Web architecture-based intelligent registration and management method and system for teacher workload

Abstract

The application relates to the technical field of educational informatization, and discloses a teacher workload intelligent registration and management method and system based on a Web architecture, wherein the method comprises the following steps: receiving a workload declaration request and extracting a declaration event attribute list; querying and merging to generate a teacher time constraint dataset; performing structured constraint verification on the declaration event attribute list to generate a constraint verification result set; performing student affiliation conflict verification based on student rotation data; performing Z-score standardization on conflict severity score and abnormal risk score, and then performing weighted fusion to obtain comprehensive abnormal risk score; and pushing an ordered review queue in descending order of the comprehensive abnormal risk score.

Description

Technical Field

[0001] This invention relates to the field of educational informatization technology, specifically to a method and system for intelligent registration and management of teacher workload based on a Web architecture. Background Technology

[0002] The workload management system for faculty in universities and medical colleges is usually based on a web architecture. Faculty members submit workload declaration records through browser forms, and the backend system performs compliance verification on the declaration data before pushing the records into the review queue.

[0003] The existing workload declaration verification module in the web system only performs statistical compliance checks on the declared values, such as verifying whether the declared class hours exceed the limit or whether the declaration frequency is abnormal. This type of verification method cannot perform logical cross-validation between the declaration records and the teaching calendar, schedule, course schedule, and student rotation data already stored in the system.

[0004] When a teacher's submitted application records meet the numerical standards, but logically contradict their work schedule, teaching calendar, or the actual departments the students rotate through, such records cannot be automatically identified by the system and will directly enter the review queue. This deficiency forces reviewers to spend a lot of time manually verifying each record, and there is a risk that logically contradictory application records will be missed and enter the calculation process. Summary of the Invention

[0005] This invention provides a method and system for intelligent registration and management of teacher workload based on a web architecture, which solves the technical problems in related technologies such as the lack of multi-dimensional logical cross-verification of teacher workload declaration records and the difficulty in automatically identifying and quantitatively sorting abnormal declarations.

[0006] This invention discloses a method for intelligent registration and management of teacher workload based on Web architecture, including the following steps: Step 1, the Web system backend receives workload application requests, obtains the target teacher identifier, accounting cycle number and set of application records to be reviewed, extracts the application date, time period, task type, teaching location and set of participating students for each application record, and generates a list of application event attributes;

[0007] Step 2: Using the target teacher identifier and accounting cycle number as the query key, query the database for teaching calendar data, target teacher schedule data, and target teacher timetable data, and merge them to generate a teacher time constraint dataset;

[0008] Step 3: For each record in the event attribute list, perform set membership checks on the declaration date and the list of statutory holidays, calculate the overlapping intervals between the declaration date and the time period and the schedule, and perform matching calculations between the declaration time period and the teaching location and the timetable. Then, add holiday conflict markers, schedule conflict markers, and timetable conflict markers respectively, and summarize to generate a set of structured constraint verification results.

[0009] Step 4: Use the student identification list and application date as the query keys to obtain student rotation schedule data, compare the student rotation department identification with the target teacher's department identification, and add a student belonging conflict mark to the application records of students with conflicting affiliations.

[0010] Step 5: Perform severity score conversion on various conflict markers to obtain conflict severity scores. Obtain statistical anomaly risk score ,right and After Z-score standardization, the weighted sums are used to obtain the comprehensive anomaly risk score. ;

[0011] Step 6, for Declaration records exceeding a preset threshold generate structured anomaly report data objects, which are then processed according to... The data is pushed to the reviewers in descending order, generating an anomaly-marked review queue response data.

[0012] The comprehensive abnormal risk score mentioned above The calculation formula is:

[0013]

[0014] in, To comprehensively score the abnormal risk, The conflict severity score is the result of Z-score standardization. The statistical anomaly risk score is calculated using the Z-score standardization method. To be applied to The preset weight parameters, To be applied to The preset weight parameters satisfy and .

[0015] Furthermore, the teaching calendar data includes semester start and end dates, a list of statutory holidays, a mapping of teaching week numbers, and an exam week marker field;

[0016] The target teacher scheduling data includes a daily scheduling type field and a scheduling time period field; the target teacher course schedule data includes fields for scheduled courses, times, and locations.

