User Requirement Analysis Method and System for Technology Transfer Service Platform

Abstract

The present invention discloses a method and system for user demand analysis of a technology transfer service platform. The method includes: S1. Setting a specific network port through the technology transfer service platform to collect user demand data, and constructing a user portrait by using data analysis and mining techniques; S2. Conducting in-depth analysis of the collected user data, identifying the interest descriptions and demand characteristics of users, and defining the demand proportion of users; S3. Constructing a matching model through a deep learning algorithm to perform intelligent demand matching between the user demand proportion and the technical resources of the technology transfer service platform; S4. Constructing a permission technology repository to store the technical resources of the client side and setting permissions for resource protection. The present invention realizes personalized recommendation through constructing a user portrait and in-depth demand analysis, realizes intelligent matching between technical resources and user demands through constructing a matching model, establishes a permission technology repository and sets permissions to ensure the security protection of technical resources, and improves information security and confidentiality.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and specifically to a user demand analysis method and system for a technology transfer service platform. Background Art

[0002] The technology transfer platform is a medium and bridge between technology producers and demanders, and has unique advantages in promoting the transformation of scientific and technological achievements. With the continuous deepening of the activity of the technology trading market, the service demands of technology transfer entities are also developing in the direction of specialization, comprehensiveness, and in-depth.

[0003] Currently, technology transfer service platforms of various scales have been established across the country. The service processes of most platforms mainly focus on the display and release stages of technology achievements, lacking targeted user demand analysis and personalized service design. As a result, the upstream link of the service is emphasized, while the downstream link of user demand is neglected. At the same time, service resources and information resources cannot be effectively integrated in a timely manner, and technology achievements and demand information cannot be effectively matched, resulting in the failure to meet user demands in a timely and accurate manner. Summary of the Invention

[0004] The purpose of the present invention is to provide a user demand analysis method and system for a technology transfer service platform, to achieve more accurate user demand analysis and matching, and to provide personalized technology resource recommendations.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] The present application provides a user demand analysis method for a technology transfer service platform, including the following steps:

[0007] S1. Set a specific network port on the technology transfer service platform to perform data interaction and information transmission with the technology transfer service platform. Users log in and interact on the technology transfer service platform. The specific network port collects demand data composed of users' personal information, behavior data, and preferences during the user's use process, cleans and integrates the demand data, and uses data analysis and mining technologies to extract and analyze the features of the cleaned data, identify the key features of users' basic information, preferences, and behavior data, and then combine the key features together to construct a user portrait model;

[0008] S2. Deeply analyze the collected user data, select split hierarchical clustering to identify the user's interest descriptions and demand characteristics, define the user's demand ratio, and perform precise personalized recommendations according to the demand ratio, including: assigning different weights to the behavior data and interest description data of different user groups, setting the ratio coefficient to 1. When the proportion of user behavior data is greater than 0.8, the technology transfer platform automatically recommends content related to the user's behavior data, and preferentially displays technical resources and information related to the user's actual behavior; when the proportion of interest description data is the same as that of the user's behavior data, the content displayed by the technology transfer platform is still mainly interest description data;

[0009] S3. Based on the user portrait and the results of demand analysis, construct a matching model through deep learning algorithms, and perform intelligent demand matching between the user demand ratio and the technical resources of the technology transfer service platform, including: based on the user portrait and the results of demand analysis, extract basic information, preferences and key features of behavior data from the user portrait, and convert the key features into numerical feature vectors. Use the neural network model of deep learning algorithms to construct a matching model, set the number of input layer nodes for the number of user data features, and design the hidden layer structure and the number of neurons, including multiple hidden layers, and then the number of output layer nodes and the predicted values. Match the user demand ratio with the technical resources, and train the model through the backpropagation algorithm. The trained matching model is deployed to the technology transfer service platform for real-time user demand matching tasks; the matching model recommends resources by calculating the matching degree between the user demand and the technical resources, including setting two vectors to represent the user demand characteristics and the technical resource characteristics, which are respectively and , where n represents the number of features; calculate the Euclidean distance to measure the similarity between the two vectors

[0010] Then set the weights of each feature, expressed as a weight vector;

[0011] Obtain the weighted matching degree score according to the weight vector and the Euclidean distance, that is, obtain the comprehensive matching degree score;

[0012] S4. Based on the technology transfer service platform, construct a permission technology repository for storing the technical resources of the client, set permissions for resource protection, and then authorize through setting intelligent windows and manual windows. The permission technology repository includes a second technology repository for storing the latest research results, patented technologies and innovative solutions.

