Innovation and entrepreneurship guidance service system for educational institutions

Abstract

The invention relates to the technical field of innovation and entrepreneurship guidance service, and discloses an educational institution-oriented innovation and entrepreneurship guidance service system, which comprises a data acquisition and modeling module for acquiring student professional information, regional industrial characteristics and historical entrepreneurship data of an educational institution, and constructing a personalized evaluation model based on the information; the dynamic knowledge base module is used for continuously updating a knowledge base by collecting policy and regulation, market trend and industry dynamic information in real time and utilizing an information processing algorithm to screen, classify and prioritize the information; and an intelligent matching and analysis module. According to the system, policy and regulation, market trend and industry dynamic information are collected and processed in real time through the dynamic knowledge base module, continuous and automatic updating of the knowledge base is achieved in combination with a multi-source data collection channel and a priority updating mechanism, and the problems that updating of the knowledge base lags behind and depends on static data in an existing system are effectively solved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of innovation and entrepreneurship guidance service, in particular to an innovation and entrepreneurship guidance service system for educational institutions. BACKGROUND

[0002] With the deepening of innovation development, various educational institutions generally introduce or develop special guidance service systems to cultivate students' innovation and entrepreneurship ability. The systems usually integrate basic functions such as project declaration, course learning and tutor matching, aiming to provide informationization support for students' innovation and entrepreneurship practice and improve the management efficiency and the convenience of guidance to a certain extent. However, most of these systems are built on static or semi-static knowledge bases and evaluation models, and their service mode tends to be standardized and procedural.

[0003] However, the existing service systems generally have the following problems: the knowledge base updating mechanism is lagging behind, and it seriously depends on the initial constructed data, lacking the real-time capturing and integration ability of external information such as policies and regulations, industry dynamics and market trends, resulting in insufficient timeliness of the provided guidance information, and secondly, the accuracy of personalized guidance of the system is low, since the system uses a general evaluation model, it fails to deeply integrate the professional characteristics and skill background of students, resulting in serious homogenization of direction suggestions, which is difficult to meet individual needs. Based on this, the application designs an innovation and entrepreneurship guidance service system for educational institutions to solve the above problems. SUMMARY

[0004] The application aims to provide an innovation and entrepreneurship guidance service system for educational institutions, which solves the problems of insufficient timeliness and serious homogenization in the background art.

[0005] To solve the above technical problems, the application provides the following technical solutions: An innovation and entrepreneurship guidance service system for educational institutions, comprising: a data acquisition and modeling module for acquiring students' professional information of an educational institution, regional industrial characteristics and historical entrepreneurship data, and constructing a personalized evaluation model based thereon; a dynamic knowledge base module for acquiring policy regulations, market trends and industry dynamic information in real time, and using information processing algorithms to filter, classify and prioritize the information for continuously updating the knowledge base; an intelligent matching and analysis module for calculating the multidimensional matching degree and analyzing the feasibility of an input innovation and entrepreneurship project through the personalized evaluation model and the updated knowledge base; a resource docking and scheme generation module for generating customized resource docking schemes and achievement transformation suggestions according to the output results of the intelligent matching and analysis module, and pushing them to the user end.

[0006] Preferably, in the data acquisition and modeling module, constructing a personalized evaluation model further comprises: The clustering algorithm is used to automatically classify the professional direction of the student.

[0007] A multi-dimensional evaluation index is set, which at least includes a professional course matching degree, a regional industry matching degree, and a historical entrepreneurship success rate.

[0008] The comprehensive evaluation value E of the project is calculated based on the following formula: ; Among them, M represents the professional matching degree, the value range is 0-1, the higher the value represents the higher the matching degree of the project and the student's professional background; A represents the market potential, the value range is 0-1, the higher the value represents the better the market prospect of the project; I represents the innovation potential, the value range is 0-1, the higher the value represents the stronger the innovation of the project; a, b, c are the weight coefficients of M, A, I respectively, which are positive numbers based on the dynamic adjustment of regional industry characteristics, and satisfy a+b+c=1.

[0009] Preferably, in the dynamic knowledge base module, updating the knowledge base further includes: Establish a multi-source data collection channel to access government databases, news platforms, and public opinion monitoring sources; Use text mining algorithms to extract keywords from policies and regulations and automatically classify them; The update priority score S of the information is calculated by the following formula: ; Among them, R represents the release heat score of the information release source, the value range is 1-5, which is used to measure the information heat; P impact represents the policy influence index, which is set by the system in advance, the value range is 0.5-1, the higher the value represents the wider the influence; Q represents the information source authority score, the value range is 1-5, the higher the value represents the more authoritative the source; C category represents the weight of the category, which is set by the system according to the correlation with the education institution, the value range is 0.5-2.

