A dynamic post description generation type nuclear power talent training system and method

Abstract

The present application belongs to the technical field of nuclear facility intelligent training, and particularly relates to a dynamic post description generation type nuclear power talent training system and method. The method comprises the following steps: step 1: global data tracing and twin construction; step 2: task-Agent mapping rule construction; step 3: intelligent knowledge fusion and data governance; step 4: multi-dimensional knowledge graph drawing; step 5: dynamic post description intelligent generation; step 6: training system adaptation and intelligent pushing; step 7: training feedback and data iteration closed loop; and step 8: big data visualization decision support. The beneficial effects are that the dynamic post description updating timely rate is improved by 40%, real-time adaptation after the change of tasks, technologies and regulations is realized, and compliance is significantly enhanced; the post responsibility definition clarity is improved by 50%, the problem of multi-post cross task responsibility ambiguity is effectively solved, and the collaboration efficiency is greatly improved; the talent and post matching error rate is reduced to below 5%, and the human resource allocation efficiency is significantly improved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent training technology for nuclear facilities, specifically relating to a dynamic job description generation system and method for nuclear power talent training. Background Technology

[0002] Nuclear power, as a core pillar of national energy security, is crucial for ecological well-being, public health, and social stability through its safe and stable operation. Talent, as the core productive force in the nuclear power industry, is key to ensuring a precise match between their skills and job requirements. From a regulatory perspective, the state imposes stringent mandatory requirements on the definition of qualifications, division of responsibilities, and implementation of talent training for nuclear power positions. Job descriptions, as the core basis for talent management, directly determine the effectiveness of compliance management through their accuracy and timeliness. From a technical perspective, nuclear power plant systems are complex and diverse, spanning multiple disciplines such as nuclear physics, electrical engineering, and mechanical automation. Furthermore, continuous advancements in equipment iteration, process upgrades, and emergency response optimization make static job descriptions ill-suited for dynamic tasks. From a talent perspective, over 700 positions cover 50 job categories and a 5-level hierarchical system. The rapid updating of talent knowledge and skills and the dynamic changes in job requirements necessitate efficient coordination to overcome the talent-job mismatch dilemma. From a strategic perspective, the synchronized advancement of corporate talent reserve planning and strategic goals relies on real-time, accurate job data support; static job descriptions cannot meet the needs of rapid decision-making.

[0003] The traditional nuclear power talent training and post management model has the following prominent defects, mainly reflected in the following 10 aspects: Job descriptions are outdated and cannot be updated in real time to keep up with dynamic changes in work tasks, technological advancements, and regulatory requirements.

[0004] There is a lack of a precise mapping mechanism between tasks and agent capabilities, and the job descriptions lack quantitative support for defining the boundaries of responsibilities and capability requirements.

[0005] Job descriptions are disconnected from talent knowledge and skills profiles, and there is a lack of scientific data links for matching people with jobs.

[0006] Manuals are compiled manually, which is time-consuming, costly, and their updates lag behind actual changes in needs.

[0007] The job descriptions and training system are disconnected, and the training content cannot be dynamically optimized based on job requirements.

[0008] The overlapping tasks of multiple positions are poorly defined, and static job descriptions make it difficult to clearly define the division of responsibilities and collaboration standards.

[0009] There is a lack of visual connection between employee career development paths and the competency requirements in job descriptions, resulting in unclear growth directions.

[0010] Enterprises lack quantitative analysis based on dynamic job data in their talent strategy decisions, resulting in a lag in the prediction of talent gaps.

[0011] When new positions are established or old positions are adjusted, the preparation of instruction manuals lacks standardized processes and data support, resulting in low efficiency.

[0012] In cross-departmental collaboration scenarios, the instruction manual does not reflect the requirements for coordination, resulting in poor work flow and reduced efficiency.

