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

Abstract

The invention provides an intelligent course matching and promotion path recommendation method and device based on post portraits. The method comprises the steps of obtaining internal multi-source heterogeneous data of an enterprise and performing preprocessing to obtain a standardized multi-dimensional feature data set; constructing a dynamic post capability portrait by adopting a semi-definite programming method of an asymmetric random block model; analyzing a causal relationship between capability dimensions by applying a linear time primitive algorithm to obtain an employee capability assessment report; a constraint solution algorithm meeting excessive dissipation is adopted to calculate the capability gap matrix to obtain a personalized capability improvement requirement; screening and combining the learning resources through a weighted matching algorithm to obtain a personalized learning recommendation plan; and adopting a promotion path planning algorithm to construct multi-dimensional promotion possibility evaluation to obtain personalized promotion path suggestions. According to the invention, a closed-loop feedback mechanism of learning, evaluation and promotion is realized, and a systematic solution is provided for enterprise talent development.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of enterprise talent training and development, in particular to an intelligent course matching and promotion path recommendation method and device based on job portrait, which is used to provide personalized ability development planning and career promotion suggestions for enterprise employees. BACKGROUND

[0002] In the field of enterprise talent training and development, the systematic solution for employee ability improvement and career development has always been an important issue for organizational development. With the deepening of digital transformation, the internal learning and development system of enterprises has gradually evolved from traditional passive training management to active ability development platform.

[0003] The common enterprise learning system on the market mainly adopts a recommendation method based on content tag matching, such as extracting and classifying keywords from course content, and then simply matching with tags such as employee's department and position. Another type of system uses a collaborative filtering method to recommend courses by analyzing the learning tracks of similar employees, but these methods often ignore individual ability differences and organizational development needs.

[0004] The existing more advanced solution attempts to combine employee ability assessment and course recommendation, and recommends corresponding courses after evaluating employees by establishing an ability model. This technology analyzes the ability gap of employees, combines with preset course ability tags, and provides personalized learning suggestions for employees. However, this method has obvious limitations in the implementation process.

[0005] Firstly, the existing technology cannot effectively integrate learning behavior data and performance result data, resulting in a lack of scientific correlation between recommended courses and actual work performance improvement; secondly, the promotion path planning is too static and does not consider organizational structure changes and individual differences, which cannot provide dynamic optimization of career development suggestions for employees; finally, the existing system generally lacks a closed-loop feedback mechanism for learning, evaluation and promotion, making it difficult to achieve continuous optimization of talent development strategies. SUMMARY

[0006] The purpose of the present application is to provide an intelligent course matching and promotion path recommendation method and device based on job portrait, which aims to solve the problems existing in the prior art, to realize the effective integration of learning behavior data and performance result data, to provide dynamic optimization of career development suggestions, and to establish a closed-loop feedback mechanism for learning, evaluation and promotion.

[0007] To achieve the above-mentioned purpose, the technical solutions provided by the present application are as follows:

[0008] An intelligent course matching and promotion path recommendation method based on job portrait, comprising the following steps:

[0009] Obtaining multi-source heterogeneous data in an enterprise, performing data cleaning, standardization and feature extraction processing on the multi-source heterogeneous data to obtain a standardized multi-dimensional feature dataset;

[0010] Based on the standardized multi-dimensional feature dataset, a semi-definite programming method of an asymmetric random block model is used to map the job capability requirements and the competent person features to obtain a dynamic job capability portrait;

[0011] Based on the standardized multi-dimensional feature dataset and the dynamic job capability portrait, a linear time element algorithm is applied to analyze the causal relationship between the capability dimensions to obtain an employee capability evaluation report;

[0012] Based on the dynamic job capability portrait and the employee capability evaluation report, a constraint solving algorithm of dissipation over-satisfaction is used to calculate the gap matrix between the current capability of the employee and the target job requirements through iterative calculation of the capability gap value and the weight coefficient to obtain individualized capability improvement needs;

[0013] Based on the individualized capability improvement needs, combined with learning resources from the standardized multi-dimensional feature dataset, a weighted matching algorithm is used to screen and combine the learning resources to obtain an individualized learning recommendation plan;

[0014] Based on the dynamic job capability portrait, the employee capability evaluation report and the organizational structure data of the standardized multi-dimensional feature dataset, a promotion path planning algorithm is used to construct a multi-dimensional promotion possibility evaluation to obtain an individualized promotion path suggestion.

[0015] Preferably, the data cleaning, standardization and feature extraction processing on the multi-source heterogeneous data to obtain a standardized multi-dimensional feature dataset comprises:

[0016] Based on the multi-source heterogeneous data, data integrity checking and outlier processing are performed to obtain a dataset after integrity checking and outlier processing;

[0017] The dataset after integrity checking and outlier processing is subjected to dimension unification and normalization processing to obtain a standardized dataset;

[0018] Based on the standardized dataset, learning behavior features, capability evaluation features and performance features are extracted to obtain the standardized multi-dimensional feature dataset.

[0019] Preferably, the semi-definite programming method of the asymmetric random block model is used to map the job capability requirements and the competent person features to obtain a dynamic job capability portrait, comprising:

[0020] Based on the standardized multi-dimensional feature data set, a standardized capability framework system covering dimensions including technical capability, management capability and general capability is established;

[0021] Based on the standardized multi-dimensional feature data set of enterprise organization structure and job description, job basic information is extracted and mapped to the standardized capability framework system to obtain a job capability requirement mapping matrix;

[0022] Based on the standardized multi-dimensional feature data set of the competent person's characteristics, the actual capability performance of the competent person is mapped to the standardized capability framework system to obtain a competent person capability characteristic mapping matrix;

[0023] Based on the job capability requirement mapping matrix and the competent person capability characteristic mapping matrix, an asymmetric random block model is constructed to obtain an initial block association model;

[0024] The semi-definite programming algorithm is applied to the initial block association model for optimization and solution to obtain an optimized job capability weight matrix;

[0025] Based on the optimized job capability weight matrix, a multi-dimensional capability portrait is constructed in combination with the competent person's characteristics to obtain the dynamic job capability portrait.

[0026] Preferably, the linear time element algorithm is applied to analyze the causal relationship between the capability dimensions to obtain an employee capability evaluation report, including:

[0027] Based on the standardized multi-dimensional feature data set of the employee's historical learning records, evaluation results and work performance data, multi-dimensional data integration is performed to obtain an employee historical capability performance data set;

[0028] Based on the employee historical capability performance data set, a graphical causal reasoning method is applied to identify the causal relationship between each capability dimension to obtain a capability causal relationship network;

[0029] The capability causal relationship network is topologically sorted and path tracked, and a linear time element algorithm is applied for direct and indirect influence calculation to obtain a capability weight relationship matrix;

[0030] Based on the capability weight relationship matrix and the employee's latest evaluation data of the standardized multi-dimensional feature data set, a comprehensive capability score calculation is performed through a weighted network propagation algorithm to obtain an employee capability multi-dimensional score result;

[0031] Based on the employee capability multi-dimensional score result, an intuitive capability evaluation report is generated in combination with the dynamic job capability portrait to obtain the employee capability evaluation report.

[0032] Preferably, the solving algorithm for solving the constraint of over-satisfaction of dissipation calculates the gap matrix between the current ability of the employee and the requirements of the target post by iteratively calculating the ability gap value and the weight coefficient, and obtains the individualized ability improvement requirement, including:

[0033] Based on the employee ability assessment report and the dynamic post ability portrait, the gap value of each ability dimension is calculated by one-to-one dimension mapping, and an initial ability gap matrix is obtained.

[0034] Based on the initial ability gap matrix, an optimization model containing hard constraint conditions and dissipation constraint conditions is constructed, and a dissipation constraint model is obtained.

[0035] The 3-SAT solving algorithm with over-satisfaction constraint is applied to the dissipation constraint model for iterative solving, and the individualized ability improvement requirement is obtained.

[0036] Preferably, the learning resources are screened and combined by the weighted matching algorithm to obtain an individualized learning recommendation plan, including:

[0037] Based on the learning resources including courses, projects and case resources in the enterprise learning resource library in the standardized multi-dimensional feature data set, content features, ability labels and difficulty level information are extracted to obtain a learning resource feature matrix.

[0038] Based on the individualized ability improvement requirement and the learning resource feature matrix, the correlation degree of each learning resource to the target ability improvement is calculated to obtain an ability-resource correlation degree matrix.

[0039] Based on the ability-resource correlation degree matrix, a preliminary learning path scheme is obtained by a path optimization algorithm considering learning order, time constraints and resource dependency relationships.

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

[0041] The individualized learning recommendation plan is set with a phased target and a progress tracking mechanism to obtain a complete learning development scheme.

[0042] Preferably, the individualized promotion path suggestion is obtained by constructing a multi-dimensional promotion possibility evaluation using a promotion path planning algorithm, including:

[0043] Based on the organization structure data of the standardized multi-dimensional feature data set, a connection relationship network between posts is constructed and possible promotion channels are identified to obtain an organization post network diagram.

[0044] Based on the employee capability assessment report, the dynamic job capability profile, and the historical promotion case data of the standardized multi-dimensional feature data set, the asymmetric random block model is applied to calculate the promotion fitness of employees in different positions, and a promotion possibility score matrix is obtained;

[0045] Based on the promotion possibility score matrix and the organizational position network graph, a longitudinal promotion path and a horizontal development path are generated, and a multi-dimensional promotion path candidate set is obtained;

[0046] Based on the multi-dimensional promotion path candidate set, a path optimization is performed by a multi-constraint optimization model and a random walk strategy using a dissipation constraint solving technique, and an optimized promotion path scheme is obtained;

[0047] Based on the optimized promotion path scheme, a detailed suggestion including time nodes, milestones, and key capability improvement points is generated by combining the individualized learning recommendation plan, and the individualized promotion path suggestion is obtained.

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

[0049] Based on the evaluation results, application situations, and performance changes of employees after completing learning based on the standardized multi-dimensional feature data set, the effectiveness of learning resources is evaluated, and a learning effect evaluation report is obtained;

[0050] Based on the successful promotion employee cases of the standardized multi-dimensional feature data set, the capability improvement trajectory, learning path, and key success factors are analyzed, and a promotion success mode analysis result is obtained;

[0051] Based on the learning effect evaluation report and the promotion success mode analysis result, a three-dimensional closed-loop feedback model of learning-evaluation-promotion is constructed, and an optimization direction is obtained;

[0052] Based on the three-dimensional closed-loop feedback model, the parameters of the core algorithms including position profile construction, capability assessment, and resource matching are adaptively adjusted, and an optimized algorithm parameter set is obtained;

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

[0054] Preferably, based on the multi-source heterogeneous data, data integrity check and outlier processing are performed, and a data set after integrity check and outlier processing is obtained, including:

[0055] Based on the multi-source heterogeneous data, data source access and integration are performed through API interface and data synchronization tool, and an original multi-source data stream is obtained;

[0056] Perform data integrity checking, outlier processing and missing value filling operations on the original multi-source data stream to obtain the data set after integrity checking and outlier processing.

[0057] The application also provides an intelligent course matching and promotion path recommendation device based on post portraits, comprising:

[0058] A data preprocessing module is configured to acquire multi-source heterogeneous data in an enterprise, perform data cleaning, standardization and feature extraction processing on the multi-source heterogeneous data, and obtain a standardized multi-dimensional feature data set.

[0059] A post portrait construction module is configured to map post capability requirements and competent person features by using a semi-definite programming method of an asymmetric random zone group model based on the standardized multi-dimensional feature data set, and obtain a dynamic post capability portrait.

[0060] An employee capability evaluation module is configured to analyze the causal relationship between capability dimensions by using a linear time cell algorithm based on the standardized multi-dimensional feature data set and the dynamic post capability portrait, and obtain an employee capability evaluation report.

[0061] A capability gap analysis module is configured to calculate a gap matrix between the current capability of an employee and the requirements of a target post by iteratively calculating a capability gap value and a weight coefficient by using a constraint solving algorithm of dissipation over-satisfaction based on the dynamic post capability portrait and the employee capability evaluation report, and obtain individualized capability improvement requirements.

[0062] A learning resource matching module is configured to filter and combine learning resources by using a weighted matching algorithm based on the individualized capability improvement requirements and the learning resources from the standardized multi-dimensional feature data set, and obtain an individualized learning recommendation plan.

[0063] A promotion path planning module is configured to construct a multi-dimensional promotion possibility evaluation by using a promotion path planning algorithm based on the dynamic post capability portrait, the employee capability evaluation report and organizational structure data of the standardized multi-dimensional feature data set, and obtain an individualized promotion path suggestion.

[0064] The application has the following beneficial effects:

[0065] 1. The asymmetric random zone group model and the semi-definite programming method are used to realize accurate mapping of post capability requirements and competent person features, and a dynamic post capability portrait is constructed, thereby providing a scientific basis for employee capability evaluation and development planning.

[0066] 2. The linear time cell algorithm is used to analyze the causal relationship between capability dimensions, thereby revealing the mutual influence mechanism between different capabilities and making employee capability evaluation more comprehensive and systematic.

[0067] 3. Using a constraint solving algorithm that overcomes dissipation, the gap between employee capabilities and job requirements is accurately calculated, providing personalized capability improvement needs, making learning resource matching more accurate;

[0068] 4. By using a weighted matching algorithm to filter and combine learning resources, an individualized learning recommendation plan is generated, improving learning resource utilization efficiency and learning effectiveness;

[0069] 5. Using a promotion path planning algorithm to construct a multi-dimensional promotion possibility evaluation, personalized promotion path suggestions are provided to employees, helping them achieve long-term career development goals;

[0070] 6. A three-dimensional closed-loop feedback model of learning-evaluation-promotion is established, realizing the continuous optimization and iteration of the system, and improving the effectiveness and adaptability of talent development strategies. BRIEF DESCRIPTION OF DRAWINGS

[0071] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

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

[0073] Figure 2 is the flowchart of the job portrait construction module provided by the present application;

[0074] Figure 3 is the structural block diagram of the intelligent course matching and promotion path recommendation device based on job portrait provided by the present application. DETAILED DESCRIPTION

[0075] The present application will be further described in detail below in combination with the drawings and embodiments.

