An integrated service system for college students' mental health and career development

Abstract

This invention relates to the field of information technology applications, specifically to an integrated service system for university students' mental health and career development. The system includes an information interaction platform, a student terminal, and a university administrator terminal. The information interaction platform integrates and facilitates data exchange between a user management module, a one-stop support module for student growth, a campus-based AI consultation module, and a campus-specific community module. It integrates psychological assessment, resource services, AI consultation, and community interaction onto a unified platform, achieving integrated services for mental health management, academic planning, career development, and campus community interaction, breaking down the fragmented nature of traditional services. This invention supports unified identity authentication for universities and possesses functions such as scenario-based assessment, intelligent resource recommendation, AI consultation and feedback, and content review and classification, effectively solving the problems of fragmented functions, lack of campus-specific focus, and low resource integration in existing platforms.

Description

Technical Field

[0001] This invention relates to the field of information technology applications, specifically to an integrated service system for the mental health and career development of university students. Background Technology

[0002] With increasingly fierce social competition, college students generally face multiple challenges such as academic pressure, career planning confusion, and psychological anxiety. Their needs for mental health support, access to academic resources, and career development guidance are becoming increasingly urgent and diverse. Currently, although some general mental health assessment platforms, career planning tools, and campus social applications exist in the market, these services generally suffer from the following problems: Functional fragmentation and service disconnect: Mental health services, career guidance, and academic resources are typically provided by different independent platforms, with data not shared and functions not coordinated between them. Students need to frequently switch between multiple platforms to obtain comprehensive support, which is cumbersome and significantly reduces user experience and the effectiveness of support. Lack of Campus-Specificity: Existing platforms often employ generic designs for their assessment dimensions and consultation suggestions, failing to closely integrate with the unique campus life of university students. For example, psychological assessments pay little attention to campus-specific issues such as GPA anxiety, pressure from postgraduate / civil service exams, dormitory interpersonal relationships, and balancing internships and studies; the suggestions provided are often based on general theories, lacking actionable, personalized solutions that integrate with specific university resources (such as the university's psychological counseling center, career guidance office, and library databases), resulting in insufficient practicality of the services. Low resource integration and accuracy: Existing platforms mostly use a generalized collection model for learning and career resources, lacking refined tag management and intelligent recommendation mechanisms based on dimensions such as the student's university, major, and year. Students need to manually sift through massive and disorganized resource libraries, which is not only inefficient but also makes it easy to obtain invalid information that does not match their own situation. For example, lower-year students may mistakenly click on materials for core professional courses of higher-year students, or liberal arts students may search for a large number of STEM technical documents, resulting in serious information overload and wasted time. Therefore, it is necessary to design an integrated service system for college students' mental health and career development to improve the above-mentioned problems. Summary of the Invention

[0003] To address the problems of existing technologies, this invention provides an integrated service system for the mental health and career development of university students, comprising an information interaction platform, a student terminal, and a university administrator terminal. The information interaction platform is connected to both the student terminal and the university administrator terminal, and student users and university administrators log in to the information interaction platform through the student terminal and the university administrator terminal, respectively. The information exchange platform includes: The user management module is configured to implement student user authentication, access control, password management, and university-specific management functions; by establishing an interface with the university's unified identity authentication system, it can verify the campus identity of student users and obtain and store the identity identification information of student users; The one-stop support module for student growth includes a campus-specific psychological assessment module and an academic-career resource integration module with interconnected data. It is configured to provide students with mental health assessment services and academic and career resource services. After a student user completes the psychological assessment by selecting a target psychological assessment unit, the campus-specific psychological assessment module generates a campus-based assessment report for subsequent querying. Simultaneously, the student user, after successful identity verification, performs data resource retrieval, collection, and download operations through the student terminal. The academic-career resource integration module collects student user behavior data generated during the execution process. The campus-based AI consultation module is configured to provide personalized, real-time consultation services to student users based on AI technology, and to generate scenario-based suggestions by integrating assessment data and university resources. The campus-exclusive community module is designed to promote academic exchange, career sharing, and social interaction among students. Authenticated student users can categorize, publish, and review content through the student app.

[0004] Furthermore, the user management module includes: a campus identity authentication unit, configured to establish a secure connection link with the university's unified identity authentication system through the OAuth 2.0 protocol, obtain the student user's basic campus information based on the connection link, use the basic campus information as the student user's identity identification information, and encrypt and store the student user's identity identification information. The access control unit, based on a role-based access control mechanism, divides users of the information interaction platform into at least student roles and university administrator roles, and assigns corresponding operation permissions to different roles. The password management unit is configured to allow student users or university administrators to set, reset, and retrieve passwords for login requests according to set rules. The university management unit is configured to provide university administrators with campus service management functions, including viewing a summary of students' mental health status, issuing instructions to optimize service resource allocation, and adjusting system operating parameters.

[0005] Furthermore, the campus-specific psychological assessment module includes: The assessment selection unit is configured to display the tag information corresponding to each psychological assessment unit, receive the assessment selection instruction triggered by the student user, and jump to the target psychological assessment unit selected by the student user based on the assessment selection instruction. The interactive question-answering unit is configured to generate suitable interactive psychological assessment questions based on the assessment theme and dimensions of the target psychological assessment unit, thus constructing an interactive assessment link with student users. The interactive question-answering unit has a built-in sub-module for saving the answer progress and a sub-module for resuming the answer midway. The sub-module for saving the answer progress stores the student user's answer progress data in real time. The sub-module for resuming the answer midway restores the answer progress to the answer state before the interruption when the student user re-enters the same target psychological assessment unit after interrupting the assessment, thus realizing the functions of saving and resuming the answer midway. The assessment submission unit is configured to receive psychological assessment data submitted by student users after completing all interactive psychological assessment questions, perform structured processing on the psychological assessment data and encapsulate it into a JSON format assessment data packet, and transmit the assessment data packet to the assessment result processing unit. The assessment result processing unit is configured to receive the JSON-formatted assessment data packet, parse and extract the psychological assessment data; quantify the psychological assessment data using a weighted scoring method to obtain the corresponding assessment score; match the assessment score to the corresponding psychological stress level based on a preset score-stress level mapping rule, and generate a campus-based scenario-based assessment report by combining the psychological stress level with university resources; and associate and store the campus-based scenario-based assessment report with the student user's identity information to form a traceable personal assessment file.

[0006] Furthermore, the academic-career resource integration module includes: The resource classification unit is configured to establish a preset resource classification system, which includes at least academic resources, career resources, and psychological resources. University resources are classified and stored according to the preset resource classification system, and each type of resource is assigned multi-dimensional tag information based on university code, major code, and grade identifier to form a standardized resource tag library. The resource submission unit is configured to provide an original resource upload interface, receive original resources and resource description information uploaded by student users; push the original resources and resource description information to the university administrator for review; if the review is approved, trigger the tag addition mechanism to match standardized multi-dimensional tags for the original resources and publish them online; synchronously record the identity information of the uploader of the original resources and associate and bind it with the identity information of the student users; The resource filtering unit is configured to receive multi-dimensional filtering conditions input by student users, perform matching queries based on the filtering conditions and multi-dimensional tag information in the resource tag library, and obtain target resources that meet the conditions; use a comprehensive scoring algorithm of matching degree weight and historical download volume to prioritize the target resources; and display the sorted high-quality target resources that meet the conditions to student users in a list format to achieve accurate resource filtering output. The resource recommendation unit is configured to use a collaborative filtering algorithm to perform correlation analysis on the historical behavior data of student users and the behavior data of student users with similar characteristics to construct a user resource demand model; based on the user resource demand model and combined with the student user's campus-based assessment report, it proactively pushes resources that match the student user's academic planning and career development needs to the student user.

[0007] Furthermore, the campus-based AI consultation module includes: The consultation input unit has a built-in context semantic storage submodule, which is configured to receive text consultation content input by student users through the student terminal interactive interface; the context semantic storage submodule caches semantic information in real time during the consultation session, realizing continuous memory and associated response of the consultation context; The assessment data fusion unit is configured to retrieve associated stored campus-based scenario assessment reports based on the student user's identity information; extract assessment type, psychological status evaluation results, and university coding information from the campus-based scenario assessment reports, and encapsulate the extracted information into AI consultation context initialization data; The university resource docking unit is configured to establish a real-time data interaction channel with university resources through a standardized API interface; acquire exclusive university resource data including the university's lecture schedule, psychological counseling appointment portal, professional resource links, and special event information, forming a dynamically updated university resource database; the university resource docking unit is set with a data synchronization frequency to automatically synchronize university resource data and complete data updates and storage; The suggestion generation unit is configured to build a multi-source data fusion model, which deeply integrates the text consultation content of student users, the initialization data of AI consultation context, and relevant resource data in the university resource library; calls the AI ​​model to generate scenario-based suggestions that include at least academic guidance, career recommendation, and psychological counseling; and embeds relevant real-time resource jump links in the scenario-based suggestions, so that student users can directly click to access the corresponding resources; The consultation and feedback unit is configured to provide a satisfaction rating entry and an opinion feedback input box, receive student users' satisfaction ratings and improvement suggestions for the scenario-based suggestions, and synchronize the satisfaction ratings and improvement suggestions to the information interaction platform to optimize the accuracy of AI-generated suggestion content and resource matching. The interactive data storage unit is configured to synchronously record consultation interaction data during the consultation interaction process, including text consultation content, AI-generated suggestion content and resource jump records, and to associate and store the consultation interaction data with the student user identity information that initiated the consultation request, forming a traceable consultation file.