[0013] Further, in step S1, a user portrait model is constructed to describe and analyze the characteristics and needs of users. At the same time, the technology transfer service platform continuously optimizes the user portrait by continuously collecting user data, analyzing user behavior and feedback information.

[0014] Furthermore, based on the in-depth analysis of the collected user data in step S2, a clustering tree is constructed by gradually splitting data points through hierarchical clustering. By defining a similarity measurement method to measure the similarity between different users, the splitting hierarchical clustering is selected to identify the interest descriptions and demand characteristics of users, specifically including:

[0015] S21. Initialization: Each user is regarded as a separate cluster.

[0016] S22. Similarity calculation: Calculate the similarity between each pair of users.

[0017] S23. Splitting: Split the most similar user cluster according to the similarity to form new clusters.

[0018] S24. Repeat steps S22 and S23 until all users are merged into a large cluster.

[0019] Furthermore, based on the clustering results and the generated clustering tree, different user groups and categories are identified.

[0020] Furthermore, in step S3, a comprehensive matching score is calculated to quantify the matching degree between user needs and technical resources.

[0021] Furthermore, the prerequisite for the intelligent window to grant permissions is that the user sends an application signal for communication. The intelligent window receives the application and performs intelligent authorization within 10 seconds. The manual window requires a specific permission application. By receiving the user application and conducting a review, the user identity and needs are verified, and whether to authorize the user to access permissions is decided based on the review results.

[0022] Furthermore, the permission technology repository also includes a first technology repository for storing general and conventional technical resources.

[0023] This application also provides a user demand analysis system for a technology transfer service platform, including a demand analysis module, an intelligent matching module, and a permission technology repository;

[0024] The demand analysis module includes a user demand unit and a user identification and division unit. The user demand unit is used to collect and analyze user demand information, including the description and characteristics of user needs. The user identification and division unit is used to identify and divide different user groups, establish user portraits, understand user behavior data and preferences, and provide customized services for different user groups;

[0025] The intelligent matching module includes a user portrait unit and a user matching unit. The user portrait unit constructs a user portrait based on the user's personal information, behavior data, and demand characteristics. The user matching unit is used to intelligently match user needs and technical resources;

[0026] The described permission technology repository includes a technology storage unit and a permission management unit. The technology storage unit includes a first technology repository and a second technology repository, which are used to store various types of technical resources and information;

[0027] The permission management unit includes a first permission management and a second permission management. The first permission management is connected to the intelligent window and intelligently grants permissions to users. By intelligently granting permissions, users can view the technical content in the first technology repository; the second permission management is connected to the manual window, and users can apply for the second permission through the manual window to have the right to view the technical content in the second technology repository.

[0028] The beneficial effects of the present invention are as follows:

[0029] By describing and analyzing user characteristics and needs through the user portrait model, the user needs and preferences can be accurately grasped, providing personalized technical resource recommendations and customized services for users;

[0030] By gradually splitting data points through hierarchical clustering to construct a clustering tree, the integrated service resources and information resources can be effectively integrated. Then, according to the clustering results and the clustering tree generated by the analysis and the proportion of user needs, the technology transfer platform can conduct more accurate and targeted user needs analysis and personalized recommendations, and avoid recommending too many technical resources irrelevant to user needs, which can improve user satisfaction, increase user stickiness to the platform, and at the same time help improve the utilization efficiency of technical resources. By reasonably setting and using the proportion of user needs, the technology transfer platform can achieve personalized services, realize more accurate user needs analysis and personalized recommendations, and improve the accuracy of recommendations and user satisfaction;

