Scientific and technological innovation management system based on big data analysis

Abstract

The invention discloses a scientific and technological innovation management system based on big data analysis, which relates to the technical field of big data services and comprises a data collection module, a processing module, an analysis module, a calculation management module and an identification module. According to the method, factors such as professional backgrounds and working experience of the scientific researchers can be comprehensively considered, the efficiency of the scientific researchers can be evaluated more accurately, managers can better understand the actual ability and contribution of the scientific researchers, reasonable allocation of scientific research project resources can be achieved through optimization analysis, and the scientific research project efficiency can be improved. The scientific research project can obtain enough resource support, waste and idleness of resources are avoided, the efficiency of researchers, resource configuration of the scientific research project and the actual utilization condition of the scientific research resources can be comprehensively considered, the comprehensive utilization rate of the scientific research resources is evaluated, and the scientific research project resource utilization rate is improved. Managers can understand the overall utilization efficiency and existing problems of scientific research resources, and a scientific basis is provided for further optimal configuration and efficient utilization of the resources.

Description

Technical Field

[0001] The present invention relates to the field of big data service technology, and specifically to a scientific and technological innovation management system based on big data analysis. Background Art

[0002] With the rapid development of information technology, big data analysis technology has become an important means to promote scientific and technological innovation management. By deeply mining and analyzing massive data, big data technology can reveal the hidden laws and trends behind the data, and provide scientific basis and decision-making support for scientific and technological innovation management. In the field of scientific research project management, the application of big data analysis technology is particularly critical. It can help managers more accurately evaluate the efficiency of scientific researchers and optimize the resource allocation of scientific research projects, thereby improving the quality of scientific research and the output of results.

[0003] However, in the current scientific and technological innovation management methods, most people involved in scientific research projects may complete their scientific research tasks on a part-time basis, and only a few project leaders can guarantee full-time commitment. This phenomenon of part-time scientific research prevents technical personnel from fully devoting themselves to scientific research, which greatly reduces the quality and results of scientific research. At the same time, large-scale scientific research facilities and instruments lack unified management and effective sharing, resulting in a waste of scientific research resources. Summary of the invention

[0004] The purpose of the present invention is to provide a scientific and technological innovation management system based on big data analysis, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a scientific and technological innovation management system based on big data analysis, comprising: Data collection module: collects the number of papers, number of projects, years of work and proportion of time spent on scientific research of each researcher from the database, and inputs the collected data into the processing module; Processing module: clean the data of each researcher’s number of papers, number of projects, years of work, and proportion of time spent on scientific research to remove duplicate and invalid data, then convert data in different formats into a unified format and store the processed data; Analysis module: Input the number of papers, number of projects, years of work and proportion of time spent on scientific research of each researcher after processing by the processing module, and output the professional background score ELOA of the i-th researcher i , the research background score of the i-th researcher ELOB i , the work experience score of the i-th researcher ELOC i and the time ratio of the i-th researcher to research ELOD i ; Calculation management module: calculate the professional background score ELOA of the i-th researcheri , the research background score of the i-th researcher ELOB i , the work experience score of the i-th researcher ELOC i and the time ratio of the i-th researcher to research ELOD i Input, output the efficiency evaluation value ELO of the i-th researcher i , the optimal value of resource allocation for the jth scientific research project RDG j and the comprehensive utilization rate of scientific research resources (UCD); Identification module: The efficiency evaluation value ELO of the i-th researcher i , the optimal value of resource allocation for the jth scientific research project RDG j and comprehensive utilization of scientific research resources UCD input, identification module for ELO i ,RDG j By comparing and analyzing with UCD, efficient and inefficient scientific researchers can be identified, problems with irrational resource allocation, and the reasons for low utilization of scientific research resources can be discovered. Then, based on the analysis results, the task allocation of scientific researchers can be adjusted, resource allocation can be optimized, and resource utilization efficiency can be improved.

[0006] Optionally, the computing management module includes: a personnel efficiency analysis submodule, a project resource analysis submodule and a comprehensive resource utilization submodule.

[0007] Optionally, the calculation formula of the personnel efficiency analysis submodule is as follows: ; in: ELO i refers to the efficiency evaluation value of the i-th researcher, EA refers to the weight coefficient of the professional background score, EB refers to the weight coefficient of the scientific research background score, EC refers to the weight coefficient of the work experience score, ED refers to the weight coefficient of the combined score of the time ratio and the scientific research output rate, ELOA i Refers to the professional background score of the i-th researcher, ELOB i Refers to the research background score and ELOC of the i-th researcher i Refers to the work experience score and ELOD of the i-th researcher i Refers to the proportion of time that the i-th researcher devotes to scientific research, ELOE i Refers to the research output rate of the i-th researcher, EC×ELOC i Refers to the contribution value of work experience score, reflecting the importance of work experience in the efficiency evaluation of researchers (EA×ELOA i +EB×ELOB i ) refers to the contribution of the professional and scientific research background scores of researchers to the efficiency evaluation value; The processing process of the personnel efficiency analysis submodule is as follows: the professional background score ELOA of the i-th researcher is calculated as follows: i , the research background score of the i-th researcher ELOB i , the work experience score of the i-th researcher ELOC i and the time ratio of the i-th researcher to research ELOD i Input into the personnel efficiency analysis submodule, and based on the research output rate ELOE of the i-th researcher i Output the efficiency evaluation value ELO of the i-th researcher i .