[0013] The core problems of existing technologies can be summarized into four dimensions: Job descriptions lack dynamism. Most positions lack job descriptions; those that exist are static, with an average update cycle of 8-10 months. Over 45% of positions exhibit a disconnect between responsibilities and actual work, highlighting compliance risks.

[0014] The efficiency of matching people to jobs is low. Lacking a task-agent mapping mechanism and dynamic knowledge graph support, the error rate of manual matching based on static specifications is as high as 40%, leading to frequent job allocation errors.

[0015] Training resources are being wasted in significant ways. Training content is designed based on fixed manuals, with over 30% of courses failing to meet actual job requirements. Furthermore, adjustments to training content are delayed by more than six months after manual updates, and training management costs account for 13-16% of nuclear power plant operating costs.

[0016] The decision-making support capability is weak. There is a lack of dynamic analysis linking job data and talent data, and the prediction cycle for talent gaps and strategic deviations exceeds 4 months, which cannot support rapid strategic response. Summary of the Invention

[0017] The purpose of this invention is to provide a dynamic job description generation nuclear power talent training system and method, which can solve problems such as untimely job matching, low matching degree, and high training cost in nuclear power plants.

[0018] The technical solution of the present invention is as follows: A dynamic job description generation nuclear power talent training system, comprising a global data foundation storage layer, a task-Agent mapping core layer, an intelligent data governance central layer, a multi-dimensional knowledge graph construction layer, a dynamic job description generation layer, a training adaptation and execution layer, and a big data visualization decision-making layer; The full-domain data foundation storage layer stores full-dimensional power plant twin data, with data sources including system mapping import, manual precise entry, synchronization with external regulatory database interfaces, and linkage with equipment operation data; The task-agent mapping core layer establishes a precise association between tasks and agent capabilities, transforming abstract work tasks into concrete and quantifiable capability requirements. The intelligent data governance central layer standardizes and refines the raw data, providing high-quality input for knowledge graph drawing and instruction manual generation; The multi-dimensional knowledge graph construction layer is based on the governed data and mapping rules to construct three core graphs; The dynamic job description generation layer automatically generates and iterates job descriptions. The training adaptation and execution layer is based on the comparison results between dynamic job descriptions and talent competency profiles. After employees pass the assessment, the training results are automatically fed back to the talent competency profile map, triggering data iteration. Big data visualization enables decision-makers to integrate, analyze, and visualize data.

[0019] A dynamic job description generation method for nuclear power talent training includes the following steps: Step 1: Full-domain data traceability and twin construction; Step 2: Construct task-agent mapping rules; Step 3: Intelligent knowledge fusion and data governance; Step 4: Creating a multi-dimensional knowledge graph; Step 5: Intelligent generation of dynamic job descriptions; Step 6: Training system adaptation and intelligent push; Step 7: Training feedback and data iteration closed loop; Step 8: Big data visualization for decision support.

[0020] Step 1 includes collecting data on nuclear power plant work tasks, agent capabilities, basic job information, technology iteration data, regulatory updates, historical job descriptions, training resources, and talent files to construct a digital twin model of the task-capability-job-talent quadruple.

[0021] Step 2 includes using a large language model to deeply decompose the task data and extract core elements; constructing an Agent capability model adapted to nuclear power positions and clarifying quantitative evaluation indicators; and establishing an intelligent mapping rule base between tasks and Agent capabilities to achieve accurate transformation from task requirements to capability requirements.

[0022] Step 3 includes accessing multi-source heterogeneous data through a data acquisition architecture, processing data in real time through a large language model, applying knowledge graph technology and semantic association engine to build a standardized data governance system. This includes establishing a nuclear power industry-specific standard dictionary and standard metadata, data format mapping and conversion, data alignment and cleaning, knowledge completion, outlier detection, data integrity verification, formulating unified coding rules, and introducing an expert appraisal and calibration mechanism for difficult-to-calculate and controversial data. This achieves deep integration, accurate extraction, intelligent completion, and trend prediction of multi-source data, ensuring the uniqueness, computer identifiability, and computability of the data.