[0010] Based on the update priority score S, the knowledge base update task is triggered regularly.

[0011] Preferably, in the intelligent matching and analysis module, the project matching and feasibility analysis further includes: A mapping relationship is established between the project type and the industry classification.

[0012] The project matching degree score M is calculated based on the following formula: score ; Among them, W i represents the weight of the i-th evaluation factor, which is a pre-set positive value, and all W i ​and the sum is 1; V i represents the matching value of the i-th evaluation factor, and the value range is 0-1, and the higher the value is, the better the matching degree is.

[0013] The Bayesian network model is applied to determine the comprehensive feasibility of the project in combination with market demand, policy support and resource availability.

[0014] Preferably, in the resource docking and scheme generation module, generating the customized scheme further comprises: identifying the resource preferences of the user in terms of technology, funds and tutors; formulating differentiated support strategies in combination with the life cycle stage of the project; generating a resource recommendation score R according to the following formula score : ; wherein, P i represents the matching degree of the i-th resource and the user demand, and the value range is 0-1; L i represents the availability level of the i-th resource, and the value range is an integer from 1 to 5, and the higher the value is, the easier the resource is to obtain.

[0015] Preferably, the data collection and modeling module is further used to build a personalized growth profile of the student, and the building of the growth profile comprises: integrating the classroom performance, practical activities, psychological evaluation and social network data of the student; calculating a student comprehensive ability score O score : ; wherein, Aca represents an academic performance score, which is quantified from course grades and homework quality, and the value range is 0-1; Psy represents a psychological index, which is quantified from standardized psychological evaluation results, and the value range is 0-1; Act represents a social participation degree, which is quantified from club activities and internship experience, and the value range is 0-1; ρ1, ρ2 and ρ3 are weight coefficients of the above three items respectively, and satisfy ρ1+ρ2+ρ3=1.

[0016] Preferably, the intelligent matching and analysis module is further used to: Based on the growth records of students, a student feature vector is constructed, which takes academic ability, psychological characteristics, skill tags and social participation as the core; at the same time, based on the project requirements, a project feature vector is constructed, which takes the required professional background, innovation ability, market attribute and team structure as the core; the cosine similarity algorithm is used to calculate the similarity between the student feature vector and the project feature vector, the similarity is determined by calculating the cosine value of the angle between the two vectors in the multidimensional space, the closer the value is to 1, the higher the matching degree of the student and the project; the team cooperation potential of the student is extracted, and the collaborative filtering algorithm is used to mine and recommend potential innovation and entrepreneurship partners for the current student based on the student group data with similar characteristics and behavior history.

[0017] Preferably, the resource docking and scheme generation module is further used for: Automatically generating a project incubation plan template using natural language processing technology; Combining policy support information to recommend eligible funding and subsidy sources for users; Generating a project risk warning prompt, the risk warning is calculated based on the following formula to calculate the risk score Risk: ; Wherein, Market represents the market uncertainty factor, the value range is 0-1, which is obtained based on market trend analysis; Funding represents the degree of funding gap, the value range is 0-1, which is calculated based on the project budget and the current financing amount; Legal represents the legal risk factor, the value range is 0-1, which is obtained based on policy compliance analysis; k1, k2 and k3 are the weight coefficients of the above risk factors, which are preset positive numbers.

[0018] Preferably, the system further comprises a feedback optimization mechanism for: Collecting user feedback on the satisfaction of recommended schemes and resource docking; Based on the feedback data, the parameters Z of the individual evaluation model and the resource recommendation algorithm are dynamically adjusted by the following formula: ; Wherein, rate represents the user praise rate, the value range is 0-1; count represents the number of negative feedback of the user, which is a non-negative integer, T is the total number of feedback in a time window; λ and μ are the adjustment coefficients of positive and negative feedback respectively, which are preset positive numbers.

[0019] Preferably, the step of pushing customized resource matching solutions and results transformation suggestions based on the analysis results further includes: allocating dedicated resource channels according to project type; setting up a resource matching satisfaction evaluation mechanism and continuously optimizing the push strategy based on user ratings; using a Bayesian network model to perform probabilistic extrapolation on the resource integration process to improve the scientific nature of decision-making; and introducing regional parameters, which are quantitative indicators constructed based on regional industrial characteristics, local support policies, and local market size data.