[0031] By constructing a matching model through the neural network model, the user needs and technical resources are intelligently matched, and then the comprehensive matching degree score is calculated to quantitatively represent the matching degree between user needs and technical resources, providing a more accurate technical resource recommendation service that meets user needs, solving the problem of low matching degree between technical achievements and demand information, enabling users' needs to be responded to in a timely manner, and improving the utilization efficiency of technical resources;

[0032] By constructing a permission technology repository to store various types of technical resources and information, and setting access permissions on the technology transfer service platform, and authorizing through the intelligent window and the manual window, the user permissions are effectively managed, the security and confidentiality of technical resources are protected, and the orderly circulation and utilization of technical resources are promoted. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] For a better understanding and implementation, the technical solutions of the present application will be described in detail below with reference to the accompanying drawings.

[0034] Figure 1It is the flowchart of the steps of the user demand analysis method for the technology transfer service platform provided in Embodiment 1 of this application;

[0035] Figure 2 It is the flowchart of the steps of constructing a clustering tree for the user demand analysis method for the technology transfer service platform provided in Embodiment 1 of this application;

[0036] Figure 3 It is the structural block diagram of the user demand analysis system for the technology transfer service platform provided in Embodiment 1 of this application. Detailed implementation manners

[0037] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the exemplary embodiments will be described in detail herein, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with this application. On the contrary, they are only examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0038] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms "a", "the", and "said" used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0039] The following will detail the specific implementation manners, features, and effects of the present invention in conjunction with the drawings and preferred embodiments.

[0040] Embodiment 1

[0041] Please refer to Figures 1 - 3 , this embodiment provides a user demand analysis method and system for the technology transfer service platform, which realizes more accurate user demand analysis and matching and provides personalized technology resource recommendations.

[0042] The present invention provides a user demand analysis method for the technology transfer service platform, including the following steps:

[0043] S1. Set specific network ports through the technology transfer service platform to collect demand data such as users' personal information, behavior data, and preferences, and use data analysis and mining technologies to construct user portraits, including users' basic information, preferences, behavior data, etc.;

[0044] S2. Deeply analyze the collected user data, identify the user's interest descriptions and demand characteristics, define the proportion of the user's demands, and perform precise personalized recommendations according to the proportion of demands;

[0045] S3. Based on the user portrait and the results of demand analysis, construct a matching model through deep learning algorithms, and perform intelligent demand matching between the proportion of user demands and the technical resources of the technology transfer service platform;

[0046] S4. Build a permission-based technology repository based on the technology transfer service platform to store the technical resources of the client, and set permissions for resource protection.

[0047] Through user portrait and demand analysis, personalized recommendations are realized to improve user satisfaction, and a matching model is constructed using deep learning algorithms to achieve intelligent matching of technical resources and user demands, thereby enhancing the service level, strengthening the efficiency and quality of the technology transfer service platform. By establishing a permission-based technology repository and setting permissions for resource protection, the security and protection of technical resources are ensured.

[0048] Furthermore, according to step S1, set a specific network port on the technology transfer service platform, perform data interaction and information transmission with the technology transfer service platform through the specific network port. Users log in and perform interaction operations on the technology transfer service platform. The specific network port collects the user's personal information, behavior data, and preference and other demand data during the user's use process, including the user's basic information such as name, age, occupation, behavior data such as browsing records, click behaviors, and preference data such as favorite fields, preference types;

[0049] Clean and integrate the collected user demand data, use data analysis and mining techniques to extract and analyze the features of the cleaned data, identify the key features of the user's basic information, preferences, and behavior data, and then combine the key features together to construct a user portrait model for describing and analyzing the user's features and demands. At the same time, the technology transfer service platform continuously optimizes the user portrait by continuously collecting user data, analyzing user behaviors, and feedback information to ensure the accuracy and practicality of the user portrait.