[0008] Optionally, the calculation formula of the project resource analysis submodule is as follows: ; in: RDG j Refers to the resource allocation optimization value of the jth scientific research project, PD j Refers to the set of researchers participating in the jth scientific research project, RDGA i，j Refers to the task allocation coefficient of the i-th researcher in the j-th project, RDGB i，j Refers to the resource demand coefficient and ELO of the i-th researcher in the j-th project i ×RDGA i，j Refers to the contribution of researchers to the optimization of project resource allocation, Refers to the sum of the contribution values ​​of all researchers participating in the project to the optimization of project resource allocation, which is used to normalize the contribution values ​​to ensure the rationality and comparability of the resource allocation optimization value RDGj; The processing process of the project resource analysis submodule is as follows: the efficiency evaluation value ELO of the i-th researcher is i Input to the project resource analysis submodule, and based on the set of researchers involved in the jth scientific research project PD j and the task allocation coefficient RDGA of the i-th researcher in the j-th project i，j Output the resource allocation optimization value RDG of the jth scientific research project j .

[0009] Optionally, the calculation formula of the comprehensive resource utilization submodule is as follows: ; in: UCD refers to the comprehensive utilization rate of scientific research resources, UCDA j Refers to the scale of the jth scientific research project, UCDB jRefers to the resource efficiency actually used by the jth scientific research project, UCDC j Refers to the research resource cost and ELO required by the i-th researcher i ×UCDC j Refers to the contribution of researchers to the use of scientific research resources. Refers to the sum of the contribution values ​​of all scientific researchers and scientific research projects to the utilization of scientific research resources, and is used to normalize the contribution values; The processing process of the comprehensive resource utilization submodule is as follows: the efficiency evaluation value ELO of the i-th researcher is i and the optimal resource allocation value RDG of the jth scientific research project j Input to the comprehensive resource utilization submodule, based on the research resource cost UCDC required by the i-th researcher j Output the comprehensive utilization rate of scientific research resources (UCD).

[0010] Optionally, the processing process of the analysis module is as follows: Input the number of papers, number of projects, years of work experience and the proportion of time spent on research for each researcher into the analysis module; The professional background score ELOA for the i-th researcher i ; If the number of papers published by the i-th researcher is less than 3, then ELOA i The output is 0; If the number of papers published by the i-th researcher is greater than or equal to 3 and less than 10, then ELOA i The output is 5; If the number of papers published by the i-th researcher is greater than or equal to 10, then ELOA i The output is 10.

[0011] Optionally, the research background score of the i-th researcher is ELOB i The processing process is as follows: If the number of scientific research projects participated by the i-th researcher is less than 2, then ELOB i The output is 0; If the number of scientific research projects participated by the i-th researcher is greater than or equal to 2 and less than 5, then ELOB i The output is 4; If the number of scientific research projects participated by the i-th researcher is greater than 5, then ELOB i The output is 10.

[0012] Optionally, the work experience score ELOC of the i-th researcher i The processing process is as follows: If the i-th researcher has worked for less than or equal to 3 years, then ELOCi The output is 0; If the working years of the i-th researcher are greater than 3 years and less than or equal to 6 years, then ELOC i The output is 4; If the working years of the i-th researcher are greater than 6 years and less than or equal to 10 years, then ELOC i The output is 8; If the i-th researcher has worked for more than 10 years, then ELOC i The output is 15; The proportion of time ELOD that the i-th researcher devotes to scientific research i The processing process is as follows: If the i-th researcher spends less than or equal to 50% of his time on research, then ELOD i The output is 3; If the proportion of time devoted by the i-th researcher to research is greater than 50% and less than 75%, then ELOD i The output is 5; If the i-th researcher devotes more than 75% of his time to research, then ELOD i The output is 10.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention outputs the efficiency evaluation value of the ith scientific researcher through the personnel efficiency analysis submodule. By calculating the efficiency evaluation value of the scientific researcher, the efficiency of each scientific researcher can be accurately evaluated, and then which scientific researchers are efficient and which are inefficient can be identified, which helps managers make more informed decisions when allocating resources and allocate more resources to efficient scientific researchers to improve overall scientific research efficiency. The evaluation results of the efficiency evaluation values ​​of scientific researchers can also be used as a basis for incentives and training. For efficient scientific researchers, more rewards and promotion opportunities can be given, and for inefficient scientific researchers, targeted training and development plans can be provided to help them improve their scientific research efficiency.

[0014] 2. The present invention outputs the resource allocation optimization value of the jth scientific research project through the project resource analysis submodule. The resource allocation of scientific research projects can be optimized by calculating the resource allocation optimization value of scientific research projects. Managers can reasonably allocate resources according to the needs of scientific research projects and the efficiency evaluation results of scientific researchers to ensure that each project can obtain sufficient resource support while avoiding waste and repeated allocation of resources. The evaluation results of the resource allocation optimization value of scientific research projects can also help managers determine the priority of projects. For projects with large resource requirements, high scientific research efficiency and significant expected results, higher priority and more resource support can be given. Through the evaluation results of the resource allocation optimization value of scientific research projects, managers can promptly discover potential risks and problems in the project to reduce the risk of project failure.

