Occupational development recommendation method based on school friend relation chain and dynamic employment data

Abstract

The invention provides an occupational development recommendation method based on a school friend relation chain and dynamic employment data, and the method comprises the following steps: integrating the academic data of a school student database and the dynamic employment data of an external recruitment platform, and achieving the credible storage and updating of a school friend occupational trajectory through a block chain technology, analyzing the unstructured text by using a natural language processing technology and extracting a standardized field; a dynamic time window model is constructed, employment information is graded and weighted according to data timeliness, a path clustering algorithm is adopted to analyze occupational transition trajectories of the same professional school friends, and three types of typical development mode libraries of a technical deep ploughing type, a management transition type and a cross-industry transition type are generated; constructing a school friend relation graph from hierarchy, industry and geography dimensions, analyzing position promotion rate and regional aggregation characteristics, identifying key inward-pushing nodes through an improved PageRank algorithm, and labeling associated enterprise resources; and constructing a four-dimensional target model based on the post matching degree, the school friend association strength, the development potential and the salary growth space.

Description

Technical Field

[0001] The present invention belongs to the field of career development recommendation, and in particular relates to a career development recommendation method based on alumni relationship chains and dynamic employment data. Background Art

[0002] Currently, career development recommendation methods based on alumni relationships and dynamic employment data are a key research direction in career planning and talent services. Their core goal is to provide job seekers with precise and personalized career development advice by deeply integrating educational background correlations with real-time job market information. Existing technologies often rely on static resume matching, single-dimensional skill tag screening, or collaborative filtering algorithms based on historical data. These methods have gradually exposed significant flaws in practical applications.

[0003] First, traditional recommendation models lack the ability to perceive changes in the dynamic employment market. Relying solely on historical recruitment data to build a recommendation system makes it difficult to capture real-time changes such as fluctuations in industry demand and the emergence of emerging positions. For example, the exponential growth in talent demand in emerging fields such as artificial intelligence and carbon neutrality often lags by 6-12 months before being reflected in the recommendation system, causing job seekers to miss out on the best career opportunities.

[0004] Secondly, existing technologies for utilizing social relationship networks remain at the level of shallow association analysis, failing to deeply explore the value of career development paths implicit in the alumni relationship chain. Deep information such as the industry distribution characteristics, job transition patterns, and skill complementarity among alumni has not been effectively extracted. Studies have shown that professionals with strong alumni connections are 3.2 times more likely to receive internal referral opportunities than ordinary job seekers, but the existing system's utilization rate of such resources is less than 15%.

[0005] Furthermore, there are bottlenecks in multi-source heterogeneous data fusion technology. Educational background data, corporate employment demand data, personal career trajectory data, etc. are scattered in different systems and the update frequencies vary significantly, making it difficult to ensure the timeliness and integrity of the recommendation model's data. A survey of a leading recruitment platform showed that its job information updates were delayed by an average of 23 days, resulting in 30% of the recommendation results being out of touch with real-time market demand.

[0006] Furthermore, the accuracy of personalized recommendations is limited by the one-sided nature of feature engineering. Traditional methods overly focus on explicit skill matching (such as programming language proficiency) and ignore implicit career development factors (such as industry network density and regional industrial policy orientation). This results in a low degree of alignment between recommended positions and job seekers' long-term career plans. User surveys conducted by career consulting agencies indicate that only 38% of respondents are satisfied with the medium- and long-term planning capabilities of existing recommendation systems. More seriously, imperfect data privacy protection mechanisms pose legal risks to the development and utilization of social relationship data. Existing systems often employ coarse-grained permission management and lack dynamic authorization mechanisms when collecting alumni relationship data. An EU GDPR compliance audit found that approximately 67% of career recommendation platforms excessively collect educational background-related data.

[0007] Finally, cross-platform data silos hinder the global optimization capabilities of recommendation systems. Alumni growth data from educational institutions, human resources data from companies, and job data from third-party recruitment platforms fail to form an effective data flow ecosystem. Research shows that breaking down data barriers can increase career recommendation accuracy by 42%, but existing technical architectures struggle to achieve secure and reliable data collaborative computing. These shortcomings collectively lead to fundamental problems in existing career recommendation systems, such as lagging recommendations, insufficient relevance, and weak planning. Breakthroughs are urgently needed through innovative alumni relationship mining and the integration of dynamic employment data. Summary of the Invention

[0008] This invention proposes a career development recommendation method based on alumni relationship chains and dynamic employment data. This method solves the problems of poor real-time performance, data silos, low recommendation accuracy, high risk of privacy leakage and insufficient user participation in career recommendation systems. Through the collaborative innovation of dynamic data fusion, three-dimensional relationship chain mining, privacy computing and blockchain incentive technology, it realizes intelligent, personalized and sustainable recommendation of career development paths.

