A college credit point automatic accounting and early warning system and method

Abstract

The application discloses a kind of higher education credit point automatic accounting and early warning system and method, including educational administration data acquisition and standardization module, training scheme and point rule analysis module, credit point automatic accounting module, academic risk assessment and early warning module, early warning information push and feedback module, data archiving and dynamic updating module;The application is replaced artificial accounting process by multi-source data standardization and automatic accounting model, eliminates human error, realizes the full-process automation of point accounting, and the accounting caliber is unified, the result is accurate, adapts the differentiated conversion needs of difference of conventional course and special course.Cultivation scheme and accounting rule analysis storage mechanism are used, the decoupling of rule and business logic is realized, and the differentiated requirements of different majors, different grades and different education levels can be quickly adapted, and the rule is updated conveniently, and the system has strong expansibility.

Description

Technical Field

[0001] This invention relates to the field of higher education academic affairs management technology, specifically to an automatic calculation and early warning system and method for higher education credit grade points. Background Technology

[0002] With the continuous advancement of higher education popularization and the informatization of academic affairs management, GPA (Grade Point Average) serves as a core basis for measuring students' academic level, awarding scholarships, recommending students for postgraduate studies without examination, and verifying graduation qualifications. The accuracy and timeliness of its calculation directly affect the efficiency of academic affairs management and students' academic development. Currently, the following technical problems generally exist in the calculation of GPA and academic early warning systems in universities:

[0003] The accounting process is highly dependent on manual operation, requiring manual extraction of multi-source data such as course grades, credits, and curriculum plans. This can easily lead to problems such as data entry errors, inconsistent accounting standards, and duplicate accounting. Furthermore, it cannot adapt to dynamic conversion rules for special scenarios such as retaking courses, exemptions, cross-major course selection, and innovative credit recognition.

[0004] The GPA calculation rules are closely tied to the curriculum. Traditional systems use hard-coded methods to implement the rules, which cannot quickly adapt to the differentiated calculation standards of different academic levels, majors and grades. The rule update costs are high and the compatibility is poor.

[0005] The academic early warning mechanism is outdated and simplistic, relying solely on a fixed GPA threshold as the basis for warnings. It fails to comprehensively consider multiple indicators such as credit completion rate, number of failed courses, and course attendance status, resulting in insufficient accuracy in early warnings and a lack of tiered early warning and closed-loop feedback mechanisms.

[0006] The academic affairs data is isolated, with grade data, student status data, curriculum plan data, and course selection data unable to be shared in real time. The calculation results cannot be dynamically updated, and the channels for pushing early warning information are limited, making it impossible for students and counselors to obtain their academic status in a timely manner.

[0007] The lack of a long-term archiving and retrospective mechanism for academic data makes it difficult to manage historical accounting data, early warning records, and rectification feedback information in a unified manner, thus hindering the tracking of academic status and the optimization of academic management decisions.

[0008] Existing technologies have not formed a systematic solution that integrates multi-source data standardization, rule-adaptive parsing, accurate GPA calculation, multi-dimensional risk assessment, hierarchical early warning push, and dynamic data management, and cannot meet the actual needs of refined and intelligent academic affairs management in higher education. Summary of the Invention

[0009] The purpose of this invention is to provide an automatic calculation and early warning system and method for higher education credit grade points, so as to solve the problems existing in the prior art.

[0010] To achieve the above objectives, the present invention provides the following technical solution: an automatic calculation and early warning system for higher education credit grade points, comprising a module for academic affairs data collection and standardization, a module for curriculum scheme and grade point rule analysis, a module for automatic calculation of credit grade points, a module for academic risk assessment and early warning, a module for early warning information push and feedback, and a module for data archiving and dynamic updating;

[0011] The academic affairs data collection and standardization module is used to connect to the multi-source academic affairs management system to complete the automatic capture, cleaning, verification and format standardization of academic data.

[0012] The training program and GPA rule parsing module is used to parse the training program structure and GPA calculation rules, and to build a structured training program library and rule library;

[0013] The automatic GPA calculation module is based on standardized data and rule base to complete the calculation of course GPA, average GPA, and conversion of GPA for special courses.

[0014] The academic risk assessment and early warning module constructs a risk assessment model based on multi-dimensional academic indicators, and completes the calculation of academic risk index and determination of early warning level.