[0017] Furthermore, step 3, which involves attaching a holiday conflict flag, includes attaching a holiday conflict flag to the record when the declaration date falls on a statutory holiday and the task type field value does not belong to the special approval type set; wherein the special approval type set is a task type enumeration set pre-configured in the system parameter table.

[0018] Furthermore, step 3, which involves adding a scheduling conflict flag, includes adding a scheduling conflict flag to the record when the declared time interval overlaps with the time interval of the scheduling record, the scheduling type field value is clinical on-call or surgical type, and the declared teaching location field value is not a clinical site identifier.

[0019] Furthermore, step 3, which involves attaching a timetable conflict flag, includes matching the declared time period and teaching location field values ​​with the target teacher's timetable data. When there are teaching records in different locations within the same time period, a timetable conflict flag is attached to that record.

[0020] Furthermore, step 4 also includes: when there are students with conflicting affiliations in the application record, writing the student's actual rotation department information field into the application record.

[0021] Furthermore, step 5, which involves performing severity scoring conversion on various conflict markers, includes mapping holiday conflict markers and scheduling conflict markers to high severity scores.

[0022] Class schedule conflict markers are mapped to medium severity scores; student affiliation conflict markers are mapped to severity scores that can be controlled by configurable parameters; the above scores are then summed to obtain the conflict severity score. .

[0023] Furthermore, the statistical anomaly risk score The system is generated by the historical time series feature analysis module, which takes teacher identification as input and outputs the degree of deviation of the frequency and quantity of applications within the accounting period from the historical distribution.

[0024] Furthermore, the structured anomaly report data object in step 6 includes a conflict type field, a conflict evidence summary field, and a statistical anomaly indicator field, wherein the conflict evidence summary field references the corresponding schedule record primary key and teaching calendar entry identifier.

[0025] The method further includes: performing grouping and aggregation calculations on various constraint verification results and conflict detection count data according to the target teacher identification dimension and department dimension, generating conflict occurrence frequency statistics and distribution characteristic data, and writing them into the analysis database.

[0026] This invention provides a web-based intelligent registration and management system for teacher workload, including: an application receiving module, used to receive workload application requests and extract and generate an application event attribute list;

[0027] The constraint data query module is used to query teaching calendar data, target teacher schedule data, and target teacher time schedule data, and merge them to generate a teacher time constraint dataset.

[0028] The structured constraint verification module is used to perform set member detection, overlapping interval calculation, and matching calculation on the list of declared event attributes and to attach corresponding conflict markers.

[0029] The student affiliation verification module is used to obtain student rotation schedule data and perform an equivalent comparison to add a student affiliation conflict flag;

[0030] The risk scoring module is used to score the severity of the conflict. With statistical anomaly risk score The comprehensive anomaly risk score is obtained by performing Z-score standardization and weighted summation. The exception report push module is used to generate structured exception report data objects and push them according to... Push the results to the reviewers in descending order.

[0031] The present invention has the following beneficial effects.

[0032] Firstly, the backend reuses stored structured data such as teaching calendars, class schedules, course schedules, and student rotations to perform multi-dimensional logical cross-validation on each application record. This covers logical contradictions in the time and space dimensions that cannot be detected by pure numerical verification, enabling the system to automatically identify records with compliant numerical values ​​but logical contradictions, thus reducing the risk of such records being missed and entering the accounting process.

[0033] Secondly, by verifying student affiliation through student rotation scheduling data, the verification coverage is extended from the teacher side to the student side, so that the system can identify the application records of students who are not actually in the target teacher's department but are included in the student group.

[0034] Third, scoring the severity of the conflict. With statistical anomaly risk score After Z-score standardization, a weighted fusion was performed to eliminate the impact of the difference in dimensionality between the two methods on the score merging. This allowed logical conflicts and frequency-based anomalies to be incorporated into a unified risk measurement framework, resulting in a comprehensive anomaly risk score. .