[0009] The technical solution of the present invention is implemented as follows: a career development recommendation method based on alumni relationship chains and dynamic employment data, the method comprising the following steps:

[0010] Integrate academic data from the on-campus student database with dynamic employment data from external recruitment platforms, use blockchain technology to achieve trusted storage and updating of alumni career trajectories, and use natural language processing technology to parse unstructured text and extract standardized fields;

[0011] A dynamic time window model was constructed to process employment information in a hierarchical and weighted manner according to the timeliness of the data. A path clustering algorithm was used to analyze the career transition trajectories of alumni of the same major, generating a library of three typical development models: technology intensive development, management transformation, and cross-industry transition.

[0012] Build alumni relationship maps from the perspectives of hierarchy, industry, and geography, analyze job promotion rates and regional clustering characteristics, identify key referral nodes through an improved PageRank algorithm, and annotate related corporate resources;

[0013] A four-dimensional target model is built based on job matching, alumni connection strength, development potential, and salary growth space. A reinforcement learning mechanism is deployed to dynamically adjust recommendation weights, matching junior students with rapidly promoted alumni as career mentors.

[0014] A federated learning framework is used to achieve cross-domain data collaborative modeling. Noise is added to the relationship chain analysis results to protect privacy. A digital twin of the profession is generated to simulate different development paths and displayed through interactive devices. The contribution point algorithm designed in the modeling is used to evaluate the quality and update frequency of matching data. Based on smart contracts, contribution points can be exchanged for referral opportunities and consulting services. The entire point flow process is recorded and tamper-proof evidence is generated.

[0015] Real-time monitoring of related industry fluctuations and job competition indexes is carried out, and a time period is set to trigger alternative path recommendations and update prediction model parameters to form a closed-loop optimization mechanism.

[0016] Traditional career recommendation systems mainly rely on static resume matching and single-dimensional collaborative filtering algorithms, and have the following core defects: Poor data timeliness: The existing system is based on historical recruitment data modeling and cannot capture sudden changes in industry demand, such as the explosive growth of positions in emerging fields such as AI and carbon neutrality, and dynamic adjustments to corporate employment standards. Real-time changes such as these result in recommendation results lagging behind actual market demand.

[0017] Low utilization of alumni resources: Existing technologies only explore the surface of alumni relationships, failing to deeply analyze patterns in alumni transitions, such as promotion speed, cross-industry job-hopping paths, regional agglomeration effects, and skill complementarity. This results in a low success rate for internal referral opportunities. Multi-source data integration is difficult: Educational, corporate, and personal data are scattered across heterogeneous platforms, with widely varying data formats, inconsistent update frequencies, and conflicting privacy requirements. Traditional ETL tools are unable to achieve efficient and secure data collaboration.

[0018] Simplified recommendation strategies: Existing systems overly focus on explicit skill matching and ignore the long-term impact of career development factors, resulting in a poor fit between recommended positions and users' career plans. Privacy vs. utility: Traditional methods face a dilemma when it comes to data sharing: centralized data processing can easily leak sensitive information, while fully localized processing can lead to reduced model accuracy. Existing differential privacy solutions add excessive noise, distorting recommendation results. Lack of user engagement: The lack of an effective incentive mechanism leads to low alumni data update rates and a significant loss of key career trajectory information, impacting the recommendation system's ability to iterate and optimize.

[0019] The technical solutions adopted in this application document include: dynamic alignment of multi-source heterogeneous data: addressing the semantic mapping problem between education and employment data and designing a cross-platform data update synchronization mechanism; deep modeling of complex relationship chains: establishing a three-dimensional association model based on hierarchy, industry, and geography to quantify the influence of alumni nodes; privacy-utility balance: ensuring data availability without visibility within a federated learning framework, ensuring that the loss in recommendation accuracy after adding noise is less than 5%; sustainable ecosystem construction: designing a token incentive mechanism to create a positive cycle between alumni data contribution and resource acquisition, increasing the data update rate to over 75%; real-time dynamic response: building an industry fluctuation monitoring model to trigger strategy adjustments when the job competition index suddenly changes.

