Student information management system

Abstract

The invention relates to the technical field of information management, in particular to a student information management system, which comprises a data acquisition module used for acquiring multi-dimensional data of students from a plurality of heterogeneous data sources; the user interaction module is used for acquiring position information of a user and carrying out identity authentication on the user to generate an identity tag with a unique number; the data fusion analysis module is used for cleaning, standardizing and fusing the collected multi-dimensional data to generate a unified data portrait of each student; the computing node management module is used for dynamically distributing edge computing nodes according to the geographical location information of the user; the authority level management module dynamically adjusts the access authority according to the geographical location information and the identity information of the user; the analysis and early warning module is used for analyzing the unified data portrait, identifying potential risks and generating an early warning signal; and the early warning management module is used for executing a management strategy according to the received early warning signal.

Description

Technical Field

[0001] This invention relates to the field of information management technology, and more particularly to a student information management system. Background Technology

[0002] In today's era of digital education, student information management systems have become an indispensable tool for the daily operations of schools. With the continuous expansion of education and the increasing number of students, student information is characterized by its large volume, complexity, and frequent updates. This places extremely high demands on the efficient operation and rapid response of student information management systems.

[0003] The rise of edge computing technology has provided a new way to improve the performance of student information management systems. By offloading some computing tasks to edge nodes closer to the data source, data transmission latency can be effectively reduced, and the load on the core network can be lowered. However, existing student information management systems have exposed a serious problem in the process of integrating with edge computing: the inability to dynamically allocate edge computing nodes.

[0004] Currently, most student information management systems employ a statically configured edge computing node allocation method. In this model, the task scope and processing capacity of each edge node are fixed at system deployment, lacking a mechanism for flexible adjustment based on real-time business needs and node status. For example, during peak exam result inquiry periods, a large number of students simultaneously access the system, causing a sudden surge in the volume of data reading and processing. However, due to the limited pre-allocated computing resources of some edge nodes, they cannot cope with the sudden increase in load, leading to slow processing speeds or even lag. Meanwhile, other edge nodes may be idle due to fewer tasks, unable to promptly support heavily loaded nodes. This static allocation method results in low system resource utilization, failing to fully leverage the advantages of edge computing, and consequently severely reducing the overall efficiency of the student information management system in handling access tasks. Summary of the Invention

[0005] To address the above problems, this invention provides a student information management system.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A student information management system, comprising:

[0008] The data acquisition module is used to obtain multi-dimensional data about students from multiple heterogeneous data sources;

[0009] The user interaction module is used to obtain the user's geographical location information and perform identity authentication to generate a unique identity tag.

[0010] The data fusion and analysis module is used to preprocess the collected multi-dimensional data and generate a unified data profile for each student.

[0011] The computing node management module dynamically allocates edge computing nodes based on the user's geographic location information;

[0012] The access control module dynamically adjusts access permissions based on the user's geographic location and identity information.

[0013] The analysis and early warning module is used to analyze the unified data profile, identify potential risks, and generate early warning signals.

[0014] The early warning management module is used to execute management strategies based on the received early warning signals.

[0015] Preferably, the workflow of the computing node management module includes:

[0016] The user terminal device sends an access request to the center and the system, and the system extracts the access task from the access request.

[0017] The nearest computing node is matched based on the user's geographical location information and used as a candidate node;

[0018] Obtain the current QoS metrics of the candidate nodes, and determine whether the QoS metrics of the candidate nodes meet the threshold based on the access task.

[0019] Preferably, the QoS metrics include CPU utilization, memory availability, throughput and latency, task response time and task completion rate.

[0020] Preferably, the step of obtaining the current QoS index of the candidate node and determining whether the QoS index of the candidate node meets the threshold based on the access task specifically includes:

[0021] If the QoS metric of a candidate node does not meet the threshold for access tasks, a new candidate node will be obtained.

[0022] When the Oos metric of a candidate node meets the threshold, the candidate node will be used as the task computation node.

