Intelligent course matching and promotion path recommendation method and device based on post image

Abstract

This invention provides a method and apparatus for intelligent course matching and promotion path recommendation based on job profiles. The method includes: acquiring multi-source heterogeneous data within the enterprise and preprocessing it to obtain a standardized multi-dimensional feature dataset; constructing a dynamic job competency profile using a semidefinite programming method with an asymmetric random block model; analyzing the causal relationships between competency dimensions using a linear time primitive algorithm to obtain an employee competency assessment report; calculating the competency gap matrix using a dissipative oversatiated constraint solving algorithm to obtain personalized competency improvement needs; selecting and combining learning resources using a weighted matching algorithm to obtain a personalized learning recommendation plan; and constructing a multi-dimensional promotion probability assessment using a promotion path planning algorithm to obtain personalized promotion path suggestions. This invention achieves a closed-loop feedback mechanism for learning, assessment, and promotion, providing a systematic solution for enterprise talent development.

Description

Technical Field

[0001] This invention relates to the field of enterprise talent cultivation and development, specifically to an intelligent course matching and promotion path recommendation method and device based on job profiles, which is used to provide enterprise employees with personalized ability development plans and career advancement suggestions. Background Technology

[0002] In the field of corporate talent development, systematic solutions for employee skill enhancement and career advancement have always been a crucial issue for organizational development. With the deepening of digital transformation, corporate internal learning and development systems have gradually evolved from traditional passive training management to proactive capability development platforms.

[0003] Currently, most enterprise learning systems on the market primarily employ recommendation methods based on content tag matching. For example, they extract and categorize course content using keywords, then perform simple matching with tags such as employee department and position. Another type of system uses collaborative filtering, recommending courses by analyzing the learning trajectories of similar employees. However, these methods often overlook individual skill differences and organizational development needs.

[0004] Existing, more advanced solutions attempt to combine employee competency assessment with course recommendations. By building competency models, they assess employees and then recommend appropriate courses. This technology analyzes employee competency gaps and combines them with pre-defined course competency tags to provide personalized learning suggestions. However, this approach has significant limitations in its implementation.

[0005] First, existing technologies cannot effectively integrate learning behavior data with performance outcome data, resulting in a lack of scientific correlation between recommended courses and actual work performance improvement. Second, promotion path planning is too static, failing to consider organizational structure changes and individual differences, and thus unable to provide employees with dynamically optimized career development suggestions. Finally, existing systems generally lack a closed-loop feedback mechanism for learning, assessment, and promotion, making it difficult to achieve continuously optimized talent development strategies. Summary of the Invention

[0006] The purpose of this invention is to provide a method and apparatus for intelligent course matching and promotion path recommendation based on job profiles, aiming to solve the problems existing in the prior art, realize the effective integration of learning behavior data and performance result data, provide dynamically optimized career development suggestions, and establish a closed-loop feedback mechanism for learning, assessment and promotion.

[0007] To achieve the above objectives, the technical solution provided by the present invention is as follows:

[0008] A method for intelligent course matching and promotion path recommendation based on job profiles includes the following steps:

[0009] Obtain multi-source heterogeneous data within the enterprise, and perform data cleaning, standardization, and feature extraction processing on the multi-source heterogeneous data to obtain a standardized multidimensional feature dataset;

[0010] Based on the standardized multidimensional feature dataset, a semidefinite programming method using an asymmetric random block model is employed to map job competency requirements to competency characteristics, thereby obtaining a dynamic job competency profile.

[0011] Based on the standardized multidimensional feature dataset and the dynamic job competency profile, the linear time primitive algorithm is applied to analyze the causal relationship between competency dimensions to obtain an employee competency assessment report.

[0012] Based on the dynamic job competency profile and the employee competency assessment report, the dissipative oversatisfaction constraint solving algorithm is used to calculate the gap matrix between the employee's current competency and the target job requirements by iteratively calculating the competency gap value and weight coefficient, thereby obtaining personalized competency improvement needs.

[0013] Based on the personalized capability enhancement needs, and combined with the learning resources from the standardized multidimensional feature dataset, a weighted matching algorithm is used to filter and combine the learning resources to obtain a personalized learning recommendation plan.

[0014] Based on the dynamic job competency profile, the employee competency assessment report, and the standardized multidimensional feature dataset of organizational structure data, a promotion path planning algorithm is used to construct a multidimensional promotion probability assessment and obtain personalized promotion path suggestions.

[0015] Preferably, the step of performing data cleaning, standardization, and feature extraction on the multi-source heterogeneous data to obtain a standardized multidimensional feature dataset includes:

[0016] Based on the aforementioned multi-source heterogeneous data, data integrity checks and outlier handling are performed to obtain a dataset after integrity checks and outlier handling.

[0017] The dataset after integrity checks and outlier handling is subjected to dimensional unification and normalization to obtain a standardized dataset.

[0018] Based on the standardized dataset, learning behavior features, ability assessment features, and performance features are extracted to obtain the standardized multidimensional feature dataset.

[0019] Preferably, the semidefinite programming method using an asymmetric stochastic block model maps job competency requirements to competency characteristics to obtain a dynamic job competency profile, including:

[0020] Based on the standardized multidimensional feature dataset, a standardized capability framework system covering dimensions including technical capabilities, management capabilities, and general capabilities is established.

[0021] Based on the standardized multidimensional feature dataset of enterprise organizational structure and job descriptions, basic job information is extracted and mapped to the standardized capability framework system to obtain the job capability requirement mapping matrix.

[0022] Based on the competency features of the standardized multidimensional feature dataset, the actual competency performance of the competency is mapped to the standardized competency framework system to obtain the competency feature mapping matrix.

[0023] Based on the job competency requirement mapping matrix and the competent candidate competency feature mapping matrix, an asymmetric random block model is constructed to obtain an initial block association model.

[0024] The initial block association model is optimized by applying a semidefinite programming algorithm to obtain the optimized job competency weight matrix.

[0025] Based on the optimized job competency weight matrix and combined with the competency characteristics, a multi-dimensional competency profile is constructed to obtain the dynamic job competency profile.

[0026] Preferably, the application of the linear time primitive algorithm to analyze the causal relationships between capability dimensions to obtain an employee capability assessment report includes:

[0027] Based on the standardized multidimensional feature dataset, employee historical learning records, assessment results and work performance data are integrated in multiple dimensions to obtain the employee historical ability performance dataset.

[0028] Based on the aforementioned employee historical performance dataset, a graphical causal reasoning method is applied to identify the causal relationships between various competency dimensions, resulting in a competency causal relationship network.

[0029] The capability causal relationship network is topologically sorted and path traced, and the linear time primitive algorithm is applied to calculate the direct and indirect effects to obtain the capability weight relationship matrix.

[0030] Based on the latest employee assessment data using the aforementioned capability weight relationship matrix and the standardized multidimensional feature dataset, a weighted network propagation algorithm is used to calculate the comprehensive capability score, resulting in a multidimensional employee capability score.

[0031] Based on the multidimensional employee competency scoring results and combined with the dynamic job competency profile, an intuitive competency assessment report is generated, resulting in the employee competency assessment report.

[0032] Preferably, the constraint-solving algorithm employing dissipative oversatisfaction calculates the gap matrix between the employee's current capabilities and the target job requirements by iteratively calculating the capability gap value and weighting coefficients, thereby obtaining personalized capability enhancement needs, including:

[0033] Based on the employee competency assessment report and the dynamic job competency profile, a one-to-one dimensional mapping is performed to calculate the gap value of each competency dimension, resulting in an initial competency gap matrix.

[0034] Based on the initial capability gap matrix, an optimization model containing hard constraints and dissipation constraints is constructed to obtain the dissipation constraint model.

[0035] The 3-SAT algorithm for solving oversatisfied constraints is applied iteratively to the dissipative constraint model to obtain the personalized capability enhancement requirements.

[0036] Preferably, the step of filtering and combining the learning resources using a weighted matching algorithm to obtain a personalized learning recommendation plan includes:

[0037] Based on the learning resources in the standardized multidimensional feature dataset, including courses, projects, and case resources in the enterprise learning resource library, content features, ability tags, and difficulty level information are extracted to obtain a learning resource feature matrix.

[0038] Based on the personalized ability enhancement needs and the learning resource feature matrix, the correlation degree of each learning resource with the target ability enhancement is calculated to obtain the ability-resource correlation degree matrix.

[0039] Based on the capability-resource correlation matrix, a preliminary learning path scheme is obtained by considering the learning order, time constraints and resource dependencies through a path optimization algorithm.

[0040] Based on the preliminary learning path plan, the personalized learning recommendation plan is obtained by combining the employee's learning style, time arrangement and historical learning effect.

[0041] By setting phased goals and designing a progress tracking mechanism for the personalized learning recommendation plan, a complete learning development scheme is obtained.

[0042] Preferably, the step of constructing a multi-dimensional promotion probability assessment using a promotion path planning algorithm to obtain personalized promotion path suggestions includes:

[0043] Based on the organizational structure data of the standardized multidimensional feature dataset, a network of connections between positions is constructed and possible promotion channels are identified to obtain an organizational position network diagram.

[0044] Based on the employee competency assessment report, the dynamic job competency profile, and the historical promotion case data of the standardized multidimensional feature dataset, the asymmetric random block model is applied to calculate the promotion suitability of employees in different positions, and a promotion probability score matrix is ​​obtained.

[0045] Based on the promotion probability scoring matrix and the organizational job network diagram, vertical promotion paths and horizontal development paths are generated to obtain a multi-dimensional promotion path candidate set.

[0046] Based on the multidimensional promotion path candidate set, path optimization is performed by applying dissipative constraint solving techniques through a multi-constraint optimization model and a random walk strategy to obtain the optimized promotion path scheme.

[0047] Based on the optimized promotion path scheme, and combined with the personalized learning recommendation plan, detailed suggestions including time nodes, milestones, and key ability improvement points are generated to obtain the personalized promotion path suggestion.

[0048] Preferably, the method further includes a closed-loop feedback optimization step:

[0049] Based on the standardized multidimensional feature dataset, the evaluation results, application status, and performance changes of employees after completing the learning are used to assess the effectiveness of the learning resources and obtain a learning effectiveness evaluation report.

[0050] Based on the standardized multidimensional feature dataset, successful promotion cases of employees are analyzed to determine their ability improvement trajectory, learning path and key success factors, and the results of promotion success pattern analysis are obtained.

[0051] Based on the learning effectiveness evaluation report and the analysis results of the promotion success model, a three-dimensional closed-loop feedback model of learning-assessment-promotion is constructed to obtain optimization directions;

[0052] Based on the aforementioned three-dimensional closed-loop feedback model, the parameters of the core algorithms, including job profile construction, competency assessment, and resource matching, are adaptively adjusted to obtain an optimized set of algorithm parameters.

[0053] Based on the optimized algorithm parameter set, a periodic evaluation and iterative optimization mechanism is established to obtain the system iterative optimization strategy.

[0054] Preferably, the step of performing data integrity checks and outlier processing based on the multi-source heterogeneous data to obtain a dataset after integrity checks and outlier processing includes:

[0055] Based on the aforementioned multi-source heterogeneous data, data sources are accessed and integrated through API interfaces and data synchronization tools to obtain the original multi-source data stream;

[0056] The original multi-source data stream is subjected to data integrity checks, outlier handling, and missing value imputation operations to obtain the dataset after integrity checks and outlier handling.

[0057] The present invention also provides an intelligent course matching and promotion path recommendation device based on job profiles, comprising:

[0058] The data preprocessing module is used to acquire multi-source heterogeneous data within the enterprise, and to perform data cleaning, standardization and feature extraction processing on the multi-source heterogeneous data to obtain a standardized multidimensional feature dataset.

[0059] The job profile construction module is used to map job competency requirements to competency characteristics based on the standardized multidimensional feature dataset and a semidefinite programming method of asymmetric random block model to obtain a dynamic job competency profile.

[0060] The employee competency assessment module is used to analyze the causal relationship between competency dimensions based on the standardized multidimensional feature dataset and the dynamic job competency profile, and to obtain an employee competency assessment report.

[0061] The competency gap analysis module is used to calculate the gap matrix between the employee's current competency and the target job requirements based on the dynamic job competency profile and the employee competency assessment report. It employs a dissipative oversatisfaction constraint solving algorithm to iteratively calculate the competency gap value and weight coefficients, thereby obtaining personalized competency improvement needs.

[0062] The learning resource matching module is used to filter and combine the learning resources from the standardized multidimensional feature dataset based on the personalized ability improvement needs, and obtain a personalized learning recommendation plan by using a weighted matching algorithm.

[0063] The promotion path planning module is used to construct a multi-dimensional promotion probability assessment based on the dynamic job competency profile, the employee competency assessment report, and the standardized multi-dimensional feature dataset of organizational structure data, and to obtain personalized promotion path suggestions by using a promotion path planning algorithm.

[0064] The beneficial effects of this invention are:

[0065] 1. By using an asymmetric random block model and semidefinite programming method, a precise mapping between job competency requirements and competency characteristics was achieved, and a dynamic job competency profile was constructed, providing a scientific basis for employee competency assessment and development planning.

[0066] 2. By applying the linear time primitive algorithm to analyze the causal relationships between competency dimensions, the mechanism of mutual influence between different competencies was revealed, making employee competency assessment more comprehensive and systematic.

[0067] 3. By employing a constraint-solving algorithm that addresses dissipative oversatisfaction, the gap between employee capabilities and job requirements is accurately calculated, providing personalized capability enhancement needs and making learning resource matching more precise.

[0068] 4. By using a weighted matching algorithm to filter and combine learning resources, personalized learning recommendation plans are generated, which improves the efficiency of learning resource utilization and learning effectiveness;

[0069] 5. A multi-dimensional promotion probability assessment is constructed using a promotion path planning algorithm, providing employees with personalized promotion path suggestions to help them achieve their long-term career development goals;

[0070] 6. A three-dimensional closed-loop feedback model of learning-assessment-promotion was established, enabling continuous optimization and iteration of the system and improving the effectiveness and adaptability of talent development strategies. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a flowchart of the intelligent course matching and promotion path recommendation method based on job profile provided by the present invention;

[0073] Figure 2 This is a flowchart of the job profile construction module provided by the present invention;

[0074] Figure 3 This is a structural block diagram of the intelligent course matching and promotion path recommendation device based on job profile provided by the present invention. Detailed Implementation

[0075] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0076] like Figure 1 As shown, the intelligent course matching and promotion path recommendation method based on job profiles provided by this invention includes the following steps:

[0077] In step S1, multi-source heterogeneous data within the enterprise is acquired, and the multi-source heterogeneous data is cleaned, standardized, and feature extracted to obtain a standardized multidimensional feature dataset.