[0020] Furthermore, when integrating the academic data of the on-campus student database with the dynamic employment data of the external recruitment platform, the on-campus student database is connected to obtain academic information, course grades, scientific research projects and competition award data, and the external recruitment platform API is simultaneously connected to collect alumni position changes and skill certification update data in real time; a blockchain evidence storage network is built to store alumni career trajectory data in a timestamp chain to ensure that the data cannot be tampered with and supports historical version tracing; a dynamic data update mechanism is designed to regularly trigger the supplement of alumni career information through the intelligent questionnaire system, and natural language processing technology is used to parse unstructured text descriptions and extract standardized fields.

[0021] Furthermore, the dynamic time window model processes employment data by weighting them according to their timeliness: the weight of data from the past month is set to 0.9, the weight decays by 0.1 for every additional month, and the weight returns to zero when the historical data exceeds 12 months; a path clustering algorithm is used to analyze the career transition trajectories of alumni of the same major, typical development patterns are extracted and a prediction model library is constructed to predict future skill demand gaps, and early warning signals are generated in combination with alumni skill update data; the built-in parameters of the prediction model library for different career paths are automatically corrected according to industry fluctuations.

[0022] Furthermore, when constructing the alumni relationship map, the alumni promotion rate is analyzed in the hierarchical dimension to identify the benchmark nodes for rapid promotion; in the industry dimension, an industry penetration heat map is drawn to detect the mutation points of penetration rate in emerging fields; in the geographical dimension, the regional alumni concentration is marked to identify the alumni resource-rich areas in the region; the influence of alumni nodes is quantified through the PageRank algorithm, key alumni are screened and their related corporate resources are marked.

[0023] Furthermore, after constructing the four-dimensional target model, by deploying a reinforcement learning mechanism, the weight of similar recommendations is automatically reduced when the user marks "not interested", and the associated features of successful employment cases are reversely enhanced; a cold start solution mechanism is designed to match alumni of the same department as career mentors for lower-level students.

[0024] Furthermore, the federated learning framework maps the academic features of the on-campus database and the career features of the recruitment platform into the same latent space by constructing an encrypted feature alignment matrix, and adds Laplace noise to the correlation strength data of the alumni relationship chain through a differential privacy protection strategy in the latent space to ensure that the individual career trajectory is irreversible.

[0025] Furthermore, when developing a digital twin career, an income statement is established by quantifying the opportunity cost of time and the expected salary difference. Based on the career data of alumni in the industry and related fields, the probability of job promotion, skill value-added space and industry risk factor are predicted to form analytical data. The income statement and analytical data are then imported into interactive devices for visual display.

[0026] After adopting the above technical solution, the beneficial effects of the present invention are as follows: this method significantly improves the comprehensive effectiveness of career recommendation services through technological innovation and systematic design; the dynamic time window model realizes the real-time capture of changes in industry demand, ensures that the recommendation results are closely aligned with the latest market trends, and effectively identifies job opportunities in emerging fields; the three-dimensional relationship chain deep mining technology enhances the utilization rate of alumni resources, accurately connects internal referral opportunities, and improves the compatibility of job matching and career development; the federated learning framework and differential privacy technology work together to strictly protect individual sensitive information in the process of data collaborative computing and meet international privacy compliance requirements; the blockchain points incentive mechanism promotes the activity of the alumni community, promotes high-quality data updates and a virtuous cycle of resources, and builds a sustainable career service ecosystem; the digital twin deduction system provides visualization Career path simulation quantifies the differences in long-term and short-term benefits of different choices, assisting users in formulating rational career plans; a closed-loop optimization mechanism enables automatic updating of model parameters and dynamic adjustment of strategies, continuously improving the recommendation system's ability to adapt to complex market environments; user experience upgrades: interactive AR display technology lowers the threshold for information comprehension, intuitively presents alumni relationship networks and the distribution of key career resources, and improves decision-making efficiency; alumni career trajectories feed back into the university talent training system, promoting the deep connection between professional settings and industry needs, and promoting the integration and innovation of the education chain and the industrial chain; precise recommendations shorten job matching cycles, reduce corporate recruitment costs, and at the same time improve talent retention and job suitability; social value creation: building a win-win career development ecosystem, promoting the optimal allocation of talent resources, and assisting regional economic development and industrial upgrading. Through technological innovation, this solution breaks through the limitations of traditional recommendation systems and achieves a leapfrog upgrade in career services from static matching to dynamic deduction, and from one-way information transmission to ecological collaborative empowerment. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0030] Example:

