Intelligent post matching and employee growth path planning system and method, and storage medium

Abstract

The invention provides an intelligent post matching and employee growth path planning system and method and a storage medium. The system comprises a data acquisition and processing module, an employee portrait construction module, an intelligent post matching module and an occupational path planning module. The data acquisition and processing module collects employee performance data, education background, work experience and other information from internal and external data sources. The employee portrait construction module constructs a comprehensive portrait of each employee by using a clustering analysis and factor analysis technology; the intelligent post matching module performs intelligent matching by applying a recommendation system algorithm according to post demands and employee portraits, and finds the posts most suitable for the current capabilities and development desires of the employees; the occupational path planning module analyzes the industry development trend and future skill requirements based on the prediction model, and provides personalized occupational development paths and training suggestions for employees. According to the invention, historical performance evaluation and industry trend analysis of employees can be integrated, and comprehensive post matching and occupational planning solutions are provided.

Description

Technical Field

[0001] The present invention belongs to the field of data analysis and artificial intelligence technology, and in particular relates to an intelligent job matching and employee growth path planning system, method and storage medium. Background Art

[0002] In the existing field of human resource management and job matching, existing technologies usually use manual or semi-automatic methods to assign jobs and plan careers for employees. These methods mainly include the following categories:

[0003] 1. Rule-based manual matching method: Traditional human resource management relies on the experience and judgment of human resource managers to match employees’ superficial information such as skills, educational background and work experience. This method is inefficient, relies on the subjective judgment of managers, is prone to deviations, and it is difficult to ensure the fairness and consistency of matching results.

[0004] 2. Simple keyword search and matching system: Some existing systems match positions and employees through keyword search. This method can only match based on keywords in the position description and employee resume, and fails to fully consider the employee's personality characteristics, soft skills and actual job requirements, resulting in inaccurate matching results, which cannot meet the company's requirements for efficient and accurate matching.

[0005] 3. Career planning tools based on static models: Some career planning tools provide career path suggestions based on employees’ historical data, but most of these tools use static models and cannot dynamically track industry changes or employee capabilities. This makes the career development suggestions employees receive lack of personalization and flexibility, making it difficult to motivate employees to achieve long-term career goals.

[0006] The above-mentioned existing technologies have many shortcomings, mainly reflected in low data utilization, lack of dynamic adaptability, lack of intelligence in the matching process, and insufficient personalization of career planning. The existing technologies lack systematic integration and analysis of multi-source data in the collection and utilization of employee data, resulting in incomplete and inaccurate matching results and inability to provide effective decision support. In addition, traditional methods are difficult to dynamically track industry changes and employee capacity improvement, and lack flexibility. The matching process mainly relies on simple rules or keyword retrieval, fails to use artificial intelligence technology to fully explore employees' personality characteristics and soft skills, and the matching results are not accurate enough. Career planning also usually relies on static models, lacks personalization, and is difficult to meet employees' long-term development needs. : When providing career development advice to employees, existing systems usually rely on static career development paths, and fail to fully combine employees' personal growth trajectories, skill improvement, and dynamic changes in external industry needs to generate personalized development paths. This limits employees' career development space, resulting in a lack of flexibility and foresight in career planning, and it is difficult to effectively motivate employees to achieve long-term career goals.

[0007] In order to solve the above technical problems, the present invention proposes an intelligent job matching and employee growth path planning system and method based on data analysis and artificial intelligence technology. By integrating multi-source data (including internal performance data, industry trends, skill certification, employee feedback, etc.), applying machine learning technology to build employee portraits, and combining recommendation system algorithms and predictive models, it can more comprehensively match job requirements with employee characteristics and dynamically plan employees' career growth paths. Summary of the invention

[0008] In view of the deficiencies in the prior art, the present invention provides an intelligent job matching and employee growth path planning system, method and storage medium, which integrates employees' historical performance evaluation and industry trend analysis to provide a comprehensive job matching and career planning solution.

[0009] The present invention achieves the above technical objectives through the following technical means.

[0010] An intelligent job matching and employee growth path planning system, including a data collection and processing module, an employee portrait building module, an intelligent job matching module, and a career path planning module;

[0011] The data collection and processing module collects relevant information from multiple internal and external data sources, including employee performance data, skill certificates, educational background, work experience, and performs pre-processing; the employee portrait construction module uses machine learning technology to build a comprehensive portrait of each employee, including skill level, work preferences, and potential capabilities; the intelligent job matching module uses the recommendation system algorithm to perform intelligent matching based on the company's job requirements and employee portraits to find jobs that match employees' current capabilities and development aspirations; the career path planning module analyzes industry development trends and future skill requirements based on predictive models, and provides employees with career development paths and training recommendations.

[0012] Furthermore, the data acquisition and processing module includes a data acquisition unit, a data cleaning unit, a data normalization processing unit, and a data integration and conversion unit; the data normalization processing unit is used to realize the normalization of numerical features, and is also used to realize categorical feature encoding; the data integration and conversion unit is used to merge data sources, integrate information from different data sources together to form a complete data set, and is also used for feature conversion, transforming or combining original features.

