill star map hybrid data from the skill star map generation module, calculates the fit between the user's skills and the target position through a cosine similarity matching algorithm, and generates a career path. The career path includes the job name, skill gaps, and learning suggestions.

[0024] The potential assessment unit receives node weight data from the dynamic skill star map hybrid data of the skill star map generation module and demand weight data from the industry trend analysis unit. It then calculates the user's success probability in the target position using a weighted average algorithm, with the probability value ranging from 0% to 100%.

[0025] The career trajectory simulation module transmits the career path and success probability in JSON format to the career time capsule module for storage via the control flow management module. The career trajectory simulation module also pushes the career path and success probability to the user in the form of a visual trajectory map through the user interaction terminal.

[0026] The user provides career goal adjustment data through the user interaction terminal. The career goal adjustment data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module updates the node weights of the dynamic skill star map hybrid data according to the career goal adjustment data.

[0027] Preferably, the skill challenge module includes a task generation unit and a scoring and evaluation unit;

[0028] The task generation unit receives dynamic skill star map hybrid data and user career goals from the skill star map generation module, and generates customized interactive tasks through the rule engine. The customized interactive tasks include multiple-choice questions, simulated scenarios, and open-ended questions.

[0029] The scoring and evaluation unit receives user task completion data and calculates a skill score using a weighted scoring algorithm. The weighted scoring algorithm is based on selection accuracy, completion time, and answer quality, with a score range of 0 to 100.

[0030] The skill challenge module transmits skill score data in JSON format to the skill certification chain module and the skill energy pool module through the control flow management module. The skill certification chain module generates digital badges, and the skill energy pool module updates the energy value.

[0031] The skill challenge module transmits skill score data to the career time capsule module for storage via the control flow management module. The user provides task experience data through the user interaction terminal. The task experience data is transmitted to the skill star map generation module in JSON format via the control flow management module. The skill star map generation module updates the node weights of the dynamic skill star map mixed data based on the task experience data.

[0032] Preferably, the skill certification chain module includes a blockchain recording unit and a badge generation unit;

[0033] The blockchain recording unit receives skill score data from the skill challenge module, records the evaluation results through an Ethereum smart contract, and generates immutable skill metadata, which includes skill category, skill score, and evaluation time.

[0034] The badge generation unit receives skill metadata and generates digital badges, which contain a unique identifier and a verification link.

[0035] The skill certification chain module pushes a digital badge verification link through the user interaction terminal. The digital badge data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module updates the node weights of the dynamic skill star map hybrid data according to the digital badge data.

[0036] The user provides feedback on digital badge usage data through the user interaction terminal. The digital badge usage data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module optimizes the correlation strength of the dynamic skill star map mixed data based on the digital badge usage data.

[0037] Preferably, the future skills navigation module includes a trend prediction unit and a learning recommendation unit;

[0038] The trend prediction unit receives demand weight data from the industry trend analysis unit, predicts skill demand for the next three years using a time series analysis algorithm, and generates a demand priority list.

[0039] The learning recommendation unit receives node weight data and a demand priority list from the dynamic skill star map hybrid data of the skill star map generation module, and generates a personalized learning plan through a collaborative filtering algorithm. The personalized learning plan includes course recommendations, project recommendations, and community activity recommendations.

[0040] The future skills navigation module pushes personalized learning plans once or twice a week through the user interaction terminal. The personalized learning plans are transmitted in JSON format to the career time capsule module for storage through the control flow management module.

[0041] The user provides feedback on learning progress data through the user interaction terminal. The learning progress data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module updates the node weights of the dynamic skill star map hybrid data based on the learning progress data.

[0042] Preferably, the skill collaboration ecosystem module includes a user matching unit and a community interaction unit;

[0043] The user matching unit receives dynamic skill star map mixed data from the skill star map generation module, calculates the skill similarity and complementarity between users through a cosine similarity matching algorithm, and generates a collaboration potential index, the value of which ranges from 0 to 100.

[0044] The community interaction unit supports user communication and project collaboration through the platform's messaging system. The community interaction unit generates collaborative data, which is transmitted in JSON format to the skill challenge module and career story generation module through the control flow management module.

[0045] The skills challenge module receives collaborative data, verifies collaborative results, and generates skills score data.

[0046] The career story generation module receives collaborative data and generates collaborative stories;

[0047] The user provides collaborative experience data through the user interaction terminal. The collaborative experience data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module updates the node weights of the dynamic skill star map hybrid data according to the collaborative experience data.

[0048] The user matching unit receives growth report data from the career time capsule module, combines it with dynamic skill star map mixed data, and recommends career mentors through a priority ranking algorithm. The career mentor recommendation data is transmitted to the user interaction terminal in JSON format through the control flow management module.

[0049] Preferably, the skill evolution tree module includes a growth path unit and an advancement recommendation unit;

[0050] The growth path unit receives dynamic skill star map mixed data from the skill star map generation module and generates a tree-like skill path through a hierarchical clustering algorithm. The tree-like skill path includes prerequisite skills and advanced skills.

[0051] The advanced recommendation unit receives demand weight data from the industry trend analysis unit and generates advanced task recommendations through a priority ranking algorithm.

[0052] The skill evolution tree module transmits advanced task recommendations in JSON format to the skill challenge module through the control flow management module. The skill challenge module verifies the completion status of the advanced tasks and generates skill score data.

[0053] The skill challenge module transmits skill score data to the skill star map generation module and the skill certification chain module through the control flow management module. The skill star map generation module updates the node weights of the dynamic skill star map hybrid data according to the skill score data, and the skill certification chain module generates digital badges according to the skill score data.

[0054] The user provides feedback on the advanced task completion experience data through the user interaction terminal. The advanced task completion experience data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module optimizes the skill path of the dynamic skill star map mixed data based on the advanced task completion experience data.

[0055] Preferably, the career story generation module includes a narrative generation unit and a template customization unit;

[0056] The narrative generation unit receives dynamic skill star map hybrid data from the skill star map generation module and skill score data from the skill challenge module, and generates personalized career stories through a template filling algorithm. The personalized career stories include skill descriptions, career achievements, and goal statements.

[0057] The template customization unit receives the user's career goals and generates career scenario narrative templates through the rule engine;

[0058] The career story generation module pushes personalized career story previews through the user interaction terminal, and the personalized career stories are transmitted in JSON format to the career time capsule module for storage through the control flow management module.

[0059] The user provides personalized career story editing data through the user interaction terminal. The personalized career story editing data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module updates the node weights of the dynamic skill star map hybrid data according to the personalized career story editing data.

[0060] Preferably, the skill energy pool module includes an energy calculation unit and a resource exchange unit;

[0061] The career time capsule module includes a recording and storage unit and a growth analysis unit;

[0062] The energy calculation unit receives node weight data from the dynamic skill star map hybrid data of the skill star map generation module and skill score data from the skill challenge module, and calculates the skill energy value through a weighted average algorithm. The skill energy value ranges from 0 to 100.

[0063] The resource exchange unit receives skill energy value data and supports users in exchanging professional resources through a points exchange algorithm. The professional resources include professional mentor consultation and priority in community activities.

[0064] The skill energy pool module pushes skill energy value reports through the user interaction terminal, and the skill energy value data is transmitted in JSON format to the career time capsule module for storage through the control flow management module.

[0065] The record storage unit receives dynamic skill star map hybrid data, user career goals, and user reflection data from the skill star map generation module, and generates an immutable record through blockchain storage.

[0066] The growth analysis unit receives dynamic skill star map mixed data from the current skill star map generation module, and generates a growth report through a comparative analysis algorithm at the user-set unlock time. The growth report includes skill progress and goal achievement.

[0067] The career time capsule module pushes growth reports through the user interaction terminal, the user provides feedback on growth report data through the user interaction terminal, the growth report data is transmitted to the skill star map generation module in JSON format through the control flow management module, and the skill star map generation module updates the node weights of the dynamic skill star map hybrid data according to the growth report data;

[0068] The instruction distribution unit of the control flow management module distributes dynamic skill star map mixed data, skill score data, digital badge data, personalized learning plan, collaboration data, advanced task recommendations, personalized career stories, skill energy value data, and growth report data through the 5G network according to instruction priority. The data transmission monitoring unit monitors transmission latency and packet loss rate. The feedback loop optimization unit adjusts the instruction execution frequency and data collection cycle according to the amount of user feedback data, optimizing once to twice per hour.