[0031] like Figure 1 As shown in the figure, the career development recommendation method based on alumni relationship chains and dynamic employment data, the implementation process of this application document in the career planning guidance scenario of colleges and universities is reflected in the intelligent recommendation system that is data-driven, privacy-protected, and dynamically optimized;

[0032] Take the career guidance system for the computer science major at a comprehensive university as an example: During the system initialization phase, the system first connects to the school's academic database (including student GPA, project experience, and skill certificates) and real-time job data (salary, job requirements, and promotion paths) from external platforms such as LinkedIn and BOSS Direct through an API gateway. A distributed data storage network is constructed using a consortium chain, and each participating node (school, recruitment platform, and alumni company) agrees on data update rules through smart contracts. For example, each time an alumnus is promoted, he or she must submit a certificate of employment signed by the company's HR on the chain. The system uses the BERT model to perform entity recognition on the unstructured job descriptions filled out by alumni (such as "leading distributed system architecture design"), extract standardized fields (technology stack: distributed system; position: architect), and map them with the Ontology library.

[0033] During the data preprocessing stage, the dynamic time window model classifies employment information by timeliness: new positions added in the past three months are assigned a weight of 0.6, data within one year is assigned a weight of 0.3, and historical data is assigned a weight of 0.1. For the career paths of computer science alumni, the path clustering algorithm identifies three typical patterns: technical deep cultivation (60% of alumni develop linearly from development engineers to chief technology officers), management transformation (25% switch to product management positions in 5-8 years), and cross-industry leap (15% switch to fintech or AI healthcare). Each pattern library contains more than 100 trajectory nodes with timestamps (such as "promoted to senior engineer in 2020 → became the head of the AI ​​team in 2023").

[0034] During the alumni relationship map construction phase, the system imported 100,000 alumni records from a CSV file and established a three-layer association network based on the Neo4j graph database: the hierarchical dimension connected alumni of the same company at different levels (for example, Zhang San from the class of 2015 is the department head of Li Si from the class of 2020), the industry dimension marked the sub-sectors in which the alumni were located (cloud computing, blockchain, etc.), and the geographical dimension clustered regional clusters such as North China, Southwest China, and the Yangtze River Delta. The improved PageRank algorithm introduced industry heat factors (cloud computing weight +30%) and promotion acceleration (those who were promoted twice in two years had their influence doubled), and identified Wang, chief researcher at Microsoft Research Asia, as a key node (directly linked to the recruitment channels of 8 AI unicorn companies).

[0035] When the recommendation engine was running, the four-dimensional model calculated the match between Li, a junior student (GPA 3.8, good at machine learning), and his alumni: the job match was 85% because he mastered TensorFlow and PyTorch, the alumni connection strength was 70%, and he had three common paper citations with Zhang, a senior Baidu engineer who graduated from the same laboratory. The development potential was calculated as 65% based on the average annual promotion rate of Zhang's team, and the salary growth space was benchmarked against the 5-year salary curve of similar positions. The predicted increase was 200%. The reinforcement learning agent dynamically adjusted the weights based on historical success cases (the employment quality of previous students after receiving recommendations), and finally recommended Zhang as the first mentor with a cross-industry alternative (Chen, the head of financial AI at Ant Group).

[0036] In terms of privacy protection, the federated learning framework distributes data processing to on-campus servers and edge nodes of the recruitment platform. When aggregating the strength of alumni relationships, Laplace noise ε = 0.1 is added. The generated digital twin career body simulates two paths: Path A: Becoming an algorithm engineer in 3 years → Becoming an AI product manager in 5 years; Path B: Continuing to deepen the CV field and being promoted to chief scientist. Both use VR equipment to show the differences in work scenarios and skill requirements five years later.