[0013] Furthermore, the employee portrait construction module includes a cluster analysis unit and a factor analysis unit;

[0014] The clustering analysis unit is used to select corresponding features from the employee data obtained by the data collection and processing module. These features include numerical features and categorical features. It is also used to perform clustering analysis using the spectral clustering algorithm according to the characteristics of the data and business needs, and use the K-means algorithm in the last step of spectral clustering to complete the cluster allocation, that is, spectral clustering first processes the data through graph theory methods to extract the low-dimensional representation of the data, and then uses the K-means algorithm to perform the final clustering step in the low-dimensional space to identify complex employee group characteristics;

[0015] The factor analysis unit is used to achieve factor extraction, factor rotation, factor interpretation, and building a comprehensive portrait; the maximum likelihood estimation method is used in factor extraction to extract a set of main factors related to employee characteristics from the original employee data; factor rotation is to rotate the extracted factors to ensure the independence and interpretability of the factors; factor interpretation is to interpret the extracted factors to determine the specific meaning of each factor; building a comprehensive portrait means comprehensively considering the influence of each factor to construct a comprehensive portrait of the employee.

[0016] Furthermore, the intelligent job matching module includes a collaborative filtering unit and a content recommendation unit;

[0017] Based on the collaborative filtering algorithm, the collaborative filtering unit provides users with personalized job recommendation results by analyzing the similarities between employees and positions. At the same time, it builds user portraits based on employees' personal characteristics and historical behavior data, uses similarity measurement methods to calculate the similarity between employee portraits and job requirements, and measures the degree of match between employees and positions. Based on the similarity calculation results, it generates a list of matching job recommendations, filters and sorts the recommended results, and eliminates positions that do not meet employees' interests or skills.

[0018] The content recommendation unit is based on the content recommendation algorithm. It analyzes the similarities between items to provide users with recommendation results related to their past interests. It extracts key features from texts including job descriptions, responsibilities and requirements, and uses similarity measurement methods to calculate the similarities between different jobs to measure the degree of similarity between jobs. Based on the employees' past work experience and skill levels, it generates a job recommendation list related to their past interests for employees, and filters and sorts the generated recommendation results.

[0019] Further, the career path planning module includes a prediction model analysis unit and a training suggestion unit;

[0020] The forecasting model analysis unit is used to collect multi-dimensional industry data, including economic indicators, technology development trends, and corporate recruitment needs. It also extracts representative features based on the characteristics of different industries and positions to build input features for the forecasting model. It uses machine learning algorithms to train and verify historical data to build a forecasting model. It interprets and analyzes forecast results to identify the main trends and key driving factors of future industry development, and provide career development directions and suggestions for employees.

[0021] The training recommendation unit combines employees' learning history and feedback information to guide the recommendation of training resources, and matches training needs based on employees' personal profiles and career development paths, and recommends training content based on employees' current skill levels, career goals, and industry development trends.

[0022] An intelligent job matching and employee growth path planning method using the above intelligent job matching and employee growth path planning system includes the following process:

[0023] Step 1: The data acquisition and processing module performs data acquisition and preprocessing;

[0024] Step 2: Employee portrait construction module to construct employee portraits;

[0025] Step 2.1: The cluster analysis unit of the employee portrait construction module selects corresponding employee features from the employee data obtained by the data collection and processing module, uses the spectral clustering algorithm to analyze the selected employee features, constructs an employee similarity matrix, calculates the similarity through the Gaussian kernel function, and performs spectral decomposition. Finally, the K-means clustering method is used to obtain the group clustering results of the employees;

[0026] Step 2.2: The factor analysis unit uses the maximum likelihood estimation method to extract the main factors related to employee characteristics from the original employee data matrix, including skill level, work ability, and leadership ability. The factor loading matrix is ​​then optimized through Varimax rotation to generate a personalized comprehensive portrait of each employee.

[0027] Step 3: The intelligent job matching module performs job demand analysis;

[0028] The content recommendation unit of the intelligent job matching module uses a content recommendation algorithm to combine the skills and experience in the employee profile with the requirements in the job description, and calculates and generates recommendation results by matching through text similarity calculation;

[0029] Step 4: The intelligent job matching module performs intelligent job matching;

[0030] The collaborative filtering unit of the intelligent job matching module is based on the collaborative filtering algorithm of matrix decomposition. It adopts the latent semantic model to decompose the employee-job interaction matrix into low-dimensional latent vectors of employees and jobs. It uses the gradient descent method to optimize the latent vectors and minimize the matching loss function. Finally, it generates a job recommendation list through the inner product of the employee and job latent vectors.

[0031] Step 5: Career path planning module to plan career path;

[0032] The prediction model analysis unit of the career path planning module predicts industry development trends and future skill requirements based on time series analysis or regression analysis models, plans career development paths for employees based on the prediction results, and recommends training courses and development directions based on job requirements and employee potential capabilities;

[0033] The training recommendation unit of the career path planning module combines employees' learning history and feedback information to analyze their personal learning preferences and habits, matches training needs based on their personal portraits and career development paths, and recommends training content based on their current skill levels, career goals, and industry development trends.

