A method and system for employee skills iteration and job adaptation management based on AI Agent

Abstract

The application relates to an AI Agent-based employee skill iteration and post adaptation management method and system, which comprises the following steps: recognizing post skill items and scoring by analyzing enterprise strategy documents to form a skill demand vector. Integrating employee skill tests and work logs, extracting implicit features through standardization and semantic analysis to construct a skill status vector. Determine the skill to be improved by comparing the similarity of the two vectors. Optimize and combine training resources from the resource library to generate a personalized training plan. Collect learning and practice data during execution, establish a multi-level evaluation system, generate a training effect index, and accordingly correct the skill vector and update the dynamic profile. Match the updated profile with the post database to generate adaptation suggestions. The system continuously monitors feedback and strategic change signals, automatically triggers adjustments to the skill demand vector or matching threshold according to rules, and realizes closed-loop optimization. The application improves the adaptation efficiency and accuracy of employee skills and enterprise needs.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method and system for managing employee skill iteration and job adaptation based on AI Agent. Background Technology

[0002] With the deepening of digital transformation, business models and technological environments are undergoing rapid iteration, which places higher demands on the dynamic adaptability of talent structures and employee skills within organizations. Traditional enterprises mostly rely on periodic performance evaluations, static job descriptions, and relatively rigid training systems to manage employee skills development. This model can still operate in environments with high stability.

[0003] However, against the backdrop of rapid technological change and intensified market competition, the frequency of corporate strategic adjustments has accelerated significantly, and new job positions are constantly emerging. This makes the gap between employees' existing skills and future job requirements an increasingly dynamic and unpredictable challenge. At the same time, the rise of technologies such as artificial intelligence, while automating some routine tasks, has also created demand for new skills. How to systematically and scalably help employees iterate their skills has become a key issue for companies to maintain their core competitiveness.

[0004] Currently, most companies' identification of job skill requirements often lags behind strategic adjustments and relies on managers' subjective experience for general descriptions, leading to a discrepancy between skills training direction and the company's actual needs. Furthermore, companies often rely on periodic exams or supervisor evaluations to assess employee skills. This not only results in limited data sources and slow updates but also makes it difficult to capture the implicit skills and application abilities employees demonstrate in daily work, failing to create a comprehensive and objective dynamic skills profile. Secondly, traditional employee training programs often lack personalized design, and the evaluation of training effectiveness focuses on superficial indicators such as course completion rates, failing to scientifically measure the conversion of skills into practical work, leading to a poor return on investment. In addition, the process of matching employee skills development with internal job positions is usually disconnected. Newly acquired skills cannot be automatically and accurately matched with internal roles, preventing the timely conversion of employee skills into value, resulting in talent waste and increased external recruitment costs. Summary of the Invention

[0005] To address the aforementioned technical issues, this application provides a method and system for managing employee skill iteration and job adaptation based on AI Agent.

[0006] Firstly, this application provides a method for employee skill iteration and job adaptation management based on AI Agent, employing the following technical solution: Job description information for target positions is obtained from corporate strategy documents and natural language processing is performed to identify multiple skill items associated with the target positions. An importance score is assigned to each skill item to generate a job skill requirement vector. Collect skill test data and work system log data of target employees, standardize the skill test data, extract implicit skill features through semantic analysis technology, and combine the standardization results and implicit skill features to generate an employee skill status vector. Based on the similarity matching between the job skill requirement vector and the employee skill status vector, at least one skill item that needs to be improved is identified if the similarity difference exceeds a preset difference range. Based on the at least one skill to be improved, corresponding training resources are selected from the enterprise training resource library, and an optimization algorithm is used to sort and combine the selected training resources to generate a personalized skill training plan. During the process of the target employee implementing the personalized skills training program, training process data of the target employee is collected, including learning progress data and project practice data; Based on the cultivation process data, multiple evaluation indicators are determined and a multi-level evaluation indicator system is established. Fuzzy mathematics is used to quantify each evaluation indicator, and the quantification results of each evaluation indicator are combined to generate a cultivation effect index. The employee's current skill status vector is corrected based on the training effect index to obtain updated skill status data, which is then stored in the target employee's dynamic skill profile. The current skill data in the dynamic skill profile is matched with the job requirement data in the enterprise job database to generate job matching suggestions; Monitor the implementation feedback data of the job matching suggestions or corporate strategy change signals, and trigger the adjustment process of the job skill requirement vector and / or similarity matching threshold parameters according to preset rules.

[0007] By adopting the aforementioned technical solutions, vague strategic needs are transformed into clear skills development paths, subjective competency assessments are converted into objective data profiles, extensive experience-based training is transformed into scientific, personalized solutions, and static job management is transformed into dynamic, real-time matching. This not only improves the efficiency and accuracy of matching employee skills with corporate needs but also effectively revitalizes internal talent resources, reduces hidden costs and recruitment expenses caused by skills mismatch, and enhances employee sense of belonging and retention through continuous attention and empowerment of employee growth. Thus, it builds a sustainable competitive advantage for the company in a dynamically changing market environment. In practical application, this application introduces an AI Agent as the core intelligent hub, constructing a closed-loop iterative system integrating needs analysis, training implementation, matching application, and effect evaluation, achieving the coordinated development and dynamic adaptation of employee skills and job requirements.

[0008] Secondly, this application provides an employee skills iteration and job adaptation management system based on AI Agent, which adopts the following technical solution: The job skill requirement vector generation module is used to obtain the job description information of the target position from the enterprise strategic document data and perform natural language processing to identify multiple skill items associated with the target position, assign importance scores to each skill item, and generate a job skill requirement vector. The employee skill status vector generation module is used to collect skill test data and work system log data of target employees, standardize the skill test data, extract implicit skill features through semantic analysis technology, and generate an employee skill status vector by combining the standardization results and implicit skill features. The skills gap analysis module is used to perform similarity matching between the job skills requirement vector and the employee skills status vector to identify at least one skill item that needs to be improved if the similarity difference exceeds a preset difference range. The personalized training program optimization module is used to select corresponding training resources from the enterprise training resource library based on the at least one skill item to be improved, and to sort and combine the selected training resources using an optimization algorithm to generate a personalized skill training program. The training process data acquisition module is used to collect training process data of the target employee during the execution of the personalized skills training program, including learning progress data and project practice data. The cultivation effect evaluation module is used to determine multiple evaluation indicators based on the cultivation process data and establish a multi-level evaluation indicator system. It uses fuzzy mathematics to quantify each evaluation indicator and generates a cultivation effect index by combining the quantification results of each evaluation indicator. The skills profile update module is used to correct the employee's current skills status vector based on the training effect index, obtain updated skills status data, and store it in the target employee's dynamic skills profile. The job matching recommendation module is used to match the current skill data in the dynamic skill profile with the job requirement data in the enterprise job database to generate job matching suggestions. The closed-loop feedback adjustment module is used to monitor the implementation feedback data of the job matching suggestions or corporate strategy change signals, and trigger the adjustment process of the job skill requirement vector and / or similarity matching threshold parameters according to preset rules.

[0009] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as in any of the methods in the first aspect.

[0010] In summary, this application includes at least one of the following beneficial technical effects: Through precise skills gap analysis and personalized training, this application significantly reduces enterprises' external recruitment costs and internal training investment. Simultaneously, the dynamic job matching mechanism improves talent allocation efficiency, thereby enhancing employee job satisfaction and retention rates, ensuring continuous synergy between employee skills development and organizational strategic needs, and achieving precise allocation and continuous value-added of human resources. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the first process of an employee skills iteration and job adaptation management method according to one embodiment of this application.

[0012] Figure 2 This is a schematic diagram of the second process of an employee skills iteration and job matching management method according to one embodiment of this application.

[0013] Figure 3 This is a schematic diagram of the third process of an employee skills iteration and job matching management method according to one embodiment of this application.

[0014] Figure 4 This is a schematic diagram of the fourth process of the employee skills iteration and job adaptation management method according to one embodiment of this application.

[0015] Figure 5 This is a schematic diagram of the fifth process of an employee skills iteration and job matching management method according to one embodiment of this application.

[0016] Figure 6 This is a schematic diagram of the sixth process of an employee skills iteration and job adaptation management method according to one embodiment of this application.

[0017] Figure 7 This is a schematic diagram of the seventh process of the employee skill iteration and job adaptation management method according to one embodiment of this application.

[0018] Figure 8 This is a schematic diagram of the eighth process of the employee skills iteration and job adaptation management method according to one embodiment of this application. Detailed Implementation

[0019] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1-8 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0020] This application discloses a method for managing employee skill iteration and job adaptation based on AI Agent.

[0021] Reference Figure 1 A management method for employee skill iteration and job adaptation based on AI Agent, the management method includes: Step S101: Obtain the job description information of the target position from the enterprise strategic document data and perform natural language processing to identify multiple skill items associated with the target position, assign importance scores to each skill item, and generate a job skill requirement vector. The underlying logic of this step is to transform abstract corporate strategies and job responsibilities into quantifiable, machine-readable data. First, responsibilities for the target position are extracted from unstructured text data such as corporate strategy documents and job descriptions. Then, NLP techniques such as Named Entity Recognition (NER) and keyword extraction are used to identify skill entities (such as "Python programming," "project management," and "data analysis") embedded in the text.

