A method and system for industry-university-research cooperation based on transformation of scientific and technological innovation capability

Abstract

The application discloses a kind of based on scientific and technological innovation ability transformation industry-university-research cooperation method and system, comprising: collection scientific research data, carry out normalization processing, based on scientific research achievement type and innovation ability data, construct multidimensional quantitative evaluation model.According to the evaluation result, carry out industry-university-research resource dynamic matching.According to the matching result, design specific transformation path.The effect evaluation is carried out to the completed transformation path, collects actual transformation data, optimizes transformation model and path planning algorithm.The potential value of scientific and technological achievement is accurately evaluated in the application, and scientific basis is provided for subsequent resource matching, which significantly improves the evaluation accuracy.Industry-university-research resources are dynamically matched.The step greatly improves resource utilization and matching efficiency, effectively shortens the technology transformation cycle.Scientific and technological achievement transformation is more flexible and efficient, and the failure risk is significantly reduced.

Description

Technical Field

[0001] This invention relates to the field of technological innovation and technology transfer technology, specifically to a method and system for industry-university-research cooperation based on the transformation of technological innovation capabilities. Background Technology

[0002] With the rapid improvement of global technological innovation capabilities, industry-academia-research collaboration has become an important model for promoting the transformation of scientific and technological achievements. Universities and research institutions focus on basic research and technology development, while enterprises emphasize market demand analysis and technology application. The combination of these two approaches can greatly improve the actual efficiency of scientific research results transformation. However, existing industry-academia-research collaboration models are still largely human-driven, with many subjective aspects in the process and a lack of scientific and dynamic evaluation methods. This leads to low resource matching efficiency, long technology transformation cycles, and difficulty in guaranteeing the success rate of technology promotion. Furthermore, in recent years, technologies such as artificial intelligence and big data have provided new opportunities for the transformation of scientific and technological achievements. Using data-driven methods to intelligently evaluate research results and dynamically match resources has become an important direction for the innovation of scientific and technological achievement transformation models.

[0003] While some research methods and systems exist for the commercialization of scientific and technological achievements, they primarily focus on single-dimensional technology assessment or experience-based resource matching, lacking a comprehensive scientific evaluation system. Existing technologies suffer from significant shortcomings in several aspects: First, technology assessment lacks dynamism, relying solely on data from a single point in time, making it impossible to accurately predict potential development trends. Second, resource matching is inefficient, typically relying on simple associations based on domain matching while neglecting crucial factors such as technology maturity and market adaptability. Third, the commercialization path is singular, lacking diversified commercialization solutions tailored to the characteristics of different achievements. These problems not only delay the efficiency of technology commercialization but also limit the promotion and application of high-potential research results in the market. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: insufficient dynamic evaluation: to solve the problem that the potential value of scientific research results cannot be dynamically evaluated in the existing technology, by introducing a multi-dimensional quantitative evaluation model and a dynamic balance mechanism, the dynamic quantitative evaluation of the value of scientific research results is realized.

[0006] Low resource matching efficiency: It solves the problem of single and inefficient matching of existing technology resources. Through a dynamic matching strategy of dividing intervals, it significantly improves the matching efficiency of resources and scientific research results.

[0007] Single transformation path: It overcomes the problem of the lack of flexible transformation paths in existing technologies and provides diversified transformation solutions to adapt to the characteristics of different results.

[0008] Lack of closed-loop evaluation and optimization: This addresses the problem of the lack of a closed-loop evaluation and optimization mechanism in existing technologies. Through effect evaluation and path planning optimization, a complete improvement system is formed, which improves the efficiency and accuracy of subsequent technology transfer.

[0009] To address the aforementioned technical problems, this invention provides the following technical solution: a method for industry-academia-research collaboration based on the transformation of scientific and technological innovation capabilities, comprising: collecting scientific research data, performing normalization processing, and constructing a multi-dimensional quantitative evaluation model based on the type of scientific research results and innovation capability data.

[0010] Based on the evaluation results, dynamic matching of industry-academia-research resources will be carried out.

[0011] Based on the matching results, design specific conversion paths.

[0012] Evaluate the effectiveness of completed conversion paths, collect actual conversion data, and optimize conversion models and path planning algorithms.

[0013] As a preferred embodiment of the industry-university-research cooperation method based on the transformation of scientific and technological innovation capabilities described in this invention, the collection of scientific research data includes collecting data on the types of scientific research results, data on innovation capabilities, and resource-related data, and performing normalization processing.

[0014] As a preferred embodiment of the industry-university-research collaboration method based on the transformation of scientific and technological innovation capabilities described in this invention, the construction of a multi-dimensional quantitative evaluation model based on scientific research achievement types and innovation capability data includes: evaluating the potential using weighted sum nonlinear functions for scientific research achievement type data, expressed as:

[0015]

[0016] in, This represents the j-th data item of the i-th type of result. This represents the resource support data corresponding to the i-th type of result. This represents the innovation capability data corresponding to the i-th type of achievement.

[0017] For the evaluation of innovation capability data, an integral and nonlinear dynamic equilibrium mechanism is used to assess potential, expressed as:

[0018]

[0019] in, A time-dependent dynamic variable representing technology maturity. This indicates the evaluation results of data on different types of scientific research achievements. This indicates the results of the data assessment of innovation capabilities.