[0069] The skill collaboration ecosystem module receives skill energy value data and growth report data, and generates cross-regional collaboration recommendations through a cosine similarity matching algorithm. The cross-regional collaboration recommendations are transmitted to the user interaction terminal in JSON format through the control flow management module.

[0070] Beneficial effects

[0071] This invention provides a vocational competency assessment system based on skill maps. It has the following beneficial effects:

[0072] 1. This invention, by constructing a dynamic skill star map and combining it with real-time data transmission via 5G network, can promptly collect users' professional experience, learning records, and test data. Through semantic analysis and weighted scoring algorithms, it dynamically adjusts node weights, effectively overcoming the problems of strong subjectivity, low efficiency, and inaccurate quantification of abilities inherent in traditional manual assessments and online tests. Compared to the time-consuming and limited coverage of manual assessments in the background technologies, and the static limitations of online tests, this invention integrates structured professional data, skill proficiency data, and industry demand weights through a skill star map generation module to generate personalized, dynamic skill star map hybrid data. This data is transmitted in JSON format to the control flow management module via 5G network, enabling feedback optimization once to twice per hour. This significantly improves the real-time performance and quantification accuracy of the assessment, providing users with an accurate skill baseline.

[0073] 2. This invention, by introducing Ethereum blockchain technology to record assessment data from the skills challenge module and generating tamper-proof digital badges, effectively addresses the issues of low credibility and limited workplace acceptance of existing certification results in the background technology. Compared to the limitations of static records in data analysis tools and paper certificates, the skills certification chain module utilizes blockchain recording units and badge generation units to ensure the security and transparency of skills metadata (including skill category, score, and time), and enhances the credibility of users' professional competitiveness by pushing verification links through user interaction terminals. Simultaneously, the skills star map generation module optimizes the association strength based on digital badge usage data, forming a closed-loop management system, further compensating for the lack of dynamic feedback in existing methods.

[0074] 3. This invention, relying on a career trajectory simulation module, a future skills navigation module, and a skills evolution tree module, can comprehensively analyze the fit between user skills and job requirements, predict skill needs for the next three years, and generate a tree-like growth path through cosine similarity matching, time series analysis, and hierarchical clustering algorithms. This effectively solves the problems of non-real-time monitoring and limited applicability in the background technology. Compared to the shortcomings of periodic testing or periodic data updates, this invention achieves multi-module collaboration through the instruction distribution unit of the control flow management module, generating personalized career paths, learning plans, and advanced task recommendations. It also dynamically updates the dynamic skills star map hybrid data based on user feedback data, supporting users' timely decision-making and continuous improvement in the early stages of career transition, thereby significantly improving the dynamism and personalization of career ability assessment. Attached Figure Description

[0075] Figure 1 This is a system flowchart of the present invention;

[0076] Figure 2 This is a diagram illustrating the skill map construction steps of the present invention;

[0077] Figure 3 This is the dynamic skill star map of the present invention;

[0078] Figure 4 This is a bar chart illustrating the career path analysis of the present invention.

[0079] Figure 5 This is a line graph showing the changes in skill scores according to the present invention. Detailed Implementation

[0080] 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. Specific Implementation Example 1:

[0082] like Figures 1 to 5As shown, the "Skill Graph-Based Career Competency Assessment System" is an integrated and dynamic platform designed to assess, enhance, and guide users' career abilities through a skill graph framework. It constructs a personalized, data-driven ecosystem through interconnected modules, not only assessing users' current skills but also predicting future career paths, validating abilities, and promoting collaborative growth. As described in the claim, the system comprises eleven core modules: a Skill Star Map generation module, a Career Trajectory Simulation module, a Skill Challenge module, a Skill Certification Chain module, a Future Skill Navigation module, a Skill Collaboration Ecosystem module, a Skill Evolution Tree module, a Career Story Generation module, a Skill Energy Pool module, a Career Time Capsule module, and a Control Flow Management module. These modules collaborate in a closed-loop manner. The Skill Star Map generation module, as the core data center, generates dynamic skill star map hybrid data for other modules to execute specific functions. The Control Flow Management module ensures smooth data flow, optimizes instruction priority and feedback loops, and guarantees system real-time performance and stability.

[0083] The detailed operation of each module is as follows:

[0084] The Skill Star Map generation module is the core data processing center of the system. It is responsible for collecting users' career experiences, learning records, test data, and industry trends to generate dynamic skill star map hybrid data, which represents the user's skill network (nodes are skills, and edges represent the strength of the association between skills). Its purpose is to provide a unified data foundation for other modules, supporting career path planning, ability verification, and learning recommendations.

[0085] Implementation steps and contact information:

[0086] Data Collection: The experience collection unit collects resumes (supporting PDF / Word formats), project experience, and learning records (including online course certificates) through user interaction terminals (supporting web and mobile applications). Users can manually upload data or import it through professional social networking platform interfaces to generate structured professional data, including fields such as user ID, skill category, work duration, and proficiency level. The testing and assessment unit provides online tests, including multiple-choice questions (testing basic knowledge), case studies (testing application skills), and simulated tasks (including writing a business plan), generating skill proficiency data with a scoring range of 0 to 100. The industry trend analysis unit extracts skill requirement keywords by crawling professional discussions from public recruitment websites and social media platforms, generating demand weight data with a weight range of 0 to 100.

[0087] Data Integration: The skill star map generation module integrates structured occupational data, skill proficiency data, and demand weight data, and uses a semantic analysis algorithm (based on word frequency and semantic relevance) to generate dynamic skill star map hybrid data. The data is represented by nodes (skill name, proficiency score, demand weight) and edges (skill association strength), stored in JSON format, and encrypted using AES-256.

[0088] Data transmission: The dynamic skill star map hybrid data is transmitted to the control flow management module via the 5G network. The instruction distribution unit then distributes it to the career trajectory simulation module, skill challenge module, skill certification chain module, future skill navigation module, skill collaboration ecosystem module, skill evolution tree module, career story generation module, skill energy pool module, and career time capsule module.

[0089] Feedback Update: Users provide feedback on career goal adjustment data, task experience data, etc. through the user interaction terminal. This data is sent back to the skill star map generation module in JSON format through the control flow management module to update the node weights and form a closed loop.

[0090] Connections and Functions: The Dynamic Skill Star Map Hybrid Data is the core data of the system. The Career Trajectory Simulation Module uses it to plan paths, the Skill Challenge Module generates tasks, the Skill Certification Chain Module verifies abilities, the Future Skill Navigation Module recommends learning plans, the Skill Collaboration Ecosystem Module matches users, the Skill Evolution Tree Module constructs growth paths, the Career Story Generation Module generates narratives, the Skill Energy Pool Module calculates energy values, and the Career Time Capsule Module stores historical records.

[0091] Career trajectory simulation module:

[0092] Definition and Uses: The Career Trajectory Simulation Module generates personalized career paths and predicts the probability of success based on dynamic skill star map mixed data. It aims to help users understand the matching degree between skills and career goals and plan their career development direction.

[0093] Implementation steps and contact information:

[0094] Path Planning: The path planning unit receives dynamic skill star map hybrid data and compares the weights of the user's skill nodes with the skill requirements of the target position (obtained from the industry trend analysis unit) using a cosine similarity matching algorithm. It then generates a career path, including the job title, skill gaps, and learning suggestions. For example, if a user's skill includes "data analysis" (weight 80), and the target position "data scientist" requires "machine learning" (missing), the path suggestion would be to take relevant courses.

[0095] Potential Assessment: The potential assessment unit receives node weight data and demand weight data, and calculates the success probability using a weighted average algorithm. The weight allocation is: skill proficiency 60% and industry demand 40%. The results are transmitted to the career time capsule module for storage in JSON format (including path and probability).

[0096] User Interaction: The career trajectory simulation module displays the path as a visual trajectory map through the user interaction terminal. Users can adjust their target positions, and the results are updated in real time. Push notifications are sent once a week, including a path summary.

[0097] Feedback Update: User feedback on career goal adjustments is sent back to the skill star map generation module through the control flow management module, updating node weights and affecting subsequent path planning.

[0098] Connections and Functions: The Career Trajectory Simulation Module relies on dynamic skill star map mixed data, outputs path and probability data for storage by the Career Time Capsule Module, optimizes the skill star map generation module based on user feedback, and influences the task generation of the Skill Challenge Module and the recommendations of the Future Skill Navigation Module.

[0099] The Skills Challenge module generates customized interactive tasks to assess users' skill performance, aiming to verify actual abilities and provide feedback for improvement.

