AI-based rural education resource intelligent matching and collaborative management system

Abstract

The invention discloses an AI-based rural educational resource intelligent matching and collaborative management system, and relates to the technical field of educational resource configuration and collaborative management. Comprising a user management module, a data acquisition module, a project generation module, an intelligent matching engine module, a project execution monitoring module, a fund supervision module, a certificate generation module and an incentive distribution module. The data acquisition module acquires service capability information and education demand information; the system has the beneficial effects that through multi-role authority management, multi-dimensional data acquisition, an intelligent matching engine and a dynamic monitoring mechanism, a full-process management system covering resource matching, project execution and fund supervision is constructed, and efficient integration and accurate matching of educational resources are realized; the method has the advantages that the resource matching accuracy is improved, the project implementation quality is guaranteed, the multi-party cooperation efficiency is enhanced, and the fund supervision transparency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of educational resource allocation and collaborative management, and in particular to an AI-based intelligent matching and collaborative management system for rural educational resources. Background Art

[0002] The rational allocation and effective utilization of rural education resources have become key factors in promoting educational equity and improving the quality of rural education. Currently, the matching and management of rural education resources mainly rely on traditional manual docking methods, lacking intelligent and systematic collaborative management mechanisms, leading to problems such as uneven resource allocation and inefficient utilization.

[0003] Several AI-based educational resource recommendation systems exist in the prior art. For example, CN116628339B discloses an AI-based educational resource recommendation method and system. This system intelligently recommends educational resources by determining resource characteristics, reading target users' resource needs and demand characteristics, analyzing users' historical learning information to determine user interest characteristics, and determining recommendation values based on matching scores.

[0004] CN114723390A proposes an artificial intelligence-based rural education information retrieval system, which includes a volunteer side, a teaching side, and a server. The AI feedback unit intelligently screens the teaching content and volunteer information, making it easier for volunteers and teaching villages to select each other.

[0005] CN119128275B describes an educational resource recommendation method and system based on artificial intelligence. The system builds an educational resource engine, collects basic user information to generate user portraits, and dynamically updates user portraits based on user personalized needs. It uses collaborative filtering and content-based recommendation methods to provide users with personalized learning resources.

[0006] In terms of collaborative resource management, CN119417417A discloses a collaborative method and system for art education practice resources. The system extracts resource information from information collection forms, classifies and assigns resource tags, and calculates resource adaptability based on demand fields in the database, thereby matching resources with demand.

[0007] CN115526749A proposes an Internet shared teaching cloud platform with interactive functions, including a front-end display unit, an operation management unit and an operation environment unit, which realizes the sharing of educational resources among multi-level education systems.

[0008] However, the existing technology still has the following shortcomings:

[0009] First, the existing education resource recommendation system mainly focuses on recommending learning content, and lacks a unified management and coordination mechanism for multiple user roles such as volunteers, parents, governments and enterprises; second, the existing system is relatively simple in matching algorithms, fails to fully consider the particularity and urgency of rural education needs, and cannot achieve accurate matching based on multi-dimensional features; third, it lacks management functions for the entire life cycle of the project, and cannot effectively track and evaluate the actual effects of education resource investment; fourth, the existing system generally lacks a transparent fund flow management mechanism, making it difficult to ensure the rational use and openness and transparency of donated funds.

[0010] Therefore, an AI-based intelligent matching and collaborative management system for rural education resources is proposed. Summary of the Invention

[0011] In view of the above-mentioned state of the art, this application is proposed. The embodiments of this application provide an AI-based intelligent matching and collaborative management system for rural education resources, which can improve the accuracy of education resource matching, ensure the quality of education project implementation, enhance the efficiency of multi-party collaboration, and improve the transparency of fund supervision.

[0012] According to one aspect of the present application, an AI-based intelligent matching and collaborative management system for rural education resources is provided, including: a user management module, configured to perform multi-role registration for volunteers, parents, education institution administrators, government users and corporate users, and assign differentiated operation permissions based on role types; a data acquisition module, configured to obtain service capability information of volunteers and education demand information of rural children, the service capability information including skill descriptions, geographic coordinates, serviceable time windows and skill tags selected from a preset tag library input through natural language text, the education demand information including learning goal descriptions, geographic coordinates and subject demand tags selected from a preset tag library input through natural language text; a project generation module, configured to create corresponding education projects according to the education demand information, and establish a binding relationship between the education project and the education demand information; an intelligent The matching engine module is configured to perform multi-dimensional similarity calculations on the service capability information and the educational demand information of the educational project, and output a matching list of volunteers and educational projects; the project execution monitoring module is configured to record the service log data submitted by volunteers in the process of executing the educational project, and generate a project progress visualization report based on the log data; the fund supervision module is configured to receive fund donation requests from corporate users for designated educational projects, and generate a fund flow tracking chain, which includes a hash value of the donation amount, the recipient's account and the fund usage certificate; the certificate generation module is configured to verify the volunteer's service log data according to preset certificate issuance conditions, generate a digital certificate after verification, and write it into the blockchain for evidence storage; the incentive allocation module is configured to allocate the incentive amount from the donation fund pool to the volunteer account according to a preset ratio based on the completion index in the project progress visualization report.

