College institution service intelligent response system integrating self-service questions and answers and task work orders

Abstract

The invention relates to the cross technical field of a computer information technology and a college management information system, and provides a college institution service intelligent response system fusing self-service questions and answers and task work orders. The system comprises a user interaction module, a self-service question and answer engine, a work order generation and distribution module, a background processing and feedback module and a service statistics and optimization module. According to the invention, an advanced natural language processing technology, knowledge graph construction and intelligent work order management are fused, so that a college institution service intelligent response system integrating self-service question answering and task work orders is constructed. The system not only realizes seamless access and efficient intelligent interaction of multiple channels and multiple terminals, but also ensures that various problems are timely and accurately processed through automatic classification, responsibility distribution and closed-loop management.

Description

A smart response system for university and government services that integrates self-service Q&A and task orders Technical Field

[0001] This invention relates to the interdisciplinary field of computer information technology and university management information systems, specifically to an intelligent response system for government services that integrates natural language processing (NLP), knowledge graphs, self-service question answering, and workflow work order collaboration, applicable to teaching management and administrative service scenarios in universities or similar institutions. Background Technology

[0002] Currently, administrative staff in vocational colleges often rely on manual responses, telephone consultations, and email communication when facing frequent inquiries and requests for assistance from faculty and students. These methods have the following problems: 1. High repetition: Many questions focus on common matters such as course selection, grade inquiries, certificate applications, and evaluation criteria; 2. Low response efficiency: Manual responses depend on staff rotation, often resulting in delays or information omissions; 3. Lack of transparency in task tracking: Tracking relies on paper or email, lacking closed-loop management; 4. Lack of service data analysis: It is difficult to quantify service quality and optimize work arrangements.

[0003] Although some universities have built "smart campus" platforms, their "service-oriented" capabilities are still limited to static information display or form submission, lacking a closed-loop "question-answer-process" capability.

[0004] Therefore, this solution proposes an intelligent response system for university administrative services that integrates self-service Q&A and task work orders to address the aforementioned issues. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide an intelligent response system for university and government services that integrates self-service Q&A and task work orders.

[0006] To achieve the aforementioned objectives, the technical solution of this invention is implemented as follows: A smart response system for university administrative services integrating self-service Q&A and task work orders. The system includes: a user interaction module, used to support access from multiple terminals such as web pages, WeChat official accounts, mini-programs, and desktop terminals, and integrates a natural language processing interface to achieve semantic understanding, keyword extraction, and sentiment judgment; a self-service Q&A engine, used to automatically respond to users' common inquiries based on a pre-built university knowledge graph, performing FAQ semantic matching, synonym expansion, question completion, and multi-round Q&A; a work order generation and dispatch module, used to guide users to submit work orders when the self-service Q&A engine cannot match user questions, automatically identify the question type, determine the responsible department, and dispatch the work order, while supporting time-limited response and overdue reminder mechanisms; a background processing and feedback module, used for administrators to process work orders, upload supplementary materials and replies, automatically record the processing process and notify users, and collect user satisfaction feedback; and a service statistics and optimization module, used to automatically count indicators such as consultation volume, response rate, satisfaction, and work order processing time, and recommend optimization of knowledge base content and service processes based on data analysis results.

[0007] Preferably, the natural language processing interface includes a semantic understanding module, a keyword extraction module, and an emotion recognition module, which can realize user intent recognition and early warning of abnormal mental health.

[0008] Preferably, the university knowledge graph covers multiple knowledge domains such as courses, evaluations, certificates, student status, finance, and competitions, supports multi-round question answering and semantic expansion, and has an automatic response rate of over 70%.

[0009] Preferably, the work order generation and dispatch module supports the automatic generation of personalized work orders, setting work order processing time limits, and automatically reminding relevant responsible personnel and users when the time limit is exceeded.

[0010] Preferably, the back-end processing and feedback module provides a work order processing interface, supports material uploading, supplementary responses, and user satisfaction evaluation, and realizes closed-loop management of work orders.

[0011] Preferably, the service statistics and optimization module, based on historical service data, enables the ranking of hot issues, statistics of overdue work orders, and satisfaction analysis, assisting government departments in optimizing service strategies.

[0012] Preferably, the system supports a unified access architecture for multiple terminals, enabling seamless collaborative access across web pages, WeChat, mini-programs, and desktop devices.

[0013] Preferably, the system is applied to university administrative service scenarios, including but not limited to the Academic Affairs Office, Student Affairs Office, Human Resources Office, Logistics Support Department, Psychological Counseling Center, and Youth League Committee, to achieve intelligent service collaboration across departments and business areas.