[0020] By combining the evaluation results of data on the types of scientific research achievements and the evaluation results of data on innovation capabilities, a multidimensional quantitative evaluation model is constructed:

[0021]

[0022] in, This represents the final comprehensive evaluation value of innovation capability. Indicates the adjustment parameter. The balancing factor is represented by k, which represents the k-th category. The category index is from 1 to m, and m represents the total number of scientific research achievement categories.

[0023] Comprehensive evaluation value of innovation capability The range of its value is from 0 to positive infinity.

[0024] As a preferred embodiment of the industry-university-research cooperation method based on the transformation of scientific and technological innovation capabilities described in this invention, the step of dynamically matching industry-university-research resources according to the evaluation results includes dividing the evaluation results E into intervals, including a low-potential interval of 0~50, a medium-potential interval of 50~80, and a high-potential interval of 80~∞.

[0025] The low-potential range matching strategy includes analyzing the shortcomings of the research findings and matching them with university laboratories possessing strong R&D capabilities. Priority is given to matching the findings with laboratory resources that possess specialized experimental equipment and align with the research direction, based on the technological field of the research. The findings are included in basic research special funds for support, dynamically linked to basic research fund projects. The E-value is adjusted based on feedback from the resource providers' experimental results, and subsequent R&D directions are dynamically adjusted accordingly.

[0026] Extract matching university and research institution resource information from the database, and calculate the matching degree using an algorithm:

[0027]

[0028] in, Indicates the matching degree of university laboratory resources. Indicates the degree of matching with target requirements. This indicates the adjustment parameter.

[0029] The mid-potential range matching strategy includes matching research findings with relevant enterprises, with enterprises providing demand scenarios and participating in R&D, and collaborating with universities to optimize technical details. The focus is on promoting the transformation of laboratory results into pilot enterprise products. Enterprise resources are prioritized for pilot projects with marketability, providing opportunities for scenario testing of research results. Domain experts are organized to conduct a comprehensive assessment of technical feasibility and market potential, dynamically adjusting subsequent resource allocation plans.

[0030] Based on the field of the research findings, target companies matching the needs are selected from the enterprise database using a multi-target matching algorithm.

[0031]

[0032] in, Indicates the market demand weight of enterprises. This indicates the score for the company's testing site resources. Indicates the target innovation index. This represents the achievement innovation index.

[0033] The high-potential matching strategy includes ensuring that research findings closely align with industry needs, directly introducing them to target companies, and having these companies lead the productization process. A fast-track approach is provided for rapid marketization, including simplified approval procedures. Research findings are recommended for entry into specialized technology incubators, leveraging their funding, technology, and market resources for rapid implementation. Venture capital and angel investment are introduced, prioritizing projects with high growth potential.

[0034] Extract matching information from the enterprise and incubator resource database and dynamically calculate the matching degree:

[0035]

[0036] in, This represents the target company's market capability score. This indicates the incubator's resource suitability.

[0037] As a preferred embodiment of the industry-university-research cooperation method based on the transformation of scientific and technological innovation capabilities described in this invention, the step of designing a specific transformation path based on the matching results includes matching the university laboratory resources first, matching the enterprise-university joint development model, and matching the enterprise direct introduction model.

[0038] The priority matching and transformation path for university laboratory resources is as follows:

[0039] Results Optimization and Experimental Verification Phase: Matching research findings are introduced into university laboratories, and specific research objectives are defined. The laboratories conduct basic verification experiments, generating detailed experimental reports and optimization suggestions.

[0040] Multi-round feedback and iteration phase: Based on laboratory feedback, the results are gradually improved, and their core technical parameters are refined. An expert review mechanism is introduced to regularly evaluate the progress of the optimization of the results.

[0041] Preparation phase for results dissemination: Based on the experimental results, prepare the project's technical specifications and application report to prepare for subsequent technical cooperation and pilot applications. After transitioning to the medium-potential range, resource matching will re-enter the process.

[0042] The matching and transformation path for the enterprise-university joint development model is as follows:

[0043] Technology verification and demand matching phase: Universities and enterprises jointly establish project teams and clarify project objectives. Enterprises provide real-world demand scenarios, and university teams conduct technology verification.

[0044] Pilot project implementation phase: Conduct technology pilots in enterprise settings, collect actual data, and assess the technology's adaptability. Carry out small- to medium-scale trial production.

[0045] Joint optimization and results sharing phase: The project team optimizes the technology and improves the results based on pilot data. The allocation of technology intellectual property rights is clarified, and a sharing mechanism is established.

[0046] The direct conversion path for enterprises using pattern matching is as follows:

[0047] Technology introduction and adaptation phase:

[0048] The target company acquires the right to use the research results through a technology transfer agreement. The company's technical team then conducts secondary development of the results to adapt them to its existing product line.

[0049] Large-scale production and market promotion stage: Enterprises directly invest resources to transform technology into marketable products. Market feedback is collected through large-scale pilot production.

[0050] Results Feedback and Upgrade Phase: Optimize and iterate the technology based on market feedback. Further apply for patent protection and explore new market areas.

