Production-university-research cooperation method and system based on scientific and technological innovation capability transformation

Abstract

The invention discloses an industry-university-research cooperation method and system based on scientific and technological innovation ability conversion, and the method comprises the steps: collecting scientific research data, and constructing a multi-dimensional quantitative evaluation model based on the scientific research achievement type and innovation ability data. And carrying out industry-university-research resource dynamic matching according to an evaluation result. And designing a specific conversion path according to a matching result. And performing effect evaluation on the completed conversion path, collecting actual conversion data, and optimizing a conversion model and a path planning algorithm. According to the method, the potential value of the scientific and technological achievements is accurately evaluated, a scientific basis is provided for subsequent resource matching, and the evaluation accuracy is remarkably improved. And college, enterprise and incubator resources are dynamically matched. According to the steps, the resource utilization rate and the matching efficiency are greatly improved, and the technology conversion period is effectively shortened. Scientific and technological achievement transformation is more flexible and efficient, and the failure risk is remarkably reduced.

Description

Technical Field

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

[0002] With the rapid advancement of global scientific and technological innovation capabilities, industry-university-research collaboration has become a key model for promoting the commercialization of scientific and technological achievements. Universities and research institutions focus on basic research and technological development, while enterprises prioritize market demand analysis and technological application. This combination of the two can significantly improve the efficiency of the practical application of scientific research results. However, the existing industry-university-research collaboration model remains largely human-driven, with processes subject to significant subjective factors and a lack of scientific, dynamic evaluation methods. This results in inefficient resource matching, long technology transfer cycles, and uncertain success rates for technology promotion. Furthermore, in recent years, technologies such as artificial intelligence and big data have provided new opportunities for the commercialization of scientific and technological achievements. Using data-driven approaches to intelligently evaluate research results and dynamically match resources has become a key direction for the innovation of scientific and technological achievement commercialization models.

[0003] Although there are some research methods and systems for the transformation of scientific and technological achievements, they mainly focus on single-dimensional technology evaluation or experience-based resource matching, and lack a comprehensive scientific evaluation system. Existing technologies have significant shortcomings in the following aspects: First, technology evaluation lacks dynamism. It is only based on data from a single time point and cannot accurately predict the potential development trend of the technology; second, resource matching is inefficient. Usually, simple associations are made based on field matching, while ignoring key factors such as technology maturity and market adaptability; third, the transformation path is single, and there is a lack of diversified transformation plans tailored to the characteristics of different achievements. These problems not only slow down the efficiency of the transformation of scientific and technological achievements, but also limit the promotion and application of high-potential scientific 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 the present invention is: insufficient dynamic evaluation: solving the problem that the potential value of scientific research results cannot be dynamically evaluated in the existing technology, by introducing a multidimensional quantitative evaluation model and a dynamic balance mechanism, a dynamic quantitative evaluation of the value of scientific research results is realized.

[0006] Low resource matching efficiency: This solves the problem of single and inefficient resource matching in existing technologies. Through the dynamic matching strategy between partitions, the matching efficiency of resources and scientific research results is significantly improved.

[0007] Single transformation path: It overcomes the problem of 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 for evaluation and optimization: This solves the problem of lack of closed loop for evaluation and optimization in existing technologies. Through effect evaluation and path planning optimization, a complete improvement system is formed, which improves the efficiency and accuracy of subsequent transformation of scientific and technological achievements.

[0009] To solve the above technical problems, the present invention provides the following technical solutions: a method for industry-university-research cooperation based on the transformation of scientific and technological innovation capabilities, comprising: collecting scientific research data, and constructing a multidimensional quantitative evaluation model based on the types of scientific research results and innovation capability data.

[0010] Based on the evaluation results, dynamic matching of industry-university-research resources is carried out.

[0011] Design a specific conversion path based on the matching results.

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

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

[0014] As a preferred solution of the industry-university-research cooperation method based on the transformation of scientific and technological innovation capabilities described in the present invention, the multidimensional quantitative evaluation model based on the scientific research results type and innovation capability data includes evaluating the potential using a weighted and nonlinear function for the scientific research results type data evaluation, which is expressed as:

[0015] in, Represents the jth data of the i-th category achievement, Indicates the resource support data corresponding to the i-th category of results, Represents the innovation capability data corresponding to the i-th category of achievements.

[0016] For the evaluation of innovation capability data, the potential is evaluated using the integration and nonlinear dynamic balance mechanism, which is expressed as:

[0017] in, A time-dynamic variable representing the maturity of technology, Indicates the evaluation results of scientific research results type data, Represents the results of the evaluation of innovation capability data.

[0018] Combining the evaluation results of scientific research results and innovation capabilities, a multi-dimensional quantitative evaluation model is constructed:

[0019] in, Represents the final comprehensive evaluation value of innovation capability, represents the adjustment parameter, represents the balance factor, k represents the data normalization steepness parameter, and m represents the total number of scientific research results categories.