[0022] Then, instead of simply listing skills, the system intelligently assigns an importance score to each skill by analyzing word frequency, inverse document frequency (TF-IDF), and the co-occurrence relationship between skill keywords and strategic goal keywords. For example, a skill like "machine learning," which frequently appears alongside "big data" in a "digital transformation" strategic document, will have a significantly higher importance score than other skills. Ultimately, all skill items and their importance scores together constitute a multi-dimensional job skill requirement vector. This vector not only defines what skills are needed for the job but also precisely characterizes the strategic weight of each skill, laying a data foundation for subsequent accurate matching.

[0023] Step S102: Collect skill test data and work system log data of target employees, standardize the skill test data, extract implicit skill features through semantic analysis technology, and generate an employee skill status vector by combining the standardization results and implicit skill features. This step integrates explicit skills assessment and implicit behavior analysis. On the one hand, standardized skills test data is processed (such as Z-score standardization or min-max normalization) to eliminate the influence of differences in scale and difficulty between different tests, making them comparable.

[0024] On the other hand, semantic analysis can be performed on log data generated by employees' daily work systems (such as code commit records, project documents, and communication and collaboration content) to extract their implicit skill characteristics. For example, by analyzing the quality and complexity of the code comments written by employees, their "code maintainability" skills can be inferred; by analyzing their participation in project discussions, their "communication and collaboration" abilities can be assessed.

[0025] Ultimately, the employee skill status vector generated by weighted splicing or attention mechanism ensures a balanced representation of explicit abilities and implicit practices. This step is a digital twin of the employee's real and dynamic skill level, which reflects their comprehensive abilities far better than a single test score or supervisor evaluation.

[0026] Step S103: Perform similarity matching between the job skill requirement vector and the employee skill status vector to identify at least one skill item that needs to be improved if the similarity difference exceeds the preset difference range. The system places the "job skill requirement vector" and the "employee skill status vector" in the same high-dimensional skill space, and evaluates the degree of alignment between their directions by calculating the cosine similarity between the two vectors.

[0027] In this embodiment, cosine similarity focuses on the direction of vectors rather than their absolute magnitude, making it particularly suitable for assessing the similarity of skill structures—that is, the matching pattern between an employee's skill set and job requirements. When the similarity is below a preset threshold (e.g., 0.6), it indicates a significant deviation between the employee's skill structure and job requirements. The system then performs further difference operations between vectors to accurately identify skill dimensions with high importance but low employee proficiency; these dimensions are the skill items to be improved. This gap identification based on a mathematical model avoids the bias of subjective judgment and ensures the accuracy and objectivity of skill improvement direction.

[0028] Step S104: Based on at least one skill to be improved, select corresponding training resources from the enterprise training resource library, and use an optimization algorithm to sort and combine the selected training resources to generate a personalized skill training plan. Once skill gaps are identified, the most efficient improvement path can be planned for employees from a vast array of corporate training resources (such as online courses, micro-certifications, practical projects, and mentorship). This is essentially a complex combinatorial optimization problem.

[0029] In this embodiment, a metaheuristic algorithm such as a genetic algorithm can be used to solve the problem. The genetic algorithm simulates the "selection, crossover, and mutation" process in biological evolution, treating different resource combinations as "chromosomes," and using "maximizing skill improvement within a predetermined time" or "minimizing learning cost" as the objective function, undergoing multiple rounds of iterative evolution. It automatically explores various resource combination schemes, evaluates their adaptability (i.e., coverage and improvement efficiency of the target skill), and ultimately evolves the optimal sequence of personalized training schemes. This ensures that the training scheme is not merely a simple accumulation of resources, but an optimal solution that has undergone global optimization and considers individual learning patterns and constraints.

[0030] Step S105: During the process of the target employee implementing the personalized skills training program, collect training process data of the target employee, including learning progress data and project practice data; The learning progress data includes time-series data such as completion rate, test scores, and duration collected from the learning management system (LMS); the project practice data can be extracted by connecting to project collaboration tools (such as Jira) via API, such as task delivery quality and code review scores.

[0031] Step S106: Based on the cultivation process data, determine multiple evaluation indicators and establish a multi-level evaluation indicator system. Use fuzzy mathematics to quantify each evaluation indicator and generate a cultivation effect index by combining the quantification results of each evaluation indicator. Specifically, the system first establishes a multi-level evaluation index system, decomposing macro-level goals such as "learning progress" and "project practice results" into observable primary and secondary indicators. Primary indicators (such as "knowledge mastery" and "application ability") are further subdivided into secondary indicators (such as "course test pass rate" and "project delivery defect rate"), forming a tree structure. Then, fuzzy mathematics methods, particularly triangular fuzzy numbers, are introduced to handle uncertainties in the evaluation. For example, the evaluation of "good" for "project application effect" is no longer a precise number, but a fuzzy interval described by a minimum, most likely, and maximum value. This aligns better with human evaluation habits; for instance, "good" can be mapped to [0.7, 0.8, 0.9].

[0032] Next, the Analytic Hierarchy Process (AHP) was used to scientifically determine the weights of each indicator, reflecting the different contributions of each indicator to the final outcome. Finally, through fuzzy synthesis, the fuzzy evaluations of all indicators were aggregated into a clear cultivation effect index (a value between 0 and 1). This index comprehensively reflects both the quantity and quality of the cultivation process, providing a reliable quantitative basis for subsequent decision-making.

[0033] Step S107: Correct the employee skill status vector based on the training effect index to obtain updated skill status data, and store it in the target employee's dynamic skill profile. The system uses the training effect index as a credibility weight or fusion coefficient to perform a weighted average or Bayesian update on the employee's original skill status vector and the skill target vector expected to be achieved through this training.

[0034] Specifically, if the training effectiveness index is high (e.g., greater than 0.7), then new skills data carries greater weight in the update; conversely, more existing data is retained. This dynamic update mechanism ensures that employees' skills profiles are no longer static snapshots, but documents that evolve continuously with their growth, providing real-time and accurate data support for precise talent allocation.

[0035] Step S108: Match the current skill status data in the dynamic skill profile with the job requirement data in the enterprise job database to generate job matching suggestions; Based on the updated dynamic skill profiles, the system intelligently searches and matches employees across the entire job ecosystem. It recalculates the cosine similarity between employee skill vectors and the cosine similarity of all potential job requirement vectors. When the matching score reaches a high threshold (e.g., 0.85), the system generates formal job reassignment suggestions to facilitate internal talent mobility.

[0036] Furthermore, for vacant positions that do not yet have a perfect match, the system demonstrates its flexibility: using skills deconstruction and recombination technology, it packages employees' strengths into specific, flexible work assignments (such as serving as a technical consultant for a project or participating in a cross-departmental innovation task) or recommends them to external collaborative projects. This enables the immediate transformation of employee skills, maximizing their human capital value, while also providing enterprises with a highly flexible talent allocation method.

[0037] Step S109: Monitor the implementation feedback data of job matching suggestions or corporate strategy change signals, and trigger the adjustment process of job skill requirement vector and / or similarity matching threshold parameters according to preset rules.

[0038] The system continuously monitors two key signals: first, the implementation feedback data of job matching suggestions (such as the performance achievement rate of employees in new positions), and second, signals of changes in corporate strategy (such as the rate of change of keywords in strategic documents).

[0039] In some embodiments, when these signals trigger preset rules (such as a performance achievement rate below 70% or a keyword change rate exceeding 15%), an adjustment process is initiated. For example, if multiple employees perform poorly after training and matching, it may indicate an error in the skill requirement vector generation rules or an unreasonable matching threshold setting. The system will then automatically adjust the weighting of the importance score calculation and / or the similarity threshold parameters. This transforms the management approach from a fixed procedure into an intelligent system capable of learning from practice and adaptively evolving with changing environments.

[0040] In the above implementation, vague strategic needs are transformed into clear skills development paths, subjective competency assessments are transformed into objective data profiles, extensive experience-based training is transformed into scientific personalized solutions, and static job management is transformed into dynamic, real-time matching. This not only improves the efficiency and accuracy of matching employee skills with corporate needs, but also effectively revitalizes internal talent resources, reduces hidden costs and recruitment expenses caused by skills mismatch, and enhances employees' sense of belonging and retention rate through continuous attention to and empowerment of employee growth. Thus, it builds a sustainable talent competitive advantage for enterprises in a dynamically changing market environment.

[0041] In practical application, this application introduces an AI Agent as the core intelligent hub, constructing a closed-loop iterative system integrating demand analysis, training implementation, matching application, and effect evaluation. This achieves the coordinated development and dynamic adaptation of employee skills and job requirements. The following is a detailed implementation example of the overall process: This application's implementation begins with the precise quantification of job skill requirements and the objective assessment of employees' current skill status. AIAgent first accesses the enterprise's strategic document library and human resource knowledge graph (such as Beamery), using natural language processing technology to analyze the job description and strategic requirements of the target position, automatically identifying multiple related skill items, and assigning an importance score to each skill item, thereby generating a structured job skill requirement vector S_d=(s_1, s_2, ..., s_n). Simultaneously, the system collects skill test data and work system log data from the target employees, standardizes the test data, and further extracts implicit skill features reflecting actual abilities from the work logs using semantic analysis technology. Finally, it combines both to generate a comprehensive employee skill status vector S_e=(t_1, t_2, ..., t_n). To accurately quantify the gap between people and jobs, AIAgent uses a cosine similarity algorithm to calculate the matching degree Sim of the two vectors. When the Sim value is lower than a preset threshold (e.g., 0.6), the skill improvement process is automatically triggered, and the skill items to be improved are precisely located.