[0034] Furthermore, the specific process of step 2.1 is as follows:

[0035] Feature selection: Select employee features from the employee data obtained from the data collection and processing module, including personal performance, skill level, work experience, and educational background;

[0036] Similarity matrix construction: Use the selected employee features to construct an employee similarity matrix. The similarity matrix represents the matrix of similarities between data points. Each element in the matrix represents the degree of similarity between two employees. Use the Gaussian kernel function to calculate the similarity between two employee data points:

[0037]

[0038] Among them, x i and x j are the feature vectors of employees i and j respectively, σ is the kernel bandwidth parameter, W ij represents the similarity between employee i and employee j;

[0039] Clustering results: The similarity matrix is ​​mapped to a low-dimensional space through spectral decomposition, and the projected data is clustered using the K-means algorithm to finally obtain the clustering results of the employee group;

[0040] The specific method of step 2.2 is as follows:

[0041] Factor extraction: Decompose the employee data matrix X into common factors F and special factors E, and fit the factor model through the maximum likelihood estimation method to extract the main common factors:

[0042] X=ΛF+E

[0043] Among them, Λ is the factor loading matrix, F is the extracted common factor matrix, and E is the special factor matrix;

[0044] Factor rotation: Varimax rotation is used to maximize the explanatory power of each factor on a small number of variables:

[0045]

[0046] Among them, Λ ij represents the loading of factor j on variable i, and n is the total number of variables;

[0047] Portrait generation: Ultimately, the results of factor analysis are combined with employees’ potential abilities and work preferences to build a personalized portrait of each employee.

[0048] Furthermore, the specific process of step 3 is as follows:

[0049] Job feature extraction: Obtain job descriptions from the company's internal human resource management system or job demand release system, use the word frequency-inverse document frequency algorithm to analyze the job descriptions, extract key features from the job, and convert these features into feature vectors to provide structured job information for subsequent job matching:

[0050]

[0051] Where f(t,d) represents the number of times keyword t appears in document d, t represents the keyword, d represents the document, N represents the total number of all documents in the corpus, ∑ t′∈d f(t′,d) represents the total frequency of all words in document d;

[0052] Job similarity calculation: Use cosine similarity to calculate the similarity between feature vectors of different jobs and generate a job feature similarity matrix to measure the similarity between different jobs:

[0053]

[0054] Among them, A is the employee feature vector, B is the position feature vector, and θ represents the directional difference between the employee feature vector and the position feature vector. The smaller the angle θ is, the higher the matching degree between the employee and the position is.

[0055] Matching and sorting: Generate a list of recommended positions for employees based on similarity scores and filter and sort them by threshold.

[0056] Furthermore, the specific process of step 4 is as follows:

[0057] Matrix decomposition: Set the employee-position interaction matrix R and decompose it into the employee latent vector matrix P and the position latent vector matrix Q. The goal is to minimize the following loss function:

[0058]

[0059] Among them, R ik represents the matching score of employee i to position k, λ is the regularization parameter, Q k represents the latent vector representation of position k, The latent vector P representing employee i i The transpose of , L represents the loss function;

[0060] Hidden vector update: Through gradient descent optimization, iteratively update the hidden vectors of employees and positions:

[0061]

[0062]

[0063] Among them, η is the learning rate, R ij represents the actual rating of employee i on position j; P i and Q j are the latent vectors of employees and positions respectively, which are updated by gradient descent;

[0064] Matching generation: Calculate the recommended job list through the inner product between the employee and job latent vectors:

[0065]

[0066] in, It indicates the predicted matching degree of employee i to position j. The higher the value, the higher the matching degree between the employee and the position.

[0067] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the intelligent job matching and employee growth path planning method described in claim 6 above.

[0068] The present invention has the following beneficial effects:

[0069] 1. Improved accuracy and efficiency of job matching: By introducing deep learning models and multi-source data integration, the present invention achieves higher accuracy in job matching, significantly improves employees' job adaptability and satisfaction, and effectively improves employees' work performance at their posts.

[0070] 2. Personalized and dynamic adjustment of career path planning: The present invention dynamically plans career development paths based on real-time data on employees’ growth trajectories, skill enhancement, and industry needs, making employees’ career development more flexible and personalized, and stimulating their work enthusiasm and long-term development motivation.

[0071] 3. Improvement of enterprise human resource management efficiency: Through intelligent matching and career path planning, the present invention reduces manual intervention, reduces deviations caused by human factors, improves the efficiency of human resource management, and helps enterprises achieve remarkable results in optimizing resource allocation, reducing employee turnover, and improving overall work efficiency.

[0072] 4. The present invention integrates multi-source data (including internal performance data, industry trends, skill certification, employee feedback, etc.), applies machine learning technology to construct employee portraits, and combines recommendation system algorithms and prediction models to more comprehensively match job requirements with employee characteristics and dynamically plan employees' career growth paths. The present invention also uses deep learning technology to explore employees' potential abilities and career interests, thereby achieving precise job matching and personalized career development, improving employees' work enthusiasm, job adaptability and the overall human resource management efficiency of the enterprise.

[0073] 5. Multi-dimensional employee portrait: By comprehensively analyzing employees' performance data, skill tests, work preferences and personal goals, a comprehensive employee portrait is constructed to ensure the accuracy and personalization of job matching.

[0074] 6. Dynamic industry trend forecasting: Use time series analysis, natural language processing and other technologies to analyze industry reports, news, papers, etc., predict industry trends and future skill requirements, and guide career planning and employee development.

[0075] 7. Intelligent matching and feedback loop: The system not only provides one-time matching suggestions, but also dynamically adjusts the matching algorithm by continuously collecting employees' work feedback and performance changes to achieve more efficient job matching and career planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a framework diagram of the intelligent job matching and employee growth path planning system described in the present invention. DETAILED DESCRIPTION

[0077] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

[0078] like Figure 1 As shown, the intelligent job matching and employee growth path planning system of the present invention includes a data collection and processing module, an employee portrait construction module, an intelligent job matching module, and a career path planning module.