[0015] The early warning information push and feedback module is used to push early warning information in a targeted manner and collect feedback records to form a closed-loop management of early warning;

[0016] The data archiving and dynamic update module is used for the classification and archiving of accounting data, early warning records, and feedback information, as well as the dynamic refresh and historical retrospective of data changes.

[0017] Furthermore, the academic affairs data collection and standardization module specifically includes a data interface docking unit, a data cleaning and verification unit, and a format standardization unit;

[0018] The data interface connection unit is used to establish a communication connection with the academic affairs, student status, course selection and grade management system and automatically capture academic data; the data cleaning and verification unit is used to remove redundant data, complete missing data, and identify and remove abnormal data; the format standardization unit is used to convert multi-source heterogeneous data into a unified standard format and establish a unique identifier mapping relationship between students and courses.

[0019] Furthermore, the training program and GPA rule parsing module specifically includes a training program parsing unit, a GPA rule extraction unit, and a rule base construction unit;

[0020] The curriculum scheme parsing unit is used to extract the course types, credit allocation, study requirements and graduation credit standards of each major's curriculum scheme; the grade point rule extraction unit is used to extract the grade and grade point conversion rules, average grade point calculation rules and special course conversion rules; the rule base construction unit is used to structure and store the curriculum scheme requirements and calculation rules to form a rule base that can be dynamically called and updated.

[0021] Furthermore, the automatic GPA calculation module includes a single course GPA calculation unit, a weighted average GPA calculation unit, and a special course conversion unit;

[0022] The single-course grade point calculation unit calculates the grade point for a single course based on standardized grades and the grade-grade point conversion rules in the rule base.

[0023] The weighted average grade point calculation unit uses a weighted average model to automatically calculate the average grade point. The calculation formula is as follows:

[0024] Formula 1: Formula for calculating Grade Point Average (GPA):

[0025]

[0026] in, Grade Point Average (GPA) for students; The total number of courses taken by students and included in the accounting; For the first The credit value of a course; For the first Grade Point Average (GPA) of all courses after conversion; The total grade point average (GPA) of students; The total credit hours for courses taken by students and included in the accounting;

[0027] The special course conversion unit addresses special courses such as retakes, exemptions, cross-major course selections, make-up exams, and innovative credits. It uses a credit conversion model to standardize the conversion of credits and grade points. The conversion formula is as follows:

[0028] Formula 2: Special Course Credit GPA Conversion Formula:

[0029]

[0030] in, For the first The final grade point after conversion for special courses; For the first Grade Point Average (GPA) corresponding to the original grade of a special course; For the first The conversion factor for a special course is automatically matched by the rule base based on the course type.

[0031] Furthermore, the academic risk assessment and early warning module includes a multi-dimensional indicator extraction unit, a risk index calculation unit, and an early warning level determination unit;

[0032] Multidimensional indicator extraction unit: Extracts multidimensional indicators such as average grade point average, credit completion rate, number of failed courses, percentage of courses not passed, and core course enrollment status from standardized data and accounting results.

[0033] Risk Index Calculation Unit: An academic risk index calculation model is constructed based on multi-dimensional indicators to quantify the degree of academic risk. The calculation formula is as follows:

[0034] Formula 3: Academic Risk Index Computational model:

[0035]

[0036] in, This is a student academic risk index; the higher the value, the higher the risk. , , For the weighting coefficients, satisfying The rule base is adaptively configured according to the requirements of the training program. This is the ratio of the credits a student has earned to the credits required for graduation. The number of courses a student failed; This represents the total number of courses the student has taken. Grade Point Average (GPA) is the average grade point average for students.

[0037] Warning level determination unit: based on academic risk index The numerical range is used to classify the warning levels into no risk, general warning, key warning, and emergency warning, and at the same time, the corresponding warning reasons and improvement suggestions are generated.

[0038] Furthermore, the early warning information push and feedback module specifically includes a targeted push unit, a feedback information collection unit, and a closed-loop management unit;

[0039] The targeted push unit is used to push early warning information to students, counselors, and academic administrators according to their roles; the feedback information collection unit is used to collect academic rectification plans, assistance records, and handling opinions; the closed-loop management unit is used to associate early warning information and feedback information, and track and mark the status of early warning handling.