[0051] As a preferred embodiment of the industry-university-research collaboration method based on the transformation of technological innovation capabilities described in this invention, the evaluation of the completed transformation path includes setting evaluation indicators according to the characteristics of the transformation path, adopting a weighted multi-objective evaluation model, and combining key indicators for comprehensive evaluation.

[0052]

[0053] in, Indicates the technology maturity improvement value. Indicates the cost of commercializing research results. Indicates market return rate, Indicates the conversion cycle. Indicates the degree of technology compatibility. , , This represents the weighting coefficient of the indicator.

[0054] >0 indicates a good conversion rate. =0 indicates that the conversion rate met expectations but showed no significant improvement. A value less than 0 indicates that the conversion rate is lower than expected, and the path and resources need to be reassessed.

[0055] As a preferred embodiment of the industry-academia-research collaboration method based on the transformation of technological innovation capabilities described in this invention, the following steps—collecting actual transformation data and optimizing the transformation model and path planning algorithm—include collecting key data generated along the transformation path, including technology improvement data, economic benefit data, project progress data, and user feedback data. The collected raw data is then denoised and normalized, and the cleaned data is stored in a database, with the data source, time, and relevant stage clearly labeled.

[0056] Based on actual data, we improved the conversion model and path planning algorithm, aiming to improve resource matching efficiency, shorten the conversion cycle, and enhance the adaptability of technology to scenarios.

[0057] Design a reinforcement learning model to transform the path planning problem into a sequence decision problem:

[0058] Status: The current stage of the transformation path.

[0059] Actions: Optional optimized actions.

[0060] Rewards: Based on performance evaluation scores The reward is calculated using the following function:

[0061]

[0062] in, The penalty factor represents the conversion cycle.

[0063] Optimize the path planning algorithm by combining the output of the reinforcement learning model:

[0064]

[0065] in, This represents the optimized best path plan. Indicates the discount factor. This indicates the reward for the current stage.

[0066] A collaborative system for industry-academia-research cooperation based on the transformation of technological innovation capabilities, characterized by: including,

[0067] The calculation module collects scientific research data and constructs a multi-dimensional quantitative evaluation model based on the types of scientific research achievements and innovation capabilities.

[0068] The resource matching module dynamically matches industry-academia-research resources based on the evaluation results.

[0069] The conversion module designs specific conversion paths based on the matching results.

[0070] The optimization module evaluates the effectiveness of completed conversion paths, collects actual conversion data, and optimizes the conversion model and path planning algorithm.

[0071] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method described above.

[0072] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0073] The beneficial effects of this invention are as follows: Data collection and evaluation model construction: By collecting data on the types of scientific research achievements, innovation capabilities, and resource-related data, a multi-dimensional quantitative evaluation model is constructed. This step accurately assesses the potential value of scientific and technological achievements and provides a scientific basis for subsequent resource matching, significantly improving the accuracy of the evaluation.

[0074] Dynamic resource matching based on assessment results: The assessment results are divided into low, medium, and high potential ranges, and differentiated matching strategies are adopted according to the characteristics of different ranges to dynamically match resources from universities, enterprises, and incubators. This step significantly improves resource utilization and matching efficiency, and effectively shortens the technology transfer cycle.

[0075] Design specific transformation paths: For different matching results, three differentiated transformation paths are formulated: university laboratory verification, joint development with enterprises, and direct introduction by enterprises, ensuring that scientific and technological achievements can be commercialized in the optimal way. This step makes the transformation of scientific and technological achievements more flexible and efficient, significantly reducing the risk of failure.

[0076] Effectiveness Evaluation and Path Optimization: A weighted multi-objective evaluation model is used to assess the effectiveness of the transformation path, and reinforcement learning is combined to dynamically optimize path planning, ensuring the accuracy and efficiency of subsequent technology transfer. This step achieves a closed-loop improvement of evaluation, transformation, and optimization, contributing to continuous improvement of system efficiency. Attached Figure Description

[0077] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0078] Figure 1 The first embodiment of the present invention provides an overall flowchart of a method and system for industry-university-research cooperation based on the transformation of scientific and technological innovation capabilities. Detailed Implementation

[0079] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0080] Example 1, referring to Figure 1 As one embodiment of the present invention, a method for industry-academia-research collaboration based on the transformation of technological innovation capabilities is provided, comprising:

[0081] S1: Collect scientific research data, perform normalization processing, and construct a multi-dimensional quantitative evaluation model based on the types of scientific research achievements and innovation capability data.

[0082] Collect data on the types of scientific research achievements, innovation capabilities, and resources.

[0083] It should be noted that the data on research achievements includes papers, patents, experimental results, and technological achievements. Papers include the journal in which they were published, their impact factor, number of citations, and research field. Patents include patent type (invention, utility model, etc.), authorization status, technical field, and number of citations. Experimental results include the scale of experimental data, type of experimental result (performance testing, parameter optimization, etc.), and application scenario. Technological achievements include technology maturity level (TRL) and technology development stage (proof of concept, prototype development, etc.).

[0084] Innovation capability data includes technical indicators, market value, and academic influence. Technical indicators include percentage efficiency improvement, innovation score (expert evaluation), and techno-economic benefits (cost / benefit ratio). Market value includes potential market size and alignment with industry needs. Academic influence includes citation frequency of papers and coverage in core journals.

