Scientific and technological big data service system and method for promoting transformation of scientific and technological achievements

Abstract

The invention discloses a science and technology big data service system and method for promoting transformation of science and technology achievements, and relates to the technical field of science and technology services and big data, and the system comprises a data collection and integration module, a data analysis and mining module, an intelligent matching and recommendation module, and a transformation service support module. The data acquisition and integration module is used for acquiring data from multiple data sources and cleaning, preprocessing, integrating and storing the data; and the data analysis and mining module is used for carrying out feature extraction, market demand analysis and association relationship mining on the data. According to the science and technology big data service system and method for promoting the transformation of the science and technology achievements, the science and technology achievements and related information can be comprehensively and accurately collected through the multi-source data collection and integration module, the problem that the information is dispersed and incomplete is solved, and a solid data basis is provided for subsequent analysis and service. And the data cleaning and preprocessing unit ensures the data quality and improves the accuracy and reliability of data analysis.

Description

Technical Field

[0001] This invention relates to the fields of science and technology services and big data technology, specifically to a science and technology big data service system and method for promoting the transformation of scientific and technological achievements. Background Technology

[0002] Technology transfer is the process of transforming scientific and technological achievements from knowledge into tangible products or commodities, or non-tangible services, thereby realizing economic value. Against the backdrop of accelerated technological innovation, the number of scientific and technological achievements is growing rapidly, but the efficiency of technology transfer is unsatisfactory. On the one hand, research institutions and universities possess a large number of scientific and technological achievements but struggle to find suitable application scenarios and transfer pathways; on the other hand, enterprises have a strong demand for new technologies and products but, due to information asymmetry and other issues, are unable to obtain matching scientific and technological achievements in a timely manner.

[0003] Traditional technology transfer models, such as searching and subscribing to technology information through third-party websites or technology provider websites, are cumbersome, and the accuracy and timeliness of the information are difficult to guarantee. Furthermore, technology transfer involves multiple factors, including the maturity of the technology itself, market demand, funding support, and the policy environment. Existing technologies struggle to comprehensively analyze and process these factors, thus failing to provide efficient and accurate services for technology transfer. To address these issues, we propose a technology big data service system and method to facilitate technology transfer. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the present invention provides a big data service system and method for promoting the transformation of scientific and technological achievements, and solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a big data service system for promoting the transformation of scientific and technological achievements, comprising a data collection and integration module, a data analysis and mining module, an intelligent matching and recommendation module, and a transformation service support module; the data collection and integration module is used to collect data from multiple data sources and perform cleaning, preprocessing, integration, and storage; the data analysis and mining module is used to extract features from the data, analyze market demand, and mine correlations; the intelligent matching and recommendation module is used to construct matching models and provide users with personalized recommendations and results display; the transformation service support module is used to conduct risk assessment, transformation path planning, and policy and regulatory consultation.

[0006] Preferably, the data acquisition and integration module includes a multi-source data acquisition unit, a data cleaning and preprocessing unit, and a data integration and storage unit. The multi-source data acquisition unit collects scientific and technological achievement data, market demand data, enterprise information data, and policy and regulatory data from research institution databases, university knowledge bases, technology achievement trading platforms, enterprise technology demand databases, and government science and technology policy websites. The data cleaning and preprocessing unit removes duplicate data, erroneous data, and noisy data, processes missing values, and performs standardization. The data integration and storage unit integrates the processed data according to a data model and stores it in a distributed database, establishing a data index.

[0007] Preferably, the data analysis and mining module includes a technology achievement feature extraction unit, a market demand analysis unit, and a correlation mining unit; the technology achievement feature extraction unit uses natural language processing technology and machine learning algorithms to extract keywords, technical points, and innovation points from technology achievement data; the market demand analysis unit mines market trends, potential demands, and characteristics of different types of market demands through time series analysis and cluster analysis; the correlation mining unit mines the correlation between technology achievements and market demands and enterprises through co-occurrence analysis and establishes a correlation model.

