A method for matching and distributing homogenized teaching post resources

Abstract

This application provides a method for matching and allocating homogeneous teaching positions, including: obtaining a pre-selected list batch loading schedule, dynamically calibrating an initial threshold, assessing potential conflict risks through a conflict detection unit, assessing the occupancy status of each teaching position based on the potential conflict risks, and determining the starting point of a high-risk adjustment window; after determining the starting point of the high-risk adjustment window, using a K-means clustering algorithm to group the matching round progress, dividing the position occupancy conflict clusters under the priority constraint of the pre-selected list, and identifying the distribution pattern of secondary adjustment frequency within the clusters; for the optimized adjustment intervention timing sequence, the conflict detection unit performs temporal consistency verification on the position allocation snapshots of each adjustment intervention timing, extracts a list of compatible positions with stable matching schemes, and determines adjustment paths to reduce the frequency of secondary adjustments.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for matching and allocating resources for homogeneous teaching positions. Background Technology

[0002] In the field of educational resource allocation, research on job matching and adjustment mechanisms is of great significance, directly related to the fairness and efficiency of teaching resources, ensuring that every student can obtain suitable job opportunities. This field is not only a core link in the modernization of educational management, but also key to improving student satisfaction and teaching quality. Currently, adjustment methods in job allocation often face challenges of dynamism and coordination. For example, a job matching method, device, computer equipment, and storage medium disclosed in the prior art (publication number CN118656659A) discloses a technique that achieves resume-job category matching by clustering job data and combining object resume data with job category attention to calculate the first path weight and integrate the second path weight. However, this technique suffers from the problem of unreasonable timing of adjustment under dynamic batch information input, leading to repeated adjustments in the adjustment process, increased conflict risk, and increased management costs. Many existing solutions fail to fully consider the changes brought about by different batch information inputs when handling resource matching, resulting in a lack of flexibility in the adjustment process and a tendency to trigger multiple adjustments.

[0003] This situation not only increases management costs but may also affect the fairness of the allocation results, especially when priority constraints are involved, where the problem becomes more pronounced. A deeper technical challenge lies in balancing the timing of adjustments with the stability of the matching scheme. The timing of adjustments, as a core factor influencing allocation results, directly determines the cost and effectiveness of the adjustment. If adjustments are intervened too early, conflicts with priority lists may occur due to incomplete subsequent information; if intervention is too late, the optimal adjustment window may be missed, resulting in wasted resources and student dissatisfaction. This contradiction makes finding the appropriate timing for adjustments in a dynamic environment a pressing problem that needs to be solved.

[0004] In practice, the timing of job reassignments often involves complex risks of conflict. For example, in a round of job allocation, some students may have already been assigned to initial positions, but due to the addition of students to the priority list, the original assignments have to be readjusted. This results in some students being reassigned multiple times, potentially triggering a chain reaction that affects the job placement of even more people. Such repeated adjustments caused by inappropriate timing not only increase workload but may also cause students to lose trust in the allocation process.

[0005] Therefore, how to rationally determine the timing of adjustment intervention in a dynamically changing allocation environment, so as to reduce the number of repeated adjustments and control the scope of influence, has become a key issue that this study urgently needs to solve. Summary of the Invention

[0006] This invention provides a method for matching and allocating homogeneous teaching position resources, including: The adjustment time sequence analysis module obtains the current matching round progress, the loading schedule of the batch of internal list, the job demand for the internal list to be loaded, and the number of people in the cascade rearrangement of released teaching positions from the job allocation platform. The conflict risk index of each teaching position is calculated based on the matching round progress and the demand for positions. The conflict risk index is dynamically calibrated by combining the batch loading schedule and the number of people in the cascade reshuffle. The period when the risk index exceeds the preset range is determined as a high-risk adjustment window period. During the high-risk adjustment window period, the K-means clustering algorithm is used to group the occupancy status and demand changes of each teaching position, and to divide the position occupancy conflict cluster under the priority constraint of the pre-selected list. Extract the subset of people affected by the cascade rearrangement from the job occupancy conflict cluster, and simulate the job release scenario by combining the adjustment timing analysis module with the subsequent batch loading schedule. The release node with the lowest secondary adjustment frequency is determined as the optimized adjustment intervention timing sequence. For the aforementioned adjustment and intervention timing sequence, the conflict detection unit performs a timing consistency verification on the job allocation snapshots of each intervention timing, extracts a list of compatible jobs with stable matching schemes, and determines an adjustment path to reduce the frequency of secondary adjustments; Based on the adjustment path and the compatible job list, the cascading data of the released jobs are integrated and released. The changes in demand are classified according to the direction of the job supply and demand gap during the high-risk adjustment window period. The improvement of the stability of the matching scheme is evaluated, and the execution queue of the final adjustment scheme is obtained. The intervention point with the lowest conflict risk is selected from the execution queue. The adjustment timing analysis module generates a job reallocation matrix and updates the job allocation platform database to determine the long-term stability of the matching scheme.

[0007] Furthermore, the step of obtaining the current matching round progress, the pre-selection list batch loading schedule, the job demand for the pre-selection list to be loaded, and the number of people in the cascading reordering of released teaching positions from the job allocation platform through the adjustment time sequence analysis module includes: The job allocation records of each round are traversed from the job occupancy conflict cluster. The student IDs involved when the cascading rearrangement is triggered by the priority constraint of the pre-determined list are extracted. The students are grouped and statistically analyzed according to the round affiliation to obtain the subset of the number of people affected by the cascading rearrangement. The adjustment time sequence analysis module reads the size value of the subset of the number of people affected and compares it with a preset threshold. Based on the comparison result, the current conflict cluster is marked as a high-pressure state or a normal state. For the conflict clusters under high pressure or normal conditions, obtain the distribution pattern of the secondary adjustment frequency and the subsequent batch loading schedule. Based on the sorting position of the high-frequency adjustment rounds in the distribution pattern, and combined with the expected arrival node of each batch in the subsequent batch loading schedule, traverse the job release records corresponding to each batch after loading, count the number of job releases under each batch loading time node, and obtain the job release list corresponding to each time node. Based on the job release list, each time node is sorted from most to least number of job releases. Candidate nodes whose job releases exceed the preset supply threshold and are not related to the time nodes of the high-frequency adjustment round are selected. The candidate nodes are arranged in chronological order to obtain the optimized adjustment intervention timing sequence.