[0008] Furthermore, the campus-specific community module includes: The content classification unit is configured to divide the community sections according to preset theme categories, and to filter the divided community sections based on the university code and major code in the student user's identity information, displaying the exclusive community section of the student user's university and major; wherein, the preset theme categories include at least academic exchange, career sharing, social interaction and resource assistance; The content publishing unit is configured to provide a content publishing interface that combines text and auxiliary images, and to receive community content to be published by student users; it has a built-in anonymous publishing function option. When a student user selects the anonymous publishing function option, the student user's sensitive identity information is automatically hidden. The sensitive identity information includes at least the student ID and a custom nickname; only the major information and grade information are retained and associated with the content to be published, and then the content to be published is pushed to the dual review unit; The dual review unit is configured to employ a dual review mechanism combining AI initial review and university administrator secondary review: the AI ​​initial review is based on a preset violation feature library and a high-quality content feature library to intelligently identify the received content to be reviewed, filter out false information and violation content, and mark high-quality content containing specific resource descriptions of the university; the university administrator secondary review manually verifies and confirms the results of the AI ​​initial review, and the content that passes the review is pushed to the corresponding community section for display, while the content that is rejected is fed back to the publishing user with preset rejection reasons; The content interaction unit is configured to support student users in performing interactive actions such as liking, commenting, and replying to comments on the displayed community content; for community content and related comments that have been marked as high-quality by the dual review unit, a display priority enhancement mechanism is automatically triggered, so that they are displayed first in the community section, thereby improving the efficiency of the dissemination of high-quality content; The content browsing unit is configured to provide student users with a list-style browsing interface for community content, supporting sorting by publication time and interaction popularity, making it easy for student users to quickly search for community content of interest.

[0009] Furthermore, the university management unit includes: The university administrator identity authentication unit receives login requests sent by university administrators to the information exchange platform through the university administrator terminal. After completing identity verification, the user is redirected to the management function interface. The output end of the university administrator identity authentication unit establishes a data interaction link with the group evaluation and statistics unit, content review unit, weight configuration unit, and university resource docking unit. The group assessment and statistics unit collects psychological assessment data from student users, performs multi-dimensional aggregation and analysis on the psychological assessment data of student users according to college, major and grade, and generates a visualized overall psychological health distribution report. University administrators can obtain the psychological health status of their students based on the overall psychological health distribution report. The content review unit has a list of content to be reviewed, and receives review instructions from university administrators for original resources uploaded by students and community content published. It is configured with options for approval and rejection, and a preset rejection reason must be associated when the rejection operation is executed. The weight configuration unit establishes a data interaction channel with the campus-specific psychological assessment module, receives weight adjustment instructions input by university administrators, and customizes the weight coefficients of various psychological assessment questions in the campus-specific psychological assessment module to adapt to the personalized assessment needs of universities. Furthermore, the information interaction platform also includes: The User Guide module, including the association detection unit and the display trigger unit, is configured to automatically trigger the display process of the function usage instructions and data privacy statement when a student user first accesses the campus-specific psychological assessment module or the campus-based AI consultation module; the student user must click to confirm before entering the corresponding module; The Personal Center module, comprising an information integration unit and a personal information management unit, is configured to create a one-stop information management interface for student users. It integrates and displays student users' identity information, historical psychological assessment records, AI consultation conversation records, collected academic / career resources, and published / interactive community content. Student users can manage their personal information by changing their mobile phone number and resetting their password.

[0010] A computer-readable storage medium storing a computer program that, when executed by a processor, performs the functions of the system.

[0011] The beneficial effects of this invention are: (1) This invention is applicable to providing students with integrated services such as mental health management, academic planning guidance, career development support and campus community interaction in the context of colleges and universities. The integrated service system includes an information interaction platform, a student terminal and a college administrator terminal. The information interaction platform is connected to the student terminal and the college administrator terminal respectively. The information interaction platform includes a user management module, a one-stop support module for student growth, a campus-based AI consultation module and a campus-specific community module. By deeply integrating psychological assessment, resource services, AI consultation and community interaction into a unified platform and realizing data interoperability between modules, the traditional fragmented service status is broken. Students can obtain continuous growth support services without switching multiple applications, which greatly improves convenience and service continuity. (2) By setting up a campus-specific psychological assessment module and deeply integrating the campus-based AI consultation module with the internal resource system of universities, the present invention enables the assessment results and consultation suggestions to be closely integrated with the real campus environment in which students are located, providing practical solutions and significantly improving the practical value of the service. (3) The present invention adopts a resource management system based on multi-dimensional tags such as university, major, and grade, and combines students' personalized characteristics and behavioral history. It uses a collaborative filtering algorithm to perform correlation analysis on the historical behavioral data and the behavioral data of students with similar characteristics. According to the user resource demand model and combined with the student user's campus scenario assessment report, it actively pushes resources that match the student user's academic planning and career development needs to the student user, helping students to efficiently obtain high-quality resources and reducing information retrieval costs. (4) This invention comprehensively protects the system's operational security, user data privacy, and community content compliance through unified identity authentication in universities, strict role and permission control, encrypted data transmission and storage, and a dual content review mechanism of AI and human review, thus creating a healthy and trustworthy service ecosystem. (5) This invention provides university administrators with visualized data statistics reports, centralized content review interfaces, and flexible system parameter configuration tools, enabling universities to gain insights into the overall situation of students based on data, dynamically optimize resource allocation, and improve the intelligence and refinement of student management. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the overall system architecture interaction of the present invention; Figure 2 This is a schematic diagram of the information interaction platform module structure of the present invention. Detailed Implementation

[0013] 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.

[0014] Please see Figure 1-2 This invention provides an integrated service system for mental health and career development for college students, including an information interaction platform, a student terminal, and a university administrator terminal. The information interaction platform is connected to the student terminal and the university administrator terminal, respectively. Student users and university administrators log in to the information interaction platform through the student terminal and the university administrator terminal, respectively. The information exchange platform includes: The user management module is configured to implement student user authentication, access control, password management, and university-specific management functions; by establishing an interface with the university's unified identity authentication system, it can verify the campus identity of student users and obtain and store the identity identification information of student users; The one-stop support module for student growth includes a campus-specific psychological assessment module and an academic-career resource integration module with interconnected data. It is configured to provide students with mental health assessment services and academic and career resource services. After a student user completes the psychological assessment by selecting a target psychological assessment unit, the campus-specific psychological assessment module generates a campus-based assessment report for subsequent querying. Simultaneously, the student user, after successful identity verification, performs data resource retrieval, collection, and download operations through the student terminal. The academic-career resource integration module collects student user behavior data generated during the execution process. The campus-based AI consultation module is configured to provide personalized, real-time consultation services to student users based on AI technology, and to generate scenario-based suggestions by integrating assessment data and university resources. The campus-exclusive community module is designed to promote academic exchange, career sharing, and social interaction among students. Authenticated student users can categorize, publish, and review content through the student app.

[0015] It should be noted that the integrated service system comprises three parts: an information interaction platform, a student client, and a university administrator client. The system establishes bidirectional connections with both the student client and the university administrator client through the information interaction platform. Student users log in to the system through their dedicated student client, while university administrators access the information interaction platform through their independent university administrator client, achieving hierarchical management, access control, and service collaboration. The integrated service system is a complete system, consisting of three core components: the information interaction platform, the student client, and the university administrator client. These components communicate and interact via a network. The information interaction platform, acting as a hub, integrates six modules: user management, student growth support, AI scenario consultation, campus community, user instructions, and personal center. It handles data storage, processing, analysis, and service scheduling, and interfaces with existing university systems such as unified identity authentication, career guidance, mental health education, and library systems through standardized API interfaces (APIs). The student client, serving as the user interface for students, supports multiple access methods, including web and mobile devices, providing entry points for assessment participation, resource retrieval, AI consultation, and community interaction, meeting students' needs for accessing services anytime, anywhere. The university administrator interface provides administrators with backend management functions, including student account permission configuration, resource content review, community posting management, assessment data statistical analysis, and system parameter settings, enabling full-process monitoring and maintenance of the system operation.