[0100] Implementation steps and contact information:

[0101] Task Generation: The task generation unit receives dynamic skill star map mixed data and user career goals, and generates tasks through a rule engine, such as multiple-choice questions (testing theoretical knowledge), simulated scenarios (simulating workplace tasks), and open-ended questions (testing comprehensive abilities). Tasks are customized based on skill weights and target positions; for example, if the goal is "project manager," a team coordination task will be generated.

[0102] Scoring and Evaluation: The scoring and evaluation unit receives user task completion data and calculates skill scores using a weighted scoring algorithm (selection accuracy 40%, completion time 30%, answer quality 30%). The generated data includes task ID, skill category, and score.

[0103] Data transmission: Skill score data is transmitted in JSON format through the control flow management module to the skill certification chain module (generate badges), skill energy pool module (update energy values), and career time capsule module (store).

[0104] User Interaction and Feedback: Tasks are presented through a user interaction terminal, displaying real-time progress and error messages. Upon task completion, a rating report and improvement suggestions are pushed to the user. User feedback on the task experience is transmitted back to the skill star map generation module via the control flow management module to update node weights.

[0105] Connections and Functions: The Skill Challenge module relies on dynamic skill star map mixed data to generate tasks. The scoring data supports the Skill Certification Chain module and the Skill Energy Pool module, and is stored in the Career Time Capsule module. User feedback optimizes the Skill Star Map generation module, and influences the Career Trajectory Simulation module and the Future Skill Navigation module.

[0106] The Skills Certification Chain module uses blockchain technology to record skills assessment results and generate verifiable digital badges, aiming to enhance the credibility of competency verification.

[0107] Implementation steps and contact information:

[0108] Data Recording: The blockchain recording unit receives skill score data from the skill challenge module and generates immutable skill metadata through an Ethereum smart contract, including skill category, score, and evaluation time. User IDs are protected for privacy using SHA-256 hashing.

[0109] Badge Generation: The badge generation unit generates digital badges based on skill metadata, which include a unique identifier and verification link and are stored in JSON format.

[0110] Data transmission and interaction: Digital badge data is transmitted to the skill star map generation module via the control flow management module to update node weights. Badges are pushed through user interaction terminals, and users can share them to professional social platforms.

[0111] Feedback Update: User feedback on badge usage data (usage scenarios, feedback opinions) is sent back to the skill star map generation module through the control flow management module to optimize the association strength.

[0112] Connections and Functions: The Skill Certification Chain module relies on the scoring data of the Skill Challenge module, outputs badge data to update the Skill Star Map Generation module, stores it in the Career Time Capsule module, and affects user matching in the Skill Collaboration Ecosystem module.

[0113] The Future Skills Navigation module predicts skill demands for the next three years and generates personalized learning plans designed to help users maintain their professional competitiveness.

[0114] Implementation steps and contact information:

[0115] Trend Forecasting: The trend forecasting unit receives demand weight data from the industry trend analysis unit, predicts skill demand through time series analysis algorithms, and generates a demand priority list, which includes skill names and priority scores.

[0116] Learning Recommendation: The learning recommendation unit receives node weight data and a demand priority list from the dynamic skill star map hybrid data, and generates a learning plan through a collaborative filtering algorithm, which includes recommendations for courses, projects and community activities.

[0117] Data transmission and interaction: The learning plan is transmitted in JSON format to the Career Time Capsule module for storage via the control flow management module, and is pushed 1-2 times per week through the user interaction terminal, including resource links.

[0118] Feedback Update: User feedback on learning progress data is sent back to the Skill Star Map Generation Module through the Control Flow Management Module to update node weights.

[0119] Connections and Functions: The future skills navigation module relies on a combination of dynamic skills star map data and industry trend data to output learning plans stored in the career time capsule module, influencing the advanced recommendations of the skills evolution tree module, and optimizing the skills star map generation module based on user feedback.

[0120] The skills collaboration ecosystem module promotes user collaboration and mentor recommendations through skills matching, aiming to build a professional community and enhance skills sharing and networking.

[0121] Implementation steps and contact information:

[0122] User Matching: The user matching unit receives dynamic skill star map mixed data, calculates skill similarity and complementarity using a cosine similarity matching algorithm, and generates a collaboration potential index (0 to 100). Combined with growth report data from the career time capsule module, a priority ranking algorithm is used to recommend career mentors.

[0123] Community Interaction: The community interaction unit provides a messaging system and project management tools to support user communication and collaboration, and generate collaborative data (project ID, participants, contribution).

[0124] Data transmission: Collaborative data is transmitted through the control flow management module to the skills challenge module (to verify results) and the career story generation module (to generate collaborative stories). Mentor recommendation data is pushed through the user interaction terminal.

[0125] Feedback Update: Collaboration experience data provided by users is sent back to the Skill Star Map Generation Module via the Control Flow Management Module to update node weights.

[0126] Connections and Functions: The Skill Collaboration Ecosystem Module relies on dynamic Skill Star Map hybrid data, outputting collaboration and mentor recommendation data that influences the Skill Challenge Module, Career Story Generation Module, and Career Time Capsule Module. User feedback optimizes the Skill Star Map Generation Module.

[0127] The skill evolution tree module generates a tree-like skill growth path and recommends advanced tasks, aiming to guide users to systematically improve their skills.

[0128] Implementation steps and contact information:

[0129] Path Generation: The growth path unit receives dynamic skill star map mixed data and generates a tree-like skill path through hierarchical clustering algorithm, which includes prerequisite skills and advanced skills.

[0130] Advanced Recommendations: The advanced recommendation unit receives demand weight data and generates advanced task recommendations, such as learning advanced skills or participating in projects, through a priority ranking algorithm.

[0131] Data transmission: Advanced tasks are recommended to be transmitted to the skill challenge module for verification through the control flow management module, generate skill score data, and then transmit it to the skill star map generation module and the skill certification chain module.

[0132] Feedback Update: User feedback on the completed experience data is sent back to the skill star map generation module through the control flow management module to optimize skill paths.

[0133] Connections and Functions: The Skill Evolution Tree module relies on dynamic skill star map mixed data, outputs task recommendations that influence the Skill Challenge module and the Skill Certification Chain module, and uses user feedback to optimize the Skill Star Map generation module.

[0134] The career story generation module generates personalized career stories to enhance users' personal brand and improve their competitiveness in job hunting and social interactions.

[0135] Implementation steps and contact information:

[0136] Narrative Generation: The narrative generation unit receives dynamic skill star map mixed data and skill score data, and generates career stories through template filling algorithms, including skill descriptions, achievements and goals.

[0137] Template customization: The template customization unit generates narrative templates based on the user's career goals, supporting styles such as technical and management.

[0138] Data transmission and interaction: Career stories can be previewed through user interaction terminals, and users can edit the content. The story data is then transmitted to the Career Time Capsule module for storage.

[0139] Feedback Update: Edit data provided by users is sent back to the Skill Star Map Generation Module via the Control Flow Management Module to update node weights.

[0140] Connections and Functions: The Career Story Generation Module relies on dynamic skill star map mixed data and skill rating data, outputs stories and stores them in the Career Time Capsule Module, and optimizes the skill star map generation module based on user feedback.

[0141] The Skill Energy Pool module quantifies the value of skills, incentivizes users to accumulate energy through tasks and learning, and supports resource exchange, aiming to enhance user engagement.

[0142] Implementation steps and contact information:

[0143] Energy Calculation: The energy calculation unit receives node weight data and skill score data from the dynamic skill star map mixed data, and calculates the skill energy value from 0 to 100 through a weighted average algorithm (proficiency 40%, demand weight 30%, usage frequency 30%).

[0144] Resource Exchange: The resource exchange unit supports the exchange of professional resources based on energy points, such as mentor consultation or priority in community activities.

[0145] Data transmission and interaction: Energy value reports are pushed through the user's interactive terminal, and energy value data is transmitted to the professional time capsule module for storage.

[0146] Feedback Update: User-reported redemption data is sent back to the Skill Star Map Generation Module via the Control Flow Management Module to update node weights.

[0147] Connections and Functions: The Skill Energy Pool module relies on dynamic skill star map mixed data and skill rating data. The output energy value affects the mentor recommendation of the skill collaboration ecosystem module, is stored in the career time capsule module, and user feedback optimizes the skill star map generation module.

[0148] The Career Time Capsule module stores user skills and goal data and generates growth reports designed to track long-term career development.