[0085] Resource-related data includes team capabilities and resource availability. Team capabilities include the number of team members, their academic backgrounds, and the number of major projects they have participated in. Resource availability includes the completeness of laboratory equipment and the adequacy of research funding.

[0086] For data evaluation of different types of scientific research achievements, a weighted sum and nonlinear function are used to assess potential, expressed as:

[0087]

[0088] in, This represents the j-th data item of the i-th type of result. This represents the resource support data corresponding to the i-th type of result. This represents the innovation capability data corresponding to the i-th type of achievement.

[0089] It should be noted that different types of research results have varying degrees of impact and commercialization value. Weighting methods are used to reflect the importance of different types of results. For example, the market commercialization value of patented technology may be higher than that of basic research papers. Nonlinear functions (such as logarithmic functions, square functions, etc.) can be used to amplify or suppress research data, enhancing the impact of important data and reducing the interference of less important data. For example, The square function is used to mitigate the excessive influence of large values ​​on the overall assessment, while it can amplify the effect of key variables.

[0090] For the evaluation of innovation capability data, an integral and nonlinear dynamic equilibrium mechanism is used to assess potential, expressed as:

[0091]

[0092] in, A time-dependent dynamic variable representing technology maturity. This indicates the evaluation results of data on different types of scientific research achievements. This indicates the results of the data assessment of innovation capabilities.

[0093] It should be noted that data on innovation capability are often related to time or external conditions; therefore, an integral form is introduced to dynamically simulate how the potential for achievement changes over time or under different conditions. Nonlinear functions (such as exponential and cosine functions) are used to balance the influence of different data. For example, The potential for results can be dynamically adjusted as technology maturity changes. Technology maturity Introduced as an integral variable, the potential of simulation technology gradually increases or decreases over time.

[0094] By combining the evaluation results of data on the types of scientific research achievements and the evaluation results of data on innovation capabilities, a multidimensional quantitative evaluation model is constructed:

[0095]

[0096] in, This represents the final comprehensive evaluation value of innovation capability. Indicates the adjustment parameter. The balancing factor is represented by k, which represents the k-th category. The category index is from 1 to m, and m represents the total number of scientific research achievement categories.

[0097] Comprehensive evaluation value of innovation capability The range of its value is from 0 to positive infinity.

[0098] S2: Based on the evaluation results, conduct dynamic matching of industry-academia-research resources.

[0099] The assessment result E is divided into intervals, including a low potential interval of 0~50, a medium potential interval of 50~80, and a high potential interval of 80~∞.

[0100] It should be noted that the low potential range indicates a lower assessment result, insufficient technological maturity of research achievements, poor market fit, and weak innovation capability. The classification is based on... Primarily limited by resource support and innovation capacity data, research results below this value range are suitable for further basic research or technological improvement.

[0101] The "medium potential" range indicates a moderate assessment result. The technological maturity of the research achievements basically meets industry needs, and the innovation capability has a certain appeal, but the market prospects need further clarification. The classification is based on the fact that achievements in this range require precise resource allocation to drive the technology towards marketization.

[0102] The high-potential range indicates a high evaluation result, with research achievements demonstrating a high level of technological maturity and innovation, possessing the conditions for direct marketization or rapid application. The classification is based on the fact that achievements exceeding this range are suitable for efficient commercialization through direct introduction by enterprises or incubation.

[0103] The low-potential range matching strategy includes analyzing the shortcomings of the research findings and matching them with university laboratories possessing strong R&D capabilities. Priority is given to matching the findings with laboratory resources that possess specialized experimental equipment and align with the research direction, based on the technological field of the research. The findings are included in basic research special funds for support, dynamically linked to basic research fund projects. The E-value is adjusted based on feedback from the resource providers' experimental results, and subsequent R&D directions are dynamically adjusted accordingly.

[0104] It should be noted that university laboratories with strong R&D capabilities must meet the following conditions:

[0105] Completeness of experimental equipment: The laboratory should have high-end equipment covering the matching technology field, including but not limited to instruments, testing equipment, simulation experimental equipment, etc., which can support the basic verification and optimization of scientific research results.

[0106] Research direction alignment: The laboratory's main research direction should be highly consistent with the field of the results, and the laboratory should have rich research experience and published results in that field.

[0107] Academic influence: The research projects in which the laboratory participates must have national or provincial / ministerial level support, the research results must have a high publication rate in international core journals or authoritative conferences, and the researchers must have highly cited papers.

[0108] Technology transfer experience: The laboratory should have successful cases of technology transfer and be able to provide effective technology optimization and industrialization support.

[0109] Staffing: The laboratory research team should include senior researchers or professors, as well as technology transfer specialists familiar with market demands.

[0110] Extract matching university and research institution resource information from the database, and calculate the matching degree using an algorithm:

[0111]

[0112] in, Indicates the matching degree of university laboratory resources. Indicates the degree of matching with target requirements. This indicates the adjustment parameter.