[0040] Furthermore, the data archiving and dynamic update module includes a classification archiving unit, a dynamic refresh unit, and a history backtracking unit;

[0041] The classification and archiving unit stores accounting results, early warning records, feedback information, and rule update records by student ID, semester, and major, and establishes an indexing mechanism; the dynamic refresh unit is used to respond to instructions for grade updates, credit recognition, changes in course enrollment status, and rule adjustments, automatically re-triggering the accounting and early warning process and updating the corresponding data; the historical backtracking unit supports retrieving historical accounting data, early warning records, and feedback information by student, semester, and major, realizing full-cycle traceability of academic status.

[0042] A method for calculating and issuing early warnings in an automatic GPA calculation and early warning system for higher education includes the following steps:

[0043] Step 1: Perform multi-source academic affairs data collection, cleaning, verification, and format standardization to generate a standard academic affairs dataset;

[0044] Step 2: Analyze the curriculum and GPA calculation rules for each major, and build a curriculum and rule database;

[0045] Step 3: Use standardized data and rule bases to calculate course grade points, average grade point average (GPA), and convert special courses accordingly;

[0046] Step 4: Extract multi-dimensional academic indicators, calculate the academic risk index and determine the warning level, and generate warning content;

[0047] Step 5: Push early warning information in a targeted manner, collect feedback records, and realize closed-loop management of early warnings;

[0048] Step 6: Classify and archive the accounting results, early warning records, and feedback information, and dynamically refresh and backtrack the data in response to changes;

[0049] Step 7: After completing the entire process, the system enters standby mode.

[0050] Furthermore, in step 3, the average grade point average (GPA) calculation and special course conversion are performed simultaneously, and the calculation and conversion results are linked to the student's unique identifier in real time; in step 6, the dynamic refresh trigger conditions include grade updates, credit recognition, changes in course enrollment status, curriculum adjustments, and updates to calculation rules.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] This invention replaces manual calculation processes with a multi-source data standardization and automated calculation model, eliminating human error and achieving full automation of GPA calculation. It ensures unified calculation standards, accurate results, and adaptability to the differentiated conversion needs of regular and specialized courses. Employing a mechanism for parsing and storing curriculum plans and calculation rules, it decouples rules from business logic, allowing for rapid adaptation to the differentiated requirements of different majors, grades, and academic levels. Rule updates are convenient, and the system is highly scalable. A risk index model based on multi-dimensional academic indicators replaces single-threshold early warning methods, enabling quantitative assessment and tiered early warning of academic risks. This provides more targeted warnings and effectively improves the effectiveness of academic intervention. It breaks down data silos from multiple sources, enabling real-time data exchange and dynamic updates, and establishing a closed-loop management mechanism of early warning, push notifications, feedback, and tracking to ensure effective implementation of academic risk matters. With categorized archiving and historical review functions, it achieves full-cycle management of academic data, providing comprehensive data support for student academic tracking, counselor assistance, and academic management decisions. Attached Figure Description

[0053] Figure 1 This is a system module diagram of the present invention;

[0054] Figure 2 This is a schematic diagram of the academic affairs data collection and standardization module of the present invention;

[0055] Figure 3 This is a schematic diagram of the training scheme and GPA rule parsing module of the present invention;

[0056] Figure 4 This is a schematic diagram of the automatic GPA calculation module of the present invention;

[0057] Figure 5 This is a schematic diagram of the academic risk assessment and early warning module of the present invention;

[0058] Figure 6 This is a flowchart of the method of the present invention. Detailed Implementation

[0059] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0060] Please see Figure 1-6This invention provides an automatic calculation and early warning system and method for higher education credit grade points, including a module for academic data collection and standardization, a module for curriculum and grade point rule analysis, a module for automatic calculation of credit grade points, a module for academic risk assessment and early warning, a module for early warning information push and feedback, and a module for data archiving and dynamic updating.