[0149] Implementation steps and contact information:

[0150] Data storage: The record storage unit receives dynamic skill star map hybrid data, user goals and reflection data, and stores them through blockchain to generate tamper-proof records.

[0151] Growth Analysis: The growth analysis unit compares current and historical data at a user-defined time and generates a growth report, which includes skill progress and goal achievement.

[0152] Data transmission and interaction: The growth report is pushed through the user interaction terminal, and the report data is transmitted to the skill star map generation module.

[0153] Feedback Update: User feedback on growth report data is sent back to the skill star map generation module through the control flow management module to update node weights.

[0154] Connections and Functions: The Career Time Capsule module relies on dynamic skill star map hybrid data, stores data from the Career Trajectory Simulation module, Skill Challenge module, and Skill Energy Pool module, outputs reports that influence mentor recommendations in the Skill Collaboration Ecosystem module, and uses user feedback to optimize the Skill Star Map Generation module.

[0155] The control flow management module coordinates data flow between modules, optimizes instruction priority and feedback loop, and aims to ensure system real-time performance and stability.

[0156] Implementation steps and contact information:

[0157] Instruction distribution: The instruction distribution unit distributes data via the 5G network according to priority (high: skill score data, medium: learning plan, low: feedback data). The data format is JSON and it is encrypted using AES-256.

[0158] Transmission monitoring: The data transmission monitoring unit monitors latency (target less than 50 milliseconds) and packet loss rate (target less than 1%).

[0159] Feedback optimization: The feedback loop optimization unit adjusts the instruction execution frequency and data acquisition cycle based on the amount of feedback data, optimizing 1-2 times per hour.

[0160] Cross-regional collaboration: The control flow management module supports cross-regional collaboration recommendations in the skill collaboration ecosystem module and transmits recommendation data to the user interaction terminal.

[0161] Connections and Functions: The control flow management module connects all modules, ensuring smooth flow of dynamic skill star map mixed data, skill score data, etc., and optimizing the skill star map generation module based on user feedback to maintain a closed loop.

[0162] System optimization and user experience:

[0163] Privacy protection: User data is anonymized using SHA-256 hashing, blockchain storage ensures immutability, and 5G network transmission provides encrypted privacy protection.

[0164] Multilingual support: The user interface supports multiple languages ​​including Chinese and English, and automatically translates career stories and learning plans to suit global users.

[0165] Performance optimization: The system adopts a distributed architecture, with dynamic skill star map hybrid data stored in a NoSQL database, resulting in a query latency of less than 100 milliseconds. The control flow management module uses message queues to handle high concurrency. Specific Implementation Example 2:

[0167] like Figures 1 to 5 As shown, the key algorithm mentioned in Example 1 will be analyzed in detail below, including its core mathematical formulas and explanations:

[0168] The algorithm for the skill star map generation module:

[0169] Semantic analysis algorithms:

[0170] The formula is as follows:

[0171] ;

[0172] ;

[0173] ;

[0174] in:

[0175] :Skill The demand weight, ranging from 0 to 100 (after normalization), represents the intensity of skill demand in the industry. It is used as the node weight in dynamic skill star map hybrid data, influencing career path planning and learning recommendations. Derived from TF-IDF calculations, it addresses the problem of how to quantify the market importance of skills. :Skill The term frequency-inverse document frequency (TFM) value comprehensively measures the importance of skills in a text dataset. It is used to extract key skills from recruitment data and social media discussions crawled by the industry trend analysis unit. :Skill The word frequency represents the frequency of a skill's occurrence in a single document. It originates from a text dataset derived from the industry trend analysis unit, addressing the challenge of identifying high-frequency skills. :Skill In the dataset The frequency of appearances in (recruitment data and social media discussions) is directly calculated from text segmentation, reflecting the frequency of skill mentions. The total number of occurrences of all skills in the dataset is used to normalize word frequencies, ensuring... Between 0 and 1. :Skill Inverse document frequency measures the uniqueness of a skill. It is derived from dataset statistics and addresses the problem of how to highlight rare skills. Dataset The total number of documents in the database (such as the total number of job postings and social media discussions) is calculated by the web crawler and reflects the scale of the data. Includes skills The document count was analyzed, skill distribution was statistically analyzed, and the weight of common skills was reduced. Natural logarithm: used to smooth inverse document frequency and prevent extreme values. :Skill Examples such as "Python" and "project management" are extracted from the text through word segmentation and semantic analysis.

[0176] Problems and solutions:

[0177] Problem: Extract key skill requirements from massive amounts of unstructured text (such as job postings and social media discussions), quantify their importance, and ensure that dynamic skills star map hybrid data reflects market trends.

[0178] Handling method:

[0179] The industry trend analysis unit uses web crawlers to collect text from publicly available job posting websites and social media platforms to create a dataset. .

[0180] The text is segmented, stop words (such as “and” and “of”) are removed, and skill keywords (such as “data analysis”) are extracted.

[0181] Weighted scoring algorithm (test evaluation unit):

[0182] The formula is as follows:

[0183] ;

[0184] in:

[0185] :Skill The proficiency score ranges from 0 to 100. It represents the user's skill level in the test and is used as a node weight in the dynamic skill star map mixed data, influencing career path planning and task generation. It originates from an online test evaluation unit. The weights are 0.4, 0.3, and 0.3, respectively, summing to 1. This represents the relative importance of accuracy, time efficiency, and answer quality, addressing the challenge of balancing multi-dimensional evaluation. :Skill The accuracy rate of multiple-choice questions is calculated as follows: This data is derived from test data and reflects the user's level of mastery of basic knowledge. :Skill The number of correct answers in the test is automatically counted by the system. :Skill The total number of questions in the test is preset in the test question bank. Completion time efficiency, calculated as If the actual time exceeds the limit, take the reciprocal. This data is derived from test records and reflects user efficiency. :Skill The standard completion time for the test is based on the question bank design. The actual completion time for the user is timed by the system. Answer quality score, ranging from 0 to 1, is based on keyword matching and human scoring criteria (such as the logicality of open-ended questions). It is derived from case studies and simulated tasks, reflecting the user's overall ability.

[0186] Problems and solutions:

[0187] Problem: Objectively quantify users' skill levels, avoid bias caused by a single indicator, and ensure that the dynamic skill star map mixed data accurately reflects users' abilities.

[0188] Handling method:

[0189] The testing and assessment unit provides online tests (multiple choice, case analysis, and simulation tasks), and records answers, time, and text.

[0190] Use formulas to calculate and generate JSON data.

[0191] The skill star map generation module integrates proficiency scores and demand weight data, updates the node weights of the dynamic skill star map mixed data, and transmits them to other modules (such as the career trajectory simulation module and the skill challenge module).

[0192] The algorithm for the career trajectory simulation module:

[0193] Cosine similarity matching algorithm:

[0194] The formula is as follows:

[0195] ;

[0196] in:

[0197] : Skill fit between user U and job J, ranging from 0 to 1 (multiplied by 100 becomes 0 to 100). Represents the degree of skill matching, used to generate career paths and identify skill gaps. Derived from calculations in the path planning unit. The node weight of user skill i is derived from the dynamic skill star map hybrid data and reflects the user's ability level. The weights of skill i required for the target position J are derived from demand weight data from the industry trend analysis unit, reflecting the job requirements. Total number of skills, based on the number of nodes in the dynamic skill star map hybrid data. The dot product of user skill vectors and job skill vectors measures the degree of skill overlap. The modulus of the user skill vector, and the normalized user skill weights. The modulus of the job skill vector is the normalized job skill requirement.

[0198] Problems and solutions:

[0199] Problem: Accurately match users' skills with their career goals, identify skills gaps, and provide feasible career paths.

[0200] Handling method:

[0201] The path planning unit extracts the user's skill vector (UUU) from the dynamic skill star map hybrid data and the job skill vector from the industry trend analysis unit. The path is displayed as a visual trajectory map through the user interaction terminal, and is recalculated after the user adjusts their target job. The path data is transmitted to the career time capsule module for storage, and user feedback on career goal adjustments is sent back to the skill star map generation module to update node weights.

[0202] Weighted average algorithm:

[0203] The formula is as follows:

[0204] ;

[0205] in:

[0206] : Success probability for job j, ranging from 0% to 100%. Represents the user's potential success rate in the job, used for career trajectory prediction. Derived from the potential assessment unit. w1, w2: Weights, 0.6 and 0.4 respectively, summing to 1. Represents the relative importance of skill fit and industry demand, addressing the issue of balancing individual abilities with market trends. The skill fit between the user and job j is derived from the cosine similarity matching algorithm. The industry demand weight for job j comes from the industry trend analysis unit and reflects the intensity of market demand.