[0008] Preferably, the intelligent matching and recommendation module includes a matching model construction unit, a personalized recommendation unit, and a recommendation result display unit. The matching model construction unit uses cosine similarity algorithm and neural network algorithm to construct an intelligent matching model and calculate the matching degree based on the characteristics of scientific and technological achievements, market demand characteristics, and correlations. The personalized recommendation unit provides personalized recommendations of scientific and technological achievements based on the enterprise's historical behavior data and preference data, using collaborative filtering algorithm. The recommendation result display unit displays the recommendation results in an intuitive way, including detailed information on the scientific and technological achievements, matching degree score, and recommendation reasons.

[0009] Preferably, the transformation service support module includes a risk assessment unit, a transformation path planning unit, and a policy and regulation consultation unit; the risk assessment unit comprehensively considers technology maturity, market prospects, competition, and legal risks, and uses the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method to assess the risk level; the transformation path planning unit formulates personalized transformation paths based on the characteristics of scientific and technological achievements, enterprise needs, and risk assessment results; the policy and regulation consultation unit establishes a policy and regulation knowledge base and provides consultation services through keyword retrieval and semantic understanding.

[0010] This invention also discloses a method for promoting the transformation of scientific and technological achievements, comprising the following steps: a data collection and integration step, which involves collecting data from multiple data sources, cleaning, preprocessing, integrating, and storing the data; a data analysis and mining step, which involves extracting features from the data, analyzing market demand, and mining correlations; an intelligent matching and recommendation step, which involves building a matching model, providing personalized recommendations, and displaying the results; and a transformation service support step, which involves risk assessment, transformation path planning, and policy and regulatory consultation.

[0011] Preferably, the data acquisition and integration steps include: multi-source data acquisition, acquiring various types of data from preset data sources; data cleaning and preprocessing, removing duplicate, erroneous, and noisy data, processing missing values, and standardizing; and data integration and storage, integrating and storing the processed data into a distributed database according to a data model and establishing an index.

[0012] Preferably, the data analysis and mining steps include: feature extraction of scientific and technological achievements, using natural language processing technology and machine learning algorithms to extract keywords, technical points, and innovative points of scientific and technological achievements; market demand analysis, predicting market demand trends through time series analysis and clustering enterprises with similar demands using cluster analysis; and relationship mining, mining the relationship between scientific and technological achievements and market demand and enterprises through co-occurrence analysis and establishing a relationship model.

[0013] Preferably, the intelligent matching and recommendation steps include: matching model construction, which uses cosine similarity algorithm and neural network algorithm to construct a model to calculate the matching degree based on the characteristics of scientific and technological achievements, market demand characteristics and correlations; personalized recommendation, which uses collaborative filtering algorithm to provide personalized recommendations based on the enterprise's historical behavior and preference data; and recommendation result display, which displays detailed information on scientific and technological achievements, matching degree score and recommendation reasons in an intuitive way.

[0014] Preferably, the transformation service support steps include: risk assessment, which comprehensively considers factors such as technology maturity and market prospects, and uses the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method to assess the risk level; transformation path planning, which formulates personalized transformation paths based on the characteristics of scientific and technological achievements, enterprise needs, and risk assessment results; and policy and regulatory consultation, which obtains information from the policy and regulatory knowledge base through keyword retrieval and semantic understanding and provides it back to the user.

[0015] Beneficial effects

[0016] This invention provides a big data service system and method for promoting the transformation of scientific and technological achievements. Compared with existing technologies, it has the following advantages:

[0017] (1) This invention, through a multi-source data acquisition and integration module, can comprehensively and accurately collect scientific and technological achievements and related information, solving the problem of scattered and incomplete information, and providing a solid data foundation for subsequent analysis and services. The data cleaning and preprocessing unit ensures data quality and improves the accuracy and reliability of data analysis.

[0018] (2) The data analysis and mining module utilizes advanced technologies to deeply explore the characteristics and relationships between scientific and technological achievements and market demands, providing a scientific basis for intelligent matching and recommendation. It can discover potential opportunities for the transformation of scientific and technological achievements, improving the efficiency and accuracy of matching scientific and technological achievements with market demands.