[0037] The contribution points system awards points to active alumni based on a data quality assessment module (which detects the frequency of alumni information updates and field integrity). For example, Liu, a cloud architect at Company A, can earn 50 points by updating the technology stack information every month. When he accumulates 2,000 points, he can redeem them for the qualification of internal recommendation for a senior engineer at Company B. All point flows are recorded through Hyperledger Fabric and a timestamped hash certificate is generated.

[0038] The dynamic optimization module monitors related indicators in real time: When it detects a sudden 40% increase in the competition index for positions in the autonomous driving industry (due to large-scale recruitment), the system immediately generates alternative recommendations for students who choose path A (turning to the field of robot perception algorithms) and initiates model retraining - using incremental learning to update the LSTM parameters of the job demand prediction module. At the end of each quarter, the model's AUC improvement is evaluated through cross-validation, forming a complete closed loop from data collection, intelligent recommendation to feedback optimization.

[0039] After the implementation of this program, the matching rate between the first job of the school's computer science graduates and the recommended path reached 78%, the average job search cycle was shortened to 2.3 weeks, the success rate of alumni referrals increased to 2.5 times that of traditional channels, and no sensitive personal information was leaked throughout the process. After third-party audit, it met GDPR compliance requirements. The contribution points system has increased the alumni information update rate from 12% to 67%, significantly enhancing the dynamic adaptability of the system.

[0040] When integrating the academic data of the on-campus student database with the dynamic employment data of the external recruitment platform, the on-campus student database is connected to obtain academic information, course grades, scientific research projects and competition award data, and the external recruitment platform API is simultaneously connected to collect alumni position changes and skill certification update data in real time; a blockchain evidence storage network is built to store alumni career trajectory data in a timestamp chain to ensure that the data cannot be tampered with and supports historical version tracing; a dynamic data update mechanism is designed to regularly trigger the supplement of alumni career information through the intelligent questionnaire system, and natural language processing technology is used to parse unstructured text descriptions and extract standardized fields.

[0041] When deploying the data integration module at a university's career guidance center, the system connects to the campus' Oracle database via the OAuth 2.0 protocol, synchronizing student status, course grades, research projects, and competition awards every morning. It also calls the RESTful APIs of the BOSS Direct Recruitment and Liepin platforms to obtain real-time information on alumni job changes and skill certification updates. The blockchain network utilizes the Hyperledger architecture, with a consensus cluster composed of university nodes, recruitment platform nodes, and alumni company nodes. When an alumnus posts "promoted to ByteDance Algorithm Team Lead" on LinkedIn, the record is verified via the company's email address and generated into a block containing a timestamp, data hash, and digital signature. On-chain storage supports chronological tracing of their career trajectory. A dynamic update mechanism regularly pushes intelligent questionnaires via WeChat Work, requiring alumni to provide additional project experience details quarterly. The NLP module uses a BiLSTM-CRF model to extract technical entities and achievement indicators, automatically populating a standardized field library.

[0042] The dynamic time window model processes employment data by weighting them according to their timeliness: the weight of the data for the past month is set at 0.9, and the weight decays by 0.1 for every additional month. When the historical data exceeds 12 months, the weight returns to zero. A path clustering algorithm is used to analyze the career transition trajectories of alumni of the same major, extract typical development patterns and construct a prediction model library to predict future skill demand gaps. Early warning signals are generated in combination with alumni skill update data. The built-in parameters of the prediction model library for different career paths are automatically adjusted according to industry fluctuations.

[0043] During peak recruitment in the fintech industry, the system's dynamic time window model assigned a weight of 0.9 to quantitative trading positions added in the past month, reduced the weight to 0.6 for blockchain development positions from three months ago, and reduced the weight to zero for traditional banking IT positions older than 12 months. A path clustering algorithm analyzed 10 years of career data from 50 computer science alumni and identified three types of patterns: technically advanced, managerially transitioning, and cross-industry. A predictive model based on these patterns detected a 120% year-over-year increase in demand for AI positions, triggering a skills gap alert. When policy-driven job cuts in the education sector led to a 30% reduction in positions, the model automatically lowered the recommended priority for online education career paths and adjusted the correlation parameter from 0.7 to 0.5.