[0035] Fourth, based on the comprehensive abnormal risk score Pushing the review queue in descending order allows reviewers to prioritize high-risk records, which helps improve review efficiency. Attached Figure Description

[0036] Figure 1 This is a flowchart of the intelligent registration and management method for teacher workload based on Web architecture provided in this embodiment of the invention;

[0037] Figure 2 This is a schematic diagram comparing the severity scores of conflicts in various declaration records provided in an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram showing the comparison between the comprehensive abnormal risk score and the threshold of each declaration record provided in the embodiments of the present invention;

[0039] Figure 4 This is a schematic diagram of the heatmap distribution of conflict marker types for various declaration records provided in an embodiment of the present invention;

[0040] Figure 5 This is a schematic diagram of the distribution of conflict severity scores and statistical anomaly risk assessment dispersion points provided in the embodiments of the present invention;

[0041] Figure 6 This is a schematic diagram showing the severity scores corresponding to each conflict type provided in the embodiments of the present invention;

[0042] Figure 7 This is a schematic diagram showing the comparison of scores of each record after Z-score standardization provided in an embodiment of the present invention;

[0043] Figure 8 This is a schematic diagram illustrating the composition of the conflict severity scoring items for each declaration record provided in the embodiments of the present invention;

[0044] Figure 9 This is a schematic diagram comparing the weighted fusion comprehensive anomaly risk score calculation process provided in the embodiments of the present invention. Detailed Implementation

[0045] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0046] Example

[0047] A method for verifying teacher workload declaration records according to an embodiment of the present invention, such as... Figure 1As shown, it includes the following steps:

[0048] Step 1: Receive workload application requests and extract the application event attribute list;

[0049] The web system backend receives workload application requests submitted by teachers via browser forms from their clients. It then retrieves the target teacher identifier, the current accounting cycle number, and a complete set of pending application records from the request data. For each application record in the pending record set, it extracts the application date, time period, task type, teaching location, and participating student set fields, generates a list of application event attributes, and writes it to a temporary processing cache.

[0050] Step 2: Query and merge to generate the teacher time constraint dataset;

[0051] Using the target teacher identifier and accounting period number as query keys, query requests are sent to the database to obtain the teaching calendar data, target teacher schedule data, and target teacher course schedule data for the current accounting period.

[0052] It should be noted that the teaching calendar data includes fields for semester start and end dates, a list of statutory holidays, a mapping of teaching week numbers, and an exam week marker; the target teacher scheduling data includes fields for daily scheduling type and scheduling time period; and the target teacher course schedule data includes fields for scheduled courses, times, and locations. The teaching calendar data, target teacher scheduling data, and target teacher course schedule data are merged to generate a teacher time constraint dataset, which is then loaded into an in-memory processing queue.

[0053] Step 3: Perform structured constraint validation on the list of declared event attributes;

[0054] Perform structured constraint validation on each record in the event attribute list, which may include:

[0055] Step 3-1: Perform a set membership check between the application date field value and the list of statutory holidays in the teaching calendar data. If the application date matches a statutory holiday and the task type field value does not belong to the special approval type set, add a holiday conflict flag to the record.

[0056] Step 3-2: Perform overlap interval calculation between the application date and time period field values ​​and the time intervals of each scheduling record in the target teacher scheduling table data. When time interval overlap is detected, the scheduling type field value is clinical on-call or surgery type, and the application teaching venue field value is not a clinical site identifier, add a scheduling conflict mark to the record.

[0057] Step 3-3: Match the values ​​of the declared time period and teaching location fields with the target teacher's timetable data. When there are teaching records in different locations within the same time period, add a timetable conflict flag to the record.

[0058] Steps 3-4: Summarize holiday conflict markers, shift conflict markers, and class schedule conflict markers to generate a structured constraint verification result set.

[0059] It should be noted that the special approval type set is a task type enumeration set pre-configured in the system parameter table, such as types that can be legally declared on statutory holidays, such as "holiday on-call teaching" and "special approval centralized training"; the clinical site identifier is the enumeration value in the teaching site field that represents clinical locations such as wards, operating rooms, and outpatient clinics.

