Human resource training path planning method and system

Abstract

The present application relates to the technical field of human resource planning, and more particularly to a human resource training path planning method and system. The method comprises the following steps: receiving end collects the job profile data, post ability benchmark data and historical training implementation data of each post of the enterprise through the human resource collaboration platform, performs normalization processing, and generates post-ability corresponding benchmark data set; based on the post-ability corresponding benchmark data set, the job holder's ability gap is calculated, and the path is preliminarily matched to generate the personalized initial training path; the adaptability of the personalized initial training path is checked, and the adaptive training path is generated combined with the job holder's post work timing data; the execution trajectory data corresponding to the adaptive training path is collected in real time, the training effect closed loop verification is carried out, and the adaptive training path is iteratively optimized according to the verification result, and the final human resource training path planning result is output. The present application can continuously improve the human resource training path.

Description

Technical Field

[0001] This invention relates to the field of human resource planning technology, and in particular to a method and system for planning human resource training pathways. Background Technology

[0002] With the acceleration of enterprise digital transformation and increasingly fierce competition for talent, companies are placing higher demands on the professional capabilities and adaptability of human resources. Personalized, precise, and closed-loop optimization of talent development has become crucial for enhancing core competitiveness, placing higher demands on the scientific rigor, adaptability, and effectiveness of human resource training path planning. Traditional human resource training path planning lacks a systematic integration of job profiles, individual skill gaps, and corporate training resources, making it difficult to match the personalized training needs of different positions and individuals. Furthermore, existing methods lack a closed-loop verification and iterative optimization mechanism for training effectiveness, resulting in rigid training paths that are disconnected from job performance timelines, leading to poor training results and wasted resources. Summary of the Invention

[0003] Therefore, the present invention needs to provide a human resource training path planning method and system to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a human resource training path planning method includes the following steps: The receiving end collects job profile data, job competency benchmark data and historical training implementation data for each position in the enterprise through the human resources collaboration platform, and performs normalization processing on the collected data to generate a job-competency corresponding benchmark dataset. Based on the job-competency matching benchmark dataset, the competency gap of the employees is measured to generate an individual competency gap list; the individual competency gap list is combined with the enterprise training resource catalog to perform preliminary path matching and generate a personalized initial training path. The suitability of the personalized initial training path is verified, and the training nodes and duration are adjusted based on the job sequence data of the employees to generate a suitable training path. Real-time collection of execution trajectory data corresponding to the adaptive training path, conducting closed-loop verification of training effectiveness, iteratively optimizing the adaptive training path based on the verification results, and outputting the final human resource training path planning results.

[0005] Furthermore, the present invention also provides a human resource training path planning system, the system being used to execute the human resource training path planning method described above, the human resource training path planning system comprising the following modules: The data acquisition module is used to collect job profile data, job competency benchmark data, historical training implementation data, and job work time sequence data for each position in the enterprise through the human resources collaboration platform, and output the raw human resources training-related dataset. The data normalization module is connected to the data acquisition module and is used to clean and normalize the original human resources training-related datasets, and integrate them to generate a job-competency correspondence benchmark dataset. The competency gap assessment module is connected to the data normalization module and is used to assess the competency gap of employees based on the job-competency correspondence benchmark dataset and generate an individual competency gap list. The preliminary path matching module is connected to the capability gap assessment module. It is used to perform preliminary path matching by combining the individual capability gap list with the corporate training resource catalog to generate a personalized initial training path. The path adaptation and optimization module is connected to the path preliminary matching module. It is used to verify the adaptability of the personalized initial training path, adjust the training nodes and duration based on the job work time sequence data, and generate an adapted training path. The execution tracking and verification module is connected to the path adaptation and optimization module to collect execution trajectory data of the adaptive training path in real time, conduct closed-loop verification of training effectiveness, and output training effectiveness verification results. The path iteration output module is connected to the execution tracking and verification module. It is used to iteratively optimize the adaptive training path based on the training effect verification results and output the final human resource training path planning result.

[0006] The beneficial effects of this invention are: The human resource training path planning method proposed in this invention, compared with existing methods, has the advantage of collecting job profiles, job competency benchmarks, and historical training implementation data for each position within the enterprise through a human resource collaboration platform at the receiving end. This breaks through the limitations of traditional planning that relies solely on single job descriptions or human experience, achieving full-dimensional data coverage from the current job status and job competency requirements to past training practices. It comprehensively captures the correspondence between positions and competencies and provides historical reference for training implementation. The collected data is normalized to eliminate dimensional differences, statistical biases, and redundant information among different types of data. The resulting job-competency correspondence benchmark dataset establishes a standardized and comparable unified system for the competency requirements of each position, the job competency of personnel, and historical training data, clearly presenting the core competency benchmarks of each position and the basic status of personnel's employment. Secondly, by calculating the competency gap of employees based on a job-competency benchmark dataset, the actual competency of employees is quantitatively compared with the established competency benchmarks for their positions. This clarifies the specific shortcomings of each employee in terms of professional skills and job suitability, generating a personal competency gap list that makes the competency deficiencies clearly identifiable, breaking away from the limitations of traditional "one-size-fits-all" training that ignores individual competency differences. The personal competency gap list is then combined with the company's training resource catalog for initial path matching. Based on the specific competency gaps of each employee, suitable training courses and formats are selected, customizing personalized initial training paths for employees in different positions with different competency gaps. This avoids the problems of traditional training paths being out of touch with actual needs and resource mismatch. Then, the personalized initial training paths undergo adaptability verification, comprehensively checking from dimensions such as the feasibility of company training resources, the continuity of training content, and employee acceptance. Unreasonable or unimplementable links in the path are eliminated, ensuring that the training path has the conditions for practical execution, breaking away from the limitations of traditional planning that only focuses on competency matching while ignoring implementation feasibility. By combining job-related timeline data with that of employees, the system analyzes their daily work rhythm, busy and idle periods, and adjusts training nodes and durations in the training path accordingly. Training is scheduled during periods of lower workload, and the duration of each training session is rationally allocated to avoid conflicts with core work. This generates an adaptive training path that organically integrates training arrangements with job duties, making the training path more aligned with the company's actual production and operation rhythm and employees' work status. Finally, by collecting execution trajectory data of the adaptive training path in real time, the system comprehensively records the actual progress of training, employee participation, and course learning outcomes, breaking through the limitations of traditional training that emphasizes planning but neglects execution and lacks process monitoring. This makes the training implementation process traceable and analyzable. Based on the execution trajectory data, a closed-loop verification of training effectiveness is conducted. The post-training skill improvement is compared with the initial skill gap list to evaluate the actual effect of the training path, determine the rationality of the training content, nodes, and duration, and accurately identify problems and deficiencies in the path.Based on the verification results, the adaptability training path is iteratively optimized, unreasonable training nodes are adjusted, ineffective training content is replaced, and the allocation of training time is optimized. The training path is continuously improved and the final training path planning result is output, thus constructing a complete closed-loop system of "planning-implementation-verification-optimization". Attached Figure Description

[0007] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the steps of the human resource training path planning method of the present invention. Figure 2 This is a schematic diagram of the modules of the human resource training path planning system of the present invention. Detailed Implementation

[0008] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0009] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0010] It should be understood that although the terms "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0011] To achieve the above objectives, this invention provides a human resource training path planning method. In the embodiments of this invention, please refer to... Figure 1 The diagram shown illustrates the steps of the human resource training path planning method of the present invention. In this example, the human resource training path planning method includes the following steps: S01: The receiving end collects job profile data, job competency benchmark data and historical training implementation data for each position in the enterprise through the human resources collaboration platform, and performs normalization processing on the collected data to generate a job-competency corresponding benchmark dataset. In this embodiment of the invention, the receiving end initiates a full data collection process through the multi-terminal collection interface preset by the human resources collaboration platform. It collects three types of core data one by one according to job category and individual employee, ensuring that the collected data is comprehensive, accurate, and chronologically consistent, without omissions or deviations. The collected job profile data covers on-the-job duration, skill mastery, career development aspirations, learning preferences, and job performance evaluation. On-the-job duration is calculated based on actual on-the-job days, and skill mastery is divided into three levels: mastery, familiarity, and understanding. Job competency benchmark data is collected according to management, technical, and operational positions, including core competency items, competency standards, and competency level classifications. Historical training implementation data includes details of training courses, participation records, assessment results, and feedback information from the past 1-3 years, with assessment results recorded on a percentage basis. After data collection, cleaning and normalization processes are performed sequentially to remove invalid data that is missing key items, has logical contradictions, or is completely duplicated. Data representation standards, classification criteria, and time series formats are standardized. Competency information and training data are converted using fixed rule encoding. The association mapping method is used to establish the relationship between job positions, incumbents, and competency items, generating a clearly associated and standardized job-competency correspondence benchmark dataset.

[0012] S02: Calculate the competency gap of employees based on the job-competency correspondence benchmark dataset and generate an individual competency gap list; combine the individual competency gap list with the corporate training resource catalog to perform preliminary path matching and generate a personalized initial training path. In this embodiment of the invention, a comprehensive assessment of the competency gaps of incumbents is conducted based on a generated job-competency benchmark dataset. During the assessment, detailed competency benchmark data for each job is first extracted, clarifying core competency items, non-core competency items, achievement standards, and evaluation dimensions. Then, individual competency status data for each incumbent is extracted, clarifying skill mastery, job performance, and evaluation records. Each incumbent's competency is compared item by item with the corresponding job benchmark, identifying discrepancies, analyzing the reasons for discrepancies, distinguishing between trainable and non-training improvement gaps, and summarizing them by individual incumbent. The manifestation, scope, impact, and urgency of improvement for each gap are clarified, generating an individual competency gap list. Simultaneously, the company's training resource catalog is retrieved, core information on training resources is extracted, and suitable training resources are selected based on competency suitability, consistency of improvement direction, and job suitability. Resources are sorted by urgency of improvement, and learning details are planned based on learning preferences and job characteristics. Information is integrated to generate a personalized initial training path, completing preliminary verification, supplementing missing resources, and deleting redundant resources.

