College student innovation and entrepreneurship service management system

Abstract

The invention discloses a college student innovation and entrepreneurship service management system, and belongs to the field of entrepreneurship services, and the system comprises a multi-source information collection and processing module which is used for capturing data of policies, funds, industry dynamics and the like in real time, and constructing a standardized database through a natural language processing technology; the user portrait and project modeling module comprises user portrait construction and project modeling analysis: collecting data such as user basic information, capability labels and entrepreneurship demands, and generating personalized demand vectors through a machine learning algorithm; data such as policies and funds are updated in real time through the multi-source information acquisition module, a user portrait and an intelligent matching engine are constructed by using big data analysis and an artificial intelligence algorithm, and accurate docking of entrepreneurship resources is realized; an intelligent decision support module and a dynamic feedback mechanism are combined to provide full-process intelligent entrepreneurship service for college students, the problems of information lag, low matching efficiency and the like of an existing platform are solved, and the entrepreneurship success rate and the resource utilization efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of startup services, and particularly to a service management system for college students' innovation and entrepreneurship. Background Art

[0002] With the improvement of college students' awareness of innovation and entrepreneurship, more and more universities and social institutions have built service management systems for innovation and entrepreneurship, providing various services such as policy support, financial support, and resource docking for college students' startups. However, many current systems still face problems such as lagging information update and inaccurate resource matching. Specifically, it is manifested as follows:

[0003] Lagging policy information: The policy, financial support, and industry dynamics information in some systems is not updated in a timely manner, resulting in students failing to obtain the latest policy support and market opportunities in a timely manner.

[0004] Inaccurate resources: The startup resources (such as funds, venues, investors, etc.) provided in the system may have outdated or mismatched information, resulting in students being unable to effectively connect with relevant resources.

[0005] Therefore, there is a need for a service management system for college students' innovation and entrepreneurship that integrates "data real-time, matching precision, and service intelligence" to solve the deficiencies of the existing technology. Summary of the Invention

[0006] In view of the deficiencies of the existing technology, the present invention provides a service management system for college students' innovation and entrepreneurship, which solves the problems raised in the above background art.

[0007] Technical Solution: To solve the above technical problems, according to one aspect of the present invention, more specifically, it is a service management system for college students' innovation and entrepreneurship, including a multi-source information collection and processing module, a user portrait and project modeling module, an intelligent matching engine module, a resource docking and feedback closed-loop module, and a data security and multi-terminal adaptation module.

[0008] The multi-source information collection and processing module is used to capture data such as policies, funds, and industry dynamics in real time, and build a standardized database through natural language processing technology.

[0009] The user portrait and project modeling module includes user portrait construction and project modeling analysis:

[0010] Collect data such as user basic information, ability tags, and startup needs, and generate personalized demand vectors through machine learning algorithms (such as random forest) to dynamically update the user preference model.

[0011] Perform text parsing on the business plan, extract core elements such as industry track, technical advantages, and market positioning, and generate a project competitiveness evaluation report in combination with patent retrieval and competitor analysis.

[0012] The intelligent matching engine module, based on collaborative filtering, natural language matching, and reinforcement learning algorithms, realizes the precise matching of user needs and entrepreneurial resources; the intelligent decision-making support module provides market analysis, risk assessment, and strategic suggestions;

[0013] The resource docking and feedback closed-loop module supports one-click resource docking and dynamic optimization of recommendation strategies;

[0014] The data security and multi-terminal adaptation module ensures data security and supports cross-platform operations.

[0015] Furthermore, the multi-source information collection and processing module includes:

[0016] The web crawler unit is used to scrape public data such as government official websites, investment platforms, and social media;

[0017] The API interface unit is used to connect to third-party structured data such as enterprise credit information and patent databases;

[0018] The data parsing unit uses natural language processing (NLP) technology to parse unstructured text, extract key information (such as policy application conditions and fund declaration requirements), and construct a "policy library", "resource library", and "industry dynamics library" through data cleaning, deduplication, and standardization processing.