[0015] 3. The present invention outputs the comprehensive utilization rate of scientific research resources through the comprehensive resource utilization submodule. The calculation of the comprehensive utilization rate of scientific research resources can comprehensively reflect the utilization of scientific research resources. Through the evaluation of the comprehensive utilization rate of scientific research resources, managers can understand which resources are fully utilized and which resources are wasted or idle, which helps managers to formulate more reasonable resource utilization plans and management strategies. In view of the waste or idleness of resources found in the evaluation results of the comprehensive utilization rate of scientific research resources, managers can formulate corresponding measures to optimize the allocation and sharing. The evaluation of the comprehensive utilization rate of scientific research resources can also serve as a basis for continuous improvement and enhancement. Managers can regularly summarize and reflect on the utilization of scientific research resources based on the evaluation of the comprehensive utilization rate of scientific research resources, identify existing problems and deficiencies and formulate corresponding improvement measures and enhancement plans.

[0016] 4. The present invention iterates the resource demand coefficient of the i-th researcher in the j-th project through the comprehensive utilization rate of scientific research resources. By iteratively adjusting the resource demand coefficient, resources can be allocated more accurately according to the actual needs of the scientific research project, avoiding excessive concentration or idleness of resources, thereby improving the utilization efficiency of resources. The iterative process makes the scientific and technological innovation management method more flexible and dynamic, and can adjust the management strategy in time according to the actual situation to improve the management efficiency and response speed. This iterative form solves the problem of unreasonable resource allocation caused by the scientific research project management not entering the main process of enterprise management. The comprehensive utilization rate value of scientific research resources calculated by the comprehensive resource utilization submodule is used as a feedback signal, which can more scientifically guide the adjustment of the resource demand coefficient and make the decision-making process more data-based and accurate. The comprehensive utilization rate of scientific research resources reflects that the comprehensive utilization rate of scientific research resources is an important indicator for evaluating the effect of resource allocation, and the resource demand coefficient in the project resource analysis submodule directly determines the resource allocation of the scientific research project. Therefore, there is a direct correlation between the two. By iteratively optimizing the resource demand coefficient, resource allocation can be made more reasonable and efficient, thereby meeting the actual needs of scientific research projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flowchart of the method steps of the scientific and technological innovation management system based on big data analysis; Figure 2 This is a schematic diagram of the overall structure of the scientific and technological innovation management system based on big data analysis; Figure 3 This is a structural diagram of the computing management module in the scientific and technological innovation management system based on big data analysis. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] This science and technology innovation management system based on big data analysis is different from the existing science and technology innovation management system. The existing science and technology innovation management system often measures the efficiency of scientific researchers based on a single scientific research output, such as the number of papers and patents. However, this evaluation method ignores multi-dimensional factors such as the professional background, work experience, and proportion of time invested in scientific research of scientific researchers, resulting in inaccurate evaluation results. In addition, since most scientific researchers complete scientific research tasks part-time, their scientific research efficiency and output are also greatly affected. Due to inaccurate evaluation of scientific researchers' efficiency and lack of unified management and planning of scientific research projects, the allocation of scientific research project resources is unreasonable, which makes some scientific research projects unable to proceed smoothly due to insufficient resources, and some resources are idle or wasted and cannot be effectively utilized. This problem of unreasonable resource allocation not only affects the progress and output of scientific research projects, but also causes a waste of scientific research resources; The module of the science and technology innovation management system based on big data analysis can more accurately evaluate the efficiency of scientific researchers by comprehensively considering multi-dimensional factors such as their professional background, work experience, proportion of time invested in scientific research, and output of scientific research results. This helps managers better understand the actual capabilities and contributions of scientific researchers, and provides a scientific basis for personnel allocation and task allocation of scientific research projects. Based on the efficiency evaluation value of scientific researchers combined with the actual needs of the project and resource availability, the rational allocation of scientific research project resources is achieved through optimization algorithms, which helps to ensure that scientific research projects can obtain sufficient resource support while avoiding waste and idleness of resources.

[0020] Example 1: Please refer to Figures 1 to 3 This implementation provides a scientific and technological innovation management system based on big data analysis, including: Data collection module: collects the number of papers, number of projects, years of work and proportion of time spent on scientific research of each researcher from the database, and inputs the collected data into the processing module; Processing module: clean the data of each researcher’s number of papers, number of projects, years of work, and proportion of time spent on scientific research to remove duplicate and invalid data, then convert data in different formats into a unified format and store the processed data; Analysis module: Input the number of papers, number of projects, years of work and proportion of time spent on scientific research of each researcher after processing by the processing module, and output the professional background score ELOA of the i-th researcher i , the research background score of the i-th researcher ELOB i , the work experience score of the i-th researcher ELOC i and the time ratio of the i-th researcher to research ELOD i ; Calculation management module: calculate the professional background score ELOA of the i-th researcher i , the research background score of the i-th researcher ELOB i , the work experience score of the i-th researcher ELOC i and the time ratio of the i-th researcher to research ELOD i Input, output the efficiency evaluation value ELO of the i-th researcher i , the optimal value of resource allocation for the jth scientific research project RDG j and the comprehensive utilization rate of scientific research resources (UCD); Identification module: The efficiency evaluation value ELO of the i-th researcher i , the optimal value of resource allocation for the jth scientific research project RDG j and comprehensive utilization of scientific research resources UCD input, identification module for ELO i ,RDG j Compare and analyze with UCD to identify efficient and inefficient researchers, find out the problems of unreasonable resource allocation and the reasons for low utilization of scientific research resources, and then adjust the task allocation of researchers according to the analysis results, optimize resource allocation and improve resource utilization efficiency; The calculation management module includes: personnel efficiency analysis submodule, project resource analysis submodule and comprehensive resource utilization submodule.