[0113] The mid-potential range matching strategy includes matching research findings with relevant enterprises, with enterprises providing demand scenarios and participating in R&D, and collaborating with universities to optimize technical details. The focus is on promoting the transformation of laboratory results into pilot enterprise products. Enterprise resources are prioritized for pilot projects with marketability, providing opportunities for scenario testing of research results. Domain experts are organized to conduct a comprehensive assessment of technical feasibility and market potential, dynamically adjusting subsequent resource allocation plans.

[0114] Based on the field of the research findings, target companies matching the needs are selected from the enterprise database using a multi-target matching algorithm.

[0115]

[0116] in, Indicates the market demand weight of enterprises. This indicates the score for the company's testing site resources. Indicates the target innovation index. This represents the achievement innovation index.

[0117] The high-potential matching strategy includes ensuring that research findings closely align with industry needs, directly introducing them to target companies, and having these companies lead the productization process. A fast-track approach is provided for rapid marketization, including simplified approval procedures. Research findings are recommended for entry into specialized technology incubators, leveraging their funding, technology, and market resources for rapid implementation. Venture capital and angel investment are introduced, prioritizing projects with high growth potential.

[0118] Extract matching information from the enterprise and incubator resource database and dynamically calculate the matching degree:

[0119]

[0120] in, This represents the target company's market capability score. This indicates the incubator's resource suitability.

[0121] S3: Based on the matching results, design a specific conversion path.

[0122] The matching results include priority matching of university laboratory resources, matching of enterprise-university joint development models, and matching of enterprise direct introduction models.

[0123] It should be noted that the conversion path is shown in Table 1.

[0124] Table 1 Conversion Path

[0125]

[0126] The priority matching and transformation path for university laboratory resources is as follows:

[0127] Results Optimization and Experimental Verification Phase: Matching research findings are introduced into university laboratories, and specific research objectives are defined. The laboratories conduct basic verification experiments, generating detailed experimental reports and optimization suggestions.

[0128] Multi-round feedback and iteration phase: Based on laboratory feedback, the results are gradually improved, and their core technical parameters are refined. An expert review mechanism is introduced to regularly evaluate the progress of the optimization of the results.

[0129] Preparation phase for results dissemination: Based on the experimental results, prepare the project's technical specifications and application report to prepare for subsequent technical cooperation and pilot applications. After transitioning to the medium-potential range, resource matching will re-enter the process.

[0130] It should be noted that the priority matching path for university laboratory resources adopts a dynamic iteration and feedback mechanism, introducing a multi-dimensional evaluation model combined with expert review to ensure that the optimization of results has a scientific basis. The beneficial effects are:

[0131] Enhance the technological maturity of scientific research results: Through multiple rounds of experimental feedback and optimization, ensure that the core technical indicators of scientific research results meet the standards required for industrialization.

[0132] Enhancing the reliability and scientific rigor of the technology: Through expert review and experimental data support, a solid foundation is laid for subsequent technology promotion and cooperation.

[0133] Reduce technology transfer risks: Correct potential technical defects during the basic verification stage to avoid the risk of technology failure after entering the market.

[0134] The matching and transformation path for the enterprise-university joint development model is as follows:

[0135] Technology verification and demand matching phase: Universities and enterprises jointly establish project teams and clarify project objectives. Enterprises provide real-world demand scenarios, and university teams conduct technology verification.

[0136] Pilot project implementation phase: Conduct technology pilots in enterprise settings, collect actual data, and assess the technology's adaptability. Carry out small- to medium-scale trial production.

[0137] Joint optimization and results sharing phase: The project team optimizes the technology and improves the results based on pilot data. The allocation of technology intellectual property rights is clarified, and a sharing mechanism is established.

[0138] It should be noted that the enterprise-university joint development model integrates technology verification and demand matching, breaking through the traditional model of universities unilaterally outputting results and forming a multi-party resource interaction and optimization. The beneficial effects are:

[0139] Meet the customized needs of enterprises: By participating in real-world enterprise scenarios, we ensure the practical adaptability and market potential of our technical solutions.

[0140] Shorten the gap between pilot testing and mass production: Utilize pilot data to directly optimize technical solutions and avoid secondary development costs caused by delayed market feedback.

[0141] Promote collaborative innovation between industry, academia, and research: directly connect the research capabilities of universities with the market demands of enterprises to create a synergistic effect.

[0142] The direct conversion path for enterprises using pattern matching is as follows:

[0143] Technology introduction and adaptation phase:

[0144] The target company acquires the right to use the research results through a technology transfer agreement. The company's technical team then conducts secondary development of the results to adapt them to its existing product line.

[0145] Large-scale production and market promotion stage: Enterprises directly invest resources to transform technology into marketable products. Market feedback is collected through large-scale pilot production.

[0146] Results Feedback and Upgrade Phase: Optimize and iterate the technology based on market feedback. Further apply for patent protection and explore new market areas.

[0147] It should be noted that the direct enterprise adoption model employs a path of technology adaptation and enterprise resource integration, simultaneously conducting technology development and market promotion. The beneficial effects are:

[0148] Achieve rapid technology implementation: Directly conduct secondary development and production of the results, shortening the transformation cycle from technology to product.

[0149] Precise and efficient market promotion: Utilize existing corporate resources to directly conduct large-scale trial production and market launch.

[0150] Improve technology profitability: Maximize the value of technology applications by rapidly capturing the market.