[0019] Furthermore, the intelligent matching engine module includes:

[0020] The collaborative filtering algorithm unit, based on collaborative filtering (CF) and natural language matching (NLM) technologies, calculates the similarity between the "user demand vector" and the "resource label", and preferentially recommends resources with a matching degree ≥ 80%;

[0021] The natural language matching unit realizes the demand-resource matching of unstructured text based on semantic analysis;

[0022] The reinforcement learning optimization unit introduces the reinforcement learning (RL) algorithm, dynamically adjusts the matching weights according to the historical docking success rate (such as 40% for policy matching, 30% for fund matching, and 30% for mentor matching), and forms an adaptive matching strategy.

[0023] Furthermore, the intelligent decision-making support module includes:

[0024] The market analysis sub-module, based on industry dynamics data, generates a track trend chart and a competitor heat map, predicts the market size and growth potential, and provides SWOT analysis and business strategy suggestions;

[0025] The risk assessment sub-module constructs an assessment indicator system that includes technology, market, and team risks, outputs risk levels through the analytic hierarchy process (AHP), and recommends response strategies for high-risk links (such as connecting with emergency financing channels).

[0026] Furthermore, the resource connection and feedback closed-loop module includes:

[0027] One-click docking unit supports users to initiate docking applications through the intelligent recommendation list. The system automatically generates a docking letter containing a project summary and requirement description, pushes it to the resource provider simultaneously, and provides tools such as online negotiation and electronic signing.

[0028] The feedback optimization unit collects users' ratings of resource matching effects and project progress data, and feeds them back to the algorithm layer to optimize the matching model, thus realizing a closed loop of "recommendation-matching-feedback-optimization".

[0029] Furthermore, the data security and multi-terminal adaptation module includes:

[0030] Blockchain evidence storage unit is used for decentralized evidence storage of user data. It uses blockchain technology to store user data, encrypts and stores sensitive information (AES-256 algorithm), and uses hierarchical authority management to ensure data access security.

[0031] The multi-terminal adaptation unit supports data synchronization and function adaptation of the Web, WeChat applet and PC client, supports cross-platform data synchronization, and the applet integrates OCR recognition function to realize automatic analysis and entry of materials.

[0032] The beneficial effects of the college student innovation and entrepreneurship service management system of the present invention are:

[0033] (1) The present invention uses a multi-source information collection module to update policy, funding and other data in real time, and uses big data analysis and artificial intelligence algorithms to build user portraits and intelligent matching engines to achieve accurate docking of entrepreneurial resources; combined with an intelligent decision support module and a dynamic feedback mechanism, it provides college students with full-process intelligent entrepreneurial services, solves the problems of information lag and inefficient matching on existing platforms, and improves the success rate of entrepreneurship and resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0035] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0036] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0037] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] Refer to Figure 1 , a college student innovation and entrepreneurship service management system, including a multi-source information collection and processing module, a user portrait and project modeling module, an intelligent matching engine module, a resource docking and feedback closed-loop module, and a data security and multi-terminal adaptation module.

[0039] The multi-source information collection and processing module is used to capture data such as policies, funds, and industry dynamics in real time, and construct a standardized database through natural language processing technology.

[0040] The user portrait and project modeling module includes user portrait construction and project modeling analysis:

[0041] Collect data such as user basic information, ability tags, and entrepreneurship needs, generate personalized demand vectors through machine learning algorithms (such as random forests), and dynamically update the user preference model.

[0042] Perform text parsing on the business plan, extract core elements such as industry tracks, technical advantages, and market positioning, and generate a project competitiveness evaluation report in combination with patent retrieval and competitor analysis.

[0043] The intelligent matching engine module, based on collaborative filtering, natural language matching, and reinforcement learning algorithms, realizes the accurate matching of user needs and entrepreneurial resources; the intelligent decision-making support module provides market analysis, risk assessment, and strategy recommendations.

[0044] The resource docking and feedback closed-loop module supports one-click resource docking and dynamic optimization of recommendation strategies.