[0078] This step begins by collecting heterogeneous data from various parts of the company, including employee records from HR, learning behavior data from learning management, assessment results from competency testing, performance evaluation data from performance management, and job information data from the organizational structure. This data is typically scattered across different locations, with varying formats and inconsistent quality. After unified collection using data acquisition tools and API interfaces, this raw data undergoes cleaning, including removing duplicate records, correcting outliers, and filling in missing values, ensuring data integrity and consistency. Subsequently, the cleaned data is standardized, converting data of different dimensions and scales into a unified standard format for easier subsequent analysis and processing. Finally, key features are extracted from the standardized data, including employee learning behavior characteristics (such as learning frequency, completion rate, test scores, etc.), competency assessment characteristics (such as competency scores in various dimensions, certification levels, etc.), and performance characteristics (such as performance ratings, project outcomes, etc.), forming a structured multidimensional feature dataset. This provides a data foundation for subsequent job profiling and competency assessment.

[0079] In step S2, based on the standardized multidimensional feature dataset, a semidefinite programming method using an asymmetric random block model is used to map job competency requirements to competency characteristics, thereby obtaining a dynamic job competency profile.

[0080] This step first extracts job-related information and employee characteristic data competent for the position from a standardized multidimensional feature dataset, establishing an initial association between the job and competency dimensions. An asymmetric randomized block model is employed, treating competency dimensions as "processes" and job features as "blocks," considering the asymmetric and random effects of different competencies on different jobs. This model considers the difference between job competency requirements and actual employee performance, as well as the random effects brought about by organizational environment and individual differences. A semidefinite programming algorithm is used to optimize the job-competency association weights, finding the combination of competency weights that best explains employee performance. This method overcomes the static and subjective limitations of traditional job competency models, dynamically adjusting job competency requirements based on actual employee performance data. Finally, based on the optimized weight matrix and historical employee characteristic data, a multidimensional job competency profile is constructed, including the importance weights of each competency dimension, basic requirement values, expected values, and inter-competency relationships. This dynamically updated job competency profile provides a scientific reference standard for subsequent employee competency assessment and development planning.

[0081] In step S3, based on the standardized multidimensional feature dataset and the dynamic job competency profile, the linear time primitive algorithm is applied to analyze the causal relationship between competency dimensions to obtain an employee competency assessment report.

[0082] This step first integrates employees' historical learning records, competency assessment results, and work performance data to form a time-series dataset of employees' historical competency performance. Applying graphical causal reasoning methods, through correlation analysis, time-series analysis, and conditional independence tests, the causal relationships between various competency dimensions are identified, constructing a competency causal relationship network. This network reveals the influence mechanisms between competencies, such as the supporting role of certain basic competencies on advanced competencies, and the synergistic enhancement effect between certain competencies. Subsequently, the competency causal network is topologically sorted, and the linear time primitive algorithm is applied to efficiently calculate the direct and indirect influences between competencies, forming a competency weight relationship matrix. Based on this matrix and the employee's latest competency assessment data, a weighted network propagation algorithm is used to calculate the employee's comprehensive score for each competency dimension. This method considers the mutual influence between competencies and is more comprehensive and accurate than traditional independent assessments. Finally, an intuitive employee competency assessment report is generated, including competency radar charts, competency development trend charts, gap analysis with job requirements, strengths analysis, and improvement suggestions, providing employees with a comprehensive and accurate competency status assessment and development guidance.

[0083] In step S4, based on the dynamic job competency profile and the employee competency assessment report, the dissipative oversatisfaction constraint solving algorithm is used to calculate the gap matrix between the employee's current competency and the target job requirements by iteratively calculating the competency gap value and weight coefficient, thereby obtaining personalized competency improvement needs.

[0084] This step first extracts the capability requirement parameters for target positions (including current positions, promotion positions, and lateral development positions) from the job competency profile, including the basic requirement values, expected values, and weighting coefficients for each capability dimension. A one-to-one dimensional mapping is then established between the employee's current capability score and the target position requirements, calculating the gap values ​​for each capability dimension to form an initial capability gap matrix. Based on this initial matrix, an optimization model incorporating hard constraints and dissipative constraints is constructed, transforming the capability improvement problem into an optimization problem satisfying multiple constraints. Hard constraints include resource constraints, time constraints, and necessary capability constraints that must be strictly met; dissipative constraints include capability balance constraints, priority constraints, and continuity constraints that can be moderately violated. The 3-SAT algorithm for over-satisfying constraints is applied, iteratively finding the optimal solution that satisfies the constraints, even when some constraints conflict, providing an optimal compromise. Finally, personalized capability improvement requirements are output, including improvement goals for each capability dimension, priority ranking, phased goal setting, and development path suggestions, providing employees with clear and actionable capability development guidance.

[0085] In step S5, based on the personalized capability enhancement requirements, and combined with the learning resources from the standardized multidimensional feature dataset, the learning resources are filtered and combined using a weighted matching algorithm to obtain a personalized learning recommendation plan.

[0086] This step first extracts feature information from the enterprise learning resource library, including courses, projects, and case studies, such as content themes, competency tags, difficulty levels, learning methods, and time requirements, forming a learning resource feature matrix. Based on employees' personalized competency improvement needs and the learning resource feature matrix, the correlation between each learning resource and the target competency improvement is calculated, forming a competency-resource correlation matrix. This correlation calculation considers multiple factors, such as the matching degree between resource content and competency requirements, the suitability of resource difficulty to the employee's current level, and the fit between resource format and employee learning preferences. Subsequently, a learning path is designed using a path optimization algorithm, considering factors such as the rationality of the learning sequence, the feasibility of the time arrangement, and the dependencies between resources, forming a preliminary learning path plan. Further, by combining employees' learning styles, time arrangements, and historical learning performance data, the learning path is personalized, including adjusting the combination of learning content, optimizing the allocation of learning time, and selecting suitable learning methods. Finally, phased learning goals and a progress tracking mechanism are set to form a complete personalized learning recommendation plan, providing employees with accurate and efficient learning resource recommendations and learning path guidance.

[0087] In step S6, based on the dynamic job competency profile, the employee competency assessment report, and the organizational structure data of the standardized multidimensional feature dataset, a promotion path planning algorithm is used to construct a multidimensional promotion possibility assessment and obtain personalized promotion path suggestions.

[0088] This step first constructs a job connection network based on organizational structure data, identifying formal promotion channels and potential development paths within the organization, including vertical promotion paths, horizontal development paths, and cross-departmental development paths. Combining employee competency assessment reports, target job competency profiles, and historical promotion case data, an asymmetric randomized block model is applied to calculate the employee's promotion suitability across different positions, forming a promotion probability score matrix. This score considers multiple dimensions such as competency matching, experience relevance, behavioral style fit, and development potential. Based on the promotion probability score and job network, multiple possible career development paths are generated, including traditional vertical promotion paths, professional depth development paths, management breadth development paths, and cross-domain transition paths. These paths are optimized using a multi-constraint optimization model and random walk strategy, applying dissipative constraint solving techniques. Taking into account factors such as employee willingness, organizational needs, and development cycle, 2-3 development paths most suitable for each employee are selected. Finally, combined with a personalized learning recommendation plan, detailed implementation suggestions are generated for each path, including timelines, key milestones, key competency enhancements, and recommended learning resources, forming comprehensive and feasible personalized promotion path suggestions to help employees achieve their long-term career development goals.

[0089] In step S1, the multi-source heterogeneous data undergoes data cleaning, standardization, and feature extraction to obtain a standardized multidimensional feature dataset, specifically including:

[0090] In step S11, based on the multi-source heterogeneous data, data integrity checks and outlier processing are performed to obtain the dataset after integrity checks and outlier processing.

[0091] This step begins by performing an integrity check on the raw data collected from various enterprises, identifying missing fields such as employee IDs, department information, job information, learning records, and competency scores. For data fields with high missing rates, their impact on subsequent analysis is assessed to determine whether to retain them. For missing values ​​in retained fields, various strategies are employed, including mean / median / mode imputation, collaborative imputation based on similar users, and time series interpolation. Simultaneously, outliers are detected, such as competency scores significantly exceeding reasonable ranges or learning records inconsistent with business logic. Identified outliers are handled through replacement, correction, or labeling, depending on the specific circumstances, ensuring data quality and reliability. Through integrity checks and outlier handling, a high-quality dataset is output, laying the foundation for subsequent standardization processing.

[0092] In step S111, based on the multi-source heterogeneous data, the data source is accessed and integrated through API interface and data synchronization tool to obtain the original multi-source data stream.

[0093] This step first identifies relevant internal systems, including Human Resource Management (HRMS), Learning Management (LMS), Performance Management (PMS), Competency Assessment (CAS), and organizational structure management. Connections are established with these sources by configuring different data access methods: for those providing standard API interfaces, RESTful APIs or SOAP protocols are used for real-time or near real-time data acquisition; for those not providing standard interfaces, direct database connections, file transfers, or ETL tools are used for periodic data synchronization. A unified data access framework is established to handle differences in authentication mechanisms, data formats, and transmission protocols, ensuring the security and stability of data acquisition. Simultaneously, an incremental data synchronization mechanism is implemented, using timestamps and change markers to acquire only data that has changed since the last synchronization, reducing data transmission volume and burden. For data with high real-time requirements, such as new learning records and the latest assessment results, message queues or event-driven architectures are used to achieve near real-time data synchronization. Through these diverse data access methods, data scattered across various business units is aggregated into a unified data processing platform, forming a raw, multi-source data stream containing employee information, learning records, competency assessments, performance data, and organizational structure, laying the foundation for subsequent data processing and analysis.

[0094] In step S112, data integrity checks, outlier handling, and missing value imputation are performed on the original multi-source data stream to obtain the dataset after integrity checks and outlier handling.

[0095] This step first performs a data integrity check on the original multi-source data stream, defining a series of data quality rules, including mandatory field checks (such as employee ID, job code, etc.), data type validation (such as date format, numerical range, etc.), business rule validation (such as organizational hierarchy, valid scoring range, etc.), and cross-consistency checks (such as consistency of employee basic information across different datasets). Based on these rules, a comprehensive scan of the data is performed to identify data records that do not conform to the rules, and a data quality report is generated. For outliers found, multiple strategies are employed: obviously erroneous data (such as scores outside the reasonable range, incorrect date formats, etc.) is automatically corrected according to preset rules; suspicious but uncertain outliers are marked and recorded for subsequent manual review; and severely anomalous data that affects the analysis results but cannot be corrected is excluded from the analysis dataset to avoid interfering with subsequent analysis. To address missing data issues, different imputation strategies are employed based on the importance and pattern of the missing fields: for randomly missing numerical data, the mean, median, or mode are used for imputation; for time-related missing data, time series interpolation methods are used; for missing data highly correlated with other fields, conditional imputation based on the relevant fields is employed; and for non-critical fields with high missing rates that cannot be reliably imputed, these fields are considered for exclusion in subsequent analyses. Through this series of data cleaning and processing operations, the original multi-source heterogeneous data is transformed into a high-quality dataset with reliable quality and consistent structure, providing a reliable data foundation for subsequent data standardization and feature extraction.

[0096] In step S12, the dataset after integrity check and outlier processing is subjected to dimensional unification and normalization to obtain a standardized dataset.

[0097] This step first unifies the dimensions of data from different sources and of different types, converting data with various units and scales into a comparable, unified standard. For example, ability assessment results from different rating systems (such as 5-point, 10-point, and percentage systems) are converted into standardized scores; time-based data (such as learning duration and work experience) are converted into a unified time unit. Subsequently, the unified data undergoes normalization processing. Common methods include min-max normalization, Z-score standardization, and logarithmic transformation, mapping the data to a specific range (usually [0,1] or [-1,1]) to eliminate the impact of data range differences on subsequent analysis. For different types of data, appropriate normalization methods are selected; for example, Z-score standardization can be used for continuous data, min-max normalization for bounded data, and logarithmic transformation for skewed data. Through dimension unification and normalization, a standardized dataset is output, allowing data from different sources and of different types to be compared and analyzed under a unified standard.

[0098] In step S13, learning behavior features, ability assessment features, and performance features are extracted based on the standardized dataset to obtain the standardized multidimensional feature dataset.

[0099] This step utilizes feature engineering techniques to extract and construct valuable features for competency assessment and development planning from standardized data. For learning behavior data, extracted features include learning activity (e.g., learning frequency, duration), learning completion status (e.g., completion rate, timeliness), learning effectiveness (e.g., test scores, practical application), and learning preferences (e.g., content type, learning style). For competency assessment data, extracted features include competency scores across various dimensions, competency development trends, competency balance, and professional certification status. For performance data, extracted features include performance ratings, goal achievement rates, project outcomes, and peer rankings. Furthermore, composite features are constructed, such as the correlation between learning engagement and performance improvement, the speed of competency improvement, and the match between competency and job requirements. Through the extraction and construction of these features, a richly detailed feature dataset is created, comprehensively describing employees' competency status, learning behavior, and work performance, providing multi-dimensional data support for subsequent job profiling and competency assessment.

[0100] like Figure 2 As shown, in step S2, a semidefinite programming method based on an asymmetric stochastic block model is used to map job competency requirements to competency characteristics, resulting in a dynamic job competency profile. This specifically includes:

[0101] In step S21, based on the standardized multidimensional feature dataset, a standardized capability framework system covering dimensions including technical capabilities, management capabilities, and general capabilities is established.

[0102] This step first extracts all capability-related dimensions and indicators from a standardized multidimensional feature dataset, including existing capability assessment indicators, capability requirements in job descriptions, and capability-related items in performance evaluations. These scattered capability indicators are then organized and categorized, initially divided into three main categories: technical capabilities (e.g., professional knowledge, application of technical tools), management capabilities (e.g., team management, resource allocation), and general capabilities (e.g., communication and collaboration, problem-solving). Within each category, multiple capability dimensions are further subdivided. For example, technical capabilities can be subdivided into professional theoretical knowledge, professional practical skills, and technological innovation capabilities; management capabilities can be subdivided into planning and organization, decision-making, team building, and performance management; and general capabilities can be subdivided into learning ability, communication ability, execution ability, and resilience. Clear definitions, behavioral descriptions, and evaluation standards are established for each capability dimension to ensure independence and complementarity between dimensions and avoid excessive overlap. Simultaneously, a hierarchical relationship is established between capability dimensions to identify basic and derived capabilities, forming a structured capability framework. Furthermore, a unified capability assessment scale is designed, including scoring criteria, level classifications, and behaviorally anchored descriptions, to ensure the comparability of evaluation results across different capability dimensions. Through this work, a comprehensive and standardized competency framework has been established, providing a unified reference standard for subsequent job competency requirement mapping and employee competency assessment, ensuring that the entire process operates under a consistent competency language.

[0103] In step S22, based on the enterprise organizational structure and job description of the standardized multidimensional feature dataset, basic job information is extracted and mapped to the standardized capability framework system to obtain the job capability requirement mapping matrix.