[0023] Preferably, when the QoS metric of a candidate node does not meet the threshold for access tasks, a new candidate node is obtained, specifically including:

[0024] The access task is parsed by the task scheduler, and the feature vectors in the access task are extracted.

[0025] A task requirement profile is generated based on the feature vector, and the task requirement profile is matched with computing nodes.

[0026] The computing node with the highest overall score is selected as the candidate node.

[0027] Preferably, the system also includes a computing node storage module, which stores the latitude and longitude, network topology, and real-time load status of all computing nodes.

[0028] Preferably, the multi-dimensional data includes exam score data and behavioral information data, wherein the behavioral information data includes attendance data, consumption data, and extracurricular activity data.

[0029] Preferably, the workflow of the permission level management module includes:

[0030] Initial access permissions are set for users based on the identity tags generated by the user interaction module;

[0031] The initial access permissions are adjusted based on the user's geographical location information, and the final access permissions are generated.

[0032] Preferably, the analysis and early warning module includes:

[0033] The feature extraction unit is used to extract student information feature vectors from the unified data profile;

[0034] The machine learning model unit, trained using an ensemble learning algorithm, is used to output a risk level score based on the feature vector.

[0035] The early warning triggering unit is used to compare the risk level score with a preset threshold and generate an early warning signal of the corresponding level.

[0036] Preferably, the warning signals include academic crisis, psychological problems, behavioral abnormalities, and physical health issues.

[0037] The beneficial effects of this invention are as follows:

[0038] 1. This application selects the computing node closest to the user based on the user's location information for edge computing, so that the task scope and processing capacity of each computing node can be dynamically adjusted, avoiding the decrease in access speed due to the surge in processing tasks during peak periods.

[0039] In addition to selecting nodes based on distance, there is also a feature to build a task requirement profile based on the access task, and match the computing node based on the task requirement profile. This method can increase the adaptability between computing nodes and access tasks.

[0040] 2. In terms of permission settings, this application adds geolocation recognition on the basis of identity information recognition. By using geolocation recognition, the user's login information is prevented from being leaked when the user is off campus, thus ensuring the privacy and security of the information. Attached Figure Description

[0041] Figure 1 This is a block diagram of the student information management system in a specific embodiment of the present invention;

[0042] Figure 2 A flowchart of the computing node management module in a specific embodiment of the present invention. Detailed Implementation

[0043] 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.

[0044] Example 1

[0045] Please see Figures 1-2 As shown, the present invention relates to a student information management system, comprising:

[0046] A computing node storage module stores the latitude and longitude, network topology, and real-time load status of all computing nodes.

[0047] The data acquisition module is used to obtain multi-dimensional data about students from multiple heterogeneous data sources;

[0048] The data fusion and analysis module is used to clean, standardize, and fuse the collected multi-dimensional data to generate a unified data profile for each student.

[0049] Specifically, multiple heterogeneous data sources and multi-dimensional data include:

[0050] The academic affairs management system is used to obtain student registration information, course selection information, course schedules, exam scores, grade point average (GPA), and graduation review status.

[0051] One-card system: a data source for student campus consumption and some behavioral patterns, such as canteen consumption records, supermarket shopping records, shower / water filling records, library access gate records, and dormitory access control records.

[0052] Library Management System: Data sources reflecting students' learning habits and interests, such as book borrowing history, electronic resource access logs, study room seat reservation records, and time spent in the library.

[0053] Network Authentication and Management System: Data sources reflecting students' online behavior and daily routines, such as campus network login / logout time, online duration, website traffic (anonymized), and AP (wireless access point) switching location trajectory.

[0054] Online learning platforms: Data sources reflecting students' engagement in online learning, such as video viewing time and completion rate, assignment submission status, number of forum posts and interactions, online quiz scores, and course resource download records.

[0055] Student Activities System: A data source reflecting students' comprehensive qualities and social activities, such as club membership, activity registration and participation records, award and honor records, and volunteer service hours.

