Staged progressive talent growth path planning method and system

Abstract

The invention belongs to the technical field of talent management development, and relates to a staged progressive talent growth path planning method and system, and the system collects and integrates the static and dynamic data of talents through a talent portrait construction module to generate an initial talent portrait; the growth stage division module divides progressive growth stages and sets capability thresholds based on the initial talent portraits and the organization development objectives; the stage target setting module is used for disassembling a stage target into quantifiable sub-target schemes with time limit; the resource matching and pushing module is used for matching resources according to the target and the portrait and automatically pushing the resources to the talent end; the capability evaluation feedback module is used for evaluating a stage target achievement condition and generating an evaluation report; the path dynamic adjustment module progressively or triggers path adjustment based on the evaluation report judgment stage; and the data storage and analysis module is used for storing the whole-process data, carrying out multi-dimensional analysis and optimizing parameters. According to the invention, personalized planning, dynamic adjustment and whole-process closed-loop management of talent growth paths can be realized.

Description

Technical Field

[0001] This invention relates to the field of talent management and development technology, specifically to a phased and progressive talent growth path planning method and system. Background Technology

[0002] In today's highly competitive talent market, how to scientifically and efficiently plan and promote talent development has become a core issue for organizational development.

[0003] In existing technologies, traditional talent planning methods are often static and lack personalization, making it difficult to adapt to rapidly changing environmental needs and individual differences.

[0004] To this end, the invention patent with publication number CN119359267A discloses a talent development path planning method based on multi-dimensional data. The method is characterized by the following steps: Step 1: Calculate the matching degree between employee feature profiles and development path feature profiles using a cosine similarity algorithm, and generate a first development path recommendation list based on the matching degree score; Step 2: Construct colleague development path feature profiles, including initial cluster center selection, distance calculation, probability definition for selecting cluster centers, repeated clustering until multiple professional groups are formed, user neighbor matrix construction, sparsity processing, and neighbor learning path extraction; Step 3: Through comparison of employee... The first step involves analyzing the similarity between the employee's profile and the profiles of their colleagues' career development paths to calculate a user-colleague path matching score. Based on this score, a second career development path recommendation list is generated. The second step integrates the first and second career development path recommendation lists to generate the optimal career path. This includes identifying common elements to generate an intersection A, extracting elements that appear only in one list to generate sets B and C, and sorting the paths in sets B and C based on the weighted fusion score to generate the final recommendation list. The fifth step outputs the reasons for the career development path recommendations, including skill level matching, career interest matching, difficulty gradient matching, and company needs matching. Through multi-dimensional data analysis, the scientific and personalized nature of talent development path planning is improved.

[0005] The above-mentioned technical solutions have made progress in the personalization of planning methods, but the following technical problems still exist: The above-mentioned solutions focus on one-time path matching and recommendation, lacking a phased, progressive planning and dynamic adjustment mechanism for the talent development process; they do not build a closed-loop system for the entire process, and the coordination between various links is insufficient, making it difficult to achieve continuous iterative optimization.

[0006] In view of this, it is very necessary to provide a phased and progressive talent development path planning method and system to solve the above-mentioned defects in the prior art. Summary of the Invention

[0007] The purpose of this invention is to address the lack of a phased, progressive planning and dynamic adjustment mechanism for talent development; the absence of a closed-loop system for the entire process; and the insufficient coordination among various links. In response to the technical deficiencies of the existing technology, this invention provides a phased, progressive talent development path planning method and system to solve the aforementioned technical problems.

[0008] To achieve the above objectives, the present invention provides the following technical solution: Firstly, this invention provides a phased, progressive talent development path planning method, comprising the following steps: Step S1: The step of constructing the initial talent profile, which involves collecting static and dynamic data of talents to generate the initial talent profile; Step S2: The steps for dividing the growth stages, based on the initial talent profile and organizational development goals, are to divide the stages into progressive stages and set capability standards for each stage; Step S3: The step of setting phase goals. Based on the phase capability standards, the goals are broken down into quantifiable and time-limited monthly or quarterly sub-goals using the SMART principle. Step S4: Resource matching and push. Based on the stage goals and initial talent profile, learning, practice and guidance resources are automatically pushed through a three-dimensional matching model of resources-goals-profile. Step S5: The step of capability assessment feedback, which integrates resource usage, performance and manual assessment data, calculates capability achievement score and generates assessment report; Step S6: The steps for dynamically adjusting the path, based on the ability achievement score, determine whether to proceed to the next growth stage; Step S7: Data storage and analysis steps, accumulating data at each stage in real time, generating organizational-level performance reports regularly, and optimizing parameters for each step.