[0019] (3) The personalized recommendation function of the intelligent matching and recommendation module can accurately recommend suitable scientific and technological achievements to enterprises based on their characteristics and needs, reducing the cost of information screening for enterprises and increasing the possibility of technology transfer. The recommendation result display unit presents the results in an intuitive way, making it convenient for users to quickly understand and select.

[0020] (4) The risk assessment unit of the transformation service support module provides risk warnings for the transformation of scientific and technological achievements and helps users make reasonable decisions; the transformation path planning unit formulates personalized transformation paths according to actual conditions, which improves the success rate of the transformation of scientific and technological achievements; the policy and regulation consultation unit provides policy and regulation support to users and ensures that the transformation of scientific and technological achievements is carried out within a legal and compliant framework. Attached Figure Description

[0021] Figure 1 This is a system block diagram of the present invention;

[0022] Figure 2 This is a schematic diagram of the data acquisition and integration module of the present invention;

[0023] Figure 3 This is a schematic diagram of the data analysis and mining module of the present invention;

[0024] Figure 4 This is a schematic diagram of the intelligent matching and recommendation module of the present invention;

[0025] Figure 5 This is a schematic diagram of the conversion service support module of the present invention.

[0026] In the diagram: 01. Data Acquisition and Integration Module; 02. Data Analysis and Mining Module; 03. Intelligent Matching and Recommendation Module; 04. Transformation Service Support Module; 011. Multi-Source Data Acquisition Unit; 012. Data Cleaning and Preprocessing Unit; 013. Data Integration and Storage Unit; 021. Scientific and Technological Achievement Feature Extraction Unit; 022. Market Demand Analysis Unit; 023. Relationship Mining Unit; 031. Matching Model Construction Unit; 032. Personalized Recommendation Unit; 033. Recommendation Result Display Unit; 041. Risk Assessment Unit; 042. Transformation Path Planning Unit; 043. Policy and Regulation Consultation Unit. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The present invention provides three technical solutions, specifically including the following embodiments:

[0029] Example 1

[0030] Please see Figures 1-2 A big data service system for promoting the transformation of scientific and technological achievements includes a data acquisition and integration module 01, a data analysis and mining module 02, an intelligent matching and recommendation module 03, and a transformation service support module 04. The data acquisition and integration module 01 is used to collect data from multiple data sources and perform cleaning, preprocessing, integration, and storage. The data analysis and mining module 02 is used to extract features from the data, analyze market demand, and mine correlations. The intelligent matching and recommendation module 03 is used to build matching models and provide users with personalized recommendations and results display. The transformation service support module 04 is used to conduct risk assessment, transformation path planning, and policy and regulatory consultation.

[0031] The data acquisition and integration module 01 includes a multi-source data acquisition unit 011, a data cleaning and preprocessing unit 012, and a data integration and storage unit 013. The multi-source data acquisition unit 011 collects scientific and technological achievement data, market demand data, enterprise information data, and policy and regulatory data from research institution databases, university knowledge bases, technology achievement trading platforms, enterprise technology demand databases, and government science and technology policy websites. The data cleaning and preprocessing unit 012 removes duplicate data, erroneous data, and noisy data, processes missing values, and performs standardization. The data integration and storage unit 013 integrates the processed data according to the data model and stores it in a distributed database, establishing a data index.

[0032] Multi-source data acquisition unit 011: Collects scientific and technological achievement data, market demand data, enterprise information data, and policy and regulatory data from multiple data sources, including databases of research institutions, university knowledge bases, technology transfer platforms, enterprise technology demand databases, and government science and technology policy websites. For example, it uses web crawling technology to retrieve published scientific and technological achievement information from technology transfer platforms, including achievement name, technical field, application scope, and contact person; and it obtains technology demand information published by enterprises from enterprise technology demand databases, such as demand descriptions, expected solution timelines, and budgets.