[0060] Step 4: Perform student affiliation conflict verification based on student rotation data;

[0061] Extract the "Participant Set" field from each application record, and retrieve the corresponding student rotation schedule data from the database using the student ID list and application date as the query keys. For each student in the participant set, query the student's rotation department ID corresponding to the application date and perform an equivalence comparison with the target teacher's department ID. If the student's rotation department ID does not match the target teacher's department ID, it is determined that the student has an affiliation conflict. When there are students with affiliation conflicts in the application record, add a student affiliation conflict flag to the corresponding application record and write the student's actual rotation department information field to the record.

[0062] Step 5: Perform conflict severity score conversion and generate a comprehensive anomaly risk score;

[0063] The severity scores of various conflict markers in the structured constraint validation result set are converted into severity scores: holiday conflict markers and scheduling conflict markers are mapped to high severity scores; timetable conflict markers are mapped to medium severity scores; and student affiliation conflict markers are mapped to severity scores that can be controlled by configurable parameters. These scores are then summed to obtain the conflict severity score for each declaration record. .

[0064] Obtain the statistical anomaly risk score output by the historical time series feature analysis module. It should be noted that the statistical anomaly risk score... This embodiment generates the system's existing historical time-series feature analysis module, using teacher identifiers as input and the deviation of the frequency and quantity of applications within the accounting period from the historical distribution as output. It directly calls the statistical anomaly risk score. The output value.

[0065] Due to conflict severity rating With statistical anomaly risk score Due to the different units of measurement, the severity of the conflict was scored separately before performing the weighted summation. and statistical anomaly risk score Z-score standardization is performed to map both scores to the same dimensionless numerical space. The standardized conflict severity score and statistical anomaly risk score are then weighted and summed according to preset weight parameters to obtain the comprehensive anomaly risk score for each declaration record. The calculation formula is:

[0066]

[0067] in, To comprehensively score the abnormal risk, The conflict severity score is the result of Z-score standardization. The statistical anomaly risk score is calculated using the Z-score standardization method. To be applied to The preset weight parameters, To be applied to The preset weight parameters satisfy and .

[0068] Step 6: Generate an anomaly report and push it to the sorted review queue;

[0069] Comprehensive abnormal risk score For reports exceeding a preset threshold, a structured anomaly report data object is generated. This object includes a conflict type field, a conflict evidence summary field, and a statistical anomaly indicator field. The conflict evidence summary field references the primary key of the corresponding schedule record and the identifier of the teaching calendar entry, facilitating direct source tracing by reviewers on the web interface.

[0070] Associate structured anomaly report data objects with corresponding audit task records and score them according to comprehensive anomaly risk. The data is sorted in descending order, and the sorted audit task queue data is pushed to the audit personnel's management interface through the Web system's audit management interface to generate constraint-enhanced anomaly-marked audit queue response data.

[0071] In this embodiment, based on the generated constraint-enhanced anomaly annotation review queue response data, the following steps are also included: grouping and aggregating various constraint verification results and conflict detection count data according to the target teacher identification dimension and department dimension to generate statistical values ​​and distribution characteristics of various conflict occurrence frequencies; and writing the summarized statistical results into the analysis database for query and retrieval by the management reporting module. This step is used to provide the management with a conflict distribution profile, assisting in identifying teachers or departments with long-term logical deviations in their applications.

[0072] This implementation reuses pre-stored structured database resources such as teaching calendars, schedules, course schedules, and student rotation data in the backend of the web system, performing multi-dimensional logical cross-validation on each application record. Step 3 performs set membership checks, overlap interval calculations, and matching calculations between the application date, time period, and teaching location and three types of time constraint data: teaching calendar data, target teacher schedule data, and target teacher course schedule data. This covers logical contradictions in the time and space dimensions that cannot be detected by pure numerical verification. Therefore, records with compliant numerical values ​​but logical contradictions can be automatically identified by the system. Step 4 further utilizes student rotation schedule data to verify student affiliation, extending the verification coverage from the teacher side to the student side, thus exposing application records where students are not actually in the target teacher's department but are included in the student set. Step 5 scores the severity of the conflict. With statistical anomaly risk score After Z-score standardization, the scores are weighted and merged to eliminate the impact of the difference in dimensionality between the two methods, thus incorporating logical conflicts and frequency anomalies into a unified risk measure; Step 6: Score based on comprehensive anomaly risk. The descending order of the review queue allows reviewers to prioritize high-risk records. In summary, this implementation overcomes the lack of logical validation caused by the original system's reliance on numerical verification, solving the problems of low review efficiency and the risk of missed reviews.