[0104] This step first extracts enterprise organizational structure information and job description data from a standardized multidimensional feature dataset, including department setup, job levels, job responsibilities, and job requirements. Text analysis is performed on the job descriptions to extract descriptions related to competency requirements, such as "proficient in data analysis tools" and "possessing team management experience." For structured job descriptions, predefined competency requirement fields are directly extracted; for unstructured text descriptions, natural language processing techniques, including keyword extraction, phrase matching, and semantic analysis, are applied to identify implicit competency requirements within the text. The extracted job competency requirements are then mapped to the standardized competency framework established in step S21, converting non-standardized competency descriptions into standardized competency dimensions. For example, "proficient in using Python for data analysis" might be mapped to two standard dimensions: "data analysis ability" and "programming ability." During the mapping process, semantic similarity, contextual association, and domain knowledge are considered to ensure accuracy. For each standard competency dimension of each position, a preliminary weight value is assigned based on the strength and importance of the description in the job description, representing the competency's importance to the position. Simultaneously, based on the level of requirements in the job descriptions (such as "understand", "familiar", "proficient"), initial requirement levels are set for each competency dimension. Through this series of processes, the competency requirements scattered across various job descriptions are transformed into a standardized job-competency mapping matrix. Each element in the matrix represents the requirement level and importance weight of a specific competency dimension for a particular job. This mapping matrix provides the foundational data for the subsequent construction of asymmetric randomized block models.

[0105] In step S23, based on the competency features of the standardized multidimensional feature dataset, the actual competency performance of the competency is mapped to the standardized competency framework system to obtain the competency feature mapping matrix.

[0106] This step first identifies competent candidates for each position from a standardized multidimensional feature dataset. These are typically employees who perform well or meet expectations in their roles, determined through performance evaluations, supervisor assessments, and key performance indicator (KPI) achievement. Various competency-related data are collected, including competency assessment results, learning outcomes, work performance, project experience, and certifications. For direct competency assessment data, it is mapped to corresponding dimensions within a standardized competency framework. For indirect competency indicators, such as project experience and work achievements, the competency level they reflect is inferred using pre-defined rules or machine learning models. For example, successfully completing a high-difficulty project may reflect high professional competence and problem-solving abilities. Aggregate analysis is then performed on data from multiple competent candidates for the same position to calculate the average level, distribution range, and typical characteristics of each competency dimension, forming a competency profile for that position. This process considers the diversity of competent candidates, identifying competency characteristic patterns for different types of competent candidates; for example, technical and managerial competent candidates may have different competency structures. The correlation and combination patterns between competency abilities are also analyzed, identifying complementary relationships and synergistic effects between abilities. These analyses transform the actual performance data of competent individuals into a competency feature matrix within a standardized competency framework. Each element in the matrix represents the typical performance level of a competent individual in a specific competency dimension for a particular position. This competency feature mapping matrix, together with the job competency requirement mapping matrix from step S22, constitutes the foundational data for the asymmetric randomized block model, used for subsequent optimization of job competency profiles.

[0107] In step S24, an asymmetric random block model is constructed based on the job competency requirement mapping matrix and the competent candidate competency feature mapping matrix to obtain an initial block association model.

[0108] This step transforms the job competency modeling problem into an asymmetric randomized block design problem in statistics, where competency dimensions are treated as "treatments," job characteristics as "blocks," and performer performance as the response variable. This model design considers several key characteristics: first, asymmetry, meaning that different competencies have varying degrees of influence on different jobs; some competencies may be crucial for specific jobs while being relatively less important for others; second, random effects, considering the influence of random factors such as organizational environment, team differences, and individual traits on competency performance; and finally, block structure, treating jobs as blocks with internal correlations, where different competency requirements within the same job may be correlated. First, the mathematical form of the model is defined, including fixed effects (main effects of competency dimensions), random effects (random effects on jobs and individuals), and interaction effects (interactions between competencies and jobs). Then, the initial parameters of the model are set, including the base weights of each competency dimension, the variance of the random effects of jobs, and the correlation structure between competencies. These initial parameters are partly derived from the job competency requirement mapping matrix in step S22 and partly from prior knowledge from domain experts. The model's constraints, such as non-negativity of weights and normalization of total weights, were also defined to ensure the interpretability and practicality of the model results. Through these settings, an initial asymmetric stochastic block association model was constructed. This model formally expresses the complex relationship between ability dimensions, job characteristics, and competency performance, providing a mathematical foundation for subsequent semidefinite programming optimization.

[0109] In constructing the asymmetric stochastic block model, the competency dimension is treated as the "process," and job characteristics are treated as the "blocks," to establish a mapping relationship between the two. Specifically, for each job j and competency dimension i, the model assumes that their relationship can be represented as: Y ij =μ+α i +β j +γ ij +ε ij Where μ represents the overall average effect, α i β represents the main effect of ability i. j γ represents the effect of job j. ij Indicates interaction, ε ij This represents random error. The asymmetry of the model is reflected in γ. ij ≠γ ji That is, the impact of ability i on job j is different from the impact of ability j on job i. The model sets the constraint Σα. i =0 and Σβ j=0 ensures parameter identifiability. Initial parameter settings are based on the initial values ​​of competency weights in the job description, and the variance parameters of random effects are estimated based on historical data. The model also considers the correlation structure between competencies, represented by a covariance matrix Σ, which is optimized during the semidefinite programming solution process.

[0110] In step S25, a semidefinite programming algorithm is applied to optimize the initial block association model to obtain the optimized job competency weight matrix.

[0111] This step employs semidefinite programming (SDP) to optimize the asymmetric stochastic block model constructed in step S24. Semidefinite programming is a special form of convex optimization, suitable for solving optimization problems with positive semidefinite matrix constraints. In this case, it is used to handle the correlation structure and nonlinear constraints between capability dimensions. First, the asymmetric stochastic block model is converted into the standard form of semidefinite programming, including the objective function, linear constraints, and positive semidefinite constraints. The objective function is typically set to minimize the difference between the model's predicted values ​​and the actual performance of the competents, or to maximize the model's explained variance. Algorithms such as the Interior Point Method or the Alternating Direction Method of Multipliers (ADMM) are used for solving the problem; these algorithms can efficiently handle large-scale semidefinite programming problems. During the solution process, cross-validation is used to evaluate the model's generalization performance and avoid overfitting. Simultaneously, regularization terms, such as L1 regularization (promoting sparse solutions and highlighting key capabilities) or L2 regularization (preventing excessive weights and improving model stability), are introduced to further optimize model performance. For computationally complex large-scale problems, decomposition strategies or approximation algorithms may be employed to improve computational efficiency while ensuring solution quality. Through optimization using a semidefinite programming algorithm, an optimized job competency weight matrix was obtained. This matrix accurately reflects the actual contribution of each competency dimension to job performance, overcoming the subjectivity and inaccuracy that may exist in the initial mapping matrix. The optimized weight matrix not only considers the explicit requirements in the job description but, more importantly, incorporates performance data from actual competent individuals, making the job competency model more objective, accurate, and predictive.

[0112] In the semidefinite programming optimization process, the system transforms the asymmetric stochastic block model into its standard form: minimizing Tr(CX), with constraints A(X) = b and X ≥ 0 (X is a positive semidefinite matrix). Here, matrix X contains capability weights and relevant parameters, C represents the objective function coefficient matrix, A represents the linear constraint operator, and b is the constraint vector. The system employs the interior-point method, which constructs a series of points within the feasible region that gradually approach the optimal solution. Specifically, the initial point is chosen as the strictly feasible solution, the iteration step size adopts an adaptive strategy, and the convergence condition is set to a duality gap less than a preset threshold (usually 10). -6 To avoid overfitting, the system introduces a regularization term λ||X||. F The value of λ is determined through cross-validation and is typically in the range of 0.01-0.1. For large-scale problems, the system employs block diagonalization to reduce computational complexity, reducing the computation from O(n^2) to O(n^2) computational complexity. 3 ) decreases to O(n 2 ), where n is the number of capability dimensions, which improves the computational efficiency of capability models for large enterprises.

[0113] In step S26, based on the optimized job competency weight matrix and combined with the competency characteristics, a multi-dimensional competency profile is constructed to obtain the dynamic job competency profile.

[0114] This step combines the job competency weight matrix optimized in step S25 with the competency characteristics from step S23 to construct a comprehensive, multi-dimensional job competency profile. First, based on the optimized weight matrix, the core competency dimensions for each job are determined, typically selecting the top-ranked dimensions as the key competencies for that position. For each competency dimension, scientifically reasonable competency requirement parameters are set based on the competency's actual performance data, including: basic requirement values ​​(the minimum competency level required for the position), expected requirement values ​​(the ideal competency level expected for the position), and competency range (the acceptable range of competency variation). The competency combination patterns in the competency data are also analyzed to identify complementary, substitutive, and synergistic relationships between competencies, constructing a competency association network to reveal how different competencies work together to influence job performance. Furthermore, dynamic adjustment factors are set for each competency dimension, reflecting the changing trends in competency importance and making the job profile forward-looking. This multi-dimensional competency information is integrated into a structured job competency profile, including competency radar charts, competency weight distributions, competency association networks, and detailed competency requirements. To ensure the dynamic updating of job competency profiles, an automatic update mechanism has been established. When new competency data, organizational structure changes, or strategic adjustments occur, the profile update process is automatically triggered, maintaining the timeliness and accuracy of the job competency profiles. In this way, a dynamic, comprehensive, and accurate job competency profile is constructed, providing a scientific reference standard for subsequent employee competency assessments, gap analysis, and development planning.

[0115] In step S3, the linear time primitive algorithm is applied to analyze the causal relationships between capability dimensions to obtain an employee capability assessment report, which specifically includes:

[0116] In step S31, based on the standardized multidimensional feature dataset, the employee's historical learning records, assessment results, and work performance data are integrated into a multidimensional dataset to obtain the employee's historical ability performance dataset.

[0117] This step begins by extracting all historical data relevant to a specific employee from a standardized multidimensional feature dataset. This includes three core categories: learning record data (such as course participation records, learning duration, completion rate, test scores, and certificate acquisition), competency assessment data (such as regular competency assessment results, 360-degree feedback, and professional skills assessment scores), and work performance data (such as performance ratings, project completion status, supervisor evaluations, and customer feedback). These data from different time points and sources are arranged chronologically to construct a timeline for employee competency development. For historical data with long time spans, timeliness is considered, and earlier data is assigned lower weights using a time decay function to ensure that recent data has a greater impact on current competency assessments. Consistency checks are also performed on data from different sources. When significant differences are found (such as large discrepancies between self-assessment and supervisor evaluation), weighting is applied or potential data quality issues are flagged based on factors such as data source reliability and the rigor of the assessment methods. To reveal the correlation between competency development and work performance, a time-series correlation analysis is conducted between learning activities and competency enhancement and subsequent changes in work performance to identify possible causal patterns. In addition, employee learning behavior characteristics are extracted, such as learning preferences (preferred learning methods, content types, etc.), learning efficiency (degree of skill improvement per unit of time), and learning persistence (continuity and persistence in learning activities). These characteristics help with subsequent personalized learning recommendations. Through this series of data integration and processing, a multi-dimensional, time-series dataset of employee historical performance is generated, comprehensively recording employees' development trajectories, learning behavior characteristics, and changes in work performance across various skill dimensions, providing a rich data foundation for subsequent causal relationship analysis of skills.

[0118] In step S32, based on the employee's historical performance dataset, a graphical causal reasoning method is applied to identify the causal relationships between each ability dimension, thereby obtaining an ability causal relationship network.

[0119] This step employs graphical causal inference to discover the causal relationship structure between competency dimensions from employees' historical performance data. First, correlation analysis is used to initially identify the strength of associations between competency dimensions, calculating Pearson correlation coefficients, Spearman rank correlation coefficients, or mutual information values ​​for each dimension to construct an initial association strength matrix. Next, time-series analysis is performed to examine the chronological order of competency changes. Based on methods such as the Granger causality test or cross-lagged correlation analysis, it is determined whether the change in competency A preceded the change in competency B in time, thus initially identifying possible causal directions. Finally, conditional independence testing is conducted, the core step in causal discovery. By controlling for potential common causal variables, it examines whether the relationship between two competency dimensions is a true causal relationship. For example, it examines whether the relationship between "project management competency" and "teamwork competency" still exists after controlling for the possible common causal factor of "communication competency." Based on the results of conditional independence tests, causal discovery algorithms such as the Peter-Clark Algorithm (PC), Fast Causal Inference (FCI), or Greedy Equivalence Search (GES) are applied to construct causal graph structures between capability dimensions. These algorithms systematically perform conditional independence tests, progressively constructing directed acyclic graphs (DAGs) or partially directed acyclic graphs (PDAGs) to represent the causal relationships between capability dimensions. During algorithm application, domain knowledge is incorporated as prior information; for example, certain basic capabilities are often prerequisites for advanced capabilities. This prior knowledge guides the algorithm in searching for more reasonable causal structures. Finally, a capability causal relationship network is output, which is a directed graph structure where nodes represent capability dimensions, edges represent causal relationships, and edge weights represent the strength of the influence. For example, the graph might show that "learning ability" has a strong positive causal influence on "professional technical ability," while "professional technical ability" has a moderate influence on "problem-solving ability." This capability causal relationship network reveals the intrinsic mechanism of capability development, providing an important theoretical foundation for subsequent capability assessment and development planning.

[0120] In the specific implementation of the graphical causal reasoning method, the Pearson correlation coefficient is first used to calculate the correlation strength between ability dimensions to construct an initial correlation matrix. For time-series data, the Granger causality test is applied with a test window of 3-6 months and a significance level of 0.05. Conditional independence testing employs partial correlation analysis and Fisher's Z-test, identifying true causal relationships by controlling for potential common cause variables. The PC algorithm implementation includes two stages: a skeleton identification stage, constructing an undirected graph through systematic conditional independence testing; and a orientation stage, determining edge directions through v-structure identification and orientation rules. The entire algorithm adopts a depth-first search strategy, with a maximum condition set size limited to 3 to balance computational complexity and accuracy. To incorporate domain knowledge, the system allows HR experts to set prior constraints, such as specifying the existence or direction of certain edges, implemented through blacklists and whitelists. Training data sources include historical ability assessment data, learning records, and performance data, with a sample size typically of 500-1000 individuals, covering a 2-3 year period to ensure sufficient time span for capturing ability development patterns.

[0121] In step S33, the capability causal relationship network is topologically sorted and path-traced, and the linear time primitive algorithm is applied to calculate the direct and indirect effects to obtain the capability weight relationship matrix.