[0033] Data Cleaning and Preprocessing Unit 012: Cleans the collected data, removing duplicate, erroneous, and noisy data. Missing values ​​are handled using methods such as mean imputation and regression imputation. For example, for missing application cases in scientific and technological achievement data, if the average number of application cases for other achievements in the same field is 5, imputation can be performed based on this average. Data is standardized by unifying data formats and encodings, such as unifying the date format from different data sources to "YYYY-MM-DD".

[0034] Data Integration and Storage Unit 013: This unit integrates the cleaned and preprocessed data according to a defined data model and stores it in a distributed database, such as HBase. It also establishes data indexes for easy and rapid querying and retrieval. For example, it uses the unique identifier of a scientific or technological achievement (such as the achievement number) as an index to store detailed information about the achievement, including its technical principles, R&D team, and intellectual property details.

[0035] Example 2

[0036] Based on Example 1, see Figures 3-5 As shown, the data analysis and mining module 02 includes a technology achievement feature extraction unit 021, a market demand analysis unit 022, and a correlation mining unit 023. The technology achievement feature extraction unit 021 uses natural language processing technology and machine learning algorithms to extract keywords, technical points, and innovation points from technology achievement data. The market demand analysis unit 022 mines market trends, potential demands, and characteristics of different types of market demands through time series analysis and cluster analysis. The correlation mining unit 023 mines the correlation between technology achievements and market demands and enterprises through co-occurrence analysis and establishes a correlation model.

[0037] Technology Achievement Feature Extraction Unit 021: This unit utilizes natural language processing technology and machine learning algorithms to extract features from technology achievement data. It extracts features such as keywords, technical points, and innovative aspects from the descriptive text of the technology achievements. For example, for a technology achievement related to new energy batteries, keywords such as "high energy density," "fast charging," and "new electrode materials" are extracted as its features.

[0038] Market Demand Analysis Unit 022: Analyze market demand data to uncover market trends and potential demand. Predict changing trends in market demand for a specific technology area through time series analysis. For example, analyze the changes in demand for miniaturized and long-lasting battery technology in the smart wearable device market over the past 5 years to predict future market demand trends. Use cluster analysis to cluster companies with similar needs and identify the characteristics of different types of market demand.

[0039] Relationship Mining Unit 023: Mining the relationships between scientific and technological achievements, market demands, and enterprises. Through co-occurrence analysis, it identifies scientific and technological achievements and market demands that frequently co-occur in the same or related fields, and establishes a relationship model. For example, it discovers a strong correlation between a new sensor technology and the demand for environmental monitoring functions from companies in the smart home sector.

[0040] The intelligent matching and recommendation module 03 includes a matching model construction unit 031, a personalized recommendation unit 032, and a recommendation result display unit 033. The matching model construction unit 031 uses cosine similarity algorithm and neural network algorithm to construct an intelligent matching model and calculate the matching degree based on the characteristics of scientific and technological achievements, market demand characteristics, and correlations. The personalized recommendation unit 032 provides personalized recommendations of scientific and technological achievements based on the historical behavior data and preference data of enterprises and uses collaborative filtering algorithm. The recommendation result display unit 033 displays the recommendation results in an intuitive way, including detailed information on scientific and technological achievements, matching degree score, and recommendation reasons.

[0041] Matching Model Construction Unit 031: Based on the characteristics of scientific and technological achievements, market demand characteristics, and correlations, an intelligent matching model is constructed. Cosine similarity algorithms and neural network algorithms are used to calculate the matching degree between scientific and technological achievements and market demands. For example, the cosine similarity of the keyword vectors of scientific and technological achievements and the keyword vectors of market demands is calculated; a higher matching degree indicates a better fit between the two.

[0042] Personalized Recommendation Unit 032: Based on a company's historical behavioral data and preference data, this unit provides personalized recommendations for technological achievements. Using collaborative filtering algorithms, it identifies other companies with similar needs and behaviors to the target company and recommends the technological achievements these companies have followed or adopted to the target company. For example, if both Company A and Company B have shown interest in artificial intelligence image recognition technology in the past, then a new image recognition algorithm recently followed by Company A will be recommended to Company B.