[0013] Compared with the existing technology, the AI-based intelligent matching and collaborative management system for rural education resources according to the embodiment of this application can build a full-process management system covering resource matching, project execution, and fund supervision through multi-role authority management, multi-dimensional data collection, intelligent matching engine and dynamic monitoring mechanism, thereby realizing efficient integration and precise matching of education resources, and has the advantages of improving resource matching accuracy, ensuring project implementation quality, enhancing multi-party collaboration efficiency and improving fund supervision transparency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0015] Figure 1 This is a block diagram of an AI-based intelligent matching and collaborative management system for rural education resources in the present invention.

[0016] Figure 2 This is a block diagram of the operation of the AI assistant module of the AI-based rural education resource intelligent matching and collaborative management system of the present invention. DETAILED DESCRIPTION

[0017] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0018] Application Overview

[0019] Existing technologies in rural education resource matching systems commonly suffer from insufficient multi-dimensional matching accuracy, a lack of oversight during the service process, inefficient multi-role collaboration, and inadequate funding oversight mechanisms. Existing solutions often rely on single-dimensional keyword matching, lacking intelligent algorithms that comprehensively consider factors such as location and time windows. This makes it difficult to effectively coordinate the precise alignment of volunteer skills with educational needs. Furthermore, traditional systems lack dynamic monitoring of project execution, making it difficult to trace the flow of donated funds, and inadequate multi-user permission management mechanisms, hindering the optimal allocation of educational resources.

[0020] To address these issues, the inventors identified three core contradictions in the traditional system: first, insufficient information structuring between both the supply and demand sides of educational resources led to inefficient matching; second, a lack of a unified management platform for cross-regional, multi-role collaboration; and third, a disconnect between the incentive mechanisms and regulatory oversight of public welfare projects. By analyzing the multidimensional characteristics of volunteer service capabilities and educational needs, they realized the need for a complex matching model. They also observed the potential of blockchain technology for fund tracing and the value of machine learning in semantic understanding. Ultimately, they developed a technical approach that combines multi-objective optimization algorithms with blockchain evidence storage, achieving closed-loop management of the entire process, from demand matching to incentive feedback.

[0021] Exemplary Systems

[0022] Figure 1 The diagram shows the AI-based intelligent matching and collaborative management system for rural education resources based on this application, including a user management module, a data acquisition module, a project generation module, an intelligent matching engine module, a project execution monitoring module, a fund supervision module, a certificate generation module, and an incentive distribution module. Through data interaction and functional collaboration, each module forms a complete resource management closed loop.

[0023] Among them, the user management module refers to an access control system that implements permission segmentation through role classification. It can adopt a permission allocation mechanism based on the RBAC model, establish a five-category user account system and set an operation whitelist. This module limits the scope of data operations by distinguishing user types, and solves data security issues when multiple roles collaborate.

[0024] Among them, the data collection module refers to a collection system that combines structured and unstructured data. It can adopt a dual input method of natural language processing interface and label selector to obtain geographic location coordinates and time window parameters at the same time. This module provides multi-dimensional feature input for subsequent matching algorithms by integrating text semantics and structured labels.

[0025] Among them, the project generation module refers to the conversion unit that converts demand information into executable projects. It can adopt a dual-channel mechanism of automatic template matching and manual review to establish a mapping relationship between educational projects and original needs. This module forms allocable task units by materializing educational needs.

[0026] Among them, the intelligent matching engine module refers to a multi-dimensional feature matching system based on machine learning. It can use a hybrid recommendation algorithm that combines collaborative filtering and deep learning to calculate the comprehensive matching degree between volunteer capabilities and project requirements. This module breaks through the limitations of the traditional single matching model through multi-dimensional similarity calculation.

[0027] Among them, the project execution monitoring module refers to the service process tracking and visualization system, which can use log collection API and data dashboard technology to record teaching interaction data in real time. This module realizes traceable management of the service process by generating visual reports.

[0028] Among them, the fund supervision module refers to a donation fund management system based on blockchain, which can use smart contract technology to generate a fund flow chain containing hash values. This module ensures the transparency of fund use through an unalterable evidence storage mechanism.

[0029] Among them, the certificate generation module refers to the service results authentication system, which can use blockchain evidence technology to verify service log data and trigger the generation of digital certificates when the preset conditions are met. This module realizes the objective quantification of volunteers' contributions through a trusted evidence mechanism.

[0030] Among them, the incentive allocation module refers to a performance-related reward distribution system, which can adopt a preset proportion of the fund pool allocation algorithm to automatically calculate the incentive amount based on the project completion rate. This module enhances the enthusiasm of volunteers to participate by establishing a positive correlation between performance and benefits.

[0031] Specifically, the system establishes a permissions system for five user categories through the user management module, ensuring that each role can operate data within a secure scope. The data collection module simultaneously obtains volunteers' natural language skill descriptions and structured tags, combining them with their location and time window information to construct a multidimensional feature vector. The project generation module converts the collected educational needs into specific, executable projects, forming the basic unit of the matching operation. The intelligent matching engine performs machine learning analysis on the volunteer feature vectors and project requirements to generate an optimized matching list. During project execution, the monitoring module continuously collects service logs and generates visual reports, providing data support for subsequent incentive allocation. The fund supervision module stores corporate donation information on-chain, ensuring the traceability of each fund flow. When a project meets the preset completion criteria, the certificate generation module automatically issues a blockchain-based certificate. The incentive distribution module distributes rewards proportionally from the fund pool to the volunteer accounts based on the completion indicators shown in the visual report, forming a closed-loop incentive system.