[0009] Secondly, the present invention also provides a phased and progressive talent development path planning system, comprising: The talent profile building module is used to collect and integrate static and dynamic data of talents to generate an initial talent profile. The growth stage segmentation module, based on the initial talent profile and organizational development goals, divides the growth stages into progressive stages and sets capability thresholds; The phase goal setting module is used to break down phase goals into quantifiable, time-limited sub-goal solutions; The resource matching and push module is used to match resources based on the target and profile, and automatically push them to the talent pool. The competency assessment and feedback module is used to assess the achievement of stage goals and generate assessment reports. The path dynamic adjustment module is used to determine the stage progression or trigger path adjustments based on the assessment report; The data storage and analysis module is used to store data throughout the entire process, perform multidimensional analysis, and optimize parameters.

[0010] Each module enables bidirectional data flow and collaborative work through standardized data interfaces, achieving personalized planning, dynamic adjustment, and closed-loop management of talent development paths.

[0011] The beneficial effects of this invention are as follows: This invention constructs an initial talent profile and, in conjunction with organizational development goals, divides talent development into various stages and sets competency standards for each stage. This allows for an orderly and systematic phased approach to talent development, avoiding the lack of clear phased planning in traditional methods and making the talent development process more scientific and practical.

[0012] This invention employs the SMART principle to break down the competency standards for each stage into quantifiable, time-bound monthly or quarterly sub-goals, thereby ensuring that the goals for each growth stage are clear and actionable. In this way, every step of talent development has clearly defined tasks and objectives, effectively improving the accuracy and traceability of talent development.

[0013] This invention establishes a three-dimensional matching model of resources, goals, and profiles. Based on talent profiles and set goals, it automatically pushes relevant learning, practice, and guidance resources. This intelligent push can provide precise support according to the specific needs of talents, improving the efficiency and effectiveness of resource utilization and avoiding resource waste or mismatch problems.

[0014] This invention integrates resource usage data, performance data, and manual evaluation data to assess talent competency achievement and generate detailed evaluation reports. This mechanism ensures timely feedback during talent development, providing data-driven support for next-stage decision-making and ensuring that development paths can be dynamically adjusted based on actual circumstances.

[0015] This invention, based on evaluation feedback, determines whether talent meets the conditions for advancing to the next stage, or whether adjustments to resource allocation and goal setting are necessary. This flexible, dynamic adjustment mechanism effectively avoids the limitations of fixed paths, achieves real-time path adaptation, and overcomes the shortcomings of traditional static planning, enabling talent development paths to be continuously optimized according to actual circumstances, individual differences, and organizational needs.

[0016] This invention accumulates data at each stage in real time and generates organizational-level performance reports periodically, optimizing core parameters of each module. This mechanism enables the talent development process to be continuously optimized through data analysis, improving the overall talent development effectiveness of the organization and enhancing the system's adaptability and long-term planning capabilities.

[0017] In summary, this invention effectively addresses the problems of unclear planning, ambiguous goals, resource mismatch, insufficient evaluation and feedback, lack of progressive stage division, and lagging path adjustment in traditional talent cultivation processes through a phased and progressive talent development path planning method. It also constructs a full-process data iteration system, solving the problem of insufficient coordination among various links, and has significant practical application value.

[0018] Therefore, it is evident that the present invention has outstanding substantive features and significant progress compared with the prior art, and the beneficial effects of its implementation are also obvious. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 It is a flowchart of a phased and progressive talent development path planning method; Figure 2 This is a schematic diagram of the principle of a phased and progressive talent development path system. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following implementation methods.

[0022] Example 1: like Figure 1 As shown in the figure, this embodiment provides a phased and progressive talent development path planning method, which includes the following steps: Step S1: The step of constructing the initial talent profile, which involves collecting static and dynamic data of talents to generate the initial talent profile; Step S2: The steps for dividing the growth stages, based on the initial talent profile and organizational development goals, are to divide the stages into progressive stages and set capability standards for each stage; Step S3: The step of setting phase goals. Based on the phase capability standards, the goals are broken down into quantifiable and time-limited monthly or quarterly sub-goals using the SMART principle. Step S4: Resource matching and push. Based on the stage goals and initial talent profile, learning, practice and guidance resources are automatically pushed through a three-dimensional matching model of resources-goals-profile. Step S5: The step of capability assessment feedback, which integrates resource usage, performance and manual assessment data, calculates capability achievement score and generates assessment report; Step S6: The steps for dynamically adjusting the path, based on the ability achievement score, determine whether to proceed to the next growth stage; Step S7: Data storage and analysis steps, accumulating data at each stage in real time, generating organizational-level performance reports regularly, and optimizing parameters for each step.