[0043] Recommendation Results Display Unit 033: This unit presents the matching and recommendation results to users in an intuitive way, such as through a web interface or mobile application. The displayed content includes detailed information about the scientific and technological achievements, a matching score to the user's needs, and the reasons for the recommendation. For example, on a webpage, recommended scientific and technological achievements are displayed in a list format, with each achievement showing its name, field, and matching score (e.g., 85%). Clicking on the achievement name allows users to view detailed technical descriptions, application cases, research and development team, and other information.

[0044] The transformation service support module 04 includes a risk assessment unit 041, a transformation path planning unit 042, and a policy and regulation consultation unit 043. The risk assessment unit 041 comprehensively considers technology maturity, market prospects, competition, and legal risks, and uses the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method to assess the risk level. The transformation path planning unit 042 formulates personalized transformation paths based on the characteristics of scientific and technological achievements, enterprise needs, and risk assessment results. The policy and regulation consultation unit 043 establishes a policy and regulation knowledge base and provides consultation services through keyword retrieval and semantic understanding.

[0045] Risk Assessment Unit 041: This unit comprehensively assesses the risks associated with the commercialization of scientific and technological achievements, taking into account factors such as technological maturity, market prospects, competition, and legal risks. The analytic hierarchy process (AHP) is used to determine the weights of each risk factor, and the fuzzy comprehensive evaluation method is combined to calculate the risk level. For example, for a newly developed anticancer drug, the unit assesses its clinical trial progress (technological maturity), the market size and growth trend of cancer drugs (market prospects), competition from similar drugs, and patent infringement risks, ultimately providing a low, medium, or high risk assessment result.

[0046] Transformation Path Planning Unit 042: Based on the characteristics of scientific and technological achievements, enterprise needs, and risk assessment results, formulate personalized technology transfer paths. For scientific and technological achievements with high technological maturity and clear market demand, direct transfer or licensing to enterprises for implementation is recommended. For achievements whose technology still requires further research and development, industry-university-research cooperation is suggested for joint development and transformation. For example, for a new energy vehicle battery management system that has completed laboratory research and development but has not yet undergone industrialization verification, a cooperation path between industry, academia, and research is planned. The university research team continues to optimize the technology, while the enterprise is responsible for industrialization production and market promotion. Both parties jointly invest funds and resources and share the benefits of transformation.

[0047] Policy and Regulation Consultation Unit 043: This unit collects and organizes national and local policies and regulations concerning the commercialization of scientific and technological achievements, providing users with policy and regulation consultation services. It establishes a policy and regulation knowledge base and utilizes technologies such as keyword retrieval and semantic understanding to quickly respond to users' policy and regulation inquiries. For example, if a user inquires about tax incentives for the commercialization of scientific and technological achievements, the system retrieves relevant policy clauses from the policy and regulation knowledge base, providing detailed explanations of applicable conditions, preferential rates, application procedures, and other relevant information.

[0048] Example 3

[0049] Based on Example 2, see Figure 1 As shown, this invention also discloses a method for promoting the transformation of scientific and technological achievements, including the following steps: Data collection and integration steps:

[0050] Multi-source data acquisition: Activate the multi-source data acquisition unit to collect scientific and technological achievement data, market demand data, enterprise information data, policy and regulatory data, etc. from preset data sources such as scientific research institution databases, university knowledge bases, technology achievement trading platforms, enterprise technology demand databases, and government science and technology policy websites.

[0051] Data cleaning and preprocessing: The collected data is transmitted to the data cleaning and preprocessing unit for data cleaning, removal of duplicate, erroneous and noisy data, handling of missing values, and standardization of data format and encoding.

[0052] Data integration and storage: The cleaned and preprocessed data is integrated in the data integration and storage unit according to the data model and stored in the distributed database, and a data index is established.

[0053] Data analysis and mining steps:

[0054] Scientific and technological achievement feature extraction: Using natural language processing technology and machine learning algorithms, the feature extraction unit extracts features such as keywords, technical points, and innovative points from scientific and technological achievement data.