[0032] Compared with existing technologies, this solution achieves breakthroughs in three dimensions:

[0033] First, by integrating collaborative filtering and deep learning into a hybrid recommendation algorithm, a multi-dimensional feature matching model is constructed. Compared with traditional keyword matching solutions, this model can simultaneously handle geographic location constraints, time window matching, and semantic similarity calculation.

[0034] Second, blockchain evidence storage technology is introduced to build a fund flow tracking chain. Compared with traditional fund management systems, it can achieve tamper-proof storage and automated verification of donation certificates.

[0035] Third, a dynamic permissions management system based on a role model was established. Compared to a fixed permissions allocation mechanism, this system can flexibly adapt to the collaborative operation needs of multiple types of users. Furthermore, by dynamically linking service logs with visual reports, a complete data chain from task allocation to performance evaluation was formed.

[0036] Through the above technical solutions, this application effectively solves four technical problems: To address insufficient matching accuracy, multi-dimensional feature fusion and hybrid recommendation algorithms are used to improve the fit between volunteers and educational projects; to address the lack of process monitoring, a linkage mechanism between service logs and visual reports is established to achieve full project lifecycle tracking; to address low collaboration efficiency, a role-driven permission management system is adopted to optimize the multi-user collaboration process; and to address weak fund supervision, blockchain evidence storage technology is used to ensure transparency of fund flows. Furthermore, a dynamic incentive allocation mechanism directly links project execution results with fund allocation, forming a sustainable incentive mechanism for public welfare participation.

[0037] The present application further proposes a collaborative filtering recommendation unit, which is configured to construct a volunteer user-education project interaction matrix based on the historical matching records of volunteers and education support projects, generate a volunteer similarity matrix or a project similarity matrix through a cosine similarity algorithm, and filter objects with similarities higher than a preset threshold according to the target user type to generate a recommended matching list.

[0038] Among them, the volunteer user-education project interaction matrix refers to a two-dimensional matrix in which row vectors represent the unique identifier of volunteers and column vectors represent the unique identifier of education projects. The matrix elements use binary identifiers to indicate whether a matching result exists. Specifically, it can be implemented using sparse matrix storage technology. This structure can effectively compress data storage space and improve matrix operation efficiency.

[0039] Among them, the cosine similarity algorithm refers to a method of measuring the degree of similarity by calculating the cosine value of the angle between vectors. It can be specifically implemented using a standardized vector dot product operation. This algorithm has good noise resistance and computational efficiency when processing high-dimensional sparse data.

[0040] Among them, the recommendation matching list generation logic refers to the decision-making mechanism that dynamically switches the recommendation path according to the target user type. It can be implemented using conditional branch statements. This design can provide differentiated recommendation strategies based on the characteristics of different user groups.

[0041] Specifically, the system first converts historical matching data into an interaction matrix composed of 0s and 1s, where each element reflects the matching relationship between a specific volunteer and an educational project. By calculating the similarity of volunteer behavior by row, a similarity matrix reflecting volunteer preferences is formed. Simultaneously, the correlation of educational project needs is calculated by column, forming a matrix reflecting the similarity of project features. When processing volunteer users, the system searches for other volunteers with similar behavior patterns and extracts projects that have successfully matched these volunteers as recommendation candidates. When processing the needs of rural children, the system searches for other projects with similar features to the current project and extracts volunteers who have successfully matched these projects as recommendation candidates. This two-way recommendation mechanism leverages the advantages of both user collaborative filtering and project collaborative filtering.

[0042] Compared with existing technologies, traditional educational resource matching systems mostly adopt a one-way recommendation mechanism and rely only on a single similarity dimension, without considering the correlation characteristics between projects. However, this solution constructs a two-way collaborative filtering mechanism, which not only uses the similarity of volunteer behavior to explore potential suitable projects, but also discovers high-quality volunteer resources through project similarity, effectively overcoming the defects of the traditional system's single recommendation dimension and insufficient coverage.

[0043] Through the above technical solution, this application solves the problem of insufficient accuracy caused by single-dimensional recommendation in traditional matching systems. By constructing a two-way collaborative filtering mechanism between users and projects, volunteer users can obtain recommendations for projects successfully matched with similar volunteers, and rural children in need can obtain recommendations for volunteers successfully matched with similar projects, which significantly improves the coverage rate and matching success rate of educational resource allocation.

[0044] This application further proposes that the AI intelligent matching module also includes a content recommendation unit, which is configured to perform TF-IDF vectorization processing on the skill tag set and learning goal description, calculate the similarity between feature vectors through the cosine similarity algorithm, and generate a recommended matching list based on the calculation results.

[0045] Among them, TF-IDF vectorization processing refers to converting text data into numerical vectors that reflect the importance of words. It can be implemented using the term frequency-inverse document frequency algorithm. The weight value is calculated by counting the frequency of words in the document and their distribution in the entire corpus, thereby capturing key feature words.