[0056] A unified data profile for each student is generated based on multi-dimensional data, specifically including:

[0057] Data association is performed using a student's unique identifier (such as student ID) as the primary key. When a unique identifier is missing, a fuzzy matching algorithm (such as based on name, department, or year of enrollment) is used to determine whether records from different data sources belong to the same student.

[0058] Align and link various types of data (grades, spending, access control, etc.) with student ID as the primary key along the time dimension.

[0059] For example, to generate a record in time sequence, such as:

[0060] [Timestamp: 2023-10-27 12:00, Event: Cafeteria Consumption, Amount: 15 yuan];

[0061] [Timestamp: 2023-10-27 14:00, Event: Library access control, Location: 3rd floor];

[0062] Based on the fused multi-dimensional data, derived features are calculated, including:

[0063] Statistical characteristics: average spending over the past 7 days, number of times the library was visited in the past month, and variance of grades this semester.

[0064] Behavioral pattern characteristics: daily routine index (based on access control and internet time), learning engagement index (online learning time + library time), and consumption volatility coefficient.

[0065] Trend-based characteristics: trends in academic performance and changes in spending.

[0066] After the above steps, a structured, dynamically updated data object—a unified data profile—is generated for each student and stored in the database of the information management system in JSON or other formats.

[0067] After the above steps, a structured, dynamically updated data object—a unified data profile—is generated for each student. It is typically stored in JSON or a similar format and contains the following layers:

[0068] The user interaction module is used to obtain the user's location information and perform identity authentication to generate a unique identity tag, specifically including:

[0069] User terminal devices (such as mobile phones and computers) send requests to the central access gateway / load balancer to access the information management system.

[0070] The central access gateway extracts the source location information contained in the request. Extraction methods include, but are not limited to:

[0071] IP Geolocation Database Query: Based on the request source IP address, query a high-precision IP geolocation database to obtain the user's approximate latitude and longitude or city-level location.

[0072] GPS / Wi-Fi triangulation data (for mobile apps): If the system app has obtained user authorization, it can directly obtain more accurate GPS coordinates or connected Wi-Fi BSSID information from the device for positioning.

[0073] Network latency detection: Send a lightweight probe packet to the user terminal and estimate the approximate distance by back-calculating latency (e.g., ping, traceroute).

[0074] The compute node management module dynamically allocates edge computing nodes based on the user's geographic location information. The specific workflow includes:

[0075] The user terminal device sends an access request to the center and the system, and the system extracts the access task from the access request.

[0076] The nearest computing node is matched based on the user's geographical location information and used as a candidate node;

[0077] Obtain the current QoS metrics of the candidate nodes, and determine whether the QoS metrics of the candidate nodes meet the threshold based on the access task;

[0078] When the QoS metric of a candidate node does not meet the threshold for access tasks, re-acquire candidate nodes, including:

[0079] The access task is parsed by the task scheduler, and the feature vectors in the access task are extracted.

[0080] A task requirement profile is generated based on the feature vector, and the task requirement profile is matched with computing nodes.

[0081] The computing node with the highest overall score is selected as the candidate node;

[0082] When the Oos metric of a candidate node meets the threshold, the candidate node will be used as the task computation node.

[0083] The access control module dynamically adjusts access permissions based on the user's geographic location and identity information. It sets initial access permissions for users based on identity tags generated by the user interaction module, and then adjusts the initial access permissions based on the user's geographic location information to generate final access permissions.

[0084] The analysis and early warning module is used to analyze the unified data profile, identify potential risks, and generate early warning signals. These early warning signals include academic crises, psychological problems, behavioral abnormalities, and physical health issues. The early warning module includes:

[0085] The feature extraction unit is used to extract student information feature vectors from the unified data profile;

[0086] The machine learning model unit, trained using an ensemble learning algorithm, is used to output a risk level score based on the feature vector.

[0087] The early warning triggering unit is used to compare the risk level score with a preset threshold and generate an early warning signal of the corresponding level.

[0088] The early warning management module is used to execute management strategies based on the received early warning signals.