[0073] The following is an example of an application of the present invention, such as... Figure 2-9 As shown, the implementation process is as follows:

[0074] At the end of the fall semester accounting period of 20XX, the faculty workload management system of a medical college received a workload declaration request submitted by teacher T-071, who belongs to the Department of Internal Medicine (department code DEPT-NEI). This declaration contained four records, covering task types such as clinical teaching, mini-lectures, and operational demonstrations. The system backend performed logical cross-validation on each declaration record sequentially.

[0075] The system extracts the target teacher identifier T-071, the accounting cycle number CY-2024A, and all four pending application records from the request data submitted by the browser form. For each record, it extracts the fields of application date, time period, task type, teaching location, and participating student set, generates a list of application event attributes, and writes it to a temporary processing cache.

[0076] Table 1 List of Event Attributes for Reporting

[0077]

[0078] Using the target teacher identifier T-071 and the accounting cycle number CY-2024A as the query keys, the system queries the database for teaching calendar data, teacher scheduling data, and course schedule data respectively, and merges the three types of data to generate a teacher time constraint dataset, which is then loaded into the memory processing queue. The teaching calendar data marks October 1st as a statutory holiday for National Day; the scheduling data records that T-071's clinical on-call period is from 14:00 to 16:00 on October 9th; the course schedule records that T-071 has a mini-lecture scheduled in demonstration room A from 14:00 to 16:00 on October 9th.

[0079] Table 2 Teacher Time Constraint Dataset (Key Entries)

[0080]

[0081] Perform structured constraint validation on the four records in the event attribute list. In step 3-1, REC-01's application date of October 1st falls within the list of statutory holidays, and its task type is "clinical teaching," which does not belong to the special approval type set (the special approval set includes "holiday on-call teaching" and "special approval intensive training"). Therefore, a holiday conflict flag is added to REC-01. In step 3-2, REC-03's application time period of 14:00 to 16:00 overlaps with the clinical on-call time period of October 9th in the schedule. The schedule type is clinical on-call, but the application teaching location is "surgical ward" instead of internal medicine ward (which does not match the on-call location and is a cross-department application during clinical on-call), so a schedule conflict flag is added. REC-02's application location for the same time period is demonstration room A, which overlaps with the clinical on-call time in the schedule, but the teaching location is marked as a non-clinical site, so a schedule conflict flag is also added. In step 3-3, both REC-02 and REC-03 are submitted for a time period from 14:00 to 16:00 on October 9th. The timetable records show that T-071 has a scheduled course in demonstration room A during this time period. REC-03 is submitted for a surgical ward, which constitutes a conflict between teaching in different locations during the same time period. A timetable conflict marker is added to REC-03. Step 3-4 summarizes all markers to generate a structured constraint verification result set.

[0082] Table 3. Structured constraint verification result set

[0083]

[0084] Extract the set of participating trainees from the four application records, and retrieve rotation schedule data from the database using the trainee ID list and application date as the query keys. For each trainee, query the rotation department ID corresponding to the application date and perform an equivalence comparison with the DEPT-NEI of T-071's department. In REC-01, S-205's rotation department on October 1st was Surgery (DEPT-WAI), which is inconsistent with DEPT-NEI. It is determined that S-205 has a conflict of assignment, so a trainee assignment conflict mark is added to REC-01 and the actual rotation department information of S-205 is written. In REC-04, S-203's rotation department on October 15th was Pediatrics (DEPT-ER). It is determined that S-203 has a conflict of assignment, so a trainee assignment conflict mark is added to REC-04.