[0042] After identifying skill gaps, the AI ​​Agent enters the personalized training program generation and execution phase. Based on the skills to be improved, the system intelligently selects corresponding training resources (such as online courses and practical training projects) from the company's training resource library. To further optimize the efficiency and effectiveness of resource combinations, the AI ​​Agent employs optimization algorithms such as genetic algorithms to prioritize and combine multiple training resources within a predetermined timeframe, aiming to maximize skill improvement. This generates a unique, personalized skills training program for each employee. During program execution, the system collects real-time data on employee learning progress and project practice as a basis for subsequent effectiveness evaluation.

[0043] The evaluation of training effectiveness and the immediate conversion of skill value are key aspects of the embodiments of this application. To overcome the subjectivity of traditional evaluations, the AI ​​Agent adopts a hierarchical fuzzy comprehensive evaluation method, using learning progress, knowledge mastery, and project application effectiveness as primary indicators to construct a multi-level evaluation system. Qualitative evaluations are transformed into triangular fuzzy numbers for processing, and the weights of each indicator are determined using the analytic hierarchy process (AHP). Finally, an objective and quantitative training effectiveness index E (E∈[0,1]) is synthesized and output. Subsequently, the system uses this index to weight and correct the employee's current skill status vector, and compares the updated results with the DEEM. The dynamic assessment model integrates multi-dimensional capability data and updates it to employees' dynamic skill profiles. After the profiles are updated, AIAgent immediately matches them with the needs in the company's job database in real time, generating specific job allocation suggestions; for situations where there are no offline job vacancies, it recommends flexible work tasks or external projects to ensure the immediate conversion of skill value.

[0044] Ultimately, this application embodiment achieves system self-optimization and continuous effectiveness through a closed-loop iterative mechanism. AIAgent continuously monitors implementation feedback data of job matching suggestions (such as new job performance) or signals of corporate strategic changes. When anomalies or changes are detected, the system automatically triggers an adjustment process for job skill requirement vectors or similarity matching threshold parameters based on a preset rule base. This closed-loop design of "demand analysis - training implementation - matching application - effect evaluation" allows the entire system to dynamically evolve with the development of the enterprise and its employees, ultimately achieving comprehensive benefits such as improving employee skill matching rates, reducing recruitment and training costs, enhancing talent allocation efficiency, and increasing employee retention rates.

[0045] Reference Figure 2 As one implementation of step S101, the steps of obtaining job description information of the target position from enterprise strategic document data and performing natural language processing to identify multiple skill items associated with the target position, assigning importance scores to each skill item, and generating a job skill requirement vector include: Step S201: Collect enterprise strategic document data associated with the target position from the enterprise database and extract text fragments containing job description information; The enterprise database, as the sole trusted source, ensures the authority and consistency of the data. Its content may include structured or semi-structured documents such as strategic planning reports, departmental annual goals, and job descriptions. However, these documents are not designed for direct machine understanding, thus requiring information extraction.

[0046] Specifically, through specific data parsing techniques (such as format parsing for PDF documents and querying and relating database fields), text fragments strongly related to the "target job responsibilities" can be accurately located and extracted. For example, from a lengthy strategic planning PPT, text boxes containing clear job descriptions such as "backend development engineers need to master..." can be automatically identified and extracted.

[0047] Step S202: Perform natural language processing on the text fragment and identify the entity units describing the professional skill requirements through semantic parsing; In some embodiments, after obtaining a text fragment, dependency parsing can be used to understand the verb-object relationship between verbs such as "master" and "proficient" and nouns such as "cloud computing platform" and "distributed system," thereby confirming that it is a requirement for a skill rather than a description. Alternatively, named entity recognition technology can be used to accurately identify specific phrases in the text as proper noun entities representing skills, such as "Java programming" and "user requirements analysis." These identified "entity units" are no longer isolated words, but data objects carrying explicit semantic information (i.e., professional skill requirements).

[0048] Step S203: Based on the knowledge graph association mapping, classify the entity units into standardized skill items in the preset skill classification system; The identified entity units may be described differently in different documents (such as "Java development" and "Java programming"), and using them directly will lead to data confusion.

[0049] In this embodiment, the knowledge graph acts as a "semantic router," a pre-configured structured semantic network containing a large number of skill nodes and relationships between them (such as "synonyms," "hypernyms," and "relatedness"). The knowledge graph association mapping process involves calculating semantic similarity and logically associating potentially non-standard entity units identified in the previous step with predefined, standardized skill nodes in the knowledge graph. For example, the expression "can use Python for data analysis" in text is accurately associated with the standardized skill item "Python data analysis" through graph mapping. This not only solves the synonym problem but also automatically supplements related parent skills (e.g., mapping "TensorFlow" to "machine learning framework") through the graph's reasoning capabilities. Its output is an unambiguous, unified, standardized skill item, ensuring that all subsequent calculations and comparisons are performed on the same semantic scale, which is crucial for guaranteeing the system's objectivity and consistency.

[0050] Step S204: Calculate the importance metric value of each standardized skill item based on the contextual semantic features of the entity unit in the enterprise strategy document data; Among these, contextual semantic features serve as the basis for this evaluation. The system comprehensively analyzes various features of entity units in the original text. For example, it uses word frequency-inverse document frequency analysis to determine the frequency of the skill term in the current job description (higher frequency usually indicates greater importance); and it analyzes the positional weight of the term in the document (e.g., it has a higher weight when it appears in the "Core Requirements" section than in the "Additional Notes" section).

[0051] More importantly, it can also use technologies such as word vector models to calculate the semantic similarity between the skill term and known core keywords of corporate strategy (such as "digital transformation" and "cost reduction and efficiency improvement"). The closer the skill requirement is to the strategic goal, the higher its importance. The system will integrate these multi-dimensional features and use a preset weighted scoring model to calculate a continuous and comparable metric value (such as 0-5 points) for each standardized skill item. This makes the importance of a skill no longer a subjective assumption, but a quantitative assessment based on objective evidence in the strategic text.

[0052] Step S205: Integrate all standardized skill items and important metric values ​​to generate a job skill requirement vector stored in vector form.

[0053] In this context, each dimension of the job skill requirement vector corresponds to a skill item, and the value of the dimension is a significant metric of the skill item.

[0054] In some embodiments, the job skill requirement vector for a position may be represented as: [Programming ability: 4.5, System architecture: 4.0, Project management: 3.5, ...]. This representation compresses a complex, descriptive job requirement into a compact mathematical object, facilitating computer storage, indexing, and rapid retrieval. Furthermore, it provides a direct mathematical foundation for subsequent similarity calculations using vector space models (such as cosine similarity comparison with employee skill vectors), serving as the cornerstone for achieving accurate and automated matching.

[0055] The above implementation achieves the digitization, standardization, and quantitative modeling of job skill requirements, changing the traditional extensive management model that relies on manual interpretation and subjective judgment. Through the deep integration of natural language processing and knowledge graphs, not only is the breadth and accuracy of skill recognition improved, but context-based semantic analysis also provides an objective basis for skill importance assessment. The resulting job skill requirement vector, as a general machine language, provides a stable and reliable data foundation for building the decision-making core of the entire intelligent human resource management system. This enables large-scale, automated skills gap analysis, personalized training path planning, and precise person-job matching, fundamentally improving the agility, scientific nature, and efficiency of organizational talent management.

[0056] Reference Figure 3 As one implementation of step S102, the steps of collecting skill test data and work system log data of target employees, standardizing the skill test data, extracting implicit skill features through semantic analysis technology, and generating an employee skill status vector by combining the standardization results and implicit skill features include: Step S301: Collect structured skills test data generated by the target employee in the preset skills assessment system, as well as unstructured work system log data recorded in the office collaboration system; Traditional employee evaluations often rely on a single data source, either standardized tests detached from actual work scenarios or subjective evaluations from superiors. Both of these methods have blind spots.

[0057] In this embodiment, structured skills test data (such as objective scores from online skills testing platforms) provides a standard measure of employees' explicit knowledge in a controlled environment, offering good comparability, but may not fully reflect their application capabilities in actual work. Unstructured work system log data (such as task descriptions in project management systems, commit comments in code repositories, technical sharing in online documents, and technical discussion records in instant messaging tools) truthfully records employees' behavior, decisions, and outputs in daily work, containing extremely rich implicit skills information, such as problem-solving approaches, communication and collaboration efficiency, and code design elegance. By proactively and synchronously collecting these two types of data, the aim is to combine the theoretical level of "should know" with the practical ability of "should be," laying a data foundation for subsequently building a comprehensive model that closely approximates employees' true capabilities.

[0058] Step S302: Perform a standardization transformation on the structured skills test data to map it to a scoring space with a unified dimension; Structured skills test data from different sources often have different scoring systems and scales. For example, a programming test may have a maximum score of 100 points, while a foreign language proficiency test may use CEFR levels (A1-C2). Directly comparing or combining these data is like comparing a meter and a ruler—meaningless. Therefore, standardization and transformation are necessary.

[0059] The specific steps include: identifying the original scoring range of different assessment modules; converting the original scores to the 0-100 range using a linear normalization function; and weighting and calibrating the converted scores according to the job skill weighting table.

[0060] In this embodiment, this can be achieved through mathematical transformations (such as min-max normalization and Z-score standardization). Essentially, this maps all raw scores with different dimensions to a pre-defined, unified scoring space (e.g., a continuous interval from 0 to 1, or an integer interval from 0 to 100). This process ensures that scores from different assessment modules have the same mathematical meaning and basis for comparison, allowing "programming ability 90 points" and "project management 85 points" to be meaningfully compared and calculated on the same dimension. This is a prerequisite for subsequent precise mathematical processing.