[0020] Comprehensive evaluation value of innovation capability The value range is from 0 to positive infinity.

[0021] As a preferred solution of the industry-university-research cooperation method based on the transformation of scientific and technological innovation capabilities described in the present invention, the dynamic matching of industry-university-research resources based on 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~∞.

[0022] The low-potential matching strategy involves analyzing the shortcomings of the research results and matching them with university laboratories with strong R&D capabilities. Based on the technical areas of the research results, priority is given to matching them with laboratories with specialized experimental equipment and research directions that align with the research direction. The research results are incorporated into special funding for basic scientific research and dynamically linked to basic research fund projects. The E-value is adjusted based on experimental feedback from the resource provider, and subsequent R&D directions are dynamically adjusted.

[0023] Extract matching university and research institution resource information from the database and calculate the matching degree through the algorithm:

[0024] in, Indicates the matching degree of university laboratory resources, Indicates the target demand matching degree, Indicates the adjustment parameter.

[0025] The matching strategy for the medium-potential segment involves matching research findings with relevant enterprises, with enterprises providing demand scenarios and participating in R&D, while collaborating with universities to optimize technical details. A key focus is on promoting the transformation of laboratory findings into pilot products for enterprises. Enterprise resources are prioritized for pilot projects with marketability, providing opportunities for scenario testing of research findings. Field experts are organized to conduct comprehensive assessments of technical feasibility and market potential, dynamically adjusting subsequent resource allocation plans.

[0026] Based on the field to which the results belong, target enterprises that match the needs are selected from the enterprise database, and a multi-objective matching algorithm is used:

[0027] in, represents the weight of enterprise market demand, Indicates the distribution of enterprise test site resources. represents the target innovation index, Represents the achievement innovation index.

[0028] The high-potential matching strategy involves directly recruiting target companies based on the close alignment of research findings with industry needs, with the companies leading the product development process. A fast track for rapid marketization of research findings is provided, including streamlined approval processes. Research findings are recommended for entry into professional technology incubators, leveraging their funding, technology, and market resources for rapid implementation. Venture capital and angel investors are also introduced, prioritizing projects with high growth potential.

[0029] Extract matching information from the enterprise and incubator resource database and dynamically calculate the matching degree:

[0030] in, Indicates the target enterprise's market capability score, Indicates the adaptability of incubator resources.

[0031] As a preferred solution of the industry-university-research cooperation method based on the transformation of scientific and technological innovation capabilities described in the present invention, wherein: the specific transformation path is designed based on the matching results, and the matching results include priority matching of university laboratory resources, enterprise-university joint development model matching and enterprise direct introduction model matching.

[0032] The priority matching transformation path for university laboratory resources is: Results Optimization and Experimental Verification Phase: Matched research results are introduced to university laboratories, with specific research objectives identified. The laboratories conduct basic verification experiments and generate detailed experimental reports and optimization recommendations.

[0033] Multi-round feedback 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 optimization progress of the results.

[0034] Preparation for the promotion of results: Based on the experimental results, prepare the project technical specifications and application reports to prepare for subsequent technical cooperation and pilot applications. After entering the medium potential range, re-enter resource matching.

[0035] The transformation path of enterprise-university joint development model matching is: Technology Verification and Demand Matching Phase: Universities and enterprises jointly establish project teams and clarify project goals. The enterprise provides real-world demand scenarios, and the university team conducts technology verification.

[0036] Pilot project implementation phase: Conduct technology pilots in enterprise scenarios, collect real-world data, and evaluate technology adaptability. Conduct small- to medium-scale trial production.

[0037] Joint Optimization and Results Sharing Phase: The project team optimizes technology and refines results based on pilot data. It also clarifies technology ownership and establishes a sharing mechanism.

[0038] The enterprise directly introduces the pattern matching conversion path as follows: Technology introduction and adaptation stage: The target company obtains the right to use the scientific 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 lines.

[0039] Large-scale production and market promotion: The company directly invests resources to transform the technology into a marketable product. Market feedback is collected through large-scale trial production.

[0040] Results feedback and upgrade stage: Optimize and iterate the technology based on market feedback. Further apply for patent protection and explore new market areas.

[0041] As a preferred solution of the industry-university-research cooperation method based on the transformation of scientific and technological innovation capabilities described in the present invention, the effect evaluation of the completed transformation path includes setting the effect evaluation indicators according to the characteristics of the transformation path, using a weighted multi-objective evaluation model, and conducting a comprehensive evaluation in combination with key indicators:

[0042] in, Indicates the improvement value of technology maturity, represents the cost of converting results, represents the market rate of return, Represents the conversion cycle, Indicates technical suitability. Indicates the weight coefficient of the indicator.