[0023] Step 4 includes drawing three core graphs based on the shared metadata, semantic association, and mapping rules of the governed data: a task-agent mapping graph, a job knowledge and skills graph, and a talent capability profile graph.

[0024] Step 5 includes automatically generating standardized dynamic job descriptions in natural language with templates set, establishing a real-time update trigger mechanism for the descriptions, mapping changes in tasks, technologies, and regulations to the Agent, automatically iterating the content and retaining version records.

[0025] Step 6 includes comparing the Agent mapping with the dynamic job description and the talent competency profile to identify talent gaps; accurately pushing courses to target employees; and simultaneously recording the teaching process to achieve resource reuse.

[0026] Step 7 includes automatically collecting training outcome data, feeding it back to the global data pool, and triggering the synchronous update of the talent competency profile map and dynamic job descriptions.

[0027] Step 8 includes real-time display of the status of job descriptions, the number of qualified personnel, potential talent reserves, and the distribution of skill gaps for each position; based on multi-dimensional graph comparison, it predicts future job talent demand, providing quantitative data support for human resource allocation, talent development planning, and corporate strategic decision-making.

[0028] The beneficial effects of this invention are as follows: The timeliness of dynamic job description updates is improved by 40%, enabling real-time adaptation to changes in tasks, technologies, and regulations, significantly enhancing compliance; the clarity of job responsibility definitions is improved by 50%, effectively solving the problem of ambiguous responsibilities for overlapping tasks in multiple positions, and greatly improving collaboration efficiency; the talent-job matching error rate is reduced to below 5%, significantly improving human resource allocation efficiency; the relevance of training content is improved by 60%, reducing the waste of ineffective training resources and lowering training costs by 18-20%; the efficiency of job description preparation and updating is improved by 70%, significantly shortening the preparation cycle and reducing labor costs; the response time for identifying talent capability gaps is shortened to within one week, providing rapid support for training planning; the visualization coverage of employee career development paths reaches 100%, clarifying the direction of capability improvement and stimulating the willingness to learn proactively; the data update cycle for enterprise talent strategy decisions is shortened to within one month, significantly enhancing the scientific and forward-looking nature of decision-making; and the efficiency of cross-departmental job collaboration is improved by 35%, clarifying collaboration requirements through job descriptions and reducing obstacles to work coordination. Attached Figure Description

[0029] Figure 1 Functional layer diagram of a dynamic job description generation nuclear power talent training system provided by the present invention; Figure 2 The present invention provides a logic diagram of a dynamic job description generation nuclear power talent training system. Detailed Implementation

[0030] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] This invention provides a dynamic job description generation-based nuclear power talent training system and method. Using a task-agent mapping mechanism as its core, it applies the association analysis capabilities of knowledge graphs and the semantic understanding and logical reasoning techniques of large language models to construct a linked system for dynamic job description generation and talent training. It transforms data such as power plant work tasks, agent capability models, core job requirements, multi-dimensional talent profiles, training resources, and strategic goals into real-time updated dynamic job descriptions and visualized human-job fit maps, achieving closed-loop management of "dynamic description generation - talent capability matching - precise training empowerment - strategic decision support." This fosters a talent training ecosystem in nuclear power characterized by "dynamic matching as the foundation, precise empowerment as the core, collaborative efficiency as the guiding principle, and strategic orientation as the soul," ensuring that job descriptions always align with regulatory requirements and actual work, that talent capabilities and job needs are matched in real time, and that training resources are precisely allocated, thus laying a solid foundation for the safe and stable operation of nuclear power plants and the implementation of talent strategies.

