A scientific and technological talent long-term incentive retention management method and system based on digitized five-layer demand hierarchical matching

Abstract

In view of the technical defects of the existing scientific and creative talent incentive management, such as homogeneity, staticity, low matching degree and poor long-term effect, the present application provides a scientific and creative talent long-term incentive retention management method and system based on digital five-layer demand hierarchical matching, which realizes accurate hierarchical identification of the five-layer demands of scientific and creative staff through digital modeling, dynamically matches individualized, differentiated and hierarchical incentive schemes, completely solves the industry pain points of traditional incentive mispositioning, talent loss and insufficient innovation motivation, continuously activates the innovation potential of scientific and human capital, stabilizes the high-end research and development team, and consolidates the foundation of new human productivity of enterprises.

Description

Technical Field

[0001] This invention belongs to the fields of digital management of human capital for new quality productivity, intelligent incentives for scientific and technological innovation talents, retention of high-end R&D talents, and enterprise innovation human resource management technology. Specifically, it involves a digital intelligent management method and system based on a digital Maslow's hierarchy of needs model, which quantitatively identifies, dynamically classifies, and precisely matches incentives to the five levels of needs of scientific and technological innovation employees, thereby achieving long-term retention of high-end scientific and technological innovation talents and continuous activation of their innovation potential. It is suitable for long-term talent strategy management scenarios in cutting-edge technology R&D, patent tackling, and technology innovation teams in the information management industry. Background Technology

[0002] Currently, the information management industry generally adopts a standardized and rigid salary incentive model for scientific and technological innovation talents and high-end R&D talents. This model primarily relies on fixed salaries, project bonuses, and uniform benefits, resulting in serious homogenization and inefficient management. Traditional talent incentive systems fail to accurately identify the individualized needs of different R&D personnel at different stages and levels. They focus solely on material compensation, neglecting implicit needs such as job security, team belonging and recognition, professional dignity and respect, and self-actualization. This easily leads to problems such as misaligned incentives for high-end scientific and technological innovation talents, ineffective incentives, and the loss of core talents.

[0003] Existing technological solutions lack the ability to digitally quantify and classify the multidimensional needs of science and technology innovation talents, failing to achieve dynamic and precise matching of incentive resources with the personalized needs of talents. While short-term incentives are effective, long-term retention is weak, making it difficult to continuously activate the intrinsic innovation drive of highly skilled talents. For leading information technology companies that are planning for cutting-edge technology breakthroughs over the next 3-5 years, continuously cultivating new-quality human productivity, and stabilizing core science and technology innovation teams, traditional, experience-based, one-size-fits-all talent management models are unsuitable for the high-quality, long-term, and sustainable innovation development needs of human capital under the new-quality productivity system. The industry urgently needs a smart incentive and management system for science and technology innovation talents that can be digitally assessed, stratified, precisely matched, and effectively empowered in the long term. Summary of the Invention

[0004] I. Purpose of the invention.

[0005] To address the shortcomings of existing science and technology talent incentive management systems, such as homogenization, static nature, low matching degree, and poor long-term effectiveness, this invention provides a method and system for long-term incentive retention management of science and technology talents based on digital five-level demand hierarchical matching. Through digital modeling, it achieves accurate hierarchical identification of the five levels of needs of science and technology employees, dynamically matches personalized, differentiated, and hierarchical incentive schemes, and completely solves the industry pain points of traditional incentive misalignment, talent loss, and insufficient innovation motivation. It continuously activates the innovation potential of science and technology human capital, stabilizes high-precision R&D teams, and consolidates the foundation of new-quality human productivity for enterprises.

[0006] II. Technical Solution.

[0007] 1. A long-term incentive and retention management method for science and technology innovation talents based on digital five-layer demand hierarchical matching, characterized by the following steps: S1. Construct a digital five-layer demand assessment model adapted to the scientific and technological innovation R&D scenario, conduct full-dimensional digital assessment of scientific and technological innovation employees, quantitatively and hierarchically identify the five core demands of employees: physiological security demand, job safety demand, team belonging demand, professional respect demand, and self-worth realization demand, and generate a personalized demand level profile and demand weight distribution data for each individual. S2. Establish a dynamic matching and mapping mechanism between five levels of needs and science and technology innovation incentive resources. Based on the personalized needs profile of employees, accurately match the corresponding level of salary and benefits guarantee, job promotion channel, R&D platform empowerment, patent innovation dividends and long-term growth empowerment differentiated incentive combination schemes. S3. Implement a tiered and precise personalized science and technology innovation incentive strategy, allocate appropriate incentive resources for R&D talents at different demand levels, accurately address the shortcomings in the core needs of talents, stimulate the intrinsic innovation motivation of science and technology talents, strengthen employees' sense of job identity and professional belonging, and stabilize the high-end science and technology innovation R&D team. S4. Establish a dynamic iteration and incentive closed-loop optimization system for talent demand, track changes in employee demand levels, R&D innovation output, and job growth status in real time, dynamically update demand profiles and incentive matching schemes, continuously activate the innovation potential of science and technology human capital, retain core science and technology talents in the long term, and consolidate the company's new-quality human productivity and continuous innovation capabilities.