[0043] >0, indicating good conversion effect. =0, indicating that the conversion effect is in line with expectations but there is no significant improvement. <0 means the conversion effect is lower than expected and the path and resources need to be re-evaluated.

[0044] As a preferred embodiment of the industry-university-research collaboration method for transforming scientific and technological innovation capabilities described in the present invention, the collection of actual transformation data and optimization of the transformation model and path planning algorithm includes collecting key data generated along the transformation path, including technological improvement data, economic benefit data, project progress data, and user feedback data. The collected raw data is then de-noised and normalized, and the cleaned data is stored in a database, annotated with data source, time, and relevant stages.

[0045] Based on actual data, the conversion model and path planning algorithm are improved, with the goal of improving resource matching efficiency, shortening the conversion cycle, and improving the adaptability of technology and scenarios.

[0046] Design a reinforcement learning model to transform the path planning problem into a sequential decision-making problem: Status: The current stage of the conversion path.

[0047] Action: Optional optimization action.

[0048] Reward: Based on the effect evaluation value Calculate the reward, the reward function is:

[0049] in, Represents the penalty factor for the conversion cycle.

[0050] Combined with the reinforcement learning model output, the path planning algorithm is optimized:

[0051] in, represents the optimal path planning after optimization, represents the discount factor, Indicates the reward for the current stage.

[0052] An industry-university-research cooperation system based on the transformation of scientific and technological innovation capabilities, characterized by: including: The calculation module collects scientific research data and builds a multidimensional quantitative evaluation model based on the types of scientific research results and innovation capability data.

[0053] The resource matching module dynamically matches industry-university-research resources based on the evaluation results.

[0054] The conversion module designs a specific conversion path based on the matching results.

[0055] The optimization module evaluates the effectiveness of completed conversion paths, collects actual conversion data, and optimizes conversion models and path planning algorithms.

[0056] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0057] A computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

[0058] The present invention has the following beneficial effects: Collecting scientific research data and building an evaluation model: By collecting data on scientific research achievement types, 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, provides a scientific basis for subsequent resource matching, and significantly improves the accuracy of the evaluation.

[0059] Dynamic resource matching based on assessment results: The assessment results are categorized into low, medium, and high potential ranges, and differentiated matching strategies are adopted based on the characteristics of each range to dynamically match university, enterprise, and incubator resources. This step significantly improves resource utilization and matching efficiency, effectively shortening the technology transfer cycle.

[0060] Design specific transformation pathways: Based on different matching results, we develop three differentiated transformation pathways: university laboratory validation, enterprise joint development, and direct enterprise acquisition, ensuring that scientific and technological achievements can be commercialized in the best possible way. This step makes the transformation of scientific and technological achievements more flexible and efficient, significantly reducing the risk of failure.

[0061] Effect Evaluation and Path Optimization: We evaluate the effectiveness of transformation pathways using a weighted multi-objective evaluation model and dynamically optimize path planning using reinforcement learning to ensure the accuracy and efficiency of subsequent scientific and technological achievement transformation. This step completes a closed-loop improvement process of evaluation, transformation, and optimization, contributing to continuous improvement in system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them: Figure 1 This is an overall flow chart of an industry-university-research cooperation method and system based on the transformation of scientific and technological innovation capabilities provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0063] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0064] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides an industry-university-research cooperation method based on the transformation of scientific and technological innovation capabilities, including: S1: Collect scientific research data and build a multidimensional quantitative evaluation model based on the types of scientific research results and innovation capability data.

[0065] Collect data on scientific research results types, innovation capabilities and resource-related data and perform normalization processing.

[0066] It should be noted that data on research results includes research papers, patents, experiments, and technological achievements. Research results include information such as the journal in which they were published, their impact factor, citations, and research fields. Patent results include information such as patent type (invention, utility model, etc.), authorization status, technical field, and citations. Experimental results include experimental data size, experimental result type (performance testing, parameter optimization, etc.), and experimental application scenarios. Technological results include information such as technology readiness level (TRL) and technology development stage (proof of concept, prototype development, etc.).

[0067] Innovation capability data includes technical indicators, market value, and academic influence. Technical indicators include efficiency improvement percentage, innovation score (expert evaluation), and technical and economic benefits (cost / benefit ratio). Market value includes potential market size and alignment with industry needs. Academic influence includes citation frequency and core journal coverage.

[0068] Resource-related data includes team capacity and resource availability. Team capacity includes 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.

[0069] For the evaluation of scientific research results type data, the weighted and nonlinear functions are used to evaluate the potential, which is expressed as:

[0070] in, Represents the jth data of the i-th category achievement, Indicates the resource support data corresponding to the i-th category of results, Represents the innovation capability data corresponding to the i-th category of achievements.