[0151] Furthermore, unlike traditional single-mode transformation, this invention tailors transformation paths based on the characteristics of scientific research results, improving transformation efficiency. It introduces an evaluation and feedback loop to ensure the accuracy of resource allocation and dynamic adjustment capabilities. Combining the effectiveness evaluation of the transformation path with a reinforcement learning model, it provides continuously optimized path planning, guaranteeing a long-term success rate for technology transformation.

[0152] S4: Evaluate the effectiveness of the completed conversion path, collect actual conversion data, and optimize the conversion model and path planning algorithm.

[0153] It should be noted that the performance evaluation metrics are set based on the characteristics of the conversion path:

[0154] Technical dimension:

[0155] Technology Maturity Improvement Value: The difference between the completed technology maturity value and the initial value.

[0156] Technology adaptability: The degree to which the resulting technology matches the needs of the target scenario.

[0157] Economic dimension:

[0158] Commercialization cost: The total cost of investment from research and development to practical application.

[0159] Market return on investment: The ratio of revenue to cost of the commercialized product in the market.

[0160] Efficiency dimension:

[0161] Transformation cycle: The length of time from initial matching to market application of results.

[0162] The performance evaluation indicators are set according to the characteristics of the conversion path, and a weighted multi-objective evaluation model is adopted, which combines key indicators for comprehensive evaluation:

[0163]

[0164] in, Indicates the technology maturity improvement value. Indicates the cost of commercializing research results. Indicates market return rate, Indicates the conversion cycle. Indicates the degree of technology compatibility; , , This represents the weighting coefficient of the indicator.

[0165] It should be noted that the transformation path involves dimensions such as technology, economy, and efficiency, and key indicators are designed for each.

[0166] The importance of each indicator can be adjusted by weighting coefficients. For example, for short-term projects, the weight of efficiency indicators may be prioritized. This can be combined with negative terms (such as periodic penalties). In the comprehensive evaluation, the shortcomings of high-potential results should be balanced.

[0167] >0 indicates a good conversion rate. =0 indicates that the conversion rate met expectations but showed no significant improvement. A value less than 0 indicates that the conversion rate is lower than expected, and the path and resources need to be reassessed.

[0168] Key data generated during the transformation process are collected, including data on technological improvements, economic benefits, project progress, and user feedback. The collected raw data is then denoised and normalized, stored in a database, and labeled with its source, time, and relevant stage.

[0169] It should be noted that technical improvement data includes optimized technical parameters and performance test results. Economic benefit data includes total investment and market return. Project progress data includes time spent on each stage and completion time of key milestones. User feedback data includes market survey results and user satisfaction ratings.

[0170] Based on actual data, we improved the conversion model and path planning algorithm, aiming to improve resource matching efficiency, shorten the conversion cycle, and enhance the adaptability of technology to scenarios.

[0171] Design a reinforcement learning model to transform the path planning problem into a sequence decision problem:

[0172] Status: The current stage of the transformation path.

[0173] Actions: Optional optimized actions.

[0174] Rewards: Based on performance evaluation scores The reward is calculated using the following function:

[0175]

[0176] in, The penalty factor represents the conversion cycle.

[0177] Optimize the path planning algorithm by combining the output of the reinforcement learning model:

[0178]

[0179] in, This represents the optimized best path plan. Indicates the discount factor. This indicates the reward for the current stage.

[0180] It should be noted that all imaginary parameters (adjustment parameters, etc.) in this invention are set experimentally or based on experience, and can be changed according to the situation in actual use.

[0181] The above embodiments also include an industry-university-research cooperation system based on the transformation of scientific and technological innovation capabilities, specifically:

[0182] The calculation module collects scientific research data and constructs a multi-dimensional quantitative evaluation model based on the types of scientific research achievements and innovation capabilities.

[0183] The resource matching module dynamically matches industry-academia-research resources based on the evaluation results.

[0184] The conversion module designs specific conversion paths based on the matching results.

[0185] The optimization module evaluates the effectiveness of completed conversion paths, collects actual conversion data, and optimizes the conversion model and path planning algorithm.

[0186] The computer device can be a server. This computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data cluster data for the power monitoring system. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a collaborative industry-academia-research approach based on the transformation of technological innovation capabilities.

[0187] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0188] Example 2 is an embodiment of the present invention, which provides a method and system for industry-university-research cooperation based on the transformation of scientific and technological innovation capabilities. In order to verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0189] To verify the effectiveness of industry-academia-research collaboration methods and systems based on the transformation of scientific and technological innovation capabilities, research results in the field of new energy were selected as experimental subjects. The effects of existing technologies and this invention in research result evaluation, resource matching, and technology transfer path design were compared. Existing technologies employ traditional single-dimensional index evaluation methods and fixed matching mechanisms, while this invention introduces a multi-dimensional quantitative evaluation model, a dynamic matching algorithm, and a multi-path transformation mechanism.

[0190] Existing technology implementation process:

[0191] The current system evaluates the value of research results using an expert scoring method, with scores ranging from 0 to 100, primarily focusing on technological maturity. Results scoring above 60 are directly recommended to enterprises. The matching process uses a static resource table, with managers manually matching based on technological fields. The transformation path mainly relies on direct introduction by a single enterprise, without considering the adaptability of the results or feedback on the transformation effect. The entire process is highly dependent on human intervention and lacks dynamic adjustment capabilities.