[0023] In step S1: static and dynamic data inputs are received from the organization's internal HR system, LMS learning management system, and performance evaluation system. The static and dynamic data are cleaned, structured, and integrated. Combined with data self-reported by talents and supplementary data from third-party assessment tools, an initial talent profile is generated that includes multiple dimensions and is dynamically updated, such as ability level, learning preferences, practical experience, and interest characteristics. The initial talent profile is output in structured data form and transmitted in real time to the growth stage division step as the core basis for stage division. The data is synchronously transmitted to the data storage and analysis step for data storage.

[0024] The static data includes education level, major, years of work experience, and certifications; the dynamic data includes job performance data, skills learning data, project participation data, and career interest survey data; the third-party assessment tools include career personality assessment tools and skills assessment tools.

[0025] By constructing a complete and updatable initial talent profile, growth path planning can be based on individual differences, thereby improving the relevance and accuracy of talent development programs.

[0026] In step S2: Initial talent profile data and organizational development goals are received. The talent's competency status is compared with organizational requirements. Based on a competency threshold system, the talent growth cycle is divided into multiple progressive stages. The current initial stage that the talent is suited for is determined. After the division, the growth stage division results are output, including the talent's current stage label and the competency entry standards and competency target standards for each stage. This output result is fed back to the initial talent profile construction step to update the growth stage labels in the initial talent profile. It is also passed to the stage goal setting step as a basis for stage goal formulation. The data is synchronously transmitted to the data storage and analysis step for data storage.

[0027] Organizational development goals include a job promotion system and skills upgrade standards; the talent lifecycle is divided into multiple progressive stages through a competency threshold system. For technical personnel in enterprises, this can be divided into novice stage, proficient stage, backbone stage, and expert stage; for university students, it can be divided into basic learning stage, professional practice stage, comprehensive application stage, and innovative research stage. The competency threshold system includes setting clear competency entry standards and competency target standards for each stage. For example, the competency entry standard for novice stage personnel is to master basic operational skills, and the competency target standard is to reach the level of independently completing simple tasks.

[0028] This step ensures that the division of growth stages has an objective and quantifiable basis, and that the growth path has a clear progressive logic. It solves the problems of vagueness and inconsistent standards in traditional training stages, and establishes a clear and progressive growth stage model for talents. At the same time, this step provides a two-way transmission mechanism for competency standards and stage rules, so that competency standards can be directly called by the stage goal setting module.

[0029] In step S3: Upon receiving the growth stage segmentation results, the overall stage goal is broken down into quantifiable, time-bound monthly or quarterly sub-goals using the SMART principle. After the sub-goals are formulated into a stage goal plan, it is pushed to the talent and management side for confirmation. Simultaneously, the resource requirement type corresponding to each sub-goal is transmitted to the resource matching and push step. The generated stage goal plan is also fed back to the growth stage segmentation step to verify the match between stage goals and stages. The stage goal setting step periodically obtains resource usage data to monitor goal progress. If progress lags behind, the resource matching and push step is triggered to adjust the resource push priority, prioritizing the push of resources corresponding to the lagging goals. The period for periodically obtaining resource usage data is set to monthly.

[0030] The SMART principle is a methodology for setting clear, actionable goals that ensure they are Specific (S), Measurable (M), Achievable (A), Relevant (R), and Time-bound (T). Specifically, goals should be clear, specific, measurable by quantifiable standards, achievable, relevant to the overall strategy or needs, and have a defined completion deadline. By using the SMART principle, goals become not only clear and specific but also help individuals or teams achieve them more efficiently, improving work efficiency and results.

[0031] Resource needs include skills learning needs and practical needs. The overall stage goal is broken down into sub-goals such as capability enhancement goals, knowledge learning goals, and job practice goals. Goals in adjacent stages are progressive, with each stage's goal building upon the achievement of the previous one. For example, the capability enhancement goal for the proficiency stage can be broken down into mastering three advanced professional skills or completing two cross-departmental collaborative projects within a quarter. Through this process, the stage goal setting module can scientifically set goals for each stage based on the initial development stage of the talent, ensuring that each stage has a clear direction and timeline.

[0032] This step allows for the reasonable breakdown of the overall stage goal into specific and actionable sub-goals, ensuring the feasibility and measurability of the goals. It also provides clear guidance on the resource needs of each stage, helping to improve resource utilization efficiency and goal achievement. Furthermore, it ensures the progressiveness of each stage's goals, making the talent development path clearer and more scientific, avoiding ambiguity or overly high expectations in goal setting, and improving the effectiveness of goal management and the precision of talent development.