[0021] In this embodiment: the system can identify inefficient scientific researchers and scientific research projects with unreasonable resource allocation, make targeted improvements, and then optimize the allocation of scientific researchers, encourage full-time scientific researchers to participate in important projects, improve scientific research quality and results, and strengthen the unified management and effective sharing of scientific research materials to reduce resource waste. By optimizing resource allocation and identifying efficient scientific researchers, multiple sub-modules in the calculation management module help to improve the utilization efficiency of scientific research resources and reduce waste. Multiple sub-modules and their calculation results can provide powerful decision-making support for scientific and technological innovation management in big data analysis, helping managers make more informed decisions. Multiple sub-modules are combined together to achieve overall optimization from scientific researcher efficiency evaluation to scientific research project resource allocation optimization to scientific research resource comprehensive utilization rate evaluation. This overall optimization helps to improve scientific research efficiency and quality and reduce resource waste. The calculation results of multiple sub-modules are combined together to form a comprehensive evaluation of the resource utilization of scientific researchers and scientific research projects. This comprehensive evaluation helps to discover problems and bottlenecks in the resource utilization process and provide guidance for subsequent optimization and configuration. Multiple sub-modules and their calculation results have strong adaptability and can be adjusted and optimized according to different scientific research environments and needs. This adaptability makes this system have broad application prospects in scientific and technological innovation management of big data analysis.

[0022] See also Figures 1 to 3 ,The processing process of the personnel efficiency analysis submodule is as follows: ; in: ELO i refers to the efficiency evaluation value of the i-th researcher, EA refers to the weight coefficient of the professional background score, EB refers to the weight coefficient of the scientific research background score, EC refers to the weight coefficient of the work experience score, ED refers to the weight coefficient of the combined score of the time ratio and the scientific research output rate, ELOA i Refers to the professional background score of the i-th researcher, ELOB i Refers to the research background score and ELOC of the i-th researcher i Refers to the work experience score and ELOD of the i-th researcher i Refers to the proportion of time that the i-th researcher devotes to scientific research, ELOE i Refers to the research output rate of the i-th researcher, EC×ELOC i Refers to the contribution value of work experience score, reflecting the importance of work experience in the efficiency evaluation of researchers (EA×ELOA i +EB×ELOB i ) refers to the contribution of the professional and scientific research background scores of researchers to the efficiency evaluation value; The processing process of the personnel efficiency analysis submodule is as follows: the professional background score ELOA of the i-th researcher i , the research background score of the i-th researcher ELOB i , the work experience score of the i-th researcher ELOC i and the time ratio of the i-th researcher to research ELOD i Input into the personnel efficiency analysis submodule, and based on the research output rate ELOE of the i-th researcher i Output the efficiency evaluation value ELO of the i-th researcher i .

[0023] In this embodiment: This submodule comprehensively considers the professional background, work experience, time ratio of scientific research and scientific research output of scientific researchers, and provides an accurate method for evaluating the efficiency of scientific researchers for big data analysis. i The calculation of ELO can accurately evaluate the efficiency of each researcher, thereby identifying which researchers are efficient and which are inefficient. This helps managers make more informed decisions when allocating resources and allocate more resources to efficient researchers to improve overall research efficiency. i Based on the evaluation results, managers can build and optimize the scientific research team more scientifically. They can adjust the composition of team members according to the efficiency evaluation results of scientific researchers, ensure that the team has both efficient scientific researchers as the core force and scientific researchers with great potential as the reserve force, and form a reasonable team echelon. i The evaluation results can also be used as a basis for incentives and training. For efficient scientific researchers, more rewards and promotion opportunities can be given; for inefficient scientific researchers, targeted training and development plans can be provided to help them improve their scientific research efficiency.

[0024] See also Figures 1 to 3 , the processing process of the project resource analysis submodule is as follows: ; in: RDG j Refers to the resource allocation optimization value of the jth scientific research project, PD j Refers to the set of researchers participating in the jth scientific research project; RDGA i，j Refers to the task allocation coefficient of the i-th researcher in the j-th project, RDGA i，j Based on the proportion of tasks, the evaluation can be carried out according to factors such as the amount of tasks undertaken and the difficulty of tasks; RDGB i，j Refers to the resource demand coefficient of the i-th researcher in the j-th project, RDGBi，j Evaluation based on equipment and funding; ELO i ×RDGA i，j Refers to the contribution of researchers to the optimization of project resource allocation, Refers to the sum of the contribution values ​​of all researchers participating in the project to the optimization of project resource allocation, which is used to normalize the contribution values ​​to ensure the rationality and comparability of the resource allocation optimization value RDGj; The processing process of the project resource analysis submodule is as follows: the efficiency evaluation value ELO of the i-th researcher i Input to the project resource analysis submodule, and based on the set of researchers involved in the jth scientific research project PD j and the task allocation coefficient RDGA of the i-th researcher in the j-th project i，j Output the resource allocation optimization value RDG of the jth scientific research project j .