[0020] Compared with the prior art, the beneficial effects achieved by the present invention are: 1. This invention collects and processes policy and regulatory information, market trends, and industry dynamics in real time through a dynamic knowledge base module. Combined with multi-source data collection channels and a priority update mechanism, it achieves continuous and automated updates of the knowledge base, effectively solving the problems of lagging knowledge base updates and reliance on static data in existing systems, and significantly improving the timeliness and accuracy of innovation and entrepreneurship guidance information.

[0021] 2. This invention constructs a personalized evaluation model through a data collection and modeling module, integrates students' professional information, regional industry characteristics, and historical entrepreneurial data, and utilizes multi-dimensional evaluation indicators and a dynamic weight adjustment mechanism to achieve in-depth quantitative analysis of students' individual characteristics. This overcomes the limitations of homogeneous guidance caused by general evaluation models and significantly improves the personalization and accuracy of innovation and entrepreneurship guidance.

[0022] 3. This invention, through an intelligent matching and analysis module, combined with a personalized evaluation model and a real-time updated knowledge base, employs multi-dimensional matching degree calculation and Bayesian network analysis to achieve a scientific feasibility assessment of projects. Simultaneously, by constructing feature vectors for students and projects and applying similarity algorithms and collaborative filtering recommendations, it enhances the intelligence level of project matching and team building, further improving the systematicness and scientific nature of the guidance service. Attached Figure Description

[0023] Figure 1 This is the overall system flowchart of the present invention; Figure 2 This is a flowchart of the data acquisition and modeling module of the present invention; Figure 3 This is a flowchart of the dynamic knowledge base module of the present invention; Figure 4 This is a flowchart of the intelligent matching and analysis module of the present invention. Detailed Implementation

[0024] 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.

[0025] Example 1; Please see Figures 1-4 In this embodiment of the invention, an innovation and entrepreneurship guidance service system for educational institutions includes: a data acquisition and modeling module, used to acquire students' professional information, regional industry characteristics, and historical entrepreneurship data of educational institutions, and to construct a personalized evaluation model based on this; a dynamic knowledge base module, used to continuously update the knowledge base by collecting policy and regulation information, market trends, and industry dynamics in real time, and using information processing algorithms to filter, classify, and prioritize the information; an intelligent matching and analysis module, used to perform multi-dimensional matching degree calculation and feasibility analysis on the input innovation and entrepreneurship projects through the personalized evaluation model and the updated knowledge base; and a resource matching and solution generation module, used to generate customized resource matching solutions and results transformation suggestions based on the output results of the intelligent matching and analysis module, and push them to the user terminal.

[0026] In the data acquisition and modeling module, building a personalized evaluation model further includes: Students' majors are automatically categorized using clustering algorithms; Set up multi-dimensional evaluation indicators, which should include at least the degree of matching of professional courses, regional industry matching, and historical entrepreneurial success rate; The overall evaluation value E of the project is calculated based on the following formula: ; Here, M represents professional matching degree, calculated by comparing the similarity between project content and students' professional courses, with a value ranging from 0 to 1. A higher value indicates a better fit between the project and the student's professional background. A represents market potential, based on market research data, such as industry growth rate quantification, with a value ranging from 0 to 1. A higher value indicates a better market prospect for the project. I represents innovation potential, derived through expert scoring or patent data analysis, with a value ranging from 0 to 1. A higher value indicates stronger innovation of the project. a, b, and c are the weight coefficients of M, A, and I, respectively, which are positive numbers dynamically adjusted based on regional industrial characteristics, and satisfy a + b + c = 1. The dynamic adjustment mechanism of the weight coefficients a, b, and c ensures that the model can adapt to changes in regional industries, solving the problem of insufficient timeliness caused by static models.

[0027] The data acquisition and modeling module first obtains student major information, regional industry characteristics, and historical entrepreneurship data from educational institutions, and then constructs a personalized assessment model. This module automatically categorizes students' majors using clustering algorithms (such as K-means), reducing manual intervention and improving efficiency. Multi-dimensional assessment indicators include professional course matching (M), market potential (A), and innovation potential (I), designed to address the need for homogenization: by using quantitative indicators, it avoids the limitations of generic assessments. Through dynamic weights and multi-dimensional indicators, the model deeply integrates individual student characteristics, avoiding a one-size-fits-all assessment approach, thereby improving the accuracy of personalized guidance.