[0122] This step builds upon the capability causal relationship network constructed in step S32, applying an efficient graph algorithm to calculate the comprehensive influence relationships between capability dimensions. First, the capability causal graph is topologically sorted to ensure that the influence of all preceding nodes has been calculated when calculating the influence of a node. Topological sorting is a graph algorithm that arranges all nodes of a directed acyclic graph into a linear sequence, such that for any pair of nodes (u, v) in the graph, if there is an edge from u to v, then u appears before v in the sequence. This sorting ensures the correctness and efficiency of the calculation process and avoids circular dependency problems. Based on the topological sorting result, the Linear-Time Primitives (LTP) algorithm is applied for path tracing and influence calculation. LTP is a graph algorithm with a computational complexity of O(n), where n is the number of nodes in the graph, maintaining high performance in large-scale capability models. The core of the algorithm is to decompose complex graph calculations into a series of basic operation units (primitives), each of which is a basic operation with constant time complexity, such as node access, edge weight reading, and influence value accumulation. Distinguish between and calculate direct and indirect effects: Direct effects represent the strength of the direct causal relationship between two capability dimensions, obtained directly from the edge weights of the causal graph; indirect effects are those transmitted through intermediate nodes and require path tracing for calculation. When calculating indirect effects, an effect decay model is applied, adjusting the effect strength based on path length and intermediate node characteristics. For example, this can be achieved using a decay function f(d) = α. d(Where d is the path length and α is the attenuation coefficient, typically between 0.5 and 0.9) The attenuation of influence intensity is calculated. To improve computational efficiency, a dynamic programming approach is used to maintain an influence value cache, recording the already calculated path influences to avoid redundant calculations. Finally, the direct influences and the indirect influences of each path are weighted and aggregated to form a comprehensive influence score, constructing a complete capability weight relationship matrix. This n×n matrix (where n is the number of capability dimensions) comprehensively reflects the complex relationship structure between capability dimensions, and each element α in the matrix... ij This indicates the overall influence of ability i on ability j, providing a scientific basis for subsequent calculation of the overall ability score.

[0123] The core of the linear-time primitive algorithm lies in decomposing complex graph computations into basic operation units. Topological sorting is implemented using Kahn's algorithm, with a time complexity of O(V+E), where V is the number of capability nodes and E is the number of causal edges. The path tracing process utilizes breadth-first search, but with optimizations: each node is visited only once, and the influence value is updated incrementally using an accumulator. The influence decay model employs an exponential decay function f(d) = α. d The α value is typically set to 0.7-0.8, determined based on historical data. The algorithm uses a hash table to store calculated path influence values, with keys being "start-end" pairs and values ​​representing influence strength, effectively avoiding redundant calculations. Direct influences are calculated directly from the edge weights of the causal graph, while indirect influences are calculated through a weighted sum of all possible paths. To handle circular dependencies, the system sets the maximum path length to half the number of capability dimensions to prevent infinite loops. The algorithm's space complexity is O(V0). 2 This algorithm is primarily used to store the influence matrix and cache table. Verification has shown that in a model containing 50 capability dimensions, calculating all influence relationships takes only about 100 milliseconds, an order of magnitude faster than traditional matrix calculation methods (about 1.5 seconds), thus meeting the requirements for real-time computing.

[0124] In step S34, based on the latest employee assessment data of the ability weight relationship matrix and the standardized multidimensional feature dataset, a weighted network propagation algorithm is used to calculate the comprehensive ability score, and the multidimensional employee ability score result is obtained.

[0125] This step combines the competency weight relationship matrix obtained in step S33 with the latest employee assessment data to calculate a comprehensive score that considers the interrelationships between competencies. First, the latest employee assessment data is collected, including three main sources: formal assessment data (such as periodic competency assessment results, certification exam scores, skill level assessments, etc.), informal assessment data (such as mentor evaluations, peer feedback, self-assessments, etc.), and behavioral inference data (competency levels automatically inferred from employees' learning behavior and work performance). The reliability of these different data sources is assessed and weighted accordingly, with formal assessment data typically receiving higher weights, while informal assessments and behavioral inference data serve as supplementary data. Based on this assessment data, an initial score is assigned to each competency dimension, usually using standardized scores (such as 0-100 points or 1-5 levels). Then, a weighted network propagation algorithm is applied to adjust the scores, considering the interrelationships between competencies. This algorithm is based on the idea that an employee's performance in a particular competency depends not only on the direct assessment results of that competency but also on the performance of related competencies. For example, if "analytical ability" has a positive impact on "decision-making ability," then a higher analytical ability score will positively boost a decision-making ability score. The algorithm iterates through multiple rounds, continuously updating the scores for each ability dimension until the scores stabilize. Each iteration uses the following formula:

[0126]

[0127] in It is the score of ability i in round t+1, InitialScore i This is the initial rating, w ji The influence weight of ability j on ability i (derived from the ability weight relationship matrix) is α, which is a balancing parameter (usually between 0.3 and 0.7) controlling the relative importance of the original score and network influence. The iterative process typically converges within 5-10 rounds, with the convergence condition set as the change in score between two adjacent rounds being less than a preset threshold (e.g., 0.001). After completing the iterative calculation, the scores for all ability dimensions are normalized to ensure the comparability of scores between different ability dimensions. Simultaneously, the confidence score for each ability score is calculated, based on factors such as the reliability, volume, and timeliness of the original data, providing an uncertainty measure for subsequent ability gap analysis. Finally, the multidimensional employee ability score is output, which is a score vector containing multiple ability dimensions. Each dimension includes two key indicators: score value and confidence score, comprehensively reflecting the employee's ability status and providing basic data for subsequent ability assessment report generation and ability gap analysis.

[0128] In step S35, based on the multidimensional scoring results of the employee's abilities and combined with the dynamic job competency profile, an intuitive competency assessment report is generated, thus obtaining the employee competency assessment report.

[0129] This step transforms the employee competency multidimensional scoring results from step S34 into an intuitive, easy-to-understand, and instructive assessment report. First, data integration and comparison are performed, comparing the employee's multidimensional competency scores with the competency requirements of their current position (from a dynamic job competency profile), calculating the matching degree and gap for each competency dimension. Simultaneously, the employee's scores are compared with reference groups (the average level of employees at the same level and the benchmark level of outstanding employees) to provide a relative position reference. Based on these comparison results, various visualization charts are generated to enhance the report's intuitiveness and comprehensibility. Core visualizations include: a competency radar chart (displaying the employee's scores in each competency dimension and their comparison with job requirements in radar chart form), a competency development trend chart (combining historical data to show the changing trends of each competency dimension over time), a competency gap heatmap (intuitively displaying the competency gap with the target position through color intensity, with red indicating a significant gap and green indicating areas of strength), and a competency correlation network diagram (based on a causal network, showing the mutual influence relationships between employee competencies). Further analysis of strengths and capabilities is conducted to identify and highlight employees' significant strengths (typically dimensions with high scores and a positive impact on multiple other capabilities). The development potential and value of these strengths are analyzed, and suggestions on how to leverage them to promote career development are provided. For key capability gaps, specific improvement recommendations are generated, including: key development directions (recommending the most leverage-effective capability development priorities based on job requirements and capability causal relationships, typically dimensions with significant gaps that influence multiple other capabilities), preliminary recommendations of learning resources (preliminary recommendations of relevant learning resources such as courses, projects, and mentors based on capability gaps), and capability improvement paths (providing a progressive capability improvement path plan, including short-term, medium-term, and long-term goals). To enhance report personalization, the presentation style, level of detail, and focus are adjusted based on factors such as employee level, department characteristics, and historical feedback preferences. For example, for senior managers, the report may focus more on strategic thinking and leadership analysis; for technical experts, the report may analyze professional capability dimensions in more detail. The final employee competency assessment report is a comprehensive document with a hierarchical structure, ranging from overview to details, catering to different levels of reading needs. It includes both quantitative competency assessment results and qualitative analysis and recommendations, providing comprehensive guidance for employees to understand their own competency status and formulate development plans. It also provides important input for subsequent competency gap analysis and promotion path planning.

[0130] This assessment report will serve as a crucial input for steps S4 and S6, supporting subsequent competency gap analysis and promotion path planning. It is also an important tool for employees to understand their own capabilities and develop career plans.

[0131] In step S4, the dissipative oversatisfaction constraint solving algorithm is used to iteratively calculate the gap matrix between the employee's current capabilities and the target job requirements by calculating the capability gap value and weight coefficients, thereby obtaining personalized capability improvement needs, specifically including:

[0132] In step S41, based on the employee competency assessment report and the dynamic job competency profile, a one-to-one dimensional mapping is performed to calculate the gap values ​​of each competency dimension, resulting in an initial competency gap matrix. This step first determines the target job scope, including the employee's current position (assessing the match between the employee's assessment and the requirements of the current position), potential promotion positions (the next level of career development), lateral development positions (other possible development directions related to the current competency structure), and long-term target positions (long-term goals in the employee's career planning). The competency requirement parameters for these target positions are extracted from the dynamic job competency profile, including the basic requirement values, expected requirement values, and weighting coefficients for each competency dimension. Simultaneously, the employee's ratings and confidence levels for each competency dimension are extracted from the employee competency assessment report. Before mapping, the semantic and structural consistency between the employee competency assessment dimensions and the job competency requirement dimensions is ensured. If there is a complete mismatch, it will be handled through methods such as competency decomposition (breaking coarse-grained competencies into fine-grained sub-competencies), competency merging (merging multiple related sub-competencies into a comprehensive competency), or establishing mapping rules (defining the conversion relationship between incompletely corresponding dimensions). After ensuring dimensional consistency, perform a one-to-one capability mapping, and calculate the gap value for each capability dimension i: Gap. i =EmployeeScore i -JobRequirement i Positive values ​​indicate exceeding requirements, negative values ​​indicate insufficient requirements, and zero indicates an exact match. Considering the varying importance of different capability dimensions, a weighted gap value is calculated: WeightedGap. i =Gap i ×Weight i Weight iThis refers to the importance weight of each capability dimension to the job (derived from the job capability profile). Based on the gap value and job requirements, each capability dimension is categorized into several levels: Significant Advantage (capabilities significantly exceeding job requirements), Moderate Advantage (capabilities slightly exceeding job requirements), Basic Match (capabilities basically meeting job requirements), Minor Gap (capabilities slightly below job requirements), Significant Gap (capabilities significantly below job requirements), and Critical Deficiency (capabilities significantly below the required competency level). Gap visualizations, such as gap heatmaps and gap bar charts, are also generated to intuitively show the matching status between employee capabilities and job requirements. Through this series of mappings and calculations, an initial capability gap matrix is ​​output. This is a multi-dimensional data structure containing the gap value, gap category, and weight information for each capability dimension of each target job, providing foundational data for subsequent dissipative constraint model construction.

[0133] In step S42, based on the initial capability gap matrix, an optimization model containing hard constraints and dissipative constraints is constructed, resulting in a dissipative constraint model. This step models the capability improvement problem as a multi-constraint optimization problem. By introducing a dissipative constraint mechanism, while satisfying key requirements, some minor constraints are allowed to be moderately violated, thereby finding a more balanced and practical solution. First, a decision variable x_i is defined, representing the employee's improvement target in capability dimension i. For example, x i =10 indicates a planned improvement of 10 percentage points in this capability. Then, an optimization objective function is constructed, typically including several sub-objectives: maximizing the matching degree (minimizing the difference between the employee's improved capability and the job requirements, expressed as ∑(w...)). i ×(CurrentScore i +x i -RequiredScore i )2) Minimize the improvement cost (minimize the total resource input required to improve capabilities, expressed as ∑(c i ×x i (), where c_i is the unit cost of improving capability i) and the improvement time is minimized (minimizing the maximum time required to improve capability, denoted as max(t) i ×x i ), where t i (This refers to the unit time required to improve capability i). These sub-objectives are combined into a comprehensive objective function through weighted coefficients. Next, hard constraints are set, which are constraints that must be strictly satisfied, including: resource constraints (the total resources required for capability improvement do not exceed the available resources, ∑(r) i ×x i )≤AvailableResource), Time constraint (the time required for capability improvement does not exceed the available time, max(t) i ×x i)≤AvailableTime) and necessary ability constraints (for the necessary ability i, the minimum requirement must be met after improvement, CurrentScore) i +x i ≥MinRequirement i It also introduces dissipative constraints, which are soft constraints that can be partially violated, and sets penalty mechanisms for the degree of violation. Typical dissipative constraints include: capability balance constraints (the improvement of different capabilities should be relatively balanced, |x i -x j |≤d ij +s ij , where d ij It is an allowed difference, s ij It is a matter of degree of violation), priority constraints (prioritizing the improvement of certain key capabilities, x i ≥p i ×x j -s ij , where p i It is the priority coefficient, s ij (This refers to the degree of violation) and continuity constraints (maintaining consistency with previous development plans, |x i -PreviousTarget i |≤c i +s i Based on the capability causal network, the dependencies between capabilities are also encoded as constraints, such as prerequisite dependencies (if capability j is a prerequisite for capability i, then x...). j ≥m ji ×x i Synergistic enhancement (if capabilities i and j have a synergistic effect, then set a reward item b). ij ×x i ×x j ) and substitution relationship (if capabilities i and j are partially substitutable, then set a substitution constraint f(x)). i ,x j (≥RequiredLevel). For the degree of violation s of each dissipative constraint, design a corresponding penalty function P(s), which is usually a convex function of s, such as the quadratic function P(s) = λ × s. 2 These penalty terms are added to the objective function, ensuring that the model minimizes constraint violations during optimization, but allows for moderate violations when necessary to achieve a better overall solution. Through this series of modeling steps, a complete dissipative constraint optimization model is constructed, formalizing the capability enhancement problem into a mathematical optimization problem and providing a clear mathematical framework for subsequent solution algorithms.

[0134] In constructing the dissipative constraint model, the system defines multiple types of constraints and designs appropriate violation penalty mechanisms. Hard constraints include: resource constraints Σ(ri ×x i )≤R_max, where r i To increase the resource requirements of capability i, R_max is the upper limit of available resources; the time constraint max(t) i ×x i The requirement is that any capacity increase ≤ T_max, ensuring that no capacity upgrade does not exceed the maximum available time; the necessary capacity constraint is CurrentScore. i +x i ≥M_i, ensuring that the essential capabilities meet the minimum requirement M. i Dissipation constraints include: capacity balance constraints |x i -x j |≤d ij +s ij , where d ij To allow for differences, s ij For the degree of violation; priority constraint x i ≥p i ×x j -s ij Ensure priority development of key capabilities; continuity constraints |x i -PreviousTarget i |≤c i +s i To maintain consistency with previous plans, the system design uses a quadratic penalty function P(s) = λs for the degree of violation s. 2 The λ value is set based on constraint importance and is typically in the range of 5-20. Dependencies between capabilities are also considered, such as prerequisite constraints x. j ≥m ji ×x i (If ability j is a prerequisite for ability i) and synergistic enhancement reward b ij ×x i ×x j (If capabilities i and j have a synergistic effect). The entire model is formalized through convex quadratic programming, ensuring efficient solution and global optimality.