[0061] The academic data collection and standardization module is used to connect with university academic management systems, student status management systems, course selection systems, and grade management systems to automatically capture, clean, verify, and unify the format of multi-source academic data, eliminating data silos. The curriculum scheme and GPA rule parsing module is used to parse the course structure, credit requirements, and course order of each major and grade, while extracting GPA calculation rules and special course conversion rules to form a callable rule and scheme library. The automatic GPA calculation module, based on standardized data and parsed rules, uses a weighted calculation model to calculate the GPA of a single course, the cumulative GPA, and the average GPA. The system automatically calculates credits and grade points for special courses; the academic risk assessment and early warning module constructs an academic risk assessment model based on the calculation results and multi-dimensional academic indicators, completes the quantification of risk levels and the determination of graded early warnings, and generates targeted early warning content; the early warning information push and feedback module is used to push early warning information to corresponding students, counselors, and academic administrators, while collecting feedback information and rectification records to form an early warning closed loop; the data archiving and dynamic update module is used to classify and archive the calculation results, early warning records, and feedback information, while responding to data update commands to realize the dynamic refresh and retrospective of grade points and early warning status.

[0062] The academic affairs data collection and standardization module includes a data interface connection unit, a data cleaning and verification unit, and a format standardization unit;

[0063] Data interface connection unit: It adopts a standardized data interaction protocol to establish a communication connection with the existing academic affairs, student status, course selection and grade system of universities, and automatically captures basic student information, course information, grade information, credit information, enrollment status information and curriculum-related information according to preset data dimensions to ensure that the data source is unique and the acquisition is real-time;

[0064] Data cleaning and verification unit: performs redundancy removal, missing value completion, and outlier identification on the captured raw data; verifies the legality of grades, the rationality of credits, and the validity of student status; and removes invalid and conflicting data.

[0065] Standardized Format Unit: This unit converts academic data from different sources and in different formats into a pre-defined standard format, establishes a mapping relationship between unique student identifiers and unique course identifiers, and forms a standardized academic dataset to provide compliant data support for subsequent modules.

[0066] The curriculum and GPA rule parsing module includes a curriculum parsing unit, a GPA rule extraction unit, and a rule base construction unit;

[0067] The curriculum analysis unit analyzes the curriculum documents and structured data for each major and grade level, extracting the requirements for compulsory courses, elective courses, practical courses, and innovation credits, as well as the credit allocation, priority of study, and total graduation credit requirements.

[0068] Grade Point Rating Extraction Unit: Extracts the conversion rules for percentage grades and grade points, the conversion rules for five-level grades and grade points, the average grade point average calculation rules, and the conversion rules for retakes / exemptions / cross-major course selections / make-up exams.

[0069] Rule base construction unit: The parsed training program requirements and GPA calculation rules are stored in a structured manner to form a training program library and rule base that can be dynamically called, updated and expanded, and supports rule adaptation for different academic levels and different majors.

[0070] The automatic GPA calculation module includes a single course GPA calculation unit, a weighted average GPA calculation unit, and a special course conversion unit.

[0071] Single course grade point calculation unit: Calculate the grade point for a single course based on standardized grades and the grade-to-grade point conversion rules in the rule base;

[0072] Weighted Average Grade Point Average (GPA) Calculation Unit: This unit uses a weighted average model to automatically calculate the average grade point average (GPA). The calculation formula is as follows:

[0073] Formula 1: Formula for calculating Grade Point Average (GPA):

[0074]

[0075] in, Grade Point Average (GPA) for students; The total number of courses taken by students and included in the accounting; For the first The credit value of a course; For the first Grade Point Average (GPA) of all courses after conversion; The total grade point average (GPA) of students; The total credit hours for courses taken by students that are included in the accounting.

[0076] Special Course Conversion Unit: For special courses such as retakes, exemptions, cross-major course selections, make-up exams, and innovative credits, a credit conversion model is used to complete the standardized conversion of credits and grade points. The conversion formula is as follows:

[0077] Formula 2: Special Course Credit GPA Conversion Formula:

[0078]

[0079] in, For the first The final grade point after conversion for special courses; For the first Grade Point Average (GPA) corresponding to the original grade of a special course; For the first The conversion factor for a special course is automatically matched by the rule base based on the course type.

[0080] The academic risk assessment and early warning module includes a multi-dimensional indicator extraction unit, a risk index calculation unit, and an early warning level determination unit;

[0081] Multidimensional indicator extraction unit: Extracts multidimensional indicators such as average grade point average, credit completion rate, number of failed courses, percentage of courses not passed, and core course enrollment status from standardized data and accounting results.