[0028] In the dynamic knowledge base module, updating the knowledge base further includes: Establish multi-source data collection channels and connect to government databases, news platforms, and public opinion monitoring sources; The text mining algorithm is used to extract keywords from policies and regulations and perform automatic classification. The specific steps are as follows: segment the policy text into words and remove stop words; calculate the word frequency-inverse document frequency value and take the top ten as keywords; calculate the cosine similarity between keywords and classification labels based on a pre-trained word vector model and match the highest-scoring label.

[0029] The update priority score S of the information is calculated using the following formula: ; This formula originates from a priority scoring model in the field of information retrieval. It combines popularity, authority, and relevance to trigger update tasks quantitatively, avoiding delays caused by manual updates. Here, R represents the popularity score of the information source, calculated by analyzing the number of forwards or search indexes, with a value ranging from 1 to 5, used to measure the popularity of the information; P... impact The policy impact index is preset by the system based on the scope of policy impact; for example, a national policy is assigned a value of 1, and a local policy is assigned a value of 0.7. The value ranges from 0.5 to 1, with higher values ​​indicating wider impact. Q represents the authority rating of the information source, ranging from 1 to 5, with higher values ​​indicating more authoritative sources. C category This indicates the category weight, which is set by the system based on the relevance to the educational institution, and the value ranges from 0.5 to 2.

[0030] Based on the update priority score S, knowledge base update tasks are triggered periodically. Through automated scoring and regular updates, this module ensures that the knowledge base captures external changes in real time, addressing the issue of insufficient timeliness.

[0031] The intelligent matching and analysis module further includes project matching and feasibility analysis, which includes: Establish a mapping relationship between project types and industry classifications; Project matching score M is calculated based on the following formula. score: ; The intelligent matching and analysis module utilizes a personalized evaluation model and an updated knowledge base to calculate the multi-dimensional matching degree of innovation and entrepreneurship projects. Among them, W... i Let W represent the weight of the i-th evaluation factor, which is a preset positive value derived through optimization based on historical success rates. i The sum is 1; V i This represents the matching value of the i-th evaluation factor, calculated by a rule engine or machine learning model. For example, the market trend matching degree is based on real-time data comparison and ranges from 0 to 1. The higher the value, the better the matching degree. By applying a Bayesian network model and combining market demand, policy support, and resource availability, the overall feasibility of a project can be determined.

[0032] In the resource integration and solution generation module, the generation of customized solutions further includes: Identify users' resource preferences in terms of technology, funding, and mentors; Develop differentiated support strategies based on the project's lifecycle stage; The resource recommendation score R is generated according to the following formula. score : ; Among them, P i L represents the matching degree between the i-th resource and the user's needs, with a value ranging from 0 to 1, calculated using a user preference survey algorithm; i This represents the availability level of the i-th resource, with a value ranging from 1 to 5, where a higher value indicates easier acquisition. The value is assigned based on inventory or supplier data. Through a differentiated support strategy, this module enables personalized resource recommendations, addressing the problem of homogenized resource recommendations.

[0033] The data acquisition and modeling module is also used to build personalized growth portfolios for students. The construction of these growth portfolios includes: Integrate students' classroom performance, practical activities, psychological assessments, and social network data; The student's comprehensive ability score O is calculated using the following formula. score : ; Wherein, Aca represents academic performance score, which is quantified from course grades and assignment quality, with a value range of 0-1; Psy represents psychological index, which is quantified from standardized psychological assessment results, with a value range of 0-1; Act represents social participation, which is quantified from club activities and internship experience, with a value range of 0-1; ρ1, ρ2 and ρ3 are the weight coefficients of the above three items, and satisfy ρ1+ρ2+ρ3=1.

[0034] Example 2; Please see Figures 1-4 In this embodiment of the invention, the intelligent matching and analysis module is further used for: Based on students' growth profiles, a student feature vector is constructed with academic ability, psychological traits, skill tags, and social participation as its core. At the same time, based on project requirements, a project feature vector is constructed with the required professional background, innovation ability, market attributes, and team structure as its core. The cosine similarity algorithm is used to calculate the similarity between the student feature vector and the project feature vector. The similarity is determined by calculating the cosine value of the angle between the two vectors in multidimensional space. The closer the value is to 1, the higher the matching degree between the student and the project. Extracting students' teamwork potential and using collaborative filtering algorithms, based on student group data with similar characteristics and behavioral history, to identify and recommend potential innovation and entrepreneurship partners for current students.