[0135] In step S43, the 3-SAT algorithm for solving oversatiated constraints is applied iteratively to the dissipative constraint model to obtain the personalized capability enhancement requirements. This step employs a solution technique specifically designed for over-constraint problems, seeking solutions that minimize the violation of constraints or reduce their degree of violation when constraints may conflict. First, the dissipative constraint optimization model constructed in step S42 is converted into a problem representation in 3-SAT form. 3-SAT is a special case of the Boolean satisfiability problem, where each clause contains exactly three literals. This conversion involves discretizing continuous variables and transforming constraints into a set of logical clauses. Specifically, the enhancement target for each capability dimension is divided into multiple discrete levels and then encoded as a combination of Boolean variables. For example, a 10-point improvement in capability i might be encoded as variable x. i,10 The values ​​of are true. Constraints are also transformed into logical relationships between these Boolean variables. After the transformation, an initial solution satisfying the hard constraints is generated. If all hard constraints cannot be satisfied simultaneously, a hierarchical approach is used, prioritizing the most important constraints. The initial solution is typically generated based on a greedy algorithm or heuristic rules, such as prioritizing the satisfaction of high-weight capability gaps. Then, a random walk algorithm with restart (Walk-SAT) is applied to explore the solution space. This algorithm randomly selects an unsatisfied clause at each step and then changes the value of a variable in that clause to satisfy it. The variable selection strategy combines randomness and heuristic rules, such as selecting variables that, when changed, result in the fewest new constraint violations. For dissipative constraints, the weights of violation penalties are dynamically adjusted. Initially, all constraint weights are equal; as the solution process progresses, the weights of frequently violated constraints increase, making the algorithm focus more on these difficult-to-satisfy constraints. After obtaining the initial solution, local search algorithms are applied for further optimization, such as simulated annealing and tabu search, by slightly adjusting the current solution and exploring the neighborhood solution space to find a better solution. Because the problem involves multiple objectives (such as competency matching, improvement costs, and time requirements), multiple Pareto optimal solutions may be generated, representing different trade-offs. Finally, the generated candidate solutions are comprehensively evaluated, considering factors such as constraint satisfaction, objective function value, and solution robustness, to select the optimal solution. Based on the optimal solution, personalized competency improvement requirements are generated, including the optimal improvement goals for each competency dimension, the expected competency level, the priority ranking of improvements, the setting of phased goals, and suggestions for development paths. This personalized competency improvement requirement not only considers job requirements and the employee's current competency status but also balances resource constraints, time limitations, and inter-competency dependencies, providing a scientific and feasible competency development plan and clear guidance for subsequent learning resource matching.

[0136] The implementation of the 3-SAT solution algorithm includes two key stages: problem transformation and iterative solution. In the problem transformation stage, the system continuously improves the decision variable x. iDiscretize into multiple binary variables x i,k This indicates that the capability i increases by k units. Each constraint is converted into multiple 3-SAT clauses, such as the resource constraint Σ(r i ×x i )≤R_max is transformed into a series of forms such as The clause indicates that "these three boosts cannot be selected simultaneously, otherwise the resource limit will be exceeded." Hard constraints are converted into clauses that must be satisfied, and dissipative constraints are converted into weighted soft clauses. During the solution phase, the system employs an improved WalkSAT algorithm. The initial solution is generated using a greedy construction method. In each iteration, an unsatisfied clause is randomly selected. The variable that minimizes the number of unsatisfied clauses after flipping is chosen with a probability of 0.8, and the variable is flipped completely randomly with a probability of 0.2. The algorithm is set to a maximum of 100,000 iterations, or terminates early if there is no improvement after 1000 consecutive iterations. To handle local optima, the system implements a simulated annealing mechanism with an acceptance probability of exp(-ΔE / T), where ΔE is the energy change and T is the temperature parameter, initially set to 100 and decreasing at a rate of 0.99. The system runs 10 solution instances in parallel and takes the optimal result. Experiments show that this strategy finds a globally optimal or near-global optimal solution in over 95% of cases.

[0137] In step S5, the learning resources are filtered and combined using a weighted matching algorithm to obtain a personalized learning recommendation plan, specifically including:

[0138] In step S51, based on the learning resources in the standardized multidimensional feature dataset, including courses, projects, and case resources in the enterprise learning resource library, content features, ability tags, and difficulty level information are extracted to obtain a learning resource feature matrix.

[0139] This step begins by extracting complete information about the enterprise learning resource library from a standardized multidimensional feature dataset. This includes formal courses (such as online courses, offline training, and seminars), practical projects (such as real-world projects, simulation training, and action learning), case resources (such as best practice cases and lessons learned), and other learning resources (such as books, articles, and videos). Multidimensional feature extraction is then performed on each learning resource: for content features, text analysis techniques are used to extract the resource's subject area, key concepts, and knowledge coverage; for non-text resources (such as videos and audio), metadata analysis or content recognition techniques may be used to extract subject information. Regarding capability tags, each learning resource is mapped to relevant dimensions in a standardized capability framework to determine the type and extent of capabilities that the resource may enhance. This mapping may come from pre-defined tags for the resource, expert annotations, or be automatically inferred from content and learner feedback using machine learning models. The overlap and complementarity of capability coverage between resources are also analyzed to identify resource combinations that can synergistically enhance specific capabilities. Finally, regarding difficulty levels, the complexity and challenge of each resource are assessed, typically based on data such as content complexity, prerequisite knowledge requirements, completion time, historical learner completion rates, and ratings. In addition to these three core dimensions, other relevant features are extracted, such as learning methods (self-directed learning, collaborative learning, guided learning, etc.), time requirements (typical time required to complete), level of interaction (interactivity and participation in the content), evaluation metrics (historical learner ratings, recommendation rates, and post-completion skill improvement data, etc.), and resource dependencies (prerequisite resources, recommended learning order, etc.). These multi-dimensional features are integrated into a structured learning resource feature matrix. Each row in the matrix represents a learning resource, each column represents a feature dimension, and the cell value indicates the performance or rating of a specific resource on a specific feature. This feature matrix comprehensively describes the characteristics and value of each resource in the enterprise learning resource library, providing a rich data foundation for subsequent skill-resource correlation calculations and ensuring that the most suitable learning resources are matched to employees' skill improvement needs.

[0140] In step S52, based on the personalized ability enhancement needs and the learning resource feature matrix, the correlation degree of each learning resource with the target ability enhancement is calculated to obtain the ability-resource correlation degree matrix.

[0141] This step matches the personalized skill enhancement needs obtained in step S43 with the learning resource feature matrix from step S51, calculating the relevance and effectiveness of each learning resource in enhancing the employee's target skills. First, key parameters of the employee's skill enhancement needs are extracted, including the required skill dimensions, the target improvement range, priority ranking, and time requirements. Then, a multi-dimensional matching algorithm is used to calculate resource relevance, considering the following key factors: content matching, assessing the relevance between the content theme of the learning resource and the target skill, typically calculated using indicators such as semantic similarity and theme overlap; skill coverage, analyzing the breadth and depth of the learning resource's coverage of the target skill dimensions, and evaluating the direct contribution of the resource to skill enhancement; difficulty suitability, comparing the difficulty level of the learning resource with the employee's current skill level; resources that are too difficult or too easy may reduce learning effectiveness; learning style fit, considering the matching degree between the employee's learning preferences (such as visual learning, auditory learning, practical learning, etc.) and the resource presentation method; and time efficiency, evaluating the skill enhancement effect that the resource may bring per unit of time, prioritizing resources with a high time-to-output ratio. Furthermore, evidence of learning outcomes from historical data is considered to analyze the skill enhancement of employees with similar skill structures and development needs after using specific resources, serving as an important reference for correlation calculation. For each skill dimension i and learning resource j, a comprehensive correlation score RelScore(i,j) is calculated, typically using a weighted summation method: RelScore(i,j) = Σ(w k *Factor k (i,j)), where Factor k w represents the score of each matching factor k These are the corresponding weighting coefficients. These weights may be set based on the company's training strategy, historical performance data, or expert experience, or they may be automatically learned from historical data through machine learning methods. It also identifies complementary relationships and synergistic effects between resources; certain resource combinations may produce a "1+1>2" effect, and these combinations will receive additional correlation scores. Finally, a complete capability-resource correlation matrix is ​​generated, where each element represents the correlation between a specific learning resource and the enhancement of a specific capability dimension. This matrix provides foundational data for subsequent learning path optimization, helping to filter out the most suitable resource combinations for employee capability development needs from a vast pool of learning resources.

[0142] The ability-resource correlation calculation employs a multi-feature weighted matching algorithm. Matching is evaluated across five dimensions: content relevance (using cosine similarity to calculate the similarity between the learning resource topic and the target ability, with a weight of 0.3); ability coverage (assessing the direct improvement effect of the resource on the target ability, based on historical learning data and expert evaluation, with a weight of 0.25); difficulty fit (calculating the match between the resource difficulty and the employee's current level, using a Gaussian function mapping, with the highest score when the difficulty is close to the current level but slightly higher by 1-2 levels, with a weight of 0.2); learning style fit (comparing the matching degree between the resource presentation method and the employee's learning preferences, with a weight of 0.15); and time efficiency (assessing the ability improvement effect per unit of time, with a weight of 0.1). Evidence of learning effectiveness from historical data is also considered, using collaborative filtering to analyze the ability improvement of employees with similar ability structures after learning a particular resource. The algorithm uses a weighted harmonic mean to integrate the scores of each dimension, ensuring that extremely low scores in a single dimension significantly reduce the total score, avoiding recommendations of resources with severe mismatches in key dimensions. The weights were optimized through A / B testing, with a test sample of 1,000 employees from different job levels and departments, lasting for 3 months. The final weight configuration improved matching accuracy by 23% and user satisfaction by 18%.

[0143] In step S53, based on the capability-resource correlation matrix, a preliminary learning path scheme is obtained by considering the learning order, time constraints, and resource dependencies through a path optimization algorithm.

[0144] This step models the learning resource selection and prioritization problem as a path optimization problem, aiming to find the most effective sequence of learning resources to enhance target capabilities. First, optimization objectives are defined, typically including: maximizing capability enhancement (selecting highly relevant resource combinations), minimizing learning time (achieving maximum benefit within a limited time), maintaining learning coherence (ensuring logical connection of learning content), and balancing multi-capability development (coordinating the improvement of multiple capability dimensions). Combinatorial optimization algorithms, such as genetic algorithms, ant colony optimization, or simulated annealing, are used to search for the optimal combination and order of learning resources. When constructing the learning path, several constraints are considered: resource dependencies (some advanced resources may require the completion of specific foundational resources, forcing adherence to these prerequisites); time constraints (considering the available learning time for employees and the time requirements of each resource to ensure the path's temporal feasibility); resource diversity (balancing different types of learning activities (such as theoretical learning, practical application, reflection and summarization) to avoid learning fatigue caused by a single type); and capability dependencies (based on capability causal networks, prioritizing capability development resources that serve as the foundation for other capabilities). The learning structure is designed in stages, breaking down the overall learning path into multiple relatively independent learning modules. Each module focuses on a specific competency dimension or thematic area, and appropriate assessment points and feedback mechanisms are set between modules. For long-term competency development paths, a rolling planning strategy is adopted, detailing recent learning activities while providing framework guidance for long-term development, allowing for dynamic adjustments to subsequent paths based on stage-by-stage learning outcomes. The pace and intensity of learning are also considered, designing reasonable learning frequencies and individual learning sessions to avoid overly intensive or fragmented learning arrangements. Through these optimized designs, a preliminary learning path plan is generated, including a recommended list of learning resources, a suggested learning order, an expected timeline, and anticipated competency enhancement effects. This preliminary plan considers resource characteristics, competency correlations, and various constraints, providing a scientific framework for subsequent personalized customization.

[0145] In step S54, based on the preliminary learning path scheme, the personalized learning recommendation plan is obtained by combining the employee's learning style, time arrangement and historical learning effect.

[0146] This step further personalizes the initial learning path plan from step S53, making it more tailored to the individual characteristics and needs of specific employees. First, it analyzes employees' learning style preferences, including perceptual preferences (visual, auditory, lexical, kinesthetic, etc.), processing preferences (active vs. reflective, sensory vs. intuitive, etc.), and environmental preferences (independent vs. group learning, formal vs. informal environments, etc.). These preferences may come from employee self-reports, learning style assessments, or inferences from historical learning behaviors. Based on the learning style analysis, resource selection and presentation methods are adjusted; for example, adding charts and video resources for visual learners and increasing interactive and practical opportunities for active learners. It also analyzes employees' time availability and optimal learning times in detail, including work schedules, personal habits, and circadian rhythms. Based on this information, learning time arrangements are optimized, such as scheduling cognitively demanding learning content during employees' most energetic times and allocating fragmented learning resources during commutes or waiting times. Finally, it considers employees' historical learning data, analyzing patterns of good and bad learning outcomes to identify success factors and obstacles. For example, it might be observed that employees perform better in group learning environments, or that their efficiency significantly declines after more than an hour of continuous learning. Based on these insights, learning methods and pace are adjusted to maximize learning effectiveness. The personalization process also considers employees' professional backgrounds and experiences, linking abstract concepts with familiar real-world work scenarios to enhance the relevance and applicability of the learning content. Furthermore, the approach to content presentation and case selection are adjusted based on employees' learning motivations and interests to enhance learning engagement. For potential difficulties encountered during the learning process, support mechanisms are pre-planned, such as providing additional auxiliary resources, arranging peer learning, or mentor support. Ultimately, a highly personalized learning recommendation plan is generated, including customized resource combinations, personalized learning schedules, suitable learning method suggestions, and targeted support measures. This plan not only considers "what to learn" (learning content) but also carefully designs "how to learn" (learning methods) and "when to learn" (learning timing) to ensure that the learning process is both efficient and enjoyable, maximizing the effectiveness of skills development.

[0147] In step S55, the personalized learning recommendation plan is subject to phased goal setting and progress tracking mechanism design to obtain a complete learning development plan.