[0079] The data collection and processing module automatically collects employee performance data, skill certificates, educational background, work experience and other information from multiple internal and external data sources, and performs cleaning and normalization processing. The data collection and processing module includes a data collection unit, a data cleaning unit, a data normalization processing unit, and a data integration and conversion unit.

[0080] The data collection unit automatically obtains data from internal and external data sources regularly by writing scripts (such as Python's Scrapy, Selenium, etc.) or using data collection tools (such as ETL tools, API integration tools, etc.). Internal data sources come from the company's internal systems, databases, internal files and documents. The company's internal systems include human resource management systems, performance management systems, and training record systems. The database stores employees' basic information, performance data, job requirements, etc. Internal files and documents include electronic documents such as employee resumes and training certificates; external data sources come from industry data, third-party data providers, and external data interfaces. Industry data includes data related to industry trends and skill requirements obtained from public sources such as industry databases and industry reports. Third-party data providers include skill certification agencies, career development platforms, etc. Data on employee skill levels, career preferences, etc. are obtained through third-party data providers. External data interfaces refer to the establishment of interfaces with public data sources (such as social media) to obtain relevant information on employees on external platforms, such as professional skill certification, social activities, etc. Internal systems, databases, internal files and documents of the company, and external data sources include industry data, third-party data providers, external data interfaces, etc. By integrating multi-source data (including internal performance data, industry trends, skill certification, employee feedback, etc.), applying machine learning technology to build employee portraits, and combining recommendation system algorithms and prediction models, it is possible to more comprehensively match job requirements with employee characteristics and dynamically plan employees' career growth paths. In addition, the present invention also uses deep learning technology to mine employees' potential abilities and career interests, thereby achieving precise job matching and personalized career development, improving employees' work enthusiasm, job adaptability, and the overall human resource management efficiency of the enterprise.

[0081] The data cleaning unit is used to perform data deduplication, data filling, outlier processing, data format conversion, and data normalization.

[0082] The data normalization processing unit is used to realize the normalization of numerical features, that is, to scale the numerical features to a certain range using methods such as minimum-maximum scaling or Z-score standardization; it is also used to realize categorical feature encoding, that is, to encode categorical data, such as One-Hot Encoding.

[0083] The data integration and conversion unit is used to merge data sources, integrate information from different data sources to form a complete data set; it is also used for feature conversion, transforming or combining original features to extract more meaningful features.

[0084] The employee portrait construction module uses machine learning techniques, such as cluster analysis and factor analysis, to construct a comprehensive portrait of each employee, including skill level, work preference, potential ability, etc. The employee portrait construction module includes cluster analysis unit and factor analysis unit.

[0085] The functions of the cluster analysis unit are as follows:

[0086] Data preparation: Select appropriate features from the employee data obtained from the data collection and processing module, such as personal performance, skill level, work experience, educational background, etc. These features can include numerical features (such as personal performance, years of work, etc.) and categorical features (such as professional field, work department, etc.);

[0087] Clustering algorithm selection: According to the characteristics of the data and business needs, the traditional K-means algorithm is improved, and the spectral clustering algorithm is used for clustering analysis. That is, the K-means algorithm is used in the last step of spectral clustering to complete the cluster allocation. In other words, spectral clustering first processes the data through graph theory methods, extracts the low-dimensional representation of the data, and then uses the K-means algorithm to perform the final clustering step in this low-dimensional space. This method combines the respective advantages of spectral clustering and K-means, can better handle complex data distribution, and can identify more complex employee group characteristics.

[0088] The functions of the factor analysis unit are as follows:

[0089] Factor extraction: The maximum likelihood estimation method (ELM) is used to extract factors and extract a set of main factors related to employee characteristics from the original employee data. These factors include skill level, work ability, and leadership ability.

[0090] Factor rotation: Factor rotation is performed on the extracted factors to ensure the independence and interpretability of the factors. Commonly used factor rotation algorithms include variance maximization rotation and orthogonal rotation;

[0091] Factor interpretation: Perform factor interpretation on the extracted factors to determine the specific meaning of each factor;

[0092] Build a comprehensive portrait: Consider the impact of various factors and construct a comprehensive portrait of the employee.

[0093] The intelligent job matching module uses recommendation system algorithms (such as collaborative filtering and content recommendation algorithms) to perform intelligent matching based on the company's job requirements and employee portraits, and find the job that best suits the employee's current capabilities and development aspirations. The intelligent job matching module includes collaborative filtering units and content recommendation units.

[0094] The functions of the collaborative filtering unit are as follows:

[0095] Based on the collaborative filtering algorithm, by analyzing the similarities between employees and positions, it provides users with personalized job recommendation results. The algorithm uses the similarity between employee profiles and job requirements to recommend the most suitable positions.

[0096] User portrait construction: User portraits are constructed based on employees’ personal characteristics and historical behavior data (including skill level, work experience, educational background, etc.). These characteristics serve as input to the collaborative filtering algorithm.

[0097] Similarity calculation: Use similarity measurement methods (such as cosine similarity or Pearson correlation coefficient) to calculate the similarity between employee profiles and job requirements to measure the degree of match between employees and jobs.