[0076] As shown in Figure 1 , the intelligent course matching and promotion path recommendation method based on job portrait provided by the present application comprises the following steps:

[0077] In step S1, multi-source heterogeneous data in the enterprise is obtained, and the multi-source heterogeneous data is subjected to data cleaning, standardization and feature extraction processing to obtain a standardized multi-dimensional feature data set.

[0078] This step first collects heterogeneous data from various enterprises, including employee profile data in HR, learning behavior data in learning management, assessment results in competency assessment, performance evaluation data in performance management, and post information data in organizational structure, etc. These data are usually scattered in different places, with different formats and varying data quality. After unified collection through data collection tools and API interfaces, the raw data is cleaned, including removing duplicate records, correcting outliers, filling missing values, etc., to ensure the integrity and consistency of the data. Subsequently, the cleaned data is standardized to convert data of different dimensions and scales into a unified standard format, facilitating subsequent analysis and processing. Finally, key features are extracted from the standardized data, including employee learning behavior features (such as learning frequency, completion rate, test scores, etc.), competency assessment features (such as competency scores in each dimension, certification levels, etc.), and performance features (such as performance ratings, project results, etc.), forming a structured multi-dimensional feature dataset, providing a data foundation for subsequent post profiling and competency assessment;

[0079] In step S2, based on the standardized multi-dimensional feature dataset, a semi-definite programming method of the asymmetric random block model is used to map the post competency requirements and the competent employee features, obtaining a dynamic post competency profile.

[0080] This step first extracts post-related information and employee feature data from the standardized multi-dimensional feature dataset, establishing an initial association between posts and competency dimensions. The asymmetric random block model is used, with competency dimensions as "treatments" and post features as "block groups". The model considers the non-symmetry and random effects of the impact of different competencies on different posts. In this model, the differences between post competency requirements and actual employee performance, as well as the random effects caused by organizational environment and individual differences, are considered. Through semi-definite programming algorithm, the post-competency association weight is optimized and solved, finding the combination of competency weights that best explain the performance of competent employees. This method overcomes the limitations of traditional post competency models, which are static and subjective, and can dynamically adjust post competency requirements based on actual employee performance data. Finally, based on the optimized weight matrix, combined with historical employee feature data, a multi-dimensional post competency profile is constructed, including the importance weight of each competency dimension, the basic requirement value, the expected value, and the correlation between competencies. This dynamically updated post competency profile provides a scientific reference standard for subsequent employee competency assessment and development planning.

[0081] In step S3, based on the standardized multi-dimensional feature dataset and the dynamic post competency profile, a linear time cell algorithm is applied to analyze the causal relationship between competency dimensions, obtaining an employee competency assessment report.

[0082] The step first integrates the historical learning records, ability evaluation results and work performance data of employees to form a time series data set of employee historical ability performance. By applying graphical causal reasoning methods, the causal relationships between the various ability dimensions are identified through correlation analysis, time series analysis and conditional independence testing, and a causal relationship network of abilities is constructed. This network reveals the influence mechanism between abilities, such as the supporting role of certain basic abilities on advanced abilities and the synergistic effect between certain abilities. Subsequently, the topological sorting of the ability causal network is performed, and the direct and indirect effects between abilities are efficiently calculated using the linear time element algorithm to form an ability weight relationship matrix. Based on this matrix and the latest ability evaluation data of employees, the comprehensive scores of employees in each ability dimension are calculated through a weighted network propagation algorithm. This method takes into account the mutual influence between abilities and is more comprehensive and accurate than traditional independent assessment. Finally, an intuitive employee ability assessment report is generated, including ability radar charts, ability development trend charts, gap analysis with job requirements, advantage ability analysis and improvement suggestions, etc., to provide comprehensive and accurate ability status assessment and development guidance for employees.

[0083] In step S4, based on the dynamic job ability profile and the employee ability assessment report, a constraint solving algorithm with excessive satisfaction dissipation is used to iteratively calculate the ability gap value and weight coefficient to calculate the gap matrix between the current ability of the employee and the target job requirements, and obtain the individualized ability improvement needs.

[0084] This step first extracts the ability requirement parameters of the target job (including the current job, promotion job and horizontal development job) from the job ability profile, including the basic requirement value, expected value and weight coefficient of each ability dimension. The current ability score of the employee is mapped one-to-one with the target job requirements to calculate the gap value of each ability dimension and form an initial ability gap matrix. Based on this initial matrix, an optimization model containing hard constraints and dissipation constraints is constructed, and the ability improvement problem is converted into an optimization problem that meets multiple constraint conditions. Hard constraints include resource constraints, time constraints and necessary ability constraints, which must be strictly satisfied; dissipation constraints include ability balance constraints, priority constraints and continuity constraints, which can be moderately violated. By applying the 3-SAT solving algorithm with excessive satisfaction constraints, the optimal solution that meets the constraint conditions is found through iterative solving, even in the case of conflicting constraints, an optimal compromise solution is provided. Finally, the individualized ability improvement needs are output, including the improvement target, priority ranking, phased target setting and development approach suggestions for each ability dimension, providing clear and operable ability development guidance for employees.

[0085] In step S5, based on the individualized ability improvement needs, the learning resources are screened and combined through a weighted matching algorithm to obtain an individualized learning recommendation plan.

[0086] This step first extracts the feature information of learning resources such as courses, projects, and cases from the enterprise learning repository, including content theme, ability label, difficulty level, learning method, time requirement, etc., to form a learning resource feature matrix. Based on the individualized ability improvement needs of employees and the learning resource feature matrix, the correlation degree of each learning resource to the target ability improvement is calculated to form an ability-resource correlation degree matrix. This correlation degree calculation considers multiple factors such as the matching degree of resource content and ability demand, the adaptation degree of resource difficulty and employee current level, and the fit degree of resource form and employee learning preference. Subsequently, a learning path is designed through a path optimization algorithm, considering factors such as the rationality of learning order, the feasibility of time arrangement, and the dependency relationship between resources, to form a preliminary learning path scheme. Further combined with the learning style, time arrangement, and historical learning effect data of employees, the learning path is customized, including adjusting the learning content combination, optimizing the learning time allocation, and selecting the appropriate learning method. Finally, the stage learning goals and progress tracking mechanism are set to form a complete individualized learning recommendation plan, providing precise and efficient learning resource recommendation and learning path guidance for employees.

[0087] In step S6, based on the dynamic job capability profile, the employee capability assessment report, and the organization architecture data of the standardized multi-dimensional feature data set, a promotion path planning algorithm is used to construct a multi-dimensional promotion possibility evaluation, and an individualized promotion path suggestion is obtained.

[0088] This step first constructs a job connection relationship network based on organization architecture data, identifies the formal promotion channels and potential development paths in the organization, including vertical promotion paths, horizontal development paths, and cross-department development paths. Combined with the employee capability assessment report, the target job capability profile, and the historical promotion case data, an asymmetric random group model is applied to calculate the promotion adaptation degree of employees in different positions, forming a promotion possibility score matrix. This score considers multiple dimensions such as ability matching degree, experience relevance, behavior style fit degree, and development potential. Based on the promotion possibility score and the job network, multiple possible career development paths are generated, including traditional vertical promotion paths, professional depth development paths, management breadth development paths, and cross-field transformation paths. Through a multi-constraint optimization model and a random walk strategy, a dissipative constraint solving technique is applied to optimize these paths, considering factors such as employee willingness, organizational needs, development cycle, etc., to select the most suitable 2-3 development paths for employees. Finally, combined with the individualized learning recommendation plan, detailed implementation suggestions are generated for each path, including time nodes, key milestones, ability improvement focuses, learning resource recommendations, etc., to form a comprehensive and feasible individualized promotion path suggestion, helping employees achieve long-term career development goals.

[0089] In step S1, the multi-source heterogeneous data is subjected to data cleaning, standardization and feature extraction processing to obtain a standardized multi-dimensional feature data set, specifically including:

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

[0091] This step first performs integrity checking on the raw data collected from each enterprise, identifies the missing situation of key fields such as employee ID, department information, position information, learning record, ability score, etc. For data fields with high missing rate, the impact on subsequent analysis is evaluated to decide whether to retain the field. For missing values in the retained field, various strategies are used for processing, including mean / median / mode filling, collaborative filling based on similar users, time series interpolation, etc. At the same time, outliers in the data are detected, such as ability scores that are obviously beyond the reasonable range, learning records that do not conform to business logic, etc. For the identified outliers, replacement, correction or marking, etc. are used for processing according to the specific situation to ensure the quality and reliability of the data. Through integrity checking and outlier processing, a high-quality data set is output to lay the foundation for subsequent standardization processing.

[0092] In step S111, based on the multi-source heterogeneous data, data source access and integration are performed through API interface and data synchronization tool to obtain raw multi-source data stream.

[0093] This step first identifies various relevant internal enterprises, including human resource management (HRMS), learning management (LMS), performance management (PMS), capability assessment (CAS) and organization structure management, etc. Different data access methods are configured to connect with these sources: for those that provide standard API interfaces, RESTful API or SOAP protocol is used for real-time or quasi-real-time data acquisition; for those that do not provide standard interfaces, database direct connection, file transfer or ETL tool is used for periodic data synchronization. A unified data access framework is established to handle differences in different authentication mechanisms, data formats and transmission protocols, ensuring the security and stability of data acquisition. At the same time, incremental data synchronization mechanism is realized, through recording time stamp, change marker, etc. Only the data changed since the last synchronization is obtained to reduce data transmission volume and burden. For real-time data such as new learning records, latest evaluation results, etc., message queue or event-driven architecture is used to realize near-real-time data synchronization. Through these diversified data access methods, the data scattered in various businesses is collected to a unified data processing platform to form a raw multi-source data stream containing employee information, learning records, capability assessment, performance data and organization structure, etc. laying the foundation for subsequent data processing and analysis.

[0094] In step S112, data integrity checking, outlier processing and missing value filling operations are performed on the original multi-source data stream to obtain a data set after integrity checking and outlier processing.

[0095] In this step, data integrity checking is first performed on the original multi-source data stream, and a series of data quality rules are defined, including mandatory field checking (such as employee ID, post code, etc.), data type verification (such as date format, numerical range, etc.), business rule verification (such as organizational hierarchy relationship, score effective range, etc.), and cross-consistency checking (such as consistency of employee basic information in different systems). According to these rules, the data is comprehensively scanned to identify data records that do not meet the rules, and a data quality report is generated. For the discovered outliers, multiple strategies are used for processing: for obviously incorrect data (such as scores beyond reasonable range, incorrect date format, etc.), automatic correction is performed according to pre-set rules; for suspicious but indeterminate outliers, they are marked and recorded for subsequent manual review; for serious abnormal data that affect the analysis results but cannot be corrected, they are excluded from the analysis data set to avoid interference with subsequent analysis. For data missing problems, different filling strategies are used according to the importance of the missing field and the missing pattern: for randomly missing numerical data, mean, median or mode filling is used; for time-related missing data, time series interpolation method is used; for highly correlated missing data with other fields, conditional filling based on related fields is used; for non-key fields with high missing rate and cannot be reliably filled, the field can be excluded in subsequent analysis. Through a series of data cleaning and processing operations, the original multi-source heterogeneous data is converted into a high-quality data set with reliable quality and consistent structure, providing a reliable data foundation for subsequent data standardization and feature extraction.

[0096] In step S12, the data set after integrity checking and outlier processing is subjected to dimension unification and normalization processing to obtain a standardized data set.

[0097] This step first unifies the dimensions of data from different sources and types, converting data in various units and scales into a unified standard for comparison. For example, ability assessment results under different scoring systems (such as 5-point, 10-point, and percentage systems) are converted into standard scores; time-related data (such as learning duration and work experience) are converted into a unified time unit. Subsequently, the data with unified dimensions is normalized, commonly using methods such as min-max normalization, Z-score standardization, and logarithmic transformation, mapping the data into 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, such as Z-score standardization for continuous data, min-max normalization for bounded data, and logarithmic transformation for skewed distribution data. Through dimension unification and normalization, a standardized dataset is output, allowing data from different sources and types to be compared and analyzed under a unified standard.

[0098] In step S13, based on the standardized dataset, learning behavior features, ability assessment features, and performance features are extracted, obtaining the standardized multi-dimensional feature dataset.

[0099] This step extracts and constructs features valuable for ability assessment and development planning from the standardized data through feature engineering techniques. For learning behavior data, extracted features include learning activity (such as learning frequency and duration), learning completion (such as completion rate and timeliness), learning effectiveness (such as test scores and practical application), and learning preferences (such as content type and learning method). For ability assessment data, extracted features include dimensional ability scores, ability development trends, ability balance, and professional certification. For performance data, extracted features include performance ratings, goal achievement rates, project outcomes, and peer comparison rankings. In addition, some composite features are constructed, such as the correlation between learning investment and performance improvement, ability improvement speed, and ability-post matching degree. Through the extraction and construction of these features, a feature dataset with rich dimensions is formed, comprehensively describing the ability status, learning behavior, and work performance of employees, providing multi-dimensional data support for subsequent job profiling and ability assessment.

[0100] As shown in Figure 2 In step S2, a semi-definite programming method based on an asymmetric random block model is used to map job capability requirements and competent features, obtaining a dynamic job capability profile, which includes:

[0101] In step S21, based on the standardized multi-dimensional feature dataset, a standardized ability framework system covering dimensions including technical ability, management ability, and general ability is established.