[0035] This algorithm originates from a vector space model, measuring similarity through the cosine of the angle between the similarities, avoiding absolute value bias and improving matching accuracy. The vector dimensions are preset by the system; for example, academic ability is normalized to a range of 0-1 based on course grades, while psychological traits are quantified using assessment scales. The closer the similarity value is to 1, the higher the matching degree. This design captures individual student differences through multi-dimensional vectorization and, combined with a collaborative filtering algorithm, recommends partners based on data from similar student groups, effectively solving the problem of homogeneous guidance.

[0036] The algorithm utilizes user-based collaborative filtering to recommend partners. Specifically, this involves: vector construction, which transforms each student's "growth profile" data (academic ability, psychological traits, skill tags, and project participation history) into user feature vectors; similarity calculation, which calculates the cosine similarity between the current student's vector and the vectors of other students in the database; and recommendation generation, which analyzes the teams successfully formed by these "similar students" in the past, extracts the characteristics of their teammates (such as professional complementarity and personality traits), and recommends candidates who have not yet collaborated with the current student but frequently appear in the successful teams of "similar students" as potential partners, with reasons provided for the recommendations.

[0037] The resource matching and solution generation module is also used to: automatically generate project incubation plan templates using natural language processing technology; and recommend eligible funding and subsidy sources to users by combining policy support information.

[0038] Generate project risk warnings. The risk warning is based on the risk score (Risk) calculated using the following formula: ; Among them, Market represents the market uncertainty factor, with a value range of 0-1, derived from market trend analysis; Funding represents the funding gap, with a value range of 0-1, calculated based on the project budget and current financing amount; Legal represents the legal risk factor, with a value range of 0-1, derived from policy compliance analysis; k1, k2, and k3 are the weight coefficients of the above risk factors, which are preset positive numbers. The initial values ​​are derived from regression analysis of 100 historical startup projects and will be dynamically updated through user feedback to optimize the module.

[0039] This formula is based on a linear combination model of risk assessment and references standard methods of financial risk management. It identifies potential project risks in advance in a quantitative way and automatically generates project plan templates and risk warnings through natural language processing. This module improves the scientific nature of decision-making and responds to market changes caused by insufficient timeliness.

[0040] The system also includes a feedback optimization mechanism for: Collect user feedback on their satisfaction with the recommendation schemes and resource integration; Based on feedback data, the parameter Z of the personalized evaluation model and resource recommendation algorithm is dynamically adjusted using the following formula: ; Here, `rate` represents the user approval rating, ranging from 0 to 1, calculated based on user rating data; `count` represents the number of negative user feedbacks, a non-negative integer; `T` is the total number of feedbacks within a time window; and `λ` and `μ` are the adjustment coefficients for positive and negative feedback, respectively, and are preset positive numbers. This mechanism enables the system to continuously learn user preferences, avoid model rigidity, and thus improve the long-term effectiveness of personalized services.

[0041] Based on the analysis results, customized resource matching solutions and results transformation suggestions are pushed out, including: allocating dedicated resource channels according to project type; setting up a resource matching satisfaction evaluation mechanism and continuously optimizing the push strategy based on user ratings; using Bayesian network models to perform probabilistic extrapolation of the resource integration process to improve the scientific nature of decision-making; and introducing regional parameters, which are quantitative indicators constructed based on regional industrial characteristics, local support policies, and local market size data.

[0042] Example 3; Please see Figures 1-4To provide a specific implementation, the data acquisition and modeling module first obtains the students' major information from the university and automatically categorizes them using the K-means clustering algorithm, dividing students into three major groups: technical, management, and innovative. Regional industry characteristics are based on local science and technology parks and manufacturing industries, and historical entrepreneurial data includes statistics on the success rates of 50 projects over the past five years. In constructing the personalized evaluation model, multi-dimensional evaluation indicators are set, including the degree of matching between professional courses and regional industries, and the historical entrepreneurial success rate.

[0043] An artificial intelligence project has an M-value of 0.85 for computer science students; market potential A is assigned a value of 0.75 based on industry growth rate data; and innovation potential I is set at 0.90 based on expert scoring. The weighting coefficients a, b, and c are dynamically adjusted based on regional industry characteristics. These weighting coefficients (a (professional matching weight), b (market potential weight), and c (innovation potential weight) are not fixed values ​​but are dynamically calculated based on the real-time industry characteristics of the region where the educational institution is located. The specific adjustment logic is as follows: Data input: The system regularly obtains the GDP share of regional industries, the list of key support policies, and the talent demand index for strategic emerging industries from the government statistical database.