[0046] Among them, the cosine similarity algorithm refers to measuring the similarity between two vectors by calculating the cosine value of the angle between them in space. Specifically, it can be implemented by the ratio of the vector dot product and the module length product. This method can effectively eliminate the impact of text length differences on similarity calculations.

[0047] Specifically, the content recommendation unit first performs word segmentation on the set of structured skill tags, counts the IDF value of each tag in the preset corpus, and generates a tag vector based on the TF value in the current document. At the same time, after text cleaning and word segmentation of the natural language learning goal description, the same TF-IDF model is used to generate a text vector. After the two types of vectors are normalized, the cosine similarity algorithm is used to calculate the semantic association strength in the high-dimensional space. Finally, a recommendation list is generated based on the similarity score sorting. This process achieves dual coverage of explicit skill requirements and implicit potential requirements by integrating the standardized classification of structured tags with the semantic features of natural language text.

[0048] Compared with existing technologies, existing educational resource matching systems typically rely solely on preset tag libraries for keyword matching or perform simple word segmentation on natural language text, resulting in incomplete semantic understanding and an inability to handle unlabeled requirements. This solution collaboratively processes structured tags and natural language text to construct a unified vector space model. This not only preserves the standardization of the tag system but also mines the implicit semantic features in the text, effectively addressing matching biases caused by synonym ambiguity and missed detection of near-synonyms.

[0049] Through the above technical solution, this application achieves a refined matching of volunteer skills and educational needs. By quantitatively analyzing the semantic correlation between structured data and unstructured text, the recommendation results not only meet the preset classification specifications, but also can identify potential demand characteristics that are not clearly marked in natural language descriptions, significantly improving the accuracy and coverage of educational resource matching.

[0050] This application further proposes to perform semantic encoding on the skill description and learning goal description input in natural language text through a preset neural network, generate corresponding demand semantic vectors and volunteer semantic vectors, calculate the similarity between the demand semantic vector and the volunteer semantic vector based on the cosine similarity algorithm, and generate a recommended matching list based on the calculation results.

[0051] Among them, the preset neural network refers to a pre-trained natural language processing model, which can be implemented using a transformer architecture model such as BERT or RoBERTa. Its function is to convert unstructured text information into a vector form with semantic representation capabilities.

[0052] Among them, semantic encoding refers to the process of mapping natural language text into a high-dimensional vector space, which can be achieved through the hidden layer output of the neural network. This process can capture the implicit semantic associations and contextual relationships in the text.

[0053] Among them, the demand semantic vector and volunteer semantic vector refer to the vectorized expressions of the learning goal description and skill description in the semantic space respectively. They can be obtained by dimensionality reduction processing on the feature vector output by the neural network, so that texts with different expressions but similar semantics obtain similar vector distributions.

[0054] Among them, the cosine similarity algorithm refers to a method of measuring the similarity of two vectors by calculating the cosine value of the angle between them. Specifically, it uses the ratio of the vector dot product and the module length product for calculation, which is used to quantify the degree of semantic matching between requirements and skills.

[0055] Specifically, the natural language text input of skill descriptions and learning goal descriptions is fed into a preset neural network. The model extracts contextual features of the text through a multi-layer self-attention mechanism and generates a feature vector containing semantic information. For skill descriptions, the neural network encodes them into volunteer semantic vectors of fixed dimensions; for learning goal descriptions, it generates demand semantic vectors of the same dimensions. Subsequently, the cosine similarity between the two types of vectors is calculated in a unified semantic space. When the similarity exceeds a preset threshold, the volunteer and the educational program are added to the recommended matching list. This process breaks through the limitations of traditional keyword matching, for example, accurately associating needs and skills that have different expressions but similar semantics, such as "cultivating mathematical thinking" and "improving arithmetic ability."

[0056] Compared to existing technologies, traditional methods rely on keyword matching or TF-IDF vectorization, and are unable to identify differences in expression through synonym substitution or semantic paraphrase. For example, existing technologies consider "programming instruction" and "code training" to be unrelated keywords, while this solution uses semantic encoding to identify their proximity in vector space, thereby establishing an effective association. Furthermore, existing technologies struggle to accurately decompose complex skill descriptions such as "possessing Python programming and data analysis capabilities," while this solution automatically extracts multi-dimensional semantic features through a neural network, achieving fine-grained matching.

[0057] Through the above technical solution, this application can accurately identify the potential semantic association between volunteer skills and educational needs, and solve the problem of missing matches due to differences in expression. For example, "physics experiment guidance" and "scientific inquiry ability training" are effectively matched, even if the two do not contain the same keywords. At the same time, the solution can handle text input containing professional terms or local dialects, such as automatically associating "Olympiad tutoring" with "mathematical competition training", improving the adaptability of cross-regional educational resource matching.

[0058] This application further proposes a multi-objective optimization submodule, which is configured to construct a geographic distance cost function that minimizes the distance based on the geographic location coordinates of volunteers and rural children; construct a time window coincidence function that maximizes the coincidence rate based on the matching calculation between the volunteer's service time window and the project requirement time period; construct a skill matching function that maximizes the matching score based on the TF-IDF similarity and semantic feature vector similarity between the skill label set and the subject requirement label; use the NSGA-II genetic algorithm to solve the multi-objective optimization model and generate a Pareto optimal solution set; and filter and output the global optimal recommended matching list from the Pareto optimal solution set according to the preset strategy.