[0033] In step S4: the phase target plan and initial talent profile data are received; based on the "resource-target-profile" three-dimensional matching model, the resource demand type corresponding to each sub-target is analyzed, and corresponding learning resources, practical resources, and guidance resources are matched and automatically pushed to the talent end; at the same time, the matching results are fed back to the step of setting phase targets; the talent's resource usage data is transmitted in real time to the step of capability assessment feedback as dynamic data support for subsequent capability assessments; if the step of setting phase targets detects that the target progress is lagging behind, resource matching push can be triggered, the resource push priority is adjusted, and resources corresponding to the lagging targets are pushed first.

[0034] Talent resource utilization data includes course learning time, project participation progress, etc. Matching logic uses a three-dimensional model of "resources-goals-profiles" for decision-making: the resource dimension includes learning resources, practical resources, and mentoring resources; learning resources include online courses, textbooks, and case studies; practical resources include job tasks, project opportunities, and internships; mentoring resources include mentor matching and access to industry expert consultation; the goal dimension includes capability improvement goals, knowledge learning goals, and job practice goals; the profile dimension comes from the static and dynamic data of the initial talent profile. Through comprehensive analysis of the three-dimensional data, optimal matching of resources, goals, and talent characteristics is achieved. For example, for the technical breakthrough goals of core-stage technical personnel, high-difficulty project resources, technical expert mentors, and cutting-edge technology courses can be matched.

[0035] This step ensures that the learning, practice, and guidance resources needed for each stage of the learning process are delivered in a timely and accurate manner, achieving a high degree of matching between resources and goals. At the same time, through feedback mechanisms and real-time resource usage data collection, resource matching strategies can be continuously optimized to improve resource utilization efficiency and goal achievement rate, thereby ensuring the effectiveness and scientific nature of the talent development path.

[0036] In step S5: Data on talent resource utilization and the completion status of stage goals are received. Once a talent completes all sub-goals of the current stage or reaches the stage deadline, a multi-dimensional evaluation of the talent's stage goal achievement is conducted. The evaluation process integrates resource utilization data, performance data, and manual evaluation data to calculate a capability achievement score. After the evaluation, an evaluation report is generated and pushed to the talent end, management end, and the dynamic adjustment path step, providing a basis for subsequent adjustments. The resource utilization effectiveness analysis from the evaluation results is also fed back to the resource matching push step to optimize subsequent resource matching logic. Simultaneously, the capability achievement score and the analysis of reasons for non-achievement are transmitted to the dynamic adjustment path step as the basis for dynamic path adjustment.

[0037] The generated assessment report includes a competency achievement score, the status of achieving the stage goals, reasons for non-achievement, and improvement suggestions. The assessment method combines data-driven and manual evaluation to complete the stage competency assessment. Data-driven evaluation is automatically calculated using learning progress, task completion rate, and performance data; resource usage data includes course learning time and project participation progress; performance data includes job assessments and project results. Manual evaluation data is scored by mentors, superiors, or the assessment command center based on evaluation criteria, including the talent's practical performance and skills assessment results. The competency achievement score is calculated by combining the data-driven evaluation score and the manual evaluation score according to preset weights, with data-driven evaluation accounting for 60%-80% and manual evaluation accounting for 20%-40%.

[0038] This step enables an objective and scientific assessment of the achievement of talent development goals at each stage, allowing for comprehensive monitoring of resource utilization effectiveness, capability enhancement progress, and goal completion. The assessment report not only provides improvement directions for talent but also offers decision support for management and dynamic adjustments to the development path, ensuring that subsequent goal setting and resource allocation are more targeted and improving the effectiveness and accuracy of talent development paths.

[0039] In step S6: Based on the ability achievement score, determine whether the talent meets the conditions for entering the next stage. If the score is greater than or equal to the preset threshold, step S2 is automatically triggered to update the talent's growth stage label and proceed to the next stage of goal setting and planning, i.e., repeating steps S3-S5.

[0040] If the score is less than the preset threshold, analyze the reasons for failure and push adjustment instructions to step S3 or step S4 according to the specific reasons; If the analysis indicates a resource mismatch issue, an adjustment instruction is sent to step S4 to optimize the resource plan and better support talent development. If the issue is related to goal setting, an adjustment instruction is sent to step S3 to re-decompose the goals and ensure they are set more reasonably. After the adjustment, the talent will repeat the learning and practice steps S3-S5.

[0041] The adjusted path plan is fed back to step S5 to update the capability assessment benchmark, which will serve as the basis for updating the benchmark in the next stage of assessment.