[0071] It should be noted that different types of scientific research results have different influences and transformation values, and the importance of different types of results is reflected through weighting methods. For example, the market transformation value of patented technology may be higher than that of basic research papers. Nonlinear functions (such as logarithmic functions, square functions, etc.) are used to amplify or suppress scientific research data to enhance the influence of important data and weaken the interference of minor data. For example, It is used to reduce the excessive influence of large values on the overall evaluation, while the square function can amplify the effect of key variables.

[0072] For the evaluation of innovation capability data, the potential is evaluated using the integration and nonlinear dynamic balance mechanism, which is expressed as:

[0073] in, A time-dynamic variable representing the maturity of technology, Indicates the evaluation results of scientific research results type data, Represents the results of the evaluation of innovation capability data.

[0074] It should be noted that the data on innovation capability is usually related to time or external conditions, so an integral form is introduced to dynamically simulate the changes in the potential of achievements over time or conditions. Nonlinear functions (such as exponential functions, cosine functions, etc.) are used to balance the impact of different data. For example, The potential of results can be adjusted dynamically as the technology maturity changes. It is introduced as an integral variable to simulate the gradual improvement or decline of technological potential over time.

[0075] Combining the evaluation results of scientific research results and innovation capabilities, a multi-dimensional quantitative evaluation model is constructed:

[0076] in, Represents the final comprehensive evaluation value of innovation capability, represents the adjustment parameter, represents the balance factor, k represents the data normalization steepness parameter, and m represents the total number of scientific research results categories.

[0077] Comprehensive evaluation value of innovation capability The value range is from 0 to positive infinity.

[0078] S2: Dynamically match industry, academia and research resources based on the evaluation results.

[0079] The evaluation result E is divided into intervals, including the low potential interval 0~50, the medium potential interval 50~80, and the high potential interval 80~∞.

[0080] It should be noted that the low potential range indicates a low evaluation result, insufficient technical maturity of scientific research results, poor market fit, and weak innovation capabilities. Mainly limited by resource support and innovation capability data, scientific research results below this value range are suitable for further basic research or technological improvement.

[0081] The Medium Potential category indicates an intermediate assessment result. The technical maturity of the research results generally meets industry needs, and the innovation capabilities possess a certain appeal, but the marketability prospects require further clarification. The classification is based on the fact that research results in this category require precise resource matching to promote the commercialization of the technology.

[0082] The High Potential category indicates a high assessment result, indicating a high level of technological maturity and innovation, and the potential for direct commercialization or rapid application. Research findings above this category are suitable for efficient transformation through direct introduction or incubation by businesses.

[0083] The low-potential matching strategy involves analyzing the shortcomings of the research results and matching them with university laboratories with strong R&D capabilities. Based on the technical areas of the research results, priority is given to matching them with laboratories with specialized experimental equipment and research directions that align with the research direction. The research results are incorporated into special funding for basic scientific research and dynamically linked to basic research fund projects. The E-value is adjusted based on experimental feedback from the resource provider, and subsequent R&D directions are dynamically adjusted.

[0084] It should be noted that university laboratories with strong R&D capabilities must meet the following conditions: Completeness of experimental equipment: The laboratory must have high-end equipment covering matching technical fields, including but not limited to instruments, testing equipment, simulation experimental equipment, etc., which can support the basic verification and optimization of scientific research results.

[0085] Compatibility of research direction: The laboratory's main research direction must be highly consistent with the field in which the results are obtained, and it must have rich research accumulation and published results in this field.

[0086] Academic influence: The research projects in which the laboratory participates must have national, provincial or ministerial 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.

[0087] Technology transformation experience: The laboratory must have successful cases of transformation of scientific and technological achievements and be able to provide effective technology optimization and industrialization support.

[0088] Staffing: The laboratory research team must include senior researchers or professors, as well as technology transfer specialists who are familiar with market needs.

[0089] Extract matching university and research institution resource information from the database and calculate the matching degree through the algorithm:

[0090] in, Indicates the matching degree of university laboratory resources, Indicates the target demand matching degree, Indicates the adjustment parameter.

[0091] The matching strategy for the medium-potential segment involves matching research findings with relevant enterprises, with enterprises providing demand scenarios and participating in R&D, while collaborating with universities to optimize technical details. A key focus is on promoting the transformation of laboratory findings into pilot products for enterprises. Enterprise resources are prioritized for pilot projects with marketability, providing opportunities for scenario testing of research findings. Field experts are organized to conduct comprehensive assessments of technical feasibility and market potential, dynamically adjusting subsequent resource allocation plans.

[0092] Based on the field to which the results belong, target enterprises that match the needs are selected from the enterprise database, and a multi-objective matching algorithm is used:

[0093] in, represents the weight of enterprise market demand, Indicates the distribution of enterprise test site resources. represents the target innovation index, Represents the achievement innovation index.

[0094] The high-potential matching strategy involves directly recruiting target companies based on the close alignment of research findings with industry needs, with the companies leading the product development process. A fast track for rapid marketization of research findings is provided, including streamlined approval processes. Research findings are recommended for entry into professional technology incubators, leveraging their funding, technology, and market resources for rapid implementation. Venture capital and angel investors are also introduced, prioritizing projects with high growth potential.