[0013] S03: Perform adaptability verification on the personalized initial training path, adjust the training nodes and duration based on the job sequence data of the employees, and generate an adaptable training path; In this embodiment of the invention, a comprehensive adaptability verification is performed on the generated personalized initial training path. First, the time-series data of the initial training path is extracted, clarifying the specific time, duration, learning order, and learning format of each training node, recorded in a year-month-day-hour-minute format, consistent with the time-series format mentioned earlier. Then, the job-related time-series data of each employee is collected through a human resources collaboration platform, clarifying daily work hours, task distribution, peak periods, idle periods, and leave arrangements, generating detailed job-related time-series data. The two types of time-series data are compared period by period to identify conflicts between training nodes and work tasks, issues related to training nodes being in peak work periods, and unreasonable durations, integrating these to form the path adaptability verification result. For problematic nodes, conflicting and peak period nodes are moved to the nearest idle period, and the training duration is adjusted based on the workload and difficulty of the learning content. After adjustment, new conflicts and node clustering issues are identified, and the training pace is assessed based on learning capacity, fine-tuning the interval between nodes, clarifying the specific details of each node, and generating an adaptable training path.

[0014] S04: Collect execution trajectory data corresponding to the adaptive training path in real time, conduct closed-loop verification of training effectiveness, iteratively optimize the adaptive training path based on the verification results, and output the final human resource training path planning results.

[0015] In this embodiment of the invention, the execution trajectory data of the adaptive training path is collected in real time through the multi-terminal interface of the human resources collaboration platform. This data covers the completion status of training nodes, actual learning time, learning progress, training assessment results, and changes in job performance. Data is recorded individually for each staff member and training node to ensure real-time and complete data. A closed-loop verification of training effectiveness is conducted, comparing the actual execution data with the path planning requirements. The node completion rate and time adherence rate are checked. Combined with training assessment results and changes in job performance, the training effectiveness is evaluated to determine whether the capability gap has been narrowed and whether the training content meets the needs. Detailed records are kept of any non-compliance, identifying the non-compliance nodes, specific reasons, and impacts. A distinction is made between training execution problems and path planning problems, generating training effectiveness verification results. Based on the verification results, the adaptive training path is iteratively optimized. This includes adjusting the reminder mechanism, re-matching resources, fine-tuning the timing and duration of points, deleting nodes corresponding to gaps that have been met, and supplementing resources for gaps that have not been narrowed. Adaptability and rationality are verified again, and the final human resources training path planning result is output, completing the entire training path planning process.

[0016] Furthermore, the receiving end collects job profile data, job competency benchmark data, and historical training implementation data for each position within the enterprise through the human resources collaboration platform. The normalization processing of the collected data includes the following steps: The receiving end collects job profile data, corresponding competency benchmark data, and past training implementation data of employees in various positions within the enterprise simultaneously through the multi-terminal collection interface of the human resources collaboration platform. The job profile data covers the employee's on-the-job duration, skill mastery, career development aspirations, learning preferences, and job performance evaluation. The competency benchmark data covers the core competency items of the position, competency standards, competency level classification, and competency update cycle. The past training implementation data covers details of past training courses, training participation records, training assessment results, and training feedback information. In this embodiment of the invention, by employing multi-terminal data acquisition technology, synchronous interface calling methods, and data classification acquisition mechanisms, the receiving end initiates a multi-terminal synchronous acquisition process through the multi-terminal acquisition interface preset by the human resources collaboration platform. This enables the synchronous acquisition of three types of core data related to employees in various positions within the enterprise, maintaining data consistency and ensuring no omissions during the acquisition process. The collected job profile data covers all dimensions of job performance-related information for employees. On-duty hours are counted according to the actual number of days on duty, accurate to the calendar day; skill mastery is recorded for each skill required for the position, categorized into three levels: mastery, familiarity, and understanding; career development aspirations clearly define the employee's short-term (within 1 year) and long-term (within 3 years) career advancement and skill improvement directions; learning preferences record the employee's preferred training formats, training times, and content types; and job performance evaluations are compiled quarterly, including work completion quality, teamwork performance, and job suitability scores. Competency benchmark data is collected one by one by job category, covering core competency items, competency standards, competency levels, and competency update cycles. Core competency items are broken down according to the core job responsibilities, competency standards clearly define the qualification requirements for each competency item, and competency levels are divided into three levels: basic, intermediate, and advanced. The competency update cycle is fixed at once a year. Past training implementation data comprehensively collects relevant training information from the past 1-3 years, including details of past training courses, training participation records, training assessment results, and training feedback. Training assessment results are recorded on a 100-point scale, and training feedback includes scores and written evaluations based on dimensions such as course practicality and instructor professionalism. All collected data is categorized and labeled by job category and personnel name to ensure data traceability and categorizable processing.

[0017] Furthermore, invalid data was removed from the three types of raw data collected, including data with missing key information, logical contradictions, and duplicate records, to obtain cleaned human resources training-related data. In this embodiment of the invention, a complete invalid data removal process is performed on the three types of raw data collected: job profile data, competency benchmark data, and past training implementation data, to ensure that the data is authentic, valid, and free of redundancy. A key information verification method is used to check each piece of raw data one by one, removing data lacking key information. For example, job profile data missing any key item such as on-the-job duration or skill mastery is directly removed; competency benchmark data missing any key item such as core competency or achievement standard is directly removed; and past training implementation data missing any key item such as course details or participation records is directly removed. A logical contradiction verification method is used to compare the logical relationships between data, removing logically contradictory data, such as data showing quarterly performance evaluations for employees with less than 3 months of on-the-job time, data indicating a high competency level but failing to meet the corresponding competency achievement standard, and data where training participation records and assessment results are inconsistent. A duplicate data identification method was employed, comparing core identifying information across the data. Job profile data was compared by name and job title; competency benchmark data by job category and competency item; and past training implementation data by name, course details, and training date. Completely duplicate records were removed, retaining only one valid record. After a triple process of key information verification, logical contradiction checking, and duplicate record removal, cleaned human resource training-related data—free of invalidity, contradictions, and duplicates—was obtained, providing a reliable foundation for subsequent data processing.

[0018] Furthermore, the cleaned human resources training-related data is normalized to unify data representation standards, classification criteria, and time series formats. Different types of competency information and training data are coded and converted according to unified rules. All normalized data are integrated to establish the correlation mapping relationship between positions, employees, and competency items, generating a position-competency correspondence benchmark dataset to clarify the correspondence between each position and its corresponding competency item, and between each employee and its corresponding competency item.

[0019] In this embodiment of the invention, the cleaned human resource training data is fully normalized to unify data representation standards, classification criteria, and time sequence formats, eliminating format differences between data collected from different sources and terminals. Data representation standards are adjusted according to the company's unified human resource standards; skill mastery is uniformly expressed as "mastered," "familiar," and "understands"; training assessment results are uniformly retained to one decimal place; and textual feedback information is uniformly organized into short sentences. Classification criteria are unified according to the company's job classification system, adjusting the original job classification names from different terminals to three main categories: management, technical, and operational, plus their subcategories. The time sequence format is unified as year, month, day, hour, and minute, ensuring consistency in all time sequence data representations. An encoding conversion method is used to encode different types of capability information and training data according to unified rules. Capability levels (basic, intermediate, advanced) are coded as 01, 02, and 03 respectively; online and offline training formats are coded as 001 and 002 respectively; and skill mastery is coded as 01, 02, and 03. The encoding rules are fixed and consistent throughout the process. By integrating all the normalized data and using an association mapping method, a relationship is established between job positions, employees, and competency items. This clarifies all the core competency items corresponding to each job position, the job positions and competency items mastered by each employee, the qualification standards for each competency item, and training-related data. This generates a complete and clearly correlated job-competency correspondence benchmark dataset, in which each job position, each employee, and each competency item corresponds one-to-one, providing accurate and standardized data support for subsequent human resource training path planning.

[0020] Furthermore, the calculation of the competency gap of incumbents based on the job-ability correspondence benchmark dataset includes the following steps: Extract the competency benchmark data for each position from the job-competency correspondence benchmark dataset, clarify the core competency items, competency standards and competency level requirements for each position, and obtain detailed job competency benchmark data; In this embodiment of the invention, by retrieving the benchmark dataset corresponding to job positions and capabilities, a job-level extraction method is adopted. The benchmark capability data for each job is extracted according to three main categories: management, technical, and operational, and their subcategories, ensuring that the benchmark capability data for each job is complete and without deviation. During the extraction process, the core capability items, capability attainment standards, and capability level requirements for each job are clearly defined. Core capability items are broken down according to the core job responsibilities: core capability items for management positions include overall coordination, decision-making and execution, and team management; core capability items for technical positions include professional operation, troubleshooting, and technology updates; and core capability items for operational positions include process control, data statistics, and customer liaison. The capability attainment standards clearly define the specific qualification requirements for each capability item. Overall coordination capability requires the ability to efficiently promote cross-departmental collaboration, and professional operation capability requires the ability to independently complete the core operational tasks of the job. Capability level requirements are set according to job level: entry-level positions require a basic level, mid-level positions require an intermediate level, and senior-level positions require an advanced level. All extracted data were categorized and organized by job type and competency item, and the correspondence between each job and competency item was marked to obtain detailed job competency benchmark data with clear structure and specific parameters, providing a clear benchmark basis for subsequent individual competency comparison.

[0021] Furthermore, job profile data for each employee is extracted from the job-ability correspondence benchmark dataset to clarify each employee's current skill mastery, ability level, and job performance evaluation, thereby obtaining individual ability status data. In this embodiment of the invention, by retrieving the generated job-ability correspondence benchmark dataset, a personnel-by-person extraction method is adopted. The data is extracted one by one according to job category and personnel name, ensuring complete extraction of relevant data for each employee. During the extraction process, the current skill mastery, ability level, and job performance evaluation of each employee are clearly defined. Skill mastery is recorded one by one according to the core ability items of the corresponding job, and the mastery level of each ability item is marked, divided into three levels: mastery, familiarity, and understanding. For example, the professional operation ability of technical personnel is marked as mastery, and the troubleshooting ability is marked as familiarity. The ability level is determined comprehensively based on skill mastery and job performance, corresponding to three levels: basic, intermediate, and advanced. Those with all skills mastered and good performance evaluation are judged as intermediate level, while those with some abilities familiar are judged as basic level. Job performance evaluation extracts scores and evaluation opinions from the past two quarters, including work completion quality, teamwork performance, and job suitability scores, organized and recorded on a percentage basis, while also marking the skill strengths and weaknesses mentioned in the evaluation. The extracted personal data is categorized and summarized, with the personnel's names and corresponding positions labeled, to obtain comprehensive and detailed data on the current status of individual capabilities, clearly presenting the current capability level of each employee.