[0008] Furthermore, the conflict risk index for each teaching position is calculated based on the matching round progress and the demand for positions. This conflict risk index is then dynamically calibrated using the batch loading schedule and the number of positions to be reassigned. Periods where the risk index exceeds a preset range are designated as high-risk adjustment windows, including: An initial threshold is set based on historical job allocation data. A batch loading schedule for the internal list is obtained from the job allocation platform. The schedule records the expected loading time node and the corresponding job demand for each batch of internal list. The time nodes are arranged in chronological order. The time interval between adjacent batches is extracted. The initial threshold is reduced or increased according to the relationship between the time interval and the initial threshold to obtain a calibrated dynamic threshold. The conflict detection unit reads the calibrated dynamic threshold, traverses the current allocation record of each teaching position and the position requirement list to be loaded into the internal list, compares whether there is overlap between the assigned students and the students to be assigned in the internal list for each teaching position, marks the teaching positions with overlap as conflict positions, counts the number of conflict positions, and obtains the conflict position set. Based on the set of conflicting positions, the conflict detection unit assesses the occupancy status of each teaching position and classifies the occupancy status into three categories: idle, allocated, and pending release. For teaching positions that are pending release and are within the set of conflicting positions, the corresponding batch loading time node is extracted, and the earliest time when conflicting positions are released in a concentrated manner is determined as the starting point of the high-risk adjustment window period.

[0009] Furthermore, the method also includes: Extract the expected arrival time of the subsequent pre-selected list from the loading schedule of each batch, and compare the arrival time with the starting point of the high-risk adjustment window period in time sequence; Select batches that arrive after the starting point, and for the job requirements corresponding to the batches, check the records in the currently assigned positions that overlap with the internal list, and summarize them to obtain a list of newly added conflicting positions. For the newly added conflicting job list, obtain the release time nodes of the released teaching jobs, compare the time records of each released job being occupied by other rounds after release, mark the overlapping jobs as lost jobs, and summarize the interval from the release time node to the occupied time node corresponding to the lost jobs to obtain the adjustable resource loss interval. Based on the newly added conflict position list and the range of available resource loss, the number of newly added conflict positions and the number of lost positions are compared. The time point when the number of newly added conflict positions and the number of lost positions are both within the preset allowable range is selected to determine the optimal intervention starting boundary that takes into account both the completeness of batch information and the timeliness of the adjustment window.

[0010] Furthermore, during the high-risk adjustment window period, the K-means clustering algorithm is used to group the changes in the occupancy status and demand of each teaching position, and to divide the position occupancy conflict clusters under the priority constraint of the pre-selected list, including: Based on the starting point of the high-risk adjustment window period, the progress data of each matching round is obtained from the job allocation platform. The progress data includes the number of students who have been allocated and the number of students who are to be allocated in each round, as well as the priority constraint flag of the internal list corresponding to each round. The progress data is arranged according to the round number and time order to construct the matching round progress sequence. For the matching round progress sequence, the K-means clustering algorithm is used to group the people with the ratio of the number of people who have been assigned to the number of people who are to be assigned, using the clustering feature. Rounds with similar ratios are grouped into the same group to obtain several round progress groups. From the round progress groups, round records that contain the priority constraint mark of the pre-selected list and have overlapping job occupancy are selected and aggregated to obtain the job occupancy conflict cluster. For the aforementioned job occupancy conflict cluster, the adjustment records of each round within the cluster are read, and the number of secondary adjustments occurring in each round within the current allocation cycle is counted. The secondary adjustment frequency list for each round within the cluster is obtained by summarizing according to the round number. The secondary adjustment frequency list is sorted from high to low according to the frequency value, and rounds with frequency values ​​exceeding a preset threshold are marked as high-frequency adjustment rounds. The proportion and sorting position of high-frequency adjustment rounds within the cluster are counted to identify the distribution pattern of secondary adjustment frequency within the cluster.

[0011] Furthermore, the method also includes: For each batch loading schedule, the loading time nodes of each batch are traversed one by one. The changes in the number of job occupancy before and after the node are statistically analyzed from the job occupancy conflict cluster. The number of job vacancies after the node is subtracted from the number of job vacancies before the node to obtain the change in job vacancies under each batch loading time node. Based on the change range of the job vacancies, extract the time node corresponding to each adjustment and intervention opportunity from the optimized adjustment and intervention opportunity sequence, obtain the number of people in the cascade rearrangement and the number of people in the internal list to be assigned under the time node, divide the number of people in the cascade rearrangement by the number of people in the internal list to be assigned to obtain the matching ratio, and mark it as a high matching state or a low matching state according to the comparison result of the matching ratio and the preset matching interval. For each marked adjustment intervention opportunity, obtain its corresponding conflict risk value and job supply surplus, filter out adjustment intervention opportunities marked as high matching status with conflict risk value lower than preset risk threshold and job supply surplus exceeding preset supply threshold, and obtain a set of candidate intervention windows; The intervention windows are sorted from low to high according to the conflict risk value. The corresponding range of executable conditions is marked for each sorted intervention window to obtain an optimized intervention timing list.