[0025] In this embodiment: This submodule optimizes the resource allocation of scientific research projects based on the efficiency evaluation value of scientific researchers and project requirements. This submodule ensures that resources are allocated to the most efficient scientific researchers, improves resource utilization efficiency, and j The calculation can optimize the resource allocation of scientific research projects. Managers can reasonably allocate resources according to the needs of scientific research projects and the efficiency evaluation results of scientific researchers to ensure that each project can obtain sufficient resource support while avoiding waste and duplication of resources. j The evaluation results can also help managers determine the priority of projects. For projects with large resource requirements, high scientific research efficiency and significant expected results, higher priority and more resource support can be given. For projects with small resource requirements, low scientific research efficiency or unclear expected results, the priority and resource investment can be appropriately reduced. j The evaluation results can also help managers promptly identify potential risks and problems in the project. For example, when the resource allocation efficiency of a project continues to decline, it may mean that the project has problems such as poor management, loss of scientific researchers or technical difficulties. At this time, managers can take timely measures to intervene and adjust to reduce the risk of project failure.

[0026] See also Figures 1 to 3 ,The processing process of the comprehensive resource utilization submodule is as follows: ; in: UCD refers to the comprehensive utilization rate of scientific research resources; UCDA j Refers to the scale of the jth scientific research project, UCDA jA comprehensive evaluation can be conducted based on factors such as the project's funding scale, research cycle, and research objectives; UCDB j Refers to the actual resource efficiency of the jth scientific research project, UCDB j Evaluation based on equipment utilization and capital efficiency; UCDC j Refers to the research resource cost and ELO required by the i-th researcher i ×UCDC j Refers to the contribution of researchers to the use of scientific research resources. Refers to the sum of the contribution values ​​of all scientific researchers and scientific research projects to the utilization of scientific research resources, and is used to normalize the contribution values; The processing process of the comprehensive resource utilization submodule is as follows: the efficiency evaluation value ELO of the i-th researcher i and the optimal resource allocation value RDG of the jth scientific research project j Input to the comprehensive resource utilization submodule, based on the research resource cost UCDC required by the i-th researcher j Output the comprehensive utilization rate of scientific research resources (UCD).

[0027] In this embodiment: this submodule comprehensively considers the resource utilization of scientific researchers and scientific research projects, and provides an evaluation method for the comprehensive utilization rate of scientific research resources for big data analysis. This submodule covers two dimensions, scientific researchers and scientific research projects, to ensure the comprehensiveness of the evaluation results, provide guidance for resource optimization and allocation, and improve resource utilization efficiency. Through quantitative indicators, it is convenient to measure and compare resource utilization. The calculation results of UCD can fully reflect the utilization of scientific research resources. Through the evaluation results of UCD, managers can understand which resources are fully utilized and which resources are wasted or idle, which helps managers to formulate more reasonable resource utilization plans and management strategies. In response to the waste or idleness of resources found in the UCD evaluation results, managers can formulate corresponding measures to optimize configuration and sharing, such as establishing a scientific research resource sharing platform or mechanism to promote resource sharing and cooperation between different scientific research projects; equipment use processes and management systems can be optimized to improve equipment utilization and efficiency. The evaluation results of UCD can also be used as a basis for continuous improvement and improvement. Managers can regularly summarize and reflect on the utilization of scientific research resources based on the evaluation results of UCD, find existing problems and deficiencies, and formulate corresponding improvement measures and improvement plans.