[0016] Furthermore, the user management module includes: The campus identity authentication unit is configured to allow student users to send login requests to the information interaction platform via their student terminals. It establishes a secure connection with the university's unified identity authentication system using the OAuth 2.0 protocol. Based on this connection, it obtains the student's basic campus information (including student ID, name, university, major, and year of study) and uses this information as the student's identity identifier. This identifier is encrypted and stored using the Advanced Encryption Standard 256 (AES-256-bit) algorithm. This unit supports student users initiating login requests to the information interaction platform via various terminals, including mobile apps and web browsers. The login request must include an authentication trigger instruction initiated by the student, such as selecting the "Campus Identity Login" option. The unit has a built-in OAuth 2.0 protocol adaptation module responsible for establishing and maintaining a secure communication connection with the university's unified identity authentication system. The entire integration process strictly follows the OAuth 2.0 standard authorization workflow: After a student user triggers campus identity login, the system automatically redirects to the university's unified identity authentication page. The user enters the unified identity account assigned by the university (such as student ID) and the corresponding password. After successful verification by the university's unified identity authentication system, an authorization token is returned to this unit. The campus identity authentication unit securely obtains the student user's basic campus information based on this authorization token, including at least core fields such as student ID (as a unique identifier), name, university, major, and year. The unit integrates the above information into structured student user identity information and encrypts and stores it in the platform's student user database, serving as the basis for permission allocation and various business operations. Simultaneously, this unit supports a real-time synchronization mechanism for identity information: when student information in the university's unified identity authentication system changes, the corresponding user information in the platform can be automatically updated via the protocol interface, ensuring data consistency between the two ends.

[0017] The access control unit, based on a Role-Based Access Control (RBAC) mechanism, divides users of the information interaction platform into at least student roles and university administrator roles, assigning corresponding operation permissions to different roles. Student role permissions include participation in psychological assessments, resource retrieval and download, and community content posting and interaction; university administrator role permissions include viewing summarized mental health statuses, content review, and weight configuration, and administrators do not have access to individual students' private data. All role permissions are isolated within the system, and unauthorized operations are prevented through permission verification logic, ensuring system security and compliance.

[0018] The password management unit is configured for student users or university administrators to request passwords for login, setting, resetting, and retrieving according to set rules (password length ≥ 8 characters, including combinations of letters, numbers, and special characters). Password reset and retrieval require verification via mobile phone number or email address. All passwords are processed using the SHA-256 (Secure Hash Algorithm 256-bit) secure hash algorithm before storage and are saved in encrypted form, greatly ensuring the security of account information.

[0019] The university management unit is configured to provide university administrators with campus service management functions, including viewing and summarizing student mental health status, issuing service resource allocation optimization commands, and adjusting system operating parameters. This unit aims to help administrators efficiently grasp the overall operational status, optimize resource allocation, and ensure the platform's stable and efficient operation.

[0020] It should be noted that student user identification information mainly includes student ID, university, major, and grade, which can be used to uniquely identify a student; password setting rules are based on a combination of character length and character type requirements, and in the password reset and retrieval process, identity verification is strictly carried out using external credentials such as mobile phone number and email address to ensure account security.

[0021] Furthermore, the campus-specific psychological assessment module includes: The assessment selection unit is configured to display the tag information (including assessment topic, duration, and applicable population) corresponding to each psychological assessment unit, and to receive assessment selection instructions triggered by student users, and to jump to the target psychological assessment unit selected by the student user based on the assessment selection instructions. The interactive question-answering unit is configured to generate suitable interactive psychological assessment questions based on the assessment theme and dimensions of the target psychological assessment unit, thus constructing an interactive assessment link with student users. The interactive question-answering unit has a built-in sub-module for saving the answer progress and a sub-module for resuming the answer midway. The sub-module for saving the answer progress stores the student user's answer progress data in real time (including answers to answered questions and identifiers for unanswered questions). The sub-module for resuming the answer midway recalls the answer progress data to restore the answer state before the interruption when the student user re-enters the same target psychological assessment unit after interrupting the assessment, thus realizing the functions of saving and resuming the answer midway. The assessment submission unit is configured to receive psychological assessment data submitted by student users after completing all interactive psychological assessment questions, perform structured processing on the psychological assessment data and encapsulate it into a JSON format assessment data packet, and transmit the assessment data packet to the assessment result processing unit. The assessment result processing unit is configured to receive the JSON-formatted assessment data packet, parse and extract the psychological assessment data; use a weighted scoring method (weight coefficients are uniformly set by the university administrator through the weight configuration unit, and different assessment units can be configured differently) to quantify the psychological assessment data and obtain the corresponding assessment score; based on the system's preset scoring-stress level mapping rules, match the assessment score to the corresponding psychological stress level (such as mild, moderate, and severe anxiety); combine the psychological stress level and university resources to generate a campus-based scenario-based assessment report containing stress level analysis conclusions, cause analysis, and specific suggestions (such as making an appointment with the university's psychological center or participating in a postgraduate entrance examination calming session); and associate and store the campus-based scenario-based assessment report with the student user's identity information to form a traceable personal assessment file. It should be noted that the assessment selection unit has a built-in assessment tag system, which configures exclusive tag information for each psychological assessment unit. The tag information includes at least the assessment topic (such as academic stress assessment), assessment duration, applicable population, and assessment purpose (such as stress level assessment). The tag information is displayed in a visual form (such as a list) on the student's interactive interface, which makes it easy for students to intuitively understand the core information of each assessment unit. Students can generate trigger information by clicking, checking, and other operations. After receiving the trigger information, the assessment selection unit parses the assessment unit identifier and automatically jumps to the corresponding target psychological assessment unit. Simultaneously, it supports recommending suitable assessment units based on students' historical assessment records and personal attributes (such as grade level). A weighted scoring method (assessment score = Σ(question score × question weight), for example, in a postgraduate entrance exam stress assessment, the weight of the review efficiency anxiety question is 0.15, and the weight of the goal uncertainty question is 0.2) is used to quantify the psychological assessment data and obtain the corresponding assessment score. The logical chain of the weighted scoring method is as follows: demand contradiction (general scoring is not suitable for campus scenarios) → theoretical support (quantitative logic of weighted average) → scenario adaptation (personalized weight configuration for universities) → data adaptation (quantitative transformation of structured data) → industry reference (standardized methods of psychological assessment), ultimately forming the assessment score = Σ(question score × question weight). This formula not only solves the problem of the one-size-fits-all approach of traditional scoring, but also, through customized weights and data-driven calculations, makes the assessment results both scientific, campus-specific, and operable, providing accurate quantitative basis for the subsequent generation of campus-specific assessment reports. Furthermore, the academic-career resource integration module includes: The resource classification unit is configured to establish a preset resource classification system. This system includes at least academic resources (such as past exam papers, course materials, and exam outlines), career resources (such as recruitment information, industry reports, and internship experience), and psychological resources (such as stress management manuals and emotion management guides). University resources are categorized and stored according to this system, and each category is assigned multi-dimensional tags based on university code, major code, and grade level, forming a standardized resource tag library. University resources are readily available resources (including resources from university employment offices, psychological centers, and libraries) that can directly provide students with academic support, career guidance, or psychological counseling, integrated during the initial system phase. These resources form the foundation of the system's services and do not require student upload and review; they only need to be categorized and tagged according to the system's preset rules before being made available to students. For example: publicly available electronic textbooks, postgraduate entrance examination past papers, and professional courseware in university libraries; enterprise recruitment information, industry reports, and compilations of employment experiences from previous graduates compiled by the career services office; stress relief manuals, emotion management guides, and campus psychological support service descriptions compiled by the mental health center, etc. The resource submission unit is configured to provide an original resource upload interface, receiving original resources (including study notes, experience sharing, etc.) and resource description information uploaded by student users. The resource description information is supplementary text information (not the resource file itself, but a textual annotation of the resource) submitted simultaneously by student users when uploading original resources, used to describe the core attributes, content value, and applicable scenarios of the resource. The original resources and resource description information are pushed to the university administrator for review. If the review is approved, a tag-adding mechanism is triggered to match standardized multi-dimensional tags to the original resource and publish it online. The uploader's identity information is recorded simultaneously and associated with the student user's identity information. The resource filtering unit is configured to receive multi-dimensional filtering conditions input by student users (such as major: Computer Science, grade: third year, resource type: postgraduate entrance examination questions), perform matching queries based on the filtering conditions and multi-dimensional tag information in the resource tag library, and obtain target resources that meet the conditions; use a comprehensive scoring algorithm of matching degree weight and historical download volume to prioritize the target resources; and display the sorted high-quality target resources that meet the conditions to student users in a list format to achieve accurate resource filtering output; The resource recommendation unit is configured to use a collaborative filtering algorithm to analyze the correlation between the historical behavioral data of student users (resource browsing, downloading, and collection records) and the behavioral data of student users with similar characteristics (such as the same major, the same year, and similar resource preferences) to construct a user resource demand model. Based on the user resource demand model and combined with the student user's campus-based assessment report information (such as showing postgraduate entrance examination pressure), the unit proactively pushes resources that match the student user's academic planning and career development needs (such as analysis of the school's postgraduate entrance examination admission data for the past three years and resources sharing the interview experience of high-scoring senior students). The collaborative filtering algorithm adopts user-based collaborative filtering, which recommends resources visited by similar users to the current user by calculating the behavioral similarity between student users.