[0044] Quantitative calculations are performed, and the weights are recalculated at the beginning of each month based on the above data.

[0045] Adjustment of 'a': If the GDP share or talent demand index of technology-intensive industries (such as information technology and high-end manufacturing) in the region increases significantly, the value of 'a' will be increased, indicating that the system is more inclined to recommend projects that match the students' hard skills.

[0046] Adjustment of b: If the regional consumer market activity index is high or a consumer stimulus policy for a certain industry is introduced, the value of b will be increased to emphasize the market prospects of the project.

[0047] Adjustment of c: If the region releases a special development plan or R&D subsidy policy for future industries (such as artificial intelligence and biotechnology), the value of c will be increased to encourage highly innovative projects.

[0048] The output is normalized. After each adjustment, a, b, and c are normalized to ensure that their sum is 1. In this example, a is 0.40, b is 0.35, and c is 0.25, satisfying the condition that their sum is 1. The calculated value of E is 0.40 multiplied by 0.85, plus 0.35 multiplied by 0.75, plus 0.25 multiplied by 0.90, resulting in 0.83. This model avoids the limitations of general assessments by using quantitative indicators, thus improving the accuracy of personalized guidance.

[0049] The dynamic knowledge base module accesses government databases, news platforms, and public opinion monitoring sources through multi-source data acquisition channels, collecting information such as policies, regulations, and market trends in real time. When the system receives a new policy regarding science and technology innovation subsidies, it uses text mining algorithms to extract keywords such as "subsidy" and "science and technology innovation," and automatically categorizes it as a policy. The formula for calculating the priority score S is: S equals R multiplied by P. impact Add Q multiplied by C category The popularity score R is assigned a value of 4 based on the number of times information is forwarded; the policy impact index P... impact The system default score is 0.90; the source authority score Q is assigned a value of 5 based on government websites; the category weight C... category The relevance to educational institutions is set at 1.8. The calculated value of S is 4 multiplied by 0.90 plus 5 multiplied by 1.80, resulting in 12.60. Based on this score, the system periodically triggers a knowledge base update task to ensure information timeliness and resolve update lag issues.

[0050] The intelligent matching and analysis module utilizes the aforementioned models and knowledge base to calculate the multi-dimensional matching degree of the input innovation and entrepreneurship projects. For example, a smart agriculture project based on the Internet of Things, after establishing a mapping relationship with industry categories, achieves a matching degree score M. score The calculation formula is M score Equals summation W i Multiply by V i The evaluation factors include technological feasibility, market adaptability, and policy support. Weight W i The default values ​​are: Technology 0.50, Market 0.30, Policy 0.20; Matching value V i Real-time data comparison yielded a technical feasibility score of 0.80, a market adaptability score of 0.70, and a policy support score of 0.90. M was calculated. score The result is (0.5*0.8)+(0.3*0.7)+(0.2*0.9)=0.79.

[0051] Simultaneously, a Bayesian network model was applied, combining market demand, policy support, and resource availability, to determine that the project's overall feasibility was high. A Bayesian network was constructed with "project success" as the root node. Its child nodes included key factors such as "strong market demand," "strong policy support," "mature core technology," "reasonable team structure," and "sufficient start-up capital." The conditional probability relationships between each node were obtained through training by analyzing a large amount of historical entrepreneurial success and failure case data.

[0052] When a new innovation and entrepreneurship project is input, the system provides evidence for some of the aforementioned factor nodes based on its attributes (such as industry, technical description, team profile, and budget). Subsequently, the network performs probability propagation inference. The network outputs the posterior probability value of "project success" at the root node, serving as a quantitative indicator of the project's "overall feasibility." This probability value is combined with the matching score M. score This provides a more scientific and comprehensive basis for risk assessment in decision-making, which is superior to simple weighted scoring.

[0053] The resource matching and solution generation module generates customized solutions based on the matching results. It identifies user resource preferences, such as technical support, financial assistance, and mentorship, and develops differentiated strategies based on the project lifecycle stage. Resource recommendation score R. score The calculation formula is R score Equals summation P i Multiply by L i Among them, the resource matching degree P i Based on user survey settings, technical resources are rated 0.85, financial resources 0.75, and mentor resources 0.90; availability level L. i Based on inventory data, the following are assigned values: Technology 4, Funding 3, Mentor 5. R is calculated. score The result is 0.85 multiplied by 4, 0.75 multiplied by 3, and 0.90 multiplied by 5, totaling 10.15. The system automatically generates a project incubation plan template and recommends eligible funding sources, such as local technology funds. Simultaneously, it generates a risk warning, with a market uncertainty factor (Market) of 0.30, a funding gap of 0.40, and a legal risk factor (Legal) of 0.20; the weighting coefficients k1, k2, and k3 are preset to 0.40, 0.40, and 0.20, respectively. The calculated Risk is 0.40 multiplied by 0.30, 0.40 multiplied by 0.40, and 0.20 multiplied by 0.20, totaling 0.32, which falls into the low-risk category. Finally, the plan is pushed to the user, and parameters are continuously adjusted through a feedback optimization mechanism.