[0032] A dynamic job description generation method for nuclear power talent training includes the following steps: Step 1: Full-domain data traceability and twin construction; The system systematically collects nuclear power plant work task data (including routine tasks, emergency tasks, and new tasks), agent capability data (including professional skills, operational experience, psychological qualities, health status, and authorized qualifications), basic job information, technology iteration data (equipment updates and process optimizations), regulatory update data, historical job descriptions, training resources, and talent file information. It constructs a full-domain power plant twin data pool covering "task-capability-job-talent". Based on job setting rules, it applies agent technology, semantic association technology, knowledge graph technology, etc., to intelligently construct a digital twin model of the four-tuple of task-capability-job-talent.

[0033] Step 2: Constructing Task-Agent Mapping Rules Leveraging a large language model, task data is deeply decomposed to extract core elements (process standards, risk nodes, and capability thresholds). An Agent capability model adapted to nuclear power positions is constructed, clearly defining quantitative evaluation indicators. An intelligent mapping rule base between tasks and Agent capabilities is established to achieve precise conversion of task requirements into capability requirements, laying a core foundation for dynamic specification compilation. If new tasks or skill requirements arise, the platform fine-tunes the job skill requirements and training course design based on the new input data. The platform has designed a quantitative capability definition model, including a quantitative scoring algorithm based on the Analytic Hierarchy Process (AHP) for basic work + professional skills (knowledge depth × operational proficiency) weight calculation. Mapping rules based on the matching degree between power plant work needs and existing personnel capabilities (including machine learning, comparison of job profiles and personnel profiles, and expert experience assistance) enable the construction of task-capability-job-talent mapping and the self-resolution of conflicts, as well as rule supplementation and updates.

[0034] Step 3: Intelligent Knowledge Integration and Data Governance The system utilizes a multi-source heterogeneous data access protocol within its data acquisition architecture and a large language model to process data in real time, ensuring data comprehensiveness and timeliness. It applies knowledge graph technology and a semantic association engine to build a standardized data governance system, including establishing a nuclear power industry-specific standard dictionary and standard metadata, data format mapping and conversion, data alignment and cleaning, knowledge completion, outlier detection, data integrity verification, and the formulation of unified coding rules. For difficult-to-calculate and controversial data, an expert evaluation and calibration mechanism is introduced to achieve deep integration, accurate extraction, intelligent completion, and trend prediction of multi-source data, ensuring data uniqueness, computer identifiability, and computability.

[0035] Step 4: Drawing a multi-dimensional knowledge graph Based on the shared metadata, semantic associations, and mapping rules of the governed data, three core graphs are created: a task-agent mapping graph (visualizing the relationship between tasks and capabilities), a job knowledge and skills graph (integrating job responsibilities, capability requirements, and collaboration standards), and a talent capability profile graph (updating employee knowledge, skills, qualification status, and other information in real time). This system allows agents to perceive and map any minute changes in task data, personnel capability data, or job change data in real time, achieving a dynamically linked graph system.

[0036] Step 5: Intelligent generation of dynamic job descriptions Based on three core dynamic linkage graph systems, the Agent automatically generates standardized, dynamic, natural language job descriptions with pre-defined templates. These descriptions clearly define core responsibilities, task lists, quantifiable competency indicators, collaboration requirements, and authorization standards. The platform establishes a real-time update trigger mechanism for these descriptions. The Agent maps changes in tasks, technologies, and regulations, automatically iterates the content, and retains version records to ensure the timeliness and accuracy of the descriptions. Version control and version rollback mechanisms are also implemented.

[0037] Step 6: Training System Adaptation and Intelligent Push The Agent mapping system compares dynamic job descriptions with talent competency profiles to accurately identify talent gaps; it intelligently recommends instructors with corresponding Agent capabilities to write customized training materials and design training courses covering theory, practice, and collaboration; it accurately pushes courses to target employees, records the teaching process simultaneously to achieve resource reuse, and breaks through time and space limitations to meet fragmented learning needs.