[0085] Table 4. Results of Student Attribution Conflict Verification

[0086]

[0087] Severity scores are converted for various conflict markers: holiday conflict markers and scheduling conflict markers are mapped to a high severity score of 8 points; timetable conflict markers are mapped to a medium severity score of 5 points; student affiliation conflict markers are mapped to a configurable severity score, with a current configuration value of 3 points. The scores for each conflict marker are summarized for each record to obtain a conflict severity score. REC-01 has issues with holiday conflicts (8 points) and student affiliation conflicts (3 points). REC-02 has a scheduling conflict (8 points). REC-03 has scheduling conflicts (8 points) and class schedule conflicts (5 points). REC-04 has a student affiliation conflict (3 points). .

[0088] The historical time-series feature analysis module is invoked, and statistical anomaly risk scores for each record in the current accounting period are obtained using T-071 as input. This reflects the degree of deviation of the frequency and volume of applications from their historical distribution.

[0089] right sequence (11, 8, 13, 3) and The sequences were each subjected to Z-score normalization, as shown in the formula. ,in The mean, For the standard deviation, we get and According to preset weights , Perform a weighted summation to calculate the overall anomaly risk score for each record. .

[0090] Table 5. Calculation Results of Comprehensive Abnormal Risk Score

[0091]

[0092] The preset threshold is set to 0.5, REC-01 ( ) and REC-03 ( The threshold is exceeded. The system generates structured anomaly report data objects for both records. The conflict evidence summary field references the primary key of the corresponding scheduling record and the identifier of the teaching calendar entry. In the REC-03 report, the conflict type field is written as "scheduling conflict, class schedule conflict", referencing the scheduling record primary key SCH-20XX1009-T071 and the teaching calendar entry CAL-20XX-W41; in the REC-01 report, the conflict type field is written as "holiday conflict, student affiliation conflict", referencing the teaching calendar entry CAL-20XX-NAT01.

[0093] according to Arranged in descending order, REC-03 is placed before REC-01 and pushed to the management interface of the reviewer to generate constraint-enhanced anomaly annotation review queue response data.

[0094] Subsequently, the system performs grouping and aggregation calculations on various conflict detection count data according to the T-071 identifier dimension and the DEPT-NEI department dimension, generates conflict occurrence frequency statistics and distribution characteristic data, and writes them into the analysis database for the management terminal reporting module to call.

[0095] Throughout the process, data originates from the original fields submitted in the form. Step 1 extracts a structured attribute list; Step 2 retrieves three types of time constraint data from the database as verification benchmarks; Step 3 performs set membership checks, overlapping interval calculations, and matching calculations for each record, and adds conflict markers; Step 4 uses student ID and application date as query keys to import rotating data to complete student attribution verification; Step 5 performs Z-score standardization on the two types of heterogeneous scores and then weights and merges them into a comprehensive score with a unified dimension; Step 6 filters and sorts high-risk records based on the comprehensive score and pushes them to the review queue. The output data of each step serves as direct input for the next step, forming a complete logical cross-validation data flow chain.

[0096] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for intelligent registration and management of teacher workload based on a Web architecture, characterized in that, Includes the following steps: Step 1: The Web system backend receives the workload application request, obtains the target teacher identifier, accounting cycle number and the set of application records to be reviewed, extracts the application date, time period, task type, teaching location and participating student set fields for each application record, and generates a list of application event attributes; Step 2: Using the target teacher identifier and accounting cycle number as the query key, query the database for teaching calendar data, target teacher schedule data, and target teacher timetable data, and merge them to generate a teacher time constraint dataset; Step 3: For each record in the event attribute list, perform set membership checks on the declaration date and the list of statutory holidays, calculate the overlapping intervals between the declaration date and the time period and the schedule, and perform matching calculations between the declaration time period and the teaching location and the timetable. Then, add holiday conflict markers, schedule conflict markers, and timetable conflict markers respectively, and summarize to generate a set of structured constraint verification results. Step 4: Use the student identification list and application date as the query keys to obtain student rotation schedule data, compare the student rotation department identification with the target teacher's department identification, and add a student belonging conflict mark to the application records of students with conflicting affiliations. Step 5: Perform severity score conversion on various conflict markers to obtain conflict severity scores. Obtain statistical anomaly risk score ,right and After Z-score standardization, the weighted sums are used to obtain the comprehensive anomaly risk score. ; Step 6, for Declaration records exceeding a preset threshold generate structured anomaly report data objects, which are then processed according to... The data is pushed to the reviewers in descending order, generating an anomaly-marked review queue response data.