[0008] 2. The method according to claim 1, characterized in that the five-level digital demand assessment model in S1 is a customized Maslow's hierarchy of digitalization quantification model for science and technology innovation scenarios, and the specific hierarchical quantification method includes: For physiological security needs, a digital scoring and quantification is conducted based on salary, benefits, workload, and basic rights; for job safety needs, a quantitative assessment is conducted based on job stability, R&D resource guarantees, project fault tolerance mechanisms, and occupational risk protection; for team belonging needs, a weighted quantification is conducted based on team collaboration atmosphere, internal coordination mechanisms, talent care system, and team identification; for professional respect needs, a hierarchical quantification is conducted based on job level, innovation honors, patent recognition, and industry influence; and for self-worth realization needs, a higher-level quantification is conducted based on technological growth space, access to cutting-edge technology breakthroughs, patent layout rights, and channels for the transformation of personal scientific and technological achievements, ultimately forming a quantitative hierarchical profile from 0 to 100 points.

[0009] 3. The method according to claim 1, characterized in that the dynamic matching mapping mechanism in S2 constructs multi-layer one-to-one adaptation rules: For groups prioritizing physiological needs, incentives include flexible compensation, special benefits, and upgraded basic protections; for groups prioritizing job safety, incentives include stable R&D resource supply, project risk mitigation, and job rights protection; for groups prioritizing team belonging, incentives include team building mechanisms, care for scientific and technological talents, and collaborative empowerment resources; for groups prioritizing professional respect, incentives include promotion opportunities, innovation honors, patent authorship, and industry exposure; and for groups prioritizing self-worth, incentives include access to cutting-edge technology research, independent R&D projects, patent profit sharing, and the ability to commercialize scientific and technological achievements.

[0010] 4. The method according to claim 1, characterized in that the tiered precise incentive strategy in S3 adopts a personalized incentive model of "one person, one policy, dynamic adaptation", which focuses on basic security incentives for grassroots R&D personnel, on career advancement and belonging incentives for backbone personnel, and on value realization and innovation dividend incentives for core high-end talents, thereby eliminating the problems of wasted homogeneous incentive resources and incentive failure.

[0011] 5. The method according to claim 1, characterized in that the dynamic iterative closed-loop optimization system in S4 uses a cycle mechanism of monthly demand assessment, quarterly incentive review, and annual growth iteration to capture the leap in employee demand levels, the improvement of innovation capabilities, and changes in job requirements in real time, automatically update the demand profile and iterate the incentive matching scheme, so as to achieve long-term dynamic adaptation of incentive resources and talent status.

[0012] 6. The method according to claim 1, wherein the method is adapted to the talent management scenarios of cutting-edge technology R&D teams in the information management industry, such as AI intelligent agents, trusted data spaces, data governance, and cloud-native architecture, and is used for the digital management and control of enterprises' 3-5 year science and technology innovation talent strategic layout, core R&D team retention, and cultivation of new quality human productivity.

[0013] 7. A long-term incentive and retention management system for scientific and technological innovation talents based on digital five-layer demand hierarchical matching, characterized in that it is used to execute the method according to any one of claims 1-6, comprising: The five-layer demand digital assessment module is used to quantify and classify the multi-level demands of science and technology innovation employees and generate personalized talent demand profiles. The incentive resource dynamic matching module is used to establish the mapping relationship between demand and incentive resources, and output personalized incentive combination schemes; The tiered science and technology innovation incentive implementation module is used to implement differentiated, tiered, and personalized talent incentive strategies. The talent dynamic iteration management module is used to achieve closed-loop optimization of talent demand and incentive schemes throughout the entire lifecycle.

[0014] III. Beneficial Effects.