[0059] Among them, the geographic distance cost function refers to a mathematical model that generates a distance assessment value by calculating the difference in geographic coordinates between volunteers and rural children. Specifically, it can be implemented by using the Haversine formula to calculate the great circle distance between two points on the earth's surface, which is used to reduce transportation costs during the service process.

[0060] Among them, the time window overlap function refers to a calculation model that generates a time availability score by analyzing the degree of overlap between the volunteer service time period and the project demand time. Specifically, it can be implemented using a time interval overlap percentage algorithm to ensure the feasibility of service time arrangements.

[0061] The skill matching function refers to a composite calculation model that generates a skill fit score by fusing label statistical features with semantic features. Specifically, it can be implemented using a linear combination of TF-IDF weighted cosine similarity and semantic vector cosine similarity to improve the matching accuracy between skills and requirements.

[0062] Among them, the NSGA-II genetic algorithm refers to a multi-objective optimization algorithm based on non-dominated sorting and congestion comparison. It can be implemented using binary coding and tournament selection mechanisms to find a balanced solution among multiple conflicting objectives.

[0063] Specifically, the geographic distance cost function converts the geographic coordinates of volunteers and rural children into longitude and latitude data. Using a spatial distance calculation model, a geographic cost value is generated, which increases nonlinearly with distance. The time window overlap function converts the volunteer's service period and the project's required period into overlapping areas on the timeline. The time match score is calculated by calculating the ratio of the overlapping period to the total required period. The skill match function performs word frequency analysis and semantic encoding on the volunteer's skill tags and requirement tags, generating statistically and semantically similarity scores, respectively. A weighted summation of these scores yields a comprehensive match score. The NSGA-II genetic algorithm optimizes these three objective functions and iteratively searches the solution space through population initialization, crossover mutation, and elite retention strategies, ultimately outputting a Pareto-optimal set of solutions that satisfy multiple objective constraints. Pre-set strategies dynamically adjust objective priorities based on actual business needs, for example, prioritizing solutions with lower geographic costs in areas with limited access.

[0064] Compared to existing technologies, existing matching systems typically rely on a single-dimensional similarity calculation, such as considering only keyword matching or geographic proximity, and are unable to simultaneously optimize both time window overlap and skill matching. This solution, however, establishes a multi-objective optimization model that transforms three key factors, geographic distance, time availability, and skill compatibility, into quantifiable mathematical functions. It then employs an evolutionary algorithm for global optimization search, effectively solving the resource matching challenge under multi-dimensional constraints.

[0065] Through the above technical solution, this application achieves precise matching of volunteers and demanders in educational support projects. Geographic location optimization reduces travel time for service providers, time window matching ensures the feasibility of service plans, and improved skill fit guarantees the quality of educational resources. Through a multi-objective collaborative optimization mechanism, local optimality issues caused by prioritizing a single objective are avoided. For example, excessive pursuit of geographical proximity prevents the selection of volunteers with mismatched skills, while the pursuit of skill matching prevents the neglect of time feasibility issues.

[0066] This application further proposes a feedback optimization module, which is configured to receive feedback scores from parents or educational institution administrators on the volunteers' implementation of educational projects, and adjust the weight coefficients of each objective function in the Pareto frontier model based on the feedback scores.

[0067] Among them, feedback scoring refers to the quantitative evaluation of the quality of volunteer services by parents or administrators of educational institutions. It can be implemented using a five-point scoring scale or text sentiment analysis technology to objectively reflect the actual implementation effect of the educational project.

[0068] Among them, the Pareto front model refers to the mathematical expression of the set of non-inferior solutions in multi-objective optimization problems. Specifically, it can be implemented using the optimized solution set generated by the NSGA-II algorithm, which is used to characterize the trade-off relationship between objectives such as geographical distance, time window, and skill matching.

[0069] Among them, weight coefficient adjustment refers to dynamically changing the relative importance of each objective function in the optimization model based on user feedback. It can be implemented by weighted least squares method or fuzzy logic reasoning mechanism to adapt to the characteristic differences of educational needs in different regions.

[0070] Specifically, after completing the matching of educational projects, the system collects rating data from parents or educational institutions on dimensions such as the teaching quality and communication efficiency of volunteers through the online evaluation interface. The feedback optimization module converts discrete rating data into continuous influencing factors through data cleaning and normalization. A time series analysis model is constructed based on historical feedback data to identify the correlation between the weights of each objective function and user satisfaction. When it is detected that the geographical distance weight coefficient of a specific area is negatively correlated with the feedback score of local users, the module dynamically reduces the weight of the geographical distance cost function through the gradient descent algorithm, while increasing the weight coefficient of the skill matching function. During the iterative optimization process of the Pareto frontier model, the system recalculates the non-inferior solution set based on the updated weight coefficient, and gives priority to recommending matching solutions that meet the current user preference characteristics.

[0071] Compared to existing technologies, traditional educational resource matching systems use fixed-weight, multi-objective optimization models that are unable to adjust optimization strategies based on service performance feedback, resulting in misalignment between matching results and actual user needs. This solution, by establishing a dynamic weight adjustment mechanism driven by feedback data, enables multi-dimensional matching criteria to adapt to the evaluation preferences of different user groups, optimizing the regional adaptability and timeliness of the matching strategy.