[0014] Preferably, the intelligent response method for university administrative services includes the following steps: Step 1, the user inputs a natural language question through multiple terminals; Step 2, the system performs semantic understanding and keyword extraction through a natural language processing interface; Step 3, the system performs semantic matching in a knowledge graph and automatically answers common questions, or initiates multi-round question-and-answer interaction; Step 4, if the question cannot be automatically answered, the system guides the user to generate a work order and automatically dispatches it to the responsible department; Step 5, the responsible department processes the work order, uploads materials and replies, and the system automatically notifies the user of the processing result; Step 6, the user evaluates the service satisfaction, and the system collects feedback; Step 7, the system statistically analyzes service data to assist in optimizing the knowledge base and service processes.

[0015] Preferably, the method further includes the step of performing emotion recognition on user input, triggering an early warning of abnormal mental health, and generating an anonymous psychological counseling work order.

[0016] The beneficial effects of this invention are reflected in the following aspects: By integrating advanced natural language processing technology, knowledge graph construction, and intelligent work order management, this invention constructs an intelligent response system for university administrative services that integrates self-service Q&A and task work orders. The system not only achieves seamless access and efficient intelligent interaction across multiple channels and terminals, but also ensures timely and accurate handling of various issues through automatic classification, responsibility assignment, and closed-loop management. Simultaneously, the backend feedback and user satisfaction evaluation mechanism constitute a sound service closed loop, and the service statistics and analysis module provides strong data support for scientific decision-making by management. Furthermore, the integration of a mental health abnormality early warning function expands the system's application depth and social value, reflecting the university's responsibility for comprehensive student care. This system effectively alleviates the operational pressure on university administrative offices, improves service efficiency and quality, promotes the digital and intelligent transformation of university management, and has broad application prospects and significant social benefits. Attached Figure Description

[0017] In the accompanying drawings: Figure 1 is a schematic diagram of the connection between the various modules of the present invention; Figure 2 is a flowchart of the steps of the intelligent response method for university and government services of the present invention. Detailed Implementation

[0018] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the invention, and not all of them. Unless otherwise specified, the embodiments and features described in this application can be combined with each other. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0019] Furthermore, "multiple" refers to two or more. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the invention.

[0020] Please refer to Figures 1-2 in the specification. This invention provides an intelligent response system for university and administrative services that integrates self-service Q&A and task work orders. The system mainly includes five core subsystems: a user interaction module, a self-service Q&A engine, a work order generation and dispatch module, a background processing and feedback module, and a service statistics and optimization module. Each module achieves data flow and functional collaboration through a unified data interface and message bus.

[0021] The user interaction module supports multi-terminal access, including web pages, WeChat official accounts, mini-programs, and desktop clients, enabling unified multi-channel access. This module has a built-in natural language processing interface, including a semantic understanding module, a keyword extraction module, and an emotion recognition module. After a user inputs a natural language question through any terminal, the system first calls the semantic understanding module to perform deep semantic analysis of the input content to identify the user's intent; the keyword extraction module extracts key information words to assist in subsequent retrieval; and the emotion recognition module performs sentiment analysis on the user's text to determine the user's emotional state, triggering a mental health abnormality warning function when necessary.

[0022] The self-service question-answering engine is based on a pre-built knowledge graph of higher education institutions. This knowledge graph covers multiple knowledge domains, including course management, awards and honors, certificate processing, student registration management, financial services, and competitions. Data sources include publicly available policies, procedures, and frequently asked questions from various functional departments of universities. The engine uses semantic matching technology to perform FAQ retrieval on user questions and supports synonym expansion and question completion to improve the accuracy of question-answer matching. For complex or incomplete questions, the system supports multi-round question-answering interaction, gradually guiding users to improve their question descriptions and achieve accurate responses. Experiments have shown that the system's automatic response rate can reach over 70%, significantly improving the efficiency of user problem-solving.

[0023] When the self-service question-and-answer engine cannot match a user's question, the system automatically guides the user to generate a work order. The work order generation module automatically categorizes the user's input, identifies the question type, and determines the corresponding responsible department based on preset rules. The system supports automatically generating personalized work orders, specifying processing time limits, and implementing an automatic reminder function for timeouts, reminding responsible personnel and users to monitor the processing progress and ensuring timely response and closed-loop management of work orders.

[0024] This module provides a work order processing interface for university administrative staff, supporting the uploading of supplementary materials, supplementary responses, and online replies to users. The system automatically records the entire work order processing process, including processing time, personnel involved, and response content, ensuring data traceability. Upon completion, the system notifies users of the processing results via multiple terminals and invites them to provide satisfaction feedback. User feedback data is collected by the system for subsequent service improvements.