[0098] Recommendation generation: Generate a list of matching job recommendations based on similarity calculation results. Use neighborhood-based collaborative filtering methods (such as user-based collaborative filtering or item-based collaborative filtering) to generate recommendation results.

[0099] Recommendation result filtering: Filter and sort the recommendation results, eliminate positions that do not match the employees' interests or skills, and improve the accuracy and personalization of the recommendation results.

[0100] The functions of the content recommendation unit are as follows:

[0101] Content-based recommendation algorithms (a recommendation algorithm based on the content attributes of items) provide users with recommendation results related to their past interests by analyzing the similarities between items. In the intelligent job matching engine, content recommendation algorithms can recommend jobs that employees may be interested in based on their past work experience and skill levels.

[0102] Job feature extraction: Extract key features from job descriptions, responsibilities, and requirements, such as keywords, skill requirements, and work content. These features will serve as input to the content recommendation algorithm.

[0103] Similarity calculation: Use appropriate similarity measurement methods (such as TF-IDF, cosine similarity, etc.) to calculate the similarity between different positions; these similarity values ​​will be used to measure the degree of similarity between positions;

[0104] Recommendation generation: Generate a list of job recommendations related to employees' past interests based on their past work experience and skill levels; content-based recommendation methods can be used, such as content-based filtering, content-based sorting, etc.

[0105] Filtering of recommendation results: Filter and sort the generated recommendation results to select the most attractive positions for recommendation to employees. Consider filtering out positions that do not match the employee's skills or work experience to improve the personalization and accuracy of the recommendation results.

[0106] The career path planning module analyzes industry development trends and future skill requirements based on the prediction model, and provides employees with personalized career development paths and related training suggestions. The career path planning module includes the prediction model analysis unit and the training suggestion unit.

[0107] The functions of the prediction model analysis unit are as follows:

[0108] Data collection and preprocessing: Collect multi-dimensional industry data, including economic indicators, technology development trends, corporate recruitment needs, etc., and clean and convert the data to ensure data quality and availability;

[0109] Feature engineering: Extract representative features based on the characteristics of different industries and positions, such as skill keywords, industry hot topics, etc., to construct input features for the prediction model;

[0110] Model selection and training: Select appropriate machine learning algorithms, and train and verify historical data to build reliable forecasting models; commonly used algorithms include time series analysis, regression analysis, and neural networks;

[0111] Interpretation of forecast results: Interpret and analyze the forecast results to identify the main trends and key driving factors for future industry development, and provide employees with clear career development directions and suggestions.

[0112] The functions of the training advice unit are as follows:

[0113] Personal learning preference analysis: Analyze employees’ personal learning preferences and habits based on their learning history, feedback information, etc., to better guide the recommendation of training resources;

[0114] Matching training needs: Match appropriate training needs based on employees’ personal profiles and career development paths; consider employees’ current skill levels, career goals, and industry development trends to recommend targeted and forward-looking training content.

[0115] The intelligent job matching and employee growth path planning method using the above-mentioned intelligent job matching and employee growth path planning system includes the following process:

[0116] Step 1: The data acquisition and processing module performs data acquisition and preprocessing;

[0117] Step 1.1: The data collection unit automatically collects data from internal and external data sources through API integration tools or ETL tools;

[0118] Step 1.2: The data cleaning unit removes duplicates, fills in and processes outliers on the collected data;

[0119] Step 1.3: The data normalization processing unit performs minimum-maximum scaling or Z-score normalization on numerical features and one-hot encoding on categorical features;

[0120] Step 1.4: The data integration and conversion unit integrates data from different sources, generates structured employee and position data sets, and completes feature conversion.

[0121] Step 2: Employee portrait construction module to construct employee portraits;

[0122] Step 2.1: The cluster analysis unit selects appropriate employee features from the employee data obtained by the data acquisition and processing module, uses the spectral clustering algorithm to analyze the selected employee features, and constructs an employee similarity matrix; calculates the similarity through the Gaussian kernel function, and performs spectral decomposition, and finally uses the K-means clustering method to obtain the group classification of employees. The specific process is as follows:

[0123] Feature selection: Select appropriate employee features from the employee data obtained by the data collection and processing module, including personal performance, skill level, work experience, and educational background, as the main input features. These features include numerical and categorical types;

[0124] Similarity matrix construction: Use the selected employee features to construct an employee similarity matrix. The similarity matrix is ​​a matrix that represents the similarity between data points. Each element in the matrix represents the degree of similarity between two employees. Use the Gaussian kernel function (also known as the RBF kernel function) to calculate the similarity between two employee data points. The formula is as follows:

[0125]

[0126] Among them, x i and x j are the feature vectors of employees i and j respectively, σ is the kernel bandwidth parameter, W ij represents the similarity between employee i and employee j;

[0127] Clustering results: The similarity matrix is ​​mapped to a low-dimensional space through spectral decomposition, and the projected data is clustered using the K-means algorithm to finally obtain the clustering results of the employee group.