[0038] Step 7: Training Feedback and Data Iteration Closed Loop The system automatically collects training outcome data, such as assessment scores, quantitative evaluation of practical performance (e.g., accuracy of operation steps and time taken), and learning behavior data (video viewing progress and interaction frequency), and feeds it back to the global data pool, triggering the synchronous update of talent competency profile maps and dynamic job descriptions, forming a closed-loop management of "training-assessment-feedback-iteration".

[0039] Step 8: Big Data Visualization for Decision Support; It displays real-time information on the status of job descriptions, the number of qualified candidates, potential talent pool, and the distribution of skill gaps for each position. Based on multi-dimensional graph comparisons, it predicts future talent demand for positions, providing quantitative data support for human resource allocation, talent development planning, and corporate strategic decision-making.

[0040] This invention operates on the core logic of "data-driven, mapping construction, graph linkage, dynamic generation, precise empowerment, and decision support." By dynamically collecting data across the entire chain of "task-Agent-position-talent," it constructs a task-Agent mapping mechanism and a multi-dimensional knowledge graph. This enables the automatic generation and iteration of dynamic job descriptions, thereby linking the training system and talent management. It also connects the data flow of job management, talent development, and strategic decision-making, positively guiding employees towards high-quality growth and providing comprehensive support for the precise implementation of corporate talent strategies.

[0041] A dynamic job description generation nuclear power talent training system includes a global data foundation storage layer, a task-agent mapping core layer, an intelligent data governance hub layer, a multi-dimensional knowledge graph construction layer, a dynamic job description generation layer, a training adaptation and execution layer, and a big data visualization decision-making layer.

[0042] The comprehensive data foundation storage layer is the core data support hub for system operation, responsible for storing full-dimensional power plant twin data. It encompasses seven core modules: a work task storage module (storing routine tasks, emergency tasks, new tasks, and core element data), an agent capability storage module (collecting quantitative data such as professional skills, operational experience, and psychological qualities), a job basic information storage module (recording job classification, grading, and core business scope), a technology iteration data storage module (synchronizing equipment updates, process optimizations, and technical standard revisions), a regulatory and standard storage module (integrating national regulatory laws, industry standards, and corporate systems), a talent file storage module (gathering employee basic information, knowledge and skills, training records, and assessment results), and a training resource storage module (archiving historical teaching materials, courseware, practical scenario data, and question banks). Data sources include system mapping imports, precise manual input, synchronization with external regulatory database interfaces, and linkage with equipment operation data, providing comprehensive, accurate, and real-time data source support for subsequent modules.

[0043] Task-Agent Mapping Core Layer: This layer is the core innovation of this invention, responsible for establishing a precise association between tasks and agent capabilities. It comprises three main modules: a task decomposition module (deeply extracting core elements such as task flow, standards, risk points, and capability requirements based on a large language model), an agent capability modeling module (constructing a capability dimension system and quantitative evaluation indicators adapted to various nuclear power positions), and a mapping rule base module (storing the correspondence between task elements and agent capability dimensions, supporting manual optimization and automatic iteration of rules). Through the operation of this layer, abstract work tasks are transformed into concrete and quantifiable capability requirements, providing core logical support for the generation of dynamic job descriptions.

[0044] The intelligent data governance central layer is responsible for standardizing and refining the raw data. It comprises four main modules: a standard dictionary module (storing nuclear power industry-specific data standards and terminology definitions to ensure data alignment and consistency), a coding management module (assigning unique codes to various data types for rapid retrieval and association), an expert evaluation module (manually calibrating the rationality of complex task breakdown, capability indicator definition, and mapping rules), and a data optimization module (performing data completion, deduplication, error correction, and trend prediction to ensure data quality). Standardized data processed by this layer possesses computable, associative, and highly reliable characteristics, providing high-quality input for knowledge graph creation and instruction manual generation.