[0061] Step S303: Perform natural language processing on the unstructured work system log data to extract text paragraphs describing skill application scenarios; Natural Language Processing (NLP) technology uses a series of techniques (such as keyword matching, topic modeling, and text classification) to perform preliminary scanning and analysis of the raw text. The aim is to accurately identify and extract text segments that specifically describe the application scenarios of the skills. For example, from a large number of emails and chat logs, detailed discussions in technical groups about employees helping colleagues "debug a distributed system cache consistency problem" can be located; or from project update logs, descriptions such as "by refactoring the payment module code, the interface response time was reduced by 30%" can be extracted. This step initially structures the unstructured log data into a set of textual evidence fragments rich in skill information.

[0062] Step S304: The semantic analysis engine parses the action entities and effect description units in the text paragraph to generate implicit skill feature vectors; This process involves using a pre-trained language model to encode skill application text paragraphs; extracting semantic embedding representations describing problem-solving abilities; and generating implicit skill feature vectors by compressing the embedding dimensions using a clustering algorithm.

[0063] Specifically, the semantic analysis engine (usually based on an advanced pre-trained language model) performs a more refined analysis of the text: identifying action entities (such as verbs like "design," "optimize," "debug," and "guide"), which often relate to specific skill behaviors; and identifying effect description units (such as phrases describing results like "improved system stability" and "solved performance bottlenecks"), reflecting the effectiveness of skill application. The engine then further understands the semantic relationships between these elements (such as who, what, what was done, and what the effect was), and encodes this complex semantic information into a low-dimensional mathematical representation—the implicit skill feature vector. This vector is no longer composed of words but a set of numbers that concisely represents the type, level, and effectiveness of the skills demonstrated by the employee in the work scenario in a machine-readable way.

[0064] Step S305: Align the standardized and transformed structured skill test data with the implicit skill feature vector in the feature space; After preliminary processing, the following two types of data were obtained: one is the standardized test score (explicit skills) that directly represents the degree of knowledge mastery, and the other is the implicit skill feature vector that represents the ability to apply knowledge in practice.

[0065] In this embodiment, feature space alignment is a process of "translation" and "calibration," aiming to project these two types of data onto the same mathematical space so that their values ​​can be directly compared and calculated. For example, standardized test scores can be mapped to a vector with the same dimension and similar distribution as the implicit skill feature vector through some transformation (such as a neural network); or, a common basic coordinate system can be found for them. For example, alignment ensures that the explicit indicator of "scoring 90 points on a programming exam" and the implicit feature of "high maintainability reflected in code comments" can be fairly and reasonably considered on the same scale.

[0066] Step S306: Merge the aligned structured skill test data and implicit skill feature vectors to generate a multi-dimensional employee skill status vector, where each dimension corresponds to the evaluation value of a skill item.

[0067] In some embodiments, after alignment to ensure data comparability, the system uses specific algorithms (such as vector concatenation, weighted averaging, or fusion based on more complex models such as neural networks) to fuse explicit skill data and implicit skill features into a unified, multi-dimensional vector of employee skill status.

[0068] In this final vector, each dimension corresponds to a standardized skill item (such as "Python programming" or "system architecture design"), and the evaluation value for that dimension is a comprehensive score that combines the employee's test score (knowing what) and the level of ability demonstrated by their work behavior (knowing why). This employee skill status vector encompasses both theoretical depth and practical breadth, providing a scientific and objective basis for subsequent precise similarity matching with job requirements.

[0069] In the above implementation, natural language processing and multimodal data fusion technologies are used to systematically extract, quantify, and integrate the deep-seated abilities (such as problem-solving, innovative application, and collaborative effectiveness) that employees actually demonstrate in their work and are difficult to measure through traditional examinations. The resulting employee skill status vector, as a highly condensed and dynamically updated digital profile, greatly improves the objectivity, comprehensiveness, and real-time nature of skills assessment, laying a solid data foundation for subsequent accurate skills gap analysis, personalized training program development, and scientific person-job matching.

[0070] Reference Figure 4 As a further implementation of the employee skills iteration and job matching management method, after generating the employee skills status vector in step S102, the method further includes: Step S401: Obtain multimodal interaction data of the target employee in the office collaboration system, including meeting recordings, instant messaging texts, and project review records; Among them, meeting recordings captured employees' language expression ability, logical thinking agility, and emotional stability in real-time, highly interactive communication scenarios; instant messaging texts reflected employees' information transmission efficiency, knowledge sharing willingness, and daily communication style in asynchronous collaboration; and project review records, as pre-structured data, provided an objective correlation between the difficulty of the task and the quality of completion.

[0071] These three data sources collectively cover the entire workflow from "real-time interaction" to "asynchronous collaboration" to "deliverables," aiming to cross-validate the practical skills and professional qualities that employees demonstrate in real-world work environments—qualities that are difficult to measure through written exams—from different dimensions, providing rich and authentic raw materials for subsequent in-depth analysis.

[0072] Step S402: Convert the meeting recording into text using speech recognition technology, combine it with instant messaging text for sentiment analysis, and generate a collaborative ability feature vector. Specifically, firstly, speech recognition technology is used to convert unstructured audio streams into a unified text format, aiming to reduce data from different modalities (audio and text) to the same semantic analysis level, laying the foundation for subsequent joint analysis. Then, sentiment analysis is performed on the fused text. The system combines dependency parsing to identify proactive behaviors (such as making suggestions and resolving disagreements) and passive responses in communication, while using domain-adaptive models (such as FinBERT) to distinguish between constructive opinions (positive sentiment + high information content) and ineffective agreement (positive sentiment + low information content).

[0073] Through this deep analysis, the system can extract a series of quantitative features that characterize collaborative ability (such as the frequency of proactive communication, the effectiveness of conflict resolution, and the depth of knowledge sharing), and organize these features into a low-dimensional, dense mathematical representation, namely the collaborative ability feature vector. This vector essentially encodes the complex collaborative behavior patterns of employees into a numerical form that can be processed and compared by machines.

[0074] Step S403: Extract task assignment tags and completion quality feedback from the project review records to generate a task load capacity feature vector; Task assignment tags (such as "high priority," "cross-departmental dependency," and "technical challenge") implicitly indicate the complexity and stress level of the task. The system decodes these into a load intensity coefficient using predefined rules or models. Completion quality feedback (such as ratings and comments) measures the effectiveness of task output. By establishing a causal relationship model between these two factors, the system can assess employee performance under a specific intensity coefficient.

[0075] For example, successfully completing high-load and high-complexity tasks consecutively indicates that employees have excellent task load capacity. The system deconstructs this capacity into multiple dimensions (such as stress resistance, ability to solve complex problems, and stability of delivery quality) and generates a task load capacity feature vector based on historical data.

[0076] Step S404: Merge the collaborative ability feature vector and the task load ability feature vector to generate a latent skill supplement vector; Although both the collaboration capability feature vector and the task load capability feature vector represent capabilities, they originate from different behavioral modalities, and their feature spaces may differ.

[0077] In some embodiments, attention mechanisms may be used to weight cross features that contribute significantly to performance prediction (e.g., good collaboration skills may help gain support in high-pressure tasks, thereby improving delivery quality), or lightweight neural networks may be used to learn the nonlinear interaction between two types of features.

[0078] Through this deep integration, the system extracts a supplementary vector of implicit skills that can capture the synergistic effect between collaboration and task execution. This vector reveals composite abilities such as "mitigating project risks through effective communication" or "improving individual task efficiency in team collaboration," which are deep implicit skills that cannot be discovered by single types of data. For example, an employee may show a high tendency to share knowledge in the "collaboration vector" and exhibit rapid learning ability in the "task vector." After integration, this may generate a new dimension, "knowledge conversion efficiency," that was not covered by the original skill model.

[0079] Step S405: Overlay the implicit skill supplement vector onto the employee skill status vector.

[0080] Specifically, when the supplementary vector is superimposed on the original skill status vector, the new capability items introduced by the supplementary vector (such as "cross-team coordination") will expand the dimensional space of the original vector, resulting in an enhanced employee skill status vector.

[0081] Furthermore, during the overlay process, it is even more important to resolve conflicts. When there is a contradiction between explicit skill scores (such as high scores on programming tests) and abilities reflected in behavioral data (such as low quality of project code), the system will appropriately reduce the confidence weight of explicit scores based on the continuity and objectivity of behavioral data, and will be more inclined to believe practical evidence.

[0082] Understandably, by using the enhanced employee skill status vector, when calculating the similarity match between employees and positions, soft requirements in the job requirement vector that were previously difficult to measure precisely (such as "requiring good communication and coordination skills" and "being able to maintain work quality under pressure") can be accurately and quantitatively compared with their corresponding implicit skill dimensions. This makes the similarity matching results more predictive of an employee's actual job competency potential, rather than just the fit of theoretical knowledge. For example, an employee with a slightly lower theoretical score but outstanding practical skills may have their match score increased by the system, thus preventing excellent talent from being buried due to the one-sidedness of traditional assessments, and achieving a leap from "theoretical knowledge" to "real-world testing."

[0083] In the above implementation, soft skills and practical abilities that are difficult to quantify in the workplace, such as collaboration, communication, and stress resistance, are transformed into calculable and comparable objective indicators, which are then organically integrated with traditional skills assessment results. The resulting enhanced employee skills profile improves the authenticity, comprehensiveness, and predictability of the employee competency profile, making subsequent job matching decisions more accurate and scientific, and effectively reducing the risk of talent mismatch due to misjudgment of competency.