[0089] Example 2

[0090] Based on the above embodiment 1, furthermore, in this application, the permission adjustment of the permission level management module is continuous and runs through the entire user access process, that is, the system monitors changes in user location in real time or near real time, for example:

[0091] When a user walks from an open public area into a secure administrative office building with their mobile phone, the system detects the change in location and can automatically elevate their privilege level, allowing them to perform more operations.

[0092] Conversely, if a user moves from the office building to a coffee shop, the system can automatically downgrade their session, display a pop-up reminder that they are accessing sensitive data, and automatically lock the screen after a period of inactivity.

[0093] Furthermore, when this application is used within the school premises, different functional areas are divided according to the school's boundaries, such as:

[0094] The core layer consists of computing nodes deployed in the data center / core computer room, using high-performance server clusters covering the entire campus. It is used for unified data storage, global load balancing, and handling global, non-real-time, and massive storage tasks (such as querying all student records over the years and generating big data analysis reports).

[0095] The aggregation layer, where computing nodes are deployed in the main teaching buildings / libraries, uses edge servers and covers the entire building complex. It is used for regional task scheduling, data preprocessing, and handling complex tasks with high real-time requirements within the region (such as classroom attendance analysis, library seat guidance, and dormitory access control recognition).

[0096] The access layer, where computing nodes are deployed as micro nodes in classrooms / laboratories, uses Raspberry Pi CM4+NPU acceleration modules, and covers a single room. It is used for real-time request and response and terminal device connection. Lightweight computing units are integrated on wireless access points (APs) or IoT gateways in classrooms and corridors to handle the most extreme real-time and privacy-sensitive tasks (such as facial recognition access control and sensor data preprocessing).

[0097] By dividing the school's area into different regions, when selecting candidate nodes, the task scheduler parses the access tasks and extracts the feature vectors from the access tasks.

[0098] A task requirement profile is generated based on the feature vector, and then matched with different computing nodes within the school area.

[0099] The task requirement profile includes:

[0100] Spatial profile: physical location (latitude and longitude), building function (teaching building, dormitory), floor, and wireless AP coverage area.

[0101] Task profile: This includes the workload, computational complexity, real-time requirements, and data privacy levels for different business operations, for example:

[0102] Classroom attendance tracking: high real-time performance, low computational cost, and moderate privacy.

[0103] Library visitor flow analysis: medium real-time requirements, high computational demands (video analysis), low privacy.

[0104] Grade inquiry: low real-time requirements, low computational load, high privacy.

[0105] Furthermore, obtain the current QoS metrics of the candidate nodes, and determine whether the QoS metrics of the candidate nodes meet the thresholds based on the access task, for example:

[0106] When the access task is an online exam, set the QoS level to 0 and the network requirement threshold to <100ms;

[0107] When the access task is to download courseware, set the QoS level to 1 and the network requirement threshold.

[0108] When the access task is attendance statistics, the QoS level is set to 2, and the network requirement threshold is <2s.

[0109] In some other embodiments, the management strategy for compute nodes includes, but is not limited to, the geographical location of user access, and also allocates compute nodes based on the type of access task, such as:

[0110] When the access task type is interactive, the OOS metric requirements are latency <100ms and jitter <30ms, such as online exams and video defenses. The computing node allocation strategy is to force binding to the nearest access layer node.

[0111] When the access task type is streaming media, the OOS (Out of Service) requirements are bandwidth > 5Mbps and packet loss rate < 1%. For example, in MOOC videos and live lectures, the compute node allocation strategy is to allocate them to nodes with GPU acceleration.

[0112] When the access task type is batch processing, the OOS (Out of Memory) requirement is throughput > 1Gbps and latency < 2s, such as performance statistics and data analysis. The compute node allocation strategy is to route to the high storage node in the aggregation layer.

[0113] When the access task type is background synchronization, the OOS metric requirements are: allowable delay > 5s, reliability > 99.9%, such as data backup and log upload. The computing node allocation strategy is to schedule to any node during idle periods.