[0055] Market demand analysis: In the market demand analysis unit, time series analysis and cluster analysis are performed on market demand data to uncover market trends and potential demand, and enterprises with similar needs are clustered together.

[0056] Association mining: Using methods such as co-occurrence analysis, we can mine the associations between scientific and technological achievements, market demand, and enterprises in the association mining unit and establish an association model.

[0057] Intelligent matching and recommendation steps:

[0058] Matching model construction: Based on the characteristics of scientific and technological achievements, market demand characteristics, and correlations, an intelligent matching model is constructed in the matching model construction unit, and the matching degree is calculated using cosine similarity algorithm, neural network algorithm, etc.

[0059] Personalized Recommendation: Based on enterprises' historical behavior data, preference data, etc., collaborative filtering algorithms are used to provide enterprises with personalized recommendations of scientific and technological achievements in the personalized recommendation unit.

[0060] Recommendation Results Display: The matching and recommendation results are presented to users in an intuitive way through the recommendation results display unit, including detailed information on scientific and technological achievements, matching score, and reasons for recommendation.

[0061] Conversion service support steps:

[0062] Risk assessment: Taking into account factors such as the technological maturity, market prospects, competition, and legal risks of scientific and technological achievements, the risk assessment unit uses the analytic hierarchy process and fuzzy comprehensive evaluation method to assess the risks of technology transfer and give a risk level.

[0063] Transformation Path Planning: Based on the characteristics of scientific and technological achievements, enterprise needs, and risk assessment results, personalized technology transfer paths are developed in the transformation path planning unit, such as direct transfer, licensing, and industry-university-research cooperation.

[0064] Policy and regulation consultation: When users submit policy and regulation consultation questions, the system uses technologies such as keyword retrieval and semantic understanding to retrieve relevant policy and regulation content from the policy and regulation knowledge base in the policy and regulation consultation unit and provides it back to the user.

[0065] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0066] The embodiments of the invention have been described in detail above, but the content described is only a preferred embodiment of the invention and should not be considered as limiting the scope of the invention. All equivalent changes and improvements made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A big data service system for promoting the transformation of scientific and technological achievements, characterized in that: It includes a data acquisition and integration module (01), a data analysis and mining module (02), an intelligent matching and recommendation module (03), and a conversion service support module (04); the data acquisition and integration module (01) is used to collect data from multiple data sources and perform cleaning, preprocessing, integration, and storage; the data analysis and mining module (02) is used to extract features from the data, analyze market demand, and mine correlations; the intelligent matching and recommendation module (03) is used to build a matching model and provide users with personalized recommendations and results display; The conversion service support module (04) is used for risk assessment, conversion path planning and policy and regulatory consultation.

2. The science and technology big data service system for promoting the transformation of scientific and technological achievements according to claim 1, characterized in that: The data acquisition and integration module (01) includes a multi-source data acquisition unit (011), a data cleaning and preprocessing unit (012), and a data integration and storage unit (013). The multi-source data acquisition unit (011) collects scientific and technological achievement data, market demand data, enterprise information data, and policy and regulatory data from scientific research institution databases, university knowledge bases, scientific and technological achievement trading platforms, enterprise technology demand databases, and government science and technology policy websites. The data cleaning and preprocessing unit (012) removes duplicate data, erroneous data, and noisy data, processes missing values, and performs standardization processing. The data integration and storage unit (013) integrates the processed data according to the data model and stores it in a distributed database, establishing a data index.

3. The science and technology big data service system for promoting the transformation of scientific and technological achievements according to claim 1, characterized in that: The data analysis and mining module (02) includes a technology achievement feature extraction unit (021), a market demand analysis unit (022), and a correlation mining unit (023). The technology achievement feature extraction unit (021) uses natural language processing technology and machine learning algorithms to extract keywords, technical points, and innovation points from technology achievement data. The market demand analysis unit (022) mines market trends, potential demands, and characteristics of different types of market demands through time series analysis and cluster analysis. The correlation mining unit (023) mines the correlation between technology achievements and market demands and enterprises through co-occurrence analysis and establishes a correlation model.