[0045] Multi-dimensional Knowledge Graph Construction Layer: This layer, based on the governed data and mapping rules, constructs three core graphs, comprising three modules: a task-agent mapping graph module (visualizing the relationship between tasks and agent capabilities), a job knowledge and skills graph module (integrating basic job information, capability requirements, and task lists to form a comprehensive job profile), and a talent capability profile graph module (based on employee profile data, updating knowledge, skills, experience levels, and qualification status in real time). These three graphs possess dynamic iteration capabilities, automatically updating as the underlying data changes to ensure data timeliness and accuracy.

[0046] Dynamic Job Description Generation Layer: This layer is the core output hub of the system, responsible for automatically generating and iterating job descriptions. It comprises three main modules: a description generation module (automatically generating standardized descriptions based on three major frameworks and regulatory standards, covering core content such as responsibilities, tasks, and competency requirements); a description iteration module (receiving trigger signals from changes in tasks, technologies, and regulations, automatically updating content, and retaining version records); and a description verification module (ensuring that descriptions meet regulatory requirements and actual needs through compliance checks, logical consistency checks, and expert review). The generated dynamic job descriptions are synchronized to modules such as talent management and training implementation, enabling real-time data sharing.

[0047] Training Adaptation and Execution Layer: This layer, based on the comparison results between dynamic job descriptions and talent competency profiles, enables precise training empowerment. It includes four modules: a competency gap identification module (comparing the talent profile with the competency requirements in the job description to pinpoint core gaps), a teaching material writing and recommendation module (recommending instructors with corresponding agent capabilities to write customized teaching materials), a course design module (designing theoretical, practical, and collaborative training courses based on gaps), and a training delivery and execution module (intelligently pushing courses to target employees, supporting integrated online and offline training, and synchronously recording courses for repeated use). After employees pass the assessment, the training results are automatically fed back to the talent competency profile, triggering data iteration.

[0048] Big Data Visualization Decision Layer: This layer is responsible for data integration, analysis, and visualization, comprising three main modules: a data integration and analysis module (linking dynamic job descriptions, talent maps, and training data to analyze job-person matching and training effectiveness), a visualization module (displaying job description status, talent pool trends, and training implementation in real-time using panoramic charts), and a decision support module (providing quantitative data support for human resource allocation, talent development planning, and corporate strategy formulation, generating decision-making recommendation reports). Authorized users can use multi-dimensional search functions to accurately query job, talent, and training-related data to meet management and decision-making needs.

[0049] A closed-loop collaborative data flow is formed among the core modules: the data storage layer provides raw data to the mapping and governance layers; the rules output by the mapping layer and the standardized data output by the governance layer support the construction of the graph; the graph provides core input for the dynamic instruction manual generation layer; the instruction manual drives the precise operation of the training adaptation layer; the training results are fed back to the data storage layer and the graph construction layer to achieve data iteration; the decision-making layer integrates the data from each module to provide panoramic support for organizational decision-making and individual development, ensuring that the system forms a closed-loop operation of the entire process of "data collection-governance-analysis-application-feedback-iteration", and achieving a deep integration of technology empowerment and talent cultivation.

Claims

1. A dynamic job description generation nuclear power talent training system, characterized in that: It includes a global data foundation storage layer, a task-agent mapping core layer, an intelligent data governance hub layer, a multi-dimensional knowledge graph construction layer, a dynamic job description generation layer, a training adaptation and execution layer, and a big data visualization decision-making layer; The full-domain data foundation storage layer stores full-dimensional power plant twin data, with data sources including system mapping import, manual precise entry, synchronization with external regulatory database interfaces, and linkage with equipment operation data; The task-agent mapping core layer establishes a precise association between tasks and agent capabilities, transforming abstract work tasks into concrete and quantifiable capability requirements. The intelligent data governance central layer standardizes and refines the raw data, providing high-quality input for knowledge graph drawing and instruction manual generation; The multi-dimensional knowledge graph construction layer is based on the governed data and mapping rules to construct three core graphs; The dynamic job description generation layer automatically generates and iterates job descriptions. The training adaptation and execution layer is based on the comparison results between dynamic job descriptions and talent competency profiles. After employees pass the assessment, the training results are automatically fed back to the talent competency profile map, triggering data iteration. Big data visualization enables decision-makers to integrate, analyze, and visualize data.