[0028] It is worth noting that the resource demand coefficient RDGB of the i-th researcher in the j-th project is iterated based on the calculation of the comprehensive utilization rate UCD of scientific research resources and the target value UCDSS of the comprehensive utilization rate of scientific research resources. i，j, thus making the resource allocation optimization value RDG of the jth scientific research project j The comprehensive utilization rate of scientific research resources (UCD) is iterated in a cycle, and the processing process is as follows: first: ; Second: Set the iteration termination condition: Termination condition 1: The number of iterations is 50; Termination condition 2: ; in: RDGB i，j，new Refers to the updated resource demand coefficient, RDGB, of the i-th researcher in the j-th project i，j，old refers to the resource demand coefficient of the i-th researcher in the j-th project before the update, SA refers to the adjustment factor, which is used to control the change range of RDGB, UCDSS refers to the target value of the comprehensive utilization rate of scientific research resources, and UCD new Refers to the updated comprehensive utilization rate of scientific research resources, UCD old Refers to the comprehensive utilization rate of scientific research resources before the update; In this embodiment: by iteratively adjusting the resource demand coefficient RDGB i，j , it can allocate resources more accurately according to the actual needs of scientific research projects, avoid excessive concentration or idleness of resources, and thus improve the efficiency of resource utilization. As resources are more reasonably allocated, scientific researchers can have more sufficient resources and support, so they can focus more on scientific research, improve scientific research quality and output results. The iterative process makes the scientific and technological innovation management method more flexible and dynamic, and can adjust the management strategy in time according to actual conditions, improving management efficiency and response speed. The contribution of this iterative form is that it solves the problem of unreasonable resource allocation caused by the failure of scientific research project management to enter the main process of enterprise management. Through big data analysis and iterative optimization, it realizes the effective integration and efficient utilization of scientific research resources, and promotes the sustainable development of scientific and technological innovation. The UCD value calculated by the comprehensive resource utilization submodule is used as a feedback signal, which can guide the resource demand coefficient RDGB more scientifically. i，j The adjustment makes the decision-making process more data-based and precise. The iterative process enables the scientific and technological innovation management method to be dynamically adjusted according to changes in actual conditions, enhancing the adaptability and flexibility of the management method. By continuously optimizing the resource demand coefficient RDGB i，j, can maximize the utilization efficiency of scientific research resources, thereby creating more value for the enterprise. By optimizing resource allocation, more support and resources can be provided to part-time scientific researchers, reducing their workload and improving their work efficiency and scientific research quality. Through unified management and effective sharing of large-scale scientific research facilities and instruments and equipment, waste of resources and duplication of construction can be avoided, and resource utilization efficiency can be improved. By iteratively optimizing resource allocation, scientific research project management can be more standardized and systematic, which helps to incorporate it into the main management process of the enterprise, thereby realizing the deep integration and coordinated development of scientific research and enterprises. Through the use of big data analysis and calculation formulas, this method realizes the quantification and visualization of data, providing strong support for management decisions. Among them, UCD reflects the comprehensive utilization rate of scientific research resources and is an important indicator for evaluating the effect of resource allocation, while RDGB in the project resource analysis submodule i，j It directly determines the resource allocation of scientific research projects, so UCD and RDGB i，j There is a direct correlation between them. By iteratively optimizing RDGB i，j , which can make resource allocation more reasonable and efficient, thus meeting the actual needs of scientific research projects.

[0029] In the specific implementation process, the various sub-modules in this method are used to form a scientific and technological innovation management system based on big data analysis. The professional background score ELOA of the i-th researcher is calculated. i , the research background score of the i-th researcher ELOB i , the work experience score of the i-th researcher ELOC i and the time ratio of the i-th researcher to research ELOD i Input into the personnel efficiency analysis submodule, and output the efficiency evaluation value ELO of the i-th researcher i , via ELO i The calculation of ELO can accurately evaluate the efficiency of each researcher, thereby identifying which researchers are efficient and which are inefficient. This helps managers make more informed decisions when allocating resources and allocate more resources to efficient researchers to improve overall research efficiency. i The evaluation results can also be used as a basis for incentives and training. For efficient scientific researchers, more rewards and promotion opportunities can be given; for inefficient scientific researchers, targeted training and development plans can be provided to help them improve their scientific research efficiency.

[0030] By using the efficiency evaluation value ELO of the i-th researcher i Input to the project resource analysis submodule, and output the resource allocation optimization value RDG of the jth scientific research project j , via RDG jThe calculation can optimize the resource allocation of scientific research projects. Managers can reasonably allocate resources according to the needs of scientific research projects and the efficiency evaluation results of scientific researchers to ensure that each project can obtain sufficient resource support while avoiding waste and duplication of resources. j The evaluation results can also help managers determine the priority of projects. For projects with large resource requirements, high scientific research efficiency and significant expected results, higher priority and more resource support can be given. j The evaluation results can also help managers to timely identify potential risks and problems in the project. For example, when the resource allocation efficiency of a project continues to decline, it may mean that the project has problems such as poor management, loss of scientific researchers or technical difficulties. At this time, managers can take timely measures to intervene and adjust to reduce the risk of project failure. By using the efficiency evaluation value ELO of the i-th researcher i and the optimal resource allocation value RDG of the jth scientific research project j Input to the comprehensive resource utilization submodule, and output the comprehensive utilization rate UCD of scientific research resources. The calculation results of UCD can fully reflect the utilization of scientific research resources. Through the evaluation results of UCD, managers can understand which resources are fully utilized and which resources are wasted or idle. This helps managers to formulate more reasonable resource utilization plans and management strategies. In response to the waste or idleness of resources found in the UCD evaluation results, managers can formulate corresponding measures to optimize allocation and sharing. The evaluation results of UCD can also serve as a basis for continuous improvement and enhancement. According to the evaluation results of UCD, managers can regularly summarize and reflect on the utilization of scientific research resources, identify existing problems and deficiencies, and formulate corresponding improvement measures and enhancement plans; Based on the comprehensive utilization rate of scientific research resources UCD, the resource demand coefficient RDGB of the i-th researcher in the j-th project is iterated i，j , by iteratively adjusting the resource demand coefficient RDGB i，j , can more accurately allocate resources according to the actual needs of scientific research projects, avoid excessive concentration or idleness of resources, and thus improve the utilization efficiency of resources. The iterative process makes the scientific and technological innovation management method more flexible and dynamic, and can adjust the management strategy in time according to the actual situation, improving management efficiency and response speed. The contribution of this iterative form is that it solves the problem of unreasonable resource allocation caused by the failure of scientific research project management to enter the main process of enterprise management. The UCD value calculated by the comprehensive resource utilization submodule is used as a feedback signal to more scientifically guide the resource demand coefficient RDGB i，j The adjustment makes the decision-making process more data-based and precise. UCD reflects the comprehensive utilization rate of scientific research resources and is an important indicator for evaluating the effect of resource allocation.i，j It directly determines the resource allocation of scientific research projects, so UCD and RDGB i，j There is a direct correlation between them. By iteratively optimizing RDGB i，j , which can make resource allocation more reasonable and efficient, thus meeting the actual needs of scientific research projects; This allows the various sub-modules to cooperate with each other in calculations, and to perform overall cycles and iterations, so that the overall system has the effect of automatic optimization and updating, and thus better adaptability.