[0054] Working Principle: This system achieves personalized innovation and entrepreneurship guidance through modular collaborative work. First, the data acquisition and modeling module collects students' professional information, regional industry characteristics, and historical entrepreneurial data to build a personalized assessment model and create student growth profiles, forming a multi-dimensional competency profile. The dynamic knowledge base module collects and processes information such as policies, regulations, and market dynamics in real time, and continuously updates it through a priority mechanism to ensure the timeliness of information.

[0055] Subsequently, the intelligent matching and analysis module, based on a personalized model and a real-time knowledge base, performs multi-dimensional matching and feasibility analysis of projects. Simultaneously, it utilizes feature vectors and collaborative filtering algorithms to recommend suitable projects and potential partners to students. The resource matching and solution generation module, based on the analysis results and combined with user preferences and project stages, generates customized resource matching solutions, incubation plans, and risk warnings, which are then pushed to users through a dedicated channel. The system also has a built-in feedback optimization mechanism that dynamically adjusts the model and recommendation parameters based on user satisfaction, achieving continuous self-optimization. The entire process forms a closed loop of "data collection—model building—real-time updates—intelligent matching—resource push—feedback optimization," aiming to improve the accuracy, timeliness, and personalization of guidance.

[0056] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An innovation and entrepreneurship guidance service system for educational institutions, characterized by, Comprise: a data acquisition and modeling module for obtaining students' professional information of an educational institution, regional industry characteristics and historical entrepreneurship data, and constructing a personalized evaluation model based thereon; a dynamic knowledge base module for collecting policy regulations, market trends and industry dynamic information in real time, and filtering, classifying and prioritizing the information using information processing algorithms for continuous updating of the knowledge base; an intelligent matching and analysis module for multi-dimensional matching degree calculation and feasibility analysis of an input innovation and entrepreneurship project through the personalized evaluation model and the updated knowledge base; a resource docking and scheme generation module for generating customized resource docking schemes and achievement transformation suggestions based on the output of the intelligent matching and analysis module, and pushing to the user end.

2. The innovation and entrepreneurship guidance service system for educational institutions according to claim 1, characterized in that, In the data acquisition and modeling module, the construction of the personalized evaluation model further comprises: automatically classifying students' professional directions through clustering algorithms; setting multi-dimensional evaluation indexes, the indexes at least including professional course matching degree, regional industry matching degree and historical entrepreneurship success rate; calculating the comprehensive evaluation value E of the project based on the following formula: ； wherein M represents professional matching degree, the value range being 0-1, the higher the value, the more suitable the project is for the students' professional background; A represents market potential, the value range being 0-1, the higher the value, the better the market prospect of the project; I represents innovation potential, the value range being 0-1, the higher the value, the stronger the innovation of the project; a, b and c are weight coefficients of M, A and I respectively, which are positive numbers based on dynamic adjustment of regional industry characteristics, and satisfy a+b+c=1.

3. The innovation and entrepreneurship guidance service system for educational institutions according to claim 1, characterized in that, In the dynamic knowledge base module, the updating of the knowledge base further comprises: establishing multi-source data acquisition channels to access government databases, news platforms and public opinion monitoring sources; using text mining algorithms to extract keywords in policy regulations and automatically classify them; calculating the update priority score S of the information through the following formula: ； Wherein, R represents the release heat score of the information release source, the value range is 1-5, which is used to measure the information heat; P impact represents the policy influence index, which is set by the system in advance, the value range is 0.5-1, and the higher the value represents the more extensive influence; Q represents the information source authority score, the value range is 1-5, and the higher the value represents the more authoritative source; C category represents the weight of the category, which is set by the system according to the correlation degree with the education institution, the value range is 0.5-2; based on the update priority score S, triggering the knowledge base updating task regularly.