[0022] Furthermore, the individual's current ability data is compared item by item with the corresponding job's ability benchmark details, and the gap between the current status of the employee and the standard for each ability item is analyzed. In this embodiment of the invention, by retrieving detailed data on job competency benchmarks and individual competency status data, and matching each individual employee's current competency status data with the detailed competency benchmark data for the corresponding job, item by item, the comparison is made to ensure that each competency item is fully compared without omissions or mismatches. During the comparison process, the core competency items for each corresponding job are analyzed one by one. For each competency item, the skill mastery, competency level, and differences from the job competency benchmark are compared, and the gap between the employee's current state and the standard for each competency item is analyzed. For example, for management personnel's coordination ability, the job benchmark requires an intermediate level and the ability to efficiently promote cross-departmental collaboration. If the individual's current competency is at the basic level and they can only cooperate to complete collaborative tasks, then there is a clear gap in this competency item. Similarly, for technical personnel's troubleshooting ability, the job benchmark requires the ability to independently troubleshoot common faults. If the individual can only troubleshoot under guidance, then there is a clear gap in this competency item. During the comparison process, the baseline requirements and current status of each competency item are recorded in detail, and the specific manifestations of the gaps are analyzed one by one to ensure that the gaps in each competency item are accurately identified and recorded in detail, providing a basis for subsequent gap classification and improvement planning.

[0023] Furthermore, it clarifies the manifestations, scope, and influencing factors of each capability gap, distinguishes between core capability gaps and non-core capability gaps, and marks the degree of impact on job performance corresponding to each capability gap. In this embodiment of the invention, a detailed breakdown method is used to identify each capability gap, clarifying the manifestation, range, and influencing factors of each gap. The manifestation specifically describes the difference between an individual's current state and the benchmark requirements. For example, a gap in coordination ability is manifested as an inability to independently initiate cross-departmental collaboration and low coordination efficiency. The range of gaps is categorized into mild, moderate, and severe based on their magnitude: mild gap is slightly below the benchmark requirements, moderate gap is significantly below the benchmark requirements, and severe gap is severely non-compliance with the benchmark requirements. Influencing factors are analyzed in conjunction with the individual's current capabilities and job performance, including insufficient skill learning, lack of practical experience, and insufficient learning initiative. For example, the influencing factors for a gap in troubleshooting ability are insufficient practical experience and lack of participation in handling complex faults. A core classification method is used to distinguish between core capability gaps and non-core capability gaps. Core capability gaps are those that affect the performance of core job responsibilities, while non-core capability gaps are those that have a smaller impact on job performance. An impact assessment method was adopted to mark the degree of impact on job performance corresponding to each capability gap, which was divided into serious impact, significant impact, and general impact. Core capability gaps were marked as serious or significant impact, while non-core capability gaps were marked as general impact, ensuring that the gap classification was clear and the degree of impact was clearly defined.

[0024] Furthermore, all capability gap information is integrated, categorized and summarized by individual personnel, and a capability gap list is compiled for each personnel. The key areas for capability improvement for each personnel are identified, and an individual capability gap list is generated. The individual capability gap list includes capability gap items, gap details, degree of impact, and urgency of improvement.

[0025] In this embodiment of the invention, by retrieving and analyzing all capability gap information, an individual classification method is used to categorize and summarize the gaps by name for each employee, integrating all capability gaps for each employee to form a capability gap list for each employee. During the list compilation process, the key areas for capability improvement for each employee are clearly defined, prioritizing core capability gaps, severe gaps, and gaps that significantly impact job performance. For example, core capability gaps for management positions are prioritized, while severe gaps for technical positions are prioritized. The generated individual capability gap list contains complete gap-related information. Each capability gap item clearly specifies the name of the specific capability and the corresponding job benchmark requirements. Gap details include the manifestation of the gap, its scope, and influencing factors. The degree of impact is marked as severe, significant, or moderate. The urgency of improvement is set in conjunction with the gap scope and degree of impact; severe and severely impactful gaps are marked as high urgency, moderate and significantly impactful gaps as moderate urgency, and minor and moderately impactful gaps as low urgency. Each employee's individual capability gap list is independently compiled and clearly marked, ensuring the completeness and clarity of the information, providing a clear and specific basis for subsequent targeted human resource training path development.

[0026] Furthermore, the step of comparing individual skill status data with the corresponding job skill benchmark details item by item, and analyzing the gap between the current status of the employee and the standard for each skill item, includes the following steps: Extract all core and non-core competency items for each position from the competency benchmark details data for the corresponding position, clarify the qualification standards, competency level classification and evaluation dimensions for each competency item, and obtain competency item evaluation standard data; In this embodiment of the invention, by retrieving detailed data on job competency benchmarks, and categorizing them into three main types—management, technical, and operational—and their sub-categories, all core and non-core competency items for each job are extracted. Core competency items are the key abilities necessary for job performance, while non-core competency items are abilities that assist in job performance, ensuring that no competency items for each job are omitted and accurately categorized. During the extraction process, the corresponding qualification standards, competency levels, and evaluation dimensions for each competency item are clearly defined. Qualification standards are refined according to competency levels: the basic level clearly defines entry-level qualification requirements, the intermediate level clearly defines proficient application requirements, and the advanced level clearly defines mastery and leadership requirements. Competency levels are uniformly divided into three levels: basic, intermediate, and advanced, consistent with the previous description. Evaluation dimensions are set according to the characteristics of each competency item. The evaluation dimensions for overall coordination ability include the ability to initiate collaboration, the ability to resolve conflicts, and coordination efficiency; the evaluation dimensions for professional operational ability include operational proficiency, operational accuracy, and the ability to handle complex tasks. Each competency item corresponds to 2-3 core evaluation dimensions. All extracted data were categorized and organized by job type and competency type, and the correspondence between each competency and evaluation dimension and standard was marked. This resulted in competency evaluation standard data with a clear structure and specific parameters, providing a clear basis for subsequent individual competency comparison and discrepancy investigation.

[0027] Furthermore, the skill mastery, job performance and related evaluation records of the corresponding employees in each ability item are extracted from the individual ability status data to obtain detailed data of individual ability items; In this embodiment of the invention, by retrieving individual ability status data, corresponding information is extracted for each individual employee, ensuring complete and unbiased extraction of ability-related data for each employee. During the extraction process, comprehensive information on all ability items for each employee in their corresponding position is clearly defined, including skill mastery, performance, and related evaluation records. Skill mastery is recorded for each ability item, labeled as mastered, familiar, and understood, corresponding to the unified standards mentioned earlier. Performance is recorded based on the job performance over the past two quarters, documenting the actual application performance corresponding to each ability item. For example, the performance corresponding to coordination ability is the completion of cross-departmental collaborative tasks, and the performance corresponding to professional operation ability is the quality of core operation tasks. Related evaluation records extract the scores, written evaluations, and improvement suggestions for each ability item from the performance evaluations. Scores are organized on a percentage basis, and written evaluations are organized into short sentences. The extracted individual ability-related data is categorized and labeled by employee name and ability item, summarizing to form comprehensive and detailed individual ability item data, clearly presenting the specific status of each employee in each ability item.

[0028] Furthermore, the detailed data of individual competency items are matched with the evaluation standard data of each competency item, and the comparison is carried out one by one according to the evaluation dimension of each competency item to identify the differences between individual competencies and the standards. In this embodiment of the invention, by retrieving the evaluation standard data for competency items and the detailed data for individual competency items, and matching them one by one by the individual employees and their corresponding positions, it is ensured that the detailed data for each employee's individual competency items accurately corresponds to the evaluation standard data for the competency items of the corresponding position, without mismatches or omissions. After matching is completed, a comparison is carried out one by one for each evaluation dimension of competency items. For each evaluation dimension, the differences between the individual's actual status and the evaluation standard for the position are compared, and all discrepancies between the individual's competency and the standard are identified. For example, for the collaborative initiation ability dimension of the overall coordination ability of management positions, the evaluation standard requires the ability to proactively initiate cross-departmental collaboration. If the detailed data for individual competency items records that the individual can only cooperate in initiating collaboration, then a discrepancy is identified in this evaluation dimension. For the operational accuracy rate dimension of the professional operation ability of technical positions, the evaluation standard requires a basic level accuracy rate of not less than 95%. If the individual's actual accuracy rate is 92%, then a discrepancy is identified in this evaluation dimension. During the comparison process, the standard requirements, the individual's actual status, and the discrepancies for each evaluation dimension are recorded in detail to ensure that all discrepancies are comprehensively investigated and recorded in detail.

[0029] Furthermore, a detailed analysis of the differences corresponding to each competency item is conducted to clarify the specific manifestations, scope, and causes of the differences, and to distinguish between gaps that can be improved through training and gaps that cannot be improved through training. In this embodiment of the invention, a comprehensive and detailed analysis is performed on the discrepancies corresponding to each identified capability item to clarify the specific manifestations, scope, and causes of the discrepancies, ensuring that the analysis is in-depth, specific, and avoids vague descriptions. The specific manifestations of the discrepancies are described in detail along with the evaluation dimensions. For example, a difference in collaboration initiation capability manifests as an inability to proactively propose collaboration needs or an incomplete collaboration plan. The scope is divided according to the evaluation dimensions covered by the discrepancy: a difference in a single evaluation dimension is considered a local difference, while a difference in multiple evaluation dimensions is considered a comprehensive difference. The causes are analyzed comprehensively based on the individual's current capabilities, job performance, and past training experience, categorized into types such as insufficient skill reserves, lack of practical application, inadequate learning, and attitude problems. For example, a difference in operational accuracy is caused by insufficient practice attempts or failure to master standardized operating methods. A classification method is used to distinguish between gaps that can be improved through training and gaps that cannot. Differences caused by insufficient skill reserves, lack of practical application, or inadequate learning are classified as gaps that can be improved through training; differences caused by attitude problems or limitations in personal qualifications are classified as gaps that cannot be improved through training, ensuring accurate gap classification and alignment with training planning needs.

[0030] Furthermore, the gaps that can be improved through training are marked in detail, and the corresponding competency items, differences, and directions for improvement for each gap are recorded.

[0031] In this embodiment of the invention, a comprehensive and detailed annotation process is performed on all identified gaps that can be improved through training. The annotation information is complete and standardized, ensuring that each gap is clearly recorded and traceable. During the annotation process, the core information corresponding to each gap is recorded one by one. The competency items clearly specify the name of the specific competency item to which the gap belongs and the corresponding job position, such as coordination ability for management positions and troubleshooting ability for technical positions. The difference details include the corresponding evaluation dimensions, specific manifestations, scope, and causes of the difference, presenting a complete picture of the gap's full dimensions. Improvement directions are formulated based on the causes of the gap and job competency standards, aligning with achievable training goals. For example, for gaps caused by insufficient skill reserves, the improvement direction is set as systematically learning corresponding skill courses and participating in practical skills training; for gaps caused by a lack of practical application, the improvement direction is set as increasing practical exercises and participating in case study training. After annotation, the information is categorized and organized by individual personnel and competency items to ensure that the annotation information is clear, logical, and accurately correlated. This provides specific and clear support for subsequent targeted development of human resource training paths, achieving a precise correspondence between training and gap improvement.