[0084] Reference Figure 5 As one implementation of step S104, the steps of selecting corresponding training resources from the enterprise training resource library based on at least one skill to be improved, and using an optimization algorithm to sort and combine the selected training resources to generate a personalized skills training program include: Step S501: parse the skill type tag and ability level tag of at least one skill item to be improved; Among them, the skill type tags (such as "programming language", "project management", "soft skills" etc.) define the knowledge domain and behavioral pattern to which the skill belongs, which directly determines the basic direction and screening strategy for subsequent search for training resources. For example, for "programming language" skills, more emphasis will be placed on practical training and coding practice, while "soft skills" may lean towards case analysis and role-playing.

[0085] The ability level labels (such as "beginner," "intermediate," and "advanced") accurately depict the gap between the current skill level and the target level, providing a key constraint for developing a gradual improvement path that aligns with learning patterns. The parsing process typically relies on a pre-built skill ontology or knowledge graph, automatically assigning standardized labels to each skill item through semantic mapping, thereby transforming vague learning needs into structured, computable input parameters.

[0086] Step S502: Based on the skill type tags, filter the set of candidate training resources in the enterprise training resource library whose relevance exceeds the preset relevance threshold; Once the skill type is identified, a large-scale resource search and initial screening can be conducted. The goal is to quickly locate all potentially relevant resources from a massive corporate training resource library (including online courses, internal training, project practice, micro-certifications, etc.). The relevance assessment is not a simple keyword matching process, but rather a deep learning process based on semantic similarity.

[0087] The screening process includes: calculating the semantic similarity between the resource description text and the skill type tag; including resource items in the candidate set when the semantic similarity exceeds a preset association threshold; and filtering resource items that have been completed or expired by the current employee.

[0088] Specifically, the system compares the semantic meaning of resource description text (such as course outlines and project objectives) with skill type tags. Only when the similarity exceeds an adjustable preset association threshold is the resource included in the candidate set. This process ensures that the selected resources are highly relevant to the skills to be improved in terms of content theme, providing a sufficient and high-quality pool of alternative solutions for subsequent refined optimization. At the same time, the threshold filters out a large number of irrelevant or weakly relevant resources, improving the efficiency of subsequent calculations.

[0089] Step S503: Configure multi-dimensional weighting factors for each cultivation resource in the candidate cultivation resource set, including learning duration weight, cost weight, and expected conversion rate weight; Specifically, the system dynamically configures a set of multi-dimensional weighting factors for each candidate resource. These factors together constitute a comprehensive index system for evaluating the value of the resource. The configuration steps include: training a weight prediction model based on historical training data, inputting employee competency level labels and resource metadata, and outputting quantitative values ​​for learning duration weight, cost weight, and expected conversion rate weight.

[0090] The learning time weight quantifies the cost of time investment; for employees with heavy workloads, resources with high time weights may not be suitable. The cost weight is directly related to economic expenditures, helping to control training budgets. The expected conversion rate weight is a predictive indicator that estimates the effectiveness of the resource for improving the skills of current employees based on historical data (such as the skill improvement effects of similar employees using the resource).

[0091] In this embodiment, by assigning weights to these three dimensions, the system transforms each resource into an object with multiple value attributes, enabling subsequent optimization algorithms to weigh and compare within a unified and quantifiable framework, thereby finding a solution that achieves the best balance in terms of time, money, and effectiveness.

[0092] Step S504: Construct skill improvement path constraints based on ability level labels; In this embodiment, the skill enhancement path constraints are a set of rules defined by educational theory and domain expert knowledge. For example, the rules might require that for a particular skill, one must first complete resources labeled "beginner" before accessing "intermediate" resources, and finally learn "advanced" resources; or they might require that theoretical learning of resources precede practical application of resources. These constraints logically connect discrete training resources, forming a directed, non-cyclic learning path graph. The ability level labels play a crucial role in defining the path nodes and their order.

[0093] Understandably, pre-constructing these constraints essentially encodes experts' teaching experience into hard rules or soft preferences that machines can understand and execute, thereby ensuring that the final solution is not just a collection of resources, but a scientific, coherent, and executable growth ladder.

[0094] Step S505: Call the optimization algorithm engine and input the candidate culture resource set, multi-dimensional weight factors, and path constraints; Specifically, the outputs of all the aforementioned steps are integrated into a complex optimization problem, which is then solved by a specialized optimization algorithm engine (such as a genetic algorithm or simulated annealing algorithm). The engine's input is a structured decision-making scenario: the set of candidate training resources defines the search space; multidimensional weighting factors define the optimization objectives (e.g., the shortest total learning time, the lowest total cost, the highest total expected conversion rate, or a weighted combination of these objectives); and path constraints define which paths are feasible in the search space.

[0095] By invoking the optimization algorithm engine, the system shifts from static resource evaluation to dynamic, global optimal solution search. It will intelligently find the optimal path from a vast array of discrete possible solutions.

[0096] Step S506: The candidate cultivation resource set is sorted and combined using an optimization algorithm engine to output the optimal resource sequence that satisfies the path constraints. In this embodiment, the optimization algorithm engine performs the following specific steps: modeling the resource sorting problem as a constrained traveling salesman problem, generating candidate sequences through crossover and mutation operations of a genetic algorithm, and iteratively solving the objective function of minimizing total cost and maximizing conversion rate.

[0097] Specifically, the optimization algorithm engine continuously generates and evaluates different resource ranking and combination schemes based on its inherent mechanisms (such as selection, crossover, and mutation in genetic algorithms). Each evaluation checks whether the scheme satisfies all path constraints and calculates its overall utility value (based on multi-dimensional weighting factors). Through multiple iterations, the algorithm evolves or searches for the scheme with the highest overall utility value under all constraints, i.e., the optimal resource sequence. This sequence is not a simple list, but an organic whole that considers resource dependencies, learning pace, and overall benefits. It represents the most effective learning path that the computer can find for that employee under given conditions and objectives.

[0098] Step S507: Encapsulate the optimal resource sequence into a structured training task flow to generate a personalized skills training plan.

[0099] The encapsulation process includes adding specific execution metadata to each resource node in the optimal resource sequence, such as suggested start and end times, completion criteria, and dependencies, and organizing it into a structured data format (such as JSON, XML, or a definition language based on the workflow engine).

[0100] The resulting structured training task flow is a complete and actionable personalized skills training program that clearly specifies "when, in what order, and what tasks to complete," thereby turning the insights generated by intelligent computing into concrete action guidelines to drive employee skills improvement.

[0101] The above implementation method changes the traditional extensive model of training program development, which relies on human experience, lacks personalized consideration, and struggles to balance multiple objectives. By introducing a series of technical means, such as semantic-based resource selection, multi-dimensional weight factor evaluation, path constraint modeling, and optimization algorithm solutions, it systematically achieves data-driven, multi-objective optimization, and personalized customization of training programs. This technical solution can tailor a learning and development path for each employee that is highly relevant in content, conforms to cognitive laws in path, and achieves an optimal balance in cost-effectiveness. This greatly improves the relevance, scientific nature, and return on investment efficiency of skills training, providing precise and scalable technical support for the company's talent development strategy.

[0102] Reference Figure 6 As one implementation of step S106, the steps of determining multiple evaluation indicators based on cultivation process data and establishing a multi-level evaluation indicator system, quantifying each evaluation indicator using fuzzy mathematics, and generating a cultivation effect index by integrating the quantification results of each evaluation indicator include: Step S601: Receive learning progress data and project practice data from the training process data acquisition module; Learning progress data (such as course completion rate, chapter quiz scores, and learning time distribution) provides an objective measure of employees' input and absorption at the "knowledge" level, reflecting the execution volume and immediate results of the training process. Project practice data (such as the completion rate of real-world projects, code quality scores, project report review comments, and records of effectiveness in solving practical business problems) reflects employees' ability to apply their knowledge to solve real-world problems at the "action" level, serving as key evidence of skill transfer effectiveness. Receiving both types of data means that the evaluation model simultaneously focuses on process and results, input and output, providing multi-dimensional raw signals for building a comprehensive evaluation system.

[0103] Step S602: Map the learning progress data and project practice data to the corresponding initial evaluation index set according to the preset capability dimension framework; In this application embodiment, a preset capability dimension framework (such as DEEM) is used. Models, ONET skill classifications, etc., play the role of a "standardized dictionary" and "taxonomy," defining a well-organized, mutually exclusive, and complete set of competency items.

[0104] Specifically, by establishing a mapping rule, each piece of data is associated with a corresponding capability dimension within the framework. For example, the learning progress data "completed the online course on advanced Python features" is mapped to the initial evaluation metric "programming language understanding"; the project practice data "successfully optimized database queries in a project, reducing response time by 20%" is mapped to multiple initial evaluation metrics such as "performance optimization capability" and "problem-solving capability." Through this mapping, disorganized data is categorized and organized into a series of initial evaluation metrics with clear business significance, preparing the ground for building a structured evaluation system later.

[0105] Step S603: Construct a multi-level evaluation index system, including classifying the initial evaluation indexes according to logical relationships to form primary indicators, and defining a subset of related secondary indicators for each primary indicator; If a large number of initial evaluation indicators are arranged in parallel, they will appear cluttered and fail to reflect the inherent logic between the indicators. In this embodiment, the multi-level evaluation indicator system is constructed by stratifying and classifying these indicators. For example, secondary indicators such as "course completion rate" and "average test score" are classified under the primary indicator "learning engagement"; secondary indicators such as "project code quality", "solution innovation", and "problem-solving efficiency" are classified under the primary indicator "practical application ability".