[0025] The service statistics and optimization module automatically calculates key indicators such as consultation volume, automatic response rate, satisfaction rating, and work order processing time based on historical service data. The system generates a ranking of hot issues, statistical reports on overdue work orders, and satisfaction analysis reports to assist university administration in optimizing knowledge base content and service processes, thereby improving overall service quality and efficiency.

[0026] The emotion recognition module in the natural language processing interface not only judges the user's emotions but also identifies potential signs of mental health abnormalities. Once an abnormal emotion is detected, the system automatically triggers a mental health alert, anonymously generates a psychological counseling work order, and dispatches it to relevant personnel at the mental health center to ensure timely intervention.

[0027] This system is applicable to multiple departments within university administrations, including the Academic Affairs Office, Student Affairs Office, Human Resources Department, Logistics Support Department, Psychological Counseling Center, and Youth League Committee, enabling intelligent collaborative services across departments and business areas. The system supports seamless access via web, WeChat, mini-programs, and desktop platforms, allowing users to freely choose their preferred access device, ensuring broad service coverage and convenient use.

[0028] 1. When a student enters "How do I apply for the National Scholarship?" in the WeChat official account, the system recognizes the keyword "scholarship," matches it with an entry in the knowledge base, and returns the selection criteria, timeline, and download link for attachments. If the student continues to ask, "My grades aren't high, can I still apply?" the system will initiate multiple rounds of interaction.

[0029] 2. When a teacher submits a "Consultation on Modifying Course Arrangement Process," the system automatically generates a work order and dispatches it to the Academic Affairs Office because the knowledge base does not match the request. After processing the request in the background, the Academic Affairs Office uploads an explanatory document, and the system pushes the processing result to the teacher.

[0030] 3. Administrators regularly review "Top Hot Issues of the Month," "Overdue Unprocessed Work Orders," and "Consultation Satisfaction Rate Statistics" to assist in improving the agency's service processes. Example 1: New Student Q&A Scenario (Student Affairs Office) Application Process: A new student asks through the mini-program: "How do I apply for a student loan?" The system matches the keyword "student loan," automatically retrieves the latest policy explanations and application procedures from the knowledge base, and pushes a reply link and operation steps.

[0031] If a student continues to ask, "What should I do if the village doesn't have a certificate?" the system cannot answer and will automatically generate a personalized work order, which will be handled by a dedicated person from the student financial aid center.

[0032] Effects: Relieves hotline pressure at the start of the new semester; allows for closed-loop processing of issues, avoiding the omission of individual special cases. Example 2: Teacher professional title application consultation (Personnel Department) application process: Teachers log in to the PC system and ask: "What are the requirements for associate professor professional title?" The system retrieves and displays the application conditions, scoring standards, and answers to frequently asked questions from the past three years.

[0033] Teachers can download the template by clicking "Review Material Template".

[0034] The system did not match the question "Have the teaching evaluation standards changed in the past two years?" and a work order was generated for the personnel officer to reply to.

[0035] Effects: Teachers can quickly obtain policy information without making phone calls or queuing; the personnel department reduces the workload of repeated responses. Example 3: Elective Course Conflict Handling (Academic Affairs Office) Application Process: A student asks, "What should I do if my two classes conflict on Thursday afternoon?" The system identifies the problem type as "course conflict," generates a work order, and reminds the student to supplement the course code and teacher's name; after the work order is submitted, the system automatically forwards it to the academic affairs staff and sets a 48-hour processing time limit; the academic affairs office coordinates the class scheduling and replies to the student, and records the result. Effects: The process is streamlined and structured, reducing information loss caused by email or paper communication; unified management and data archiving are achieved. Example 4: Logistics Repair Collaboration (Logistics Support Department) Application Process: Students take photos of damaged dormitory doors through a mini-program and upload them, describing "the door lock is broken" in voice; the system identifies the problem type as "dormitory door lock repair," automatically generates a repair work order; the system automatically pushes it to the logistics repair team for on-site handling; after repair, photos of the repair are uploaded and the work order is automatically closed. Results: Enables rapid service response and visible results; the system automatically compiles monthly repair hotspots, supporting preventative logistical maintenance. Example 5: Psychological health intervention (psychological center) application process: The system triggers an emotion recognition mechanism based on students' language in the question-and-answer system (e.g., "I'm under a lot of pressure," "I don't want to go to class"); if the emotional risk reaches a set threshold, the system can generate an anonymous psychological warning work order and push it to the psychological counselor; the counselor follows up with intervention according to the process and fills in feedback.