[0012] Furthermore, for the aforementioned adjustment and intervention timing sequence, the conflict detection unit performs a time-series consistency verification on the job allocation snapshots for each intervention timing, extracts a list of compatible jobs with stable matching schemes, and determines adjustment paths to reduce the frequency of secondary adjustments, including: For the optimized adjustment and intervention timing sequence, the conflict detection unit compares the job allocation snapshots of adjacent adjustment and intervention timings, counts the job numbers whose job occupancy status has not changed between adjacent timings, and obtains the stable job set for each adjacent timing pair. Based on the set of stable positions, extract the position numbers that remain in an occupied state in all adjacent time pairs, and aggregate them to form a list of compatible positions; For the list of compatible positions, obtain the secondary adjustment records of each compatible position in the historical allocation cycle, count the cumulative number of times each compatible position has been subject to secondary adjustment, and mark it as a high-frequency adjustment position or a low-frequency adjustment position based on the comparison result of the cumulative number of times with the preset frequency threshold. Based on the low-frequency adjustment positions, target positions that match the job requirements of students on the pre-selected list are selected. The number of category differences between the original position category and the target position category is counted as the adjustment span. The target positions are sorted from smallest to largest according to the adjustment span, and the target position with the smallest adjustment span is selected as the adjustment destination. The adjustment path that reduces the frequency of secondary adjustments is determined.

[0013] Furthermore, the process of integrating the cascading data of job impact based on the adjustment path and the compatible job list, classifying demand changes according to the direction of job supply and demand gaps during the high-risk adjustment window, evaluating the improvement in the stability of the matching scheme, and obtaining the execution queue of the final adjustment scheme includes: According to the adjustment path, obtain the release job records involved in each adjustment path from the job allocation platform, traverse the cascading rearrangement student list corresponding to each release job, count the number of students and the number of jobs involved that trigger subsequent adjustments due to the release of the job, and summarize the cascading data set affected by the release job according to the release job number. For each released position in the cascaded data set, obtain the demand change record of the corresponding position during the high-risk adjustment window period, extract the change magnitude and the direction of the supply and demand gap, and classify the change in job demand into release type or shortage type according to the combination of the change magnitude and the direction of the supply and demand gap. Based on the type of change in job demand, the number of stable jobs before and after implementation is calculated separately for both released and scarce jobs under the priority constraint of the pre-selected list for each adjustment path. The stability improvement is calculated by subtracting the number of stable positions before execution from the number of stable positions after execution. The adjusted paths are then sorted from highest to lowest according to the stability improvement to obtain the execution queue of the final adjustment plan.

[0014] Furthermore, the step of selecting the intervention point with the lowest conflict risk from the execution queue, the adjustment timing analysis module generating a job reallocation matrix and updating the job allocation platform database, and determining the long-term stability assessment conclusion of the matching scheme, includes: Sort the execution queue of the final adjustment scheme from low to high according to the conflict risk value, and select the adjustment path with the lowest conflict risk value as the current execution intervention point; The adjustment time sequence analysis module extracts the student ID, original position ID, and target position ID based on the adjustment path corresponding to the intervention point, and constructs a position redistribution matrix. The rows of the position redistribution matrix represent the student IDs to be adjusted, the columns represent the target position IDs, and the matrix elements represent the allocation relationship between the corresponding students and positions. Based on the job redistribution matrix, the allocation relationships in the matrix are written into the database of the job allocation platform one by one, and the occupancy status and student number records of each teaching job are updated. The conflict detection unit reads the secondary adjustment records within the current allocation cycle, counts the cumulative number of secondary adjustments before and after the update, calculates the change value obtained by subtracting the cumulative number before the update from the cumulative number after the update, and determines whether the frequency of secondary adjustments shows a downward trend based on the change value. If the downward trend continues to exist in multiple consecutive allocation cycles, the assessment conclusion that the matching scheme is stable in the long term is determined.

[0015] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a method for matching and allocating homogeneous teaching positions. Addressing the challenges of high potential conflict risks, difficulty in accurately determining the timing of adjustments, and excessive resource loss and secondary adjustments in teaching position allocation, this invention proposes a systematic solution. It dynamically calibrates the intervention timing threshold through an adjustment timing analysis module, assesses position occupancy status and potential risks using a conflict detection unit, accurately identifies high-risk adjustment windows, and utilizes K-means clustering to divide conflict clusters and optimize the adjustment intervention timing sequence. Simultaneously, this invention uses cross-validation to filter a stable and compatible position list, integrates cascaded data to classify changes in position demand, and ultimately generates the execution queue with the lowest conflict risk, ensuring long-term stability of the matching scheme. The overall technical effect is a significant reduction in the frequency of secondary adjustments, improved efficiency and stability of position resource allocation, and provides scientific and efficient decision support for teaching position allocation. Attached Figure Description

[0016] Figure 1 This is a flowchart of a method for matching and allocating homogeneous teaching position resources according to the present invention.

[0017] Figure 2 This is a schematic diagram of a method for matching and allocating homogeneous teaching position resources according to the present invention.

[0018] Figure 3 This is another schematic diagram of a method for matching and allocating homogeneous teaching position resources according to the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0020] like Figures 1-3 This embodiment of a method for matching and allocating homogeneous teaching position resources may specifically include: Step S101: The adjustment timing analysis module obtains the current matching round progress, the job demand of the pre-selected list to be loaded, and the number of people in the cascaded rearrangement of released teaching positions from the job allocation platform to determine potential conflict risks and obtain the initial threshold for adjustment intervention.