[0106] Understandably, the subset of secondary indicators under each primary indicator is a more detailed measure of different aspects of that primary indicator. This tree structure not only makes the evaluation system clear and easy to manage, but also allows the evaluation to be carried out at different granularities. It can examine details (secondary indicators) and grasp the whole (primary indicators), providing a scientific framework for subsequent weighted aggregation.

[0107] Step S604: Perform fuzzification transformation on each secondary indicator to convert the original data values ​​into a membership degree distribution vector; The fuzzification transformation process includes: defining a fuzzy comment set containing evaluation levels for each secondary indicator; generating a triangular fuzzy number function based on the number of evaluation levels; and converting the original data values ​​into a membership degree distribution vector of the comment levels through a membership function.

[0108] Understandably, many evaluations are inherently vague, such as "good code quality" or "good communication," and are difficult to quantify with a precise number.

[0109] In this embodiment, the task of the fuzzification processing unit is to handle this uncertainty. It defines a fuzzy rating set (e.g., {Poor, Average, Good, Excellent}) for each secondary indicator and a membership function (commonly triangular or trapezoidal fuzzy numbers) for each rating level. Then, the unit inputs the specific original data value (e.g., a code review score of 85 points) into the membership function and calculates the membership degree of this 85 points to the rating levels "Good" and "Excellent" (e.g., the degree to "Good" is 0.3, and the degree to "Excellent" is 0.7). All these membership degrees constitute a membership degree distribution vector. This vector is no longer an isolated, potentially controversial score, but a comprehensive description of the probability that an evaluation object belongs to each rating level. It better aligns with the fuzziness of human thinking and can handle the uncertainty information in the evaluation process more delicately.

[0110] Step S605: Assign corresponding weight coefficients to each indicator in the multi-level evaluation index system; The logic behind this step is to quantify the differences in the relative importance of different evaluation indicators in the final comprehensive evaluation. Not all indicators are equally important. For example, for the training of a senior technical position, the weight of "project practical application ability" may be much higher than that of "theoretical learning performance." The weight coefficient is used to represent this relative importance, and usually the sum of the weights of all indicators of the same level is 1.

[0111] In some embodiments, the configuration of weight coefficients can be based on domain expert experience (such as the Delphi method) or calculated using more scientific mathematical methods (such as the Analytic Hierarchy Process, AHP) to ensure that the weight allocation is reasonable and consistent. Specifically, the weight coefficient configuration step in the embodiments of this application includes: based on DEEM A weight allocation rule library is established for the 27 dimensions of the competency model; the weight coefficients are dynamically adjusted according to the employee's current skill level.

[0112] Understandably, by assigning weights to each layer and each indicator, value judgments and experiential knowledge are essentially encoded into the evaluation model, so that the final aggregated results can reflect the value orientation desired by the organization, that is, which aspects of training effectiveness are valued more.

[0113] Step S606: Perform multi-level aggregation operation, and calculate the comprehensive evaluation value of each level index by combining the membership distribution vector and weight coefficient. Specifically, the multi-level aggregation operation starts from the lowest level of secondary indicators: First, the membership distribution vector of each secondary indicator is fuzzily weighted with its weight coefficient to obtain the weighted evaluation vector of that secondary indicator. Then, the weighted evaluation vectors of all secondary indicators belonging to the same primary indicator are fuzzily synthesized (e.g., using fuzzy weighted averaging) to obtain a composite vector representing the evaluation of that primary indicator. This process is repeated level by level until the top-level comprehensive evaluation vector is calculated.

[0114] Understandably, this bottom-up aggregation approach fully considers the information of all underlying indicators and reflects their different importance through weights. The final top-level evaluation value is a structured result that integrates all the subtle evaluation information.

[0115] Step S607: Normalize the comprehensive evaluation value of the top-level indicator into a scalar value within a preset range and output it as the cultivation effect index.

[0116] Specifically, the comprehensive evaluation value of the top-level indicator may still be in the form of a fuzzy set. It needs to be defuzzified using methods (such as the centroid method) to map the fuzzy set into a definite scalar value. For ease of use and interpretation, this scalar value will be normalized to a preset interval, such as between 0 and 1, or between 0 and 100. Ultimately, the output scalar value is the training effect index.

[0117] In this application embodiment, the training effectiveness index is a highly condensed quantitative indicator that comprehensively reflects all information from specific learning behaviors to project practice results and competency assessments at all levels. It provides managers with an objective, scientific, and comparable measurement standard to judge the effectiveness of the training program and serves as an important basis for subsequent skills file updates and job matching decisions.

[0118] In the above implementation, by introducing data mapping based on a capability framework, a hierarchical indicator system, fuzzy mathematics for handling uncertainty, and weighted multi-level aggregation operations, this technical solution systematically achieves a comprehensive, multi-granular, and quantifiable precise measurement of training effectiveness. The resulting training effectiveness index, as a robust and information-rich comprehensive indicator, greatly enhances the scientific rigor, objectivity, and operability of the evaluation results, providing data-driven insights for optimizing training strategies, assessing return on investment, and supporting subsequent talent management decisions.

[0119] Reference Figure 7 As a further implementation of the employee skills iteration and job matching management method, the management method also includes: Step S701: After generating a personalized skills training program, monitor the task release data of the enterprise project demand pool in real time. In particular, after the training program is generated, the enterprise project demand pool (such as the core database of project management tools such as Jira and Asana) is continuously monitored in real time to capture the latest task release data.

[0120] Specifically, based on an event-driven architecture, the system can detect new task releases or existing task updates in real time. The monitoring focuses not only on the skill keywords described in the task itself (such as "requires mastery of Python pandas for data analysis"), but also on the business context information of the task, such as priority, urgency, and the value of associated strategic projects.

[0121] Step S702: Identify high-priority tasks in the task release data that are strongly correlated with the skills to be improved; Specifically, the identification process is a multi-criteria decision-making process that comprehensively utilizes semantic analysis and business rules. First, at the semantic level, the system uses natural language processing technology to calculate the semantic similarity between the task description and the employee's skills to be improved, in order to determine the strength of the association. For example, an employee whose skill to be improved is "user experience design" will have a high degree of association with the task of "optimizing the UI of the APP ordering process".

[0122] Secondly, at the business level, the system determines the priority of tasks based on preset rules (such as whether the task is related to core KPIs, whether it is marked as "urgent" or "high value"). Only tasks that simultaneously meet the dual conditions of "strong relevance" (ensuring content relevance) and "high priority" (ensuring business value) will be selected. This step ensures that the work integrated into the training program is real and has a significant business impact, rather than secondary or trivial tasks.

[0123] Step S703: Decompose high-priority tasks into micro-task units and insert them into the task sequence of the personalized skills training program; A complete business task (such as "developing a customer profile analysis dashboard") is usually highly complex and involves a wide range of aspects. Directly including it in the training program may leave employees at a loss or disrupt their original learning path. Therefore, it is necessary to break it down, that is, to use the functional decomposition method to break down the complex task into a series of atomic, independently executable micro-task units (such as "data extraction → data cleaning → feature engineering → visualization configuration").

[0124] At the same time, the system will analyze the logical dependencies between these micro-tasks, form a directed graph, and intelligently insert them into the most appropriate position in the task sequence of the personalized skills training program based on the employee's current ability level and the progress of the original plan (e.g., insert the "visualization configuration" micro-task after completing the "data visualization basics" course).

[0125] Step S704: When the target employee executes the micro-task unit, collect the actual task delivery quality data. Specifically, the collected actual task delivery quality data includes not only objective and quantifiable output indicators (such as test pass rate after code submission, timeliness of task completion, and accuracy of data reports), but may also include subjective evaluation feedback from upstream and downstream collaborators (such as product managers' ratings of deliverable quality). This assessment data, based on real business scenarios, reflects employees' skill application level and problem-solving ability under real work pressure and environment far better than test scores from training platforms, providing highly reliable data support for the assessment.

[0126] Step S705: The actual task delivery quality data is used as an enhancement signal for the project practice data and injected into the cultivation process data to obtain enhanced cultivation process data. Among these, actual task delivery quality data is considered a reinforcing signal because it originates directly from production practice, and its credibility and value are weighted higher than data from simulated environments. The injection operation refers to integrating this high-quality data into a training process dataset used to evaluate employee training effectiveness.

[0127] It should be noted that during the data injection process, delivery data from real-world tasks are given higher weight, thus having a more significant impact on the overall training effectiveness assessment. This allows the system to judge employee capabilities more based on actual practical performance, rather than solely relying on exercise results, significantly improving the reliability and predictive validity of the assessment results.

[0128] Step S706: Based on the enhanced cultivation process data, dynamically adjust the weight coefficients of the project practice indicators in the multi-level evaluation index system.

[0129] Specifically, in the multi-level evaluation index system, project practice indicators are a key dimension for measuring the effectiveness of skills application. The system dynamically adjusts the weighting factors of this category of indicators and their specific sub-indicators based on continuously injected reinforcement signals (i.e., the quality of real-world task delivery). For example, if a certain type of real-world task (such as "high-concurrency system debugging") occurs frequently and delivery quality generally becomes a bottleneck, the system may automatically increase the weight of indicators related to "performance optimization practice" in the evaluation system, thereby emphasizing this aspect of ability in subsequent training effectiveness assessments. Conversely, for indicators that have already been stably met, their weights may be appropriately reduced.