[0042] The adjustment rules are as follows: Set assessment result thresholds: A score of ≥80 indicates the ability to proceed to the next stage; if the score is between 60 and 79, an adjustment instruction is sent to step S4 to adjust the resource matching plan and increase targeted tutoring resources; if the score is <60, an adjustment instruction is sent to step S3, requiring the stage goals to be revised.

[0043] This step allows for dynamic assessment of talent's competence, ensuring that goal setting and resource allocation align with talent development needs. It also provides a more accurate reference and foundation for the next stage of evaluation and goal setting, ensuring the flexibility and precision of talent development paths.

[0044] In step S7: the raw and output data from the previous steps are received via a data interface, stored, and their potential value is mined using big data technology to generate analysis results. These results are then pushed back to steps S1-S6 for parameter optimization in each step. For example, step S2 adjusts capability thresholds based on a talent growth trend report, and step S4 optimizes the three-dimensional matching model based on a resource planning effectiveness evaluation report. When generating analysis results, the analytical dimensions to be considered include average growth cycle analysis at each stage, path fit rate analysis for talents with different profiles, and correlation analysis between resource utilization rate and target achievement rate. The above process can also be described as follows: storing and analyzing the initial talent profile and growth stage division results, generating an initial stage analysis report and providing feedback to verify the rationality of the stage division; receiving and synchronously storing resource usage data during the learning and practice stages; storing the assessment report and analyzing the target achievement rate and resource utilization rate after the capability assessment is completed, and outputting a stage effect analysis report; after path adjustment, the module integrates the path adjustment results and stage progression data, updates the talent growth trend analysis model, and continuously accumulates data for each stage in the entire cycle of stage progression.

[0045] Each time an employee completes the planning and evaluation for a growth stage, they move on to the planning process for the next stage, forming "Stage 1". Assessment 1 Phase 2 Assessment 2 … Stage N The evaluation process involves a phased, progressive cycle of "N" assessments. By accumulating data at each stage in real time, organizational-level talent development plan effectiveness reports are generated periodically and fed back to all steps to optimize parameters. The periodic generation of these reports is set at quarterly.

[0046] Big data technology includes big data storage technology, data analysis and mining technology, correlation analysis and parameter optimization technology, periodic automated report generation technology, and dynamic parameter optimization technology based on analysis results. Specifically, big data storage technology is used for centralized management of raw and result data uploaded at each step; data analysis and mining technology is used to generate initial stage analysis reports, stage effect analysis reports, and growth trend analysis models; correlation analysis and parameter optimization technology is used to evaluate resource matching effectiveness and goal achievement; periodic automated report generation technology is used to output organizational-level planning effectiveness reports quarterly; and dynamic parameter optimization technology based on analysis results is used to adjust the capability thresholds and resource matching weight coefficients for growth stage divisions in real time, achieving dynamic iteration throughout the entire cycle.

[0047] This step not only provides centralized storage and comprehensive analysis of data at each stage, but also provides strong support for optimizing parameters at each stage. This helps improve the accuracy and efficiency of talent development, further promotes the organization's long-term development, and ensures that the talent development path is more scientific and reasonable.

[0048] Example 2: like Figure 2 As shown in the figure, this embodiment provides a phased and progressive talent development path planning system, including: The Talent Profile Building Module 1 collects and integrates static and dynamic data on talent to construct a multi-dimensional, dynamically updated initial talent profile. Static data includes education, major, years of work experience, and certifications, while dynamic data includes job performance, skills learning progress, project participation records, and career interest survey results. Data is obtained through interfaces from the organization's internal HR system, learning management system, and performance evaluation system, supplemented by information self-reported by talent and data from third-party career and skills assessment tools. This module provides a realistic, granular, and timely data foundation for career development stage segmentation and goal setting, enabling subsequent planning to accurately match individual talent characteristics.

[0049] Module 2, the talent development stage segmentation module, divides the talent development cycle into multiple progressive stages based on initial talent profiles and organizational development goals. It uses competency thresholds as core indicators, setting competency entry standards and target standards for each stage. The stage format can be tailored to specific application scenarios. For example, for corporate technical talent, it can be divided into novice, proficient, core, and expert stages; for university students, it can be divided into basic learning, professional practice, comprehensive application, and innovative research stages. This module enables precise stratification of different talents, providing a clear structured basis for subsequent goal setting, resource allocation, and development pace.

[0050] Based on the capability thresholds and stage divisions, Module 3 for setting phase goals establishes quantifiable capability improvement goals, knowledge learning goals, and job practice goals for each growth stage. Using the SMART principle, the overall stage goal is broken down into measurable, actionable, and time-bound monthly or quarterly sub-goals, ensuring that the achievement of the current stage goal is a prerequisite for the next stage goal. This module ensures a clear, progressive, and actionable goal system, avoiding learning obstacles caused by vague or disjointed goals, thereby improving the controllability and effectiveness of stage progress.