[0082] Risk Index Calculation Unit: An academic risk index calculation model is constructed based on multi-dimensional indicators to quantify the degree of academic risk. The calculation formula is as follows:

[0083] Formula 3: Academic Risk Index Computational model:

[0084]

[0085] in, This is a student academic risk index; the higher the value, the higher the risk. , , For the weighting coefficients, satisfying The rule base is adaptively configured according to the requirements of the training program. This is the ratio of the credits a student has earned to the credits required for graduation. The number of courses a student failed; This represents the total number of courses the student has taken. Grade Point Average (GPA) is the average grade point average for students.

[0086] Warning level determination unit: based on academic risk index The numerical range is used to classify the warning levels into no risk, general warning, key warning, and emergency warning, and at the same time, the corresponding warning reasons and improvement suggestions are generated.

[0087] The early warning information push and feedback module includes a targeted push unit, a feedback information collection unit, and a closed-loop management unit;

[0088] Targeted push unit: Based on the warning level and user role, the warning information is pushed to the student terminal, counselor terminal, and academic affairs administrator terminal, and the push channel is compatible with multiple terminals;

[0089] Feedback Information Collection Unit: Collects students' academic improvement plans, counselors' assistance records, and academic administrators' handling opinions to form a feedback dataset;

[0090] Closed-loop management unit: Links and binds early warning information with feedback information, tracks the progress of early warning processing, and marks the status as processed, in progress, or unprocessed to ensure the closed-loop implementation of early warning matters.

[0091] The data archiving and dynamic update module includes a categorized archiving unit, a dynamic refresh unit, and a historical backtracking unit;

[0092] Categorized archiving unit: The accounting results, early warning records, feedback information, and rule update records are categorized and stored according to student ID, semester, and major, and an indexing mechanism is established;

[0093] Dynamic refresh unit: In response to instructions such as grade updates, credit recognition, changes in course enrollment status, and rule adjustments, it automatically re-triggers the accounting and early warning process and updates the corresponding data;

[0094] Historical Retrospective Unit: Supports retrieving historical accounting data, early warning records, and feedback information by student, semester, and major, enabling full-cycle tracking of academic status.

[0095] A method for calculating and issuing early warnings in an automatic GPA calculation and early warning system for higher education includes the following steps:

[0096] Step 1: Multi-source academic affairs data collection and standardization processing

[0097] The academic data collection and standardization module is activated. Through the data interface connection unit, the original academic data of the academic affairs, student status, course selection and grade system is automatically captured. After the data cleaning and verification unit completes the redundancy removal, missing data completion and anomaly verification, the format standardization unit unifies the data format and establishes a standard academic dataset.

[0098] Step 2: Analysis and input of training program and GPA rules into the database

[0099] The curriculum and grade point rule analysis module analyzes the course structure, credit requirements and study rules of the curriculum for each major and grade. It also extracts the rules for grade-to-grade point conversion, special course conversion, and average grade point calculation to build a structured curriculum library and rule library.

[0100] Step 3: Automatic GPA Calculation

[0101] The automatic GPA calculation module calls the standard academic dataset and rule base data to first calculate the GPA for a single course, then calculate the average GPA using Formula 1, and convert the GPA for special courses using Formula 2, generating a complete GPA calculation result.

[0102] Step 4: Academic Risk Assessment and Early Warning Level Determination

[0103] The academic risk assessment and early warning module extracts multi-dimensional academic indicators, calculates the academic risk index using Formula 3, determines the early warning level based on the index range, and generates early warning content that includes the early warning level, the cause of the risk, and improvement suggestions.

[0104] Step 5: Targeted Push and Feedback Collection of Early Warning Information

[0105] The early warning information push and feedback module pushes early warning information to users based on their roles, while collecting student rectification plans, counselor assistance records, and academic affairs handling opinions to form a closed-loop management of early warning, feedback, and tracking.

[0106] Step 6: Data Archiving and Dynamic Updates

[0107] The data archiving and dynamic update module classifies and archives accounting results, early warning records, and feedback information. When academic data, training programs, or accounting rules change, dynamic refresh is automatically triggered to recalculate and update the early warning status. It also supports historical data backtracking and querying.

[0108] Step 7: End of accounting and early warning process

[0109] Once the entire process of data processing, pushing, feedback, archiving, and updating is completed, the system enters a standby state, awaiting the next round of data update or calculation instructions.