[0130] Understandably, this dynamic adjustment mechanism makes the evaluation system no longer static, but an intelligent system that can respond to changes in business needs and evolve adaptively, thereby continuously guiding training activities to focus on the most urgent and valuable business capability gaps.

[0131] The above implementation not only greatly improves the real-time nature and relevance of training content, ensuring that employee skill development always resonates with the company's development, but also uses real business results as the highest standard to verify training effectiveness and calibrate the evaluation system, thereby making the entire skill iteration process more accurate and efficient, and directly generating business value.

[0132] Reference Figure 8 As one implementation of step S109, the steps of monitoring the implementation feedback data of job matching suggestions or corporate strategy change signals, and triggering the adjustment process of job skill requirement vector and / or similarity matching threshold parameters according to preset rules include: Step S801: Continuously monitor the execution status data stream of job matching suggestions in the enterprise business system and obtain the implementation feedback parameter set; Specifically, the system integrates with the enterprise's existing HR business systems (such as recruitment systems, performance management systems, and project management systems) via API to establish a continuous data flow monitoring channel. The implementation feedback parameter set is a multi-dimensional data set, and its specific parameters may include, but are not limited to: the employee performance change rate after job reassignment, the adaptation period for the new position (the time from starting the job to achieving expected performance), the completion quality and satisfaction rating of recommended flexible tasks, and the secondary job reassignment rate due to skill mismatch, etc.

[0133] Understandably, these parameters objectively reflect the accuracy of the initial "job skill requirement vector" and the effectiveness of the "personalized skills training program." For example, if multiple employees experience a significant decline in performance after being recommended by the system for a particular job, this data signal suggests that the current job skill requirement vector may have deviated from actual business needs.

[0134] Step S802: Simultaneously scan the structured documents of the enterprise's strategic information release channels and capture structured document update events; Among them, by utilizing natural language processing technology, the system actively monitors official information sources that represent changes in the company's strategic direction. The structured documents include approved and released annual strategic plans, quarterly business adjustment reports, new business launch announcements, and notices of major organizational restructuring.

[0135] Specifically, the system periodically scans these documents and uses text difference comparison or key event detection algorithms (such as the appearance of keywords like "business transformation," "strategic focus adjustment," or "establishment of a new business unit") to capture document update events. This scanning mechanism enables the system to keenly detect adjustments in corporate strategic direction, shifts in business focus, or the emergence of new skill requirements. This is the prerequisite for the system to proactively respond to changes and avoid making erroneous decisions based on outdated information, demonstrating its dynamic perception capability of the external environment.

[0136] Step S803: When the parameter value in the implementation feedback parameter set exceeds the preset tolerance threshold, or when a structured document update event is identified, the rule engine analysis module is activated. The preset tolerance threshold defines the acceptable normal operating boundaries of the system. For example, the performance achievement rate of employees after job reassignment should not be lower than 70%. When the implemented feedback parameters (such as the actual performance achievement rate being only 65%) exceed (fall below) this threshold, it indicates that there may be problems with the current matching model or training program, requiring review. Simultaneously, any structured document update event may signify a fundamental shift in the job skill requirements. Either of these scenarios implies that the current state of the system may no longer be optimal.

[0137] At this point, the system will activate the rule engine analysis module, transitioning from standby to analysis and decision-making. This design ensures that adjustments are not made periodically and blindly, but are only triggered when there is clear evidence that adjustments are needed, thus guaranteeing the efficiency and stability of the system.

[0138] Step S804: The rule engine analysis module parses abnormal parameter values ​​or change events to generate impact scope labels; In some embodiments, the step of marking abnormal parameter values ​​includes: extracting the performance change rate within three months after job reassignment, and simultaneously calculating the delivery quality score of flexible work tasks; when the performance change rate is lower than a threshold α or the delivery quality score is lower than a threshold β, it is marked as an abnormal parameter.

[0139] Specifically, the rule engine analysis module performs in-depth analysis of the input abnormal parameter values ​​or change events. Its purpose is to determine the breadth and depth of the signal's impact and label it with an impact scope tag. The generated impact scope tag is a structured qualitative conclusion that accurately locates the impact boundary of the problem or change (whether it is a specific position, a specific department, or the entire company) and the core content (which skills or business processes are affected).

[0140] For example, if the analysis reveals that an employee's performance is substandard solely due to a lack of proficiency in a specific task, the impact might be categorized as an "individual skill item." If multiple employees with similar training paths are found to be performing poorly in their new roles, it might be categorized as a "deficiency in the training program." If the strategic update document explicitly states a "full transition to cloud-native architecture," the impact might be categorized as "technology stack level" or "department level." This analytical process is based on pre-defined rules and pattern recognition, aiming to accurately pinpoint the root cause and scope of the problem, providing a basis for selecting the correct adjustment strategy.

[0141] In addition, the rule engine may also include the following rules: IF (multiple employees' performance parameters in Category A positions exceed the standard simultaneously) AND (performance decline is related to a specific skill S) THEN (generates an impact scope label of "Category A positions - inaccurate demand for skill S"). Another example is IF (identifies a strategic document mentioning "full-scale entry into the AI ​​field") THEN (generates an impact scope label of "company-wide - general increase in demand for AI-related skills").

[0142] Step S805: Query the predefined rule decision tree based on the scope of influence label, and output the adjustment instruction type and / or similarity matching threshold parameter adjustment instruction type for the job skill requirement vector; The predefined rule decision tree is a logical tree containing various "IF-THEN" branches. The system uses the generated "scope of influence label" as the input key to traverse the decision tree and output a clear "adjustment instruction type".

[0143] For example, for the label "Class A Position - Skill S Requirement Inaccurate", the decision tree might output the instruction "Fine-adjust the weight of skill S in the skill requirement vector for Class A positions"; for the label "Company-wide - Increased Demand for AI-Related Skills", the output instruction might be "Initiate a comprehensive review and reconstruction of the skill requirement vector for all positions across the company", possibly accompanied by "Temporarily lower the similarity matching threshold for AI skill items to accelerate adaptation". These instruction types serve as the basis for the system's self-adjustment, clarifying the object (requirement vector or threshold) that needs adjustment and the nature of the adjustment (fine-tuning, reconstruction, threshold change).

[0144] Step S806 involves binding the adjustment instruction type with the version identifier of the current job skill requirement vector and the version identifier of the similarity matching threshold parameter, triggering the requirement vector generation module to execute the job skill requirement vector regeneration process, and / or triggering the threshold parameter update process of the similarity matching module.

[0145] Specifically, the generated adjustment instructions are bound to the key parameters of the currently running system (precisely specified by the "version identifier"). This binding operation means that the adjustment is based on which specific version of the system state, ensuring the accuracy of the adjustment.

[0146] Subsequently, based on the instructions, the system automatically triggers the corresponding functional modules: if the requirement vector needs to be updated, the "Requirement Vector Generation Module" is invoked to re-execute the generation process; if the threshold needs to be updated, the "Similarity Matching Module" is notified to load the new threshold parameters. This means that the entire system's workflow will restart from a new starting point (based on new inputs and instructions), thereby generating updated job skill requirement vectors and / or similarity matching threshold parameters.

[0147] In the above implementation, continuous monitoring of internal execution feedback and external strategic changes allows for automatic root cause analysis, intelligent decision-making, and precise re-execution of preceding core modules when key signals are triggered. This mechanism ensures the long-term accuracy and effectiveness of the entire management approach, reduces management costs caused by model lag or decision bias, and enables the organization's talent management strategy to maintain continuous agility and vitality, thereby building a solid and flexible competitive advantage in talent within a rapidly changing market environment.

[0148] This application also discloses an employee skills iteration and job adaptation management system based on AI Agent.

[0149] An AI Agent-based employee skills iteration and job adaptation management system, specifically including: The job skill requirement vector generation module is used to obtain the job description information of the target position from the enterprise strategic document data and perform natural language processing to identify multiple skill items associated with the target position, assign importance scores to each skill item, and generate a job skill requirement vector. The Employee Skill Status Vector Generation Module is used to collect skill test data and work system log data of target employees, standardize the skill test data, extract implicit skill features through semantic analysis technology, and combine the standardization results and implicit skill features to generate an employee skill status vector. The skills gap analysis module is used to perform similarity matching between the job skills requirement vector and the employee's current skills vector to identify at least one skill item that needs to be improved if the similarity difference exceeds a preset range. The personalized training program optimization module is used to select corresponding training resources from the enterprise training resource library based on at least one skill to be improved, and to sort and combine the selected training resources using an optimization algorithm to generate a personalized skill training program. The training process data acquisition module is used to collect training process data of target employees during the implementation of personalized skills training programs, including learning progress data and project practice data. The cultivation effect evaluation module is used to determine multiple evaluation indicators based on cultivation process data and establish a multi-level evaluation indicator system. It uses fuzzy mathematics to quantify each evaluation indicator and generates a cultivation effect index by combining the quantification results of each evaluation indicator. The skills profile update module is used to correct the employee's current skills status vector based on the training effect index, obtain updated skills status data, and store it in the target employee's dynamic skills profile. The job matching recommendation module is used to match the current skill data in the dynamic skill profile with the job requirement data in the enterprise job database to generate job matching suggestions. The closed-loop feedback adjustment module is used to monitor the implementation feedback data of job matching suggestions or corporate strategy change signals, and trigger the adjustment process of job skill requirement vector and / or similarity matching threshold parameters according to preset rules.