[0051] The resource matching and push module 4 constructs a three-dimensional matching logic of "resource-goal-profile" based on the stage goals and the initial talent profile. It matches learning resources, practical resources, and guidance resources and automatically pushes them to the talent end to support the completion of stage goals. For example, for the technical breakthrough goals of core technical talents, it matches a combination of high-difficulty project resources, expert mentors, and cutting-edge courses. This module can improve the accuracy and adaptability of resource allocation, significantly enhance the effect of goal achievement, and avoid resource waste.

[0052] The competency assessment feedback module 5 is used to automatically and quantitatively assess learning progress, task completion, and performance at the end of each stage. This is combined with human evaluations from mentors, superiors, or the assessment command center to generate a competency achievement score and improvement suggestions. The competency achievement score is calculated by weighting the data-driven assessment and human evaluation according to preset weights, with data-driven assessment accounting for 60%-80% and human evaluation accounting for 20%-40%. This module can construct an objective and comprehensive competency assessment system that truly reflects the quality of talent development at the current stage, making competency gaps and improvement directions clearly visible.

[0053] The dynamic path adjustment module 6 determines whether a talent should proceed to the next stage based on their ability achievement score. When the score reaches a set threshold, the next stage planning is automatically initiated. If the score is in the middle range, an adjustment instruction is sent to the resource matching module to enhance resource adaptation. If the score is below 60, an instruction is sent to the goal setting module to revise the stage goals or learning and practice plan. This module ensures that the growth path is adjusted in real time according to the talent's actual performance and resource utilization, achieving a continuously iterative dynamic growth mechanism.

[0054] The data storage and analysis module 7 centrally stores initial talent profiles, stage goals, evaluation reports, resource usage data, and path adjustment records. Based on big data analytics, it conducts multi-dimensional assessments, including average growth cycle analysis at each stage, path fit rate analysis for different talent profiles, and correlation analysis between resource utilization and goal achievement rate. It also dynamically optimizes parameters such as capability thresholds and capability achievement scoring weights, and generates quarterly organizational-level talent development plan effectiveness reports. This module provides organizations with data-driven growth insights and decision-making support, enabling dynamic iteration and continuous optimization of each module throughout its entire lifecycle.

[0055] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the systems disclosed in the embodiments; relevant details can be found in the method section.

[0056] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0057] In the embodiments provided by this invention, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0058] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0059] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit.

[0060] Similarly, in the various embodiments of the present invention, each processing unit can be integrated into a functional module, or each processing unit can exist physically, or two or more processing units can be integrated into a functional module.

[0061] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0062] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0063] The above-disclosed embodiments are merely preferred embodiments of the present invention, but the present invention is not limited thereto. Any non-creative variations that can be conceived by those skilled in the art, as well as any improvements and modifications made without departing from the principles of the present invention, should fall within the protection scope of the present invention.

Claims

1. A phased and progressive talent development path planning method, characterized in that, Includes the following steps: Step S1: The step of constructing the initial talent profile, which involves collecting static and dynamic data of talents to generate the initial talent profile; Step S2: The steps for dividing the growth stages, based on the initial talent profile and organizational development goals, are to divide the stages into progressive stages and set capability standards for each stage; Step S3: The step of setting phase goals. Based on the phase capability standards, the goals are broken down into quantifiable and time-limited monthly or quarterly sub-goals using the SMART principle. Step S4: Resource matching and push. Based on the stage goals and initial talent profile, learning, practice and guidance resources are automatically pushed through a three-dimensional matching model of resources-goals-profile. Step S5: The step of capability assessment feedback, which integrates resource usage, performance and manual assessment data, calculates capability achievement score and generates assessment report; Step S6: The steps for dynamically adjusting the path, based on the ability achievement score, determine whether to proceed to the next growth stage; Step S7: Data storage and analysis steps, accumulating data at each stage in real time, generating organizational-level performance reports regularly, and optimizing parameters for each step.

2. The phased and progressive talent development path planning method according to claim 1, characterized in that, In step S1: static and dynamic data inputs are received from the organization's internal HR system, LMS learning management system, and performance evaluation system. The static and dynamic data are cleaned, structured and integrated, and combined with the data self-reported by talents and supplementary data from third-party assessment tools to generate an initial talent profile. The static data includes education level, major, years of work experience, and certifications; the dynamic data includes job performance data, skills learning data, project participation data, and career interest survey data; the third-party assessment tools include career personality assessment tools and skills assessment tools.