[0207] Algorithm for the skill collaboration ecosystem module:

[0208] Cosine similarity matching algorithm:

[0209] The formula is as follows:

[0210] ;

[0211] ;

[0212] ;

[0213] in:

[0214] :user and A collaboration potential index, ranging from 0 to 100. Used to recommend partners and mentors. Weights are set to 0.5 and 0.5 respectively. This balances similarity and complementarity. :user and Skill similarity is based on dynamic skill star map hybrid data. Skill complementarity measures skill differences. :user skills Weights. Total number of skills.

[0215] Problems and methods to be solved:

[0216] Problem: Matching partners and mentors with complementary skills to improve collaboration efficiency. Solution: User matching unit calculation. Generate recommendations: .

[0217] Collaborative data is transmitted to the Skill Challenge module and Career Story generation module, while user feedback is transmitted back to the Skill Star Map generation module.

[0218] Priority sorting algorithm:

[0219] The formula is as follows:

[0220] ;

[0221] in: : Mentor m's recommendation rating. Used for recommending career mentors. The weights are 0.4, 0.4, and 0.2, respectively. Similarity of skills between users and mentors. The mentor's skill weighting. User skill improvement comes from the Career Time Capsule module.

[0222] Problem Solving:

[0223] Question: Recommend suitable career mentors to improve the quality of guidance. Specific Implementation Example 3:

[0225] like Figures 1 to 5 As shown, the hardware composition and hardware of each module in Embodiment 1 are described below:

[0226] Skill Star Map Generation Module: This module collects user career experience, learning records, test data, and industry trends to generate a dynamic skill star map. It utilizes two high-performance computing servers (multi-core CPUs, high-capacity memory, and large-capacity SSD storage), a distributed storage cluster (4 nodes, each with a large-capacity HDD, supporting redundancy), a 10Gbps Ethernet switch, two GPU accelerators (optional), and user interaction terminals (web and mobile applications). The computing servers then support semantic analysis and weighted scoring algorithms to process resume, test, and industry trend data. Distributed storage ensures data reliability and scalability. Finally, the switch supports 5G network encrypted transmission, GPU-accelerated semantic analysis, and the terminal displays the skill star map and collects feedback.

[0227] Career trajectory simulation module: Function overview: It generates career paths and success probabilities using cosine similarity and weighted average algorithms. It consists of a computing server (multi-core CPU, high-capacity memory, SSD storage), a Redis cache server, a 10Gbps Ethernet switch, and a user interaction terminal. The computing server then processes path planning and probability prediction, the cache server accelerates data retrieval, and finally the switch transmits data to the recording module. The terminal displays the trajectory map and supports target adjustment.

[0228] Skills Challenge Module: Function Overview: This module generates customized tasks and evaluates user performance. It consists of two application servers (multi-core CPUs, high-capacity memory, SSD storage), one MongoDB database server, a 10Gbps Ethernet switch, and a user interaction terminal. The application servers support task generation and scoring, the database stores task and scoring data, the switch transmits the scores to other modules, and the terminal provides task interaction.

[0229] Skills Certification Chain Module: Functional overview: It records scores through blockchain and generates digital badges. It consists of 2 blockchain node servers (multi-core CPU, high-capacity memory, SSD storage), 1 storage server (large-capacity HDD), 10Gbps Ethernet switch, and user interaction terminal. Then, the blockchain nodes execute smart contracts to generate metadata, the storage server saves badge data, the switch encrypts and transmits data, and the terminal displays the badge.

[0230] Future Skills Navigation Module: Function Overview: Predicts skill needs and generates learning plans. It consists of a computing server (multi-core CPU, high-capacity memory, SSD storage), a Redis cache server, a 10Gbps Ethernet switch, and a user interaction terminal. The computing server predicts needs and recommends plans, the cache server accelerates queries, the switch transmits the plans, and the terminal pushes the learning plans.

[0231] Skills Collaboration Ecosystem Module: Functional overview: Matching collaborative users and mentors to build a community. It consists of 2 application servers (multi-core CPU, high-capacity memory, SSD storage), 1 Kafka message server, 10Gbps Ethernet switch, and user interaction terminals. The application servers support matching and recommendation, the message server processes collaborative data, the switch transmits data, and the terminals support chat.

[0232] Skill Evolution Tree Module: Function Overview: This module generates a tree-like skill path and recommends advanced tasks. It consists of one computing server (multi-core CPU, high-capacity memory, SSD storage), one MongoDB database server, a 10Gbps Ethernet switch, and a user interaction terminal. The computing server generates the path and tasks, the database stores the path data, the switch transmits the tasks, and the terminal displays the path.

[0233] Career Story Generation Module: Function Overview: This module generates personalized career stories. It consists of one application server (multi-core CPU, high-capacity memory, SSD storage), one storage server (large-capacity HDD), a 10Gbps Ethernet switch, and a user interaction terminal. The application server generates the story, the storage server saves the template, the switch transmits the data, and the terminal supports editing.

[0234] Skill Energy Pool Module: Function Overview: It calculates energy values ​​and supports resource exchange. It consists of a computing server (multi-core CPU, high-capacity memory, SSD storage), a MongoDB database server, a 10Gbps Ethernet switch, and a user interaction terminal. The computing server calculates the energy, the database stores the exchange records, the switch transmits the data, and the terminal displays the report.

[0235] Career Time Capsule Module: Function Overview: It stores data and generates growth reports. It consists of 2 blockchain node servers (multi-core CPU, high-capacity memory, SSD storage), 1 storage server (large-capacity HDD), 10Gbps Ethernet switch, and user interaction terminal. Then, the blockchain nodes store records, the storage server saves reports, the switch transmits data, and the terminal displays reports.

[0236] Control Flow Management Module: Functionally designed to coordinate data flow and optimize feedback loops, this module consists of two management servers (multi-core CPUs, high-capacity memory, and SSD storage), one RabbitMQ message queue server, a 10Gbps Ethernet switch, and user interaction terminals. The management servers optimize distribution and feedback, the message queue processes concurrent data, the switch encrypts transmission and monitors latency, and the terminals push notifications. Specific Implementation Example 4:

[0238] like Figures 1 to 5 As shown, the following provides a complete use case:

[0239] User Background: Zhang Wei, 28 years old, data analyst with 3 years of data analysis experience, proficient in Python and SQL programming languages, aims to transition into a data scientist to enhance his career competitiveness. He registered an account through the system's web interface (deployed on a cloud server, supporting multiple languages) and occasionally uses a mobile device (iPhone 14, 8GB RAM, quad-core processor) to check for updates and receive push notifications.

[0240] Skills Star Map Generation Module: Zhang Wei uploaded his resume (detailing 3 years of data analyst experience, Python and SQL skills, and experience with a retail data analysis project), Coursera Python certificate, and learning records via a web interface. He then completed an online test, including 20 Python multiple-choice questions (testing syntax and data structure knowledge), an SQL case study (designing sales data report queries), and a simulated task (analyzing customer purchasing behavior trends). The system then used web crawlers to collect data from recruitment websites (such as Zhaopin.com) and social media platforms, analyzing the requirements for "data scientist" positions. It found that machine learning, Python, and SQL were high-frequency skills. A high-performance server (equipped with a multi-core CPU and GPU accelerator) used semantic analysis algorithms to process recruitment texts and... Social media discussions ranked the importance of computational skills: Python was in the highest demand (90 points), followed by Machine Learning (85 points), and SQL was slightly lower (80 points). A dynamic skill star chart was generated, and subsequent tests showed that Zhang Wei's Python accuracy was 90%, completion time was 20% faster than the standard, and his answer demonstrated strong logic, resulting in an overall score of 88. His SQL accuracy was 85%, completion time was slightly slower, resulting in an overall score of 85. Machine Learning was initially set to 0. The skill star chart was stored in a distributed storage cluster (supporting data backup) and transmitted to other modules via a 10Gbps Ethernet switch. Zhang Wei viewed the skill star chart on a web browser (similar to a constellation diagram, with nodes and connections indicating skill strength). He found that Machine Learning was a weakness and requested that it be prioritized for improvement. The system recorded this feedback and adjusted the node weights for Machine Learning in the skill chart.