4. A big data service system for promoting the transformation of scientific and technological achievements according to claim 1, characterized in that: The intelligent matching and recommendation module (03) includes a matching model construction unit (031), a personalized recommendation unit (032), and a recommendation result display unit (033). The matching model construction unit (031) constructs an intelligent matching model based on the characteristics of scientific and technological achievements, market demand characteristics, and correlations, and uses cosine similarity algorithm and neural network algorithm to calculate the matching degree. The personalized recommendation unit (032) provides personalized recommendations of scientific and technological achievements based on the enterprise's historical behavior data and preference data, and uses collaborative filtering algorithm. The recommendation result display unit (033) displays the recommendation results in an intuitive way, including detailed information on scientific and technological achievements, matching degree score, and recommendation reasons.

5. A big data service system for promoting the transformation of scientific and technological achievements according to claim 1, characterized in that: The transformation service support module (04) includes a risk assessment unit (041), a transformation path planning unit (042), and a policy and regulation consultation unit (043); the risk assessment unit (041) comprehensively considers technology maturity, market prospects, competition, and legal risks, and uses the analytic hierarchy process and fuzzy comprehensive evaluation method to assess the risk level; The transformation path planning unit (042) formulates personalized transformation paths based on the characteristics of scientific and technological achievements, enterprise needs, and risk assessment results; the policy and regulation consultation unit (043) establishes a policy and regulation knowledge base and provides consultation services through keyword retrieval and semantic understanding.

6. A method for promoting the transformation of scientific and technological achievements using the system described in any one of claims 1-5, characterized in that: Includes the following steps: The data acquisition and integration process involves collecting data from multiple data sources, cleaning, preprocessing, integrating, and storing the data. The data analysis and mining steps involve feature extraction, market demand analysis, and correlation mining of the data; the intelligent matching and recommendation steps involve building a matching model, providing personalized recommendations, and displaying the results; and the conversion service support steps involve risk assessment, conversion path planning, and policy and regulatory consultation.

7. The method for promoting the transformation of scientific and technological achievements according to claim 6, characterized in that: The data acquisition and integration steps include: multi-source data acquisition, collecting various types of data from preset data sources; data cleaning and preprocessing, removing duplicate, erroneous and noisy data, handling missing values ​​and standardizing; and data integration and storage, integrating and storing the processed data into a distributed database according to the data model and establishing an index.

8. The method for promoting the transformation of scientific and technological achievements according to claim 6, characterized in that: The data analysis and mining steps include: feature extraction of scientific and technological achievements, using natural language processing technology and machine learning algorithms to extract keywords, technical points, and innovative points of scientific and technological achievements; market demand analysis, predicting market demand trends through time series analysis and clustering enterprises with similar needs using cluster analysis; and relationship mining, mining the relationship between scientific and technological achievements and market demand and enterprises through co-occurrence analysis and establishing a relationship model.

9. A method for promoting the transformation of scientific and technological achievements according to claim 6, characterized in that: The intelligent matching and recommendation steps include: matching model construction, which uses cosine similarity algorithm and neural network algorithm to construct a model to calculate the matching degree based on the characteristics of scientific and technological achievements, market demand characteristics and correlations; personalized recommendation, which uses collaborative filtering algorithm to provide personalized recommendations based on the enterprise's historical behavior and preference data; and recommendation result display, which displays detailed information on scientific and technological achievements, matching degree score and recommendation reasons in an intuitive way.

10. A method for promoting the transformation of scientific and technological achievements according to claim 6, characterized in that: The transformation service support steps include: risk assessment, which comprehensively considers factors such as technology maturity and market prospects, and uses the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method to assess the risk level; transformation path planning, which formulates personalized transformation paths based on the characteristics of scientific and technological achievements, enterprise needs, and risk assessment results; and policy and regulatory consultation, which obtains information from the policy and regulatory knowledge base through keyword retrieval and semantic understanding and provides it back to the user.

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