[0102] The step first extracts all dimensions and indicators related to ability from the standardized multi-dimensional feature data set, including existing ability evaluation indicators of the enterprise, ability requirements in job descriptions, and ability-related items in performance evaluation, etc. These scattered ability indicators are sorted and classified, and are preliminarily divided into three categories: technical ability (such as professional knowledge, technical tool application, etc.), management ability (such as team management, resource allocation, etc.), and general ability (such as communication and cooperation, problem solving, etc.). Under each major category, further subdivide multiple ability dimensions, for example, technical ability can be subdivided into professional theoretical knowledge, professional practical skill, technical innovation ability, etc.; management ability can be subdivided into planning and organization, decision-making ability, team building, performance management, etc.; general ability can be subdivided into learning ability, communication ability, execution, stress resistance, etc. Establish clear definitions, behavior descriptions, and evaluation standards for each ability dimension to ensure the independence and complementarity between dimensions and avoid excessive overlap. At the same time, establish hierarchical relationships between ability dimensions, identify basic and derived abilities, and form a structured ability framework. In addition, a unified ability evaluation scale is designed, including scoring standards, grade division, and behavior anchoring description, to ensure the comparability of evaluation results for different ability dimensions. Through these works, a comprehensive, standardized ability framework system is established, providing a unified reference standard for subsequent job ability requirement mapping and employee ability evaluation, ensuring that the entire system operates under a consistent ability language.

[0103] In step S22, based on the enterprise organizational structure and job description of the standardized multi-dimensional feature data set, job basic information is extracted and mapped to the standardized ability framework system to obtain a job ability requirement mapping matrix.

[0104] This step first extracts enterprise organizational structure information and job description data from the standardized multi-dimensional feature dataset, including department setup, job level, job responsibilities, job requirements, etc. Text analysis is performed on the job description to extract descriptions related to capability requirements, such as "proficient in data analysis tools", "experience in team management", etc. For structured job descriptions, predefined capability requirement fields are directly extracted; for unstructured text descriptions, natural language processing techniques are applied, including keyword extraction, phrase matching, and semantic analysis, to identify implicit capability requirements in the text. The extracted job capability requirements are mapped to the standardized capability framework established in step S21, converting non-standardized capability descriptions into standardized capability dimensions. For example, "proficient in Python for data analysis" may be mapped to "data analysis capability" and "programming capability" two standard dimensions. In the mapping process, semantic similarity, contextual association and domain knowledge are considered to ensure the accuracy of the mapping. For each standard capability dimension of each job, a weight value is initially assigned according to the description strength and importance in the job description, indicating the importance of the capability to the job. At the same time, according to the requirement degree (such as "understand", "familiar", "master" etc.) in the job description, the initial requirement level is set for each capability dimension. Through this series of processing, the capability requirements scattered in various job descriptions are converted into a job-capability mapping matrix under a unified standard, and each element in the matrix represents the requirement level and importance weight of a specific job for a specific capability dimension. This mapping matrix provides basic data for the subsequent asymmetric random zone group model construction.

[0105] In step S23, based on the standardized multi-dimensional feature dataset of the competent person, the actual capability performance of the competent person is mapped to the standardized capability framework system to obtain a competent person capability feature mapping matrix.

[0106] This step first identifies the competent persons for each position from the standardized multi-dimensional feature data set, usually the employees who perform well or achieve the expected performance in the position, which can be determined by performance evaluation results, supervisor evaluation, key performance indicator (KPI) achievement, etc. Collect various ability-related data of these competent persons, including ability assessment results, learning achievements, work performance, project experience, certification qualifications, etc. For direct ability assessment data, map it to the corresponding dimension of the standardized ability framework; for indirect ability indicators such as project experience, work results, etc., infer the ability level reflected by them through pre-set rules or machine learning models. For example, a successfully completed high-difficulty project may reflect higher professional ability and problem-solving ability. Aggregate and analyze the data of multiple competent persons for the same position to calculate the average level, distribution range and typical characteristics of each ability dimension, forming the ability feature profile of the competent persons for the position. In this process, the diversity of competent persons is considered, and the ability feature patterns of different types of competent persons are identified, such as technical competent persons and management competent persons may have different ability structures. The correlation and combination patterns between the abilities of competent persons are also analyzed to identify the complementary relationship and synergistic effect between the abilities. Through these analyses, the actual performance data of the competent persons are converted into the ability feature matrix under the standardized ability framework, and each element in the matrix represents the typical performance level of the competent person for a specific position in a specific ability dimension. This competent person ability feature mapping matrix and the position ability requirement mapping matrix of step S22 together constitute the basic data of the asymmetric random block model, which is used for subsequent optimization of the position ability profile.

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

[0108] This step converts the job competency modeling problem into an asymmetric random block design problem in statistics, where the competency dimensions are treated as "treatments", the job characteristics are treated as "blocks", and the incumbent performance is treated as the response variable. This model design considers several key characteristics: first, the asymmetry, i.e., the degree of influence of different competencies on different jobs varies, some competencies may be crucial for a particular job, while relatively secondary for other jobs; second, the random effect, the model considers the influence of random factors such as organizational environment, team differences, individual characteristics, etc. on the performance; finally, the block structure, the model considers the job as a block with internal correlation, there may be correlations between different competency requirements within the same job. First, define the mathematical form of the model, including fixed effect terms (main effects of competency dimensions), random effect terms (random effects of jobs and individuals), and interaction effect terms (interaction between competencies and jobs). Then, set the initial parameters of the model, including the basic weights of each competency dimension, the random effect variance of the job, the correlation structure between competencies, etc. These initial parameters are partly from the job competency requirement mapping matrix in step S22, and partly from the prior knowledge of experts in the field. The constraints of the model are also defined, such as non-negativity of weights, normalization of total weights, etc., to ensure the interpretability and practicality of the model results. Through these settings, an initial asymmetric random block correlation model is constructed, which formally expresses the complex relationship between competency dimensions, job characteristics and performance, providing a mathematical basis for subsequent semi-definite programming optimization.

[0109] In the process of constructing the asymmetric random block model, the competency dimensions are treated as "treatments" and the job characteristics are treated as "blocks" to establish the mapping relationship between them. Specifically, for each job j and competency dimension i, the model assumes that the relationship can be represented as: Y ij = μ + α i i + β j j + γ ij ij + ε ij , where μ represents the overall average effect, α i represents the main effect of competency i, β j represents the effect of job j, γ ij represents the interaction, and ε ij represents the random error. The asymmetry of the model is reflected in γ ij ≠ γ ji , i.e., the influence of competency i on job j is different from the influence of competency j on job i. The model sets the constraints Σα i = 0 and Σβ j= 0 to ensure the identifiability of parameters. The initial parameter settings are based on the ability weight initial values 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 abilities, which is represented by introducing a covariance matrix Σ that is optimized in the process of solving the semidefinite programming.

[0110] In step S25, a semidefinite programming algorithm is applied to the initial zone group association model to obtain an optimized job ability weight matrix.

[0111] This step uses the semidefinite programming (SDP) method to optimize and solve the asymmetric random zone group model constructed in step S24. Semidefinite programming is a special form of convex optimization, which is suitable for solving optimization problems with semidefinite matrix constraints. In this context, it is used to handle the correlation structure and non-linear constraints between ability dimensions. First, the asymmetric random zone group model is converted into the standard form of semidefinite programming, including the objective function, linear constraints, and semidefinite constraints. The objective function is usually set to minimize the difference between the model prediction and the actual performance of the competent person, or maximize the explained variance of the model. Interior Point Method or Alternating Direction Method of Multipliers (ADMM) algorithms are used for solving, which can efficiently handle large-scale semidefinite programming problems. In the solving process, the cross-validation method is used to evaluate the generalization performance of the model to avoid overfitting. At the same time, regularization terms such as L1 regularization (promote sparse solution, highlight key abilities) or L2 regularization (prevent weight too large, improve model stability) are introduced to further optimize the model performance. For large-scale complex problems, decomposition strategies or approximation algorithms can be used to improve computational efficiency while ensuring solution quality. Through the optimization and solution of the semidefinite programming algorithm, the optimized job ability weight matrix is obtained, which accurately reflects the actual contribution of each ability dimension to the 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, it integrates the performance data of actual competent persons, making the job ability model more objective, accurate, and predictive.

[0112] In the process of semi-definite programming optimization solution, the system converts the asymmetric random group model into a standard form: minimizing Tr(CX), with constraints A(X) = b and X ≥ 0 (X is a semi-positive definite matrix). Where the matrix X contains the capability weight and related parameters, C represents the objective function coefficient matrix, A represents the linear constraint operator, and b is the constraint condition vector. The system uses the interior point method for solution, which constructs a series of points gradually approaching the optimal solution inside the feasible region. In specific implementation, the initial point of the algorithm is selected as a strictly feasible solution, the iteration step size uses an adaptive strategy, and the convergence condition is set as the dual gap being less than a preset threshold (usually 10 -6 ). To avoid overfitting, the system introduces a regularization term λ||X|| F , and the value of λ is determined by cross-validation, usually in the range of 0.01-0.1. For large-scale problems, the system uses the block diagonalization technique to reduce the computational complexity, reducing the calculation from O(n 3 ) to O(n 2 ), where n is the number of capability dimensions, improving the calculation efficiency of large enterprise capability models.

[0113] In step S26, based on the optimized post capability weight matrix, combined with the competency characteristics, a multi-dimensional capability portrait is constructed, and the dynamic post capability portrait is obtained.

[0114] The step combines the post ability weight matrix obtained by optimizing step S25 with the competency capability characteristics of step S23 to construct a comprehensive and multi-dimensional post capability portrait. First, according to the optimized weight matrix, the core capability dimensions of each post are determined, and usually the several dimensions with high weight ranking are selected as the key capabilities of the post. For each capability dimension, combined with the actual performance data of the competent person, scientific and reasonable capability requirement parameters are set, including: basic requirement value (the minimum capability level required by the post), expected requirement value (the ideal capability level expected by the post), capability range (the acceptable capability variation range), etc. The capability combination mode in the competent person data is also analyzed to identify the complementary relationship, substitution relationship and synergistic relationship between capabilities, construct a capability correlation network, and reveal how different capabilities jointly act on the post performance. In addition, combined with the organization development strategy and industry development trend, dynamic adjustment factors are set for each capability dimension to reflect the change trend of the importance of the capability, so that the post portrait has foresight. These multi-dimensional capability information is integrated into a structured post capability portrait, including capability radar chart, capability weight distribution, capability correlation network, capability requirement details and other forms of expression. To ensure the dynamic updating of the post capability portrait, an automatic updating mechanism is established, which automatically triggers the portrait updating process when there are new competent person data, organizational structure changes or strategy adjustments, to maintain the timeliness and accuracy of the post capability portrait. In this way, a dynamic, comprehensive and accurate post capability portrait is constructed, which provides a scientific reference standard for subsequent employee capability assessment, gap analysis and development planning.

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

[0116] In step S31, based on the standardized multi-dimensional feature data set, the employee historical learning records, evaluation results and work performance data are integrated to obtain an employee historical capability performance data set.

[0117] This step first extracts all historical data related to a specific employee from the standardized multi-dimensional feature dataset, including three major categories of core data: learning record data (such as course participation records, learning duration, completion rate, test scores, certificate acquisition, etc.), ability assessment data (such as regular ability assessment results, 360-degree assessment feedback, professional skill examination scores, etc.), and work performance data (such as performance rating, project completion, supervisor evaluation, customer feedback, etc.). These data from different time points and different sources are arranged in chronological order to construct a timeline of employee ability development. For historical data with a long time span, the timeliness factor is considered, and a time decay function is used to assign lower weights to earlier data, ensuring that recent data have a greater impact on current ability assessment. Consistency checks are also performed on different sources of data, and when significant differences are found between data (such as a large difference between self-evaluation results and supervisor evaluation), weighting processing or potential data quality issues are marked based on factors such as data source reliability and evaluation method rigor. To reveal the correlation between ability development and work performance, learning activities and ability improvement are correlated with subsequent changes in work performance to identify possible causal relationship patterns. In addition, employee learning behavior characteristics such as learning preferences (preferred learning methods, content types, etc.), learning efficiency (degree of ability improvement per unit time), and learning persistence (continuity and persistence of learning activities) are extracted, which can help with subsequent personalized learning recommendations. Through this series of data integration and processing, a multi-dimensional, time-sequenced employee historical ability performance dataset is generated, which comprehensively records the development trajectory of employees in each ability dimension, learning behavior characteristics, and changes in work performance, providing a rich data foundation for subsequent ability causal relationship analysis.

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

[0119] The step adopts a graphical causal inference method to discover the causal relationship structure between the ability dimensions from the historical ability performance data of the employees. First, the correlation analysis is used to preliminarily identify the correlation strength between the ability dimensions, the Pearson correlation coefficient, the Spearman rank correlation coefficient or the mutual information value between the dimensions is calculated, and an initial correlation strength matrix is constructed. Then, the time series analysis is performed to test the sequence of the ability changes, and based on the Granger causality test or the cross-lagged correlation analysis, it is determined whether the change of the ability A is prior to the change of the ability B in time, so as to preliminarily determine the possible causal direction. Next, the conditional independence test is performed, which is the core step of the causal discovery. By controlling the potential common cause variable, it is tested whether the relationship between the two ability dimensions is a real causal relationship. For example, it is tested whether the relationship between the project management ability and the team cooperation ability still exists after the communication ability as a possible common cause is controlled. Based on the result of the conditional independence test, the PC algorithm (Peter-Clark Algorithm), the FCI algorithm (Fast Causal Inference) or the GES algorithm (Greedy Equivalence Search) and other causal discovery algorithms are applied to construct the causal graph structure between the ability dimensions. These algorithms perform the conditional independence test step by step to gradually construct the directed acyclic graph (DAG) or the partial directed acyclic graph (PDAG) to represent the causal relationship between the ability dimensions. In the process of applying the algorithm, the domain knowledge is integrated as prior information, such as some basic abilities are usually the premise of advanced abilities. These prior knowledge can guide the algorithm to search for a more reasonable causal structure. Finally, the ability causal relationship network is output, which is a directed graph structure, wherein the nodes represent the ability dimensions, the edges represent the causal influence relationship, and the weights of the edges represent the influence strength. For example, the graph may show that the learning ability has a relatively strong positive causal influence on the professional technical ability, and the professional technical ability has a medium strength influence on the problem solving ability. The ability causal relationship network reveals the internal mechanism of the ability development, and provides an important theoretical basis for the subsequent ability evaluation and development planning.