[0032] Furthermore, the detailed analysis of the differences corresponding to each competency item, clarifying the specific manifestations, scope, and causes of the differences, and distinguishing between gaps that can be improved through training and gaps that cannot be improved through training, includes the following steps: The differences for each ability item were categorized and analyzed, and classified into knowledge-based differences, skill-based differences, and competency-based differences according to their manifestation, resulting in difference point classification data. In this embodiment of the invention, by retrieving all capability items and corresponding discrepancies, a full classification and sorting operation is performed according to the specific manifestation of the discrepancies to ensure that each discrepancy is accurately classified without omission. During the classification process, the core defining criteria for three types of discrepancies are clarified: knowledge-related discrepancies manifest as insufficient mastery of basic theoretical and professional knowledge required for the position, such as technical personnel's unfamiliarity with equipment working principles or management personnel's insufficient mastery of relevant planning and coordination theories; skill-related discrepancies manifest as failure to meet the benchmark requirements for practical skills and application abilities required for the position, such as insufficient data statistics skills for operations personnel or lack of proficiency in troubleshooting for technical personnel; and competency-related discrepancies manifest as non-compliance with the required professional qualities and behavioral habits, such as insufficient sense of responsibility, poor communication skills, and a lack of teamwork awareness. Each discrepancy is compared against the defining criteria, its type is marked, and it is classified and organized according to discrepancy type and job category, resulting in a clearly structured and accurately categorized discrepancy classification data, providing a classification basis for subsequent discrepancy range analysis and cause investigation.

[0033] Furthermore, for each type of difference, we analyze its scope to clarify whether the difference exists in a specific work scenario, a specific skill module, or a comprehensive difference, and obtain difference scope analysis data. In this embodiment of the invention, the specific scope of each category of differences—knowledge, skills, and qualities—is analyzed one by one to clarify the coverage and specific scenarios of the differences, ensuring a comprehensive and specific analysis. During the analysis, the scope of differences is precisely defined according to scenario type. Specific work scenario differences refer to differences that exist only in a specific work scenario, such as technical personnel having differences in troubleshooting only in complex equipment maintenance scenarios, or operations personnel having differences in data statistics only in monthly data aggregation scenarios. Specific skill module differences refer to differences that exist only in a specific skill sub-module, such as differences in conflict resolution only in coordination ability, or differences in complex task handling only in professional operation ability. Comprehensive differences refer to differences that exist in all evaluation dimensions and all work scenarios for that ability, such as communication and expression ability having differences in fluency in all work communication scenarios within the qualities category. The scope of each type of difference is marked in detail, and the specific scenarios and modules corresponding to the differences are recorded, summarizing to form complete difference scope analysis data.

[0034] Furthermore, by combining the job profile data of the employees, job performance evaluations and historical training records, we can analyze the core reasons for each difference and investigate whether the differences are due to insufficient knowledge reserves, inadequate skills training, limited learning ability or insufficient job suitability. In this embodiment of the invention, by retrieving generated difference point classification data and difference range analysis data, and simultaneously retrieving job profile data, job performance evaluations, and generated past training records, the core reasons for each difference point are analyzed from multiple dimensions. During the analysis, four core reasons are investigated one by one: insufficient knowledge reserves manifest as a lack of mastery of the relevant theoretical knowledge required for the position and untimely knowledge updates, corresponding to the main reasons for knowledge-related differences; insufficient skills training manifests as insufficient practical exercises and a lack of targeted training, corresponding to the main reasons for skills-related differences; limited learning ability manifests as poor absorption of knowledge and skills from past training and low efficiency in self-learning, which can lead to various differences; insufficient job suitability manifests as a mismatch between personal traits and abilities and job requirements, such as introverted individuals exhibiting a difference in personal qualities for communication-related positions. For each difference point, a comprehensive judgment is made based on multi-dimensional data to identify the core reasons, eliminate secondary factors, and record the reason analysis process and conclusions to ensure that the reason investigation is accurate and based on evidence.

[0035] Furthermore, based on the causes and types of differences, gaps that can be improved through training and gaps that cannot be improved through training are distinguished. Among them, knowledge-based gaps, skill-based gaps, and some quality-based gaps are classified as gaps that can be improved through training, while gaps caused by insufficient job suitability are classified as gaps that cannot be improved through training. In this embodiment of the invention, by distinguishing between gaps that can be improved through training and those that cannot, based on the core reasons for the differences and the types of differences, the classification is clear and unambiguous. During the classification process, clear classification criteria are established: knowledge-related differences, regardless of their cause, are classified as gaps that can be improved through training, which can be remedied through theoretical training and knowledge explanation; skill-related differences are all classified as gaps that can be improved through training, which can be improved through practical training and case studies; some competency-related differences, such as communication and teamwork, can be improved through competency training and scenario exercises, and are classified as gaps that can be improved through training; however, competency-related differences that cannot be changed through training due to personal character traits are classified as gaps that cannot be improved through training; all differences caused by insufficient job fit, regardless of the type of difference, are classified as gaps that cannot be improved through training, as training cannot fundamentally solve the fit problem. Each gap is compared against the criteria, its category is marked, and the classification basis is recorded to ensure accurate classification, align with the training path planning needs, and provide support for clarifying the direction of subsequent training.

[0036] Furthermore, the gaps that can be improved through training are further refined, and the training and improvement directions corresponding to each gap are clarified. Gaps that are not improved through training are marked and explained, and detailed data on the differences are generated.

[0037] In this embodiment of the invention, by further refining the defined trainable improvement gaps, specific training and improvement directions are clarified for each gap, aligning with the type of gap, its core cause, and job requirements. Knowledge-related gaps are addressed through theoretical courses, knowledge assessment and consolidation, and online / offline Q&A sessions; for example, gaps in equipment principle knowledge correspond to theoretical courses. Skill-related gaps are addressed through practical training, case studies, and mentorship; for example, gaps in troubleshooting correspond to practical drills and complex case analysis. Trainable competency-related gaps are addressed through scenario simulations, competency lectures, and interactive exercises; for example, gaps in communication skills correspond to communication scenario simulation training. For non-training improvement gaps, detailed annotations are provided, recording the difference points, core causes, and reasons for annotation, clearly explaining why training cannot improve the gaps; for example, insufficient job fit is noted as a mismatch between personal traits and job requirements, or training cannot fundamentally improve the situation. All detailed information and annotations are integrated and categorized by gap type and individual personnel to generate a complete and comprehensive gap analysis dataset, providing support for subsequent human resource training path development.

[0038] Furthermore, the preliminary path matching based on the individual competency gap list and the corporate training resource directory includes the following steps: Retrieve the enterprise training resource catalog, extract the core information of all training resources in the catalog, covering training courses, training formats, training cycles, training content, applicable skills and applicable positions, and obtain a detailed dataset of enterprise training resources; In this embodiment of the invention, by retrieving the enterprise training resource catalog and employing a classification extraction method, the core information of all training resources in the catalog is extracted one by one according to the training type, ensuring that the information of each training resource is complete and accurate, and comprehensively covering all relevant parameters of the entire training process. The extracted core information includes training courses, training formats, training cycles, training content, applicable competencies, and applicable positions. Training courses are divided into knowledge-based, skills-based, and competency-based categories according to competency type, corresponding to the previously mentioned difference types. The training formats are fixed as online courses, offline practical training, case studies, and mentorship. The training cycle is set according to the difficulty of the course: knowledge-based courses have a cycle of 3-5 days, skills-based practical training courses have a cycle of 7-10 days, and competency-based courses have a cycle of 2-3 days. The training content revolves around the corresponding competency items, accurately matching competency improvement needs. The applicable competency items clearly define the specific competency items corresponding to each course, and the applicable positions clearly define the management, technical, operational, and sub-sub-positions corresponding to the courses. All extracted training resource information is categorized and organized according to training type and applicable job position. The correspondence between each resource and skill item and job position is marked, generating a complete and detailed dataset of enterprise training resources to support subsequent matching of individual training resources.

[0039] Furthermore, the individual skill gap list is used to extract the skill gap items, improvement directions, and urgency of improvement for each employee, thus obtaining data on individual skill improvement needs. In this embodiment of the invention, by retrieving the generated personal competency gap list for each employee, core improvement needs information is extracted one by one for each employee, ensuring that each employee's improvement needs are fully extracted and accurately matched. During the extraction process, the competency gap items, improvement directions, and urgency for each employee are clearly defined. The competency gap items correspond to the specific competency items and gap details specified in the list, such as the coordination ability gap for management personnel and the troubleshooting ability gap for technical personnel. The improvement directions correspond to the training and improvement directions marked in step S2435, set based on the gap type and core reasons: knowledge gaps correspond to theoretical learning directions, and skill gaps correspond to practical training directions. The urgency for improvement strictly follows the three levels marked in the list: high, medium, and low. High urgency corresponds to core, severe gaps; medium urgency corresponds to moderate gaps; and low urgency corresponds to mild, non-core gaps. The extracted information for each employee is categorized and labeled, recording the employee's name and corresponding position, sorted and organized by urgency, and summarized to form comprehensive and accurate personal competency improvement needs data.

[0040] Furthermore, the data on individual skill enhancement needs are matched with the detailed dataset of corporate training resources. Based on the adaptability of skill items, the consistency of improvement direction, and the job adaptability, training resources that meet the individual skill enhancement needs are selected to obtain a set of individual-suitable training resources. In this embodiment of the invention, by retrieving individual skill enhancement needs data and detailed datasets of corporate training resources, a matching process is conducted for each individual employee to ensure accurate matching and relevance to their individual needs. During the matching process, suitable training resources are screened according to three core dimensions: Skill matching (the matching skill items of the training resources are completely consistent with the individual's skill gap items, such as matching training courses for coordination skills to address a gap in overall planning ability); Improvement direction consistency (the training content and format of the training resources correspond to the individual's improvement direction, such as matching offline practical training or mentorship training resources for practical improvement); and Job matching (the matching job of the training resources completely corresponds to the individual's current job, ensuring that the training content aligns with the actual work requirements of the position). For each employee's skill enhancement needs, training resources that meet the requirements of the three dimensions are compared and screened one by one. Mismatched resources are eliminated, and the resources are categorized and organized according to skill gap items to generate a personalized set of suitable training resources for each employee, clearly defining the correspondence between resources and gap items.