[0110] Example 1: Undergraduate Students' Regular Semester Grade Point Calculation and Academic Early Warning

[0111] Implementation scenario:

[0112] A second-year undergraduate student at a university is taking 8 required courses and 2 elective courses. There are no special courses that need to be retaken, exempted, or taken in a different major. The student is required to complete the semester's GPA calculation and routine academic warning.

[0113] Implementation process:

[0114] Step 1: Data Collection and Standardization

[0115] The academic affairs data collection and standardization module connects to the school's academic affairs system, grade system, and course selection system through data interfaces, automatically capturing the student's basic information, grades, credits, enrollment status, and student status for 10 courses; the data cleaning and verification unit removes invalid and redundant data, verifies that all grades are valid percentage data, and there are no missing or abnormal data; the format standardization unit unifies the data into a standard structured format and binds the student's unique student ID and the course's unique number.

[0116] Step 2: Analysis of the training program and rules

[0117] The curriculum and grade point rule analysis module retrieves the curriculum of the student's major and year, extracts the requirements for 8 required courses and 2 elective courses, the total credits required for graduation, and the credits required per semester; at the same time, it extracts the school's percentage system grades and grade point conversion rules, and the average grade point calculation rules. If there are no special course conversion requirements, the rule base automatically matches the conventional calculation rules.

[0118] Step 3: Automatic GPA Calculation

[0119] The automatic GPA calculation module first calculates the GPA for each of the 10 courses according to the conversion rules; then it calls Formula 1 and substitutes the credit hours for each course. With course grade The student's average grade point average for the semester was calculated. Since there are no special courses, there is no need to perform the conversion operation of Formula 2. The system generates a complete semester GPA calculation report, including the GPA of individual courses, cumulative GPA, average GPA, and total credits earned.

[0120] Step 4: Academic Risk Assessment and Early Warning

[0121] The academic risk assessment and early warning module extracts the student's average grade point average. Credit completion rate Number of failed courses Total number of courses completed Rule base adaptive configuration of weight coefficients , , Substitute into Formula 3 to calculate the academic risk index. After assessment, the risk index is determined to be in the risk-free range. The system marks the student as being in a risk-free state and generates feedback indicating that their academic status is normal.

[0122] Step 5: Early Warning Push and Feedback

[0123] The early warning information push and feedback module pushes a notification that the student's academic status is normal, and at the same time pushes the student's academic status report to the counselor; since there is no early warning, the feedback information collection unit only records the viewing status, and the closed-loop management unit marks it as processed and without abnormalities.

[0124] Step 6: Data Archiving and Dynamic Updates

[0125] The data archiving and dynamic update module archives the student's semester accounting results and academic status records by student ID, semester, and major, and creates an index. If there are no triggering conditions such as grade changes or rule adjustments in the semester, the system maintains data stability and supports subsequent retrieval of historical data for academic tracking.

[0126] Step 7: Process End

[0127] The system has completed all processes and entered standby mode, awaiting data collection instructions or data update triggers for the next semester.

[0128] Example 2: Credit GPA Calculation and Emergency Warning for Courses Including Retakes and Cross-Disciplinary Course Selection

[0129] Implementation scenario:

[0130] A third-year undergraduate student at a university has taken 9 required courses and 3 elective courses, including 1 retaken course and 1 cross-major elective course. There are 2 failed courses, and grade point calculation and accurate early warning are required.

[0131] Implementation process:

[0132] Step 1: Data Collection and Standardization

[0133] The academic data collection and standardization module captured data from 12 courses for the student, identifying 1 retaken course, 1 course taken outside the major, and 2 failed courses; the data cleaning and verification unit confirmed that all data was legal and valid, with no missing or abnormal data; the format standardization unit completed the format unification and marked special course types.

[0134] Step 2: Analysis of the training program and rules

[0135] The curriculum and grade point rule analysis module analyzes the curriculum of this major, confirms the credit recognition rules for retaken courses and cross-major courses, extracts the conversion coefficients for retaken courses and cross-major courses, and incorporates them into the rule base for matching and calling.