3. The phased and progressive talent development path planning method according to claim 1 or 2, characterized in that, In step S2: Initial talent profile data and organizational development goals are received; the talent's ability status is compared with organizational requirements; the talent growth cycle is divided into multiple progressive stages based on the ability threshold system; the current initial stage of the talent is determined; after the division is completed, the growth stage division result is output, which includes the talent's current stage label and the ability access standards and ability target standards for each stage; this output result is fed back to the initial talent profile construction step to update the growth stage labels in the initial talent profile, and is also transmitted to the stage goal setting step as the basis for stage goal setting; the data is synchronously transmitted to the data storage and analysis step to store the data. Organizational development goals include a job promotion system and skills upgrade standards; the talent lifecycle is divided into multiple progressive stages through a competency threshold system; the competency threshold system includes setting clear competency entry standards and competency target standards for each stage.

4. The phased and progressive talent development path planning method according to claim 3, characterized in that, In step S3: the growth stage division results are received, and the overall stage goal is broken down into quantifiable, time-limited monthly or quarterly sub-goals using the SMART principle. After the sub-goals are formed into a stage goal plan, it is pushed to the talent and management end for confirmation. The resource requirement type corresponding to each sub-goal is transmitted to the resource matching and push step. The generated stage goal plan is fed back to the growth stage division step to verify the matching between the stage goal and the stage. The stage goal setting step regularly obtains resource usage data and monitors the goal progress. If the progress is lagging, the resource matching and push step is triggered to adjust the resource push priority, prioritizing the push of resources corresponding to the lagging goals. The period for regularly acquiring resource usage data is set to monthly. The types of resource needs include skills learning needs and practical needs. The sub-goals of the overall stage goal include capability improvement goals, knowledge learning goals, and job practice goals. Furthermore, the goals of adjacent stages are progressive, with the goals of the next stage based on the achievement of the goals of the previous stage.

5. The phased and progressive talent development path planning method according to claim 4, characterized in that, In step S4: the phase target plan and initial talent profile data are received. Based on the "resource-goal-profile" three-dimensional matching model, the resource demand type corresponding to each sub-goal is analyzed, and corresponding learning resources, practical resources, and guidance resources are matched and automatically pushed to the talent end. At the same time, the matching results are fed back to the step of setting phase targets. The talent's resource usage data is transmitted to the capability assessment feedback step in real time. If the step of setting phase targets detects that the target progress is lagging behind, the resource matching and push module can be triggered to adjust the resource push priority, prioritizing the push of resources corresponding to the lagging targets. Among them, the resource dimension includes learning resources, practical resources, and guidance resources. Learning resources include online courses, textbooks, and case studies. Practical resources include job tasks, project opportunities, and internship positions. Guidance resources include mentor matching and industry expert consultation channels. The target dimension includes capability improvement targets, knowledge learning targets, and job practice targets. The profile dimension comes from the static and dynamic data of the initial talent profile.

6. The phased and progressive talent development path planning method according to claim 5, characterized in that, In step S5: receiving talent resource usage data and stage goal completion status; when a talent completes all sub-goals of the current stage or reaches the stage deadline, conducting a multi-dimensional evaluation of the talent's stage goal achievement; during the evaluation process, integrating resource usage data, performance data, and manual evaluation data, and finally calculating the capability achievement score; after the evaluation is completed, generating an evaluation report, and pushing it to the talent end, management end, and dynamic adjustment path step respectively; also feeding back the resource usage effect analysis in the evaluation results to the resource matching and pushing step; and simultaneously transmitting the capability achievement score and the analysis of reasons for non-achievement to the dynamic adjustment path step. The generated assessment report includes a competency achievement score, the status of achievement of stage goals, reasons for non-achievement, and improvement suggestions. The assessment method combines data-driven and manual assessment to complete the stage competency assessment. Data-driven assessment is automatically calculated through learning progress, task completion rate, and performance data; resource usage data includes course learning time and project participation progress, and performance data includes job assessments and project results; manual assessment data is scored by mentors, superiors, or the assessment command center based on assessment criteria, including the talent's practical performance and skills assessment results. The competency achievement score is calculated by combining the data-driven assessment score and the manual assessment score according to preset weights, with data-driven assessment accounting for 60%-80% and manual assessment accounting for 20%-40%.