[0021] The adjustment timing analysis module reads the remaining number of people to be assigned in the current matching round from the job allocation platform, obtains the job demand corresponding to the pre-selected list to be loaded, counts the number of people involved in the cascading rearrangement of released teaching positions, and divides the number of people in the cascading rearrangement by the job demand to obtain a quantitative indicator of potential conflict risk. For the quantitative indicator of potential conflict risk, if the indicator exceeds a preset risk threshold, the current round is marked as a high-sensitivity state; if the indicator is within the preset risk threshold, it is marked as a normal state. The module then queries a pre-established state-threshold comparison table to determine the time range corresponding to the high-sensitivity or normal state, and establishes an initial threshold for the adjustment intervention timing. This initial threshold represents the time span from the current time point to the suggested start of the adjustment operation.

[0022] A data channel is established between the time-series analysis module and the job allocation platform. The module reads the remaining number of students awaiting allocation in the current matching round from the job allocation platform. This number reflects the scale of students who have not yet completed job matching in the current round. At the same time, the time-series analysis module obtains the job demand corresponding to the internal list to be loaded. This demand represents the total number of teaching positions required by the students in the internal list.

[0023] In one embodiment, the cascading reassignment count is based on released teaching positions. When a teaching position is released due to the loading of a pre-selected list, the students originally assigned to that position will be reassigned. The total number of students involved in this chain reaction adjustment is the cascading reassignment count. The adjustment timing analysis module divides the cascading reassignment count by the position demand to obtain a quantitative indicator of potential conflict risk. The higher the value of this indicator, the greater the likelihood that the current round will trigger a large-scale adjustment due to the loading of the pre-selected list.

[0024] Specifically, the time-series analysis module compares the quantitative indicators of potential conflict risks with preset risk thresholds. If the indicator exceeds the risk threshold, the current round's progress is marked as a high-sensitivity state; if it is within the risk threshold, it is marked as a normal state. A state-to-threshold lookup table is pre-established in the time-series analysis module. This table records the numerical ranges corresponding to the high-sensitivity and normal states, respectively. The time-series analysis module queries the corresponding numerical range from the lookup table based on the state to determine the initial threshold for intervention.

[0025] Step S102: Obtain the pre-selected list batch loading schedule, dynamically calibrate the initial threshold, assess potential conflict risks through the conflict detection unit, assess the occupancy status of each teaching position based on the potential conflict risks, and determine the starting point of the high-risk adjustment window period.

[0026] Obtain the scheduled time table for loading the pre - determined list batches from the job assignment platform. The time table records the estimated loading time nodes of the pre - determined lists for each batch and the corresponding job requirements. Arrange the time nodes in chronological order, extract the time intervals between adjacent batches. If the time interval is less than the initial threshold of the intervention timing for adjustment, reduce the initial threshold to the value range of the time interval. If the time interval is greater than the initial threshold, increase the initial threshold to the value range of the time interval to obtain the calibrated dynamic threshold. The conflict detection unit reads the calibrated dynamic threshold, traverses the current assignment records of each teaching position and the job requirement list of the pre - determined list to be loaded, and compares one by one whether there is an overlapping occupation of both the assigned students and the students waiting for the pre - determined list assignment for each teaching position. Mark the teaching positions with overlapping occupation as conflict positions, count the number of the conflict positions to obtain the conflict position set. According to the conflict position set, the conflict detection unit evaluates the occupation status of each teaching position, divides the occupation status into three categories: idle, assigned, and to be released. For the teaching positions with the occupation status of to be released and within the conflict position set, extract their corresponding batch loading time nodes, and determine the moment when the earliest concentrated release of conflict positions appears among these time nodes as the starting point of the high - risk adjustment window period.

[0027] The scheduled time table for loading the pre - determined list batches records the estimated time nodes when the pre - determined student lists for each batch enter the job assignment process, and each batch corresponds to a specific number of teaching job requirements.

[0028] In one embodiment, the assignment of teaching internship positions in a certain university involves three batches of pre - determined lists. The first batch is expected to be loaded in the fifth week, the second batch in the seventh week, and the third batch in the ninth week. After the adjustment timing analysis module arranges them in chronological order, the time intervals between adjacent batches are extracted as two weeks respectively.

[0029] Specifically, the calibration process of the dynamic threshold is based on the numerical comparison between the time interval and the initial threshold. Let the initial threshold be T0, and the time interval between adjacent batches be T i (i = 1, 2,..., n - 1, n is the number of batches), take the minimum value T min = min(T1, T2,..., T {n-1} ). If T min < T0, the calibrated dynamic threshold T d = T min × α, where α is a preset reduction coefficient and 0 < α < 1, and the reduction coefficient α is determined according to the adjustment response time in the historical assignment cycle; if T min ≥ T0, then T d = T0+(T min-T0)×β, where β is a preset expansion coefficient and 0<β<1. This calibration mechanism allows the threshold to adapt to changes in the loading rhythm of different batches.

[0030] In one possible implementation, the conflict detection unit reads the current allocation record of each teaching position, which includes the student ID, major, and allocation time of the assigned students. Simultaneously, the conflict detection unit reads the position requirement list for each student in the pending shortlist, comparing each teaching position to see if it appears in both the allocated record and the shortlist. If a teaching position has students in both the allocated and shortlisted lists, it is marked as a conflicting position, and all conflicting positions are aggregated to form a conflicting position set.

[0031] It should be noted that the occupancy status is determined based on the actual usage of each teaching position at the current time point. An idle status indicates that the position has not yet been assigned to any student; an assigned status indicates that the position is already occupied by a student and is not included in the pre-selection list for release; a pending release status indicates that although the position is occupied by a student, it needs to be reassigned due to the loading of the pre-selection list. The conflict detection unit extracts the corresponding batch loading time point for teaching positions in the pending release status that are within the conflict position set.