[0095] Extract matching information from the enterprise and incubator resource database and dynamically calculate the matching degree:

[0096] in, Indicates the target enterprise's market capability score, Indicates the adaptability of incubator resources.

[0097] S3: Design a specific conversion path based on the matching results.

[0098] The matching results include priority matching of university laboratory resources, enterprise-university joint development model matching, and enterprise direct introduction model matching.

[0099] It should be noted that the conversion path is shown in Table 1.

[0100] Table 1 Conversion Path

[0101] The priority matching transformation path for university laboratory resources is: Results Optimization and Experimental Verification Phase: Matched research results are introduced to university laboratories, with specific research objectives identified. The laboratories conduct basic verification experiments and generate detailed experimental reports and optimization recommendations.

[0102] Multi-round feedback 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 optimization progress of the results.

[0103] Preparation for the promotion of results: Based on the experimental results, prepare the project technical specifications and application reports to prepare for subsequent technical cooperation and pilot applications. After entering the medium potential range, re-enter resource matching.

[0104] It should be noted that the priority matching path for university laboratory resources adopts a dynamic iteration and feedback mechanism, introduces a multi-dimensional evaluation model combined with expert review, and ensures that the optimization of results has a scientific basis. The beneficial effects are: Improve the technical 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.

[0105] Enhance technical reliability and scientificity: Through expert review and experimental data support, lay a solid foundation for subsequent technology promotion and cooperation.

[0106] Reduce the risk of technology transfer: Correct possible technical defects during the basic verification stage to avoid the risk of technology failure after entering the market.

[0107] The transformation path of enterprise-university joint development model matching is: Technology Verification and Demand Matching Phase: Universities and enterprises jointly establish project teams and clarify project goals. The enterprise provides real-world demand scenarios, and the university team conducts technology verification.

[0108] Pilot project implementation phase: Conduct technology pilots in enterprise scenarios, collect real-world data, and evaluate technology adaptability. Conduct small- to medium-scale trial production.

[0109] Joint Optimization and Results Sharing Phase: The project team optimizes technology and refines results based on pilot data. It also clarifies technology ownership and establishes a sharing mechanism.

[0110] It should be noted that the enterprise-university joint development model integrates technology verification and demand matching, breaking through the traditional university one-way output model and forming a multi-party resource interactive optimization. The beneficial effects are: Meeting the customized needs of enterprises: Ensure the practical adaptability and market potential of technical solutions through participation in real-world enterprise scenarios.

[0111] Shorten the gap between pilot testing and mass production: Use pilot data to directly optimize technical solutions and avoid secondary development costs caused by delayed market feedback.

[0112] Promote collaborative innovation between industry, academia and research: directly connect the scientific research capabilities of universities with the market needs of enterprises to form a synergistic effect.

[0113] The enterprise directly introduces the pattern matching conversion path as follows: Technology introduction and adaptation stage: The target company obtains the right to use the scientific 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 lines.

[0114] Large-scale production and market promotion: The company directly invests resources to transform the technology into a marketable product. Market feedback is collected through large-scale trial production.

[0115] Results feedback and upgrade stage: Optimize and iterate the technology based on market feedback. Further apply for patent protection and explore new market areas.

[0116] It should be noted that the enterprise direct introduction model adopts the path of technology adaptation and enterprise resource integration, and carries out technology development and market promotion simultaneously. The beneficial effects are: Achieve rapid implementation of technology: Directly carry out secondary development and production of results, shortening the transformation cycle from technology to product.

[0117] Accurate and efficient market promotion: Utilize the company's existing resources to directly conduct large-scale trial production and market launch.

[0118] Improve technology profitability: Maximize the value of technology applications by quickly capturing the market.

[0119] Furthermore, unlike traditional single-source transformation models, this invention tailors transformation pathways based on the characteristics of scientific research results to improve conversion efficiency. It also incorporates an evaluation and feedback loop to ensure accurate resource allocation and dynamic adjustment capabilities. Combining transformation pathway performance evaluation with reinforcement learning models provides continuously optimized path planning, ensuring the long-term success rate of technology transfer.

[0120] S4: Evaluate the effectiveness of the completed conversion path, collect actual conversion data, and optimize the conversion model and path planning algorithm.

[0121] It should be noted that the indicators for effect evaluation are set according to the characteristics of the conversion path: Technical dimension: Technology maturity improvement value: the difference between the completed value and the starting value of the technology maturity.

[0122] Technical adaptability: the degree of fit between the outcome technology and the requirements of the target scenario.

[0123] Economic dimension: Cost of transforming results: the total investment cost from research and development to practical application.

[0124] Market return rate: the ratio of revenue to cost of transformation results in the market.