[0136] Step 3: Automatic GPA Calculation

[0137] The automatic GPA calculation module first calculates the GPA for regular courses, and then applies Formula 2 for retaken courses and courses taken outside the major to complete the converted GPA. Calculate; then use Formula 1 to calculate the average grade point average (GPA) including special courses. The process involves calculating and generating an accounting report that includes details of the conversion of special courses.

[0138] Step 4: Academic Risk Assessment and Early Warning

[0139] The academic risk assessment and early warning module extracts multi-dimensional indicators, which are then substituted into Formula 3 to calculate the academic risk index. Calculations showed that the risk index was in the emergency warning range, and the system determined it to be an emergency warning level, generating warning content and targeted improvement suggestions that included failed courses, low GPA, and insufficient credit completion rate.

[0140] Step 5: Early Warning Push and Feedback

[0141] The early warning information push and feedback module pushes emergency early warning information to students, counselors, and college academic affairs administrators; counselors enter support plans, students submit academic improvement plans, academic affairs administrators mark tracking responsibilities, and the closed-loop management unit binds early warning information and feedback information and marks it as being processed.

[0142] Step 6: Data Archiving and Dynamic Updates

[0143] The data archiving and dynamic update module archives and stores the calculation results, emergency warning records, and feedback information. If the student passes the make-up exam for the failed course or updates the grade for the retake, the system will automatically trigger a dynamic refresh to recalculate the grade point, update the risk index and warning level, and retain historical warning records for academic tracking.

[0144] Step 7: Process End

[0145] The system completes the entire process, continuously monitors data changes, and maintains dynamic responsiveness.

[0146] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. 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 automatic calculation and early warning system for higher education grade points, characterized in that: It includes modules for academic data collection and standardization, curriculum and grade point rule analysis, automatic GPA calculation, academic risk assessment and early warning, early warning information push and feedback, and data archiving and dynamic updating. The academic affairs data collection and standardization module is used to connect to the multi-source academic affairs management system to complete the automatic capture, cleaning, verification and format standardization of academic data. The training program and GPA rule parsing module is used to parse the training program structure and GPA calculation rules, and to build a structured training program library and rule library; The automatic GPA calculation module is based on standardized data and rule base to complete the calculation of course GPA, average GPA, and conversion of GPA for special courses. The academic risk assessment and early warning module constructs a risk assessment model based on multi-dimensional academic indicators, and completes the calculation of academic risk index and determination of early warning level. The early warning information push and feedback module is used to push early warning information in a targeted manner and collect feedback records to form a closed-loop management of early warning; The data archiving and dynamic update module is used for the classification and archiving of accounting data, early warning records, and feedback information, as well as the dynamic refresh and historical retrospective of data changes.

2. The automatic calculation and early warning system for higher education grade points according to claim 1, characterized in that: The academic affairs data collection and standardization module specifically includes a data interface connection unit, a data cleaning and verification unit, and a format standardization unit; The data interface connection unit is used to establish a communication connection with the academic affairs, student status, course selection and grade management system and automatically capture academic data; the data cleaning and verification unit is used to remove redundant data, complete missing data, and identify and remove abnormal data; the format standardization unit is used to convert multi-source heterogeneous data into a unified standard format and establish a unique identifier mapping relationship between students and courses.

3. The automatic calculation and early warning system for higher education grade points according to claim 1, characterized in that: The training program and GPA rule parsing module specifically includes a training program parsing unit, a GPA rule extraction unit, and a rule base construction unit; The curriculum scheme parsing unit is used to extract the course types, credit allocation, study requirements and graduation credit standards of each major's curriculum scheme; the grade point rule extraction unit is used to extract the grade and grade point conversion rules, average grade point calculation rules and special course conversion rules; the rule base construction unit is used to structure and store the curriculum scheme requirements and calculation rules to form a rule base that can be dynamically called and updated.