[0120] In the specific implementation of the graphical causal inference method, the Pearson correlation coefficient is used to calculate the correlation strength between the ability dimensions to construct an initial correlation matrix. For time series data, Granger causality test is applied, with a test window of 3-6 months and a significance level of 0.05. Conditional independence test uses partial correlation analysis and Fisher's Z test to identify true causal relationships by controlling potential common cause variables. The specific implementation of the PC algorithm includes two stages: skeleton identification stage, which constructs an undirected graph through systematic conditional independence test; directional stage, which determines the direction of the edge through v-structure identification and directional rules. The entire algorithm uses a depth-first search strategy, with a maximum conditional set size limit of 3 to balance computational complexity and accuracy. To incorporate domain knowledge, the system allows human resource experts to set prior constraints, such as specifying the existence or direction of certain edges, which are implemented through black and white list mechanisms. The training data sources include historical ability assessment data, learning records, and performance data, with a sample size of 500-1000 people and a coverage period of 2-3 years, ensuring that the time span of the data is sufficient to capture the ability development pattern.

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

[0122] This step is based on the ability causal relationship network constructed in step S32, and applies an efficient graph algorithm to calculate the comprehensive influence relationship between the ability dimensions. First, the ability causal graph is topologically sorted to ensure that all predecessor node influences have been calculated when calculating node influence. Topological sorting is a graph algorithm that arranges all nodes of a directed acyclic graph in a linear sequence, so that in the graph, any pair of nodes (u, v) has an edge from u to v, and u appears before v in the sequence. This sorting ensures the correctness and efficiency of the calculation process, avoiding circular dependency problems. Based on the topological sorting result, a linear-time primitive (Linear-Time Primitives) algorithm is applied for path tracing and influence calculation. Linear-time primitives are graph algorithms with a computational complexity of O(n), where n is the number of nodes in the graph, which can maintain high efficiency in large-scale ability 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 constant-time complexity basic operation, such as node access, edge weight reading, and influence value accumulation. Direct and indirect influences are distinguished and calculated: direct influence is the direct causal relationship strength between two ability dimensions, which is directly obtained from the edge weight of the causal graph; indirect influence is the influence transmitted through intermediate nodes, which needs to be calculated through path tracing. When calculating indirect influence, an influence decay model is applied to adjust the influence strength based on path length and intermediate node characteristics. For example, through the decay function f(d) = α dwhere d is path length, a is attenuation coefficient, usually taking a value between 0.5-0.9) to calculate the attenuation of influence strength. To improve the efficiency of calculation, the dynamic programming idea is adopted to maintain an influence value cache to record the calculated path influence and avoid repeated calculation. Finally, the direct influence and the indirect influence of each path are aggregated by weighting to form a comprehensive influence score, and a complete ability weight relationship matrix is constructed. This n x n matrix (n is the number of ability dimensions) comprehensively reflects the complex correlation structure between the ability dimensions, and each element a ij in the matrix represents the comprehensive influence degree of ability i on ability j, which provides a scientific basis for subsequent comprehensive ability score calculation.

[0123] The core of the linear time primitive algorithm is to decompose complex graph calculation into basic operation units. Topological sorting is implemented using the Kahn algorithm with a time complexity of O(V+E), where V is the number of ability nodes and E is the number of causal relationship edges. The path tracking process uses breadth-first search, but is optimized: each node is accessed only once, and the influence value is updated by incrementing the accumulator. The influence attenuation model uses an exponential decay function f(d) = a d , and the value of a is usually set to 0.7-0.8, which is determined based on historical data verification. The algorithm uses a hash table to store the calculated path influence values, with the key being the "start-end" pair and the value being the influence strength, effectively avoiding repeated calculation. The direct influence is directly taken from the edge weight of the causal graph, and the indirect influence is calculated by the weighted sum of all possible paths. To handle ring dependencies, the system sets the maximum path length to half the number of ability dimensions to prevent infinite loops. The space complexity of the algorithm is O(V 2 ), mainly used to store the influence matrix and cache table. It has been verified that in a model containing 50 ability dimensions, the algorithm only takes about 100 milliseconds to calculate all influence relationships, which is an order of magnitude faster than the traditional matrix calculation method (about 1.5 seconds), meeting the real-time calculation requirements.

[0124] In step S34, based on the ability weight relationship matrix and the standardized multi-dimensional feature data set of the latest employee evaluation data, the comprehensive ability score calculation is performed through the weighted network propagation algorithm to obtain the multi-dimensional score result of the employee's ability.

[0125] This step combines the competency weight relationship matrix obtained in step S33 with the latest assessment data of the employee to calculate a comprehensive score considering the mutual influence between competencies. First, collect the latest assessment data of the employee, including three main sources: formal assessment data (such as periodic competency assessment results, certification examination scores, skill level evaluation, etc.), informal assessment data (such as mentor evaluation, colleague feedback, self-evaluation, etc.), and behavior inference data (automatically inferred competency level based on the employee's learning behavior in the system, work performance). Perform reliability assessment and weight allocation on these different sources of data, usually giving higher weight to formal assessment data, and using informal assessment and behavior inference data as supplements. Based on these assessment data, assign an initial score value to each competency dimension, usually using standardized scores (such as 0-100 points or 1-5 levels). Then, apply the Weighted Network Propagation algorithm to adjust the score considering the mutual influence between competencies. This algorithm is based on the following idea: the performance of an employee in a certain competency is not only determined by the direct assessment result of that competency, but also influenced by the performance of related competencies. For example, if "analytical ability" has a positive influence on "decision-making ability", then the employee's high analytical ability score will have a positive addition to the decision-making ability score. The algorithm updates the score of each competency dimension through multiple iterations until the score value converges. Each iteration uses the following formula:

[0126]

[0127] where is the score of competency i in the t+1th iteration, InitialScore i is the initial score, w ji is the influence weight of competency j on competency i (from the competency weight relationship matrix), and α is the balance parameter (usually between 0.3-0.7) that controls the relative importance of the original score and network influence. The iteration process usually converges within 5-10 iterations, with the convergence condition set as the score change between adjacent two iterations being less than a pre-set threshold (such as 0.001). After completing the iteration calculation, normalize the scores of all competency dimensions to ensure the comparability of scores between different competency dimensions. At the same time, calculate the confidence of each competency score based on the reliability, data volume, timeliness, and other factors of the original data, providing uncertainty measurement for subsequent competency gap analysis. Finally, output the multi-dimensional competency score result of the employee, which is a score vector containing multiple competency dimensions, each dimension containing score value and confidence two key indicators, fully reflecting the competency status of the employee, providing basic data for subsequent competency evaluation report generation and competency gap analysis.

[0128] In step S35, based on the employee capability multi-dimensional score results, combined with the dynamic post capability image, an intuitive capability evaluation report is generated, and the employee capability evaluation report is obtained.

[0129] This step converts the employee capability multi-dimensional score results of step S34 into an intuitive, easy-to-understand and guiding evaluation report. First, data integration and comparison are performed, and the employee's multi-dimensional capability score is compared with the capability requirements of the current post (from the dynamic post capability image), and the matching degree and gap of each capability dimension are calculated. At the same time, the employee's score is compared with the reference group (such as the average level of employees of the same level, the benchmark level of excellent employees), and the relative position reference is provided. Based on these comparison results, a variety of visual charts are generated to enhance the intuitiveness and understandability of the report. The core visualizations include: capability radar chart (showing the employee's score in each capability dimension and the comparison with the post requirements in the form of radar chart), capability development trend chart (combining historical data to show the trend of employee's capability dimensions over time), capability gap heat map (directly showing the capability gap with the target post through color depth, red indicating significant gap, green indicating advantage field) and capability correlation network chart (based on causal relationship network, showing the mutual influence relationship between employee's capabilities). Further advantage capability analysis is performed to identify and emphasize the employee's significant advantage capability (usually the dimension with high score and positive influence on multiple other capabilities), analyze the development potential and value of these advantage capabilities, and provide suggestions on how to utilize these advantages to promote career development. For key capability gaps, specific improvement suggestions are generated, including: key development direction (according to post requirements and capability causal relationship, recommending the most leveraged capability development focus, usually the dimension with larger gap and influence on multiple other capabilities), learning resource pre-recommendation (based on capability gap, preliminary recommendation of relevant learning resources such as courses, projects, mentors, etc.), and capability improvement path (providing progressive capability improvement path planning, including short-term, medium-term and long-term goals). In order to enhance the personalization of the report, the presentation style, detail level and focus of the report are adjusted according to the employee's job level, department characteristics, historical feedback preferences, etc. 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 capability evaluation report is a comprehensive document that adopts a hierarchical structure, from overview to details, to meet different depth reading needs, containing not only quantitative capability evaluation results, but also qualitative analysis and suggestions, providing comprehensive guidance for employees to understand their own capability status and develop plans, while also providing important input for subsequent capability gap analysis and promotion path planning.

[0130] The evaluation report will be an important input for steps S4 and S6, supporting the subsequent capability gap analysis and promotion path planning. At the same time, it is also an important tool for employees to understand their own ability status and develop development plans.

[0131] In step S4, the dissipation over-satisfied constraint solving algorithm is used to calculate the gap matrix between the employee's current ability and the target job requirements by iteratively calculating the ability gap value and the weight coefficient, and obtain the individualized ability improvement needs, including:

[0132] In step S41, based on the employee ability evaluation report and the dynamic job ability portrait, the gap value of each ability dimension is calculated by one-to-one dimension mapping, and the initial ability gap matrix is obtained. This step first determines the target job range, including the employee's current job (the matching degree of the evaluation and the current job requirements), the potential promotion job (the next level job of career development), the horizontal development job (other possible development directions related to the current ability structure) and the long-term target job (the long-term goal in the employee's career planning). Extract the ability requirement parameters of these target jobs from the dynamic job ability portrait, including the basic requirement value, the expected requirement value, the weight coefficient, etc. of each ability dimension. At the same time, extract the employee's score and confidence in each ability dimension from the employee ability evaluation report. Before mapping, first ensure the consistency of the employee ability evaluation dimension and the job ability requirement dimension in semantics and structure. If there is an incomplete correspondence, it will be handled by ability splitting (splitting coarse-grained ability into fine-grained sub-ability), ability merging (merging multiple related sub-abilities into comprehensive ability) or establishing mapping rules (defining the conversion relationship between non-completely corresponding dimensions). After ensuring the consistency of the dimensions, one-to-one ability mapping is performed. For each ability dimension i, the gap value is calculated: Gap i =EmployeeScore i -JobRequirement i , where positive value indicates exceeding the requirement, negative value indicates insufficient, and zero indicates exact match. Considering the importance difference of the ability dimensions, the weighted gap value is calculated: WeightedGap i =Gap i ×Weight i , where Weight iis the importance weight of the capability dimension to the post (from the post capability profile). Based on the gap value and the post requirement, each capability dimension is classified into several levels: significant advantage (capability significantly exceeds the post requirement), moderate advantage (capability slightly exceeds the post requirement), basic match (capability basically meets the post requirement), slight gap (capability slightly lower than the post requirement), significant gap (capability significantly lower than the post requirement), and critical missing (capability significantly lower than the essential capability requirement). Gap visualization representations such as gap heat map, gap bar chart, etc. are also generated to intuitively show the matching of employee capabilities and post requirements. Through this series of mapping and calculation, the initial capability gap matrix is output, which is a multi-dimensional data structure containing the gap value, gap category, weight information of the employee in each capability dimension of each target post, providing basic data for the subsequent dissipation constraint model construction.

[0133] In step S42, based on the initial capability gap matrix, an optimization model containing hard constraints and dissipation constraints is constructed to obtain the dissipation constraint model. This step models the capability improvement problem as a multi-constraint optimization problem, allowing some secondary constraints to be moderately violated while meeting the key requirements by introducing a dissipation constraint mechanism, so as to find a more balanced and practical solution. First, define the decision variable x_i, which represents the improvement target of the employee in the capability dimension i. For example, x_i = 10 indicates a plan to improve 10 percentage points in that capability. Then, the optimization objective function is constructed, which usually includes several sub-goals: maximum matching (minimize the difference between the employee's improved capability and the post requirement, represented as ∑(w_i × (CurrentScore_i + x_i - RequiredScore_i)2)), minimum improvement cost (minimize the total resource input required for capability improvement, represented as ∑(c_i × x_i)), and minimum improvement time (minimize the longest time required for capability improvement, represented as max(t_i × x_i)), where c_i is the unit cost of improving capability i, and t_i is the unit time required to improve capability i). These sub-goals are combined into a comprehensive objective function through weight coefficients. Next, set the hard constraints, which 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 constraints (the time required for capability improvement does not exceed the available time, max(t_i × x_i) ≤ AvailableTime), and capability constraints (the improved capability must meet the post requirement, x_i ≥ RequiredScore_i). Then, set the dissipation constraints, which are allowed to be moderately violated, including: resource dissipation constraints (the total resources required for capability improvement do not exceed the available resources, ∑(r_i × x_i) ≤ AvailableResource), time dissipation constraints (the time required for capability improvement does not exceed the available time, max(t_i × x_i) ≤ AvailableTime), and capability dissipation constraints (the improved capability must meet the post requirement, x_i ≥ RequiredScore_i). Finally, the optimization model is solved to obtain the dissipation constraint model, which contains the optimal improvement target of the employee in each capability dimension. i i i i i i i i i i i i i i ​​​​​​​​​​​​​)≤AvailableTime) and essential capability constraints (for essential capability i, the post-improvement must meet the minimum requirement, CurrentScore i +x i ≥MinRequirement i ). Dissipation constraints are also introduced, which are soft constraints that can be violated to some extent, with a penalty mechanism for the violation degree. Typical dissipation constraints include: capability balance constraint (the improvement degree of different capabilities should be relatively balanced, |x i -x j |≤d ij +s ij , where d ij is the allowed difference, s ij is the violation degree), priority constraint (priority improvement of certain key capabilities, x i ≥p i ×x j -s ij , where p i is the priority coefficient, s ij is the violation degree), and continuity constraint (consistent with the previous development plan, |x i -PreviousTarget i |≤c i +s i ). Based on the capability causal network, the dependency relationship between capabilities is also encoded as a constraint condition, such as prerequisite dependency (if capability j is a prerequisite for capability i, then x j ≥m ji ×x i ), synergistic enhancement (if capabilities i and j have synergistic effects, then set a reward term b ij ×x i ×x j ), and substitution relationship (if capabilities i and j can partially substitute each other, then set a substitution constraint f(x i ,x j )≥RequiredLevel). For the violation degree s of each dissipation constraint, a corresponding penalty function P(s) is designed, which is usually a convex function of s, such as a quadratic function P(s) = λ × s 2 . These penalty terms are added to the objective function, so that the model will try to minimize constraint violation during optimization, but allow moderate violation when necessary to obtain a better overall solution. Through this series of modeling, a complete dissipation constraint optimization model is constructed, which formalizes the capability improvement problem as a mathematical optimization problem, providing a clear mathematical framework for subsequent solution algorithms.