7. The phased and progressive talent development path planning method according to claim 6, characterized in that, In step S6: Based on the ability achievement score, determine whether the talent meets the conditions for entering the next stage. If the score is greater than or equal to the preset threshold, step S2 will be automatically triggered to update the talent's growth stage label and proceed to the next stage of goal setting and planning. If the score is less than the preset threshold, analyze the reasons for failure and push adjustment instructions to step S3 or step S4 according to the specific reasons; If the analysis result indicates a resource adaptation problem, an adjustment instruction is pushed to step S4 to optimize the resource plan; if the problem is a target setting problem, an adjustment instruction is pushed to step S3 to re-decompose the target. After the adjustment, the talent will repeat the learning and practice in steps S3-S5; the adjusted path plan is fed back to step S5 to update the capability assessment benchmark.

8. The phased and progressive talent development path planning method according to claim 7, characterized in that, In step S7: the raw data and output data from the above steps are received through the data interface, stored, and their potential value is mined using big data technology to generate analysis results. The analysis results are then pushed back to steps S1-S6 for parameter optimization in each step. When generating analysis results, the analysis dimensions to be considered include average growth cycle analysis at each stage, path adaptation rate analysis for talents with different profiles, and correlation analysis between resource utilization rate and target achievement rate.

9. A phased, progressive talent development path planning system, characterized in that, include: Talent profile construction module (1), growth stage division module (2), stage goal setting module (3), resource matching and push module (4), ability assessment feedback module (5), path dynamic adjustment module (6), data storage and analysis module (7); The talent profile construction module (1) is used to collect and integrate static and dynamic data of talents to generate an initial talent profile; The growth stage division module (2) divides progressive growth stages and sets capability thresholds based on the initial talent profile and organizational development goals; The stage target setting module (3) is used to decompose the stage target into quantifiable and time-limited sub-target schemes; The resource matching and push module (4) is used to match relevant resources with talent profiles according to stage goals and automatically push them to the talent end; The capability assessment feedback module (5) is used to assess the achievement of stage goals and generate an assessment report; The path dynamic adjustment module (6) is used to determine the stage progression and trigger path adjustment based on the evaluation report; The data storage and analysis module (7) is used to store the data of the entire process, perform multidimensional analysis, and optimize parameters.

10. A phased, progressive talent development path planning system according to claim 9, characterized in that, The talent profile construction module (1) collects and integrates static and dynamic data of talents to construct a multi-dimensional and dynamically updated initial talent profile; among which, static data includes education, major, years of work experience, and certificate qualifications, and dynamic data includes job performance, skills learning status, project participation records and career interest survey results. The data is obtained through the data interfaces of the organization's internal HR system, learning management system and performance evaluation system, and is supplemented by information filled in by talents themselves and data from third-party career assessment tools and skills assessment tools. The growth stage division module (2) divides the talent growth cycle into multiple progressive stages based on the initial talent profile and organizational development goals, and sets capability access standards and capability target standards for each stage with capability threshold as the core indicator; the stage form can be constructed according to the application scenario. Based on the capability threshold and stage division, the stage goal setting module (3) sets quantifiable capability improvement goals, knowledge learning goals and job practice goals for each growth stage, and uses the SMART principle to break down the overall stage goal into monthly or quarterly sub-goals that are measurable, executable and time-limited, so that the achievement of the current stage goal is a prerequisite for the next stage goal. The resource matching and push module (4) constructs a three-dimensional matching logic of "resource-goal-profile" based on the stage goals and the initial talent profile, matches learning resources, practice resources and guidance resources, and automatically pushes them to the talent end to support the completion of stage goals; The capability assessment feedback module (5) is used to conduct automated quantitative assessment based on learning progress, task completion, and performance data after each stage, and at the same time, combined with the manual evaluation of the tutor, superior or assessment command center, to form a capability achievement score and improvement suggestions. The competency achievement score is calculated by weighting data-driven assessment and manual assessment according to preset weights, with data-driven assessment accounting for 60%-80% and manual assessment accounting for 20%-40%. The path dynamic adjustment module (6) is used to determine whether a talent should enter the next stage based on the ability achievement score. When the score reaches the set threshold, the next stage planning will be started automatically. If the score is in the middle range, the adjustment instruction will be pushed to the resource matching module to enhance resource adaptation. If the score is below 60 points, the instruction will be pushed to the target setting module to re-formulate the stage goal or learning practice plan. The data storage and analysis module (7) is used to centrally store initial talent profiles, stage goals, evaluation reports, resource usage data and path adjustment records. Based on big data analysis technology, it conducts multi-dimensional evaluations including average growth cycle analysis at each stage, path adaptation rate analysis for talents with different profiles, and correlation analysis between resource usage rate and goal achievement rate. At the same time, it dynamically optimizes the capability threshold and capability achievement score weight parameters, and generates an organizational-level talent growth plan effect report on a quarterly basis.

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