[0041] Furthermore, the training resources in the individual's training resource pool are sorted according to the urgency of improving individual ability gaps. Priority is given to matching training resources corresponding to ability gaps with high urgency, followed by matching training resources corresponding to ability gaps with medium and low urgency, thus determining the learning order of training resources. In this embodiment of the invention, by retrieving a personalized training resource set and data on individual skill enhancement needs, all training resources in the personalized training resource set are ordered according to the urgency of improving individual skill gaps, clearly defining the learning order and ensuring that the ordering logic is clear and aligned with the key improvement priorities. During the ordering process, the principle of urgency priority is strictly followed. Training resources corresponding to skill gaps with high urgency are prioritized for learning; for example, training courses corresponding to high urgency gaps in management coordination skills are ranked first. Next, training resources corresponding to skill gaps with moderate urgency are matched, arranged after high-urgency resources. Finally, training resources corresponding to skill gaps with low urgency are matched, arranged last. Training resources of the same urgency level are ranked according to the degree of impact of the skill gap, with resources corresponding to core skill gaps taking precedence over resources corresponding to non-core skill gaps. After ordering, the learning order number of each training resource is clearly marked, the ordering basis is recorded, and the learning order of training resources for each employee is determined, providing sequential support for subsequent training path planning.

[0042] Furthermore, based on the learning preferences and job characteristics of the employees, the learning duration, learning nodes, and learning formats of each training resource are planned. The sorted training resources and planning information are integrated to generate a unique and personalized initial training path for each employee. The path is then preliminarily verified, missing core training resources are supplemented, and redundant training resources are deleted.

[0043] In this embodiment of the invention, by retrieving the determined learning order of training resources, the learning preferences of employees, and the characteristics of their individual job positions, the learning details of each training resource are comprehensively planned, and a personalized initial training path is generated. During the planning process, the learning format is determined based on learning preferences; online courses are prioritized for those who prefer online learning, while offline practical resources are optimized for those who prefer hands-on learning. Learning duration and learning nodes are planned based on job characteristics, avoiding peak work periods. Learning duration is broken down according to the training cycle, with daily learning time controlled at 2-3 hours. Learning nodes are set according to the job's work rhythm to ensure that normal job performance is not affected. The integrated and sorted training resources, along with the planned learning duration, nodes, and format information, generate a unique personalized initial training path for each employee. The initial training path is initially verified to check for missing core training resources; any missing resources are promptly supplemented with appropriate resources. Redundant training resources are also checked and immediately deleted to ensure the training path is concise, efficient, and tailored to individual and job requirements.

[0044] Furthermore, the adaptation verification of the personalized initial training path, and the adjustment of training nodes and duration based on the job sequence data of the incumbents, includes the following steps: Extract training nodes, training duration, learning sequence, and learning format from the personalized initial training path to obtain the time-series data of the initial training path. In this embodiment of the invention, by retrieving the generated personalized initial training path, core time-series related information is extracted from the path for each individual employee, ensuring that the time-series information of each training path is complete and without deviation, comprehensively covering the time and format parameters of the entire training implementation process. The extracted information includes training nodes, training duration, learning order, and learning format. Training nodes clearly define the planned start and preliminary end times of each training resource, recorded in a year-month-day-hour-minute format, consistent with the time-series format mentioned above. Training duration extracts the total duration and daily breakdown duration for each training resource, with the total duration following the cycle standard of the corresponding training resource, and the daily breakdown duration controlled within 2-3 hours. The learning order strictly extracts the sorting number determined in step S34, clarifying the order of learning for each training resource. The learning format extracts the specific forms such as online courses and offline practical exercises corresponding to each training resource, maintaining consistency with the training resource format in step S31. All extracted time-series information is categorized and organized by employee name and learning order, and the correspondence between each piece of information is marked, resulting in initial training path time-series data with a clear structure and specific parameters, providing a time-series basis for subsequent path adaptability verification.

[0045] Furthermore, by collecting job work sequence data for each employee through the human resources collaboration platform, including daily work hours, work task distribution, peak work periods, off-peak work periods, and leave arrangements, detailed job work sequence data is obtained. In this embodiment of the invention, the job sequence data collection process is initiated through the multi-terminal collection interface preset by the human resources collaboration platform. Job sequence information for each employee is collected individually and for each corresponding job position, ensuring that the collected data is comprehensive, accurate, and relevant to the actual work situation. The collected job sequence data covers daily work hours, task distribution, peak work periods, off-peak hours, and leave arrangements. Daily work hours are fixed as fixed start and end times, accurate to the minute. Task distribution is categorized by weekdays and weekends, recording specific daily tasks and corresponding execution times. Peak work periods are clearly defined for each day and month, such as the last 5 days of the month for operations staff and 9-11 AM daily. Off-peak hours are clearly defined for each day, such as 3-4 PM and the hour before the end of the workday. Leave arrangements record the planned leave periods for paid leave, personal leave, sick leave, etc. All collected data is categorized and organized by employee name and job category, with the specific range of each time sequence parameter marked, generating complete detailed job sequence data.

[0046] Furthermore, the initial training path time sequence data is compared with the job work time sequence details to analyze the suitability of training nodes, training duration and job work, and to identify problems such as training time conflicts with work tasks, training periods during peak work periods and unreasonable training duration, so as to obtain the path suitability verification results. In this embodiment of the invention, by retrieving the initial training path time sequence data and the detailed job work time sequence data, a comparison is conducted for each individual employee to ensure accurate and error-free comparison and a comprehensive analysis of the suitability of training and work. During the comparison process, the focus is on analyzing the suitability of training nodes, training duration, and job duties, identifying three core issues: 1) Training time conflict with work tasks: The planned training node time period completely overlaps with the specific work task execution time period, such as a training plan to be held at 10:00 AM, while the employee needs to perform core work tasks during that time; 2) Training period falling within peak work hours: The planned training node time period falls within the daily or monthly peak work period for the position, such as the operations position training plan being scheduled for the last 3 days of the month; 3) Unreasonable training duration: The daily training duration exceeds 3 hours, or the total training duration occupies too much work time, affecting the normal progress of job duties. Each identified problem is recorded, noting the problem type, the relevant training nodes, and the specific time period, summarizing to form a detailed path suitability verification result, clarifying the suitability items and those requiring adjustment.

[0047] Furthermore, based on the path adaptability verification results, the personalized initial training path is adjusted to avoid peak work periods and important work task periods, and training nodes are prioritized to be arranged during off-peak work periods. The training duration is adjusted according to the distribution of work tasks to avoid excessively long training durations from affecting job performance. In this embodiment of the invention, based on the path adaptability verification results, the personalized initial training path is adjusted individually for each staff member to ensure that the adjusted path avoids various adaptability issues and fits the job's work sequence. During the adjustment process, the principles of work priority and training adaptability are strictly followed, avoiding peak work periods and important task periods. Training nodes that were originally in peak periods or conflicted with important tasks are prioritized and adjusted to the staff's off-peak hours. For example, the end-of-month training for operations staff is adjusted to the middle of the month during off-peak hours, and daily peak training is adjusted to the afternoon during off-peak hours. Training duration is adjusted according to the distribution of job tasks. During periods of heavy workload, the daily training duration is shortened to less than 2 hours; during periods of lighter workload, a daily duration of 3 hours can be maintained to ensure that the training duration does not affect the normal operation of the job. The adjusted training nodes and training durations are re-recorded, and the learning sequence is updated synchronously to ensure continuity. After the adjustment is completed, a preliminary optimized training path is formed, and the basis for the adjustment and the changes before and after the adjustment are recorded.

[0048] Furthermore, a second adaptability check is performed on the adjusted training path. The rationality of the training pace is assessed in conjunction with the learning and acceptance capabilities of the employees, and problems such as training nodes being too dense or too sparse are identified. The results of the second check are obtained. Based on the results of the second check, the training path is fine-tuned and optimized, and the specific start and end times, learning duration and learning format of each training node are clarified to generate an adaptable training path.

[0049] In this embodiment of the invention, a secondary adaptability check is performed on the adjusted training path to ensure comprehensiveness and detail, addressing potential issues with unreasonable pacing. During the check, the rationality of the training pace is assessed in conjunction with the learning comprehension abilities of the personnel. Learning comprehension abilities are comprehensively evaluated based on job performance evaluations, learning preferences, and past training assessment results. Personnel with strong learning comprehension abilities can appropriately accelerate the pace, while those with weaker abilities need to slow down. Two types of pacing issues are specifically investigated: overly dense training sessions (e.g., scheduling too many training sessions in a short period, such as 3 hours of training per day for 5 consecutive days without rest or adjustment time); and overly loose training sessions (e.g., excessively long intervals between training sessions, such as more than 7 days between sessions for a single course, affecting learning continuity). Based on the results of the secondary check, the training path is fine-tuned and optimized. Overly dense sessions are appropriately spaced with 1-2 days of rest time; overly loose sessions are appropriately shortened to ensure a reasonable pace. After fine-tuning, the specific start and end times, learning duration, and learning format of each training node are clarified, the execution requirements of each node are marked, and a highly adaptable training path with a reasonable pace that fits the individual and job requirements is generated, thus completing the final optimization of the training path.

[0050] Furthermore, the comparison of the initial training path time series data with the job work time series details includes the following steps: Extract the specific time, duration and corresponding learning format of each training node from the initial training path time series data to obtain detailed training node data; In this embodiment of the invention, by retrieving the initial training path timeline data, core detailed information is extracted one by one by individual personnel and training nodes to ensure that the information of each training node is complete and accurate, precisely corresponding to the implementation details of each training resource. During the extraction process, the specific time, duration, and corresponding learning format of each training node are clearly defined. The specific time is recorded precisely in year-month-day-hour-minute format, clearly defining the planned start and end times of each training node, maintaining consistency with the timeline format mentioned above. The duration is distinguished between the total duration of a single node and the daily breakdown duration. The total duration of a single node follows the cycle standard of the corresponding training resource, while the daily breakdown duration is controlled within 2-3 hours, clearly defining the duration of the specific learning period each day. The learning format strictly extracts the specific type of the corresponding training resource, categorized into four types: online courses, offline practical exercises, case studies, and mentor-led instruction, consistent with the training resource format and timeline data. All extracted training node information is categorized and organized by personnel name and learning order, and the correspondence between each node and its duration and learning format is marked, resulting in clearly structured and parameter-specific detailed training node data, providing a clear basis for subsequent timeline comparison and conflict resolution.