[0050] Furthermore, according to step S2, deeply analyze the collected user data, construct a clustering tree by gradually splitting data points through hierarchical clustering, define a similarity measurement method for measuring the similarity between different users, and select split hierarchical clustering to identify the user's interest descriptions and demand characteristics, specifically including:

[0051] S21. Initialization, regard each user as a separate cluster;

[0052] S22. Similarity calculation, calculate the similarity between each pair of users;

[0053] S23. Split, split the clusters of the most similar users according to the similarity to form new clusters;

[0054] S24. Repeat steps S22 and S23 until all users are merged into one large cluster.

[0055] By performing hierarchical clustering analysis on user data and identifying user groups and demand characteristics, the technology transfer service platform can more effectively integrate and provide service resources and information resources that meet user needs, improve the user experience and the service level of the platform. The integrated service and information resources will be more targeted and attractive, which helps to meet user needs.

[0056] Furthermore, based on the clustering results and the clustering tree generated by the analysis, different user groups and categories are identified, different weights are assigned to the behavior data and interest description data of different user groups, the proportion coefficient is set to 1, and the ratio between the user behavior data and the interest description data is judged. When the proportion of user behavior data is greater than 0.8, it indicates that the user is more inclined to express needs through behavior data. The technology transfer platform automatically recommends content related to the user's behavior data, and preferentially displays technical resources and information related to the user's actual behavior to improve the accuracy of recommendations and user satisfaction. When the proportion of interest description data is the same as that of user behavior data, the content displayed by the technology transfer platform is still mainly based on interest description data.

[0057] By setting the proportion of user needs, the technology transfer platform can recommend more precisely according to the user's needs, avoid recommending too many technical resources irrelevant to the user's needs, improve user satisfaction, increase user stickiness to the platform, and at the same time help to improve the utilization efficiency of technical resources. By reasonably setting and using the proportion of user needs, the technology transfer platform can achieve personalized services, improve the user experience and the competitiveness of the platform.

[0058] Furthermore, according to the user portrait and the results of demand analysis in step S3, basic information, preferences and key features of behavior data are extracted from the user portrait, and the key features are transformed into numerical feature vectors. A matching model is constructed using the neural network model of the deep learning algorithm. The number of user data feature quantities is set as the number of input layer nodes, and the hidden layer structure and the number of neurons, including multiple hidden layers, and then the number of output layer nodes and the predicted values are used to match the proportion of user needs with technical resources. The model is trained through the backpropagation algorithm, and the trained matching model is deployed to the technology transfer service platform for real-time user demand matching tasks.

[0059] Through the technology transfer service platform, based on the user portrait and the results of demand analysis, a matching model can be constructed using deep learning algorithms to achieve intelligent matching of user needs according to user needs and technical resources, providing more accurate recommendations for technical resources, thereby improving user satisfaction and experience.

[0060] Furthermore, the matching model recommends resources by calculating the matching degree between user needs and technical resources. Two vectors are set to represent user need characteristics and technical resource characteristics, respectively, as and , where n represents the number of characteristics; the Euclidean distance is used to measure the similarity between the two vectors, and the calculation formula is:

[0061] ;

[0062] Then a weight vector is set to represent the weights of each characteristic, and the matching degree score calculated by weighted calculation is: ;

[0063] The weighted similarity results are weighted and summed to obtain a comprehensive matching degree score, ;

[0064] Through the above calculation formula, according to the importance and influence of the characteristics, combined with the similarity results calculated by the Euclidean distance, a comprehensive matching degree score can be calculated to quantitatively represent the matching degree between user needs and technical resources.

[0065] Furthermore, according to the construction of the permission technology repository in step S4, it is used to store various technical resources and information, and then authorization is performed through setting up an intelligent window and a manual window. The premise for the intelligent window to grant permission is that the user sends an application signal for communication. The intelligent window receives the application and performs intelligent authorization within 10 seconds. The manual window needs to apply for specific permissions, receives the user application and conducts a review, verifies the user identity and needs, and decides whether to grant the user access permission according to the review results.