[0128] Step 2.2: The factor analysis unit uses the maximum likelihood estimation method to extract the main factors (common factors) related to employee characteristics from the original employee data matrix X, which comes from the previous data collection and processing module. These factors include skill level, work ability, leadership ability, etc.; then, the factor loading matrix is ​​optimized through Varimax rotation to improve the interpretability of the factors and generate a personalized comprehensive portrait of each employee; the specific method is as follows:

[0129] Factor extraction: Decompose the employee data matrix X into common factors F and special factors E, and fit the factor model through the maximum likelihood estimation method to extract the main common factors (i.e., potential characteristics such as employee skill level, work ability, and leadership ability); the maximum likelihood estimation method is used to estimate the parameters of the factor model to help extract common factors from employee data that can explain the main structure of the data. The specific formula is as follows:

[0130] X=ΛF+E

[0131] Among them, Λ is the factor loading matrix, F is the extracted common factor matrix, and E is the special factor matrix;

[0132] Factor rotation: Varimax rotation is used to maximize the explanatory power of each factor on a small number of variables. The specific formula is:

[0133]

[0134] Among them, Λ ij represents the loading of factor j on variable i, and n is the total number of variables;

[0135] Portrait generation: Ultimately, the results of factor analysis (i.e., the common factors and special factors of employees extracted during the factor extraction process) are combined with the employees' potential abilities, work preferences and other characteristics to build a personalized portrait of each employee; among them, "potential abilities, work preferences and other factors" refer to the common factors obtained during the factor extraction process. These factors reflect the employees' potential characteristics in terms of skill level, work ability, leadership ability, etc. Through these factors, the employees' ability tendencies, career interests and future development potential can be more comprehensively portrayed, thereby providing personalized and accurate suggestions for job matching.

[0136] Step 3: The intelligent job matching module performs job demand analysis;

[0137] The content recommendation unit uses a content recommendation algorithm (Content-based Filtering) to combine the skills and experience in the employee profile with the requirements in the job description, and calculates and generates recommendation results through text similarity calculation. The specific process is as follows:

[0138] Job feature extraction: Obtain job descriptions from the company's internal human resource management system or job demand release system. Job descriptions usually include detailed information such as skill requirements and job responsibilities. Use the term frequency-inverse document frequency (TF-IDF) algorithm to analyze the job descriptions, extract key features of the job (such as required skills, job responsibilities, job content, etc.), and convert these features into feature vectors to provide structured job information for subsequent job matching:

[0139]

[0140] Where f(t,d) represents the term frequency (TF), which indicates the number of times keyword t appears in document d. t represents the keyword (term), that is, the important word to be calculated. d represents the document (document), which refers to the job description document. N represents the total number of all documents in the corpus. ∑ t′∈d f(t′,d) represents the total frequency of all words in document d, that is, the sum of the number of occurrences of all words in the document.

[0141] Job similarity calculation: Use cosine similarity to calculate the similarity between feature vectors of different jobs and generate a job feature similarity matrix. This matrix is ​​used to measure the similarity between different jobs and is used to screen and recommend the most suitable jobs for employees in the intelligent job matching process to improve the accuracy of matching:

[0142]

[0143] Among them, A is the employee feature vector, B is the position feature vector, and θ represents the directional difference between the employee feature vector and the position feature vector. In the feature vector space, these vectors represent the employee's skills, experience and other attributes and the job requirements. By calculating the angle between these two vectors, their similarity can be measured:

[0144] If the angle θ is close to 0 degrees (the angle is very small), it means that the employee's characteristics are very similar to the job requirements and the match is high;

[0145] If the angle θ is 90 degrees, it means that there is almost no correlation or relevance between the employee's characteristics and the job requirements, and the match is low;

[0146] If the angle θ is close to 180 degrees (opposite direction), it means that the employee’s characteristics do not match the job requirements, or are even completely opposite;

[0147] By using cosine similarity to calculate this angle, we can determine whether an employee is suitable for a certain position. The smaller the angle, the higher the match.

[0148] Matching and sorting: Generate a list of recommended positions for employees based on similarity scores, and filter and sort by thresholds to ensure the accuracy of recommendations.

[0149] Step 4: The intelligent job matching module performs intelligent job matching;

[0150] The collaborative filtering unit is based on the collaborative filtering algorithm of matrix decomposition. It uses the latent factor model to decompose the employee-position interaction matrix into low-dimensional latent vectors of employees and positions. The gradient descent method is used to optimize the latent vectors and minimize the matching loss function. Finally, the position recommendation list is generated through the inner product of the employee and position latent vectors. The specific process is as follows:

[0151] Matrix decomposition: Set the employee-position interaction matrix R (representing the employee's fitness for the position) and decompose it into the employee latent vector matrix P and the position latent vector matrix Q. The goal is to minimize the following loss function:

[0152]

[0153] Among them, R ik represents the matching score of employee i to position k, λ is the regularization parameter, Q k The latent vector representation of position k, the feature vector in the low-dimensional space, represents the characteristics or attributes of the position, The latent vector P representing employee i i L represents the loss function, which is used to measure the difference between the model prediction value and the actual value.

[0154] Hidden vector update: Through gradient descent optimization, the hidden vectors of employees and positions are iteratively updated. The formula is:

[0155]

[0156]

[0157] Among them, η is the learning rate, R ij represents the actual score (or adaptability) of employee i to position j; P i and Q j are the latent vectors of employees and positions respectively, which are updated by gradient descent.

[0158] Matching generation: The recommended job list is calculated by the inner product between the employee and job latent vectors. The formula is:

[0159]

[0160] in, Indicates the predicted match (recommendation score) of employee i for position j. The higher the value, the higher the match between the employee and the position, and the stronger the adaptability.