[0072] Through the above technical solution, this application effectively solves the matching bias problem caused by the rigid weighting of multi-objective optimization models, achieving dynamic closed-loop optimization of educational resource supply and demand. The system automatically balances key factors such as geographic proximity, time adaptability, and skill matching based on user feedback. While ensuring the stability of the matching algorithm, it significantly improves user acceptance of recommendation results, promoting the continuous improvement of the quality of educational support services.

[0073] This application further proposes that the project creation unit includes a templated automatic creation unit and a manual review creation unit. The templated automatic creation unit creates an education project using a preset project template based on education demand information, and the manual review creation unit receives the preset project template filled in by the government user based on the education demand information and creates the education project.

[0074] Preset project templates are data structure templates containing standardized fields. Specifically, they use JSON format to define required fields such as project name, subject category, and target age range. Each field is assigned data validation rules to achieve standardized input. This template ensures the integrity of project information through structured field constraints, providing unified data specifications for both automated generation and manual review.

[0075] Among them, the manual review creation unit refers to an interactive interface with exclusive permissions for government users. Specifically, the RBAC permission model can be used to set the project approval role of government users, present the preset project template fields through a visual form, and support uploading attached documents to supplement explanatory materials. This unit implements access control for sensitive operations through permission isolation, and meets administrative supervision requirements while inheriting template standardization.

[0076] Specifically, once educational demand information is uploaded through the data collection module, a templated automatic creation unit parses key elements in the natural language text. For example, it automatically maps "primary school mathematics tutoring" to the subject category field, generating an educational project instance that conforms to the preset template. For projects involving policy support or special funds, the system automatically triggers a manual review process. Government users use a dedicated interface to verify the match between project information and policy requirements. Once confirmed, they submit the project for formal project generation. Both creation modes share the same template data structure, ensuring that automatically generated projects and manually reviewed projects are completely consistent in field format and data specifications. At the same time, permission control enables differentiated processing of creation paths.

[0077] Compared with existing technologies, traditional education project creation systems mostly adopt a single automated generation or a completely manual approval model. The former has policy compliance risks, and the latter leads to low administrative efficiency. This solution uses a dual-path collaborative mechanism to retain the efficiency advantages of templated automatic generation. On the basis of this, it adds a government manual review link for specific project types, avoiding the regulatory loopholes that may arise from full automation and overcoming the time-consuming defects of traditional manual approval.

[0078] Through the above technical solutions, this application effectively balances the contradictions between standardization and customization in the creation of educational projects, improving project generation efficiency while ensuring policy compliance. Template-based automatic creation shortens the time required to generate regular projects, and the manual review process conducts targeted verification of key fields for key projects, significantly improving review efficiency compared to traditional full-field verification. Both creation modes operate based on the same data template, ensuring that project data generated by different paths is comparable and traceable, laying the data foundation for subsequent intelligent matching and collaborative management.

[0079] This application further proposes differentiated operation permissions including a volunteer operation permission set, a parent and educational institution administrator operation permission set, a government user operation permission set, and an enterprise user operation permission set.

[0080] Among them, the volunteer operation permission set means that volunteers can only perform operations such as browsing educational projects, submitting matching applications, uploading service logs, and applying for certificates. This can be implemented using a role-based access control model, and access to the user operation interface can be restricted through a predefined role permission table.

[0081] Among them, the operation permission set for parents and educational institution administrators allows them to submit educational needs information, query project progress, and submit feedback scores. This can be achieved by using dynamic form generation and data isolation technology, and controlling the data reading and writing scope through independent database views.

[0082] Among them, the government user operation permission set refers to the authority granted to approve the creation of education projects, supervise the flow of funds, and approve the issuance of certificates. It can be implemented using a multi-level approval workflow engine, and the non-repudiation of approval operations can be ensured through digital signature technology.

[0083] Among them, the enterprise user operation permission set refers to the functions of open education project browsing, fund donation, fund flow query and social benefit report generation. It can be specifically implemented using blockchain smart contract technology, and the transparency and traceability of fund flows can be ensured through on-chain data storage.

[0084] Specifically, the solution achieves functional isolation by establishing four independent permission models. After a volunteer account logs into the system, the interface layer calls the permission verification module to verify its role identification. Only the project browsing interface and application submission interface are open, and the service log upload function uses a file encryption storage mechanism. When a parent user submits a request, the system generates an independent project number and establishes a data sandbox. Administrators of educational institutions can only view projects associated with their respective institutions. When government users create projects, a multi-level approval process is triggered. The fund supervision function connects to the application program interface of the bank system to achieve real-time data synchronization. When a corporate user makes a donation, the system automatically generates a smart contract with a timestamp. The fund usage certificate generates a unique identifier through a hash algorithm and is written to the distributed ledger.

[0085] Compared with existing technologies, exclusive permission modules for government supervision and corporate donations have been added. Through the dual isolation of functional interfaces and data access, different roles can complete core business operations in a unified system while avoiding security risks caused by cross-border data access.

[0086] Through the above technical solution, this application effectively solves the problem of permission conflicts in multi-role collaboration scenarios.