4. The automatic calculation and early warning system for higher education grade points according to claim 1, characterized in that: The automatic GPA calculation module includes a single course GPA calculation unit, a weighted average GPA calculation unit, and a special course conversion unit. The single-course grade point calculation unit calculates the grade point for a single course based on standardized grades and the grade-grade point conversion rules in the rule base. The weighted average grade point calculation unit uses a weighted average model to automatically calculate the average grade point. The calculation formula is as follows: Formula for calculating Grade Point Average (GPA): in, Grade Point Average (GPA) for students; The total number of courses taken by students and included in the accounting; For the first The credit value of a course; For the first Grade Point Average (GPA) of all courses after conversion; The total grade point average (GPA) of students; The total credit hours for courses taken by students and included in the accounting; The special course conversion unit addresses special courses such as retakes, exemptions, cross-major course selections, make-up exams, and innovative credits. It uses a credit conversion model to standardize the conversion of credits and grade points. The conversion formula is as follows: Formula for converting special course credits to grade point average: in, For the first The final grade point after conversion for special courses; For the first Grade Point Average (GPA) corresponding to the original grade of a special course; For the first The conversion factor for a special course is automatically matched by the rule base based on the course type.

5. The automatic calculation and early warning system for higher education grade points according to claim 1, characterized in that: The academic risk assessment and early warning module includes a multi-dimensional indicator extraction unit, a risk index calculation unit, and an early warning level determination unit. Multidimensional indicator extraction unit: Extracts multidimensional indicators such as average grade point average, credit completion rate, number of failed courses, percentage of courses not passed, and core course enrollment status from standardized data and accounting results. Risk Index Calculation Unit: An academic risk index calculation model is constructed based on multi-dimensional indicators to quantify the degree of academic risk. The calculation formula is as follows: Academic Risk Index Computational model: in, This is a student academic risk index; the higher the value, the higher the risk. , , For the weighting coefficients, satisfying The rule base is adaptively configured according to the requirements of the training program. This is the ratio of the credits a student has earned to the credits required for graduation. The number of courses a student failed; This represents the total number of courses the student has taken. Grade Point Average (GPA) is the average grade point average for students. Warning level determination unit: based on academic risk index The numerical range is used to classify the warning levels into no risk, general warning, key warning, and emergency warning, and at the same time, the corresponding warning reasons and improvement suggestions are generated.

6. The automatic calculation and early warning system for higher education grade points according to claim 1, characterized in that: The early warning information push and feedback module specifically includes a targeted push unit, a feedback information collection unit, and a closed-loop management unit; The targeted push unit is used to push early warning information to students, counselors, and academic administrators based on their roles. The feedback information collection unit is used to collect academic rectification plans, assistance records and processing opinions; the closed-loop management unit is used to associate early warning information and feedback information, and track and mark the early warning processing status.

7. The automatic calculation and early warning system for higher education grade points according to claim 1, characterized in that: The data archiving and dynamic update module includes a classification archiving unit, a dynamic refresh unit, and a history backtracking unit; The classification and archiving unit stores accounting results, early warning records, feedback information, and rule update records by student ID, semester, and major, and establishes an indexing mechanism. The dynamic refresh unit is used to respond to instructions for grade updates, credit recognition, changes in course enrollment status, and rule adjustments, automatically re-triggering the accounting and early warning process and updating the corresponding data; the historical backtracking unit supports retrieving historical accounting data, early warning records, and feedback information by student, semester, and major, enabling full-cycle traceability of academic status.

8. The calculation and early warning method of the automatic calculation and early warning system for higher education grade point average (GPA) according to any one of claims 1-7, characterized in that: Includes the following steps: Step 1: Perform multi-source academic affairs data collection, cleaning, verification, and format standardization to generate a standard academic affairs dataset; Step 2: Analyze the curriculum and GPA calculation rules for each major, and build a curriculum and rule database; Step 3: Use standardized data and rule bases to calculate course grade points, average grade point average (GPA), and convert special courses accordingly; Step 4: Extract multi-dimensional academic indicators, calculate the academic risk index and determine the warning level, and generate warning content; Step 5: Push early warning information in a targeted manner, collect feedback records, and realize closed-loop management of early warnings; Step 6: Classify and archive the accounting results, early warning records, and feedback information, and dynamically refresh and backtrack the data in response to changes; Step 7: After completing the entire process, the system enters standby mode.

9. The calculation and early warning method of the automatic calculation and early warning system for higher education credit grade points according to claim 8, characterized in that: In step 3, the average grade point average (GPA) calculation and special course conversion are performed simultaneously, and the calculation and conversion results are linked to the student's unique identifier in real time. In step 6, the dynamic refresh trigger conditions include grade updates, credit recognition, changes in course enrollment status, curriculum adjustments, and updates to calculation rules.

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