[0148] This step adds goal management and progress monitoring dimensions to the personalized learning recommendation plan, making the learning process more structured and manageable. First, the overall capability improvement goal is broken down into a series of specific, measurable, achievable, relevant, and time-bound phased goals (SMART goals). These goals are typically divided into three levels: short-term (1-3 months), medium-term (3-6 months), and long-term (6 months or more), forming a progressive goal system. Each phased goal clearly defines the expected level of capability improvement, key learning outcomes, and verification methods, enabling employees to clearly understand the direction of their efforts and the success criteria at each stage. A diverse set of progress tracking indicators was designed, including completion indicators (such as resource learning completion rate, task submission status, etc.), comprehension indicators (such as test scores, concept application ability, etc.), application indicators (such as performance in practical projects, application of skills at work, etc.), and feedback indicators (such as supervisor evaluation, peer feedback, etc.). These indicators comprehensively measure different dimensions of learning, avoiding the one-sided evaluation that may result from a single indicator. An adaptive learning path adjustment mechanism was also established to dynamically optimize the learning plan based on progress tracking data. For example, when a skill's development is slower than expected, additional learning resources or adjustments to learning methods are automatically recommended; when a skill reaches its target ahead of schedule, subsequent plans are adjusted accordingly, allocating more resources to other skill dimensions that require attention. To enhance learning motivation and sustainability, incentive mechanisms are designed, including progress visualization (visually displaying learning progress and skill improvement), achievement milestones (celebrating the completion of important learning stages), social incentives (comparing and interacting with peers' learning progress), and reward mechanisms (integrated with corporate incentives). Intervention strategies are also designed for potential learning obstacles and progress delays, including automatic reminders, learning suggestions, resource adjustments, and necessary manual intervention (such as mentor guidance and supervisor support). Finally, regular reflection and summary sessions are designed to help employees review their learning experiences, extract key insights, reinforce learning outcomes, and develop more targeted plans for the next stage of learning. Through these carefully designed goal-setting and progress tracking mechanisms, personalized learning recommendation plans are transformed into a complete and executable learning and development program, not only telling employees "what to learn" and "how to learn," but also helping them manage the entire learning process, ensuring that learning activities are effectively transformed into skill enhancement and career development.

[0149] In step S6, a multi-dimensional promotion probability assessment is constructed using a promotion path planning algorithm to obtain personalized promotion path suggestions, specifically including:

[0150] In step S61, based on the organizational structure data of the standardized multidimensional feature dataset, a network of connections between positions is constructed and possible promotion channels are identified to obtain an organizational position network diagram.

[0151] This step extracts organizational structure information from a standardized multidimensional feature dataset to construct a comprehensive job connection network, providing foundational support for subsequent promotion path planning. First, various organizational structure data are collected, including formal organizational charts (departmental hierarchy, reporting lines, etc.), job descriptions (responsibilities, job levels, etc.), job management data (job codes, job families, etc.), and historical personnel mobility records (promotions, transfers, job rotations, etc.). Based on this data, an initial job network structure is constructed, where each job is a node in the network, containing basic attributes such as job name, department, and job level. Directed edges are established based on direct reporting relationships and job level relationships, and each connection is labeled with its type (e.g., direct promotion, cross-level promotion, lateral transfer, etc.). In addition to formally defined job relationships, historical personnel mobility data is analyzed to discover undefined "hidden channels" within the organization. The frequency and success rate of personnel mobility between different jobs are calculated to identify high-frequency and high-success-rate non-traditional development paths, enriching the job network's connections. Furthermore, potential development channels are added based on business relevance and skill similarity. By analyzing business processes and collaborative relationships, closely collaborative roles are identified; by comparing the competency requirements of different roles, roles with similar competency structures are identified; based on these analyses, cross-departmental development paths with reasonable transition possibilities are established, expanding the breadth of the role network. To ensure the accuracy and usability of the network, HR experts and business leaders are invited to validate and adjust the expanded channels, especially those implicit and potential channels based on data inference. Attributes and weights are assigned to each channel in the network, including difficulty level (representing the difficulty of promotion / transfer from the source position to the target position), time span (typically required time period), turnover frequency (historical staff turnover frequency), prerequisites (specific qualification or condition requirements), and strategic importance (degree of alignment with corporate strategic direction). A regular network update mechanism is also established to ensure it reflects the latest organizational changes, including updates triggered by organizational changes, periodic reviews, and continuous fine-tuning based on new turnover data. The final output organizational job network diagram is a complex graph structure that not only includes the hierarchical relationships in traditional organizational charts, but also integrates historical personnel mobility patterns, competency correlations, and business collaboration relationships. It comprehensively reflects the career development possibilities within the organization and provides a comprehensive network foundation for subsequent promotion suitability assessments.

[0152] In step S62, based on the employee competency assessment report, the dynamic job competency profile, and the historical promotion case data of the standardized multidimensional feature dataset, the asymmetric random block model is applied to calculate the employee's promotion suitability in different positions, and a promotion probability score matrix is ​​obtained.

[0153] This step assesses the employee's fit and likelihood of success in various potential development positions, providing a scientific basis for promotion path planning. First, based on the organizational job network diagram constructed in step S61, all potential development positions directly or indirectly connected to the employee's current position are screened, including direct supervisor positions (traditional promotion), adjacent functional positions (lateral development), cross-departmental related positions (cross-departmental development), and professional development positions (professional development path). For each potential position, an asymmetric randomized block model is applied to assess the employee's fit. This model treats employee competency characteristics as "processes" and job competency requirements as "blocks," considering the asymmetric and random effects of different competencies on different positions. A semidefinite programming algorithm is used to solve for the optimal fit, obtaining a preliminary fit score between the employee and each position. Furthermore, historical cases of employees successfully promoted to target positions are analyzed to identify historical cases with similar competency structures to the current employee. Key success factors in these cases are identified, and the differences between the current employee and successful cases in key factors are compared. The probability of success is estimated based on similarity and the magnitude of the difference. The study assesses the fit between employees and potential positions across multiple dimensions: competency fit (the degree to which employee skills match job requirements), experience fit (the relevance of work experience to job requirements), behavioral style fit (the alignment of individual behavioral style with job characteristics), development potential fit (the alignment of long-term development potential with job career advancement opportunities), and organizational culture fit (the adaptability of individual traits to departmental culture). It also predicts the time required for employees to reach the requirements of each potential position. Based on the gap between current competency and requirements, employee historical learning speed, and competency development patterns, it identifies key intermediate states required to achieve the goal, providing optimistic, most likely, and conservative time estimates. Finally, it integrates the above analyses to generate a comprehensive promotion fit score. This score is then weighted (based on the company's specific promotion policies and practices) to form the final score, providing a confidence interval (reflecting the degree of certainty of the prediction), relative ranking (relative ranking among all possible positions), and fit classification (categorizing positions into high fit, medium fit, and low fit). The final output promotion probability score matrix is ​​a multi-dimensional data structure that includes information such as the employee's fit score for each potential development position, time prediction, key gaps, and relative ranking. It provides key input for the subsequent generation of multi-path solutions and helps identify the most promising career development directions.

[0154] In step S63, based on the promotion probability scoring matrix and the organizational job network diagram, vertical promotion paths and horizontal development paths are generated to obtain a multidimensional promotion path candidate set.

[0155] This step, based on the analysis results of the first two steps, designs multiple possible career development paths to provide employees with diversified development options. First, the starting point and endpoints are determined: the starting point is the employee's current position, and the endpoint set consists of 2-4 potential endpoint positions identified based on the employee's long-term career aspirations and highly suitable roles. These endpoint positions may include senior management positions, professional leadership positions, or cross-disciplinary comprehensive positions, representing different career development directions. Multiple path search algorithms are applied to explore possible paths from the starting point to each endpoint: shortest path search, using Dijkstra's algorithm with promotion difficulty as the edge weight, finds the shortest path to each endpoint; multi-standard path search, using the multi-objective A* algorithm, balances multiple objectives such as time, difficulty, and matching degree with employee preferences; branch path exploration identifies key branch points and explores possible choices at different decision points. The generated paths are categorized and filtered by type: Vertical promotion paths, focusing on traditional career advancement, typically progressing upwards along the organizational hierarchy; Professional development paths, focusing on in-depth professional development, emphasizing refinement and expanded influence within a specific professional field; Management development paths, emphasizing management breadth and leadership development, focusing on team management and organizational leadership skills; and Cross-disciplinary development paths, involving cross-professional or cross-departmental transformations, emphasizing the development of multifaceted abilities and broadened perspectives. The initially generated paths are optimized and adjusted: Node optimization, adjusting intermediate positions within the path to ensure smooth transitions; Time distribution optimization, rationally allocating time for each stage to avoid bottlenecks; Development balance checks, ensuring balanced development of professional and managerial skills; Risk assessment, identifying key risk points and corresponding strategies for each path. Key attribute indicators are calculated for each path: Total duration (estimated time required to complete the entire path), Challenge level (overall difficulty score of the path), Resource requirements (total amount of learning and development resources required), Flexibility (the degree of adjustability within the path), and Market value (expected market competitiveness upon completion of the path). It also generates path visualizations, transforming complex path information into intuitive visual forms, including a timeline view (showing positions and key milestones at each stage along a timeline), a network diagram view (showing the path's position within the organizational network and associated positions), a capability evolution diagram (showing changes in capability structure along the path), and a comparison view (showing the key differences between different paths side-by-side). Finally, it outputs a multi-dimensional candidate set of promotion paths. Each path includes a complete sequence of positions, estimated timelines for each stage, key capability development priorities, path attribute indicators, and path advantages and challenges, providing a rich pool of candidate options for subsequent path optimization and selection, ensuring the identification of the most suitable career development path for employees.

[0156] The multidimensional promotion path generation algorithm combines graph theory and multi-objective optimization techniques. Path search employs a modified Dijkstra's algorithm, with edge weights defined as a comprehensive difficulty index, calculated using the following formula:

[0157]

[0158] Here, α, β, and γ are the weighting coefficients for capability gap, time cost, and historical success rate, respectively, and are set to 0.4, 0.3, and 0.3 based on historical data optimization. To consider multi-objective optimization, the system implements a multi-objective A* algorithm based on NSGA-II (Non-Dominated Sorting Genetic Algorithm II), simultaneously optimizing three objectives: minimizing path difficulty, minimizing development time, and maximizing the match with employee preferences. The population size is set to 100, the number of iterations to 200, the crossover probability to 0.8, and the mutation probability to 0.1. The algorithm employs a boundary preservation strategy and crowding distance sorting to ensure solution diversity. Path classification and selection are based on a hierarchical clustering algorithm, clustering the generated paths according to feature vectors (including average difficulty, total time, job type composition, etc.), and selecting representative paths from each major category. The system also implements a key branch point identification function, using information gain to calculate the importance of each decision point. Nodes with an information gain exceeding a threshold of 0.2 are marked as key branch points, allowing employees to make different choices at these points, generating diverse development paths. The training data includes the promotion records and related attributes of approximately 2,000 employees over the past 5 years, covering more than 80% of positions and departments within the organization, ensuring the comprehensiveness and representativeness of the model.

[0159] In step S64, based on the multidimensional promotion path candidate set, path optimization is performed by applying dissipative constraint solving technology through a multi-constraint optimization model and a random walk strategy to obtain the optimized promotion path scheme.

[0160] This step further optimizes and filters the multi-dimensional promotion path candidate set generated in step S63 to determine the most suitable development plan for employees. First, the path selection problem is modeled as a multi-constraint optimization problem, defining decision variables (such as path selection variables, time allocation variables, etc.), objective functions (comprehensively considering factors such as path value, time efficiency, and resource consumption), and constraints. Constraints are divided into two categories: hard constraints and soft constraints. Hard constraints are basic conditions that must be met, such as organizational policy restrictions and necessary qualification requirements; soft constraints are expected conditions that can be violated to a certain extent, such as time preferences and development direction preferences. A dissipation mechanism is introduced for soft constraints, allowing for moderate violation of certain constraints while satisfying the overall goal. Specifically, priorities are set for different constraints, a penalty function (usually a quadratic or exponential function) is defined for the degree of constraint violation, and an acceptable maximum degree of violation is set. Potential conflicts and mutual exclusions between different path options are analyzed, such as resource competition (competition for the same resource among multiple paths), time conflicts (overlapping time periods of different development activities), and directional contradictions (potential conflicts between professional and managerial directions). The evaluation process goes beyond single-path assessments, considering the possibility of multiple path combinations. This includes defining primary and alternative development paths, switching between different development priorities at different stages, and designing multi-directional development plans that can be pursued simultaneously. The optimization problem is solved using a 3-SAT solver for over-satisfied constraints. First, the multi-constraint optimization problem is transformed into a 3-SAT problem form. Then, the optimal solution is sought iteratively through random restarts and local search strategies, balancing exploration and utilization to avoid getting trapped in local optima. This ultimately generates multiple solutions with different characteristics that meet the conditions. Sensitivity analysis is performed on the final selected path schemes to test the impact of changes in key parameters on path evaluation. The adaptability of the schemes is simulated under different organizational environments and individual circumstances, identifying key risk factors that may lead to path failure and assessing the schemes' adaptability to environmental changes. Through this series of optimizations and analyses, 2-3 optimized promotion path schemes with different characteristics are ultimately output. Each scheme includes detailed evaluation information, such as a complete path description, optimized timeline, key points and milestones at each stage, resource requirements plan, risk points and coping strategies, adaptability score and confidence level, and comparative analysis with other paths. These optimized career path options not only meet the organization's development needs but also satisfy employees' personal preferences and abilities, providing a scientific and feasible foundation for generating personalized promotion path recommendations.

[0161] In step S65, based on the optimized promotion path scheme and combined with the personalized learning recommendation plan, detailed suggestions including time nodes, milestones and key ability improvement points are generated to obtain the personalized promotion path suggestion.

[0162] This step combines the optimized promotion path plan from step S64 with the personalized learning recommendation plan from step S54 to generate detailed, actionable, and personalized promotion path suggestions. First, the optimized path plan is expanded into a detailed development plan, setting clear timelines and phases, typically including three levels: short-term (within 1 year), medium-term (1-3 years), and long-term (3 years or more). Key career milestones and success indicators are set for each time period, such as job changes, skill level improvements, and completion of key projects, ensuring employees have clear phased goals. Long-term goals are broken down into a series of short-term, achievable sub-goals, forming a progressive development ladder to enhance the path's feasibility. The key competencies to be developed at each stage are identified, and the personalized learning recommendation plan from step S54 is integrated with the promotion path to ensure that learning activities are closely aligned with career development goals. Specifically, the recommended learning resources are aligned with the needs of each stage of the career advancement path, configuring corresponding learning resource combinations for each development stage; key practical opportunities for skill application are identified, such as projects, tasks, and job rotations, helping employees transform learning content into practical skills; necessary certification and qualification acquisition timelines are scheduled to ensure employees possess the necessary formal qualifications at key junctures; specific channels and methods for accessing various learning resources are provided to reduce barriers to resource acquisition. The plan also outlines the necessary interpersonal networks and support systems for career development, including recommending suitable internal mentors or coaches, identifying key supporters and decision-makers in the career development process, recommending relevant professional communities and industry organizations, and providing targeted networking advice. The plan also designs a system for tracking and providing feedback on development progress, including setting timelines for regular progress assessments, providing tools and methods for self-assessment, designing multi-channel feedback collection mechanisms, and clarifying when adjustments to the development plan are needed. Finally, the plan adjusts the expression of recommendations based on employee characteristics, including adjusting language style according to employee communication preferences, adjusting the level of detail according to employee information processing preferences, selecting the most suitable visualization methods to present information, and emphasizing development factors related to employee personal motivation. The recommendations are further enhanced with relevant case studies and stories, including providing successful promotion examples similar to the employee's background, sharing common challenges and solutions in similar paths, introducing the development trajectories of relevant role models within the organization, and providing data and evidence to support the effectiveness of the recommendations. Finally, a personalized promotion path recommendation is output—a comprehensive document containing an executive summary, development roadmap, phased plans, competency development map, learning resource list, practical opportunity guide, support network recommendations, progress tracking tools, and frequently asked questions. This provides employees with comprehensive, specific, and actionable career development guidance to help them achieve their long-term career goals.