[0087] This application further proposes preset certificate issuance conditions, including that the number of educational support projects completed by volunteers reaches a quantity threshold and the volunteer service time reaches a preset time threshold.

[0088] Among them, the quantity threshold refers to the lower limit of the number of completed education support projects set by the system. Specifically, the number of projects in which volunteers have participated in history can be automatically counted through a counter. When the cumulative number exceeds the preset threshold, the condition verification mechanism is triggered to screen volunteers with cross-project service capabilities.

[0089] Among them, the duration threshold refers to the lower limit of the cumulative service time set by the system. Specifically, the timer can automatically calculate the total effective service time of volunteers in each project. When the cumulative duration exceeds the preset threshold, the condition verification mechanism is triggered to screen volunteers with stable service cycles.

[0090] Specifically, when the statistical value of the number of educational support projects associated with a volunteer account reaches the minimum number of projects set by the system, it indicates that the volunteer has experience in multi-scenario service. At the same time, when the cumulative effective service time recorded by the service timer reaches the minimum duration standard set by the system, it indicates that the volunteer has the ability to provide continuous service. After the two conditions are verified simultaneously, the system automatically generates digital certificate data containing the number of projects and service time, and encrypts the key data of the verification process and writes it to the blockchain. For example, when the system sets the quantity threshold to 3 projects and the duration threshold to 50 hours, volunteers must complete at least 3 independent education projects and provide a cumulative 50 hours of effective service in these projects before they can obtain a digital certificate.

[0091] Compared to existing technologies, existing systems often use a single evaluation metric, such as counting only service hours or the number of projects, which can easily lead to evaluation bias. For example, one system may require only two projects to be completed before issuing a certificate, but volunteers may only serve on a single project for a short period of time. This solution effectively identifies volunteers with the ability to provide sustained service through cross-validation of dual metrics, and combines blockchain technology to ensure the immutability of certificate issuance records.

[0092] Through the above technical solutions, this application has established a quantifiable service evaluation system that accurately distinguishes between occasional volunteers and those who contribute regularly. This system ensures the continuity of service in educational support projects through objective threshold conditions. The automated issuance of digital certificates reduces manual review costs, and blockchain-based evidence storage enhances the credibility of certificates, thereby increasing volunteer participation in educational support projects.

[0093] This application further proposes an AI assistant module, which parses user natural language queries through a pre-trained language model and generates multimodal response content in combination with a knowledge graph. The knowledge graph contains education policies and regulations, project operation guidelines, and solutions to common education project problems.

[0094] Among them, the pre-trained language model refers to a deep learning model trained with large-scale text data. It can be implemented using the BERT or GPT series models, which are used to perform semantic analysis and intent recognition on user-entered query statements, solving the semantic ambiguity problem caused by traditional keyword matching.

[0095] Among them, the knowledge graph refers to a structured knowledge base organized in the form of a graph structure, which can be implemented using the Neo4j graph database. Its nodes represent education policy clauses, project operation steps and problem-solving entities, and the edges represent the relationships between entities, which are used to provide accurate and traceable knowledge support for response generation.

[0096] Among them, multimodal response content refers to a composite information form containing text, charts and structured data elements. It can be implemented by combining natural language generation technology with visualization tools to provide an adapted response form based on the user's query scenario and reduce the user's cognitive load.

[0097] Specifically, when a user inputs a query request through natural language, the pre-trained language model first performs word segmentation and vectorization on the query statement, extracts its semantic feature vector and performs similarity matching with the entities in the knowledge graph to determine the associated set of knowledge nodes. Subsequently, based on the mapping relationship between the query intent and the knowledge node, the system calls the corresponding response template and fills in the dynamic parameters to generate multimodal content containing the original reference of the policy clause, the operation step flowchart and the solution to common problems. For example, when a volunteer queries "how to apply for a cross-regional education project", the system extracts the cross-regional service policy documents, application process forms and review cycle instructions from the knowledge graph by parsing key intents such as "cross-region" and "application", and generates a text-and-image response page.

[0098] Compared to existing technologies, existing education support systems typically rely on rule-based keyword matching or fixed question-and-answer libraries, which are unable to handle complex semantic queries and provide monotonous responses. This solution achieves deep semantic understanding through pre-trained language models. Combined with the associative reasoning capabilities of knowledge graphs, it accurately captures user intent and provides dynamically generated responses, effectively addressing the response delays and content fragmentation issues inherent in traditional systems.

[0099] Through the above technical solution, this application realizes the intelligent processing of user queries in the education support scenario, shortens the response time of policy interpretation and operation guidance, reduces the repetitive work of manual customer service, and at the same time improves the efficiency of information transmission through multimodal content presentation, so that users of different roles can quickly obtain the required support information.