[0114] Furthermore, by using the identity tags generated by the user interaction module, initial access permissions can be set for users;

[0115] Adjust initial access permissions based on the user's geographic location information to generate final access permissions, for example:

[0116] The campus is physically fenced off, providing access to resources such as the academic affairs system and exam question bank. Verification is achieved through a combination of Bluetooth beacon and GPS positioning.

[0117] Within a specific building, the accessible resource is a laboratory equipment reservation system, verified via Wi-Fi fingerprint positioning (error <3m).

[0118] The authorized off-campus area allows access to resources such as the course selection system and grade inquiry, with verification methods including IP whitelisting and VPN two-factor authentication.

[0119] Accessible from any location, including open course videos and campus news, with no geographical restrictions and no verification required.

[0120] Specifically, initial access permissions are set based on the user's unique identification tag, for example:

[0121] School leaders have access to: all school financial / personnel data;

[0122] Department administrators have the following access permissions: teaching / research data of their department;

[0123] For regular teachers, access permissions are limited to: data on the classes they teach;

[0124] Students have access permissions for: personal student status / grade data.

[0125] Furthermore, in some other embodiments, the geographical location of this application is not limited to distinguishing only between on-campus and off-campus locations, but rather defines a more granular geographical area bound to business logic:

[0126] High-trust zone (e.g., department office building, academic affairs office): Users in this zone can be granted higher or more sensitive permissions (e.g., teachers can enter grades and access the research management system).

[0127] Standard access areas (e.g., classrooms, libraries, campus public areas): Users enjoy all permissions defined by their standard roles (e.g., students can access course selection and check grades).

[0128] Restricted access areas (e.g., dormitories, gymnasiums): For privacy or security reasons, certain sensitive operations are automatically restricted (e.g., prohibiting access to other students' personal information in dormitory areas to prevent screen snooping).

[0129] Low Trust / External Zones (e.g., Off-campus, Overseas): Enable enhanced security verification (e.g., enforce two-factor authentication 2FA) and restrict core functions (e.g., allow viewing only, prohibit downloading batch data, prohibit financial operations).

[0130] Enhanced security: Significantly reduces the risk of data breaches due to stolen credentials or in untrusted network environments. Even if account passwords are compromised, attackers cannot perform high-risk operations if they are not in a "trusted location".

[0131] Context-based intelligence: Permissions are no longer static, but are intelligently adjusted according to the user's actual situation, making them more suitable for business needs (such as being able to post announcements in the office or sign in in the classroom).

[0132] Compliance assurance: Automatically enforce compliance requirements such as "data not leaving the country" and "internal data access within the country" to reduce human error.

[0133] User experience optimization: Reduce unnecessary authentication attempts while maintaining security (no need to frequently enter passwords within the secure zone), achieving seamless security enhancement.

[0134] Example 3

[0135] Based on the above embodiment 1, in this embodiment, the analysis of the warning module output includes, but is not limited to, warning signals, warning levels, warning types, and warning objects;

[0136] The intervention strategy is a hierarchical, multi-dimensional, and configurable set of strategies, specifically including:

[0137] 1. Notifications and reminders (mild intervention)

[0138] Automatic push notifications: Send reminder messages to students via the app, email, or SMS. For example: "Student, the system has detected that you have been staying up late for several days in a row. Please remember to get some rest!"

[0139] Remind teachers / counselors: Send an alert notification to the counselor's or class teacher's work platform, reminding them to pay attention. For example: "Zhang San in your class has an academic warning. Please pay attention to it promptly."

[0140] 2. Resource guidance and support (moderate intervention)

[0141] Personalized resource recommendations: Automatically push relevant learning resources or support channels to students. For example, accurately push links to "Advanced Mathematics Difficult Points Explanation Videos" or "Top Student Tutoring Appointment" to students with academic difficulties.

[0142] Psychological support connection: For psychological risk warnings, the system can automatically provide students with appointment links to psychological counseling centers or self-help stress reduction materials.