[0036] Effects: Expands the system's functional boundaries and assists in mental health screening; utilizes natural language processing to improve the ability to identify hidden problems. Example 6: Automatic guidance for competition registration consultation (Youth League Committee) Application process: A student asks, "When is the registration for the Challenge Cup?" The system returns the current round notification, eligibility requirements, and registration channel link; the system can automatically determine whether a student is suitable for participation based on grade, college, and whether they have participated before; if a student says, "I don't know how to write a project proposal," the system guides them to the "Project Guidance Resource Library" or guides them to fill out a tutoring application form.

[0037] 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.

[0038] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0039] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A smart response system for university administrative services that integrates self-service question answering and task work orders, characterized in that, The system includes: a user interaction module, supporting access from multiple terminals such as web pages, WeChat official accounts, mini-programs, and desktops, and integrating a natural language processing interface to achieve semantic understanding, keyword extraction, and sentiment analysis; a self-service question-answering engine, which uses a pre-built university knowledge graph to perform FAQ semantic matching, synonym expansion, question completion, and multi-round question-answering to automatically respond to users' common inquiries; a work order generation and dispatch module, which guides users to submit work orders when the self-service question-answering engine cannot match user questions, automatically identifies the question type, determines the responsible department, and dispatches the work order, while supporting time-limited response and overdue reminder mechanisms; a backend processing and feedback module, which allows administrators to process work orders, upload supplementary materials and replies, automatically record the processing process and notify users, and collect user satisfaction feedback; and a service statistics and optimization module, which automatically calculates indicators such as consultation volume, response rate, satisfaction, and work order processing time, and recommends optimizations to the knowledge base content and service processes based on data analysis results.

2. The intelligent response system for university administrative services integrating self-service question answering and task work orders as described in claim 1, characterized in that, The natural language processing interface includes a semantic understanding module, a keyword extraction module, and an emotion recognition module, which can realize user intent recognition and early warning of abnormal mental health.

3. The intelligent response system for university administrative services integrating self-service question answering and task work orders as described in claim 1, characterized in that, The university knowledge graph covers multiple knowledge domains such as courses, evaluations, certificates, student status, finance, and competitions. It supports multi-round question answering and semantic expansion, with an automatic response rate of over 70%.

4. The intelligent response system for university administrative services integrating self-service question answering and task work orders as described in claim 1, characterized in that, The work order generation and dispatch module supports the automatic generation of personalized work orders, setting work order processing time limits, and automatically reminding relevant responsible personnel and users when the time limit is exceeded.

5. The intelligent response system for university administrative services integrating self-service question answering and task work orders as described in claim 1, characterized in that, The background processing and feedback module provides a work order processing interface, supports material uploading, supplementary responses, and user satisfaction evaluation, and realizes closed-loop management of work orders.

6. The intelligent response system for university administrative services integrating self-service question answering and task work orders as described in claim 1, characterized in that, The service statistics and optimization module, based on historical service data, enables the ranking of hot issues, statistics of overdue work orders, and satisfaction analysis, assisting government departments in optimizing service strategies.

7. A smart response system for university administrative services integrating self-service question answering and task work orders as described in any one of claims 1 to 6, characterized in that, The system supports a unified access architecture across multiple terminals, enabling seamless collaborative access from web pages, WeChat, mini-programs, and desktop devices.

8. A smart response system for university administrative services integrating self-service question answering and task work orders as described in any one of claims 1 to 7, characterized in that, The system is applied to service scenarios in university administration departments, including but not limited to the Academic Affairs Office, Student Affairs Office, Human Resources Office, Logistics Support Department, Psychological Counseling Center, and Youth League Committee, to achieve intelligent service collaboration across departments and business areas.

9. A smart response method for university administrative services based on the system described in any one of claims 1 to 8, characterized in that, Includes the following steps: Step 1: Users input natural language questions via multiple terminals; Step 2: The system performs semantic understanding and keyword extraction through a natural language processing interface; Step 3: The system performs semantic matching in the knowledge graph and automatically answers common questions, or initiates multi-round question-and-answer interactions; Step 4: If the question cannot be answered automatically, the system guides the user to generate a work order and automatically dispatches it to the responsible department; Step 5: The responsible department processes the work order, uploads materials and replies, and the system automatically notifies the user of the processing result; Step 6: Users rate their service satisfaction, and the system collects feedback; Step 7: The system statistically analyzes service data to assist in optimizing the knowledge base and service processes.

10. The intelligent response method for university administrative services according to claim 9, characterized in that, It also includes steps for emotion recognition of user input, triggering early warnings of abnormal mental health and generating anonymous psychological counseling work orders.

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