[0022] It should be noted that the resource filtering unit allows student users to input multi-dimensional filtering conditions through the filtering interface. Each filtering condition corresponds one-to-one with multi-dimensional tag information. Student users can independently combine filtering conditions to meet their precise search needs. The filtering unit establishes data communication with the server, converting the filtering conditions input by the student user into query commands. The server searches the university resource database based on a tag matching algorithm, filtering out target resources that completely or partially match the conditions, ensuring the relevance of the query results. It incorporates a comprehensive scoring algorithm combining matching degree weight and historical download volume. Specifically: ① Matching degree weight is assigned based on the degree of fit between the filtering conditions and resource tags (e.g., a perfect match receives full marks, while a partial match is scored according to the proportion of the matching dimensions); ② Historical download volume is statistically analyzed based on the number of downloads within a preset period (e.g., the last 30 days) and standardized (to avoid scoring bias caused by differences in resource upload times). The overall score is calculated as follows: matching score × preset weight coefficient + historical download score × preset weight coefficient (the weight coefficient can be customized according to the needs of universities). Target resources are sorted from high to low according to the overall score to ensure that high-quality and highly relevant resources are displayed first. The sorted resources are displayed to student users in a list format, which includes key information such as resource name, tag information, overall score, number of downloads, and upload time, so that student users can quickly judge the value of the resources. Furthermore, the campus-based AI consultation module includes: The consultation input unit has a built-in context semantic storage submodule, which is configured to receive text consultation content input by student users through the student terminal interactive interface (such as "I am preparing for the postgraduate entrance examination recently and I feel very anxious and my efficiency is low. What should I do?"). The context semantic storage submodule caches semantic information in the consultation conversation in real time. It not only temporarily saves the dialogue in a single round, but also maintains the dialogue state across rounds, realizing continuous memory and associated response of the consultation context, and significantly improving the coherence of the dialogue. The assessment data fusion unit is configured to retrieve associated stored campus-based scenario assessment reports (such as the most recent academic stress assessment report) based on the student user's identity information (such as student ID); extract the assessment type, psychological status evaluation result (such as moderate anxiety), and university coding information from the campus-based scenario assessment report; and encapsulate the extracted information into AI consultation context initialization data to provide data support for consultation responses. The university resource docking unit is configured to establish a real-time data interaction channel with university resources through a standardized API interface; it acquires exclusive university resource data, including the university's lecture schedule, psychological counseling appointment portal, professional resource links, and special event information (such as postgraduate entrance examination meditation sessions and online mindfulness training courses), forming a dynamically updated university resource database; the university resource docking unit is set with a data synchronization frequency (once a day by default), automatically synchronizing university resource data and completing data updates and storage; The suggestion generation unit is configured to build a multi-source data fusion model, which deeply integrates the text consultation content of student users, the initialization data of AI consultation context, and relevant resource data in the university resource library; calls the AI ​​model (such as Wenyan Yixin) to generate scenario-based suggestions that include at least academic guidance, career recommendation, and psychological counseling; and embeds relevant real-time resource jump links (such as online mindfulness training course reservation links) in the scenario-based suggestions, so that student users can directly click to access the corresponding resources, realizing a one-stop closed-loop service of "suggestion-resource"; The consultation and feedback unit is configured to provide a satisfaction rating entry (1-5 stars) and an opinion feedback input box, receiving student users' satisfaction ratings and improvement suggestions for the scenario-based suggestions; the satisfaction ratings and improvement suggestions are synchronized to the information interaction platform to optimize the accuracy of AI-generated suggestion content and resource matching.

[0023] The interactive data storage unit is configured to synchronously record consultation interaction data during the consultation interaction process, including text consultation content, AI-generated suggestion content and resource jump records, and to associate and store the consultation interaction data with the student user identity information that initiated the consultation request, forming a traceable consultation file.

[0024] It should be noted that the consultation input unit allows student users to flexibly input text consultation content through the student-side interactive interface (such as input boxes). The text consultation content can broadly cover high-frequency core needs on campus, such as academic questions (e.g., postgraduate entrance exam planning), psychological distress (e.g., stress management), and career direction consultation. The system is compatible with mixed Chinese and English input, common punctuation marks, and special scenario keywords (e.g., postgraduate recommendation), and has the ability to tolerate typos and correct semantic errors, ensuring the completeness and accuracy of the user's expressed needs. The context memory function is implemented through a built-in context semantic storage module. This module performs real-time association storage and deep semantic analysis of multiple rounds of text input in a single consultation session. For example, when a student user first inputs "I am a third-year computer science student and want to take the postgraduate entrance exam," and then subsequently inputs "recommend suitable review materials," the system can automatically associate the preceding context using dialogue state tracking technology to accurately determine that "review materials" refers to the "computer science postgraduate entrance exam" related type, avoiding the student user from repeatedly inputting background information, thereby significantly improving the coherence of the consultation interaction and service efficiency. Meanwhile, the system supports cross-conversation context memory with student user authorization, persistently storing core needs, preferences, and solutions from historical consultations to provide personalized references for subsequent consultations. Campus-based scenario-based assessment reports are linked to student user identification information. Based on the student's unique identification information (such as student ID), the system automatically and accurately retrieves and reads the latest associated psychological assessment report (which is periodically generated and centrally stored by the campus-specific psychological assessment module), ensuring the timeliness and relevance of the extracted data. The system extracts three key types of information from the latest campus-based scenario-based assessment reports: ① Assessment type (such as academic stress assessment, career planning psychological assessment), used to clarify the psychological and ability dimensions that the student user previously focused on; ② Psychological status evaluation results (such as structured conclusions about mild academic stress), helping to grasp the student user's current core needs and potential concerns; ③ University code and department information, ensuring accurate access to the corresponding university's dedicated resources and service interfaces. The extraction process employs a structured parsing algorithm combining rules and machine learning to avoid information omissions or misinterpretations, ensuring the quality and consistency of the initial data. The AI ​​consultation context initialization data encapsulation stage encapsulates the extracted key information according to a preset data format (such as JSON key-value pair structure) to form an AI consultation context initialization data package. This data format is fully compatible with the input requirements of subsequent AI services, ensuring that when AI initiates a consultation response, it can directly load the student's personalized information, quickly locate pain points and potential service gaps, and improve response speed and accuracy. The university resource docking unit has a built-in standardized API interface adaptation module, which establishes secure authentication and communication connections with the university's employment office system, psychological health center system, library system, and academic support system through API interfaces (such as REST API) or custom interface protocols (such as internal university data middleware), ensuring that the data interaction process complies with campus information security standards.

[0025] The construction process of the multi-source data fusion model includes: The data preprocessing layer standardizes student users' text consultation content, campus-based scenario assessment reports, and university-specific resource data. Unstructured text consultation content is converted into semantic feature vectors, structured campus-based scenario assessment report data is converted into assessment feature vectors, and semi-structured university resource data is converted into resource feature vectors with scenario matching. The feature fusion layer introduces a scenario attention mechanism to calculate the matching weights of the three types of feature vectors with student consultation scenarios (such as postgraduate entrance exam anxiety). The weighted feature vectors are then concatenated and fused across dimensions to generate a unified-dimensional fused feature tensor. The output layer inputs the fused feature tensor into a fine-tuned AI model. Through prompt word engineering constraints, the model outputs a comprehensive response including psychological counseling plans, specific action suggestions, and university resource recommendations. Based on preset anchor point rules, appointment links for corresponding resources are embedded in the response. The model optimization layer iteratively fine-tunes the model using a joint loss function based on satisfaction scores and improvement suggestions collected from the consultation feedback unit, optimizing the feature fusion weights and response generation logic. In practice, the text consultation content is first converted into a 128-dimensional semantic vector using the Word2Vec model. The university resource data is then structured according to the "resource type-scene label-access address". Then, dynamic weights are assigned to the three types of data (text consultation content weight 0.4, evaluation data weight 0.3, and university resource data weight 0.3) through an attention mechanism. Feature fusion is then performed to generate a fused feature tensor. Furthermore, the campus-specific community module includes: The content classification unit is configured to divide the community sections according to preset theme categories, and to filter the divided community sections based on the university code and major code in the student user's identity information, displaying the exclusive community section of the student user's university and major; wherein, the preset theme categories include at least academic exchange, career sharing, social interaction and resource assistance; The content publishing unit is configured to provide a content publishing interface that combines text and auxiliary images, and to receive community content to be published by student users; it has a built-in anonymous publishing function option. When a student user selects the anonymous publishing function option, the student user's sensitive identity information is automatically hidden. The sensitive identity information includes at least the student ID and a custom nickname; only the major information and grade information are retained and associated with the content to be published, and then the content to be published is pushed to the dual review unit; The dual review unit is configured with a dual review mechanism combining AI initial review and university administrator secondary review: The AI ​​initial review is based on a preset violation feature library (containing 2000+ violation keywords and semantic features of false information) and a high-quality content feature library (containing keywords for resource descriptions of the university and semantic templates for experience sharing). It intelligently identifies the received content to be reviewed, filters false information and violation content, and marks high-quality content containing specific resource descriptions of the university; The university administrator secondary review manually verifies the results of the AI ​​initial review (verification criteria: content marked as violation by AI needs to verify the basis for the violation, and content marked as high-quality by AI needs to confirm the authenticity of the resource). Content that passes the review is pushed to the corresponding community section for display, and content that is rejected is fed back to the publishing user with preset rejection reasons (such as content violation). The content interaction unit is configured to support student users in performing interactive actions such as liking, commenting, and replying to comments on the displayed community content; for community content and related comments that have been marked as high-quality by the dual review unit, a display priority enhancement mechanism is automatically triggered, so that they are displayed first in the community section, thereby improving the efficiency of the dissemination of high-quality content; The content browsing unit is configured to provide student users with a list-style browsing interface for community content, supporting sorting by publication time and interaction popularity, making it easy for student users to quickly search for community content of interest.