[0051] Furthermore, the work task time period, peak work period, idle work period and work task priority of each employee are extracted from the detailed work time sequence data to obtain work time sequence analysis data; In this embodiment of the invention, by retrieving detailed work sequence data for each position, core work sequence information is extracted for each individual employee, ensuring that the work sequence data for each employee is complete and accurately reflects the actual job requirements. During the extraction process, the work task periods, peak work periods, idle work periods, and work task priorities for each employee are clearly defined. Work task periods are recorded one by one for each specific daily task, specifying the start and end times of each task. Peak work periods are distinguished between daily and monthly peak periods, with specific time ranges marked, such as 9-11 AM daily for operations staff and the last 5 days of each month being peak periods. Idle work periods are clearly defined for each day's work, with specific start and end times marked, such as 3-4 PM daily and the last hour before the end of the workday. Work task priorities are divided into two levels: important and general. Important tasks refer to those that affect the core performance of the job and cannot be delayed, while general tasks refer to auxiliary tasks that can be flexibly adjusted. The extracted work sequence information is categorized and organized by employee name and task priority, summarizing to form comprehensive and detailed work sequence analysis data.

[0052] Furthermore, the detailed data of training nodes are compared with the work time sequence analysis data one by one to check whether the time of each training node overlaps with the time of important work tasks or is in the peak work period, so as to obtain the time conflict investigation results. In this embodiment of the invention, detailed training node data and work sequence analysis data are retrieved, and comparisons are conducted for each individual employee and training node at each time period to ensure comprehensive, accurate, and error-free comparisons. During the comparison process, two types of time-related issues are specifically investigated: first, whether the training node time overlaps with the time period of important work tasks. Overlap refers to the complete or partial overlap between the planned start and end times of the training node and the execution start and end times of the important work task, such as a training node planned for 9-11 am, while the employee needs to perform important data aggregation tasks during that time; second, whether the training node time falls within the peak work period, i.e., the planned time period of the training node falls entirely within the peak work period, such as an operations staff training node scheduled for the last 3 days of each month, or within the peak period of 9-11 am each day. Each identified issue is recorded, noting the involved training node, the specific conflicting time period, and the type of conflict, summarizing to form a detailed time conflict investigation result, clearly identifying nodes without conflicts and nodes with conflicts.

[0053] Furthermore, we analyzed whether the duration of each training session matched the workload of the corresponding period, and investigated whether the training duration was too long and affected the completion of the work tasks, or whether the training duration was too short and could not guarantee the learning effect, so as to obtain the results of the investigation on the rationality of the training duration. In this embodiment of the invention, by retrieving detailed training node data and work sequence analysis data, the rationality of the duration of each training node is analyzed for individual employees and each training node, ensuring that the duration is set in line with the workload and does not affect work and learning effectiveness. During the analysis, the workload of the corresponding time period for each training node is considered to determine whether the training duration is appropriate. Two types of problems are identified: first, training duration is too long, affecting the completion of work tasks, i.e., the daily training time exceeds 3 hours, or the total time of a single node occupies too much work time, resulting in the inability to complete the work tasks on time during the corresponding period, such as arranging 3.5 hours of training per day during periods of heavy workload; second, training duration is too short, failing to guarantee learning effectiveness, i.e., the daily training time is less than 1 hour, or the total time of a single node does not meet the minimum requirements of the corresponding training resources, resulting in ineffective absorption of knowledge and skills, such as only 0.5 hours of practical training per day for skills training. The rationality of the duration of each training node is determined one by one, recording reasonable and unreasonable nodes, noting the problem type, specific duration, and impact, and summarizing the results to form the rationality investigation of training duration.

[0054] Furthermore, the results of time conflict investigation and training duration rationality investigation are integrated to mark training nodes with time conflicts, peak work periods, and unreasonable training durations, clarifying the specific manifestations and scope of impact of each problem; the marked problems are classified and sorted to distinguish between core problems and secondary problems. Core problems include serious time conflicts and seriously unreasonable training durations, while secondary problems include slight time overlaps, generating complete path adaptability verification results.

[0055] In this embodiment of the invention, a full integration operation is performed by retrieving the results of time conflict investigation and training duration rationality investigation to ensure that all issues are addressed without omission and are clearly categorized. During the integration process, training nodes with time conflicts, those occurring during peak work periods, and those with unreasonable training durations are marked one by one. The specific manifestations and scope of impact of each issue are clearly defined. The specific manifestations are described in detail, such as time conflicts manifesting as overlap between training and important data aggregation tasks, and unreasonable durations manifesting as only 1 hour of practical training per day, which cannot guarantee effectiveness. The scope of impact clearly defines the work tasks, learning outcomes, and the number of training nodes involved. All marked issues are categorized and sorted to distinguish between core and secondary issues. Core issues include severe time conflicts and severely unreasonable training durations. Severe time conflicts refer to complete overlap with important work tasks that cannot be adjusted, and severely unreasonable durations refer to daily durations exceeding 4 hours or less than 0.5 hours. Secondary issues include slight time overlaps, i.e., training partially overlaps with general work tasks and can be flexibly adjusted. The problems are categorized and organized according to their type and severity, and a complete set of path adaptability verification results with a comprehensive structure and detailed information is compiled, providing a clear basis for subsequent fine-tuning and optimization of the training path.

[0056] Furthermore, adjusting the personalized initial training path based on the path adaptability verification results includes the following steps: Extract the problematic training nodes, problem types, and impact scope from the path adaptability verification results to obtain path adjustment requirement data; In this embodiment of the invention, by retrieving the path adaptability verification results, the core information of the problems in the verification results is extracted one by one for each individual employee, ensuring that the information of each problematic training node is complete and without deviation, and comprehensively covering the key parameters required for adjustment. The extracted information covers the problematic training nodes, problem types, and scope of impact. For problematic training nodes, specific node numbers, original planned start and end times, and corresponding training resources are clearly defined, consistent with the detailed training node data. Problem types strictly follow the classification in the verification results, divided into three categories: time conflict, peak work period, and unreasonable training duration, clearly defining the specific problem category for each node. The scope of impact extracts the work tasks, learning outcomes, and number of nodes affected by the problems recorded in the verification results, such as impacting the completion of important work tasks or preventing the absorption of learning content. All extracted adjustment-related information is categorized and organized by employee name and problem type, and the correspondence between each problem and training node and scope of impact is marked, resulting in clearly structured and parameter-specific path adjustment requirement data, providing a clear basis for subsequent training node adjustments.

[0057] Furthermore, for training sessions that conflict with time or fall during peak work periods, the idle time slots in the job sequence details are queried. Based on the learning needs and priorities of the training sessions, the corresponding training sessions are adjusted to the nearest idle time slot to ensure that the adjusted training sessions do not conflict with work tasks or fall during peak work periods. In this embodiment of the invention, training nodes marked as time-conflicted or in peak work periods in the path adjustment demand data are retrieved one by one from the job time sequence analysis data. A time-slot query method is used to accurately locate the corresponding job's free time slots, clarifying the specific start and end times, duration, and workload of each free time slot. During the search process, the learning needs and priorities of each problematic training node are considered. The learning needs clarify the difficulty of the training content and the required learning time for that node. Priority follows the marked urgency level, with high-urgency nodes being prioritized for matching the most recent free time slots. The corresponding training node is adjusted to the most recent free time slot, ensuring that the new training node does not overlap with any work task time slots or fall outside of peak work periods. For example, if the original training node needs adjustment due to the end-of-month peak period for operations positions, and a free time slot is found for the middle of the month from 3-5 pm, the node is adjusted to that time slot. Simultaneously, the duration of the adjusted time slot is ensured to meet the daily learning needs of the training node. After the adjustment, the node time before and after the adjustment and the basis for the adjustment are recorded in detail to ensure that each problematic node is adjusted accordingly.

[0058] Furthermore, for training sessions with unreasonable durations, the training duration can be adjusted based on the workload and difficulty of the learning content during the corresponding period. The training duration can be appropriately extended during periods with less workload and appropriately shortened during periods with more workload, while ensuring that the learning content can be fully mastered. In this embodiment of the invention, by targeting training nodes marked as having unreasonable training durations in the path adjustment demand data, the generated job work sequence analysis data is retrieved one by one to clarify the workload of each problem node's corresponding time period. Two levels are distinguished: heavy workload and light workload. Simultaneously, the difficulty of the learning content corresponding to that node is considered, categorized into knowledge-based, skill-based, and competency-based, with skill-based content being more difficult than knowledge-based and competency-based content. Training duration is adjusted according to workload and difficulty. For periods with light workloads, the original daily duration can be extended by 0.5-1 hour; for example, knowledge-based training originally 2 hours per day can be adjusted to 2.5 hours per day to ensure more detailed explanations of the learning content. For periods with heavy workloads, the original daily duration can be shortened by 0.5-1 hour; for example, skill-based training originally 3 hours per day can be adjusted to 2 hours per day to avoid affecting the completion of work tasks. During the adjustment process, the duration should be strictly controlled. The daily time for knowledge-based learning should be no less than 1.5 hours and no more than 3.5 hours, and the daily time for skills-based learning should be no less than 2 hours and no more than 3 hours. At the same time, it should be ensured that the learning content can be fully mastered. After the adjustment is completed, the duration before and after the adjustment, the basis for the adjustment, and the corresponding workload should be recorded to ensure that the time adjustment fits the actual needs.

[0059] Furthermore, the adjusted training schedule was reviewed to identify any new time conflicts or excessively dense training schedules, and the results of the review were obtained. In this embodiment of the invention, all adjusted training nodes are sequentially analyzed according to individual personnel and learning order. During this process, the specific start and end times, duration, learning order, and corresponding work periods for each adjusted training node are clearly defined to ensure clear temporal logic and seamless node connection. Simultaneously, a secondary review is conducted, focusing on two types of new issues: first, whether new time conflicts arise after the adjustment, i.e., whether the adjusted training nodes overlap with other training nodes or job work periods, such as overlapping periods between two adjusted training nodes or overlapping periods between adjusted nodes and newly added work tasks; second, whether the adjustment results in an overly dense schedule of training nodes, i.e., scheduling too many adjusted training nodes in a short period, such as scheduling 3.5 hours of training per day for four consecutive days without rest or adjustment time. Each new issue identified is recorded, noting the issue type, the training nodes involved, the specific time period, and the impact. A detailed review result is compiled, identifying problem-free nodes and newly problematic nodes, providing a basis for subsequent fine-tuning.

[0060] Furthermore, the training nodes are fine-tuned based on the results of the post-adjustment investigation to ensure that the timing of the adjusted training nodes is reasonable and conflict-free, and that the training duration is adapted to the distribution of work tasks; all adjustment information is integrated to generate the adjusted training path timing data.