[0032] For example, the earliest moment when multiple conflicting positions simultaneously enter the pending release state is selected from the time nodes. This moment is the starting point of the high-risk adjustment window period, indicating that the position occupancy conflict is most concentrated when adjustment intervention is carried out at this time.

[0033] Extract the expected arrival time of the subsequent pre-selected list in the loading schedule of each batch, assess the list of positions that will cause new conflicts with the pre-selected list due to the lack of subsequent batch information when intervention is too early, identify the range of adjustable resources lost due to the release of positions being occupied by other rounds when intervention is too late, and determine the optimal intervention starting boundary that balances the completeness of batch information and the timeliness of the adjustment window.

[0034] Extract the expected arrival times of subsequent pre-selected lists from the loading schedule of each batch. Compare these arrival times with the starting points of high-risk adjustment windows to filter out batches that arrive after the starting points. For each batch's corresponding job requirements, check the currently assigned jobs for records that overlap with the pre-selected lists. Summarize the overlapping jobs to obtain a list of newly conflicting jobs. For this list, obtain the release times of released teaching positions and compare the time records of each released position being occupied by students in other rounds. If the release time overlaps with the time occupied by other rounds, mark the position as a lost position. Summarize the interval between the release time and the occupied time of the lost positions to obtain the available resource loss interval. Based on the list of newly conflicting jobs and the available resource loss interval, compare the number of newly conflicting jobs with the number of lost positions. Select the time point where both the number of newly conflicting jobs and the number of lost positions are within a preset allowable range to determine the optimal intervention starting boundary that balances batch information completeness and adjustment window timeliness.

[0035] In teaching position allocation scenarios, the loading times of the pre-selected lists for each batch differ, and the expected arrival time of subsequent batches of lists determines the timing of adjustment intervention. By extracting the expected arrival time of subsequent pre-selected lists from the batch loading schedule and comparing these arrival times with the starting point of the high-risk adjustment window, batches of lists that arrive after the starting point can be identified.

[0036] Specifically, if the expected arrival time of a certain batch of pre-selected candidates is later than the start time of the current adjustment window, then the job requirements corresponding to that batch of candidates have not yet been clarified when the adjustment is initiated.

[0037] In one embodiment, regarding the allocation of teaching positions in teacher training colleges, the first batch of pre-selected candidates includes outstanding provincial graduates, the second batch includes students in targeted training programs, and the third batch includes candidates for special talent introduction. The arrival dates for each batch are spaced several days apart. If the adjustment intervention point is set before the arrival of the second batch, the position requirements corresponding to the second and third batches have not yet been considered for adjustment. If positions already allocated to ordinary students overlap with subsequent pre-selected candidates, this will create new conflicting positions.

[0038] It should be noted that the process of identifying newly conflicting positions relies on a one-to-one comparison between the currently assigned positions and the requirements of the priority list. After aggregating the overlapping positions, a list of newly conflicting positions is obtained, which reflects the potential conflicts in position allocation that may arise from premature intervention.

[0039] In one possible implementation, released teaching positions refer to positions temporarily vacated due to prior adjustments. After obtaining the release time of each released teaching position, the time records of each released position being occupied by students in other rounds are compared. If the release time overlaps with the occupation time of other rounds, the position is marked as a lost position. The time intervals corresponding to the lost positions are summarized to obtain the available resource loss interval, which represents the range of reduction in available resources due to late intervention.

[0040] For example, based on the list of newly added conflict positions and the range of available resource losses, the number of newly added conflict positions is compared with the number of lost positions. The number of newly added conflict positions, N, is selected. c Number of positions lost N l At the same time, the time nodes must fall within a preset allowable range, where the allowable range is pre-set based on the total number of teaching positions and historical adjustment data. N is calculated for each time node. c and N l The optimal intervention threshold is determined by whether the situation is within the allowable range, thus balancing the completeness of batch information with the timeliness of the adjustment window, thereby achieving reasonable control over the timing of adjustments during the job allocation process.

[0041] Step S103: After determining the starting point of the high-risk adjustment window period, the K-means clustering algorithm is used to group the matching round progress, divide the job occupancy conflict clusters under the priority constraint of the pre-selected list, and identify the distribution pattern of the frequency of secondary adjustments within the clusters.

[0042] Based on the starting point of the high-risk adjustment window, progress data for each matching round is obtained from the job allocation platform. This progress data includes the number of students already allocated, the number of students awaiting allocation, and the priority constraint flag for the pre-selected list corresponding to each round. The progress data is arranged according to round number and time sequence to construct a matching round progress sequence. For this matching round progress sequence, a K-means clustering algorithm is used to group the progress data for each round. The ratio of the number of students already allocated to the number awaiting allocation is used as the clustering feature. Rounds with similar ratios are grouped into the same group, resulting in several round progress groups. From these round progress groups, round records containing the pre-selected list priority constraint flag and exhibiting overlapping job occupancy are selected and aggregated to obtain a job occupancy conflict cluster. For this job occupancy conflict cluster, adjustment records for each round within the cluster are read from the job allocation platform. The number of secondary adjustments occurring in each round within the current allocation cycle is counted, and these secondary adjustment counts are summarized according to round number to obtain a secondary adjustment frequency list for each round within the cluster. Based on the secondary adjustment frequency list, each round in the cluster is sorted from high to low according to the frequency value. Rounds with frequency values ​​exceeding a preset threshold are marked as high-frequency adjustment rounds. The proportion and sorting position of high-frequency adjustment rounds in the cluster are statistically analyzed to identify the distribution pattern of secondary adjustment frequencies in the cluster.