[0134] In the construction of the dissipation constraint model, the system defines multiple types of constraint conditions and designs appropriate violation penalty mechanisms. Hard constraints include: resource constraints Σ(ri x i ≤ R_max, where r i is the resource requirement for the promotion of ability i, R_max is the upper limit of available resources; time constraint max(t i x i ) ≤ T_max, to ensure that any ability promotion does not exceed the maximum available time; essential ability constraint CurrentScore i + x i ≥ M_i, to ensure that the essential ability meets the minimum requirement M i . Dissipation constraints include: ability balance constraint |x i - x j | ≤ d ij + s ij , where d ij is the allowed difference and s ij is the violation degree; priority constraint x i ≥ p i x j - s ij , to ensure that key abilities are developed with priority; continuity constraint |x i - PreviousTarget i | ≤ c i + s i , to maintain consistency with previous plans. For the violation degree s, the system designs a quadratic penalty function P(s) = λs 2 , with λ value set based on the importance of the constraint, usually in the range of 5-20. Inter-ability dependencies are also considered, such as prerequisite dependency constraint 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 abilities i and j have a synergistic effect). The entire model is formalized through convex quadratic programming, ensuring the efficiency and global optimality of the solution.

[0135] In step S43, an over-constrained 3-SAT solving algorithm is iteratively applied to the dissipation constraint model to obtain the personalized capability improvement requirements. This step employs a specialized solving technique for over-constrained problems, which seeks a solution that violates the fewest constraints or minimizes the violation level when constraints may conflict. First, the dissipation constraint optimization model constructed in step S42 is converted into a 3-SAT problem representation. 3-SAT is a special case of Boolean satisfiability problem, where each clause contains exactly three literals. This conversion involves discretizing continuous variables and converting constraint conditions into a set of logical clauses. Specifically, the improvement target of each capability dimension is divided into multiple discrete levels, which are then encoded as combinations of Boolean variables. For example, an improvement of 10 points in capability i can be encoded as the value of variable x i,10 being true. Constraint conditions are also converted into logical relationships on these Boolean variables. After conversion, an initial solution that satisfies hard constraints is generated. If it is not possible to satisfy all hard constraints simultaneously, a hierarchical approach is used to prioritize the most important constraints. The initial solution is usually generated based on a greedy algorithm or heuristic rules, such as prioritizing the improvement of capability gaps with high weights. 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 changes the value of a variable in that clause to satisfy it. The variable selection strategy combines randomness and heuristic rules, such as selecting the variable whose change results in the fewest new violated constraints. For dissipation constraints, the weights of violation penalties are dynamically adjusted. Initially, all constraint weights are equal, and as the solving process progresses, the weights of frequently violated constraints increase, causing the algorithm to pay more attention to these difficult-to-satisfy constraints. After obtaining the preliminary solution, local search algorithms are applied for further optimization, such as simulated annealing, tabu search, and other techniques. By making small adjustments to the current solution, these algorithms explore the neighborhood solution space to find better solutions. Since the problem involves multiple objectives (such as capability matching degree, improvement cost, time requirements, etc.), multiple Pareto optimal solutions may be generated, representing different trade-off schemes. Finally, the generated multiple candidate solutions are comprehensively evaluated, considering factors such as constraint satisfaction degree, objective function value, and solution robustness, to select the optimal solution. Based on the optimal solution, a personalized capability improvement requirement is generated, including the optimal improvement target of each capability dimension, the expected capability level, the priority order of improvement, the setting of stage goals, and suggestions for development paths, etc. This personalized capability improvement requirement not only considers the job requirements and the current capability status of the employee, but also balances resource constraints, time limitations, and the dependency relationships between capabilities, providing a scientific and feasible capability development plan that provides clear guidance for subsequent learning resource matching.

[0136] The implementation of the 3-SAT solving algorithm includes two key steps: problem conversion and iterative solving. In the problem conversion phase, the system discretizes the continuous capability improvement decision variables x iDiscretization into a number of binary variables x i,k , indicating that the capability i is raised by k units. Each constraint is converted into a number of 3-SAT clauses, for example the resource constraint i ×x i )≤R_max is converted into a series of clauses like , indicating that "these three raises cannot be selected simultaneously, otherwise the resource limit will be exceeded". Hard constraints are converted into clauses that must be satisfied, and dissipation constraints are converted into soft clauses with weights. In the solving phase, the system uses an improved WalkSAT algorithm, the initial solution is generated by a greedy construction method, and in each iteration, a not satisfied clause is randomly selected, and the variable that makes the number of not satisfied clauses the least after flipping is selected with a probability of 0.8, and a variable is randomly selected with a probability of 0.2. Flip. The algorithm is set to a maximum of 100,000 iterations, or 1000 consecutive iterations without improvement to terminate early. To deal with local optimization problems, the system implements a simulated annealing mechanism with an acceptance probability of exp(-ΔE / T), where ΔE is the energy change, T is the temperature parameter, the initial value is 100, and the rate is reduced by 0.99. The system runs 10 solving instances in parallel, and takes the optimal result. Experiments show that this strategy can find the global optimal or near-global optimal solution in more than 95% of cases.

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

[0138] In step S51, based on the standardized multi-dimensional feature data set including learning resources such as courses, projects and case resources in the enterprise learning resource library, content features, capability labels and difficulty level information are extracted to obtain a learning resource feature matrix.

[0139] This step first extracts the complete information of enterprise learning resources from the standardized multi-dimensional feature dataset, including formal courses (such as online courses, offline training, seminars, etc.), practical projects (such as practical projects, simulation training, action learning, etc.), case resources (such as best practice cases, failure analysis, etc.), and other learning resources (such as books, articles, videos, etc.). Multi-dimensional feature extraction is performed on each learning resource: in terms of content features, topic area, key concepts, knowledge coverage, etc. are extracted from the resource through text analysis techniques; for non-text resources (such as videos, audio), topic information can be extracted through metadata analysis or content recognition techniques. In terms of ability labels, each learning resource is mapped to the relevant dimensions in the standardized ability framework to determine the type and degree of ability that can be improved by the resource. This mapping can come from preset labels of the resource, expert annotation, or automatic inference from content and learner feedback through machine learning models. The degree of overlap and complementarity between resources is also analyzed to identify resource combinations that can synergistically improve specific abilities. In terms of difficulty level, the complexity and challenge of each resource are evaluated, usually based on content complexity, prerequisite knowledge requirements, completion time, historical learner completion rate, score, etc. 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 for completion), interaction level (content interactivity and engagement), evaluation indicators (historical learner scores, recommendation rate, post-completion ability 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, where each row represents a learning resource and each column represents a feature dimension. The cell value indicates the performance or score of the specific resource in the 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 ability-resource correlation calculation, ensuring that the most suitable learning resources can be matched for employee ability improvement needs.

[0140] In step S52, based on the personalized ability improvement needs and the learning resource feature matrix, the correlation of each learning resource to the target ability improvement is calculated to obtain an ability-resource correlation 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 capability-resource correlation degree calculation adopts a multi-feature weighted matching algorithm. The matching degree is evaluated from five dimensions: content relevance, the similarity between the learning resource theme and the target capability is calculated using cosine similarity, with a weight of 0.3; capability coverage, the direct improvement effect of the resource on the target capability is evaluated, based on historical learning data and expert evaluation, with a weight of 0.25; difficulty adaptation, the matching degree of resource difficulty and the current level of employees is calculated, using a Gaussian function mapping, with a weight of 0.2; learning style fit, the matching degree between the resource presentation method and the employee's learning preference is compared, with a weight of 0.15; time efficiency, the capability improvement effect within a unit of time is evaluated, with a weight of 0.1. The learning effect evidence in the historical data is also considered, and the capability improvement of employees after learning a certain resource with similar capability structure is analyzed through collaborative filtering technology. The algorithm uses a weighted harmonic mean to integrate the scores of each dimension, ensuring that a very low score in a single dimension will significantly reduce the total score, avoiding the recommendation of resources with serious mismatches in key dimensions. The weights are optimized through A / B testing, with a test sample of 1000 employees from different job levels and departments for 3 months, and the final weight configuration increases the matching accuracy by 23% and the 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 dependency through a path optimization algorithm.

[0144] This step models the learning resource selection and sequencing problem as a path optimization problem, aiming to find a sequence of learning resources that can most effectively enhance the target competencies. First, define the optimization objectives, which typically include: maximizing competency enhancement effect (selecting a combination of resources with high correlation), minimizing learning time (maximizing benefits within a limited time), maintaining learning coherence (ensuring logical connection of learning content), and balancing multiple competency development (coordinating the enhancement of multiple competency dimensions). Use combinatorial optimization algorithms such as genetic algorithms, ant colony optimization or simulated annealing algorithm to search for the optimal learning resource combination and sequence. When constructing the learning path, consider various constraints: resource dependency relationships, certain advanced resources may require completion of specific basic resources first, which will force compliance with these prerequisites; time constraints, considering the available learning time of employees and the time requirements of each resource, ensuring the feasibility of the path in terms of time; resource diversity, balancing different types of learning activities (such as theoretical learning, practical application, reflection and summary, etc.), avoiding learning fatigue caused by a single type; competency dependency relationship, based on the competency causal network, preferentially arranging the development of those competencies that serve as the basis for other competencies. Design a phased learning structure to decompose the overall learning path into multiple relatively independent learning modules, each focusing on a specific competency dimension or thematic area, and set appropriate evaluation points and feedback mechanisms between modules. For long-term competency development paths, use a rolling planning strategy to plan short-term learning activities in detail while providing framework guidance for long-term development, allowing dynamic adjustment of subsequent paths based on stage learning results. Also consider the pace and intensity of learning, design reasonable learning frequency and single learning duration, avoid excessive intensive or too dispersed learning arrangement. Through these optimization designs, generate a preliminary learning path scheme, including recommended learning resource list, suggested learning sequence, predicted time schedule and expected competency enhancement effect. This preliminary scheme takes into account resource characteristics, competency correlations and various constraints, providing a scientific foundation framework for subsequent personalized customization.

[0145] In step S54, based on the preliminary learning path scheme, personalized customization is carried out by combining employee learning style, time arrangement and historical learning effect, to obtain the personalized learning recommendation plan.

[0146] This step further personalizes the preliminary learning path plan from step S53 to better fit the individual characteristics and needs of the specific employee. First, analyze the employee's learning style preferences, including sensory preferences (visual, auditory, reading / writing, kinesthetic, etc.), processing preferences (active vs. reflective, sensing vs. intuitive, etc.), and environmental preferences (independent learning vs. group learning, formal environment vs. informal environment, etc.). These preferences can come from self-reports, learning style assessments, or inferences from historical learning behavior. Based on the learning style analysis, adjust resource selection and presentation methods, such as adding graphical and video resources for visual learners and interactive and practical opportunities for active learners. Also, analyze the employee's time availability and optimal learning periods in detail, including work schedule, personal habits, and biological clock patterns. Based on this information, optimize learning time scheduling, such as scheduling high cognitive load learning content during the employee's most energetic periods and scheduling fragmented learning resources during commuting or waiting times. Also, consider the employee's historical learning data to analyze patterns of past learning effectiveness and identify success factors and barrier factors. For example, it may be found that the employee performs better in a group learning environment or that efficiency significantly decreases after continuous learning for more than an hour. Based on these insights, adjust the learning methods and pace to maximize learning effectiveness. The personalization process also considers the employee's professional background and experience, linking abstract concepts to familiar real-world scenarios from the employee's work environment, enhancing the relevance and applicability of the learning content. Also, adjust the content presentation angle and case selection based on the employee's learning motivation and interest points to enhance learning appeal. For potential difficulties encountered during the learning process, preset support mechanisms, such as providing additional auxiliary resources, setting up peer learning or tutor support, etc. Finally, generate a highly personalized learning recommendation plan, including a customized resource combination, a personalized learning schedule, appropriate 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), ensuring an efficient and enjoyable learning process that maximizes the effectiveness of capability development.

[0147] In step S55, the personalized learning recommendation plan is set with stage goals 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 ability improvement goal is broken down into a series of specific, measurable, achievable, relevant, and time-limited (SMART) targets. These targets are usually divided into short-term (1-3 months), medium-term (3-6 months), and long-term (more than 6 months) three levels, forming a progressive target system. Each stage goal clearly defines the expected ability improvement level, key learning outcomes and verification methods, so that employees can clearly understand the direction of effort and success criteria at each stage. A variety of progress tracking indicators are designed, including completion indicators (such as resource learning completion rate, task submission, etc.), understanding indicators (such as test scores, concept application ability, etc.), application indicators (such as practical project performance, ability application in work, etc.), and feedback indicators (such as supervisor evaluation, colleague feedback, etc.). These indicators comprehensively measure different dimensions of learning, avoiding one-sided evaluation that may be caused by a single indicator. An adaptive learning path adjustment mechanism is also established to dynamically optimize the learning plan based on progress tracking data. For example, when it is found that a certain ability develops slower than expected, additional learning resources or learning methods will be automatically recommended; when a certain ability reaches the target ahead of schedule, the subsequent plan will be adjusted accordingly, allocating more resources to other ability dimensions that need attention. To enhance learning motivation and persistence, incentive mechanisms are designed, including progress visualization (intuitive display of learning progress and ability improvement), achievement milestones (celebrating the completion of important learning stages), social motivation (learning progress comparison and interaction with peers), and reward mechanisms (integration with enterprise incentives). Intervention strategies are also designed for possible learning obstacles and progress delays, including automatic reminders, learning suggestions, resource adjustments, and manual intervention if necessary (such as tutor guidance, supervisor support, etc.). Finally, regular reflection and summary sessions are designed to help employees review their learning experience, distill 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, the personalized learning recommendation plan is transformed into a complete and executable learning development program, not only telling employees "what to learn" and "how to learn", but also helping employees manage the entire learning process, ensuring that learning activities effectively translate into ability improvement and career development.