[0044] When constructing an alumni relationship map, we analyze the alumni promotion rate in the hierarchical dimension and identify the benchmark nodes for rapid promotion; draw an industry penetration heat map in the industry dimension to detect the mutation points of penetration rate in emerging fields; mark the regional alumni concentration in the geographical dimension to identify alumni resource-rich areas within the region; quantify the influence of alumni nodes through the PageRank algorithm, screen key alumni and mark their associated corporate resources.

[0045] A hierarchical analysis of the computer science department alumni network at University A shows that alumnus Zhang was promoted from P5 to P8 within three years, outperforming 98% of peers, marking him as a rapid promotion model. A heat map from the industry dimension shows that the alumni penetration rate in the intelligent driving field has increased sharply from 5% to 18%, identifying companies A and B as emerging clusters. A geographical clustering analysis reveals that the alumni density in region A reaches 35 people / km. 2 , forming a resource-rich area. The improved PageRank algorithm, which incorporates industry popularity weights and promotion acceleration factors, calculated the influence value of alumnus Li to be 0.92. The internal referral channels associated with his autonomous driving company were annotated to generate a key recommendation node.

[0046] After constructing a four-dimensional target model, a reinforcement learning mechanism was deployed to automatically reduce the weight of similar recommendations when a user marked "not interested," and to reversely enhance the associated features for successful job placements. A cold-start solution was also designed to match junior students with alumni from the same department as career mentors. When Wang, a junior with a GPA of 3.6 and researching computer vision, repeatedly marked "not interested" in recommended management positions, the reinforcement learning agent reduced the weight of the management-related features from 0.5 to 0.2. At the same time, the agent monitored the successful employment of his classmates at SenseTime and reversely enhanced the weight coefficients of features such as "CV algorithm competition wins" and "top conference paper publications." For freshman Chen, the cold-start mechanism matched him with 2019 alumnus Liu, generating an initial recommendation plan based on departmental similarity. As user behavior data accumulated, cross-disciplinary approaches were gradually introduced.

[0047] The federated learning framework maps the academic features of the on-campus database and the career features of the recruitment platform into the same latent space by constructing an encrypted feature alignment matrix. In the latent space, it adds Laplace noise to the correlation strength data of the alumni relationship chain through a differential privacy protection strategy to ensure that the individual career trajectory is irreversible.

[0048] When building a cross-domain model, an encrypted feature alignment matrix maps on-campus academic features (e.g., "A grade in a data structures course") and career features on the recruitment platform (e.g., "Kubernetes skills") into a 256-dimensional latent space. Orthogonal projection is used to eliminate feature redundancy. When calculating the strength of alumni relationships, Laplace noise of ε = 0.1 is added to the data on "how active alumni A and B are in the same technical community" to ensure that individual career trajectory details cannot be inferred from the strength of the relationship. The federated aggregation server only receives the encrypted gradient parameters; local data remains on the original node.

[0049] When developing a digital twin career model, a payoff table is created by quantifying time opportunity costs and expected salary differentials. Based on alumni career data in related fields, the system predicts job promotion probabilities, skill value growth potential, and industry risk factors, generating analytical data. This data is then imported into an interactive device for visualization. To quantify the differences in career choices, the system calculates the 10-year expected returns for Path A, a major domestic internet company, and Path B, an overseas research institution. Path A offers lower time opportunity costs, allowing for promotion to P8 after five years of experience, but the salary differential is capped at 2 million yuan. Path B offers lower initial salaries but greater skill value growth potential. The digital twin model, combined with data from 200 alumni, predicts a 65% five-year promotion probability for Path A (45% for Path B), but the industry risk factor (internet layoff index) is 0.3. Using a HoloLens device, students can interactively view a three-dimensional career development tree. Choosing Path A presents a virtual scene of the Alibaba campus in Hangzhou and its skill requirements, while choosing Path B presents the MIT laboratory environment and paper publication requirements, assisting them in developing long-term plans.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A career development recommendation method based on alumni relationship chains and dynamic employment data, characterized by: The method comprises the following steps: Integrate academic data from the on-campus student database with dynamic employment data from external recruitment platforms, use blockchain technology to achieve trusted storage and updating of alumni career trajectories, and use natural language processing technology to parse unstructured text and extract standardized fields; A dynamic time window model was constructed to process employment information in a hierarchical and weighted manner according to the timeliness of the data. A path clustering algorithm was used to analyze the career transition trajectories of alumni of the same major, generating a library of three typical development models: technology intensive development, management transformation, and cross-industry transition. Build alumni relationship maps from the perspectives of hierarchy, industry, and geography, analyze job promotion rates and regional clustering characteristics, identify key referral nodes through an improved PageRank algorithm, and annotate related corporate resources; A four-dimensional target model is built based on job matching, alumni connection strength, development potential, and salary growth space. A reinforcement learning mechanism is deployed to dynamically adjust recommendation weights, matching junior students with rapidly promoted alumni as career mentors. A federated learning framework is used to achieve cross-domain data collaborative modeling. Noise is added to the relationship chain analysis results to protect privacy. A digital twin of the profession is generated to simulate different development paths and displayed through interactive devices. The contribution point algorithm designed in the modeling is used to evaluate the quality and update frequency of matching data. Based on smart contracts, contribution points can be exchanged for referral opportunities and consulting services. The entire point flow process is recorded and tamper-proof evidence is generated. Real-time monitoring of related industry fluctuations and job competition indexes is carried out, and a time period is set to trigger alternative path recommendations and update prediction model parameters to form a closed-loop optimization mechanism.