[0043] In the teaching position allocation scenario, after determining the starting point of the high-risk adjustment window, progress data for each matching round is obtained from the position allocation platform. This progress data reflects the current allocation status of each round, including the number of students already allocated, the number of students awaiting allocation, and the priority constraint flag for the pre-selected list. The priority constraint flag for the pre-selected list is used to identify whether the round involves a priority allocation group of students. When a round includes provincial-level outstanding graduates, students in targeted training programs, or special talent introduction candidates, the priority constraint flag for that round is set to an active state.

[0044] Specifically, the progress data is arranged according to the round number and time order to construct a matching round progress sequence.

[0045] In one embodiment, the allocation of teaching positions at a teacher training college comprises eight matching rounds. Rounds one through three are for regular students, round four is for provincial-level outstanding graduates, round five is for students in targeted training programs, and rounds six through eight are for supplementary allocations. Progress data for each round is extracted sequentially and arranged chronologically to form a complete matching round progress sequence. For this matching round progress sequence, a K-means clustering algorithm is used to group the progress data for each round. The execution process of the K-means clustering algorithm includes initializing cluster centers, calculating the distance from each data point to the cluster center, assigning the data point to the group containing the nearest cluster center, updating the cluster center position, and repeating the iteration until the cluster centers no longer change. Let the clustering feature value of the j-th round be r. j (i.e., the ratio of the number of people who have been assigned to the number of people who are yet to be assigned), and the k-th cluster center is μ. k Then the distance from the j-th round to the k-th cluster center is d(r j ,μ k )=|r j -μ k After each iteration, the cluster centers are updated to μ. k =Σr j / n k The summation iterates through all rounds within the k-th group, where n k Let K be the number of rounds for the k-th group. In the scenario of assigning teaching positions, the elbow method is used to select the number of cluster groups K, i.e., to calculate the sum of squared errors for different values ​​of K. The K value corresponding to the obvious inflection point of the SSE decline rate is selected. The progress ratio of each round is used as a one-dimensional feature input to the K-means clustering algorithm. After several iterations, several round progress groups are obtained. Rounds within each group have similar allocation completion levels, while there are significant differences in allocation completion levels between groups.

[0046] In one possible implementation, round records containing a pre-selected list priority constraint marker and exhibiting overlapping job occupancy are filtered from the round progress groups. Overlapping job occupancy refers to the situation where the same teaching position is listed as an allocation target in different rounds, resulting in students from multiple rounds simultaneously applying for the position. The filtering process iterates through each round progress group, checking if there are any rounds within the group whose priority constraint marker is valid, and simultaneously checking if there are any overlapping job occupancy records between rounds within the group. Round records that meet both conditions are aggregated to obtain a job occupancy conflict cluster.

[0047] Exemplarily, for the job occupancy conflict cluster, read the adjustment records of each round within the cluster from the job allocation platform. The adjustment records include the time of job adjustment in each round during the current allocation cycle, the student numbers involved, the original allocated jobs and the adjusted jobs. Count the number of secondary adjustments in each round during the current allocation cycle. Secondary adjustment refers to the situation where a student is readjusted to another job due to job conflicts or priority constraints after the first allocation is completed. Summarize the number of secondary adjustments according to the round number to obtain a list of secondary adjustment frequencies for each round within the cluster. Further, according to the list of secondary adjustment frequencies, sort each round within the cluster in descending order of the frequency value. Mark the rounds with frequency values exceeding the preset threshold as high-frequency adjustment rounds, and the preset threshold is determined based on the average adjustment frequency of each round in the historical allocation cycle. Count the proportion and sorting position of the high-frequency adjustment rounds within the cluster to identify the distribution pattern of the secondary adjustment frequencies within the cluster.

[0048] It can be understood that the distribution pattern reflects the concentration degree and position characteristics of the high-frequency adjustment rounds within the cluster. The determination of the distribution pattern uses a concentration index, and calculate the mean value P of the sorting positions of the high-frequency adjustment rounds mean and the total number of rounds N within the cluster total The ratio C = P mean / N total . If C ≤ 0.3, it indicates that the high-frequency adjustment rounds are concentrated in the front-end position of the cluster, and the rounds with a higher degree of allocation completion are more likely to have secondary adjustments; if C ≥ 0.7, it indicates that the high-frequency adjustment rounds are concentrated at the end of the cluster; if 0.3 < C < 0.7, it indicates that there is no obvious correlation between secondary adjustment and the degree of allocation completion. By identifying the distribution pattern, it is possible to locate the round interval with concentrated adjustment pressure within the job occupancy conflict cluster, so as to give targeted attention to the rounds in this interval when intervening in the adjustment.

[0049] Step S104, extract the subset of the affected number of people within the cascading rearrangement range from the job occupancy conflict cluster, evaluate whether the scale of this subset exceeds the preset threshold through the adjustment time sequence analysis module, and simulate the job release scenario under the subsequent batch loading schedule in combination with the distribution pattern of the secondary adjustment frequencies to obtain an optimized adjustment intervention timing sequence.

[0050] The system iterates through the job allocation records of each round in the job occupancy conflict cluster, extracts the student IDs involved when cascading rearrangements are triggered by the priority constraint of the pre-determined list, and groups these student IDs according to their round affiliation to obtain a subset of the affected number of people in the cascading rearrangement range. The adjustment time series analysis module reads the size value of the affected number of people subset and compares it with a preset threshold. If the size value exceeds the preset threshold, the current conflict cluster is marked as a high-pressure state; if the size value is within the preset threshold, it is marked as a normal state. For the conflict clusters in the high-pressure or normal state, the distribution pattern of the secondary adjustment frequency and the subsequent batch loading schedule are obtained. Based on the sorting position of the high-frequency adjustment rounds in the distribution pattern and the expected arrival node of each batch in the subsequent batch loading schedule, the job release records corresponding to each batch loading are iterated one by one, and the number of job releases under each batch loading time node is counted to obtain the job release list corresponding to each time node. Based on the job release list, each time node is sorted from most to least number of job releases. Candidate nodes whose job releases exceed the preset supply threshold and are not related to the time nodes of the high-frequency adjustment round are selected. The candidate nodes are arranged in chronological order to obtain the optimized adjustment intervention timing sequence.