[0241] Career trajectory simulation module: The system uses a skill star map (Python 88, SQL 85, Machine Learning 0) and industry demand data. The server uses a cosine similarity matching algorithm to analyze the match between Zhang Wei's skills and the data scientist position (requirements are Python 90, SQL 80, Machine Learning 85), arriving at a 65% match. It then suggests taking a 3-month basic machine learning course to fill the skill gap. A weighted average algorithm (weight 0.6 for match rate + 0.4 for average demand) predicts a 73% success rate. The results are stored in a Redis cache server to accelerate queries. Finally, the data is transmitted via a high-speed network to the career time capsule module. Zhang Wei views the career path map (including job title, skill gaps, and learning suggestions) on the web interface. He tries adjusting his goal to "Senior Data Scientist" (requires deep learning). The system recalculates the match rate, dropping it to 60%, and adds a deep learning course suggestion. He confirms prioritizing machine learning, submits the adjusted data, and the system updates the skill map.

[0242] Skills Challenge Module: The system generates customized tasks based on the skills graph and Zhang Wei's goals, including 10 machine learning multiple-choice questions (testing basic concepts such as regression algorithms), a simulated scenario (implementing linear regression prediction using Python), and an open-ended question (designing an e-commerce recommendation system). Zhang Wei completes the tasks via a web page, and the application server records his performance: 90% accuracy on the multiple-choice questions, 25% faster completion time than the standard, and clear and innovative logic in the open-ended question, resulting in a comprehensive score of 79. The score is stored in a MongoDB database and then transmitted via a 10Gbps Ethernet switch to the Skills Certification Chain module, Skills Energy Pool module, and Career Time Capsule module. Finally, Zhang Wei receives a push notification to view a detailed score report (including improvement suggestions: strengthening algorithm theory). The feedback indicates that the task difficulty is moderate, and the system records the feedback and updates the machine learning score to 82.

[0243] Skills Certification Chain Module: Zhang Wei's machine learning score of 79 is transmitted to the Ethereum network via a blockchain node server, generating immutable metadata (including the skill category "machine learning," score 79, and evaluation time). A smart contract then executes, recording the data and generating a digital badge (containing a unique identifier and a blockchain verification link). The badge data is then stored on a storage server and finally transmitted via a high-speed network to the Skills Star Map generation module (updating weights) and the Career Time Capsule module. Zhang Wei sees the badge icon on a webpage, clicks the verification link to confirm the data's authenticity, and shares the badge on LinkedIn to attract recruiters' attention. Feedback indicates the badge enhances trust in job applications, and the system optimizes the correlation between machine learning and other skills based on this feedback.

[0244] Future Skills Navigation Module: The system uses industry data from the past 12 months and time series analysis algorithms to predict the continued rise in demand for machine learning and big data skills over the next 3 years, generating a priority list (machine learning first, big data second). Then, combined with Zhang Wei's skill map (lacking machine learning skills), a collaborative filtering algorithm recommends a personalized learning plan: a 3-month Coursera machine learning course, a Kaggle data analysis project, and an online technical seminar. The plan is then stored in a Redis cache server to accelerate retrieval and finally pushed to the Career Time Capsule module 1-2 times per week via a 10Gbps Ethernet switch. Zhang Wei views the plan on the web interface, enrolls in courses and joins projects, and reports that he has completed 30% of the course. The system records the progress and updates the machine learning score in the skill map.

[0245] Skill Collaboration Ecosystem Module: The system uses Zhang Wei's skill graph (Python 88, SQL 85, Machine Learning 82). The application server uses a cosine similarity matching algorithm to match users with similar skills, such as Li Ming (a data scientist specializing in machine learning) and Professor Wang (a machine learning expert). The collaboration potential index is calculated based on similarity and complementarity. Then, the messaging server (Kafka) supports real-time chat and project management. The system then recommends that Zhang Wei and Li Ming collaborate on a Kaggle project and consult Professor Wang about course selection. Finally, Zhang Wei contacts Li Ming through the web interface to join the project, inquires about learning resources from Professor Wang, and reports that the collaboration was pleasant and effective. The system records the feedback and updates the weights of relevant skills in the skill graph.

[0246] Skill Evolution Tree Module: The system uses Zhang Wei's skill graph and a computing server to generate a tree-like skill path (from basic Python to machine learning, and then to deep learning) using a hierarchical clustering algorithm. It determines the prerequisites and progression relationships, and then recommends advanced tasks: learning advanced machine learning algorithms (such as random forest). The tasks are then stored in a MongoDB database and finally transmitted to the skill challenge module for verification via a 10Gbps Ethernet switch. Zhang Wei views the path graph on the web page, accepts and completes the tasks, and provides feedback that the tasks are difficult. The system records the feedback and adjusts the priority of tasks in the path.

[0247] Career Story Generation Module: Based on Zhang Wei's skills (Python 88, Machine Learning 82) and goal (data scientist), the application server uses a template-filling algorithm to generate a technology-oriented career story describing his data analysis experience and machine learning learning achievements. The story is then stored on a storage server (containing multiple narrative templates) and transmitted to the career time capsule module via a 10Gbps Ethernet switch. Finally, Zhang Wei previews the story on a webpage, edits the wording, and shares it on his resume and social media platforms. Feedback indicates that the story enhances the job application's appeal. The system records the feedback and adjusts the weights of relevant skills in the skills graph accordingly.

[0248] Skill Energy Pool Module: The system calculates a skill energy value of approximately 80 based on Zhang Wei's skill score (82 from machine learning), industry demand weight (85), and task participation frequency (3 times / 10 total tasks). Zhang Wei then uses the energy to redeem a mentor consultation service. The redemption record is stored in the MongoDB database and finally transmitted to the Career Time Capsule Module and Skill Collaboration Ecosystem Module via a 10Gbps Ethernet switch. Zhang Wei can view the energy report on the webpage, select redemption, and provide feedback that the consultation is helpful for learning. The system records the feedback and updates the skill graph.

[0249] Career Time Capsule Module: The system uses Zhang Wei's skill map, career goals, and task records. The blockchain node server generates immutable records using the Ethereum blockchain. Then, the storage server generates a growth report (showing that the machine learning score has increased from 0 to 82, and the goal achievement rate is 60%). This report is then transmitted to the skill star map generation module via a 10Gbps Ethernet switch. Finally, Zhang Wei can view the report chart (including the skill progress curve) on the web page and set it to unlock and view after one year. The feedback report is intuitive and valuable. The system records the feedback and updates the skill map weights.

[0250] Control flow management module: The system distributes data through the management server according to instruction priority (skill score is the highest, learning plan is the second highest, and feedback is the lowest). The message queue server (RabbitMQ) handles high-concurrency data streams, followed by encrypted transmission through a 10Gbps Ethernet switch and monitoring latency (less than 50 milliseconds) and packet loss rate (less than 1%). Then, Zhang Wei submits feedback (task difficulty, goal adjustment) through the web interface. Finally, the system optimizes the data update frequency (1-2 times per hour) and supports cross-regional collaborative recommendations (such as the project with Li Ming). Zhang Wei receives push notifications to view all module updates.

[0251] In summary, Zhang Wei successfully transitioned from a data analyst to a data scientist through the system. The Skills Star Map generation module identified skill gaps, the Career Trajectory Simulation module planned the transition path, the Skills Challenge module verified capabilities, the Skills Certification Chain module generated credible badges, the Future Skills Navigation module recommended learning plans, the Skills Collaboration Ecosystem module matched partners and mentors, the Skills Evolution Tree module provided a systematic growth path, the Career Story Generation module enhanced personal branding, the Skills Energy Pool module motivated participation, the Career Time Capsule module tracked growth, the Control Flow Management module ensured real-time performance and stability, and the hardware supported rapid computation, reliable storage, and high-efficiency transmission.

[0252] It's worth noting the dynamic skill star map. This image shows a dynamic skill star map consisting of three nodes (Python, SQL, and Machine Learning). The nodes are connected by lines to represent the strength of the relationship between skills, and the node color and size reflect the weight of the skills (combining proficiency and demand weight). In the image, the Python and SQL nodes are located lower (lower weight), while the Machine Learning node is located higher (higher weight), indicating that Machine Learning has a higher priority or demand weight in the skill star map.