[0031] Example 2: Please refer to Figure 1 , Figure 2 and Figure 3 , the processing of the analysis module is as follows: Input the number of papers, number of projects, years of work experience and the proportion of time spent on research for each researcher into the analysis module; ELOA score of the professional background of the i-th researcher i The processing process is as follows: If the number of papers published by the i-th researcher is less than 3, then ELOA i The output is 0; If the number of papers published by the i-th researcher is greater than or equal to 3 and less than 10, then ELOA i The output is 5; If the number of papers published by the i-th researcher is greater than or equal to 10, then ELOA i The output is 10; ELOB score of the research background of the i-th researcher i The processing process is as follows: If the number of scientific research projects participated by the i-th researcher is less than 2, then ELOB i The output is 0; If the number of scientific research projects participated by the i-th researcher is greater than or equal to 2 and less than 5, then ELOB i The output is 4; If the number of scientific research projects participated by the i-th researcher is greater than 5, then ELOB i The output is 10; ELOC score of the i-th researcher's work experience i The processing process is as follows: If the i-th researcher has worked for less than or equal to 3 years, then ELOC i The output is 0; If the working years of the i-th researcher are greater than 3 years and less than or equal to 6 years, then ELOC i The output is 4; If the working years of the i-th researcher are greater than 6 years and less than or equal to 10 years, then ELOC i The output is 8; If the i-th researcher has worked for more than 10 years, then ELOC i The output is 15; The proportion of time that the i-th researcher devotes to scientific research ELOD i The processing process is as follows: If the i-th researcher spends less than or equal to 50% of his time on research, then ELOD i The output is 3; If the proportion of time devoted by the i-th researcher to research is greater than 50% and less than 75%, then ELOD i The output is 5; If the i-th researcher devotes more than 75% of his time to research, then ELOD i The output is 10.

[0032] In this embodiment: for the research output rate ELOEi of the i-th researcher, a score is given based on the ratio of the number of citations to the number of papers, and a threshold is set to 10 times per paper. If it exceeds the threshold, 10 is output, and if it is lower than the threshold, 5 is output. Then, after the system collects and uploads various information of the researchers based on the data collection module, it scores each data based on various standards, thereby normalizing the messy data into a score number to meet the input of the formula in the calculation management module of this system.

[0033] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A technology innovation management system for big data analysis, characterized by: include: Data collection module: collects the number of papers, number of projects, years of work and proportion of time spent on scientific research of each researcher from the database, and inputs the collected data into the processing module; Processing module: clean the data of each researcher’s number of papers, number of projects, years of work, and proportion of time spent on scientific research to remove duplicate and invalid data, then convert data in different formats into a unified format and store the processed data; Analysis module: Input the number of papers, number of projects, years of work and proportion of time spent on scientific research of each researcher after processing by the processing module, and output the professional background score ELOA of the i-th researcher i , the research background score of the i-th researcher ELOB i , the work experience score of the i-th researcher ELOC i and the time ratio of the i-th researcher to research ELOD i ; Calculation management module: calculate the professional background score ELOA of the i-th researcher i , the research background score of the i-th researcher ELOB i , the work experience score of the i-th researcher ELOC i and the time ratio of the i-th researcher to research ELOD i Input, output the efficiency evaluation value ELO of the i-th researcher i , the optimal value of resource allocation for the jth scientific research project RDG j and the comprehensive utilization rate of scientific research resources (UCD); Identification module: The efficiency evaluation value ELO of the i-th researcher i , the optimal value of resource allocation for the jth scientific research project RDG j and comprehensive utilization of scientific research resources UCD input, identification module for ELO i ,RDG j By comparing and analyzing with UCD, efficient and inefficient scientific researchers can be identified, problems with irrational resource allocation, and the reasons for low utilization of scientific research resources can be discovered. Then, based on the analysis results, the task allocation of scientific researchers can be adjusted, resource allocation can be optimized, and resource utilization efficiency can be improved.

2. The technology innovation management system based on big data analysis according to claim 1 is characterized by: The calculation management module includes: a personnel efficiency analysis submodule, a project resource analysis submodule and a comprehensive resource utilization submodule.