[0015] Compared with existing technologies, this invention possesses outstanding novelty, inventiveness, and practicality, forming a complete technological barrier for digital talent management: First, it pioneers a digital Maslow's hierarchy of needs assessment model for scientific and technological innovation talents, achieving full-dimensional, quantitative, and hierarchical identification of the subjective needs of these talents, filling the technological gap in the digital assessment of personalized needs for high-end scientific and technological innovation talents in the industry; Second, it constructs a precise and dynamic matching mechanism between the five-level needs and multi-dimensional incentive resources, breaking the traditional homogeneous incentive model and achieving precise "one-person-one-policy" incentives for scientific and technological innovation, significantly improving the utilization rate of incentive resources and talent satisfaction; Third, it establishes... Fourth, we focus on cultivating new-quality human productivity, continuously activating the innovative potential of high-end scientific and technological innovation teams, stabilizing the core R&D strength of enterprises, and forming a complete new-quality productivity digital management patent barrier system integrating "resource allocation - risk prevention and control - talent empowerment" with patent 14 resource risk control and patent 15 long-term risk simulation. This system is perfectly adapted to the technological innovation strategy and talent strategy layout of leading information management enterprises in the next 3-5 years. Attached Figure Description

[0016] Figure 1 This is a flowchart of the overall method of the present invention.

[0017] Figure 2 This is a diagram of the architecture of the digital five-layer demand hierarchical evaluation model of the present invention.

[0018] Figure 3 This is a schematic diagram of the dynamic and precise matching logic of the requirements-incentives of this invention.

[0019] Abstract and Figure Selection Figure 1 . Detailed Implementation

[0020] This invention discloses a long-term incentive and retention management method for science and technology innovation talents based on a five-layer digital demand-based hierarchical matching system. This method is suitable for the long-term management scenarios of cutting-edge technology science and technology innovation teams in the information management industry. The specific implementation steps are as follows: First, for science and technology R&D personnel in cutting-edge technology tracks such as enterprise AI intelligent agent R&D, trusted data space construction, full-domain data governance, and cloud-native architecture iteration, a digital assessment system is used to collect basic employee rights data, job data, team collaboration data, career development needs, and innovation achievement data. These data are then fed into a customized five-layer demand digital assessment model to quantify and calculate the scores and weights of five major needs: physiological security, job safety, team belonging, professional respect, and self-worth. This generates a unique demand level profile and core demand tags for each science and technology talent.

[0021] Secondly, the dynamic matching module for incentive resources is invoked to accurately match talent needs based on their hierarchical profiles. Employees with weak basic needs are given priority in receiving basic incentive resources such as salary and benefits and job security. For key employees with strong needs for belonging and respect, growth incentive resources such as promotion, team empowerment, and innovation honors are provided. For core top-notch R&D talents, high-value incentive resources such as access to cutting-edge technology breakthroughs, patent innovation dividends, and empowerment for the industrialization of research results are provided, forming a differentiated and personalized incentive combination plan.

[0022] Furthermore, we will implement a tiered and categorized incentive strategy for scientific and technological innovation, abandoning the traditional uniform incentive model. We will precisely address the incentive shortcomings for scientific and technological talents at different levels and with different needs, enhance employees' sense of belonging to their positions, professional identity, and sense of achievement in innovation, fully stimulate the intrinsic innovation motivation of talents, and stabilize the core high-precision R&D team.

[0023] Finally, we will launch a full-cycle dynamic iteration closed-loop mechanism to regularly update talent demand data, innovation output data, and career growth data, dynamically revise the talent demand hierarchy profile and incentive matching scheme, continuously adapt to the growth and leap of scientific and technological talents, effectively activate the innovation potential of human capital, continuously consolidate the new quality human productivity of enterprises, and provide core talent guarantee for enterprises' long-term cutting-edge technology research, patent innovation layout, and high-quality development of new quality productivity.