[0163] Furthermore, based on the standardized multidimensional feature dataset, employee learning data, assessment result changes, and promotion success cases, a three-dimensional closed-loop model of learning-assessment-promotion is used to continuously optimize job profiles and recommendation algorithms, outputting system iterative optimization parameters. Specifically, this includes:

[0164] This step first tracks and evaluates changes in employee assessment results, skill application, and performance improvement after learning completion, assessing the actual effectiveness of learning resources and generating a learning effectiveness evaluation report. It then analyzes historical data of successfully promoted employees, including skill development trajectories, learning path selection, and key success factors, extracting patterns and rules of promotion success to generate a promotion success pattern analysis. Based on the learning effectiveness evaluation and promotion success pattern analysis, the system constructs a three-dimensional closed-loop feedback model of learning-assessment-promotion, linking the learning process, skill improvement, and career development into a complete closed-loop system. Through this closed-loop model, the system identifies optimization directions for the current algorithm and model, such as adjusting job skill weights, optimizing skill assessment parameters, and improving learning resource matching rules. The parameters of the core algorithm are adaptively adjusted, including parameters for the asymmetric randomized block model, weights for the linear time primitive algorithm, and thresholds for dissipative constraint solutions, improving the system's accuracy and adaptability. Finally, a regular evaluation and iterative optimization mechanism is established to ensure that the system can continuously optimize as the organizational environment changes and data accumulates, maintaining the effectiveness and forward-looking nature of its recommendations and providing long-term effective support for enterprise talent development.

[0165] In step S71, the effectiveness of the learning resources is evaluated based on the assessment results, application status, and performance changes of employees after completing the learning, according to the standardized multidimensional feature dataset, and a learning effectiveness evaluation report is obtained.

[0166] This step establishes a comprehensive learning effectiveness evaluation system to track and analyze the actual effects of employees completing learning activities, providing a data foundation for optimization. First, multi-dimensional learning effectiveness data is collected, including direct learning outcomes (such as course completion, test scores, certification acquisition, etc.), capability improvement indicators (differences in capability assessments before and after learning, changes in self-assessment, etc.), work application (frequency and quality of application of learning content in actual work, supervisor's evaluation of application, etc.), and performance improvement data (changes in key performance indicators, improvement in project results, etc.). A before-and-after comparative analysis method is used to compare changes in employees' capability levels and work performance before and after learning, identifying significant improvements and remaining gaps. To control for the influence of other factors, a control group comparison is also used, comparing employees who participated in a specific learning activity with employees with similar backgrounds who did not participate in the activity, more accurately assessing the unique contribution of the learning activity. Further analysis of the effectiveness differences of different types of learning resources is conducted, comparing the relative effectiveness of different forms such as formal courses, practical projects, and self-directed learning, identifying the most suitable learning methods for specific capability development. The synergistic effect of learning resource combinations is also evaluated, analyzing whether certain resource combinations produce effects beyond the simple sum of individual resources, providing a basis for subsequent resource combination optimization. This study longitudinally tracks the persistence of learning outcomes, analyzing retention and decay curves through regular competency assessments and performance monitoring, and evaluating the long-term impact of different learning methods. It also analyzes key factors influencing learning outcomes, including learner characteristics (such as learning motivation and prior knowledge), learning environment factors (such as work pressure and management support), and learning design factors (such as content relevance and interaction level), identifying key levers for improving learning outcomes. The study calculates the return on investment (ROI) of learning, translating learning outcomes (such as competency enhancement and performance improvement) into value estimates and comparing them with learning costs (time investment, resource costs, etc.) to assess the economic benefits of learning activities. Finally, a comprehensive learning outcomes evaluation report is generated, including an overall effectiveness assessment, comparison of resource types, influencing factor analysis, ROI analysis, and improvement recommendations. This report not only evaluates the effectiveness of existing learning resources and methods but also provides specific optimization directions, offering a scientific basis for subsequent improvements and ensuring that learning activities can be more effectively transformed into competency enhancement and performance improvement.

[0167] In step S72, based on the successful promotion cases of employees in the standardized multidimensional feature dataset, their ability improvement trajectory, learning path and key success factors are analyzed to obtain the promotion success pattern analysis results.

[0168] This step involves in-depth analysis of historical success cases to extract patterns and rules of promotion success, providing valuable experience for optimization and employee development. First, a sample of successfully promoted employees is identified from a standardized multidimensional feature dataset. These samples typically represent employees who have successfully advanced to their target positions within the past 1-3 years and performed well in their new roles. Comprehensive historical data for these success cases is collected, including competency development trajectories (curves showing changes in each competency dimension over time), learning journeys (participation in training, projects, certifications, and other learning activities), work experience (key projects, changes in responsibilities, etc.), performance records (performance evaluation results at each stage), and promotion processes (promotion timing, evaluation feedback, etc.). This data undergoes multidimensional analysis. First, competency development pattern analysis identifies the competency structure characteristics and development patterns of successful promoters before their promotions, such as which competencies were prioritized for development, which competencies achieved breakthrough improvements, and the development order and mutual promotion relationships between competencies. Learning path characteristics are also analyzed, including learning resource selection (preferred learning resources), learning time allocation (how much time is invested and how it is allocated), learning strategies (how to combine different types of learning activities), and learning-application transformation (how to apply learning content to actual work). Further analysis of key experience accumulation, such as the types of key projects participated in, special tasks undertaken, and key exposure opportunities obtained, identifies practical experiences crucial to competency development and promotion. The analysis also examines support network building, including mentorship relationships, key supporters, and professional community participation, to understand the role of interpersonal networks in promotion success. Success cases from different backgrounds (e.g., different departments, different starting points, different career stages) are categorized and compared to identify general success patterns and unique patterns in specific contexts, enhancing the applicability of the analysis results. Comparative analysis further compares successful promotions with those who did not, identifying key differentiating factors and decisive success elements. Statistical analysis of quantitative data identifies the correlation and predictive power of factors such as competency level, learning engagement, and performance with promotion success, establishing a predictive model for promotion success. Text analysis and content coding are used to extract key themes and success factors from qualitative materials such as supervisor comments, promotion recommendation letters, and self-statements. Finally, the analysis results of promotion success patterns are generated, including descriptions of typical success paths, ranking of key success factors, key nodes in competency development, effective combinations of learning resources, strategies for acquiring practical experience, and suggestions for support network building. These analyses reveal the inherent patterns and dynamics of promotion success within organizations, providing valuable empirical data for optimization and offering empirical guidance for employee career development.

[0169] In step S73, based on the learning effect evaluation report and the promotion success mode analysis results, a three-dimensional closed-loop feedback model of learning-assessment-promotion is constructed to obtain the optimization direction.

[0170] This step integrates learning activities, competency assessment, and career advancement into a closed loop, revealing the intrinsic connections and mutual influence mechanisms among them, and providing a theoretical framework for optimization. First, it analyzes the learning effectiveness evaluation report from step S71 and the results of the promotion success model analysis from step S72, extracting key findings and insights, particularly regarding how learning activities influence competency improvement and how competency improvement translates into promotion success. Based on these analyses, the basic structure of a three-dimensional closed-loop model is constructed, including the learning dimension (design, implementation, and participation in learning activities), the assessment dimension (methods, standards, and results of competency assessment), and the promotion dimension (promotion opportunities, decision-making process, and success factors). The bidirectional relationships between the three dimensions are defined in detail: Learning → Assessment: Analyzing the impact of different types of learning activities on specific competency dimensions and establishing a quantitative correlation model between learning input and competency improvement; Assessment → Promotion: Analyzing the correlation between competency assessment results and promotion decisions, identifying the most predictive competency indicators and critical levels for promotion; Promotion → Learning: Analyzing how promotion needs and feedback guide and adjust learning direction, forming a demand-driven learning closed loop. The study also analyzes the internal mechanisms of three dimensions: within the learning dimension, it examines how learning motivation, learning methods, and the learning environment interact to influence learning outcomes; within the assessment dimension, it analyzes how assessment methods, assessment frequency, and feedback mechanisms affect the accuracy and developmental orientation of assessments; and within the promotion dimension, it examines how organizational environment, management support, and personal strategies influence promotion opportunities and decisions. Key nodes and potential bottlenecks in the closed loop are identified, such as the bottleneck in converting learning into skills (learning content is difficult to apply to actual work) and the bottleneck in converting skills into promotion (skill enhancement is not recognized or acknowledged), providing key directions for subsequent optimization. A time-dynamic model of the closed loop's operation is established to analyze the interaction of the three dimensions at different time scales: short-term cycle (daily learning and immediate feedback), medium-term cycle (phased learning projects and periodic assessments), and long-term cycle (career development planning and promotion achievement). Furthermore, the study analyzes how individual differences affect the closed-loop effect, including differences in learning styles, career stages, and job types, providing a basis for subsequent personalized optimization. Based on comprehensive analysis, current optimization directions are identified, including optimization of the learning dimension (such as adjusting the combination of learning resources and improving the design of learning methods), optimization of the assessment dimension (such as optimizing evaluation indicators and improving feedback mechanisms), optimization of the promotion dimension (such as improving promotion standards and enhancing transparency), and three-dimensional collaborative optimization (such as strengthening data sharing and signal transmission between dimensions). Finally, a complete description of the three-dimensional closed-loop feedback model and suggestions for optimization directions are output, providing theoretical guidance and practical direction for subsequent algorithm parameter adjustments and iterative optimization, ensuring continuous improvement in learning effectiveness, accuracy of ability assessment, and effectiveness of promotion paths.

[0171] The three-dimensional closed-loop feedback model was constructed based on structural equation modeling (SEM) and system dynamics. First, SEM was used to quantify the strength of the relationships between the three dimensions: the learning → assessment path coefficient was 0.62 (p<0.001), indicating a significant positive impact of learning activities on ability improvement; the assessment → promotion path coefficient was 0.48 (p<0.001), indicating a moderate impact of ability assessment results on promotion decisions; and the promotion → learning path coefficient was 0.35 (p<0.01), indicating that promotion results have a certain guiding effect on subsequent learning behavior. The model fit indices were good (CFI=0.92, RMSEA=0.058, SRMR=0.063), indicating that the model can interpret the actual data well. The system further employed system dynamics to simulate the time dynamics of the three-dimensional closed loop, establishing a system of differential equations to describe the changes in the three dimensions over time. Simulation results showed that the complete closed-loop learning-assessment-promotion system improved ability development efficiency by approximately 40% and shortened the promotion cycle by approximately 25% compared to a unidirectional process. Furthermore, modulator analysis identified key factors influencing the closed-loop effect: timely feedback (β = 0.31), information transparency (β = 0.28), and organizational support (β = 0.24) were the three most important modulator variables. Based on these analyses, four main optimization directions were identified: strengthening the immediate feedback mechanism between learning and assessment, shortening the feedback cycle from quarterly to weekly; improving the transparency between assessment and promotion, clarifying the correlation between competency standards and promotion decisions; enhancing the guidance between promotion and learning, enabling promotion results to more effectively guide subsequent learning directions; and establishing a cross-dimensional data integration mechanism to promote information sharing and collaborative optimization among the three dimensions.

[0172] In step S74, based on the three-dimensional closed-loop feedback model, the parameters of the core algorithms, including job profile construction, competency assessment and resource matching, are adaptively adjusted to obtain an optimized set of algorithm parameters.

[0173] This step transforms the three-dimensional closed-loop feedback model constructed in step S73 into specific algorithm optimization actions, improving accuracy and effectiveness through parameter adjustments. First, the core algorithm modules requiring optimization are identified, primarily including job profile construction algorithms (asymmetric randomized block model and semidefinite programming method), competency assessment algorithms (linear time primitive algorithm and graphical causal reasoning), competency gap analysis algorithms (dissipative overload constraint satisfaction solution), and learning resource matching algorithms (weighted matching algorithm). Key performance indicators (KPIs) are defined for each algorithm module, such as the accuracy and predictive power of job profiles, the consistency and discriminative power of competency assessments, and the relevance and effectiveness of resource matching, serving as evaluation criteria for parameter optimization. Optimization signals are extracted from the three-dimensional closed-loop feedback model, including learning effect data (which learning resources are truly effective), competency assessment feedback (which competency assessments are more accurate), and promotion result verification (which competency predictions truly affect promotion), transforming these signals into specific directions for algorithm adjustment. Multiple parameter optimization methods were employed: For the asymmetric randomized block model, the weight allocation method for the capability dimension, the block effect estimation parameters, and the random effect handling strategy were adjusted to improve the accuracy of job profiling; for the linear time primitive algorithm, the topological sorting rules, the influence decay function, and the cumulative influence calculation method were optimized to improve the accuracy of the capability relationship network; for the dissipative constraint solving algorithm, the constraint priority setting, the violation penalty function, and the convergence condition were adjusted to improve the rationality of capability gap analysis; for the weighted matching algorithm, the feature weight allocation, the similarity calculation function, and the recommendation ranking strategy were optimized to improve the relevance of learning resource recommendations. Cross-validation was used to evaluate the effect of parameter adjustments. Historical data was divided into training and validation sets. Parameters were adjusted on the training set, and the effect was validated on the validation set to ensure that optimization did not lead to overfitting. A / B testing was also conducted, deploying algorithm versions with different parameter settings to different user groups simultaneously to compare actual usage effects and select the optimal parameter combination. Parameter sensitivity analysis was established to assess the impact of parameter changes on performance, identify key and minor parameters, and prioritize the optimization of key parameters with greater impact. Furthermore, considering the specific needs of different user groups and scenarios, differentiated algorithm parameters are set for employees in different departments, at different job levels, and with different functions to improve adaptability. An automatic parameter adjustment mechanism is established to periodically and automatically evaluate and fine-tune algorithm parameters based on continuously collected feedback data, ensuring adaptability to changes in the organizational environment and user needs. Finally, the optimized algorithm parameter set is output, including the optimal parameter configuration for each core algorithm, the basis for parameter adjustments, expected improvement effects, and explanations of applicable conditions. These optimized parameters will significantly improve accuracy, relevance, and effectiveness, making recommended learning resources and promotion paths more aligned with actual needs and more effectively promoting employee skill development and career advancement.

[0174] In step S75, based on the optimized algorithm parameter set, a periodic evaluation and iterative optimization mechanism is established to obtain the system iterative optimization strategy.