4. The innovation and entrepreneurship guidance service system for educational institutions according to claim 1, characterized in that, In the intelligent matching and analysis module, the project matching and feasibility analysis further comprises: establishing a mapping relationship using project types and industry classification; The project matching degree score M is calculated based on the following formula score : ； wherein W i represents the weight of the ith evaluation factor, is a pre-set positive value, and the sum of all W i is 1; V i represents the matching value of the ith evaluation factor, and has a value range of 0-1, wherein the higher the value, the better the matching degree. applying a Bayesian network model to determine the comprehensive feasibility of the project in combination with market demand, policy support and resource availability.

5. The innovation and entrepreneurship guidance service system for educational institutions according to claim 1, characterized in that, In the resource docking and scheme generation module, the generation of customized schemes further comprises: identifying users' resource preferences in technology, funds and mentors; developing differentiated support strategies in combination with the life cycle stage of the project; A resource recommendation score R is generated according to the following formula score : ； wherein P i represents the matching degree of the ith resource to the user demand, and the value range is 0-1; L i represents the availability level of the ith resource, and the value range is an integer of 1-5, and the higher the value is, the easier the acquisition is.

6. The innovation and entrepreneurship guidance service system for educational institutions according to claim 1, characterized in that, The data acquisition and modeling module is also used to construct students' personalized growth archives, the construction of the growth archives comprising: integrating students' classroom performance, practical activities, psychological evaluation and social network data; The student comprehensive ability score O is calculated by the following formula score : ； Wherein, Aca represents the academic performance score, which is quantified by course grades and homework quality, and the value range is 0-1; Psy represents the psychological index, which is quantified by standardized psychological test results, and the value range is 0-1; Act represents the social participation degree, which is quantified by club activities and internship experience, and the value range is 0-1; ρ1, ρ2 and ρ3 are weight coefficients of the above three, and satisfy ρ1+ρ2+ρ3=1.

7. The innovation and entrepreneurship guidance service system for educational institutions according to claim 1, characterized in that, The intelligent matching and analysis module further comprises: Based on the growth records of students, a student feature vector is constructed with academic ability, psychological characteristics, skill labels and social participation as the core; at the same time, based on the project requirements, a project feature vector is constructed with the required professional background, innovation ability, market attribute and team structure as the core; The similarity between the student feature vector and the project feature vector is calculated by using the cosine similarity algorithm, and the similarity is determined by calculating the cosine value of the angle between the two vectors in the multi-dimensional space, and the closer the value is to 1, the higher the matching degree of the student and the project is; The team cooperation potential of the student is extracted, and the collaborative filtering algorithm is used to mine and recommend potential innovation and entrepreneurship partners for the current student based on the student group data with similar characteristics and behavior history.

8. The innovation and entrepreneurship guidance service system for educational institutions according to claim 1, characterized in that, The resource docking and scheme generation module further comprises: An incubation project plan template is automatically generated using natural language processing technology; Combined with policy support information, the user is recommended to the source of funding and subsidies that meet the conditions; A project risk warning prompt is generated, and the risk warning is calculated based on the following formula to calculate the risk score Risk: ； Wherein, Market represents the market uncertainty factor, the value range is 0-1, which is calculated based on market trend analysis; Funding represents the degree of funding gap, the value range is 0-1, which is calculated based on project budget and current financing amount; Legal represents the legal risk factor, the value range is 0-1, which is calculated based on policy compliance analysis; k1, k2 and k3 are weight coefficients of the above risk factors, which are preset positive numbers.

9. The innovation and entrepreneurship guidance service system for educational institutions according to claim 1, characterized in that, The system further comprises a feedback optimization mechanism, comprising: Collecting user feedback on the satisfaction of recommended schemes and resource docking; Based on the feedback data, the parameters Z of the individual evaluation model and the resource recommendation algorithm are dynamically adjusted by the following formula: ； Wherein, rate represents the user praise rate, the value range is 0-1; count represents the number of negative feedback of the user, which is a non-negative integer, and T is the total number of feedback in a time window; λ and μ are the adjustment coefficients of positive and negative feedback, which are preset positive numbers.

10. The innovation and entrepreneurship guidance service system for educational institutions according to claim 1, characterized in that, The customized resource docking scheme and achievement transformation suggestion pushed according to the analysis result further comprises: According to the type of the project, an exclusive resource channel is allocated; A resource matching satisfaction evaluation mechanism is set to continuously optimize the push strategy based on user ratings; A Bayesian network model is used to probabilistically infer the resource integration process, improving the scientific nature of decision-making; Regional parameters are introduced, which are quantitative indicators constructed based on regional industrial characteristics, local support policies and local market size data.

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