[0051] In the teaching position allocation scenario, the position occupancy conflict cluster contains multiple rounds of overlapping position occupancy due to the priority constraint of the pre-selected list. The student IDs involved in the cascading reshuffling triggered by the pre-selected list priority constraint are extracted by traversing the position allocation records of each round from the conflict cluster. Cascading reshuffling refers to the process where, when a student is adjusted to a target position due to a priority constraint, the students originally occupying that position are forced to be reassigned, thus triggering a chain reaction of adjustments.

[0052] Specifically, the student IDs are grouped and statistically analyzed according to their round affiliation to obtain a subset of the number of people affected by the cascading rearrangement. The adjustment time series analysis module reads the size of the subset of affected people and compares it with a preset threshold. If the size exceeds the preset threshold, it indicates that the current conflict cluster involves a wide range of cascading adjustments and is marked as a high-pressure state; if the size is within the preset threshold, it is marked as a normal state.

[0053] In one embodiment, for conflict clusters under high-pressure or normal conditions, the distribution pattern of secondary allocation frequencies and the subsequent batch loading schedule are obtained from the system scheduling database. The distribution pattern of secondary allocation frequencies reflects the ranking position of high-frequency allocation rounds within the cluster, and the subsequent batch loading schedule records the expected arrival nodes of the pre-selected lists for each batch. Based on the ranking position of high-frequency allocation rounds in the distribution pattern, and combined with the expected arrival nodes of each batch in the subsequent batch loading schedule, the corresponding job release records after each batch is loaded are iterated through one by one.

[0054] It should be noted that the job release record refers to historical data where students originally assigned to a job were removed from that job due to a student on the pre-selected list occupying it. The number of jobs released at each batch loading time point is counted to obtain a job release list corresponding to each time point. This job release list includes the number of jobs released at each time point and the specific job number released.

[0055] For example, based on the job release list, each time node is sorted from highest to lowest number of job releases. Candidate nodes whose job releases exceed a preset supply threshold and whose time nodes are offset from the high-frequency adjustment rounds are selected. The preset supply threshold is determined based on the average number of job releases in each allocation cycle and is obtained by statistically analyzing the number of job releases at each node and comparing it with the supply threshold. The time offset refers to the time interval between the candidate node and the high-frequency adjustment round exceeding a preset safety interval. The candidate nodes are arranged chronologically to obtain an optimized adjustment intervention timing sequence. Each time node in this sequence has sufficient job supply and avoids periods of concentrated adjustment pressure.

[0056] Analyze the job release scenarios corresponding to each batch loading schedule to obtain the job vacancy change range under each batch loading time node. Evaluate the matching degree between the cascading rearrangement number of people corresponding to each adjustment intervention opportunity in the optimized adjustment intervention opportunity sequence and the number of people to be allocated in the pre-determined list. Compare the conflict risk value and job supply margin corresponding to each adjustment intervention opportunity to screen the intervention window with the lowest conflict risk and sufficient job supply. Sort the intervention windows according to the conflict risk from low to high and mark the range of executable conditions corresponding to each window to obtain the optimized intervention opportunity list.

[0057] For each batch loading schedule, the loading time nodes of each batch are iterated one by one. The changes in the number of occupied positions before and after each node are statistically analyzed from the output of the S103 position occupation conflict cluster. The number of vacant positions before the node is the number of unoccupied positions in the cluster before that node, and the number of vacant positions after the node is the number of unoccupied positions in the cluster after that node. The change in the number of vacant positions after the node is subtracted from the number of vacant positions before the node to obtain the change in the number of vacant positions at each batch loading time node. Based on the change in the number of vacant positions, the time node corresponding to each adjustment intervention opportunity is extracted from the optimized adjustment intervention opportunity sequence. The number of people in the cascaded rearrangement and the number of people in the pre-selected list to be assigned at that time node are obtained. The matching ratio is obtained by dividing the number of people in the cascaded rearrangement by the number of people in the pre-selected list to be assigned. If the matching ratio is within the preset matching range, the adjustment intervention opportunity is marked as a high matching state. If the matching ratio exceeds the preset matching range, it is marked as a low matching state. For each marked adjustment intervention opportunity, its corresponding conflict risk value and job supply surplus are obtained. The conflict risk value is the proportion of the number of potentially conflicting jobs to the total number of jobs at that time. The job supply surplus is the difference between the number of vacant jobs at that time and the number of students on the pre-selected list to be assigned. Each teaching job corresponds to the allocation requirement of one student, and both are measured in the same unit. Adjustment intervention opportunities marked as high matching status with conflict risk values ​​below a preset risk threshold and job supply surplus exceeding a preset supply threshold are selected to obtain a candidate intervention window set. Based on the candidate intervention window set, each intervention window is sorted from low to high according to conflict risk values. For each sorted intervention window, its corresponding executable condition range is marked. The executable condition range includes the batch loading progress range and the range of job vacancy changes applicable to the window, resulting in an optimized intervention opportunity list.

[0058] In the scenario of teaching position allocation, the loading of pre-selected lists in each batch will cause changes in the occupancy status of positions, thereby affecting the number of available positions for adjustment. For each batch loading schedule, the loading time nodes of each batch are traversed one by one, and the changes in the number of occupied positions before and after each node are statistically analyzed. The magnitude of the change in position vacancies reflects the scale of position resources released after a certain batch is loaded; the larger the value, the more available position resources are available for adjustment near that time node.