[0125] Efficiency dimension: Conversion cycle: the length of time from the initial matching of results to market application.

[0126] The indicators for effect evaluation are set according to the characteristics of the conversion path. A weighted multi-objective evaluation model is used to conduct a comprehensive evaluation in combination with key indicators:

[0127] in, Indicates the improvement value of technology maturity, represents the cost of converting results, represents the market rate of return, Represents the conversion cycle, Indicates technical suitability. Indicates the weight coefficient of the indicator.

[0128] It should be noted that the transformation path involves dimensions such as technology, economy, and efficiency, and key indicators are designed for each dimension.

[0129] The importance of each indicator is adjusted by the weight coefficient. For example, for a short-term project, the weight of the efficiency indicator may be given priority. ), balancing the shortcomings of high-potential outcomes in comprehensive evaluations.

[0130] >0, indicating good conversion effect. =0, indicating that the conversion effect is in line with expectations but there is no significant improvement. <0 means the conversion effect is lower than expected and the path and resources need to be re-evaluated.

[0131] Collect key data generated along the conversion path, including technology improvement data, economic benefit data, project progress data, and user feedback data. De-noise and normalize the collected raw data, store the cleaned data in a database, and annotate the data source, time, and relevant stages.

[0132] It should be noted that technical improvement data includes optimized technical parameter values and performance test results. Economic benefit data includes total capital investment and market returns. Project progress data includes the duration of each phase and the completion time of key milestones. User feedback data includes market survey results and user satisfaction scores.

[0133] Based on actual data, the conversion model and path planning algorithm are improved, with the goal of improving resource matching efficiency, shortening the conversion cycle, and improving the adaptability of technology and scenarios.

[0134] Design a reinforcement learning model to transform the path planning problem into a sequential decision-making problem: Status: The current stage of the conversion path.

[0135] Action: Optional optimization action.

[0136] Reward: Based on the effect evaluation value Calculate the reward, the reward function is:

[0137] in, Represents the penalty factor for the conversion cycle.

[0138] Combined with the reinforcement learning model output, the path planning algorithm is optimized:

[0139] in, represents the optimal path planning after optimization, represents the discount factor, Indicates the reward for the current stage.

[0140] It should be noted that all imaginary parameters (adjustment parameters, etc.) in the present invention are set based on experiments or experience and can be changed according to circumstances in actual use.

[0141] The above embodiments also include an industry-university-research cooperation system based on the transformation of scientific and technological innovation capabilities, specifically: The calculation module collects scientific research data and builds a multidimensional quantitative evaluation model based on the types of scientific research results and innovation capability data.

[0142] The resource matching module dynamically matches industry-university-research resources based on the evaluation results.

[0143] The conversion module designs a specific conversion path based on the matching results.

[0144] The optimization module evaluates the effectiveness of completed conversion paths, collects actual conversion data, and optimizes conversion models and path planning algorithms.

[0145] The computer device may be a server. The computer device includes a processor, a memory, an input / output (I / O) interface, and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data cluster data of the power monitoring system. The I / O interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements an industry-university-research collaboration method based on the transformation of scientific and technological innovation capabilities.

[0146] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, 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 various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processors (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0147] Example 2 is an embodiment of the present invention, which provides an industry-university-research cooperation method and system based on the transformation of scientific and technological innovation capabilities. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0148] To verify the effectiveness of the industry-university-research collaboration method and system based on the transformation of scientific and technological innovation capabilities, we selected scientific research results in the new energy field as experimental subjects and compared the effectiveness of existing technologies and our present invention in terms of scientific research result evaluation, resource matching, and technology transformation path design. Existing technologies use traditional single-dimensional indicator evaluation methods and fixed matching mechanisms, while our present invention introduces a multidimensional quantitative evaluation model, a dynamic matching algorithm, and a multi-path transformation mechanism.

[0149] Existing technology implementation process: Existing technology assesses the value of research results using an expert scoring system ranging from 0 to 100, focusing primarily on technological maturity. Results with a score of 60 or above are directly recommended to businesses. The matching process utilizes a static resource table, with managers manually matching results based on technical fields. The transfer path primarily relies on direct introduction by a single enterprise, without considering the adaptability of the results or feedback on their effectiveness. The entire process is highly reliant on human intervention and lacks the ability to dynamically adjust.

[0150] Implementation process of the present invention: Scientific research data is collected, and a multidimensional quantitative evaluation model is constructed using normalization and nonlinear functions to dynamically calculate a comprehensive evaluation value. Based on the evaluation results, achievements are categorized into low-potential (0–50), medium-potential (50–80), and high-potential (80–100) ranges. These are then matched with university laboratory resources, corporate joint development resources, and market incubation resources, respectively. Knowledge graph analysis technology is introduced during the dynamic matching process to enhance the precise connection between resources and achievements. For achievements with medium potential identified in the evaluation results, a corporate joint development path is selected. Using actual demand scenarios provided by enterprises, universities are assisted in technology optimization and the effectiveness of pilot applications is verified. After technology transfer is completed, data is collected for a multidimensional effect evaluation, including technology maturity improvement values, market feedback data, and transfer cycle, to further optimize the model and path.