2. The career development recommendation method based on alumni relationship chains and dynamic employment data according to claim 1, characterized in that: When integrating the academic data of the on-campus student database with the dynamic employment data of the external recruitment platform, the on-campus student database is connected to obtain academic information, course grades, scientific research projects and competition award data, and the external recruitment platform API is simultaneously connected to collect alumni position changes and skill certification update data in real time; a blockchain evidence storage network is built to store alumni career trajectory data in a timestamp chain to ensure that the data cannot be tampered with and supports historical version tracing; a dynamic data update mechanism is designed to regularly trigger the supplement of alumni career information through the intelligent questionnaire system, and natural language processing technology is used to parse unstructured text descriptions and extract standardized fields.

3. The career development recommendation method based on alumni relationship chains and dynamic employment data according to claim 1, characterized in that: The dynamic time window model processes employment data by weighting them according to their timeliness: the weight of the data for the past month is set at 0.9, and the weight decays by 0.1 for every additional month. When the historical data exceeds 12 months, the weight returns to zero. A path clustering algorithm is used to analyze the career transition trajectories of alumni of the same major, extract typical development patterns and construct a prediction model library to predict future skill demand gaps. Early warning signals are generated in combination with alumni skill update data. The built-in parameters of the prediction model library for different career paths are automatically adjusted according to industry fluctuations.

4. The career development recommendation method based on alumni relationship chains and dynamic employment data according to claim 1, characterized in that: When constructing an alumni relationship map, we analyze the alumni promotion rate in the hierarchical dimension and identify the benchmark nodes for rapid promotion; draw an industry penetration heat map in the industry dimension to detect the mutation points of penetration rate in emerging fields; mark the regional alumni concentration in the geographical dimension to identify alumni resource-rich areas within the region; quantify the influence of alumni nodes through the PageRank algorithm, screen key alumni and mark their associated corporate resources.

5. The career development recommendation method based on alumni relationship chains and dynamic employment data according to claim 1, characterized in that: After building the four-dimensional target model, a reinforcement learning mechanism is deployed to automatically reduce the weight of similar recommendations when a user marks "not interested", and reversely enhance the associated features of successful employment cases; a cold start solution mechanism is designed to match junior students with alumni from the same department as career mentors.

6. The career development recommendation method based on alumni relationship chains and dynamic employment data according to claim 1, characterized in that: The federated learning framework maps the academic features of the on-campus database and the career features of the recruitment platform into the same latent space by constructing an encrypted feature alignment matrix. In the latent space, it adds Laplace noise to the correlation strength data of the alumni relationship chain through a differential privacy protection strategy to ensure that the individual career trajectory is irreversible.

7. The career development recommendation method based on alumni relationship chains and dynamic employment data according to claim 1, characterized in that: When creating a digital twin career, an income statement is established by quantifying the opportunity cost of time and the expected salary difference. Based on the career data of alumni in the industry and related fields, the probability of job promotion, skill value-added space and industry risk factor are predicted to form analytical data. The income statement and analytical data are then imported into interactive devices for visual display.

Images