[0161] Step 5: Career path planning module to plan career path;

[0162] The forecasting model analysis unit predicts industry development trends and future skill requirements based on time series analysis or regression analysis models, and extracts key features such as skill trends and job demand changes. Based on the forecast results, it plans personalized career development paths for employees and recommends appropriate training courses and development directions based on job requirements and employee potential capabilities.

[0163] The training recommendation unit analyzes employees' personal learning preferences and habits based on their learning history, feedback information, etc., to better guide the recommendation of training resources, match appropriate training needs based on their personal profiles and career development paths; consider employees' current skill levels, career goals, and industry development trends, and recommend targeted and forward-looking training content.

[0164] The present invention also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above-mentioned intelligent job matching and employee growth path planning method are implemented.

[0165] The embodiments are preferred implementations of the present invention, but the present invention is not limited to the above-mentioned implementations. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essential content of the present invention belong to the protection scope of the present invention.

Claims

1. An intelligent job matching and employee growth path planning system, characterized in that: It includes data collection and processing module, employee portrait construction module, intelligent job matching module, and career path planning module; The data collection and processing module collects relevant information from multiple internal and external data sources, including employee performance data, skill certificates, educational background, and work experience, and performs pre-processing; The employee portrait construction module uses machine learning technology to build a comprehensive portrait of each employee, including skill level, work preference, and potential ability. The intelligent job matching module uses the recommendation system algorithm to perform intelligent matching based on the company's job requirements and employee portraits to find jobs that match employees' current abilities and development aspirations. The career path planning module analyzes industry development trends and future skill requirements based on predictive models, and provides employees with career development paths and training suggestions.

2. The intelligent job matching and employee growth path planning system according to claim 1 is characterized in that: The data acquisition and processing module includes a data acquisition unit, a data cleaning unit, a data normalization processing unit, and a data integration and conversion unit; the data normalization processing unit is used to realize the normalization of numerical features and is also used to realize the encoding of categorical features; the data integration and conversion unit is used to merge data sources, integrate information from different data sources together to form a complete data set, and is also used for feature conversion, transforming or combining original features.

3. The intelligent job matching and employee growth path planning system according to claim 1 is characterized in that: The employee portrait construction module includes a cluster analysis unit and a factor analysis unit; The clustering analysis unit is used to select corresponding features from the employee data obtained by the data collection and processing module. These features include numerical features and categorical features. It is also used to perform clustering analysis using the spectral clustering algorithm according to the characteristics of the data and business needs, and use the K-means algorithm in the last step of spectral clustering to complete the cluster allocation, that is, spectral clustering first processes the data through graph theory methods to extract the low-dimensional representation of the data, and then uses the K-means algorithm to perform the final clustering step in the low-dimensional space to identify complex employee group characteristics; The factor analysis unit is used to realize factor extraction, factor rotation, factor interpretation, and the establishment of comprehensive portraits; the maximum likelihood estimation method is used in factor extraction to extract a set of main factors related to employee characteristics from the original employee data; Factor rotation is to rotate the extracted factors to ensure the independence and interpretability of the factors; factor interpretation is to interpret the extracted factors to determine the specific meaning of each factor; Establishing a comprehensive portrait means taking into account the impact of various factors and constructing a comprehensive portrait of the employee.

4. The intelligent job matching and employee growth path planning system according to claim 1 is characterized in that: The intelligent job matching module includes a collaborative filtering unit and a content recommendation unit; Based on the collaborative filtering algorithm, the collaborative filtering unit provides users with personalized job recommendation results by analyzing the similarities between employees and positions. At the same time, it builds user portraits based on employees' personal characteristics and historical behavior data, uses similarity measurement methods to calculate the similarity between employee portraits and job requirements, and measures the degree of match between employees and positions. Based on the similarity calculation results, it generates a list of matching job recommendations, filters and sorts the recommended results, and eliminates positions that do not meet employees' interests or skills. The content recommendation unit is based on the content recommendation algorithm. It analyzes the similarities between items to provide users with recommendation results related to their past interests. It extracts key features from texts including job descriptions, responsibilities and requirements, and uses similarity measurement methods to calculate the similarities between different jobs to measure the degree of similarity between jobs. Based on the employees' past work experience and skill levels, it generates a job recommendation list related to their past interests for employees, and filters and sorts the generated recommendation results.

5. The intelligent job matching and employee growth path planning system according to claim 1 is characterized in that: The career path planning module includes a prediction model analysis unit and a training suggestion unit; The prediction model analysis unit is used to collect multi-dimensional industry data, including economic indicators, technology development trends, and corporate recruitment needs. It also extracts representative features based on the characteristics of different industries and positions to construct input features for the prediction model. Use machine learning algorithms to train and validate historical data and build forecasting models; identify the main trends and key drivers of future industry development by interpreting and analyzing forecast results, and provide career development directions and suggestions for employees; The training recommendation unit combines employees' learning history and feedback information to guide the recommendation of training resources, and matches training needs based on employees' personal profiles and career development paths, and recommends training content based on employees' current skill levels, career goals, and industry development trends.