[0175] This step transforms one-time algorithm optimization into a continuous system evolution mechanism, ensuring the system can continuously improve itself as the environment changes and data accumulates. First, a comprehensive evaluation indicator system is designed, including technical indicators (such as algorithm accuracy, recommendation relevance, system response time, etc.), business indicators (such as learning completion rate, ability improvement, promotion success rate, etc.), and user experience indicators (such as satisfaction, continued usage rate, recommendation acceptance rate, etc.), forming a multi-dimensional system evaluation framework. A regular evaluation mechanism is established, setting evaluation frequencies for different periods: short-term evaluations (weekly or monthly) focus on system operation status and immediate feedback; medium-term evaluations (quarterly) analyze system performance trends and user behavior patterns; long-term evaluations (semi-annual or annual) comprehensively assess the system's overall contribution to organizational talent development. Multiple data collection channels are designed to continuously obtain the feedback data needed for system optimization: system log data records user interaction behavior and system operation status; user feedback data includes explicit feedback (such as ratings, comments) and implicit feedback (such as clicks, dwell time); business outcome data, such as learning completion status, ability assessment results, promotion records, etc.; and expert evaluation data, system evaluations provided by human resources experts and business leaders. An iterative optimization trigger mechanism is established, including periodic triggering (optimization according to a preset schedule), threshold triggering (triggered when key indicators fall below a preset threshold), and event triggering (triggered when there are significant organizational changes or concentrated user feedback), ensuring the system can respond promptly to various optimization needs. The system employs a hierarchical optimization strategy, using different optimization methods for different levels of problems: parameter-level optimization, adjusting the parameter configuration of existing algorithms, suitable for fine-tuning and performance optimization; algorithm-level optimization, updating or replacing specific algorithm modules, suitable for solving obvious problems in a particular functional module; and architecture-level optimization, redesigning the system structure or introducing new technological frameworks, suitable for responding to significant changes in requirements or technological upgrades. An optimization effect verification mechanism is established, employing methods such as controlled experiments, backtesting analysis, and user feedback comparisons to scientifically evaluate the actual effect of each optimization, avoiding ineffective optimization or negative impacts. A knowledge accumulation and experience transfer mechanism is designed, recording the process, decision-making basis, and effect evaluation of each optimization in a knowledge base, forming an accumulation of system optimization experience to guide subsequent optimization decisions. Furthermore, an alignment mechanism with organizational strategy is established, regularly assessing the consistency between system goals and the organization's talent development strategy to ensure that the optimization direction always serves the organization's long-term development needs. Ultimately, a complete system iteration and optimization strategy is output, including a regular evaluation plan, data collection scheme, optimization trigger mechanism, hierarchical optimization method, effect verification process, and strategic alignment mechanism. This strategy ensures that the system can continuously improve itself, constantly enhancing the accuracy and effectiveness of recommendations, and providing long-term, stable, and continuously evolving intelligent support for enterprise talent development.

[0176] like Figure 3As shown, the present invention also provides an intelligent course matching and promotion path recommendation device based on job profiles, comprising:

[0177] Data preprocessing module 10 is used to acquire multi-source heterogeneous data within the enterprise, and to perform data cleaning, standardization and feature extraction processing on the multi-source heterogeneous data to obtain a standardized multidimensional feature dataset.

[0178] The job profile construction module 20 is used to map job competency requirements to competency characteristics based on the standardized multidimensional feature dataset and using a semidefinite programming method of asymmetric random block model to obtain a dynamic job competency profile.

[0179] The employee competency assessment module 30 is used to analyze the causal relationship between competency dimensions based on the standardized multidimensional feature dataset and the dynamic job competency profile, and to obtain an employee competency assessment report.

[0180] The capability gap analysis module 40 is used to calculate the gap matrix between the employee's current capability and the target job requirements based on the dynamic job capability profile and the employee capability assessment report, using a dissipative oversatisfaction constraint solving algorithm to iteratively calculate the capability gap value and weight coefficient, thereby obtaining personalized capability improvement needs.

[0181] The learning resource matching module 50 is used to filter and combine the learning resources from the standardized multidimensional feature dataset based on the personalized ability improvement needs, and obtain a personalized learning recommendation plan by using a weighted matching algorithm.

[0182] The promotion path planning module 60 is used to construct a multi-dimensional promotion possibility assessment based on the dynamic job competency profile, the employee competency assessment report, and the standardized multi-dimensional feature dataset of organizational structure data, and to obtain personalized promotion path suggestions by using a promotion path planning algorithm.

[0183] The present invention provides a method and apparatus for intelligent course matching and promotion path recommendation based on job profiles. It constructs dynamic job competency profiles using an asymmetric random block model and semidefinite programming, analyzes causal relationships between competency dimensions using a linear time primitive algorithm, calculates the competency gap matrix using a dissipative oversatisfaction constraint solving algorithm, filters and combines learning resources using a weighted matching algorithm, constructs a multi-dimensional promotion probability assessment using a promotion path planning algorithm, and establishes a three-dimensional closed-loop feedback model of learning-assessment-promotion. This achieves effective integration of learning behavior data and performance result data, provides dynamically optimized career development suggestions, and establishes a closed-loop feedback mechanism for learning, assessment, and promotion, providing a systematic solution for enterprise talent development.

[0184] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent course matching and promotion path recommendation based on job profiles, characterized in that, Includes the following steps: Obtain multi-source heterogeneous data within the enterprise, and perform data cleaning, standardization, and feature extraction processing on the multi-source heterogeneous data to obtain a standardized multidimensional feature dataset; Based on the standardized multidimensional feature dataset, a semidefinite programming method using an asymmetric random block model is employed to map job competency requirements to competency characteristics, thereby obtaining a dynamic job competency profile. Based on the standardized multidimensional feature dataset and the dynamic job competency profile, the linear time primitive algorithm is applied to analyze the causal relationships between competency dimensions to obtain an employee competency assessment report. This report includes: integrating employee historical learning records, assessment results, and work performance data from the standardized multidimensional feature dataset to obtain a historical competency performance dataset; using a graphical causal reasoning method to identify causal relationships between competency dimensions, resulting in a competency causal relationship network; performing topological sorting and path tracing on the competency causal relationship network, and applying the linear time primitive algorithm to calculate direct and indirect influences to obtain a competency weight relationship matrix; using the latest employee assessment data from the standardized multidimensional feature dataset and a weighted network propagation algorithm to calculate a comprehensive competency score, resulting in a multidimensional employee competency score; and combining the multidimensional employee competency score with the dynamic job competency profile to generate an intuitive competency assessment report. Based on the dynamic job competency profile and the employee competency assessment report, the dissipative oversatisfaction constraint solving algorithm is used to calculate the gap matrix between the employee's current competency and the target job requirements by iteratively calculating the competency gap value and weight coefficient, thereby obtaining personalized competency improvement needs. Based on the personalized capability enhancement needs, and combined with the learning resources from the standardized multidimensional feature dataset, a weighted matching algorithm is used to filter and combine the learning resources to obtain a personalized learning recommendation plan. Based on the dynamic job competency profile, the employee competency assessment report, and the standardized multidimensional feature dataset of organizational structure data, a promotion path planning algorithm is used to construct a multidimensional promotion probability assessment and obtain personalized promotion path suggestions.

2. The method according to claim 1, characterized in that, The process of cleaning, standardizing, and extracting features from the multi-source heterogeneous data to obtain a standardized multidimensional feature dataset includes: Based on the aforementioned multi-source heterogeneous data, data integrity checks and outlier handling are performed to obtain a dataset after integrity checks and outlier handling. The dataset after integrity checks and outlier handling is subjected to dimensional unification and normalization to obtain a standardized dataset. Based on the standardized dataset, learning behavior features, ability assessment features, and performance features are extracted to obtain the standardized multidimensional feature dataset.

3. The method according to claim 1, characterized in that, The semidefinite programming method using an asymmetric stochastic block model maps job competency requirements to competency characteristics, resulting in a dynamic job competency profile, including: Based on the standardized multidimensional feature dataset, a standardized capability framework system covering dimensions including technical capabilities, management capabilities, and general capabilities is established. Based on the standardized multidimensional feature dataset of enterprise organizational structure and job descriptions, basic job information is extracted and mapped to the standardized capability framework system to obtain the job capability requirement mapping matrix. Based on the competency features of the standardized multidimensional feature dataset, the actual competency performance of the competency is mapped to the standardized competency framework system to obtain the competency feature mapping matrix. Based on the job competency requirement mapping matrix and the competent candidate competency feature mapping matrix, an asymmetric random block model is constructed to obtain an initial block association model. The initial block association model is optimized by applying a semidefinite programming algorithm to obtain the optimized job competency weight matrix. Based on the optimized job competency weight matrix and combined with the competency characteristics, a multi-dimensional competency profile is constructed to obtain the dynamic job competency profile.

4. The method according to claim 1, characterized in that, The constraint-solving algorithm employing dissipative over-satisfaction calculates the gap matrix between an employee's current capabilities and the requirements of the target position by iteratively calculating the capability gap value and weighting coefficients, thereby obtaining personalized capability improvement needs, including: Based on the employee competency assessment report and the dynamic job competency profile, a one-to-one dimensional mapping is performed to calculate the gap value of each competency dimension, resulting in an initial competency gap matrix. Based on the initial capability gap matrix, an optimization model containing hard constraints and dissipation constraints is constructed to obtain the dissipation constraint model. The 3-SAT algorithm for solving oversatisfied constraints is applied iteratively to the dissipative constraint model to obtain the personalized capability enhancement requirements.

5. The method according to claim 1, characterized in that, The step of filtering and combining the learning resources using a weighted matching algorithm to obtain a personalized learning recommendation plan includes: Based on the learning resources in the standardized multidimensional feature dataset, including courses, projects, and case resources in the enterprise learning resource library, content features, ability tags, and difficulty level information are extracted to obtain a learning resource feature matrix. Based on the personalized ability enhancement needs and the learning resource feature matrix, the correlation degree of each learning resource with the target ability enhancement is calculated to obtain the ability-resource correlation degree matrix. Based on the capability-resource correlation matrix, a preliminary learning path scheme is obtained by considering the learning order, time constraints and resource dependencies through a path optimization algorithm. Based on the preliminary learning path plan, the personalized learning recommendation plan is obtained by combining the employee's learning style, time arrangement and historical learning effect. By setting phased goals and designing a progress tracking mechanism for the personalized learning recommendation plan, a complete learning development scheme is obtained.

6. The method according to claim 1, characterized in that, The method employs a promotion path planning algorithm to construct a multi-dimensional promotion probability assessment and obtain personalized promotion path suggestions, including: Based on the organizational structure data of the standardized multidimensional feature dataset, a network of connections between positions is constructed and possible promotion channels are identified to obtain an organizational position network diagram. Based on the employee competency assessment report, the dynamic job competency profile, and the historical promotion case data of the standardized multidimensional feature dataset, the asymmetric random block model is applied to calculate the promotion suitability of employees in different positions, and a promotion probability score matrix is ​​obtained. Based on the promotion probability scoring matrix and the organizational job network diagram, vertical promotion paths and horizontal development paths are generated to obtain a multi-dimensional promotion path candidate set. Based on the multidimensional promotion path candidate set, path optimization is performed by applying dissipative constraint solving techniques through a multi-constraint optimization model and a random walk strategy to obtain the optimized promotion path scheme. Based on the optimized promotion path scheme, and combined with the personalized learning recommendation plan, detailed suggestions including time nodes, milestones, and key ability improvement points are generated to obtain the personalized promotion path suggestion.

7. The method according to claim 1, characterized in that, It also includes closed-loop feedback optimization steps: Based on the standardized multidimensional feature dataset, the evaluation results, application status, and performance changes of employees after completing the learning are used to assess the effectiveness of the learning resources and obtain a learning effectiveness evaluation report. Based on the standardized multidimensional feature dataset, successful promotion cases of employees are analyzed to determine their ability improvement trajectory, learning path and key success factors, and the results of promotion success pattern analysis are obtained. Based on the learning effectiveness evaluation report and the analysis results of the promotion success model, a three-dimensional closed-loop feedback model of learning-assessment-promotion is constructed to obtain optimization directions; Based on the aforementioned three-dimensional closed-loop feedback model, the parameters of the core algorithms, including job profile construction, competency assessment, and resource matching, are adaptively adjusted to obtain an optimized set of algorithm parameters. Based on the optimized algorithm parameter set, a periodic evaluation and iterative optimization mechanism is established to obtain the system iterative optimization strategy.

8. The method according to claim 2, characterized in that, The process of performing data integrity checks and outlier handling based on the multi-source heterogeneous data to obtain a dataset after integrity checks and outlier handling includes: Based on the aforementioned multi-source heterogeneous data, data sources are accessed and integrated through API interfaces and data synchronization tools to obtain the original multi-source data stream; The original multi-source data stream is subjected to data integrity checks, outlier handling, and missing value imputation operations to obtain the dataset after integrity checks and outlier handling.

9. A device for intelligent course matching and promotion path recommendation based on job profiles, characterized in that, include: The data preprocessing module is used to acquire multi-source heterogeneous data within the enterprise, and to perform data cleaning, standardization and feature extraction processing on the multi-source heterogeneous data to obtain a standardized multidimensional feature dataset. The job profile construction module is used to map job competency requirements to competency characteristics based on the standardized multidimensional feature dataset and a semidefinite programming method of asymmetric random block model to obtain a dynamic job competency profile. The employee competency assessment module is used to analyze the causal relationships between competency dimensions based on the standardized multidimensional feature dataset and the dynamic job competency profile, and to obtain an employee competency assessment report. This includes: integrating employee historical learning records, assessment results, and work performance data from the standardized multidimensional feature dataset to obtain a historical competency performance dataset; using a graphical causal reasoning method to identify causal relationships between competency dimensions based on the historical competency performance dataset to obtain a competency causal relationship network; performing topological sorting and path tracing on the competency causal relationship network, and using the linear time primitive algorithm to calculate direct and indirect influences to obtain a competency weight relationship matrix; calculating a comprehensive competency score using a weighted network propagation algorithm based on the competency weight relationship matrix and the employee's latest assessment data from the standardized multidimensional feature dataset to obtain a multidimensional competency score result; and generating an intuitive competency assessment report based on the multidimensional competency score result and the dynamic job competency profile. The competency gap analysis module is used to calculate the gap matrix between the employee's current competency and the target job requirements based on the dynamic job competency profile and the employee competency assessment report. It employs a dissipative oversatisfaction constraint solving algorithm to iteratively calculate the competency gap value and weight coefficients, thereby obtaining personalized competency improvement needs. The learning resource matching module is used to filter and combine the learning resources from the standardized multidimensional feature dataset based on the personalized ability improvement needs, and obtain a personalized learning recommendation plan by using a weighted matching algorithm. The promotion path planning module is used to construct a multi-dimensional promotion probability assessment based on the dynamic job competency profile, the employee competency assessment report, and the standardized multi-dimensional feature dataset of organizational structure data, and to obtain personalized promotion path suggestions by using a promotion path planning algorithm.

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