[0026] It should be noted that the content classification unit integrates intelligent recognition algorithms, including keyword matching and semantic analysis algorithms, to ensure accurate content categorization and support dynamic adjustment of topic categories to adapt to the needs of different universities. The keyword matching algorithm accurately matches community content (text, resource descriptions) with a pre-set keyword library, achieving initial content classification and targeted filtering based on university / major. The semantic analysis algorithm analyzes the contextual semantics and potential intent of the text to achieve more accurate content classification, especially suitable for scenarios where keywords are not explicitly stated but semantically clear. Furthermore, it provides user-defined filtering options, allowing students to further refine their browsing based on interests, while also incorporating a built-in topic category recognition rule library for automatic classification. Academic exchange categories cover scenarios such as course learning, academic discussions, and exam preparation; career sharing categories correspond to job-seeking experiences, internship insights, and industry trends; social interaction categories support interest groups, event invitations, and networking; and resource assistance categories include scenarios such as sharing learning materials, seeking answers to questions, and requesting campus services. The content classification unit also provides an encoding-compatible interface, adapting to national standard coding systems (such as the Ministry of Education's university identification code) or university-defined coding rules, possessing good scalability and flexibility. After classification, the content classification unit supports receiving filtering instructions from student users, allowing targeted searches by university code, major code, or a combination thereof. It can also perform secondary sorting based on parameters such as publication time, interaction popularity, and content relevance to improve content matching efficiency. If incomplete coding information or empty matching results are encountered, the content classification unit automatically triggers a fault-tolerance mechanism, providing feedback and recommending content with similar codes to ensure a consistent user browsing experience. The content publishing unit supports multiple media formats, such as images and video links, and features real-time preview functionality, ensuring users can view the effect before publishing. After a publishing request is submitted, the content is automatically pushed to the dual-review unit and placed in a waiting queue. The content publishing unit also provides a draft saving function, allowing users to temporarily save unfinished content for later editing and publishing, enhancing the flexibility of the user experience. The dual-review unit uses natural language processing algorithms to parse text content and combines image semantic recognition technology to analyze auxiliary images, achieving efficient and intelligent filtering. This step can identify and automatically block false information (such as exaggerated claims) and illegal content (such as uncivilized language), while also marking high-quality content (such as experiences using campus resources). The university administrator's second review process allows for confirmation, rejection, or modification of markings, ensuring the transparency and fairness of the review mechanism. The dual review unit also records all review operations and generates review reports for quality monitoring and model optimization, improving long-term review accuracy. The content interaction unit is configured to support student users in performing interactive actions such as liking, commenting, and replying to comments on displayed community content. For community content and related comments marked as high-quality by the dual review unit, a display priority enhancement mechanism is automatically triggered, giving them priority display in the community section and improving the efficiency of high-quality content dissemination.This unit also includes a notification function, sending real-time alerts when user content is interacted with, enhancing user engagement. It also supports @mention functionality, allowing users to directly notify other users in comments, promoting deeper communication and community interaction, and fostering a vibrant community atmosphere. The content browsing unit interface is simple and intuitive, supporting keyword search and filters to help students quickly find relevant information. Furthermore, the unit offers personalized recommendations based on user history and preferences, suggesting content that may be of interest. The interface adopts a responsive design, adapting to various devices such as mobile phones, tablets, and computers, ensuring a good browsing experience across different platforms and improving information retrieval efficiency and user satisfaction. The entire module design is student-centric, emphasizing privacy protection and content quality. Through intelligent technology and administrator collaboration, it ensures a safe, efficient, and easy-to-use community environment. Smooth data interaction between units achieves a seamless user experience, supporting the continuous development and optimization of the campus community.

[0027] Furthermore, the university management unit includes: The university administrator identity authentication unit receives login requests sent by university administrators to the information exchange platform through the university administrator terminal. After completing identity verification, the user is redirected to the management function interface. The output end of the university administrator identity authentication unit establishes a data interaction link with the group evaluation and statistics unit, content review unit, weight configuration unit, and university resource docking unit. The group assessment and statistics unit collects psychological assessment data from student users. It performs multi-dimensional aggregation analysis on the psychological assessment data of student users according to the dimensions of college, major and grade (the smallest statistical dimension is the combination of major and grade, and class subdivision is not supported), and generates a visualized overall distribution report of mental health (such as an anxiety ratio distribution chart). University administrators can obtain the mental health status of their students based on the overall distribution report of mental health. Among them, for students with abnormal mental health, university administrators develop targeted intervention strategies. The content review unit features a list of content awaiting review and receives review instructions from university administrators for original resources uploaded by students and community content published online. It includes options for approval and rejection, and when rejecting a review, a pre-defined rejection reason (such as commercial promotion or content violation) must be associated. The content review unit also has a review time limit (≤2 business days for general resources, ≤12 hours for priority review resources). The weight configuration unit establishes a data interaction channel with the campus-specific psychological assessment module, receives weight adjustment instructions input by university administrators, and customizes the weight coefficients of various psychological assessment questions in the campus-specific psychological assessment module (the total weight is 1, and the weight of a single question ranges from 0.05 to 0.2), adapting to the personalized assessment needs of universities. Furthermore, the information interaction platform also includes: The User Guide module, including the association detection unit and the display trigger unit, is configured to automatically trigger the display process of the function usage instructions (including operation procedures and function boundaries) and the data privacy statement (including data collection scope, usage method, and storage period) when a student user accesses the campus-specific psychological assessment module or the campus-based AI consultation module for the first time. The student user must click to confirm before entering the corresponding module. This module uses intelligent recognition technology to ensure that the display is only triggered on the first visit, avoiding repeated interference. It also supports multi-terminal adaptation, providing a consistent interactive experience on both web and mobile devices, thus improving user-friendliness.

[0028] The Personal Center module, comprising an information integration unit and a personal information management unit, is configured to create a one-stop information management interface for student users. It integrates and displays students' identity information, historical psychological assessment records, AI consultation conversation records, collected academic / career resources, and published / interactive community content. Students can manage their personal information by changing their mobile phone number and resetting their password, achieving centralized management and self-control of personal data. This module also provides data export and download options, allowing students to back up their campus-based assessment reports and consultation records at any time, enhancing data transparency and user control.

[0029] It should be noted that the built-in module association detection unit in the User Guide module monitors the access status of student users to modules such as the campus-specific psychological assessment module and the campus-based AI consultation module in the information interaction platform in real time. When a student user initiates access for the first time (without a recorded confirmation operation), a display mechanism is automatically triggered. A pop-up window displaying usage instructions appears on the student's interface, or the user is redirected to a dedicated display page. This ensures that students receive necessary information before using core functions. The display content includes two main parts: ① Function usage instructions, which clearly explain the module's core functions (the answering process for psychological assessments, the interaction method for AI consultations), operation steps (how to submit an assessment, how to initiate a consultation), and the path to view results, using a combination of text and images to lower the barrier to entry for students. Example scenarios and frequently asked questions are also provided to help students get started quickly. ② Data privacy statement, which clearly informs students of the scope of data collection (assessment data, consultation content, and identity information), the purpose of data use (such as generating campus-based scenario-based assessment reports, optimizing AI suggestions), data storage methods (such as encrypted storage), and privacy protection measures (such as prohibiting unauthorized access), ensuring students' right to know and right to privacy, and complying with relevant data security regulations. The statement is updated regularly to reflect the latest policies, and changes are prominently displayed to prompt users to view them. The bottom of the display page features two options: "Confirm Agree" and "Disagree For Now". Selecting "Confirm Agree" allows student users to access the module and use its functions, while selecting "Disagree For Now" exits the access. The system records this action and prompts that it can be retried later. This mechanism not only ensures compliance but also strengthens user education and promotes the rational use of the platform's functions by student users.

[0030] The user management module implements student user authentication, access control, password management, and university-specific management functions. Specifically, the campus authentication unit securely interfaces with the university's unified identity authentication system using the OAuth 2.0 protocol. Student users can log in using their student ID and password, and the system automatically retrieves identity information including name, major, grade, and university code, effectively ensuring the authenticity and uniqueness of user identities. The access control unit is based on a Role-Based Access Control (RBAC) mechanism, distinguishing between student and university administrator roles. Student roles have permissions to participate in psychological assessments, resource retrieval, and community content posting; university administrators have management permissions such as data statistical analysis, content review, and weight parameter configuration. Strict isolation of permissions between different roles ensures the security and standardization of system operations. The password management unit allows students and administrators to set, reset, and retrieve passwords according to set rules. These rules integrate multiple security mechanisms, and all passwords are stored after being processed by encryption algorithms, effectively protecting account security. Users can receive verification information via their bound mobile phone number or email address to complete the password retrieval process.