[0192] Implementation process of this invention:

[0193] Research data is collected, and a multi-dimensional quantitative evaluation model is constructed using normalization and nonlinear functions to dynamically calculate the comprehensive evaluation value. Based on the evaluation results, achievements are divided into low-potential (0–50), medium-potential (50–80), and high-potential (80–100) ranges, and matched with university laboratory resources, enterprise joint development resources, and market incubation resources respectively. Knowledge graph analysis technology is introduced during the dynamic matching process to enhance the accurate matching capability between resources and achievements. For medium-potential achievements in the evaluation results, an enterprise joint development path is selected. Through actual demand scenarios provided by enterprises, universities are assisted in optimizing technologies and verifying the effects of pilot applications. After technology transfer, data is collected for multi-dimensional effect evaluation, including technology maturity improvement value, market feedback data, and transfer cycle, to further optimize the model and path.

[0194] The experimental results are shown in Table 2.

[0195] Table 2 Experimental Results

[0196]

[0197] Comparative experimental data clearly shows that the present invention significantly outperforms existing technologies in all key indicators:

[0198] In existing technologies, the technology maturity improvement values ​​of Result A and Result B are 10% and 12%, respectively, while this invention achieves improvements of 25% and 30% through dynamic evaluation and optimization. This demonstrates that the multidimensional quantitative evaluation model of this invention can effectively identify technological shortcomings and quickly compensate for them by accurately matching resources, thereby improving technology maturity.

[0199] The market demand matching rate of existing technologies is 50% and 55%, significantly lower than the 75% and 78% of this invention. This invention comprehensively considers technological maturity and market demand through a dynamic matching algorithm, making resource matching more accurate and improving the practical application value of scientific research results.

[0200] The academic impact scores of this invention are 88 and 85, a significant improvement over the 70 and 72 of the prior art. This indicates that this invention is better able to identify high-potential research results and, through resource support, transform them into results with real impact.

[0201] This invention shortens the conversion cycle to 120 days and 105 days, while existing technologies require 180 days and 210 days. This invention significantly improves conversion efficiency through dynamic adjustment of the conversion path and a real-time feedback mechanism.

[0202] The success rate of the transformation of this invention is as high as 85% and 90%, while that of the prior art is only 60% and 62%. This result verifies that the path optimization mechanism of this invention significantly improves the reliability and success rate of the transformation of results.

[0203] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A production-study-research cooperation method based on transformation of scientific and technological innovation capability, characterized in that, Comprise: Collect scientific research data, normalize, based on scientific research type and innovation ability data, construct multi-dimensional quantitative evaluation model; According to the evaluation results, the dynamic matching of industry-university-research resources is carried out; According to the matching results, the specific transformation path is designed; Effect evaluation is carried out on the completed transformation path, actual transformation data is collected, and transformation model and path planning algorithm are optimized; Based on scientific research type and innovation ability data, a multi-dimensional quantitative evaluation model is constructed, which includes scientific research type data evaluation, using weighted and nonlinear function to evaluate potential, represented as: wherein, represents the jth data of the ith type of achievement, represents the resource support data corresponding to the ith type of achievement, represents the innovation capability data corresponding to the ith type of achievement; For innovation ability data evaluation, use integral and nonlinear dynamic balance mechanism to evaluate potential, represented as: wherein, represents a time dynamic variable of technical maturity, represents the evaluation result of the scientific research achievement type data, represents the evaluation result of the innovation capability data; Combine the evaluation results of scientific research type data and the evaluation results of innovation ability data to construct a multi-dimensional quantitative evaluation model: wherein, denotes the final innovation capability comprehensive evaluation value, denotes the adjustment parameter, denotes the balance factor, k denotes the kth category, which is a category index from 1 to m, and m denotes the total number of scientific research achievement categories; Comprehensive evaluation value of innovation capability The range of its value is from 0 to positive infinity.

2. The industry-university-research cooperation method based on the transformation of scientific and technological innovation capability according to claim 1, characterized in that: The collection of scientific research data includes the collection of scientific research type data, innovation ability data and resource related data. 3.The industry-university-research cooperation method based on the transformation of scientific and technological innovation capability of claim 2, characterized in that: According to the evaluation results, the dynamic matching of industry-university-research resources includes dividing the evaluation results E into intervals, including low potential interval 0~50, medium potential interval 50~80 and high potential interval 80~∞; The matching strategy of low potential interval includes analyzing the shortcomings of the achievements, matching the achievements with the experimental laboratories of universities with strong research and development capabilities; According to the technical field of the achievements, the experimental laboratory resources with special experimental equipment and research direction matching are preferentially matched; The achievements are included in the basic scientific research special fund support, and the basic research fund project is dynamically associated; Through the experimental results feedback of resource party, E value is corrected, and subsequent research and development direction is dynamically adjusted; Extract matching university and scientific research institution resource information from database, calculate matching degree by algorithm: wherein, represents the matching degree of the laboratory resource of the university, represents the matching degree of the target demand, represents the adjustment parameter; The matching strategy of medium potential interval includes matching the achievements with related enterprises, providing demand scenarios and participating in research and development by enterprises, and optimizing technical details with universities; Focus on promoting laboratory achievements to enterprise pilot product direction transformation; Enterprise resources preferentially match pilot projects with marketization ability to provide opportunities for scientific research achievements to test scenarios; Organize field experts to comprehensively evaluate the technical feasibility and market potential, and dynamically correct the subsequent resource investment scheme; Combine the fields of the achievements to select target enterprises with demand matching from enterprise database, and use multi-objective matching algorithm: wherein, represents the enterprise market demand weight, represents the enterprise test site resource score, represents the target innovation index, represents the achievement innovation index; The matching strategy of high potential interval includes that the achievements are highly matched with industrial demand, and the target enterprises are directly introduced for productization; Provide a green channel for rapid marketization achievements, including simplifying the approval process; Recommend achievements to enter professional technology incubator to quickly land with its funds, technology and market resources; Introduce venture capital and angel investment, and preferentially match achievements with high growth potential; Extract matching information from enterprise and incubator resource database, and dynamically calculate matching degree: wherein, represents the target enterprise market capability score, represents the incubator resource fitness.