[0024] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A long-term incentive and retention management method for scientific and technological innovation talents based on digital five-layer demand hierarchical matching, characterized in that, Includes the following steps: S1. Construct a digital five-layer demand assessment model adapted to the scientific and technological innovation R&D scenario, conduct full-dimensional digital assessment of scientific and technological innovation employees, quantitatively and hierarchically identify the five core demands of employees: physiological security demand, job safety demand, team belonging demand, professional respect demand, and self-worth realization demand, and generate a personalized demand level profile and demand weight distribution data for each individual. S2. Establish a dynamic matching and mapping mechanism between five levels of needs and science and technology innovation incentive resources. Based on the personalized needs profile of employees, accurately match the corresponding level of salary and benefits guarantee, job promotion channel, R&D platform empowerment, patent innovation dividends and long-term growth empowerment differentiated incentive combination schemes. S3. Implement a tiered and precise personalized science and technology innovation incentive strategy, allocate appropriate incentive resources for R&D talents at different demand levels, accurately address the shortcomings in the core needs of talents, stimulate the intrinsic innovation motivation of science and technology talents, strengthen employees' sense of job identity and professional belonging, and stabilize the high-end science and technology innovation R&D team. S4. Establish a dynamic iteration and incentive closed-loop optimization system for talent demand, track changes in employee demand levels, R&D innovation output, and job growth status in real time, dynamically update demand profiles and incentive matching schemes, continuously activate the innovation potential of science and technology human capital, retain core science and technology talents in the long term, and consolidate the company's new-quality human productivity and continuous innovation capabilities.

2. The method according to claim 1, characterized in that, The S1 five-level digital demand assessment model is a customized Maslow's hierarchy of digitalization quantification model for science and technology innovation scenarios. Specific hierarchical quantification methods include: For physiological security needs, a digital scoring and quantification is conducted based on salary, benefits, workload, and basic rights; for job safety needs, a quantitative assessment is conducted based on job stability, R&D resource guarantees, project fault tolerance mechanisms, and occupational risk protection; for team belonging needs, a weighted quantification is conducted based on team collaboration atmosphere, internal coordination mechanisms, talent care system, and team identification; for professional respect needs, a hierarchical quantification is conducted based on job level, innovation honors, patent recognition, and industry influence; and for self-worth realization needs, a higher-level quantification is conducted based on technological growth space, access to cutting-edge technology breakthroughs, patent layout rights, and channels for the transformation of personal scientific and technological achievements, ultimately forming a quantitative hierarchical profile from 0 to 100 points.

3. The method according to claim 1, characterized in that, S2's dynamic matching mapping mechanism constructs multi-layered one-to-one adaptation rules: For groups prioritizing physiological needs, incentives include flexible compensation, special benefits, and upgraded basic protections; for groups prioritizing job safety, incentives include stable R&D resource supply, project risk mitigation, and job rights protection; for groups prioritizing team belonging, incentives include team building mechanisms, care for scientific and technological talents, and collaborative empowerment resources; for groups prioritizing professional respect, incentives include promotion opportunities, innovation honors, patent authorship, and industry exposure; and for groups prioritizing self-worth, incentives include access to cutting-edge technology research, independent R&D projects, patent profit sharing, and the ability to commercialize scientific and technological achievements.

4. The method according to claim 1, characterized in that, The S3 tiered and precise incentive strategy adopts a personalized incentive model of "one policy for one person and dynamic adaptation". It focuses on basic security incentives for grassroots R&D personnel, career advancement and belonging incentives for backbone personnel, and value realization and innovation dividend incentives for core high-end talents, thus avoiding the waste of homogeneous incentive resources and the problem of incentive failure.

5. The method according to claim 1, characterized in that, The dynamic iterative closed-loop optimization system in S4 uses a cyclical mechanism of monthly demand assessment, quarterly incentive review, and annual growth iteration to capture in real time the leap in employee demand levels, the improvement of innovation capabilities, and changes in job requirements. It automatically updates the demand profile and iterates the incentive matching scheme to achieve long-term dynamic adaptation between incentive resources and talent status.

6. The method according to claim 1, characterized in that, The method is adapted to talent management scenarios for cutting-edge technology R&D teams in the information management industry, such as AI intelligent agents, trusted data spaces, data governance, and cloud-native architecture. It can be used for the digital management and control of enterprises' 3-5 year science and technology innovation talent strategic layout, core R&D team retention, and cultivation of new human productivity.

7. A long-term incentive and retention management system for scientific and technological innovation talents based on digital five-layer demand hierarchical matching, characterized in that, For performing the method according to any one of claims 1-6, comprising: The five-layer demand digital assessment module is used to quantify and classify the multi-level demands of science and technology innovation employees and generate personalized talent demand profiles. The incentive resource dynamic matching module is used to establish the mapping relationship between demand and incentive resources, and output personalized incentive combination schemes; The tiered science and technology innovation incentive implementation module is used to implement differentiated, tiered, and personalized talent incentive strategies. The talent dynamic iteration management module is used to achieve closed-loop optimization of talent demand and incentive schemes throughout the entire lifecycle.

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