[0061] In this embodiment of the invention, based on the results of the post-adjustment review, training nodes with newly emerging issues are fine-tuned one by one for each individual employee. This ensures that the adjusted training node sequence is reasonable, free of any time conflicts, and that the training duration is adapted to the workload distribution. During the fine-tuning process, for newly emerging time conflict nodes, available time slots are re-queried and adjusted, prioritizing available time slots that are close to the original adjusted time slots and have less workload. For nodes that are too densely packed, the intervals between nodes are appropriately increased, a one-day rest period is inserted, or the daily duration of the corresponding node is shortened by 0.5 hours to alleviate the density. After the fine-tuning is completed, all training nodes are checked again to confirm that there are no time conflicts, no dense or loose issues, and that the duration is adapted to the workload. All adjustment information is integrated, including node information before adjustment, adjustment measures, adjustment basis, and node information after adjustment. The information is categorized and organized by employee name and learning order, and the time sequence parameters and corresponding work time slots of each node are labeled. This generates a complete, reasonable, and highly adaptable adjusted training path time sequence data, providing support for the final optimization of the subsequent training path.

[0062] Furthermore, the present invention also provides a human resource training path planning system, the system being used to execute the human resource training path planning method described above, the human resource training path planning system comprising the following modules: The data acquisition module is used to collect job profile data, job competency benchmark data, historical training implementation data, and job work time sequence data for each position in the enterprise through the human resources collaboration platform, and output the raw human resources training-related dataset. In this embodiment of the invention, a full data collection process is initiated through the multi-terminal collection interface preset by the human resources collaboration platform. Four core data categories are collected one by one according to job category and individual employee, ensuring that the collected data is comprehensive, accurate, and consistent in timeline, without omissions or deviations. The collected job profile data covers on-the-job duration, skill mastery, career development aspirations, learning preferences, and job performance evaluation. On-the-job duration is calculated based on actual on-the-job days, and skill mastery is divided into three levels: mastery, familiarity, and understanding. Job competency benchmark data is collected according to management, technical, and operational positions, including core competency items, competency standards, and competency level classifications. Historical training implementation data includes details of training courses, participation records, assessment results, and feedback information from the past 1-3 years, with assessment results recorded on a percentage basis. Job work timeline data covers daily work hours, task distribution, peak periods, off-peak periods, and leave arrangements, clearly defining the specific timeline range for each day and month. All collected raw data is categorized and labeled according to data type and job category, and integrated to form a complete and traceable raw human resources training-related dataset, which is directly output to the data normalization module to provide raw support for subsequent data processing.

[0063] The data normalization module is connected to the data acquisition module and is used to clean and normalize the original human resources training-related datasets, and integrate them to generate a job-competency correspondence benchmark dataset. In this embodiment of the invention, the system directly connects to the data acquisition module, receives its output of raw human resource training-related datasets, and sequentially performs cleaning and normalization processes to ultimately generate a job-competency correspondence benchmark dataset. The cleaning process employs a three-pronged approach: key information verification, logical contradiction checking, and duplicate data identification. Invalid data, such as data with missing key items, logical contradictions, or complete duplication, is removed. For example, data with quarterly performance evaluations despite less than three months of on-the-job experience is directly discarded. The normalization process standardizes data representation, classification criteria, and time sequence format. Skill mastery levels, training assessment results, and job classifications are adjusted according to unified standards, and the time sequence format is standardized to year, month, day, hour, and minute. Simultaneously, competency information and training data undergo fixed-rule encoding conversion. After integration, an association mapping method is used to establish the relationship between job positions, incumbents, and competency items. This clarifies the corresponding competency items for each job position and the corresponding job positions and competencies for each incumbent, generating a clearly defined and standardized job-competency correspondence benchmark dataset, which is then output to the competency gap calculation module.

[0064] The competency gap assessment module is connected to the data normalization module and is used to assess the competency gap of employees based on the job-competency correspondence benchmark dataset and generate an individual competency gap list. In this embodiment of the invention, by connecting with the data normalization module, the system receives the job-competency corresponding benchmark dataset output by the module, comprehensively conducts competency gap assessment for incumbents, and ultimately generates an individual competency gap list. During the assessment process, detailed competency benchmark data for each job is first extracted, clarifying core competency items, non-core competency items, achievement standards, and evaluation dimensions. Then, individual competency status data for each incumbent is extracted, clarifying skill mastery, job performance, and evaluation records. Each incumbent's competency is compared item by item with the corresponding job benchmark, identifying discrepancies, analyzing the reasons for discrepancies, and distinguishing between trainable and non-training-based gaps. All competency gaps are summarized by individual incumbent, clarifying the manifestation, scope, degree of impact, and urgency of improvement for each gap. Core competency gaps are marked as serious or significantly impactful, while non-core gaps are marked as moderately impactful. A complete and well-organized individual competency gap list is then compiled and output to the path preliminary matching module.

[0065] The preliminary path matching module is connected to the capability gap assessment module. It is used to perform preliminary path matching by combining the individual capability gap list with the corporate training resource catalog to generate a personalized initial training path. In this embodiment of the invention, by connecting to the capability gap assessment module, the system receives the individual capability gap list output by the module and simultaneously retrieves the enterprise training resource catalog to conduct preliminary path matching and generate a personalized initial training path. During the matching process, the core information of the enterprise training resources is first extracted, covering training courses, formats, cycles, applicable capability items, and job positions, categorized into knowledge, skills, and competency categories. Then, individual capability improvement needs are extracted, clarifying capability gap items, improvement directions, and urgency. Suitable training resources are selected for each employee based on three dimensions: capability item suitability, consistency of improvement directions, and job suitability. Suitable resources are sorted by improvement urgency, with resources corresponding to high-urgency gaps prioritized. For gaps of the same urgency, resources are sorted by the degree of impact. Combining learning preferences and job characteristics, the learning duration, nodes, and formats for each resource are planned. The sorted resources and planning information are integrated to complete preliminary verification, supplement missing resources, delete redundant resources, generate a personalized initial training path, and output it to the path matching optimization module.

[0066] The path adaptation and optimization module is connected to the path preliminary matching module. It is used to verify the adaptability of the personalized initial training path, adjust the training nodes and duration based on the job work time sequence data, and generate an adapted training path. In this embodiment of the invention, a personalized initial training path is received by connecting to the initial path matching module. Simultaneously, job-related time sequence data is retrieved to perform adaptability verification and adjustment, generating an adaptable training path. During the verification process, the initial training path time sequence data and detailed job-related time sequence data are extracted and compared time-by-time to identify conflicts between training nodes and work tasks, issues related to peak work periods, and unreasonable durations. These are then integrated to form the path adaptability verification result. For problematic nodes, conflicting and peak-period nodes are moved to the nearest available time slot. The training duration is adjusted based on the workload and the difficulty of the learning content. After adjustment, new conflicts and issues with dense nodes are identified again, and fine-tuning is performed. Considering the learning and acceptance capabilities of the employees, the rationality of the training pace is assessed, identifying issues of dense or sparse nodes, fine-tuning the intervals between nodes, and clarifying the specific start and end times, duration, and format of each node. This generates a highly adaptable training path that does not disrupt work and is output to the execution tracking verification module.

[0067] The execution tracking and verification module is connected to the path adaptation and optimization module to collect execution trajectory data of the adaptive training path in real time, conduct closed-loop verification of training effectiveness, and output training effectiveness verification results. In this embodiment of the invention, by connecting to the path adaptation and optimization module, the system receives the adaptive training path output by the module, initiates a real-time data collection and closed-loop verification process, and outputs the training effectiveness verification results. During the data collection process, the system collects execution trajectory data of the adaptive training path in real time through the multi-terminal interface of the human resources collaboration platform. This data covers the completion status of training nodes, actual learning time, learning progress, training assessment results, and changes in job performance. Data is recorded individually for each staff member and training node to ensure real-time and complete data. During the verification process, the system compares the actual execution data with the path planning requirements, checks the node completion rate and time completion rate, and evaluates the training effectiveness by combining training assessment results and changes in job performance. This determines whether the ability gap has been narrowed and whether the training content meets the needs. Detailed records are kept of any non-compliance, identifying the non-compliance nodes, specific reasons, and impacts. The system distinguishes between training execution problems and path planning problems, integrates these into a comprehensive and detailed training effectiveness verification result, and outputs it to the path iteration output module.

[0068] The path iteration output module is connected to the execution tracking and verification module. It is used to iteratively optimize the adaptive training path based on the training effect verification results and output the final human resource training path planning result.

[0069] In this embodiment of the invention, by connecting with the execution tracking and verification module, the system receives the training effectiveness verification results output by the module. Combined with all the process data described above, the system iteratively optimizes the adaptability of the training path, outputting the final human resource training path planning result. During the iterative optimization process, for each issue that fails to meet the verification results, optimization measures are formulated. For training execution issues, the learning node reminder mechanism is adjusted, and the adaptability of the learning format is optimized. For path planning issues, training resources are re-matched, and the node sequence and duration are adjusted. Based on the reduction in capability gaps, the training improvement direction is updated. For gaps where improvement goals have been achieved, the corresponding training nodes are deleted; for gaps that have not been reduced, adaptable training resources are added, and the corresponding duration is extended. After iterative optimization, the adaptability, rationality, and achievability of the training path are checked again to ensure that the path aligns with individual capability needs, job work sequences, and training resource availability. All optimization information is integrated to generate a structurally complete, logically clear, and directly executable final human resource training path planning result, completing the entire training path planning process.

[0070] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the same elements of the application are intended to be embraced within the invention.

[0071] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for planning human resource training pathways, characterized in that, Includes the following steps: The receiving end collects job profile data, job competency benchmark data and historical training implementation data for each position in the enterprise through the human resources collaboration platform, and performs normalization processing on the collected data to generate a job-competency corresponding benchmark dataset. Based on the job-competency correspondence benchmark dataset, the competency gap of employees is measured, and an individual competency gap list is generated. By combining the individual competency gap list with the corporate training resource catalog, a preliminary path matching is performed to generate a personalized initial training path; The suitability of the personalized initial training path is verified, and the training nodes and duration are adjusted based on the job sequence data of the employees to generate a suitable training path. Real-time collection of execution trajectory data corresponding to the adaptive training path, conducting closed-loop verification of training effectiveness, iteratively optimizing the adaptive training path based on the verification results, and outputting the final human resource training path planning results.