[0045] The data security and multi-terminal adaptation module ensures data security and supports cross-platform operations.

[0046] Furthermore, the multi-source information collection and processing module includes:

[0047] The web crawler unit is used to capture public data such as government official websites, investment platforms, and social media.

[0048] The API interface unit is used to dock with third-party structured data such as enterprise credit information and patent databases.

[0049] The data parsing unit uses natural language processing (NLP) technology to parse unstructured text, extract key information (such as policy application conditions and fund application requirements), and construct a "policy library", a "resource library", and an "industry dynamics library" through data cleaning, deduplication, and standardization processing.

[0050] Furthermore, the intelligent matching engine module includes:

[0051] The collaborative filtering algorithm unit calculates the similarity between the "user demand vector" and the "resource tag" based on collaborative filtering (CF) and natural language matching (NLM) technology, and gives priority to recommending resources with a matching degree ≥ 80%;

[0052] Natural language matching unit, which realizes demand-resource matching of unstructured text based on semantic analysis;

[0053] The reinforcement learning optimization unit introduces the reinforcement learning (RL) algorithm to dynamically adjust the matching weights (such as policy matching 40%, funding matching 30%, and mentor matching 30%) according to the historical matching success rate to form an adaptive matching strategy.

[0054] Furthermore, the intelligent decision support module includes:

[0055] The market analysis submodule generates track trend charts and competitive product heat maps based on industry dynamic data, predicts market size and growth potential, and provides SWOT analysis and business strategy recommendations;

[0056] The risk assessment sub-module constructs an assessment indicator system that includes technology, market, and team risks, outputs risk levels through the analytic hierarchy process (AHP), and recommends response strategies for high-risk links (such as connecting with emergency financing channels).

[0057] Furthermore, the resource connection and feedback closed-loop module includes:

[0058] One-click docking unit supports users to initiate docking applications through the intelligent recommendation list. The system automatically generates a docking letter containing a project summary and requirement description, pushes it to the resource provider simultaneously, and provides tools such as online negotiation and electronic signing.

[0059] The feedback optimization unit collects users' ratings of resource matching effects and project progress data, and feeds them back to the algorithm layer to optimize the matching model, thus realizing a closed loop of "recommendation-matching-feedback-optimization".

[0060] Furthermore, the data security and multi-terminal adaptation module includes:

[0061] Blockchain evidence storage unit is used for decentralized evidence storage of user data. It uses blockchain technology to store user data, encrypts and stores sensitive information (AES-256 algorithm), and uses hierarchical authority management to ensure data access security.

[0062] The multi-terminal adaptation unit supports data synchronization and function adaptation of the Web, WeChat applet and PC client, supports cross-platform data synchronization, and the applet integrates OCR recognition function to realize automatic analysis and entry of materials.

[0063] Example 1: User Registration and Portrait Generation. Students upload their resumes and summaries of startup projects through a WeChat mini-program. The system extracts information such as majors and startup experiences through OCR recognition; uses NLP to parse the project business plan (BP) and extracts tags such as "artificial intelligence" and "need 5 million yuan in angel investment" to generate an initial user demand vector; the machine learning model dynamically adjusts the tag weights according to historical data to form a personalized user portrait.

[0064] Example 2: Intelligent Recommendation and Resource Matching. The system monitors that a certain science and technology park has issued a "subsidy policy for AI startup enterprises". The matching degree is calculated to be 92% through the collaborative filtering algorithm and is pushed to the corresponding students; when the students click "apply with one click", the system automatically generates a docking letter containing the technical advantages of the project and the subsidy amount and sends it to the park administrator; after the park administrator approves it, the system opens an online negotiation room to support real-time communication and e-signature between both parties.

[0065] Example 3: Dynamic Feedback and Strategy Optimization. After the students match resources, they rate the professionalism of the tutor 4.5 points (out of 5). The feedback data flows back to the reinforcement learning model; the model adjusts the weight of the "tutor matching" dimension from 25% to 35% to optimize the subsequent recommendation strategy; tracks the project progress. If the students successfully obtain financing, the system automatically switches the recommendation focus to market expansion resources (such as channel docking and brand promotion).