3. The technology innovation management system based on big data analysis according to claim 2 is characterized by: The calculation formula of the personnel efficiency analysis submodule is as follows: ； in: ELO i refers to the efficiency evaluation value of the i-th researcher, EA refers to the weight coefficient of the professional background score, EB refers to the weight coefficient of the scientific research background score, EC refers to the weight coefficient of the work experience score, ED refers to the weight coefficient of the combined score of the time ratio and the scientific research output rate, ELOA i Refers to the professional background score of the i-th researcher, ELOB i Refers to the research background score and ELOC of the i-th researcher i Refers to the work experience score and ELOD of the i-th researcher i Refers to the proportion of time that the i-th researcher devotes to scientific research, ELOE i Refers to the research output rate of the i-th researcher, EC×ELOC i Refers to the contribution value of work experience score, reflecting the importance of work experience in the efficiency evaluation of researchers (EA×ELOA i +EB×ELOB i ) refers to the contribution of the professional and scientific research background scores of researchers to the efficiency evaluation value; The processing process of the personnel efficiency analysis submodule is as follows: the professional background score ELOA of the i-th researcher is calculated as follows: i , the research background score of the i-th researcher ELOB i , the work experience score of the i-th researcher ELOC i and the time ratio of the i-th researcher to research ELOD i Input into the personnel efficiency analysis submodule, and based on the research output rate ELOE of the i-th researcher i Output the efficiency evaluation value ELO of the i-th researcher i .

4. The technology innovation management system based on big data analysis according to claim 3 is characterized by: The calculation formula of the project resource analysis submodule is as follows: ； in: RDG j Refers to the resource allocation optimization value of the jth scientific research project, PD j Refers to the set of researchers participating in the jth scientific research project, RDGA i，j Refers to the task allocation coefficient of the i-th researcher in the j-th project, RDGB i，j Refers to the resource demand coefficient and ELO of the i-th researcher in the j-th project i ×RDGA i，j Refers to the contribution of researchers to the optimization of project resource allocation, Refers to the sum of the contribution values ​​of all researchers participating in the project to the optimization of project resource allocation, which is used to normalize the contribution values ​​to ensure the rationality and comparability of the resource allocation optimization value RDGj; The processing process of the project resource analysis submodule is as follows: the efficiency evaluation value ELO of the i-th researcher is i Input to the project resource analysis submodule, and based on the set of researchers involved in the jth scientific research project PD j and the task allocation coefficient RDGA of the i-th researcher in the j-th project i，j Output the resource allocation optimization value RDG of the jth scientific research project j .

5. The technology innovation management system based on big data analysis according to claim 4 is characterized by: The calculation formula of the comprehensive resource utilization submodule is as follows: ； in: UCD refers to the comprehensive utilization rate of scientific research resources, UCDA j Refers to the scale of the jth scientific research project, UCDB j Refers to the resource efficiency actually used by the jth scientific research project, UCDC j Refers to the research resource cost and ELO required by the i-th researcher i ×UCDC j Refers to the contribution of researchers to the use of scientific research resources. Refers to the sum of the contribution values ​​of all scientific researchers and scientific research projects to the utilization of scientific research resources, and is used to normalize the contribution values; The processing process of the comprehensive resource utilization submodule is as follows: the efficiency evaluation value ELO of the i-th researcher is i and the optimal resource allocation value RDG of the jth scientific research project j Input to the comprehensive resource utilization submodule, based on the research resource cost UCDC required by the i-th researcher j Output the comprehensive utilization rate of scientific research resources (UCD).

6. The technology innovation management system based on big data analysis according to claim 3 is characterized by: The processing of the analysis module is as follows: Input the number of papers, number of projects, years of work experience and the proportion of time spent on research for each researcher into the analysis module; For the professional background score of the i-th researcher, ELOA i ; If the number of papers published by the i-th researcher is less than 3, then ELOA i The output is 0; If the number of papers published by the i-th researcher is greater than or equal to 3 and less than 10, then ELOA i The output is 5; If the number of papers published by the i-th researcher is greater than or equal to 10, then ELOA i The output is 10.

7. The technology innovation management system based on big data analysis according to claim 6 is characterized by: The research background score ELOB of the i-th researcher i The processing process is as follows: If the number of scientific research projects participated by the i-th researcher is less than 2, then ELOB i The output is 0; If the number of scientific research projects in which the i-th researcher participates is greater than or equal to 2 and less than 5, then ELOB i The output is 4; If the number of scientific research projects participated by the i-th researcher is greater than 5, then ELOB i The output is 10.

8. The technology innovation management system based on big data analysis according to claim 6 is characterized by: The work experience score ELOC of the i-th researcher i The processing process is as follows: If the i-th researcher has worked for less than or equal to 3 years, then ELOC i The output is 0; If the working years of the i-th researcher are greater than 3 years and less than or equal to 6 years, then ELOC i The output is 4; If the working years of the i-th researcher are greater than 6 years and less than or equal to 10 years, then ELOC i The output is 8; If the i-th researcher has worked for more than 10 years, then ELOC i The output is 15; The proportion of time ELOD that the i-th researcher devotes to scientific research i The processing process is as follows: If the i-th researcher spends less than or equal to 50% of his time on research, then ELOD i The output is 3; If the proportion of time devoted by the i-th researcher to research is greater than 50% and less than 75%, then ELOD i The output is 5; If the i-th researcher devotes more than 75% of his time to research, then ELOD i The output is 10.

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