[0149] In step S6, a promotion path planning algorithm is used to construct a multi-dimensional promotion possibility evaluation, and a personalized promotion path suggestion is obtained, which specifically includes:

[0150] In step S61, based on the standardized multi-dimensional feature data set of the organizational structure data, a connection relationship network between positions is constructed and possible promotion channels are identified, and an organizational position network diagram is obtained.

[0151] This step extracts enterprise organizational structure-related information from the standardized multi-dimensional feature dataset, builds a comprehensive post connection relationship network, and provides infrastructure support for subsequent promotion path planning. First, collect various organizational structure data, including formal organizational charts (department hierarchy relationships, reporting lines, etc.), job descriptions (job scope, job level, etc.), position management data (post code, post group, etc.), and historical personnel flow records (promotion, transfer, rotation, etc.). Based on these data, an initial post network structure is built, where each post is a node in the network, containing basic attributes such as post name, department, and job level. Direct reporting relationships, job level relationships, and other connections are established, and each connection is labeled with a type (such as direct promotion, cross-level promotion, lateral transfer, etc.). In addition to formally defined post relationships, "implicit channels" that exist in the organization but are not formally defined are discovered by analyzing historical personnel flow data. The frequency and success rate of personnel flow between different posts are calculated, and non-traditional development paths with high frequency and high success rate are identified to enrich the connection relationships of the post network. Potential development channels are also added based on business relevance and ability similarity. By analyzing business processes and collaboration relationships, closely collaborating posts are identified; by comparing the ability requirements of different posts, posts with similar ability structures are identified; based on these analyses, cross-department development paths with reasonable transition possibilities are established to expand the breadth of the post network. To ensure the accuracy and practicality of the network, invite human resources experts and business managers to verify and adjust the extended channels, especially those implicit channels and potential channels inferred based on data. Assign attributes and weights to each channel in the network, including difficulty coefficient (indicating the difficulty of promotion / transferring from the source post to the target post), time span (typical time period required), flow frequency (historical personnel flow frequency), pre-requisites (special qualifications or condition requirements), and strategic importance (degree of consistency with the company's strategic direction). A regular update mechanism for the network is also established to ensure that it reflects the latest organizational changes, including organization change-triggered updates, regular audits, and continuous fine-tuning based on new flow data. The final output of the organizational post network diagram is a complex graph structure that not only contains the hierarchical relationships in the traditional organizational chart but also integrates historical personnel flow patterns, ability relevance, and business collaboration relationships, providing a comprehensive reflection of career development possibilities in the organization and a comprehensive network foundation for subsequent promotion suitability assessment.

[0152] In step S62, based on the employee capability assessment report, the dynamic post capability profile, and the historical promotion case data of the standardized multi-dimensional feature dataset, the asymmetric random block model is applied to calculate the promotion suitability of employees in different posts, obtaining a promotion possibility score matrix.

[0153] This step evaluates the employee's matching degree and success probability in each potential development position, providing a scientific basis for promotion path planning. First, based on the organizational position network graph constructed in step S61, all potential development positions directly or indirectly connected with the employee's current position are selected, including direct superior positions (traditional promotion), adjacent functional positions (horizontal development), cross-department related positions (cross-department development), and professional deepening positions (professional development route). For each potential position, the asymmetric random block model is applied to evaluate the employee's adaptability. This model takes employee ability characteristics as "processing" and job ability requirements as "block", considering the asymmetric and random effects of different abilities on different positions. The optimal matching degree is solved by semi-definite programming algorithm, and the preliminary adaptability score of the employee to each position is obtained. Historical cases of employees successfully promoted to the target position are also analyzed to find similar historical cases with the current employee's ability structure, identify the key success factors in these cases, and compare the current employee's gap with the key factors in the successful cases, based on the similarity and gap size to estimate the success probability. The matching degree of the employee to each potential position is evaluated from multiple dimensions: ability matching degree (compliance of employee ability with job requirements), experience matching degree (relevance of work experience to job experience requirements), behavior style matching degree (fit of personal behavior style with job characteristics), development potential matching degree (matching degree of long-term development potential with job development space), and organizational culture fit (adaptability of personal traits to department culture). The time required for the employee to meet the requirements of each potential position is also predicted, based on the gap between current ability and requirements, employee historical learning speed and ability development mode, identifying the key intermediate states required to reach the target, and giving optimistic, most likely, and conservative time estimates. Finally, the comprehensive promotion adaptability score is generated by integrating the above analyses, the final score is formed by weighted calculation (weight allocation according to enterprise-specific promotion policies and practices), and the confidence interval of the evaluation result (reflecting the degree of certainty of the prediction), relative ranking (relative ranking among all possible positions), and adaptability classification (classification of positions into high, medium, and low adaptability) are provided. The final output of the promotion probability score matrix is a multi-dimensional data structure containing the employee's adaptability score, time prediction, key gap, and relative ranking for each potential development position, providing key input for subsequent multi-path scheme generation, and helping to identify the most potential career development direction.

[0154] In step S63, based on the promotion probability score matrix and the organizational position network graph, vertical promotion paths and horizontal development paths are generated to obtain a multi-dimensional promotion path candidate set.

[0155] This step is based on the analysis results of the previous two steps, and designs multiple possible career development paths to provide employees with diversified development options. First, determine the starting point and the end point: the starting point is the current position of the employee, and the end point set is 2-4 potential end positions determined according to the long-term career intention and high adaptation positions of the employee. These end positions may include senior management positions, professional leadership positions or cross-domain comprehensive positions, etc., representing different career development directions. A variety of path search algorithms are applied to explore possible paths from the starting point to each end point: shortest path search, applying Dijkstra algorithm, taking promotion difficulty as edge weight, to find the shortest path to each end point; multi-criteria path search, applying multi-objective A* algorithm, balancing time, difficulty, and matching degree of employee preference, etc. multiple goals; branch path exploration, identifying key branch points, exploring possible selection branches at different decision points. The generated paths are classified and screened by type: vertical promotion path, focusing on traditional promotion path of job level promotion, usually developing upward along the organizational hierarchy; professional development path, focusing on path of professional depth promotion, emphasizing improvement and influence expansion in a specific professional field; management development path, focusing on path of management breadth and leadership development, focusing on team management and organizational leadership ability training; cross-domain development path, involving cross-professional or cross-departmental transformation path, emphasizing the development of composite skills and the expansion of vision. Optimize and adjust the preliminary generated paths: node optimization, adjust the intermediate positions in the path to ensure smooth transition; time distribution optimization, reasonably arrange the time of each stage to avoid bottlenecks; development balance check, ensure balanced development of professional ability and management ability; risk assessment, identify key risk points and coping strategies for each path. Calculate key attribute indicators for each path: total time (estimated time required to complete the entire path), challenge level (overall difficulty score of the path), resource demand (total amount of learning and development resources required), flexibility (adjustable degree in the path), and market value (expected market competitiveness after completing the path). Also generate path visualization, which converts complex path information into intuitive visual forms, including timeline view (showing each stage position and key milestones on the time axis), network graph view (showing the location and associated positions of the path in the organizational network), ability evolution graph (showing the changes in the ability structure along the path development process), and comparison view (showing the key differences between different paths). Finally, output a set of multi-dimensional promotion path candidates, each path containing complete position sequence, stage time estimation, key ability development focus, path attribute indicators, and path advantages and challenges, etc. information, providing rich candidate schemes for subsequent path optimization and screening, ensuring that the most suitable career development path for the employee can be found.

[0156] The multi-dimensional promotion path generation algorithm combines graph theory and multi-objective optimization techniques. The path search uses an improved Dijkstra algorithm, with edge weight defined as the comprehensive difficulty index, and the calculation formula is:

[0157]

[0158] where a, b, g are the weight coefficients of capability gap, time cost and historical success rate respectively, which are set to 0.4, 0.3 and 0.3 by 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), which simultaneously optimizes three objectives: minimization of path difficulty, minimization of development time and maximization of matching degree with employee preferences. The population size is set to 100, the iteration number is 200, the crossover probability is 0.8, and the mutation probability is 0.1. The algorithm uses boundary reservation strategy and crowded distance sorting to ensure the diversity of solutions. Path classification and screening are based on hierarchical clustering algorithm, which clusters the generated paths according to the feature vector (including average difficulty, total time length, job type composition, etc.). The system also implements a key branch point identification function, which uses information gain to calculate the importance of each decision point. Nodes with information gain exceeding the threshold of 0.2 are marked as key branch points, allowing employees to make different choices at these points and generate diverse development paths. The training data includes the promotion records and related attributes of about 2000 employees in the past 5 years, covering more than 80% of the positions and departments in the organization, ensuring the comprehensiveness and representativeness of the model.

[0159] In step S64, based on the candidate set of multi-dimensional promotion paths, a dissipation constraint solving technique is applied through a multi-constraint optimization model and a random walk strategy to optimize the path, obtaining an 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 the employee. First, the path selection problem is modeled as a multi-constrained optimization problem, defining decision variables (such as path selection variables, time allocation variables, etc.), objective functions (comprehensively considering path value, time efficiency, resource consumption, etc.), and constraint conditions. The constraint conditions are divided into two categories: hard constraints and soft constraints. Hard constraints are basic conditions that must be met, such as organizational policy restrictions, necessary qualification requirements, etc. Soft constraints are desirable conditions that can be moderately violated, such as time preferences, development direction preferences, etc. A dissipation mechanism is introduced for soft constraints, allowing moderate violation of certain constraints while meeting overall objectives. Specifically, different constraints are set with priorities, a penalty function (usually quadratic or exponential) is defined for the degree of constraint violation, and the maximum acceptable violation degree is set. Analyze the potential conflicts and mutual exclusion relationships between different path plans, such as resource competition (competition for the same resource by multiple paths), time conflict (overlap of different development activities), and direction conflict (potential conflict between professional direction and management direction). Not only evaluate single paths, but also consider the possibility of multiple path combinations, such as setting primary development paths and alternative paths, switching different types of development focus at different stages, designing multi-directional development plans that can be simultaneously promoted, etc. Apply the 3-SAT solver to solve the optimization problem by first converting the multi-constrained optimization problem into a 3-SAT problem form, then iteratively finding the optimal solution through random restart and local search strategies, balancing exploration and utilization, avoiding getting stuck in local optima, and finally generating multiple different characteristic solutions that meet the conditions. Perform sensitivity analysis on the final filtered path plan to test the impact of key parameter changes on path evaluation, simulate the adaptability of the plan under different organizational environments and individual circumstances, identify key risk factors that may lead to path failure, and evaluate the plan's ability to adapt to environmental changes. Through this series of optimization and analysis, 2-3 optimized promotion path plans with different characteristics are finally output, each containing detailed evaluation information such as path complete description, optimized time schedule, key points and milestones at each stage, resource demand plan, risk points and coping strategies, adaptability score and confidence, and comparison analysis with other paths. These optimized path plans not only meet the development needs of the organization, but also satisfy the personal preferences and ability characteristics of the employees, providing scientific and feasible basic plans for subsequent personalized promotion path suggestion generation.

[0161] In step S65, based on the optimized promotion path plan, a detailed suggestion containing time nodes, milestones, and key ability improvement points is generated by combining the personalized learning recommendation plan, obtaining 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 a detailed, executable, and personalized promotion path suggestion. First, the optimized path plan is expanded into a detailed development plan, with clear time nodes and stage divisions, typically including short-term (within 1 year), medium-term (1-3 years), and long-term (more than 3 years) three levels. Set key career milestones and success indicators for each time period, such as job change, ability level improvement, key project completion, etc., to ensure that employees have clear stage goals. Break down long-term goals into a series of short-term, achievable sub-goals to form a progressive development ladder, enhancing the executability of the path. Clearly define the key capabilities that need to be developed in each stage, and integrate the personalized learning recommendation plan from step S54 with the promotion path to ensure that learning activities are closely aligned with career development goals. Specifically, align the recommended learning resources with the needs of each stage of the promotion path, and configure the appropriate combination of learning resources for each development stage; identify key practice opportunities for applying abilities, such as projects, tasks, job rotation, etc., to help employees convert learning content into actual abilities; arrange necessary certification and qualification acquisition time points to ensure that employees have the necessary formal qualifications at key nodes; provide specific channels and methods for obtaining various learning resources to reduce barriers to resource acquisition. Also plan the interpersonal network and support system required 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 suggestions for expanding personal networks. Design development progress tracking and feedback, including setting regular evaluation points for development progress, providing self-assessment tools and methods, designing multi-channel feedback collection mechanisms, and clarifying when development plans need to be adjusted. Adjust the expression of the suggestion according to the characteristics of the employee, including adjusting the language style according to the employee's communication preferences, adjusting the level of detail according to the employee's information processing preferences, choosing the most suitable visualization method to present information, and emphasizing development factors related to the employee's personal motivation. Also enhance the persuasiveness of the suggestion through relevant cases and stories, including providing successful promotion cases similar to the employee's background, sharing common challenges and solutions in similar paths, introducing the development course of relevant role models within the organization, and providing data and evidence to support the effectiveness of the suggestion. Finally, output the personalized promotion path suggestion, which is a comprehensive document containing an executive summary, a development path diagram, a stage plan, a capability development map, a learning resource list, a practice opportunity guide, a support network suggestion, a progress tracking tool, and a frequently asked questions section, etc. to provide comprehensive, specific, and actionable career development guidance for employees, helping them achieve long-term career goals.