[0031] The university management unit provides administrators with dedicated management functions, including group assessment statistics, content review, weight configuration, and integration with university resource integration units. Group assessment statistics support the aggregation and analysis of assessment data across multiple dimensions such as major and grade level, generating visual reports to help universities comprehensively understand students' mental health status. The content review unit reviews community content and uploaded resources posted by students, supporting approval and rejection operations, and allowing for the annotation of rejection reasons. The weight configuration unit allows administrators to adjust the weight coefficients of each indicator in the psychological assessment questions according to actual needs, adapting to the differentiated assessment requirements of different universities. The university resource integration unit interacts with systems such as university career guidance centers, mental health centers, and libraries through standardized API interfaces, automatically synchronizing information such as information sessions, psychological appointment services, and library resources, ensuring data real-time performance and consistency.

[0032] The student growth one-stop support module integrates mental health assessment with academic and career resource services, providing full-process support from "assessment to analysis to resource matching." The campus-specific psychological assessment module designs various types of assessment units for common campus scenarios, such as academic stress assessment and career anxiety assessment, providing students with personalized assessment services. The assessment selection unit displays various assessments in a tagged format; students can click to enter the corresponding assessment unit, with a simple interface and intuitive operation. The interactive question-answering unit provides suitable interactive questions for different assessment types, supports saving progress and resuming from interrupted answers, enhancing the user experience. After receiving student answer data, the assessment submission unit encapsulates it in JSON format and sends it to the assessment result processing unit, ensuring data standardization and completeness. The assessment result processing unit uses a weighted scoring algorithm to calculate scores and determines the stress level according to a preset score-stress level mapping rule (0-2 points correspond to no stress, 3-5 points to mild stress, 6-8 points to moderate stress, and 9-10 points to severe stress), ultimately generating a campus-based scenario-based assessment report with specific scenario-based suggestions. The report is linked to student identity information storage, allowing students to access it at any time, and provides data support for the campus-based AI consultation module.

[0033] The Academic-Career Resource Integration Module brings together resources in three categories: academic, career, and psychological, enabling categorized storage, review and publication, precise filtering, and intelligent recommendations. The resource classification unit divides resources into academic (e.g., course materials, exam outlines), career (e.g., job postings), and psychological (e.g., articles on psychological counseling), and labels each category with university codes, majors, grade levels, and types for structured retrieval. The resource submission unit allows students to upload original resources (e.g., study notes, job-seeking experience sharing). Uploads require administrator review; upon approval, the system automatically adds standardized tags and publishes the resource. The uploader's information is also recorded and linked to their identity to encourage the sharing of high-quality content. The resource filtering unit matches resources based on the student's input (major, grade level, resource type, etc.) using a tag system, and then sorts them comprehensively based on matching accuracy and historical download volume, presenting the results in a list format to help students efficiently access the resources they need. The resource recommendation unit uses a collaborative filtering algorithm to analyze the correlation between the historical behavior data and the behavior data of students with similar characteristics, based on students' browsing, downloading, and collection history. According to the user resource demand model and combined with the student's campus-based assessment report, the unit proactively pushes resources that match the student's academic planning and career development needs, thereby achieving personalized services.

[0034] The campus-based AI consultation module leverages artificial intelligence technology to provide students with real-time, personalized consultation services. It combines consultation content, psychological assessment data, and university resource data to generate suggestions closely aligned with campus realities. The consultation input unit supports text input and features contextual memory, enabling coherent multi-turn dialogue. The assessment data fusion unit automatically retrieves the student's latest campus-based assessment report, extracting key data such as assessment type, psychological state, and university information, and encapsulating it as contextual initialization information for AI consultation, making suggestions more relevant to the student's psychological state. The university resource integration unit obtains real-time campus data such as information sessions, psychological appointment links, and library resources through API interfaces, providing resource support for consultation responses. The suggestion generation unit integrates student input, psychological context, and real-time resource data, calling a campus-based AI model optimized based on a large language model to generate suggestions covering academics, career, and psychology, embedding accessible resource links (such as psychological counseling appointment links) in the responses. The consultation feedback unit allows students to rate the suggestions on a 1-5 scale and provide feedback. Feedback data will be used to continuously optimize the AI-generated suggestion content and improve service quality.

[0035] The campus-specific community module constructs a standardized and thematic communication space, promoting academic discussions, career experience sharing, and social interaction among students. The content categorization unit divides the community into sections such as academic exchange, career sharing, social interaction, and resource assistance, and uses university and major codes to filter content, ensuring students can only view content relevant to their major. For example, computer science students can only access sections related to their major. The content interaction unit supports likes, comments, and replies, and information involving actual university resources (such as internship recommendations from the career services office) is marked as high-quality comments and prioritized for others' reference. The content posting unit allows students to post with text and images, and provides an anonymous posting option. When anonymous, the system hides student IDs and nicknames, displaying only major and year, balancing privacy protection and freedom of expression. The dual review unit employs a dual mechanism of initial AI filtering and secondary administrator review: AI identifies false and illegal content based on keyword matching and other technologies, and initially marks high-quality posts; administrators review the AI's results to ensure content compliance and quality.

[0036] The "Instructions for Use" module is proactively displayed when students use the psychological assessment or AI consultation function for the first time, clearly explaining the function process, data usage, and privacy protection policies (such as data storage methods and scope). Students must read and confirm their agreement before continuing to use the service, ensuring their right to know and data security.

[0037] The Personal Center module integrates students' personal information and operation records, providing a one-stop self-management service. Key content displayed includes: personal identification information (such as name, major, and grade, with support for modifying phone number), historical assessment records (viewing all previous campus-based scenario-based assessment reports), consultation records (including conversation content and AI suggestions), a list of saved resources (one-click quick access), and published community content (supporting editing and deletion). It also provides a password reset entry, comprehensively facilitating users in maintaining their personal accounts and managing their data.

[0038] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the functions of the integrated service system.

[0039] Example 1: System Architecture and Deployment like Figure 1 As shown, the system of this invention adopts a layered architecture, mainly including: Information exchange platform (server-side): Deployed on the university's own server or in the cloud, it adopts a microservice architecture and is developed using languages ​​such as Java / Python. It is responsible for core business logic processing, data storage, and analysis. The database can be a relational database such as MySQL, and the cache uses a remote dictionary service (Redis, Remote Dictionary Server).

[0040] Student App: Provided as a responsive webpage or mobile app, developed using front-end frameworks such as Vue.js / React to ensure a good experience on multiple devices including PCs, mobile phones, and tablets.

[0041] University administrator side: usually a web-based management backend, which interacts with the information exchange platform through an API interface.

[0042] The system connects with the university's unified identity authentication system via the OAuth 2.0 protocol, enabling students to log in with a single click. Simultaneously, it establishes data channels with the university's career guidance system, mental health center system, and library resource system through pre-defined API interfaces, regularly synchronizing data such as information on recruitment events, appointment schedules, and electronic resources.

[0043] Example 2: Student Terminal Usage Flow Login and Authentication: Students log in using their campus identity through the student client, which will redirect them to the university's unified authentication page. They then enter their student ID and password to complete the authentication process. First-time users must read and agree to the "Instructions for Use" and the "Privacy Agreement".

[0044] Psychological Assessment: Enter the Student Growth Support module and select campus-based assessment units such as academic stress assessment. The system presents interactive questions and supports saving midway. After submission, the system generates a campus-based assessment report, including stress level analysis and specific suggestions such as scheduling an appointment with the university's psychological center and participating in special meditation sessions for postgraduate entrance exam preparation.

[0045] Resource Acquisition and Recommendation: In the resource center, students can manually filter by major: Computer Science, year: third year, type: postgraduate entrance examination past papers. The system displays the results based on tag matching and popularity sorting. Additionally, in the "Recommended for You" section on the homepage, the system will proactively push resources such as postgraduate entrance examination admission data analysis from the past three years and interview experience sharing from high-scoring senior students, based on their campus-based assessment report (showing postgraduate entrance examination pressure) and past browsing history.

[0046] AI Consultation: Students can enter the following into the AI ​​Consultation module: "I'm preparing for the postgraduate entrance exam and I feel very anxious and inefficient. What should I do?" The system will automatically link to their recent academic stress assessment report (which shows moderate anxiety) and integrate information from the university system, such as the library's postgraduate entrance exam meditation session next week and the online mindfulness training courses offered by the psychological center, to generate a response that combines psychological counseling and specific action suggestions, along with a course reservation link.

[0047] Community Interaction: Students can post requests for help anonymously or under their real names in the postgraduate entrance exam discussion area, or browse experience posts marked as high-quality by senior students, and like and comment on them. All posted content must first pass dual review by AI and administrators.

[0048] Example 3: Management Functions for University Administrators After logging into the management backend, university administrators can perform the following operations: Data Insights: On the statistics report page, viewing the overall distribution chart of student mental health statistics by college, major, and grade, it was found that the anxiety rate of third-year students in a certain college was relatively high, thus enabling the planning of targeted group counseling activities.

[0049] Content Review: Quickly review student-uploaded data structure review notes or community posts from the pending review list. Posts flagged by AI as potentially advertising will undergo manual review and be rejected with the reason of "commercial promotion".

[0050] System Configuration: In assessment management, adjust the weighting coefficients of industry awareness questions in the career confusion assessment based on the school's characteristics. In system integration, set up automatic daily synchronization of the latest recruitment information from the career services office at midnight.