[0150] An employee skills iteration and job adaptation management system based on AI Agent according to an embodiment of this application can implement any of the above methods, and the specific working process of each module in the system can refer to the corresponding process in the above method embodiments.

[0151] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of a certain module is merely a logical functional division, and in actual implementation there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0152] This application also discloses a computer-readable storage medium.

[0153] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described above in any of the AIAgent-based employee skills iteration and job matching management methods.

[0154] The computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device; the program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0155] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for employee skill iteration and job adaptation management based on AI Agent, characterized in that, The management method includes: Job description information for target positions is obtained from corporate strategy documents and natural language processing is performed to identify multiple skill items associated with the target positions. An importance score is assigned to each skill item to generate a job skill requirement vector. Collect skill test data and work system log data of target employees, standardize the skill test data, extract implicit skill features through semantic analysis technology, and combine the standardization results and implicit skill features to generate an employee skill status vector. Based on the similarity matching between the job skill requirement vector and the employee skill status vector, at least one skill item that needs to be improved is identified if the similarity difference exceeds a preset difference range. Based on the at least one skill to be improved, corresponding training resources are selected from the enterprise training resource library, and an optimization algorithm is used to sort and combine the selected training resources to generate a personalized skill training plan. During the process of the target employee implementing the personalized skills training program, training process data of the target employee is collected, including learning progress data and project practice data; Based on the cultivation process data, a multi-level evaluation index system is determined. Fuzzy mathematics is used to perform fuzzification transformation on each evaluation index, converting the original data values ​​into membership degree distribution vectors. Corresponding weight coefficients are assigned to each index in the multi-level evaluation index system. Multi-level aggregation operations are performed by combining the membership degree distribution vectors and the weight coefficients to calculate the comprehensive evaluation value of each level of index. The comprehensive evaluation value of the top-level index is normalized to a scalar value within a preset interval, which is used as the cultivation effect index. The training effect index is used as a credibility weight or fusion coefficient to perform a weighted average or Bayesian update on the employee's current skill status vector and the skill target vector expected to be achieved through the personalized skill training program, so as to obtain updated skill status data and store it in the target employee's dynamic skill profile. The current skill data in the dynamic skill profile is matched with the job requirement data in the enterprise job database to generate job matching suggestions; Monitor the implementation feedback data of the job matching suggestions or corporate strategy change signals, and trigger the adjustment process of the job skill requirement vector and / or similarity matching threshold parameters according to preset rules; After generating the personalized skills training program, the task release data of the enterprise project demand pool is monitored in real time; high-priority tasks strongly correlated with the skills to be improved are identified in the task release data; the high-priority tasks are broken down into micro-task units and inserted into the task sequence of the personalized skills training program; when the target employee executes the micro-task unit, actual task delivery quality data is collected; the actual task delivery quality data is used as a reinforcement signal for project practice data and injected into the training process data to obtain enhanced training process data; based on the enhanced training process data, the weight coefficients of project practice indicators in the multi-level evaluation indicator system are dynamically adjusted.

2. The employee skill iteration and job adaptation management method based on AI Agent according to claim 1, characterized in that, The steps involved in extracting job description information for the target position from corporate strategy document data, performing natural language processing to identify multiple skill items associated with the target position, assigning importance scores to each skill item, and generating a job skill requirement vector include: Collect corporate strategy document data related to the target position from the corporate database and extract text fragments containing job description information; Natural language processing is performed on the text fragment to identify entity units describing professional skill requirements through semantic parsing; Based on knowledge graph association mapping, the entity units are classified into standardized skill items in the preset skill classification system; Based on the contextual semantic features of the entity unit in the enterprise strategy document data, calculate the important metric value of each standardized skill item; Integrate all standardized skill items and important metric values ​​to generate a job skill requirement vector stored in vector form.

3. The employee skill iteration and job adaptation management method based on AI Agent according to claim 1, characterized in that, The steps for collecting skill test data and work system log data from target employees, standardizing the skill test data, extracting implicit skill features using semantic analysis technology, and generating an employee skill status vector by combining the standardization results and implicit skill features include: Collect structured skills test data generated by target employees in a pre-set skills assessment system, as well as unstructured work system log data recorded in an office collaboration system; The structured skills test data is subjected to a standardization transformation operation to map it to a scoring space with a unified dimension; Natural language processing is performed on the unstructured work system log data to extract text paragraphs describing skill application scenarios; The semantic analysis engine parses the action entities and effect description units in the text paragraphs to generate implicit skill feature vectors. The standardized and transformed structured skill test data is aligned with the implicit skill feature vector in the feature space; By integrating and aligning the structured skills test data with the implicit skills feature vectors, a multi-dimensional vector of the current employee skills status is generated, where each dimension corresponds to the evaluation value of a skill item.

4. The employee skill iteration and job adaptation management method based on AI Agent according to claim 3, characterized in that, Following the step of generating an employee skill status vector, the following steps are also included: Acquire multimodal interaction data of target employees in office collaboration systems, including meeting recordings, instant messaging texts, and project review records; The meeting recordings are converted into text using speech recognition technology, and then combined with the instant messaging text for sentiment analysis to generate a collaborative ability feature vector. Extract the task assignment tags and completion quality feedback from the project review records to generate a task load capacity feature vector. By fusing the collaborative ability feature vector and the task load ability feature vector, a latent skill supplement vector is generated; The implicit skill supplement vector is superimposed on the employee skill status vector.

5. The employee skill iteration and job adaptation management method based on AI Agent according to claim 1, characterized in that, Based on the at least one skill to be improved, the steps of selecting corresponding training resources from the enterprise training resource library and using an optimization algorithm to sort and combine the selected training resources to generate a personalized skills training program include: Analyze the skill type tag and ability level tag of the at least one skill item to be improved; Based on the skill type tags, a set of candidate training resources with a correlation exceeding a preset correlation threshold is selected from the enterprise training resource library; Configure a multi-dimensional weighting factor for each cultivation resource in the candidate cultivation resource set, including learning duration weight, cost weight, and expected conversion rate weight; Construct skill improvement path constraints based on the aforementioned ability level labels; Call the optimization algorithm engine, and input the candidate culture resource set, multidimensional weight factors, and path constraints; The optimization algorithm engine sorts and combines the candidate cultivation resource set to output the optimal resource sequence that satisfies the path constraints. The optimal resource sequence is encapsulated into a structured training task flow to generate a personalized skills training program.

6. A method for employee skill iteration and job adaptation management based on AI Agent according to any one of claims 1 to 5, characterized in that, The steps of monitoring the implementation feedback data of the job matching suggestions or corporate strategy change signals, and triggering the adjustment process of the job skill requirement vector and / or similarity matching threshold parameters according to preset rules, include: Continuously monitor the execution status data stream of job matching suggestions in the enterprise business system to obtain the set of implementation feedback parameters; Simultaneously scan structured documents from the company's strategic information dissemination channels and capture structured document update events; When the parameter value in the implementation feedback parameter set is detected to exceed the preset tolerance threshold, or when the structured document update event is identified, the rule engine analysis module is activated. The rule engine analysis module parses abnormal parameter values ​​or change events to generate impact range labels. Based on the influence range label, query the predefined rule decision tree and output the adjustment instruction type and / or similarity matching threshold parameter adjustment instruction type for the job skill requirement vector; The adjustment instruction type is bound to the version identifier of the current job skill requirement vector and the version identifier of the similarity matching threshold parameter, triggering the requirement vector generation module to execute the job skill requirement vector regeneration process, and / or triggering the threshold parameter update process of the similarity matching module.

7. An employee skills iteration and job adaptation management system based on AI Agent, characterized in that, The management system is used to execute the AI ​​Agent-based employee skills iteration and job adaptation management method according to any one of claims 1 to 6, the management system comprising: The job skill requirement vector generation module is used to obtain the job description information of the target position from the enterprise strategic document data and perform natural language processing to identify multiple skill items associated with the target position, assign importance scores to each skill item, and generate a job skill requirement vector. The employee skill status vector generation module is used to collect skill test data and work system log data of target employees, standardize the skill test data, extract implicit skill features through semantic analysis technology, and generate an employee skill status vector by combining the standardization results and implicit skill features. The skills gap analysis module is used to perform similarity matching between the job skills requirement vector and the employee skills status vector to identify at least one skill item that needs to be improved if the similarity difference exceeds a preset difference range. The personalized training program optimization module is used to select corresponding training resources from the enterprise training resource library based on the at least one skill item to be improved, and to sort and combine the selected training resources using an optimization algorithm to generate a personalized skill training program. The training process data acquisition module is used to collect training process data of the target employee during the execution of the personalized skills training program, including learning progress data and project practice data. The cultivation effect evaluation module is used to determine multiple evaluation indicators based on the cultivation process data and establish a multi-level evaluation indicator system. It uses fuzzy mathematics to quantify each evaluation indicator and generates a cultivation effect index by combining the quantification results of each evaluation indicator. The skills profile update module is used to correct the employee's current skills status vector based on the training effect index, obtain updated skills status data, and store it in the target employee's dynamic skills profile. The job matching recommendation module is used to match the current skill data in the dynamic skill profile with the job requirement data in the enterprise job database to generate job matching suggestions. The closed-loop feedback adjustment module is used to monitor the implementation feedback data of the job matching suggestions or corporate strategy change signals, and trigger the adjustment process of the job skill requirement vector and / or similarity matching threshold parameters according to preset rules.

8. A computer-readable storage medium, characterized in that: The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 6.

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