6. A method for intelligent job matching and employee growth path planning using the intelligent job matching and employee growth path planning system of claim 1, characterized in that: The process includes the following: Step 1: The data acquisition and processing module performs data acquisition and preprocessing; Step 2: Employee portrait construction module to construct employee portraits; Step 2.1: The cluster analysis unit of the employee portrait construction module selects corresponding employee features from the employee data obtained by the data collection and processing module, uses the spectral clustering algorithm to analyze the selected employee features, constructs an employee similarity matrix, calculates the similarity through the Gaussian kernel function, and performs spectral decomposition. Finally, the K-means clustering method is used to obtain the group clustering results of the employees; Step 2.2: The factor analysis unit uses the maximum likelihood estimation method to extract the main factors related to employee characteristics from the original employee data matrix, including skill level, work ability, and leadership ability. The factor loading matrix is ​​then optimized through Varimax rotation to generate a personalized comprehensive portrait of each employee. Step 3: The intelligent job matching module performs job demand analysis; The content recommendation unit of the intelligent job matching module uses a content recommendation algorithm to combine the skills and experience in the employee profile with the requirements in the job description, and calculates and generates recommendation results by matching through text similarity calculation; Step 4: The intelligent job matching module performs intelligent job matching; The collaborative filtering unit of the intelligent job matching module is based on the collaborative filtering algorithm of matrix decomposition. It adopts the latent semantic model to decompose the employee-job interaction matrix into low-dimensional latent vectors of employees and jobs. It uses the gradient descent method to optimize the latent vectors and minimize the matching loss function. Finally, it generates a job recommendation list through the inner product of the employee and job latent vectors. Step 5: Career path planning module to plan career path; The prediction model analysis unit of the career path planning module predicts industry development trends and future skill requirements based on time series analysis or regression analysis models, plans career development paths for employees based on the prediction results, and recommends training courses and development directions based on job requirements and employee potential capabilities; The training recommendation unit of the career path planning module combines employees' learning history and feedback information to analyze their personal learning preferences and habits, matches training needs based on their personal portraits and career development paths, and recommends training content based on their current skill levels, career goals, and industry development trends.

7. The method for intelligent job matching and employee growth path planning according to claim 6, characterized in that: The specific process of step 2.1 is as follows: Feature selection: Select employee features from the employee data obtained from the data collection and processing module, including personal performance, skill level, work experience, and educational background; Similarity matrix construction: Use the selected employee features to construct an employee similarity matrix. The similarity matrix represents the matrix of similarities between data points. Each element in the matrix represents the degree of similarity between two employees. Use the Gaussian kernel function to calculate the similarity between two employee data points: Among them, x i and x j are the feature vectors of employees i and j respectively, σ is the kernel bandwidth parameter, W ij represents the similarity between employee i and employee j; Clustering results: The similarity matrix is ​​mapped to a low-dimensional space through spectral decomposition, and the projected data is clustered using the K-means algorithm to finally obtain the clustering results of the employee group; The specific method of step 2.2 is as follows: Factor extraction: Decompose the employee data matrix X into common factors F and special factors E, and fit the factor model through the maximum likelihood estimation method to extract the main common factors: X=ΛF+E Among them, Λ is the factor loading matrix, F is the extracted common factor matrix, and E is the special factor matrix; Factor rotation: Varimax rotation is used to maximize the explanatory power of each factor on a small number of variables: Among them, Λ ij represents the loading of factor j on variable i, and n is the total number of variables; Portrait generation: Ultimately, the results of factor analysis are combined with employees’ potential abilities and work preferences to build a personalized portrait of each employee.

8. The method for intelligent job matching and employee growth path planning according to claim 6, characterized in that: The specific process of step 3 is as follows: Job feature extraction: Obtain job descriptions from the company's internal human resource management system or job demand release system, use the word frequency-inverse document frequency algorithm to analyze the job descriptions, extract key features from the job, and convert these features into feature vectors to provide structured job information for subsequent job matching: Where f(t,d) represents the number of times keyword t appears in document d, t represents the keyword, d represents the document, N represents the total number of all documents in the corpus, ∑ t′∈d f(t′,d) represents the total frequency of all words in document d; Job similarity calculation: Use cosine similarity to calculate the similarity between feature vectors of different jobs and generate a job feature similarity matrix to measure the similarity between different jobs: Among them, A is the employee feature vector, B is the position feature vector, and θ represents the directional difference between the employee feature vector and the position feature vector. The smaller the angle θ is, the higher the matching degree between the employee and the position is. Matching and sorting: Generate a list of recommended positions for employees based on similarity scores and filter and sort them by threshold.

9. The method for intelligent job matching and employee growth path planning according to claim 6, characterized in that: The specific process of step 4 is as follows: Matrix decomposition: Set the employee-position interaction matrix R and decompose it into the employee latent vector matrix P and the position latent vector matrix Q. The goal is to minimize the following loss function: Among them, R ik represents the matching score of employee i to position k, λ is the regularization parameter, Q k represents the latent vector representation of position k, The latent vector P representing employee i i The transpose of , L represents the loss function; Hidden vector update: Through gradient descent optimization, iteratively update the hidden vectors of employees and positions: Among them, η is the learning rate, R ij represents the actual rating of employee i on position j; P i and Q j are the latent vectors of employees and positions respectively, which are updated by gradient descent; Matching generation: Calculate the recommended job list through the inner product between the employee and job latent vectors: in, It indicates the predicted matching degree of employee i to position j. The higher the value, the higher the matching degree between the employee and the position.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the steps of the intelligent job matching and employee growth path planning method described in claim 6 are implemented.

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