[0051] Example 4: Security and Privacy Protection Measures Data transmission: The entire site uses the Hypertext Transfer Protocol Secure (HTTPS) protocol to encrypt communication content.

[0052] Data storage: User passwords are stored after being hashed using SHA-256; sensitive personal data (such as detailed test answers) are encrypted and stored in the database.

[0053] Access control: Strictly adhering to the principle of least privilege, students can only view and manage their own data; administrators can only view aggregated statistics and cannot access the complete assessment reports of individual students.

[0054] Privacy compliance: Clearly inform users of the scope of data collection and use, process data only to the extent necessary, and provide users with ways to export and delete their personal data.

[0055] It is worth noting that (1) this invention integrates core modules such as user management, one-stop support for student growth, campus-based AI consultation, and campus-exclusive community by constructing an integrated architecture of "information interaction platform - student terminal - university administrator terminal". This achieves data interconnection and functional collaboration among various service links, and completely breaks the fragmented state of traditional services.

[0056] The user management module securely integrates with the university's unified identity authentication system using the OAuth 2.0 protocol. Students can log in to the entire platform with a single click using their unified campus identity, eliminating the need for repeated registration and switching. Encrypted storage of identity information ensures security and uniqueness. The one-stop student growth support module deeply integrates mental health assessment with academic and career resource services. Assessment data directly supports resource recommendations and AI consultations. The campus-based AI consultation module seamlessly connects assessment results with university resources, generating suggestions that directly link to relevant service entry points. The campus-exclusive community module provides students with interactive scenarios for academic exchange and career sharing, forming a closed-loop service process encompassing "assessment-consultation-resource acquisition-community interaction." This integrated design allows students to receive consistent support across mental health, academics, and career without switching between multiple applications, greatly improving service convenience and continuity, and significantly optimizing the user experience. (2) This invention, through comprehensive campus-specific design, makes the service more in line with students' real needs, significantly enhancing its practical value. In terms of psychological assessment, the campus-specific psychological assessment module focuses on campus-specific issues such as GPA anxiety, postgraduate / civil service exam pressure, dormitory interpersonal relationships, and the balance between internships and studies, designing targeted assessment themes and dimensions. The assessment result processing unit adopts a weighted scoring method (the weights can be customized by the university), and generates a campus-specific assessment report by combining university resources. It is no longer a generalized theoretical conclusion, but includes specific and actionable scenario-based suggestions such as making an appointment with the university's psychological counseling center and participating in campus time management workshops, allowing students to directly connect with on-campus resources to solve problems. In terms of AI consultation, the campus-specific AI consultation module establishes a real-time data interaction channel with the university's employment office system, psychological health center system, and library system through a standardized API interface, obtaining exclusive resource data such as the university's recruitment schedule, psychological counseling appointment portal, and professional resource links. When generating suggestions, AI deeply integrates students' assessment data with real-time campus resources. For example, for students experiencing anxiety about postgraduate entrance exams, it not only provides psychological counseling methods but also pushes precise resources such as analysis of the university's postgraduate admission data over the past three years and links to book special meditation sessions for postgraduate entrance exam takers in the library, making the suggestions more practical. This design, closely integrated with the campus environment, transforms the service from universal to personalized and scenario-based, significantly enhancing its practicality and appeal. (3) This invention constructs an efficient resource management system through a multi-dimensional tag system, intelligent filtering algorithm and personalized recommendation mechanism, which completely solves the problems of low resource integration and insufficient accuracy; The academic-career resource integration module establishes a pre-defined resource classification system covering academic, career, and psychological resources. Each resource category is assigned multi-dimensional tags such as university codes, major codes, and grade identifiers, forming a standardized resource tag library and enabling refined classification and storage of resources. The resource filtering unit allows students to input multi-dimensional filtering conditions, quickly locating target resources through tag matching algorithms. A comprehensive scoring algorithm combining matching degree weights and historical download volume is used to prioritize resources, ensuring that high-quality, highly relevant resources are displayed first, preventing students from experiencing information overload. The resource recommendation unit, based on collaborative filtering algorithms, collects historical data on students' resource browsing, downloading, and saving activities. Combined with the behavioral preferences of students with similar characteristics, it constructs a user resource demand model. Simultaneously, this model deeply integrates students' campus-based assessment reports. For example, for a third-year computer science student whose assessment indicates postgraduate entrance exam pressure, it automatically pushes precise resources such as past exam papers for computer science at their university, high-scoring senior students' interview experience sharing, and postgraduate entrance exam psychological adjustment manuals. This design, which combines tag categorization, intelligent filtering, and personalized recommendations, allows students to efficiently access high-quality resources that are highly aligned with their academic plans, career development, and psychological needs without having to manually filter them. This significantly reduces information retrieval costs and improves resource utilization efficiency. (4) Through multiple security mechanisms and refined management functions, this invention not only ensures the security of system operation and user privacy, but also provides universities with intelligent management tools and builds a healthy and trustworthy service ecosystem. In terms of security, the system employs multiple protection measures: user authentication is implemented through a unified identity authentication system for universities, and identity information is stored encrypted; access control is based on a role-based access control mechanism, strictly distinguishing the operational permissions of student roles from those of university administrators, preventing university administrators from accessing the specific private data of individual students; data transmission uses HTTPS protocol encryption, and password storage is processed using SHA-256 hashing; content review employs a dual mechanism combining AI initial review and university administrator secondary review, effectively filtering false information and illegal content, ensuring a healthy and compliant community environment. These measures comprehensively protect system operational security, user data privacy, and community content compliance, enhancing students' and universities' trust in the platform.

[0057] In terms of university management, the system provides university administrators with visualized data statistics reports, a centralized content review interface, and flexible system parameter configuration tools. The group assessment statistics unit can perform multi-dimensional aggregation and analysis of student assessment data by college, major, and grade level, generating a visualized overall mental health distribution report to help universities accurately grasp the overall student situation. The weight configuration unit allows universities to customize and adjust the weight coefficients of psychological assessment items according to their own needs, adapting to personalized assessment requirements. The university resource integration unit supports custom data synchronization frequency, ensuring that university resource data is updated in real time. These functions enable universities to dynamically optimize resource allocation based on data insights and formulate targeted intervention strategies, significantly improving the intelligence and precision of student management.

[0058] This invention's information interaction platform comprises a user management module, a one-stop support module for student growth, a campus-specific AI consultation module, a campus-exclusive community module, a user manual module, and a personal center module, all connected sequentially. Through an architecture of "information interaction platform - student end - university administrator end," it integrates core modules for psychological assessment, resource services, AI consultation, and community interaction, achieving data interoperability and functional synergy. Students can receive consistent support without switching between multiple platforms, improving service convenience and continuity. The platform focuses on campus-specific scenarios (GPA anxiety, postgraduate entrance exam pressure, etc.) to design assessment and consultation solutions, deeply integrating with internal university resources. The generated assessment reports and consultation suggestions include specific access points to internal university resources, allowing services to move from generalized to personalized and implementable. Furthermore, it constructs a multi-dimensional tagging system and a collaborative filtering recommendation mechanism, combining student behavior data and assessment results to accurately push resources, reducing information retrieval costs and solving resource issues. Addressing issues of low integration and insufficient accuracy, this system employs OAuth 2.0-compatible authentication, tiered access control, encrypted data storage, and dual content review to comprehensively safeguard system security, user privacy, and community compliance. It provides universities with visualized statistical reports and flexible configuration tools, enabling them to gain data-driven insights into student situations, dynamically optimize resource allocation, and enhance the intelligence and precision of management. Through integrated architecture design, campus-specific scenario adaptation, intelligent resource management, comprehensive security, and refined management, it fully resolves the functional fragmentation, lack of specificity, and low resource integration issues of existing platforms. This provides university students with a coherent, practical, and efficient integrated service for mental health and career development, while offering universities intelligent management tools, creating a win-win situation that benefits both students and empowers universities. It possesses significant practical value and promotional significance.

[0059] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An integrated service system for mental health and career development for university students, characterized by: It includes an information interaction platform, a student terminal, and a university administrator terminal. The information interaction platform is connected to the student terminal and the university administrator terminal, respectively. Student users and university administrators log in to the information interaction platform through the student terminal and the university administrator terminal, respectively. The information exchange platform includes: The user management module is configured to implement student user authentication, access control, password management, and university-specific management functions; by establishing an interface with the university's unified identity authentication system, it can verify the campus identity of student users and obtain and store the identity identification information of student users; The one-stop support module for student growth includes a campus-specific psychological assessment module and an academic-career resource integration module with interconnected data. It is configured to provide students with mental health assessment services and academic and career resource services. After a student user completes the psychological assessment by selecting a target psychological assessment unit, the campus-specific psychological assessment module generates a campus-based assessment report for subsequent querying. Simultaneously, the student user, after successful identity verification, performs data resource retrieval, collection, and download operations through the student terminal. The academic-career resource integration module collects student user behavior data generated during the execution process. The campus-based AI consultation module is configured to provide personalized, real-time consultation services to student users based on AI technology, and to generate scenario-based suggestions by integrating assessment data and university resources. The campus-exclusive community module is designed to promote academic exchange, career sharing, and social interaction among students. Authenticated student users can categorize, publish, and review content through the student app.