[0253] The chart simulates the output of the skill star map generation module, generated using a weighted scoring algorithm and semantic analysis based on Zhang Wei's skill proficiency (Python 88, SQL 85, Machine Learning initial 0) and industry demand weights (Python 90, SQL 80, Machine Learning 85). Connection strength (e.g., 0.8 between Python and Machine Learning) reflects the correlation between skills (e.g., co-occurrence frequency). The color bars (from 45 to 85 on the right) represent the range of node weights, with colors gradienting from blue to red. Machine Learning nodes are closer to red, indicating their highest weight. The horizontal axis, without explicitly labeled values, represents the X-coordinate in a two-dimensional plane, used to locate the spatial distribution of skill nodes (Python, SQL, Machine Learning), and is a random layout generated based on correlation strength or weight. The vertical axis, also without explicitly labeled values, represents the Y-coordinate in a two-dimensional plane, defining node positions together with the horizontal axis and reflecting the geometric relationships between skills. Note: This graph is a node connection diagram generated by the MATLAB `graph` function. The horizontal and vertical axes are not traditional units of measurement (e.g., time or scores), but rather represent the relative positions of nodes in the graph. Weights are represented by color-coded color bars.

[0254] Analysis conclusion:

[0255] The chart shows Zhang Wei's skill network. Python and SQL are his basic skills, which he is already quite strong. Machine learning is his transition target skill with a high weight, but his initial proficiency is 0, which needs to be improved through subsequent modules (such as skill challenges). The connection (such as 0.8 from Python to Machine Learning) shows the strong correlation between Machine Learning and Python, which may indicate that Zhang Wei can learn Machine Learning through Python. The high value of the color bar (85) reflects the influence of the demand weight, which is consistent with the design of dynamically adjusting the node weight in the technical solution. The career path analysis bar chart shows two key indicators for Zhang Wei in the data scientist position: fit and success probability, with values ​​of 65% and 73% respectively, both represented by blue bars. The chart is from the career trajectory simulation module. Based on the cosine similarity matching algorithm, the fit (65%) between the user's skills (Python 88, SQL 85, Machine Learning 0) and the requirements of the data scientist position (Python 90, SQL 80, Machine Learning 85) is calculated, and the success probability (73%) is predicted by the weighted average algorithm (0.6 × fit + 0.4 × average demand weight). The bar chart visually reflects the match between Zhang Wei's current skills and the target position. The fit is lower than the success probability, indicating that the demand weight (average 85) has a positive effect on the success probability. The horizontal axis represents the indicator categories, labeled "Fitness" and "Success Probability," corresponding to two bars. The vertical axis represents percentages (%), ranging from 0 to 90, reflecting the numerical values ​​of fit and success probability (65% and 73%). Note: The vertical axis range (0-90) is slightly larger than the data values ​​(65-73), possibly to reserve space for expansion, consistent with the default behavior of the MATLAB bar function.

[0256] Analysis Conclusion: The chart demonstrates the feasibility of Zhang Wei's transition to a data scientist. A 65% fit indicates that the skills gap (machine learning) impacts the match, while a 73% success probability suggests that the goal can be achieved through learning. The bar chart supports the path planning and potential assessment functions in the technical solution; the lower fit than the success probability highlights the importance of the requirement's weight. The visualization, pushed through the user interaction terminal, meets the result display requirements of the solution.

[0257] The line graph showing the changes in Zhang Wei's scores in three skills (Python, SQL, and Machine Learning) is shown. The data points are 88, 85, and 79, respectively, and are connected to form a line.

[0258] The chart is from the Skills Challenge module, simulating Zhang Wei's score update after completing the machine learning task. His initial proficiency was Python 88, SQL 85, and Machine Learning 0. After completing the task, his machine learning score rose to 79 (based on a weighted scoring algorithm: 0.4×0.90 + 0.3×1.25 + 0.3×0.85).

[0259] The line graph reflects the evaluation results and feedback loop of the skills challenge module. The improvement in the machine learning score from 0 to 79 indicates the effectiveness of the task, while the Python and SQL scores remained stable. The horizontal axis represents the skill category, labeled "Python," "SQL," and "Machine Learning," corresponding to three data points. The vertical axis represents the skill score, ranging from 0 to 100, in points (0-100), reflecting proficiency or task performance. Note: The vertical axis range (0-100) is consistent with the scoring range in the technical solution.

[0260] Analysis Conclusion: The charts illustrate the dynamic changes in Zhang Wei's skills. The improvement in Machine Learning from 0 to 79 validates the effectiveness of the skills challenge module, while the stability of Python and SQL reflects the consolidation of fundamental skills. The line graph supports the task generation and scoring evaluation functions in the technical solution, with score data transmitted to other modules to update weights. Push notifications via the user interaction terminal conform to a feedback loop design, suggesting further improvements to the potential of Machine Learning.

[0261] 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, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0262] 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 vocational competency assessment system based on skill maps, comprising a main assessment system, characterized in that: The main assessment system includes a skill star map generation module, a career trajectory simulation module, a skill challenge module, a skill certification chain module, a future skill navigation module, a skill collaboration ecosystem module, a skill evolution tree module, a career story generation module, a skill energy pool module, a career time capsule module, and a control flow management module. The skill star map generation module collects users' professional experience, learning records and test data, and generates dynamic skill star map mixed data through semantic analysis algorithms. The dynamic skill star map mixed data is transmitted to the control flow management module in JSON format via a 5G network. The control flow management module coordinates the instruction flow between the skill star map generation module, career trajectory simulation module, skill challenge module, skill certification chain module, future skill navigation module, skill collaboration ecosystem module, skill evolution tree module, career story generation module, skill energy pool module, and career time capsule module, realizing data transmission priority management and feedback loop optimization of the system, and ensuring the real-time performance and stability of the system. The career trajectory simulation module receives dynamic skill star map hybrid data from the skill star map generation module, generates a career path, and predicts the success probability. The skill challenge module receives dynamic skill star map hybrid data from the skill star map generation module, generates customized interactive tasks based on node weights and user career goals through a rule engine, and evaluates the user's performance in completing the tasks using a weighted scoring algorithm to generate skill score data. The skill score data includes task identifier, skill category, and score information, and is transmitted in JSON format to the skill certification chain module, skill energy pool module, and career time capsule module through the control flow management module. The skill certification chain module receives skill score data from the skill challenge module, generates immutable skill metadata containing skill category, score, and evaluation time through a blockchain recording unit using an Ethereum smart contract, hashes the user identifier to protect privacy, and generates a digital badge based on the skill metadata. The digital badge data is transmitted in JSON format from the control flow management module to the skill star map generation module for updating node weights; The future skills navigation module receives dynamic skill star map hybrid data and industry trend data from the skill star map generation module to generate a learning plan; The skill collaboration ecosystem module receives dynamic skill star map hybrid data from the skill star map generation module and matches collaborating users; The skill evolution tree module receives dynamic skill star map mixed data from the skill star map generation module and generates a tree-like growth path; The career story generation module receives dynamic skill star map hybrid data from the skill star map generation module and evaluation data from the skill challenge module to generate a career narrative; The skill energy pool module receives dynamic skill star map hybrid data from the skill star map generation module and evaluation data from the skill challenge module, calculates energy value, and supports resource exchange; The Career Time Capsule module receives dynamic skill star map hybrid data and user target data from the Skill Star Map Generation module, stores it, and generates a growth report. The operation results of the Skill Star Map Generation module, Career Trajectory Simulation module, Skill Challenge module, Skill Certification Chain module, Future Skill Navigation module, Skill Collaboration Ecosystem module, Skill Evolution Tree module, Career Story Generation module, Skill Energy Pool module, and Career Time Capsule module are fed back to the user via push notifications through the user interaction terminal. The user provides task experience data through the user interaction terminal, and the task experience data is transmitted to the Skill Star Map Generation module. The Skill Star Map Generation module updates the node weights of the dynamic skill star map hybrid data based on the task experience data. The user provides digital badge usage data through the user interaction terminal, and the digital badge usage data is transmitted to the Skill Star Map Generation module. The Skill Star Map Generation module optimizes the correlation strength of the dynamic skill star map hybrid data based on the digital badge usage data. The user feedback data is transmitted to the Skill Star Map Generation module in JSON format through the Control Flow Management module. The Skill Star Map Generation module updates the node weights of the dynamic skill star map hybrid data based on the user feedback data, forming a closed-loop management system.