2. A method for generating nuclear power talent based on dynamic job descriptions, characterized in that, Includes the following steps: Step 1: Full-domain data traceability and twin construction; Step 2: Construct task-agent mapping rules; Step 3: Intelligent knowledge fusion and data governance; Step 4: Creating a multi-dimensional knowledge graph; Step 5: Intelligent generation of dynamic job descriptions; Step 6: Training system adaptation and intelligent push; Step 7: Training feedback and data iteration closed loop; Step 8: Big data visualization for decision support.

3. The method for generating nuclear power talent based on dynamic job descriptions as described in claim 2, characterized in that: Step 1 includes collecting data on nuclear power plant work tasks, agent capabilities, basic job information, technology iteration data, regulatory updates, historical job descriptions, training resources, and talent files to construct a digital twin model of the task-capability-job-talent quadruple.

4. The method for generating nuclear power talent based on dynamic job descriptions as described in claim 2, characterized in that: Step 2 includes using a large language model to deeply decompose the task data and extract core elements; constructing an Agent capability model adapted to nuclear power positions and clarifying quantitative evaluation indicators; and establishing an intelligent mapping rule base between tasks and Agent capabilities to achieve accurate transformation from task requirements to capability requirements.

5. The method for generating nuclear power talent based on dynamic job descriptions as described in claim 2, characterized in that: Step 3 includes accessing multi-source heterogeneous data through a data acquisition architecture, processing data in real time through a large language model, applying knowledge graph technology and semantic association engine to build a standardized data governance system. This includes establishing a nuclear power industry-specific standard dictionary and standard metadata, data format mapping and conversion, data alignment and cleaning, knowledge completion, outlier detection, data integrity verification, formulating unified coding rules, and introducing an expert appraisal and calibration mechanism for difficult-to-calculate and controversial data. This achieves deep integration, accurate extraction, intelligent completion, and trend prediction of multi-source data, ensuring the uniqueness, computer identifiability, and computability of the data.

6. The method for generating nuclear power talent based on dynamic job descriptions as described in claim 2, characterized in that: Step 4 includes drawing three core graphs based on the shared metadata, semantic association, and mapping rules of the governed data: a task-agent mapping graph, a job knowledge and skills graph, and a talent capability profile graph.

7. The method for generating nuclear power talent based on dynamic job descriptions as described in claim 2, characterized in that: Step 5 includes automatically generating standardized dynamic job descriptions in natural language with templates set, establishing a real-time update trigger mechanism for the descriptions, mapping changes in tasks, technologies, and regulations to the Agent, automatically iterating the content and retaining version records.

8. The method for generating nuclear power talent based on dynamic job descriptions as described in claim 2, characterized in that: Step 6 includes comparing the Agent mapping with the dynamic job description and the talent competency profile to identify talent gaps; accurately pushing courses to target employees; and simultaneously recording the teaching process to achieve resource reuse.

9. The method for generating nuclear power talent based on dynamic job descriptions as described in claim 2, characterized in that: Step 7 includes automatically collecting training outcome data, feeding it back to the global data pool, and triggering the synchronous update of the talent competency profile map and dynamic job descriptions.

10. The method for generating nuclear power talent based on dynamic job descriptions as described in claim 2, characterized in that: Step 8 includes real-time display of the status of job descriptions, the number of qualified personnel, potential talent reserves, and the distribution of skill gaps for each position; based on multi-dimensional graph comparison, it predicts future job talent demand, providing quantitative data support for human resource allocation, talent development planning, and corporate strategic decision-making.

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