2. The system according to claim 1, characterized in that, The user management module includes: The campus identity authentication unit is configured to establish a secure connection link with the university's unified identity authentication system through the OAuth 2.0 protocol, obtain the student user's basic campus information based on the connection link, use the basic campus information as the student user's identity identification information, and encrypt and store the student user's identity identification information. The access control unit, based on a role-based access control mechanism, divides users of the information interaction platform into at least student roles and university administrator roles, and assigns corresponding operation permissions to different roles. The password management unit is configured to allow student users or university administrators to set, reset, and retrieve passwords for login requests according to set rules. The university management unit is configured to provide university administrators with campus service management functions, including viewing a summary of students' mental health status, issuing instructions to optimize service resource allocation, and adjusting system operating parameters.

3. The system according to claim 2, characterized in that, The campus-specific psychological assessment module includes: The assessment selection unit is configured to display the tag information corresponding to each psychological assessment unit, receive the assessment selection instruction triggered by the student user, and jump to the target psychological assessment unit selected by the student user based on the assessment selection instruction. The interactive question-answering unit is configured to generate suitable interactive psychological assessment questions based on the assessment theme and dimensions of the target psychological assessment unit, thus constructing an interactive assessment link with student users. The interactive question-answering unit has a built-in sub-module for saving the answer progress and a sub-module for resuming the answer midway. The sub-module for saving the answer progress stores the student user's answer progress data in real time. The sub-module for resuming the answer midway restores the answer progress to the answer state before the interruption when the student user re-enters the same target psychological assessment unit after interrupting the assessment, thus realizing the functions of saving and resuming the answer midway. The assessment submission unit is configured to receive psychological assessment data submitted by student users after completing all interactive psychological assessment questions, perform structured processing on the psychological assessment data and encapsulate it into a JSON format assessment data packet, and transmit the assessment data packet to the assessment result processing unit. The assessment result processing unit is configured to receive the JSON-formatted assessment data packet, parse and extract the psychological assessment data; quantify the psychological assessment data using a weighted scoring method to obtain the corresponding assessment score; match the assessment score to the corresponding psychological stress level based on a preset score-stress level mapping rule, and generate a campus-based scenario-based assessment report by combining the psychological stress level with university resources; and associate and store the campus-based scenario-based assessment report with the student user's identity information to form a traceable personal assessment file.

4. The system according to claim 3, characterized in that, The academic-career resource integration module includes: The resource classification unit is configured to establish a preset resource classification system, which includes at least academic resources, career resources, and psychological resources. University resources are classified and stored according to the preset resource classification system, and each type of resource is assigned multi-dimensional tag information based on university code, major code, and grade identifier to form a standardized resource tag library. The resource submission unit is configured to provide an original resource upload interface, receive original resources and resource description information uploaded by student users; push the original resources and resource description information to the university administrator for review; if the review is approved, trigger the tag addition mechanism to match standardized multi-dimensional tags for the original resources and publish them online; synchronously record the identity information of the uploader of the original resources and associate and bind it with the identity information of the student users; The resource filtering unit is configured to receive multi-dimensional filtering conditions input by student users, perform matching queries based on the filtering conditions and multi-dimensional tag information in the resource tag library, and obtain target resources that meet the conditions; use a comprehensive scoring algorithm of matching degree weight and historical download volume to prioritize the target resources; and display the sorted high-quality target resources that meet the conditions to student users in a list format to achieve accurate resource filtering output. The resource recommendation unit is configured to use a collaborative filtering algorithm to perform correlation analysis on the historical behavior data of student users and the behavior data of student users with similar characteristics to construct a user resource demand model; based on the user resource demand model and combined with the student user's campus-based assessment report, it proactively pushes resources that match the student user's academic planning and career development needs to the student user.

5. The system according to claim 4, characterized in that, The campus-based AI consultation module includes: The consultation input unit has a built-in context semantic storage submodule, which is configured to receive text consultation content input by student users through the student terminal interactive interface; the context semantic storage submodule caches semantic information in real time during the consultation session, realizing continuous memory and associated response of the consultation context; The assessment data fusion unit is configured to retrieve associated stored campus-based scenario assessment reports based on the student user's identity information; extract assessment type, psychological status evaluation results, and university coding information from the campus-based scenario assessment reports, and encapsulate the extracted information into AI consultation context initialization data; The university resource docking unit is configured to establish a real-time data interaction channel with university resources through a standardized API interface; acquire exclusive university resource data including the university's lecture schedule, psychological counseling appointment portal, professional resource links, and special event information, forming a dynamically updated university resource database; the university resource docking unit is set with a data synchronization frequency to automatically synchronize university resource data and complete data updates and storage; The suggestion generation unit is configured to build a multi-source data fusion model, which deeply integrates the text consultation content of student users, the initialization data of AI consultation context, and relevant resource data in the university resource library; calls the AI ​​model to generate scenario-based suggestions that include at least academic guidance, career recommendation, and psychological counseling; and embeds relevant real-time resource jump links in the scenario-based suggestions, so that student users can directly click to access the corresponding resources; The consultation and feedback unit is configured to provide a satisfaction rating entry and an opinion feedback input box, receive student users' satisfaction ratings and improvement suggestions for the scenario-based suggestions, and synchronize the satisfaction ratings and improvement suggestions to the information interaction platform to optimize the accuracy of AI-generated suggestion content and resource matching. The interactive data storage unit is configured to synchronously record consultation interaction data during the consultation interaction process, including text consultation content, AI-generated suggestion content and resource jump records, and to associate and store the consultation interaction data with the student user identity information that initiated the consultation request, forming a traceable consultation file.

6. The system according to claim 5, characterized in that, The campus-specific community module includes: The content classification unit is configured to divide the community sections according to preset theme categories, and to filter the divided community sections based on the university code and major code in the student user's identity information, displaying the exclusive community section of the student user's university and major; wherein, the preset theme categories include at least academic exchange, career sharing, social interaction and resource assistance; The content publishing unit is configured to provide a content publishing interface that combines text and auxiliary images, and to receive community content to be published by student users; it has a built-in anonymous publishing function option. When a student user selects the anonymous publishing function option, the student user's sensitive identity information is automatically hidden. The sensitive identity information includes at least the student ID and a custom nickname; only the major information and grade information are retained and associated with the content to be published, and then the content to be published is pushed to the dual review unit; The dual review unit is configured to employ a dual review mechanism combining AI initial review and university administrator secondary review: the AI ​​initial review is based on a preset violation feature library and a high-quality content feature library to intelligently identify the received content to be reviewed, filter out false information and violation content, and mark high-quality content containing specific resource descriptions of the university; the university administrator secondary review manually verifies and confirms the results of the AI ​​initial review, and the content that passes the review is pushed to the corresponding community section for display, while the content that is rejected is fed back to the publishing user with preset rejection reasons; The content interaction unit is configured to support student users in performing interactive actions such as liking, commenting, and replying to comments on the displayed community content; for community content and related comments that have been marked as high-quality by the dual review unit, a display priority enhancement mechanism is automatically triggered, so that they are displayed first in the community section, thereby improving the efficiency of the dissemination of high-quality content; The content browsing unit is configured to provide student users with a list-style browsing interface for community content, supporting sorting by publication time and interaction popularity, making it easy for student users to quickly search for community content of interest.

7. The system according to claim 5, characterized in that, The university management unit includes: The university administrator identity authentication unit receives login requests sent by university administrators to the information exchange platform through the university administrator terminal. After completing identity verification, the user is redirected to the management function interface. The output end of the university administrator identity authentication unit establishes a data interaction link with the group evaluation and statistics unit, content review unit, weight configuration unit, and university resource docking unit. The group assessment and statistics unit collects psychological assessment data from student users, performs multi-dimensional aggregation and analysis on the psychological assessment data of student users according to college, major and grade, and generates a visualized overall psychological health distribution report. University administrators can obtain the psychological health status of their students based on the overall psychological health distribution report. The content review unit has a list of content to be reviewed, and receives review instructions from university administrators for original resources uploaded by students and community content published. It is configured with options for approval and rejection, and a preset rejection reason must be associated when the rejection operation is executed. The weight configuration unit establishes a data interaction channel with the campus-specific psychological assessment module, receives weight adjustment instructions input by university administrators, and customizes the weight coefficients of various psychological assessment questions in the campus-specific psychological assessment module to adapt to the personalized assessment needs of universities.

8. The system according to claim 1, characterized in that, The information exchange platform also includes: The User Guide module, including the association detection unit and the display trigger unit, is configured to automatically trigger the display process of the function usage instructions and data privacy statement when a student user first accesses the campus-specific psychological assessment module or the campus-based AI consultation module; the student user must click to confirm before entering the corresponding module; The Personal Center module, comprising an information integration unit and a personal information management unit, is configured to create a one-stop information management interface for student users. It integrates and displays student users' identity information, historical psychological assessment records, AI consultation conversation records, collected academic / career resources, and published / interactive community content. Student users can manage their personal information by changing their mobile phone number and resetting their password.

9. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores a computer program that, when executed by a processor, performs the functions of the system as described in any one of claims 1-8.

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