4. The industry-university-institute cooperation method based on the transformation of scientific and technological innovation capability according to claim 3, characterized in that: According to the matching results, the specific transformation path is designed, which includes university laboratory resource preferential matching, enterprise-university joint development mode matching and enterprise direct introduction mode matching; The transformation path of university laboratory resource preferential matching is: Result optimization and experimental verification stage: Matched scientific research results are introduced into university laboratories, and specific research goals are clearly defined. Laboratories conduct basic verification experiments and generate detailed experimental reports and optimization suggestions. Multi-round feedback iteration stage: Based on laboratory feedback, gradually improve the results and perfect their core technical parameters. Introduce expert review mechanism to regularly evaluate the optimization progress of the results. Result promotion preparation stage: Based on experimental results, prepare project technical specifications and application reports to prepare for subsequent technical cooperation and pilot application. Re-enter resource matching after entering the medium potential interval. Enterprise-university joint development mode matching transformation path is: Technology verification and demand docking stage: Universities and enterprises jointly establish project teams and clearly define project goals. Enterprises provide real demand scenarios, and university teams conduct technology verification. Pilot project landing stage: Conduct technology pilot in enterprise scenarios, collect actual data, and evaluate technology adaptability. Carry out small and medium-scale trial production. Joint optimization and result sharing stage: The project team optimizes the technology based on pilot data and improves the results. Clearly define technology property rights allocation and develop a sharing mechanism. Enterprise direct introduction mode matching transformation path is: Technology introduction and adaptation stage: Target enterprises obtain the right to use scientific research results through technology transfer agreements. Enterprise technology teams conduct secondary development on the results and adapt them to existing product lines. Large-scale production and market promotion stage: Enterprises directly invest resources to transform technology into marketable products. Collect market feedback through large-scale trial production. Result feedback and upgrade stage: Optimize and iterate technology based on market feedback. Further apply for patent protection and explore new market areas.

5. The industry-university-institute cooperation method based on the transformation of scientific and technological innovation capability according to claim 4, characterized in that: The effect evaluation of the completed transformation path includes that the indicators of effect evaluation are set according to the characteristics of the transformation path, a weighted multi-objective evaluation model is used, and key indicators are comprehensively evaluated: wherein, represents a value of technology maturity improvement, represents a cost of achievement transformation, represents a market return rate, represents a transformation period, represents a technology adaptation degree; , , represents a weight coefficient of the index; >0, indicates that the conversion effect is good, =0, indicates that the conversion effect meets the expectation but no significant improvement, <0, indicates that the conversion effect is lower than the expectation, the path and resources need to be re-evaluated.

6. The industry-university-institute cooperation method based on the transformation of scientific and technological innovation capability according to claim 5, characterized in that: The collection of actual transformation data, optimization of transformation model and path planning algorithm includes the collection of key data generated in the transformation path, including technology improvement data, economic benefit data, project progress data and user feedback data. The collected raw data is denoised and normalized, and the cleaned data is stored in the database with data source, time and related stage marked. According to the actual data, improve the transformation model and path planning algorithm, the goal is to improve the resource matching efficiency, shorten the transformation cycle, and improve the adaptability of technology and scene; Design a reinforcement learning model to convert the transformation path planning problem into a sequence decision problem: State: the current stage of the transformation path; Action: the available optimization action; Reward: according to the effect evaluation value The reward is calculated, with the reward function being: wherein, represents a penalty factor for the conversion period; Optimize the path planning algorithm combined with the output of the reinforcement learning model: wherein, represents the optimized best path planning, represents the discount factor, represents the reward of the current stage.

7. A science and technology innovation capability transformation-based industry-university-research cooperation system using the method of any one of claims 1-6, characterized by: A calculation module collects scientific research data, constructs a multi-dimensional quantitative evaluation model based on scientific research result types and innovation capability data; A matching resource module performs dynamic matching of industry-university-research resources based on the evaluation results; A transformation module designs a specific transformation path based on the matching results; An optimization module performs effect evaluation on the completed conversion path, collects actual conversion data, and optimizes the conversion model and path planning algorithm.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 6.

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