[0100] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An AI-based intelligent matching and collaborative management system for rural education resources, characterized by: include: The user management module is configured to register multiple roles for volunteers, parents, educational institution administrators, government users, and corporate users, and allocate differentiated operation permissions based on role types; a data acquisition module configured to acquire service capability information of volunteers and educational needs information of rural children, wherein the service capability information includes skill descriptions inputted via natural language text, geographic coordinates, serviceable time windows, and skill tags selected from a preset tag library; and the educational needs information includes learning goal descriptions inputted via natural language text, geographic coordinates, and subject need tags selected from a preset tag library; a project generation module configured to create a corresponding education project according to the education demand information and establish a binding relationship between the education project and the education demand information; an intelligent matching engine module configured to perform multi-dimensional similarity calculation on the service capability information and the educational demand information of the educational program, and output a matching list of volunteers and educational programs; A project execution monitoring module is configured to record service log data submitted by volunteers during the execution of the educational project and generate a project progress visualization report based on the log data; A fund supervision module is configured to receive donation requests from corporate users for designated educational projects and generate a fund flow tracking chain containing a hash value of the donation amount, the recipient's account, and the fund use certificate; The certificate generation module is configured to verify the volunteer's service log data according to the preset certificate issuance conditions. After the verification is passed, a digital certificate is generated and written into the blockchain for storage; The incentive allocation module is configured to allocate the incentive amount from the donation fund pool to the volunteer account according to a preset ratio based on the completion index in the project progress visualization report.

2. The AI-based intelligent matching and collaborative management system for rural education resources according to claim 1 is characterized in that: The AI intelligent matching module includes: Collaborative filtering recommendation unit, configured as: Based on the historical matching records between volunteers and educational support projects, a volunteer user-education project interaction matrix is constructed, where the row vector represents the unique identifier of the volunteer, the column vector represents the unique identifier of the educational support project, and the matrix element value represents the binary identifier of the matching result; Through the cosine similarity algorithm, the behavioral similarity between volunteers is calculated based on row vectors to generate a volunteer similarity matrix, or the demand correlation between education support projects is calculated based on column vectors to generate a project similarity matrix; If the target user is a volunteer, other volunteers with similarities higher than a preset threshold are screened, and a list of successfully matched educational projects is extracted as a recommended matching list; If the target user is a rural child, other educational projects with a similarity to the current educational project higher than a preset threshold are screened, and a list of volunteers who have successfully matched them is extracted as a recommended matching list.

3. The AI-based intelligent matching and collaborative management system for rural education resources according to claim 2 is characterized in that: The AI intelligent matching module also includes: The content recommendation unit is configured to perform TF-IDF vectorization processing on the skill tags and learning goal descriptions, calculate the similarity between feature vectors using a cosine similarity algorithm, and generate a recommendation matching list based on the calculation results.

4. The AI-based intelligent matching and collaborative management system for rural education resources according to claim 3 is characterized in that: The AI intelligent matching module also includes: The deep learning unit is configured to semantically encode the skill description and learning goal description of the natural language text input through a preset neural network, generate corresponding demand semantic vectors and volunteer semantic vectors, calculate the similarity between the demand semantic vector and the volunteer semantic vector based on the cosine similarity algorithm, and generate a recommended matching list based on the calculation results.

5. The AI-based intelligent matching and collaborative management system for rural education resources according to claim 4 is characterized in that: The AI intelligent matching module also includes: Multi-objective optimization submodule, configured as: Construct a geographic distance cost function that minimizes the distance between volunteers and rural children based on their geographic coordinates; Construct a time window coincidence function based on the matching degree between the volunteer's service time window and the project demand time period to maximize the coincidence rate; Construct a skill matching function that maximizes the matching score based on the TF-IDF similarity and semantic feature vector similarity between skill tags and subject requirement tags; Using NSGA-II genetic algorithm to solve the multi-objective optimization model and generate a Pareto optimal solution set; According to a preset strategy, the Pareto optimal solution set is filtered and a global optimal recommended matching list is output.

6. The AI-based intelligent matching and collaborative management system for rural education resources according to claim 5 is characterized in that: Also includes: The feedback optimization module is configured to receive feedback scores from parents or educational institution administrators on the volunteers' implementation of the educational program, and adjust the weight coefficients of each objective function in the Pareto frontier model according to the feedback scores.

7. The AI-based intelligent matching and collaborative management system for rural education resources according to claim 6 is characterized in that: The project creation unit includes: a templated automatic creation unit configured to create an educational project using a preset project template according to the educational demand information; The manual review and creation unit is configured to receive a preset project template filled in by a government user based on the education demand information and create an education project.

8. The AI-based intelligent matching and collaborative management system for rural education resources according to claim 7 is characterized in that: The differentiated operating permissions include: Volunteer operation permission set: browse education projects, submit education project matching applications, upload service logs, and apply for digital certificates; Operation permission set for parents and educational institution administrators: submit education demand information, query project progress, and submit feedback and ratings; Government user operation permission set: education project creation, fund flow supervision, and certificate issuance approval; Enterprise user operation permission set: education project browsing, fund donation, fund flow query, and social benefit report generation.

9. The AI-based intelligent matching and collaborative management system for rural education resources according to claim 1 is characterized in that: The preset certificate issuance conditions include: The number of education support projects completed by volunteers reaches the quantitative threshold; The volunteer service time reaches the preset time threshold.

10. The AI-based intelligent matching and collaborative management system for rural education resources according to claim 1 is characterized in that: Also includes: The AI assistant module is configured to parse user natural language queries through a pre-trained language model and generate multimodal response content in combination with a knowledge graph, which contains education policies and regulations, project operation guidelines, and solutions to common education project problems.

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