2. The vocational competency assessment system based on skill graphs according to claim 1, characterized in that: The skill star map generation module includes an experience collection unit, a testing and evaluation unit, and an industry trend analysis unit. The experience collection unit collects user resumes, project experiences, and learning records through a user interaction terminal to generate structured professional data, which is stored in JSON format. The testing and evaluation unit generates skill proficiency data through online testing. The skill proficiency data is calculated using a weighted scoring algorithm, with a score range of 0 to 100. The industry trend analysis unit extracts industry skill requirements by crawling publicly available recruitment data and career discussions on social media platforms, and generates requirement weight data with a weight value range of 0 to 100. The skill star map generation module integrates structured occupational data, skill proficiency data, and demand weight data to generate dynamic skill star map hybrid data. The dynamic skill star map hybrid data is represented in the form of nodes and lines, where nodes represent skills and lines represent the strength of skill association. The control flow management module includes an instruction distribution unit, a data transmission monitoring unit, and a feedback loop optimization unit. The instruction distribution unit receives dynamic skill star map hybrid data from the skill star map generation module and distributes it to the career trajectory simulation module, skill challenge module, skill certification chain module, future skill navigation module, skill collaboration ecosystem module, skill evolution tree module, career story generation module, skill energy pool module, and career time capsule module. The career trajectory simulation module, skill challenge module, skill certification chain module, future skill navigation module, skill collaboration ecosystem module, skill evolution tree module, career story generation module, skill energy pool module, and career time capsule module perform path planning, task generation, badge generation, learning recommendation, user matching, growth path generation, career narrative generation, energy calculation, and growth report generation operations based on the received dynamic skill star map hybrid data.

3. The vocational competency assessment system based on skill graphs according to claim 2, characterized in that: The career trajectory simulation module includes a path planning unit and a potential assessment unit; The path planning unit receives dynamic skill star map hybrid data from the skill star map generation module, calculates the fit between the user's skills and the target position through a cosine similarity matching algorithm, and generates a career path. The career path includes the job name, skill gaps, and learning suggestions. The potential assessment unit receives node weight data from the dynamic skill star map hybrid data of the skill star map generation module and demand weight data from the industry trend analysis unit, and calculates the user's success probability in the target position through a weighted average algorithm, with the probability value ranging from 0% to 100%. The career trajectory simulation module transmits the career path and success probability in JSON format to the career time capsule module for storage via the control flow management module. The career trajectory simulation module also pushes the career path and success probability to the user in the form of a visual trajectory graph through the user interaction terminal. The user provides career goal adjustment data through the user interaction terminal. The career goal adjustment data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module updates the node weights of the dynamic skill star map hybrid data according to the career goal adjustment data.

4. The vocational competency assessment system based on skill graphs according to claim 2, characterized in that: The future skills navigation module includes a trend prediction unit and a learning recommendation unit; The trend prediction unit receives demand weight data from the industry trend analysis unit, predicts skill demand for the next three years using a time series analysis algorithm, and generates a demand priority list. The learning recommendation unit receives node weight data and a demand priority list from the dynamic skill star map hybrid data of the skill star map generation module, and generates a personalized learning plan through a collaborative filtering algorithm. The personalized learning plan includes course recommendations, project recommendations, and community activity recommendations. The future skills navigation module pushes personalized learning plans once or twice a week through the user interaction terminal. The personalized learning plans are transmitted in JSON format to the career time capsule module for storage through the control flow management module. The user provides feedback on learning progress data through the user interaction terminal. The learning progress data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module updates the node weights of the dynamic skill star map hybrid data based on the learning progress data.

5. The vocational competency assessment system based on skill graphs according to claim 2, characterized in that: The skill collaboration ecosystem module includes a user matching unit and a community interaction unit; The user matching unit receives dynamic skill star map mixed data from the skill star map generation module, calculates the skill similarity and complementarity between users through a cosine similarity matching algorithm, and generates a collaboration potential index, the value of which ranges from 0 to 100. The community interaction unit supports user communication and project collaboration through the platform's messaging system. The community interaction unit generates collaborative data, which is transmitted in JSON format to the skill challenge module and career story generation module through the control flow management module. The skills challenge module receives collaborative data, verifies collaborative results, and generates skills score data. The career story generation module receives collaborative data and generates collaborative stories; The user provides collaborative experience data through the user interaction terminal. The collaborative experience data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module updates the node weights of the dynamic skill star map hybrid data according to the collaborative experience data. The user matching unit receives growth report data from the career time capsule module, combines it with dynamic skill star map mixed data, and recommends career mentors through a priority ranking algorithm. The career mentor recommendation data is transmitted to the user interaction terminal in JSON format through the control flow management module.

6. The vocational competency assessment system based on skill graphs according to claim 2, characterized in that: The skill evolution tree module includes a growth path unit and an advancement recommendation unit; The growth path unit receives dynamic skill star map mixed data from the skill star map generation module and generates a tree-like skill path through a hierarchical clustering algorithm. The tree-like skill path includes prerequisite skills and advanced skills. The advanced recommendation unit receives demand weight data from the industry trend analysis unit and generates advanced task recommendations through a priority ranking algorithm. The skill evolution tree module transmits advanced task recommendations in JSON format to the skill challenge module through the control flow management module. The skill challenge module verifies the completion status of the advanced tasks and generates skill score data. The skill challenge module transmits skill score data to the skill star map generation module and the skill certification chain module through the control flow management module. The skill star map generation module updates the node weights of the dynamic skill star map hybrid data according to the skill score data, and the skill certification chain module generates digital badges according to the skill score data. The user provides feedback on the advanced task completion experience data through the user interaction terminal. The advanced task completion experience data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module optimizes the skill path of the dynamic skill star map mixed data based on the advanced task completion experience data.

7. The vocational competency assessment system based on skill graphs according to claim 2, characterized in that: The professional story generation module includes a narrative generation unit and a template customization unit; The narrative generation unit receives dynamic skill star map hybrid data from the skill star map generation module and skill score data from the skill challenge module, and generates personalized career stories through a template filling algorithm. The personalized career stories include skill descriptions, career achievements, and goal statements. The template customization unit receives the user's career goals and generates career scenario narrative templates through the rule engine; The career story generation module pushes personalized career story previews through the user interaction terminal, and the personalized career stories are transmitted in JSON format to the career time capsule module for storage through the control flow management module. The user provides personalized career story editing data through the user interaction terminal. The personalized career story editing data is transmitted to the skill star map generation module in JSON format through the control flow management module. The skill star map generation module updates the node weights of the dynamic skill star map hybrid data according to the personalized career story editing data.

8. The vocational competency assessment system based on skill graphs according to claim 2, characterized in that: The skill energy pool module includes an energy calculation unit and a resource exchange unit; The career time capsule module includes a recording and storage unit and a growth analysis unit; The energy calculation unit receives node weight data from the dynamic skill star map hybrid data of the skill star map generation module and skill score data from the skill challenge module, and calculates the skill energy value through a weighted average algorithm. The skill energy value ranges from 0 to 100. The resource exchange unit receives skill energy value data and supports users in exchanging professional resources through an points exchange algorithm. The professional resources include professional mentor consultation and priority access to community activities. The skill energy pool module pushes skill energy value reports through the user interaction terminal, and the skill energy value data is transmitted in JSON format to the career time capsule module for storage through the control flow management module. The record storage unit receives dynamic skill star map hybrid data, user career goals and user reflection data from the skill star map generation module, and generates an immutable record through blockchain storage; The growth analysis unit receives dynamic skill star map mixed data from the current skill star map generation module, and generates a growth report through a comparative analysis algorithm at the user-set unlock time. The growth report includes skill progress and goal achievement. The career time capsule module pushes growth reports through the user interaction terminal, the user provides feedback on growth report data through the user interaction terminal, the growth report data is transmitted to the skill star map generation module in JSON format through the control flow management module, and the skill star map generation module updates the node weights of the dynamic skill star map hybrid data according to the growth report data; The instruction distribution unit of the control flow management module distributes dynamic skill star map mixed data, skill score data, digital badge data, personalized learning plan, collaboration data, advanced task recommendations, personalized career stories, skill energy value data and growth report data through the 5G network according to instruction priority. The data transmission monitoring unit monitors transmission latency and packet loss rate. The feedback loop optimization unit adjusts the instruction execution frequency and data collection cycle according to the amount of user feedback data, 1 to 2 times per hour. The skill collaboration ecosystem module receives skill energy value data and growth report data, and generates cross-regional collaboration recommendations through a cosine similarity matching algorithm. The cross-regional collaboration recommendations are transmitted to the user interaction terminal in JSON format through the control flow management module.

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