2. The method for intelligent registration and management of teacher workload based on a Web architecture as described in claim 1, characterized in that, The teaching calendar data includes semester start and end dates, a list of statutory holidays, a mapping of teaching week numbers, and an exam week marker field; The target teacher scheduling data includes a daily scheduling type field and a scheduling time period field; the target teacher course schedule data includes fields for scheduled courses, times, and locations.

3. The method for intelligent registration and management of teacher workload based on a Web architecture as described in claim 1, characterized in that, Step 3, which involves attaching a holiday conflict flag, includes: when the application date falls on a statutory holiday and the task type field value does not belong to the special approval type set, attaching a holiday conflict flag to the record; wherein the special approval type set is a task type enumeration set pre-configured in the system parameter table.

4. The method for intelligent registration and management of teacher workload based on a Web architecture as described in claim 1, characterized in that, The additional scheduling conflict flag in step 3 includes: when it is detected that the declared time interval overlaps with the time interval of the scheduling record, the scheduling type field value is clinical on-call or surgical type, and the declared teaching venue field value is not a clinical site identifier, a scheduling conflict flag is added to the record.

5. The method for intelligent registration and management of teacher workload based on a Web architecture as described in claim 1, characterized in that, Step 3, which involves adding a timetable conflict flag, includes matching the declared time period and teaching location field values ​​with the target teacher's timetable data. When there are teaching records in different locations within the same time period, a timetable conflict flag is added to that record.

6. The method for intelligent registration and management of teacher workload based on Web architecture according to claim 1, characterized in that, Step 4 further includes: when there are students with conflicting affiliations in the application record, writing the student's actual rotation department information field into the application record.

7. The method for intelligent registration and management of teacher workload based on a Web architecture as described in claim 1, characterized in that, Step 5 involves performing severity scoring conversion on various conflict markers, including mapping holiday conflict markers and scheduling conflict markers to high severity scores. Class schedule conflict markers are mapped to medium severity scores; student affiliation conflict markers are mapped to severity scores that can be controlled by configurable parameters; the above scores are then summed to obtain the conflict severity score. .

8. The method for intelligent registration and management of teacher workload based on a Web architecture as described in claim 1, characterized in that, The statistical anomaly risk score The system is generated by the historical time series feature analysis module, which takes teacher identification as input and outputs the degree of deviation of the frequency and quantity of applications within the accounting period from the historical distribution.

9. The method for intelligent registration and management of teacher workload based on a Web architecture as described in claim 1, characterized in that, The structured anomaly report data object in step 6 includes a conflict type field, a conflict evidence summary field, and a statistical anomaly indicator field, wherein the conflict evidence summary field references the corresponding schedule record primary key and teaching calendar entry identifier. The method further includes: performing grouping and aggregation calculations on various constraint verification results and conflict detection count data according to the target teacher identification dimension and department dimension, generating conflict occurrence frequency statistics and distribution characteristic data, and writing them into the analysis database.

10. A web-based intelligent registration and management system for teacher workload, used to execute the web-based intelligent registration and management method for teacher workload as described in any one of claims 1-9, characterized in that, include: The application receiving module is used to receive workload application requests and extract and generate a list of application event attributes; The constraint data query module is used to query teaching calendar data, target teacher schedule data, and target teacher time schedule data, and merge them to generate a teacher time constraint dataset. The structured constraint verification module is used to perform set member detection, overlapping interval calculation, and matching calculation on the list of declared event attributes and to attach corresponding conflict markers. The student affiliation verification module is used to obtain student rotation schedule data and perform an equivalent comparison to add a student affiliation conflict flag; The risk scoring module is used to score the severity of the conflict. With statistical anomaly risk score The comprehensive anomaly risk score is obtained by performing Z-score standardization and weighted summation. The exception report push module is used to generate structured exception report data objects and push them according to... Push the results to the reviewers in descending order.

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