[0066] Furthermore, the permission technology repository includes a first technology repository and a second technology repository. The first technology repository is used to store general and conventional technical resources, including various technical solutions and case analyses; the second technology repository is used to store core technical resources and important technical achievements, including the latest research results, patented technologies, innovative solutions, etc.;

[0067] The user demand analysis system for the technology transfer service platform includes a demand analysis module, an intelligent matching module, and a permission technology repository;

[0068] The requirement analysis module includes a user requirement unit and a user identification and division unit. The user requirement unit is used to collect and analyze user requirement information, including the description and characteristics of user requirements. The user identification and division unit is used to identify and divide different user groups, establish user portraits, understand user behavior data and preferences, so as to better provide customized services for different user groups.

[0069] The intelligent matching module includes a user portrait unit and a user matching unit. The user portrait unit constructs a user portrait based on the user's personal information, behavior data and requirement characteristics. The user matching unit is used to intelligently match user requirements and technical resources to ensure that users receive the most suitable technical support and services.

[0070] The permission technology repository includes a technology storage unit and a permission management unit. The technology storage unit includes a first technology repository and a second technology repository, which are used to store various technical resources and information, including technical achievements, solutions, etc., to provide users with the required technical support.

[0071] The permission management unit includes a first permission management and a second permission management. The first permission management is connected to the intelligent window and intelligently grants permissions to users. By intelligently granting permissions, users can view the technical content in the first technology repository. The second permission management is connected to the manual window. Users can apply for the second permission through the manual window to have the right to view the technology in the second technology repository.

[0072] The specific content includes: The premise for the intelligent window to grant permissions is that the user needs to send an application signal for communication, and the intelligent window receives the application and authorizes within 10 seconds. Within 10 seconds, the intelligent window reviews the user's information and sets a permission barrier to ensure that the user is verified by real name, ensuring the authenticity and validity of the user's identity. And according to the user's identity and application content, it decides whether to grant the user the corresponding access permission. Through the establishment of the permission barrier, the intelligent window can quickly and securely grant permissions after the user sends an application for communication, protecting the security and privacy of user information, ensuring the compliance and smooth progress of the communication process.

[0073] The technology transfer service platform can achieve personalized analysis and intelligent matching of user requirements, improve service quality and user experience. The settings of the intelligent window and the manual window can effectively manage user permissions and protect the security of technical information, ensuring the security and compliance of information, improving the service level and credibility of the technology transfer platform, thereby making up for the deficiencies in the platform service process for user requirements, enhancing the comprehensiveness and professionalism of services, and at the same time protecting the security and confidentiality of technical resources, promoting the orderly circulation and utilization of technical resources.

[0074] The above are only the preferred embodiments of the present invention and do not impose any formal limitations on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A user demand analysis method for a technology transfer service platform, characterized by: The steps include: S1. Set up a specific network port on the technology transfer service platform to interact with the technology transfer service platform and transmit information. Users log in and interact with the technology transfer service platform. The specific network port collects the user's personal information, behavior data and preference-based demand data during the user's use. The demand data is cleaned and integrated. Data analysis and mining technology is used to extract and analyze the cleaned data to identify the user's basic information, preferences and key features of behavior data. The key features are then combined to build a user portrait model. S2. Conduct in-depth analysis of the collected user data, select split hierarchical clustering to identify the user's interest description and demand characteristics, define the user's demand proportion, and accurately perform personalized recommendations based on the demand proportion, including: assigning different weights to the behavior data and interest description data of different user groups, setting the proportion coefficient to 1. When the proportion of user behavior data is greater than 0.8, the technology transfer platform automatically recommends content related to the user behavior data, and gives priority to displaying technical resources and information related to the user's actual behavior; when the proportion of interest description data and user behavior data is the same, the content displayed by the technology transfer platform is still mainly based on the interest description data; S3. Based on user portraits and demand analysis results, a matching model is constructed through a deep learning algorithm to intelligently match the user demand ratio with the technical resources of the technology transfer service platform, including: based on user portraits and demand analysis results, basic information, preferences and key features of behavioral data are extracted from user portraits, and the key features are converted into numerical feature vectors, and a matching model is constructed using a neural network model of a deep learning algorithm, the number of user data features is set as the number of input layer nodes, and a hidden layer structure and the number of neurons are designed, including multiple hidden layers, and then the number of output layer nodes and the predicted value are matched, the user demand ratio is matched with technical resources, and the model is trained through a back propagation algorithm, and the trained matching model is deployed to the technology transfer service platform for real-time user demand matching tasks; the matching model recommends resources by calculating the matching degree between user needs and technical resources, including setting two vectors to represent user demand features and technical resource features, respectively and , where n represents the number of features; the similarity between two vectors is measured by calculating the Euclidean distance; Then set the weight of each feature, expressed as a weight vector; A weighted matching score is obtained according to the weight vector and the Euclidean distance, that is, a comprehensive matching score is obtained; S4. Build an authorized technology repository based on the technology transfer service platform to store the client's technical resources, set permissions for resource protection, and then authorize by setting up smart windows and manual windows. The authorized technology repository includes a second technology repository for storing the latest research results, patented technologies and innovative solutions.