[0163] In addition, based on the standardized multi-dimensional feature data set of employee learning data, evaluation result changes and promotion success cases, through a learning-evaluation-promotion three-dimensional closed loop model, the post image and recommendation algorithm are continuously optimized, and the system iteration optimization parameters are output. Specifically, it includes:

[0164] This step first tracks and evaluates the evaluation result changes, ability application and performance improvement of employees after completing learning, evaluates the actual effectiveness of learning resources, and forms a learning effect evaluation report. Analyze the historical data of successful promotion employees, including ability improvement trajectory, learning path selection, key success factors, etc., extract the promotion success mode and rule, and form the promotion success mode analysis result. Based on the learning effect evaluation and promotion success mode analysis, the system constructs a three-dimensional closed loop feedback model of learning-evaluation-promotion, which relates the three dimensions of learning process, ability improvement and career development, and forms a complete closed loop system. Through this closed loop model, the system identifies the optimization direction of the current algorithm and model, such as post ability weight adjustment, ability evaluation parameter optimization, learning resource matching rule improvement, etc. The parameters of the core algorithm are adjusted adaptively, including the parameters of the asymmetric random zone group model, the weight of the linear time element algorithm, and the threshold of the dissipation constraint solution, etc., to improve the accuracy and adaptability of the system. 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, maintain the effectiveness and forward-looking of the recommendation, and provide long-term effective support for enterprise talent development.

[0165] In step S71, based on the standardized multi-dimensional feature data set of employee learning data, evaluation result changes and promotion success cases, through a learning-evaluation-promotion three-dimensional closed loop model, the post image and recommendation algorithm are continuously optimized, and the system iteration optimization parameters are output. Specifically, it includes:

[0166] This step establishes a comprehensive learning effectiveness evaluation system, tracks and analyzes the actual effectiveness of employees after completing learning activities, and provides data basis for optimization. First, multi-dimensional learning effectiveness data is collected, including direct learning achievements (such as course completion, test scores, certification acquisition, etc.), capability improvement indicators (difference between pre-learning and post-learning capability assessment, self-assessment changes, etc.), work application situations (application frequency and quality of learning content in actual work, supervisor evaluation of application situation, etc.), and performance improvement data (changes in key performance indicators, project results improvement, etc.). Using before-and-after comparative analysis method, the capability level and work performance changes of employees before and after learning are compared, and significant improvement points and remaining gaps are identified. In order to control the influence of other factors, a control group comparison is also used, comparing employees who participated in specific learning activities with employees who did not participate in the activities but have similar backgrounds, to more accurately assess the unique contribution of learning activities. Further analysis of the effectiveness differences of different types of learning resources is conducted to compare the relative effectiveness of formal courses, practical projects, self-directed learning, and other forms to identify the most suitable learning method for specific capability development. The synergistic effect of learning resource combinations is also evaluated to analyze whether certain resource combinations have produced an effect beyond the simple addition of individual resources, providing a basis for subsequent resource combination optimization. The durability of learning effectiveness is tracked longitudinally through regular capability assessment and performance monitoring to analyze the retention and decay curve of learning effectiveness and assess the long-term impact of different learning methods. Key factors affecting learning effectiveness are also analyzed, including learner characteristics (such as learning motivation, prior knowledge level, etc.), learning environment factors (such as work pressure, management support, etc.), and learning design factors (such as content relevance, interaction level, etc.), to identify key leverage points for improving learning effectiveness. Learning return on investment (ROI) is calculated to convert learning outcomes (such as capability improvement, performance improvement, etc.) into value estimates and compare them with learning costs (time investment, resource expenses, etc.) to evaluate the economic benefits of learning activities. Finally, a comprehensive learning effectiveness evaluation report is generated, including overall effectiveness evaluation, resource type effectiveness comparison, influence factor analysis, investment return analysis, and improvement suggestions, etc. This report not only evaluates the effectiveness of existing learning resources and methods, but also provides specific optimization directions, providing a scientific basis for subsequent improvement, ensuring that learning activities can be more effectively converted into capability improvement and performance improvement.

[0167] In step S72, based on the successful promotion cases of employees in the standardized multi-dimensional feature data set, the capability improvement trajectory, learning path and key success factors are analyzed to obtain promotion success mode analysis results.

[0168] This step extracts the promotion success patterns and rules by in-depth analysis of historical success cases, providing experience and reference for optimization and employee development. First, identify successful promotion employee samples from the standardized multi-dimensional feature data set. These samples are usually employees who successfully promoted to the target position within the past 1-3 years and performed well in the new position. Collect comprehensive historical data of these successful cases, including capability development trajectory (change curve of each capability dimension over time), learning history (training, projects, certification, and other learning activities), work experience (key projects, job responsibility changes, etc.), performance record (performance evaluation results at each stage), and promotion process (promotion timing, evaluation feedback, etc.). Perform multi-dimensional analysis on these data. First, analyze the capability development pattern to identify the characteristics and development rules of the successful promotion employee's capability structure before promotion, such as which capabilities are prioritized for development, which capabilities have achieved breakthrough improvement, the development sequence and mutual promotion relationship between capabilities, etc. Also analyze the learning path characteristics, including learning resource selection (prefer which type of learning resource), learning time allocation (how much time is invested and how it is allocated), learning strategy (how to combine different types of learning activities), and learning-application transformation (how to apply learning content to actual work). Further analyze key experience accumulation, such as the types of key projects participated in, special tasks undertaken, and key exposure opportunities obtained, to identify critical practical experience for capability development and promotion. Also analyze support network construction, including mentor relationships, key supporters, and professional community participation, to understand the role of interpersonal networks in successful promotion. Compare successful cases from different backgrounds (such as different departments, different starting points, and different career stages) to identify common success patterns and unique patterns in specific situations, enhancing the applicability of the analysis results. Also, through comparative analysis, compare successful promotion employees with those who failed to promote to identify key differentiating factors and decisive success elements. Perform statistical analysis on quantitative data to find the correlation and predictive power of factors such as capability level, learning investment, and performance to promotion success, and establish a promotion success prediction model. Also, through text analysis and content coding, extract key themes and success factors from qualitative materials such as supervisor comments, promotion recommendation letters, and self-statements. Finally, generate promotion success pattern analysis results, including typical success path descriptions, key success factor rankings, capability development key nodes, effective learning resource combinations, practical experience acquisition strategies, and support network construction suggestions. These analysis results reveal the internal rules and patterns of successful promotion within the organization, providing valuable experience data for optimization and empirical basis for employee career development guidance.

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

[0170] The step integrates learning activities, capability assessment, and career promotion into a closed loop, revealing the internal relationship and mutual influence mechanism among the three, and providing a theoretical framework for optimization. First, analyze the learning effect evaluation report of step S71 and the promotion success mode analysis result of step S72, extract key findings and insights, especially about how learning activities affect capability improvement, and how capability improvement translates into promotion success. Based on these analyses, build the basic structure of the three-dimensional closed loop model, including the learning dimension (design, implementation, and participation of learning activities), the assessment dimension (methods, standards, and results of capability assessment), and the promotion dimension (promotion opportunities, decision-making process, and success factors). Define the two-way relationship between the three dimensions in detail: learning→assessment relationship, analyze the impact of different types of learning activities on specific capability dimensions, and establish a quantitative correlation model between learning input and capability improvement; assessment→promotion relationship, analyze the correlation between capability assessment results and promotion decision, identify the most predictive capability indicators and critical levels for promotion; promotion→learning relationship, analyze how promotion needs and promotion feedback guide and adjust learning direction, forming a demand-driven learning closed loop. Also analyze the internal mechanisms of the three dimensions: within the learning dimension, study how learning motivation, learning methods, and learning environment interact to affect learning effectiveness; within the assessment dimension, analyze how assessment methods, assessment frequency, and feedback mechanisms affect the accuracy and development orientation of assessment; within the promotion dimension, study how organizational environment, management support, and individual strategies affect promotion opportunities and decision-making. Identify key nodes and potential bottlenecks in the closed loop, such as the learning-to-capability conversion bottleneck (learning content is difficult to apply to actual work), the capability-to-promotion conversion bottleneck (capability improvement is not recognized or recognized), etc., to provide focus directions for subsequent optimization. Establish a time dynamic model for the operation of the closed loop, analyze the interaction of the three dimensions at different time scales: short-term cycle (daily learning and immediate feedback), medium-term cycle (phase learning project and regular assessment), and long-term cycle (career development planning and promotion realization). Also analyze how individual difference factors affect the closed loop effect, including learning style differences, career stage differences, job type differences, etc., to provide the basis for subsequent personalized optimization. Based on comprehensive analysis, identify the current optimization direction, including learning dimension optimization (such as adjusting learning resource combination, improving learning method design), assessment dimension optimization (such as optimizing evaluation indicators, improving feedback mechanism), promotion dimension optimization (such as perfecting promotion standards, enhancing transparency), and three-dimensional collaborative optimization (such as strengthening data sharing and signal transmission between dimensions). Finally, output the complete description of the three-dimensional closed loop feedback model and optimization direction suggestions, providing theoretical guidance and practical direction for subsequent algorithm parameter adjustment and iterative optimization, ensuring continuous improvement of learning effectiveness, capability assessment accuracy, and promotion path effectiveness.

[0171] The three-dimensional closed-loop feedback model is constructed based on structural equation modeling (SEM) and system dynamics method. First, the SEM is used to quantify the relationship strength among the three dimensions: the learning→evaluation path coefficient is 0.62 (p<0.001), indicating that the learning activity has a significant positive impact on the improvement of ability; the evaluation→promotion path coefficient is 0.48 (p<0.001), indicating that the ability assessment result has a moderate influence on the promotion decision; the promotion→learning path coefficient is 0.35 (p<0.01), indicating that the promotion result has a certain guiding effect on the subsequent learning behavior. The model fitting indexes are good (CFI=0.92, RMSEA=0.058, SRMR=0.063), indicating that the model can well explain the actual data. The system further uses the system dynamics method to simulate the time dynamic characteristics of the three-dimensional closed loop, and establishes a set of differential equations to describe the relationship between the three dimensions and time. The simulation results show that the complete closed-loop learning-evaluation-promotion system improves the ability development efficiency by about 40% and shortens the promotion cycle by about 25% compared with the one-way process. Through modulator analysis, the key factors affecting the closed-loop effect are identified: feedback timeliness (β=0.31), information transparency (β=0.28), and organizational support (β=0.24) are the three most important modulating variables. Based on these analyses, four main optimization directions are identified: strengthen the immediate feedback mechanism between learning and evaluation, shorten the feedback cycle from quarterly to weekly; improve the transparency between evaluation and promotion, clarify the correlation between ability standards and promotion decisions; strengthen the guidance between promotion and learning, so that the promotion result can more effectively guide the subsequent learning direction; establish 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 algorithm including post image construction, ability assessment and resource matching are adaptively adjusted to obtain an optimized algorithm parameter set.

[0173] This step converts the three-dimensional closed-loop feedback model constructed in step S73 into specific algorithm optimization actions to improve accuracy and effectiveness through parameter adjustment. First, identify the core algorithm modules that need to be optimized, including job profile construction algorithms (asymmetric random block model and semi-definite programming method), ability assessment algorithms (linear time element algorithm and graphical causal reasoning), ability gap analysis algorithms (dissipation over-satisfaction constraint solving), and learning resource matching algorithms (weighted matching algorithm). Define key performance indicators (KPIs) for each algorithm module, such as the accuracy and predictive power of job profiles, the consistency and discrimination of ability assessment, the relevance and effectiveness of resource matching, and other evaluation criteria for parameter optimization. Extract optimization signals from the three-dimensional closed-loop feedback model, including learning effect data (which learning resources are truly effective), ability assessment feedback (which ability assessment is more accurate), and promotion result verification (which ability prediction truly affects promotion), and convert these signals into specific directions for algorithm adjustment. Use multiple parameter optimization methods: for the asymmetric random block model, adjust the ability dimension weight allocation method, block effect estimation parameter, and random effect processing strategy to improve the accuracy of job profiles; for the linear time element algorithm, optimize the topological sorting rule, influence decay function, and cumulative influence calculation method to improve the accuracy of the ability relationship network; for the dissipation constraint solving algorithm, adjust the constraint priority setting, violation penalty function, and convergence condition to improve the rationality of the ability gap analysis; for the weighted matching algorithm, optimize the feature weight allocation, similarity calculation function, and recommendation sorting strategy to improve the relevance of learning resource recommendations. Use cross-validation methods to evaluate the effects of parameter adjustment, divide historical data into training and validation sets, adjust parameters on the training set, and verify effects on the validation set to ensure that optimization does not cause overfitting. Also conduct A / B testing by deploying algorithm versions with different parameter settings to different user groups and comparing actual usage effects to select the optimal parameter combination. Establish parameter sensitivity analysis to evaluate the impact of parameter changes on performance, identify key parameters and secondary parameters, and prioritize optimization of key parameters with greater impact. Also consider the special needs of different user groups and scenarios, set different algorithm parameters for different departments, job levels, and functional employee groups to improve adaptability. Establish an automatic parameter adjustment mechanism to automatically evaluate and fine-tune algorithm parameters on a regular basis based on continuously collected feedback data to ensure that the system can adapt to changes in organizational environment and user needs. Finally, output the optimized algorithm parameter set, including the optimal parameter configuration of each core algorithm, parameter adjustment basis, expected improvement effect, and applicable condition description. These optimized parameters will significantly improve accuracy, relevance, and effectiveness, making recommended learning resources and promotion paths more in line with actual needs and more effective in promoting employee ability 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 a system iterative optimization strategy.