[0151] The experimental results are shown in Table 2.

[0152] Table 2 Experimental results

[0153] By comparing the experimental data, it can be clearly seen that the performance of the present invention is significantly better than that of the prior art in various key indicators: In existing technologies, the technical maturity improvements for Outcomes A and B were 10% and 12%, respectively. However, the present invention achieved improvements of 25% and 30% through dynamic evaluation and optimization. This demonstrates that the present invention's multi-dimensional quantitative evaluation model can effectively identify technical shortcomings and rapidly address them through precise resource matching, thereby improving technical maturity.

[0154] The market demand fit of existing technologies is 50% and 55%, significantly lower than the 75% and 78% of the present invention. The present invention uses a dynamic matching algorithm to comprehensively consider technology maturity and market demand, making resource matching more accurate and enhancing the practical application value of scientific research results.

[0155] The academic impact scores of this invention are 88 and 85, which are significantly higher than the existing technologies of 70 and 72. This shows that this invention can better identify high-potential scientific research results and transform them into results with practical influence through resource support.

[0156] The present invention shortens the conversion cycle to 120 days and 105 days, while the existing technology requires 180 days and 210 days. The present invention significantly improves conversion efficiency by dynamically adjusting the conversion path and implementing a real-time feedback mechanism.

[0157] The conversion success rates of the present invention are as high as 85% and 90%, while the existing technologies are only 60% and 62%. This result verifies that the path optimization mechanism of the present invention significantly improves the reliability and success rate of research results conversion.

[0158] 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 the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method of industry-university-research cooperation based on the transformation of scientific and technological innovation capabilities, characterized by: include: Collect scientific research data and build a multi-dimensional quantitative evaluation model based on the types of scientific research results and innovation capabilities; Dynamically match industry, academia and research resources based on the evaluation results; Design a specific conversion path based on the matching results; Evaluate the effectiveness of completed conversion paths, collect actual conversion data, and optimize conversion models and path planning algorithms.

2. The industry-university-research cooperation method based on the transformation of scientific and technological innovation capabilities according to claim 1 is characterized by: The collecting of scientific research data includes collecting scientific research achievement type data, innovation capability data and resource-related data, and performing normalization processing.

3. The method for industry-university-research cooperation based on the transformation of scientific and technological innovation capabilities according to claim 2, characterized in that: The multi-dimensional quantitative evaluation model based on the scientific research results type and innovation capability data includes evaluating the potential using weighted and nonlinear functions for the scientific research results type data, which can be expressed as: in, Represents the jth data of the i-th category achievement, Indicates the resource support data corresponding to the i-th category of results, represents the innovation capability data corresponding to the i-th category of achievements; For the evaluation of innovation capability data, the potential is evaluated using the integration and nonlinear dynamic balance mechanism, which is expressed as: in, A time-dynamic variable representing the maturity of technology, Indicates the evaluation results of scientific research results type data, It represents the result of the evaluation of innovation capability data; Combining the evaluation results of scientific research results and innovation capabilities, a multi-dimensional quantitative evaluation model is constructed: in, Represents the final comprehensive evaluation value of innovation capability, represents the adjustment parameter, represents the balance factor, k represents the data normalization steepness parameter, and m represents the total number of scientific research results categories; Comprehensive evaluation value of innovation capability The value range is from 0 to positive infinity.

4. The method for industry-university-research cooperation based on the transformation of scientific and technological innovation capabilities according to claim 3, characterized in that: The dynamic matching of 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-∞; The low-potential interval matching strategy includes analyzing the shortcomings of the results and matching them with university laboratories with strong R&D capabilities; prioritizing matching with laboratories with specialized experimental equipment and research directions based on the technical fields of the results; incorporating the results into special funds for basic scientific research and dynamically linking them to basic research fund projects; revising the E value through experimental result feedback from the resource party and dynamically adjusting the subsequent R&D direction; Extract matching university and research institution resource information from the database and calculate the matching degree through the algorithm: in, Indicates the matching degree of university laboratory resources, Indicates the target demand matching degree, represents the adjustment parameter; The matching strategy for the medium-potential range includes matching research results with relevant enterprises, with enterprises providing demand scenarios and participating in R&D, and collaborating with universities to optimize technical details; focusing on promoting the transformation of laboratory results into enterprise pilot products; prioritizing enterprise resources with pilot projects with marketability, providing scenarios for testing research results; organizing field experts to conduct comprehensive assessments of technical feasibility and market potential, and dynamically revising subsequent resource allocation plans; Based on the field to which the results belong, target enterprises that match the needs are selected from the enterprise database, and a multi-objective matching algorithm is used: ； in, represents the weight of enterprise market demand, Indicates the distribution of enterprise test site resources. represents the target innovation index, represents the innovation index of achievements; The high-potential segment matching strategy includes: directly introducing the research results to target enterprises when they are highly consistent with industry needs, with the enterprises taking the lead in productization; providing a green channel for rapid marketization of research results, including streamlining the approval process; recommending research results to professional technology incubators for rapid implementation with the help of their funding, technology, and market resources; and introducing venture capital and angel investment, prioritizing research results projects with high growth potential. Extract matching information from the enterprise and incubator resource database and dynamically calculate the matching degree: ； in, Indicates the target enterprise's market capability score, Indicates the adaptability of incubator resources.