2. The human resource training path planning method according to claim 1, characterized in that, The receiving end collects job profile data, job competency benchmark data, and historical training implementation data for each position in the enterprise through the human resources collaboration platform. The normalization processing of the collected data includes the following steps: The receiving end collects job profile data, corresponding competency benchmark data, and past training implementation data of employees in various positions within the enterprise simultaneously through the multi-terminal collection interface of the human resources collaboration platform. The job profile data covers the employee's on-the-job duration, skill mastery, career development aspirations, learning preferences, and job performance evaluation. The competency benchmark data covers the core competency items of the position, competency standards, competency level classification, and competency update cycle. The past training implementation data covers details of past training courses, training participation records, training assessment results, and training feedback information. The three types of raw data collected were processed to remove invalid data, including data with missing key information, logical contradictions, and duplicate records, to obtain cleaned human resources training-related data. After cleaning, the human resources training-related data is normalized to unify data representation standards, classification criteria, and time series formats. Different types of competency information and training data are coded and converted according to unified rules. All normalized data are integrated to establish the correlation mapping relationship between positions, employees, and competency items, generating a benchmark dataset of position-competency correspondences, and clarifying the correspondence between each position and its corresponding competency item, and between each employee and its corresponding competency item.

3. The human resource training path planning method according to claim 1, characterized in that, The calculation of the competency gap of incumbents based on the job-competency correspondence benchmark dataset includes the following steps: Extract the competency benchmark data for each position from the job-competency correspondence benchmark dataset, clarify the core competency items, competency standards and competency level requirements for each position, and obtain detailed job competency benchmark data; Job profile data for each employee is extracted from the job-ability correspondence benchmark dataset to clarify the current skill mastery, ability level and job performance evaluation of each employee, thus obtaining individual ability status data. Compare individual competency data with the competency benchmark details for the corresponding position item by item, and analyze the gap between the current status of the employee and the standard for each competency item. Clearly define the manifestations, scope, and influencing factors of each capability gap, distinguish between core capability gaps and non-core capability gaps, and indicate the degree of impact of each capability gap on job performance. Integrate all capability gap information, categorize and summarize it by individual staff members, compile a capability gap list for each staff member, identify the key areas for capability improvement for each staff member, and generate an individual capability gap list. The individual capability gap list includes capability gap items, gap details, degree of impact, and urgency of improvement.

4. The human resource training path planning method according to claim 3, characterized in that, The process of comparing individual skill status data with the corresponding job skill benchmark details item by item, and analyzing the gap between the current status of the employee and the standard for each skill item, includes the following steps: Extract all core and non-core competency items for each position from the competency benchmark details data for the corresponding position, clarify the qualification standards, competency level classification and evaluation dimensions for each competency item, and obtain competency item evaluation standard data; Extract the skill mastery, job performance and related evaluation records of the corresponding employees in each ability item from the current data of individual abilities to obtain detailed data of individual ability items; The detailed data of individual competencies are matched with the evaluation criteria data of each competency item, and the comparison is made one by one according to the evaluation dimensions of each competency item to identify the differences between individual competencies and the standards. A detailed analysis of the differences for each competency item was conducted to clarify the specific manifestations, scope, and causes of the differences, and to distinguish between gaps that can be improved through training and gaps that cannot be improved through training. The gaps that can be improved through training should be marked in detail, and the corresponding competency items, differences, and directions for improvement should be recorded for each gap.

5. The human resource training path planning method according to claim 4, characterized in that, The detailed analysis of the differences for each competency item, clarifying the specific manifestations, scope, and causes of these differences, and distinguishing between gaps that can be improved through training and those that cannot, includes the following steps: The differences for each ability item were categorized and analyzed, and classified into knowledge-based differences, skill-based differences, and competency-based differences according to their manifestation, resulting in difference point classification data. For each type of difference, analyze its scope to clarify whether the difference exists in a specific work scenario, a specific skill module, or is a comprehensive difference, and obtain difference scope analysis data; By combining the job profile data of the employees, job performance evaluations and historical training records, we can analyze the core reasons for each difference and investigate whether the differences are due to insufficient knowledge reserves, inadequate skills training, limited learning ability or insufficient job suitability. Based on the causes and types of differences, gaps can be distinguished between those that can be improved through training and those that cannot be improved through training. Among them, knowledge-based differences, skill-based differences, and some competency-based differences are classified as gaps that can be improved through training, while differences caused by insufficient job suitability are classified as gaps that cannot be improved through training. The gaps that can be improved through training are further refined, and the training and improvement directions corresponding to each gap are clarified. The gaps that are not improved through training are marked and explained, and detailed data on the difference point analysis is generated.

6. The human resource training path planning method according to claim 5, characterized in that, The initial path matching process, which combines the individual competency gap list with the corporate training resource catalog, includes the following steps: Retrieve the enterprise training resource catalog, extract the core information of all training resources in the catalog, covering training courses, training formats, training cycles, training content, applicable skills and applicable positions, and obtain a detailed dataset of enterprise training resources; Extract the skill gap items, improvement directions, and urgency of improvement for each employee from the individual skill gap list to obtain data on individual skill improvement needs. By matching individual skill enhancement needs data with enterprise training resource details dataset, training resources that meet individual skill enhancement needs are selected based on skill item suitability, consistency of enhancement direction, and job suitability, resulting in a set of individual-suitable training resources. Based on the urgency of improving individual skill gaps, the training resources in the individual-suitable training resource pool are sorted, with priority given to matching training resources corresponding to skill gaps with high urgency, followed by matching training resources corresponding to skill gaps with medium and low urgency, thus determining the learning order of the training resources. Based on the learning preferences and job characteristics of the employees, the learning duration, learning nodes and learning methods of each training resource are planned. The training resources and planning information are integrated and sorted to generate a unique and personalized initial training path for each employee. The path is then initially verified, missing core training resources are added and redundant training resources are removed.

7. The human resource training path planning method according to claim 1, characterized in that, The process of verifying the adaptability of the personalized initial training path and adjusting the training nodes and duration based on the job sequence data of the employees includes the following steps: Extract training nodes, training duration, learning sequence, and learning format from the personalized initial training path to obtain the time-series data of the initial training path. By collecting job work sequence data of each employee through the human resources collaboration platform, covering daily work hours, work task distribution, peak work periods, off-peak work periods and vacation arrangements, detailed job work sequence data is obtained. The initial training path time sequence data is compared with the job work time sequence details to analyze the suitability of training nodes, training duration and job work, and to identify problems such as training time conflicts with work tasks, training periods during peak work periods and unreasonable training duration, so as to obtain the path suitability verification results. Based on the path adaptability verification results, the personalized initial training path is adjusted to avoid peak work periods and important work task periods. Training nodes are prioritized to be arranged during off-peak work periods, and the training duration is adjusted according to the distribution of work tasks to avoid excessively long training durations from affecting job performance. The adjusted training path undergoes a second adaptability check. The rationality of the training pace is assessed in conjunction with the learning and acceptance capabilities of the employees, and issues such as training nodes being too dense or too sparse are identified. The results of the second check are obtained. Based on the results of the second check, the training path is fine-tuned and optimized, and the specific start and end times, learning duration and learning format of each training node are clarified to generate an adaptable training path.

8. The human resource training path planning method according to claim 7, characterized in that, The comparison of the initial training path time series data with the job work time series details includes the following steps: Extract the specific time, duration and corresponding learning format of each training node from the initial training path time series data to obtain detailed training node data; Extract the work task time period, peak work period, idle work period and work task priority of each employee from the detailed work time sequence data to obtain work time sequence analysis data; By comparing the detailed data of training nodes with the work time sequence analysis data one by one, we can check whether the time of each training node overlaps with the time of important work tasks or whether it is in the peak work period, and obtain the time conflict investigation results. Analyze whether the duration of each training session matches the workload of the corresponding period, identify problems such as excessively long training sessions affecting the completion of work tasks, or excessively short training sessions failing to guarantee learning effectiveness, and obtain the results of the reasonableness check of training duration. By integrating the results of time conflict investigation and training duration rationality investigation, training nodes with time conflicts, those in peak work periods, and those with unreasonable training durations are marked, and the specific manifestations and scope of impact of each problem are clarified. The marked problems are classified and sorted to distinguish between core problems and secondary problems. Core problems include serious time conflicts and seriously unreasonable training durations, while secondary problems include slight time overlaps, generating complete path adaptability verification results.

9. The human resource training path planning method according to claim 8, characterized in that, The adjustment of the personalized initial training path based on the path adaptability verification results includes the following steps: Extract the problematic training nodes, problem types, and impact scope from the path adaptability verification results to obtain path adjustment requirement data; For training sessions that conflict with schedules or fall during peak work periods, query the job's work time sequence details for available work hours. Based on the learning needs and priorities of the training sessions, adjust the corresponding training sessions to the nearest available work hours to ensure that the adjusted training sessions do not conflict with work tasks or fall during peak work periods. For training sessions with unreasonable durations, the training duration should be adjusted based on the workload and difficulty of the learning content during the corresponding period. The training duration can be appropriately extended during periods with less workload and appropriately shortened during periods with more workload, while ensuring that the learning content can be fully mastered. The adjusted training schedule was reviewed to identify any new time conflicts or overly dense training schedules, and the results of the review were obtained. Based on the results of the post-adjustment review, the training nodes were fine-tuned to ensure that the timing of the adjusted training nodes was reasonable and conflict-free, and that the training duration was adapted to the distribution of work tasks; all adjustment information was integrated to generate the adjusted training path timing data.

10. A human resource training path planning system, characterized in that, The system is used to execute the human resource training path planning method as described in any one of claims 1-9 above, and the human resource training path planning system includes the following modules: The data acquisition module is used to collect job profile data, job competency benchmark data, historical training implementation data, and job work time sequence data for each position in the enterprise through the human resources collaboration platform, and output the raw human resources training-related dataset. The data normalization module is connected to the data acquisition module and is used to clean and normalize the original human resources training-related datasets, and integrate them to generate a job-competency correspondence benchmark dataset. The competency gap assessment module is connected to the data normalization module and is used to assess the competency gap of employees based on the job-competency correspondence benchmark dataset and generate an individual competency gap list. The preliminary path matching module is connected to the capability gap assessment module. It is used to perform preliminary path matching by combining the individual capability gap list with the corporate training resource catalog to generate a personalized initial training path. The path adaptation and optimization module is connected to the path preliminary matching module. It is used to verify the adaptability of the personalized initial training path, adjust the training nodes and duration based on the job work time sequence data, and generate an adapted training path. The execution tracking and verification module is connected to the path adaptation and optimization module to collect execution trajectory data of the adaptive training path in real time, conduct closed-loop verification of training effectiveness, and output training effectiveness verification results. The path iteration output module is connected to the execution tracking and verification module. It is used to iteratively optimize the adaptive training path based on the training effect verification results and output the final human resource training path planning result.

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