[0066] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the present invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. A college students' innovation and entrepreneurship service management system, including a multi-source information collection and processing module, a user portrait and project modeling module, an intelligent matching engine module, a resource docking and feedback closed-loop module, and a data security and multi-terminal adaptation module, characterized in that: The multi-source information collection and processing module: is used to capture policy, funding, and industry dynamics data in real time, and construct a standardized database through natural language processing technology; The user portrait and project modeling module includes user portrait construction and project modeling analysis: Collect user basic information, ability tags, and entrepreneurship demand data, generate personalized demand vectors through machine learning algorithms, and dynamically update the user preference model; Perform text parsing on the business plan, extract core elements such as industry track, technical advantages, and market positioning, and generate a project competitiveness evaluation report in combination with patent retrieval and competitor analysis; The intelligent matching engine module: based on collaborative filtering, natural language matching, and reinforcement learning algorithms, realizes the precise matching of user needs and entrepreneurial resources; an intelligent decision-making support module provides market analysis, risk assessment, and strategy recommendations; The resource docking and feedback closed-loop module: supports one-key resource docking and dynamic optimization of recommendation strategies; The data security and multi-terminal adaptation module ensures data security and supports cross-platform operations.

2. The a college student innovation and entrepreneurship service management system according to claim 1, characterized in that: The multi-source information collection and processing module includes: A web crawler unit for capturing public data on government official websites, investment platforms, and social media; An API interface unit for docking third-party structured data such as enterprise credit information and patent databases; A data parsing unit that uses natural language processing technology to parse unstructured text, extract key information, and construct a "policy library", "resource library", and "industry dynamics library" through data cleaning, deduplication, and standardization processing.

3. The a college student innovation and entrepreneurship service management system according to claim 1, characterized in that: The intelligent matching engine module includes: A collaborative filtering algorithm unit that calculates the similarity between the "user demand vector" and the "resource tag" based on collaborative filtering and natural language matching technologies, and preferentially recommends resources with a matching degree ≥ 80%; A natural language matching unit that realizes demand-resource matching of unstructured text based on semantic analysis; A reinforcement learning optimization unit that introduces a reinforcement learning algorithm to dynamically adjust the matching weight according to the historical docking success rate, and forms an adaptive matching strategy.

4. The a college students' innovation and entrepreneurship service management system according to claim 1, wherein: The intelligent decision-making support module includes: A market analysis sub-module that generates a track trend chart and a competitor heat map based on industry dynamics data, predicts the market size and growth potential, and provides SWOT analysis and business strategy recommendations; A risk assessment sub-module that constructs an evaluation index system including technology, market, and team risks, outputs the risk level through the analytic hierarchy process, and recommends coping strategies for high-risk links.

5. The college student innovation and entrepreneurship service management system according to claim 1, characterized in that: The resource docking and feedback closed-loop module includes: A one-key docking unit that supports users to initiate a docking application through the intelligent recommendation list. The system automatically generates a docking letter containing a project summary and demand description, synchronously pushes it to the resource provider, and provides online negotiation and e-signature tools; A feedback optimization unit that collects user ratings on the resource matching effect and project progress data, returns them to the algorithm layer to optimize the matching model, and realizes a closed loop of "recommendation - docking - feedback - optimization".

6. The a college students' innovation and entrepreneurship service management system according to claim 1, characterized in that: The data security and multi-terminal adaptation module includes: A blockchain evidence storage unit, which is used for the decentralized evidence storage of user data. The blockchain technology is adopted to store user data, sensitive information is encrypted and stored, and hierarchical permission management is used to ensure data access security; A multi-terminal adaptation unit, which supports data synchronization and function adaptation for the Web side, WeChat mini-programs, and PC clients, supports cross-platform data synchronization, and the mini-program integrates the OCR recognition function to realize automatic parsing and entry of materials.

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