[0175] This step converts one-time algorithm optimization into a continuous system evolution mechanism, ensuring that the system can continuously improve itself as the environment changes and data accumulates. First, design a comprehensive evaluation index system, including technical indicators (such as algorithm accuracy, recommendation relevance, system response time, etc.), business indicators (such as learning completion rate, ability improvement range, promotion success rate, etc.), and user experience indicators (such as satisfaction, continuous use rate, recommendation acceptance rate, etc.), forming a multi-dimensional system evaluation framework. Establish a regular evaluation mechanism, set different evaluation frequencies for different periods: short-period evaluation (weekly or monthly), focus on system running status and immediate feedback; medium-period evaluation (quarterly), analyze system performance trends and user behavior patterns; long-period evaluation (semi-annually or annually), comprehensively evaluate the overall contribution of the system to organizational talent development. Design multi-source data collection channels to continuously obtain feedback data required for system optimization: system log data, recording user interaction behavior and system running status; user feedback data, including explicit feedback (such as ratings, comments) and implicit feedback (such as clicks, dwell time); business results data, such as learning completion, ability assessment results, promotion records, etc.; expert evaluation data, provided by human resource experts and business leaders. Establish an iterative optimization trigger mechanism, including regular triggers (optimization according to pre-set schedule), threshold triggers (trigger when key indicators fall below pre-set thresholds), and event triggers (trigger when there are significant organizational changes or user feedback), ensuring that the system can respond to various optimization needs in a timely manner. System design adopts hierarchical optimization strategies, 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 certain functional module; architecture-level optimization, redesigning system structure or introducing new technology framework, suitable for responding to major demand changes or technology upgrades. The system establishes an optimization effect verification mechanism, using methods such as controlled experiments, backtesting analysis, and user feedback comparison to scientifically evaluate the actual effect of each optimization, avoiding ineffective optimization or negative impact. Design a knowledge accumulation and experience inheritance mechanism, record the process, decision basis, and effect evaluation of each optimization into the knowledge base, form the experience accumulation of system optimization, guide subsequent optimization decisions. Also establish an alignment mechanism with organizational strategy, regularly evaluate the consistency of system goals and organizational talent development strategy, ensure that the optimization direction always serves the long-term development needs of the organization. Finally, output a complete system iterative optimization strategy, including regular evaluation plan, data collection scheme, optimization trigger mechanism, hierarchical optimization method, effect verification process, and strategic alignment mechanism, etc. This strategy ensures that the system can continuously improve itself, constantly improve the accuracy and effectiveness of recommendations, and provide long-term, stable, and continuously evolving intelligent support for enterprise talent development.

[0176] As Figure 3As shown, the application also provides an intelligent course matching and promotion path recommendation device based on post image, comprising:

[0177] A data preprocessing module 10 is configured to acquire multi-source heterogeneous data in an enterprise, perform data cleaning, standardization and feature extraction processing on the multi-source heterogeneous data, and obtain a standardized multi-dimensional feature dataset;

[0178] A post image construction module 20 is configured to map post capability requirements and competent person features by using a semi-definite programming method of an asymmetric random block model based on the standardized multi-dimensional feature dataset, and obtain a dynamic post capability image;

[0179] An employee capability evaluation module 30 is configured to analyze the causal relationship between capability dimensions by applying a linear time cell algorithm based on the standardized multi-dimensional feature dataset and the dynamic post capability image, and obtain an employee capability evaluation report;

[0180] A capability gap analysis module 40 is configured to calculate a gap matrix between the current capability of an employee and the requirements of a target post by iteratively calculating a capability gap value and a weight coefficient by using a constraint solving algorithm of dissipation over-satisfaction based on the dynamic post capability image and the employee capability evaluation report, and obtain individualized capability improvement needs;

[0181] A learning resource matching module 50 is configured to filter and combine learning resources by using a weighted matching algorithm based on the individualized capability improvement needs and the learning resources from the standardized multi-dimensional feature dataset, and obtain an individualized learning recommendation plan;

[0182] A promotion path planning module 60 is configured to construct a multi-dimensional promotion possibility evaluation by using a promotion path planning algorithm based on the dynamic post capability image, the employee capability evaluation report and organizational structure data of the standardized multi-dimensional feature dataset, and obtain an individualized promotion path suggestion.

[0183] The intelligent course matching and promotion path recommendation method and device based on post image provided by the application construct a dynamic post capability image by using an asymmetric random block model and a semi-definite programming method, analyze the causal relationship between capability dimensions by applying a linear time cell algorithm, calculate a capability gap matrix by using a constraint solving algorithm of dissipation over-satisfaction, filter and combine learning resources by using a weighted matching algorithm, construct a multi-dimensional promotion possibility evaluation by using a promotion path planning algorithm, and establish a three-dimensional closed-loop feedback model of learning-evaluation-promotion, thereby effectively fusing learning behavior data and performance result data, providing dynamically optimized career development suggestions, establishing a closed-loop feedback mechanism of learning, evaluation and promotion, and providing a systematic solution for enterprise talent development.

[0184] The above merely illustrates the preferred embodiments of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for intelligent course matching and promotion path recommendation based on job image, characterized in that, The method comprises the following steps: obtaining multi-source heterogeneous data in an enterprise, performing data cleaning, standardization and feature extraction processing on the multi-source heterogeneous data to obtain a standardized multi-dimensional feature dataset; based on the standardized multi-dimensional feature dataset, using a semi-definite programming method of an asymmetric random block model to map post capability requirements and competent person features to obtain a dynamic post capability portrait; based on the standardized multi-dimensional feature dataset and the dynamic post capability portrait, applying a linear time element algorithm to analyze the causal relationship between the capability dimensions to obtain an employee capability evaluation report; based on the dynamic post capability portrait and the employee capability evaluation report, using a constraint solving algorithm of dissipation over-satisfaction to calculate the gap matrix between the current capability of an employee and the requirements of a target post by iteratively calculating the capability gap value and the weight coefficient to obtain individualized capability improvement requirements; based on the individualized capability improvement requirements, combining learning resources from the standardized multi-dimensional feature dataset, and screening and combining the learning resources through a weighted matching algorithm to obtain an individualized learning recommendation plan; based on the dynamic post capability portrait, the employee capability evaluation report and the organizational structure data of the standardized multi-dimensional feature dataset, using a promotion path planning algorithm to construct a multi-dimensional promotion possibility evaluation to obtain an individualized promotion path suggestion.

2. The method of claim 1, wherein, The data cleaning, standardization and feature extraction processing on the multi-source heterogeneous data to obtain a standardized multi-dimensional feature dataset comprises: based on the multi-source heterogeneous data, performing data integrity checking and outlier processing to obtain a dataset after integrity checking and outlier processing; performing dimension unification and normalization processing on the dataset after integrity checking and outlier processing to obtain a standardized dataset; based on the standardized dataset, extracting learning behavior features, capability evaluation features and performance features to obtain the standardized multi-dimensional feature dataset.

3. The method of claim 1, wherein, The semi-definite programming method of the asymmetric random block model is used to map post capability requirements and competent person features to obtain a dynamic post capability portrait, which comprises: based on the standardized multi-dimensional feature dataset, a standardized capability framework system covering dimensions including technical capability, management capability and general capability is established; based on the enterprise organizational structure and the post description of the standardized multi-dimensional feature dataset, post basic information is extracted and mapped to the standardized capability framework system to obtain a post capability requirement mapping matrix; based on the competent person features of the standardized multi-dimensional feature dataset, the actual capability performance of the competent person is mapped to the standardized capability framework system to obtain a competent person capability feature mapping matrix; based on the post capability requirement mapping matrix and the competent person capability feature mapping matrix, an asymmetric random block model is constructed to obtain an initial block correlation model; the semi-definite programming algorithm is applied to the initial block correlation model for optimization and solution to obtain an optimized post capability weight matrix; based on the optimized post capability weight matrix, combined with the competent person features, a multi-dimensional capability portrait is constructed to obtain the dynamic post capability portrait.

4. The method of claim 1, wherein, The application linear time element algorithm analyzes the causal relationship between the ability dimensions to obtain an employee ability evaluation report, including: Based on the standardized multi-dimensional feature data set, employee historical learning records, test results, and work performance data are integrated to obtain an employee historical ability performance data set; Based on the employee historical ability performance data set, a graphical causal reasoning method is applied to identify the causal relationship between each ability dimension to obtain an ability causal relationship network; The ability causal relationship network is topologically sorted and path tracked, and a linear time element algorithm is applied to calculate direct and indirect influences to obtain an ability weight relationship matrix; Based on the ability weight relationship matrix and the latest test data of the standardized multi-dimensional feature data set, a weighted network propagation algorithm is used to calculate the comprehensive ability score to obtain an employee multi-dimensional ability score result; Based on the employee multi-dimensional ability score result, a dynamic job ability profile is combined to generate an intuitive ability evaluation report to obtain the employee ability evaluation report.

5. The method of claim 1, wherein, The constraint solving algorithm based on dissipation over-satisfaction is used to calculate the gap matrix between the current ability of the employee and the requirements of the target post by iteratively calculating the ability gap value and the weight coefficient to obtain individualized ability improvement needs, including: Based on the employee ability evaluation report and the dynamic job ability profile, one-to-one dimensional mapping is performed to calculate the gap value of each ability dimension to obtain an initial ability gap matrix; Based on the initial ability gap matrix, an optimization model containing hard constraint conditions and dissipation constraint conditions is constructed to obtain a dissipation constraint model; The 3-SAT solving algorithm based on over-satisfaction constraint is applied to the dissipation constraint model to obtain the individualized ability improvement needs.

6. The method of claim 1, wherein, The learning resources are screened and combined through a weighted matching algorithm to obtain an individualized learning recommendation plan, including: Based on the learning resources in the standardized multi-dimensional feature data set, including courses, projects, and case resources in the enterprise learning resource library, content features, ability labels, and difficulty level information are extracted to obtain a learning resource feature matrix; Based on the individualized ability improvement needs and the learning resource feature matrix, the correlation degree of each learning resource to the target ability improvement is calculated to obtain an ability-resource correlation degree matrix; Based on the ability-resource correlation degree matrix, a path optimization algorithm is used to consider the learning order, time constraints, and resource dependency to obtain a preliminary learning path scheme; Based on the preliminary learning path scheme, individual customization is performed in combination with employee learning styles, time arrangements, and historical learning effects to obtain the individualized learning recommendation plan; The individualized learning recommendation plan is set with stage goals and a progress tracking mechanism to obtain a complete learning development plan.

7. The method of claim 1, wherein, The promotion path planning algorithm is used to construct a multi-dimensional promotion possibility evaluation to obtain an individualized promotion path suggestion, including: Based on the organizational structure data of the standardized multi-dimensional feature data set, a connection relationship network between posts is constructed and possible promotion channels are identified to obtain an organizational post network diagram; Based on the employee capability assessment report, the dynamic job capability profile, and the historical promotion case data of the standardized multi-dimensional feature data set, the asymmetric random block model is applied to calculate the promotion suitability of employees in different positions, and a promotion possibility score matrix is obtained; Based on the promotion possibility score matrix and the organizational position network graph, a longitudinal promotion path and a horizontal development path are generated, and a multi-dimensional promotion path candidate set is obtained; Based on the multi-dimensional promotion path candidate set, a path optimization is performed by applying a dissipation constraint solving technique through a multi-constraint optimization model and a random walk strategy, and an optimized promotion path scheme is obtained; Based on the optimized promotion path scheme, a detailed suggestion including time nodes, milestones, and key capability improvement points is generated by combining the individualized learning recommendation plan, and the individualized promotion path suggestion is obtained.

8. The method of claim 1, wherein, It also includes a closed-loop feedback optimization step: Based on the evaluation results, application situations, and performance changes of employees after completing learning of the standardized multi-dimensional feature data set, the effectiveness of learning resources is evaluated, and a learning effect evaluation report is obtained; Based on the successful promotion cases of employees of the standardized multi-dimensional feature data set, the capability improvement trajectory, learning path, and key success factors are analyzed, and a promotion success mode analysis result is obtained; Based on the learning effect evaluation report and the promotion success mode analysis result, a three-dimensional closed-loop feedback model of learning-evaluation-promotion is constructed, and an optimization direction is obtained; Based on the three-dimensional closed-loop feedback model, the parameters of the core algorithms including position profile construction, capability assessment, and resource matching are adaptively adjusted, and an optimized algorithm parameter set is obtained; Based on the optimized algorithm parameter set, a regular evaluation and iterative optimization mechanism is established, and a system iterative optimization strategy is obtained.

9. The method of claim 2, wherein, Based on the multi-source heterogeneous data, data integrity checking and outlier processing are performed, and a data set after integrity checking and outlier processing is obtained, including: Based on the multi-source heterogeneous data, data source access and integration are performed through API interface and data synchronization tool, and an original multi-source data stream is obtained; Data integrity checking, outlier processing, and missing value filling operations are performed on the original multi-source data stream, and the data set after integrity checking and outlier processing is obtained.

10. An intelligent course matching and promotion path recommendation device based on a job image, characterized in that, It includes: A data preprocessing module is used to obtain multi-source heterogeneous data within an enterprise, and the multi-source heterogeneous data is subjected to data cleaning, standardization, and feature extraction processing to obtain a standardized multi-dimensional feature data set; A position profile construction module is used to map job capability requirements and competent person features based on the standardized multi-dimensional feature data set using a semi-definite programming method of the asymmetric random block model to obtain a dynamic job capability profile; An employee capability assessment module is used to apply a linear time element algorithm to analyze the causal relationship between capability dimensions based on the standardized multi-dimensional feature data set and the dynamic job capability profile to obtain an employee capability assessment report; The capability gap analysis module is configured to calculate a gap matrix between the current capability of the employee and the target post requirement by iteratively calculating a capability gap value and a weight coefficient based on the dynamic post capability profile and the employee capability evaluation report, and obtain an individualized capability improvement requirement by using a constraint solution algorithm with dissipation over-satisfaction; The learning resource matching module is configured to filter and combine the learning resources by using a weighted matching algorithm based on the individualized capability improvement requirement and the learning resources from the standardized multi-dimensional feature data set, and obtain an individualized learning recommendation plan. The promotion path planning module is configured to construct a multi-dimensional promotion possibility evaluation by using a promotion path planning algorithm based on the dynamic post capability profile, the employee capability evaluation report, and the organizational structure data of the standardized multi-dimensional feature data set, and obtain an individualized promotion path suggestion.

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