5. The method for industry-university-research cooperation based on the transformation of scientific and technological innovation capabilities according to claim 4, characterized in that: According to the matching results, the specific transformation path is designed, including the matching results including priority matching of university laboratory resources, enterprise-university joint development model matching and enterprise direct introduction model matching; The priority matching transformation path for university laboratory resources is: Results optimization and experimental verification phase: Introducing matching scientific research results into university laboratories, clarifying specific research objectives; the laboratory conducts basic verification experiments, and generates detailed experimental reports and optimization suggestions; Multiple rounds of feedback iteration: 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 optimization progress of the results; Preparation stage for achievement promotion: Based on the experimental results, prepare the project technical specifications and application reports to prepare for subsequent technical cooperation and pilot applications; after entering the medium potential range, re-enter resource matching; The transformation path of enterprise-university joint development model matching is: Technology Verification and Demand Matching Stage: Universities and enterprises jointly establish project teams and clarify project goals. Enterprises provide real demand scenarios, and the university team conducts technology verification. Pilot project implementation phase: Conduct technology pilots in enterprise scenarios, collect actual data, and evaluate technology adaptability; Conduct small- to medium-scale trial production; Joint Optimization and Results Sharing Phase: The project team optimizes technology and improves results based on pilot data; clarifies technology property rights allocation and establishes a sharing mechanism; The enterprise directly introduces the pattern matching conversion path as follows: Technology introduction and adaptation stage: The target company obtains the right to use the scientific research results through a technology transfer agreement; the company's technical team conducts secondary development of the results to adapt them to its existing product lines; Large-scale production and market promotion stage: The company directly invests resources to transform the technology into a marketable product; collects market feedback through large-scale trial production; Results feedback and upgrade stage: optimize and iterate the technology based on market feedback; further apply for patent protection and explore new market areas.

6. The method for industry-university-research cooperation based on the transformation of scientific and technological innovation capabilities according to claim 5, characterized in that: The effect evaluation of the completed conversion path includes setting the effect evaluation indicators according to the characteristics of the conversion path, using a weighted multi-objective evaluation model, and conducting a comprehensive evaluation in combination with key indicators: in, Indicates the improvement value of technology maturity, represents the cost of converting results, represents the market return rate, Represents the conversion cycle, Indicates technology suitability; Indicates the weight coefficient of the indicator; >0, indicating good conversion effect. =0, indicating that the conversion effect is in line with expectations but there is no significant improvement. <0 means the conversion effect is lower than expected and the path and resources need to be re-evaluated.

7. The method for industry-university-research cooperation based on the transformation of scientific and technological innovation capabilities according to claim 6, characterized in that: The collecting of actual conversion data and the optimization of the conversion model and path planning algorithm include collecting key data generated in the conversion path, including technical improvement data, economic benefit data, project progress data, and user feedback data; De-noise and normalize the collected raw data, store the cleaned data in the database, and mark the data source, time and relevant stages; Based on actual data, we improve the conversion model and path planning algorithm, with the goal of improving resource matching efficiency, shortening the conversion cycle, and improving the adaptability of technology and scenarios. Design a reinforcement learning model to transform the path planning problem into a sequential decision-making problem: Status: The current stage of the conversion path; Action: optional optimization action; Reward: Based on the effect evaluation value Calculate the reward, the reward function is: ； in, represents the penalty factor of the conversion cycle; Combined with the reinforcement learning model output, the path planning algorithm is optimized: ； in, represents the optimal path planning after optimization, represents the discount factor, Indicates the reward for the current stage.

8. An industry-university-research cooperation system based on the transformation of scientific and technological innovation capabilities using the method according to any one of claims 1 to 7, characterized in that: The calculation module collects scientific research data and builds a multi-dimensional quantitative evaluation model based on the types of scientific research results and innovation capabilities; The resource matching module dynamically matches industry, academia, and research resources based on the evaluation results; The conversion module designs a specific conversion path based on the matching results; The optimization module evaluates the effectiveness of completed conversion paths, collects actual conversion data, and optimizes conversion models and path planning algorithms.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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