2. The user demand analysis method for a technology transfer service platform according to claim 1, characterized in that: In step S1, a user portrait model is constructed to describe and analyze the characteristics and needs of users. At the same time, the technology transfer service platform continuously optimizes the user portrait by continuously collecting user data, analyzing user behavior and feedback information.

3. The user demand analysis method for a technology transfer service platform according to claim 1, characterized in that: According to the in-depth analysis of the collected user data in step S2, the clustering tree is constructed by gradually splitting the data points through hierarchical clustering, and a similarity measurement method is defined to measure the similarity between different users. The split hierarchical clustering is selected to identify the user's interest description and demand characteristics, which specifically includes: S21, initialization, each user is considered as a separate cluster; S22, similarity calculation, calculating the similarity between each pair of users; S23, splitting, splitting the most similar user clusters according to similarity to form new clusters; S24. Repeat steps S22 and S23 until all users are merged into one large cluster.

4. The user demand analysis method for a technology transfer service platform according to claim 3, characterized in that: Based on the clustering results and the clustering tree generated by the analysis, different user groups and categories are identified.

5. The user demand analysis method for a technology transfer service platform according to claim 1, characterized in that: The step S3 calculates a comprehensive matching score to quantify the matching degree between user requirements and technical resources.

6. The user demand analysis method for a technology transfer service platform according to claim 1, characterized in that: The premise for the smart window to grant permissions is that the user submits an application signal for communication. The smart window receives the application and performs intelligent authorization within 10 seconds. The manual window needs to apply for specific permissions, receive the user application and review it, verify the user's identity and needs, and decide whether to authorize the user's access rights based on the review results.

7. The user demand analysis method for a technology transfer service platform according to claim 6, characterized in that: The authority technology repository also includes a first technology repository for storing common and conventional technology resources.

8. A user demand analysis system for a technology transfer service platform, characterized by: Application: A user demand analysis method for a technology transfer service platform as described in any one of claims 1 to 7, comprising a demand analysis module, an intelligent matching module, and a permission technology repository; The demand analysis module includes a user demand unit and a user identification and division unit. The user demand unit is used to collect and analyze user demand information, including descriptions and characteristics of user demands; the user identification and division unit is used to identify and divide different user groups, establish user portraits, understand user behavior data and preferences, and provide customized services for different user groups; The intelligent matching module includes a user portrait unit and a user matching unit. The user portrait unit constructs a user portrait based on the user's personal information, behavior data and demand characteristics; The user matching unit is used to intelligently match user needs and technical resources; The authority technology repository includes a technology storage unit and an authority management unit. The technology storage unit includes a first technology repository and a second technology repository for storing various types of technology resources and information.

9. The user demand analysis system for a technology transfer service platform according to claim 8, characterized in that: The permission management unit includes a first permission management and a second permission management, wherein the first permission management is connected to the smart window and intelligently grants permissions to the user, and the user can view the technical content in the first technical repository through the intelligently granted permissions; The second permission management is connected to a manual window, and a user applies for a second permission in the manual window to obtain the permission to view the technical content of the second technical repository.

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