[0143] 3. Automated Tasks (Moderate / Severe Intervention)

[0144] Automatically generate to-do tasks: Automatically create a task in the counselor's or academic affairs teacher's work list that "needs to conduct an interview with XX student" and associate it with warning information.

[0145] Notify parents: For high-risk warnings, the system can automatically generate a communication message (email or SMS) to parents, prompting them to pay attention (preset rules and authorization are required).

[0146] Automated academic warning process: Automatically generate academic warning notices and add them to the student's electronic file.

[0147] 4. Systemic Intervention (Severe Intervention)

[0148] Forced rest mechanism: In response to health alerts, the system can temporarily restrict students from accessing game servers or the campus network during specific time periods (such as early morning).

[0149] Course selection restrictions: For academic warnings (multiple failed courses), the system can automatically restrict the number of credits that the student can take in the following semester.

[0150] Trigger manual review: The highest level of warning will be directly pushed to the school-level management department, requiring the immediate initiation of the manual intervention process.

[0151] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A student information management system, characterized in that, include: The data acquisition module is used to obtain multi-dimensional data about students from multiple heterogeneous data sources; The user interaction module is used to obtain the user's geographical location information and perform identity authentication to generate a unique identity tag. The data fusion and analysis module is used to preprocess the collected multi-dimensional data and generate a unified data profile for each student. The computing node management module dynamically allocates edge computing nodes based on the user's geographic location information; The access control module dynamically adjusts access permissions based on the user's geographic location and identity information. The analysis and early warning module is used to analyze the unified data profile, identify potential risks, and generate early warning signals. The early warning management module is used to execute management strategies based on the received early warning signals.

2. The student information management system according to claim 1, characterized in that, The workflow of the computing node management module includes: The user terminal device sends an access request to the center and the system, and the system extracts the access task from the access request. The nearest computing node is matched based on the user's geographical location information and used as a candidate node; Obtain the current QoS metrics of the candidate nodes, and determine whether the QoS metrics of the candidate nodes meet the threshold based on the access task.

3. The student information management system according to claim 2, characterized in that, The QoS metrics include CPU utilization, memory availability, throughput and latency, task response time, and task completion rate.

4. The student information management system according to claim 2, characterized in that, The step of obtaining the current QoS metrics of candidate nodes and determining whether the QoS metrics of candidate nodes meet the threshold based on the access task specifically includes: If the QoS metric of a candidate node does not meet the threshold for access tasks, a new candidate node will be obtained. When the Oos metric of a candidate node meets the threshold, the candidate node will be used as the task computation node.

5. The student information management system according to claim 1, characterized in that, When the QoS metric of a candidate node does not meet the threshold for access tasks, a new candidate node is obtained, specifically including: The access task is parsed by the task scheduler, and the feature vectors in the access task are extracted. A task requirement profile is generated based on the feature vector, and the task requirement profile is matched with computing nodes. The computing node with the highest overall score is selected as the candidate node.

6. The student information management system according to claim 1, characterized in that, It also includes a computing node storage module, which stores the latitude and longitude, network topology, and real-time load status of all computing nodes.

7. The student information management system according to claim 1, characterized in that, The multi-dimensional data includes exam score data and behavioral information data, which includes attendance data, consumption data, and extracurricular activity data.

8. The student information management system according to claim 1, characterized in that, The workflow of the permission level management module includes: Initial access permissions are set for users based on the identity tags generated by the user interaction module; The initial access permissions are adjusted based on the user's geographical location information, and the final access permissions are generated.

9. The student information management system according to claim 1, characterized in that, The analysis and early warning module includes: The feature extraction unit is used to extract student information feature vectors from the unified data profile; The machine learning model unit, trained using an ensemble learning algorithm, is used to output a risk level score based on the feature vector. The early warning triggering unit is used to compare the risk level score with a preset threshold and generate an early warning signal of the corresponding level.

10. The student information management system according to claim 9, characterized in that, The warning signals include academic crisis, psychological problems, behavioral abnormalities, and physical health issues.

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