[0059] Specifically, data is extracted from the job occupancy conflict cluster, and the cumulative number of job vacancies after each batch loading time node is subtracted from the cumulative number of job vacancies before the node, i.e., the calculation formula is ΔV=V after -V before Where ΔV represents the change in job vacancies, V after V represents the cumulative number of job vacancies following the node. beforeThis indicates the cumulative number of job vacancies before the node, thus providing the change in job vacancies at each batch loading time node.

[0060] In one embodiment, the allocation of teaching positions at a teacher training college involves five batches of pre-selected lists being loaded. After the first batch is loaded, the number of vacant positions increases from twenty to twenty-eight, resulting in a change of eight positions. After the second batch is loaded, the number of vacant positions decreases from twenty-eight to twenty-five, resulting in a change of negative three positions, indicating that the loading of this batch leads to a reduction in available position resources. Based on the changes in vacant positions, the time node corresponding to each adjustment intervention opportunity is extracted from the optimized adjustment intervention timing sequence. The number of students in the cascading rearrangement and the number of students on the pre-selected list awaiting allocation at that time node are obtained. The number of students in the cascading rearrangement refers to the total number of students affected by the chain adjustment triggered by the priority constraint of the pre-selected list, and the number of students on the pre-selected list awaiting allocation refers to the number of students on the pre-selected list whose positions have not yet been allocated at that time node.

[0061] It should be noted that the matching ratio is calculated by dividing the number of students in the cascading rearrangement by the number of students on the pre-selected list awaiting allocation. This ratio reflects the balance between the adjustment pressure and the demand for allocation at the time of intervention. If the matching ratio is too high, it indicates that the number of students involved in the cascading rearrangement far exceeds the demand for allocation on the pre-selected list, and intervention may trigger a large-scale chain of adjustments; if the matching ratio is too low, it indicates that the scope of the cascading rearrangement is limited, and intervention has a relatively small impact on the overall allocation pattern. The preset matching interval is determined based on the distribution of matching ratios at various times in the historical allocation cycle. Intervention times within the matching interval are marked as high matching status, and those outside the matching interval are marked as low matching status.

[0062] In one possible implementation, for each marked adjustment intervention opportunity, the corresponding conflict risk value and job supply surplus are obtained. The conflict risk value is the proportion of the number of potentially conflicting jobs to the total number of jobs at that time. Potentially conflicting jobs refer to jobs applied for by both assigned students and students on the pre-selection list. The job supply surplus is the difference between the number of vacant jobs at that time and the number of students on the pre-selection list awaiting allocation. A positive difference indicates sufficient job resources, while a negative difference indicates insufficient job resources to meet the allocation needs of the pre-selection list.

[0063] For example, intervention opportunities marked as high-matching with conflict risk values ​​below a preset risk threshold and job supply surplus exceeding a preset supply threshold are selected to obtain a candidate intervention window set. The preset risk threshold and preset supply threshold are determined based on the actual management requirements of teaching job allocation. The risk threshold controls the upper limit of acceptable conflict ratios during intervention, while the supply threshold ensures sufficient job resource buffers during intervention. Further, based on the candidate intervention window set, each intervention window is sorted from low to high conflict risk values. The sorting result places the intervention window with the lowest conflict risk at the top of the list, facilitating priority selection of the least risky opportunity during intervention. For each sorted intervention window, its corresponding executable condition range is marked.

[0064] It is understandable that the executable condition range describes the boundaries of the business scenarios to which the intervention window applies. The executable condition range includes the batch loading progress interval and the range of job vacancy changes to which the window applies. The batch loading progress interval indicates which batch loading stages the intervention window applies to, and the range of job vacancy changes indicates the range at which the intervention window applies to the job release scale. By marking the executable condition range, an optimized intervention timing list is obtained. This list provides multiple alternative timings and their applicable conditions for adjustment execution, facilitating flexible selection of intervention timing based on the actual allocation progress.

[0065] In step S105, for the optimized adjustment and intervention timing sequence, the conflict detection unit performs timing consistency verification on the job allocation snapshots of each adjustment and intervention timing, extracts a list of compatible jobs with stable matching schemes, and determines the adjustment path to reduce the frequency of secondary adjustments.

[0066] For the optimized adjustment intervention timing sequence, the conflict detection unit performs time-series consistency verification on the job allocation snapshots for each adjustment intervention timing. These snapshots record the occupancy status of each teaching position and the assigned student ID at that timing. The unit compares the job allocation snapshots of adjacent adjustment intervention timings and counts the job IDs whose occupancy status remains unchanged between adjacent timings, obtaining a stable job set for each adjacent timing pair. Based on this stable job set, the conflict detection unit traverses the stable job sets of all adjacent timing pairs in the optimized adjustment intervention timing sequence, extracting job IDs whose occupancy status remains unchanged across all adjacent timing pairs. These job IDs are then aggregated to form a compatible job list. The matching scheme for jobs in the compatible job list remains stable under each adjustment intervention timing. For the compatible job list, the unit obtains secondary adjustment records for each compatible job in the historical allocation cycle from the job allocation platform, counts the cumulative number of secondary adjustments performed on each compatible job, and marks compatible jobs with a cumulative number exceeding a preset frequency threshold as high-frequency adjustment jobs, and compatible jobs with a cumulative number within the preset frequency threshold as low-frequency adjustment jobs. Based on the low-frequency adjustment positions, target positions that match the job requirements of students on the pre-selected list are selected. The original position number and the target position number of the student to be adjusted are obtained, and the number of category differences between the original position category and the target position category are counted as the adjustment span. The target positions are sorted from smallest to largest according to the adjustment span, and the target position with the smallest adjustment span is selected as the adjustment destination. The original position of the student to be adjusted and the adjustment destination are connected to determine the adjustment path that reduces the frequency of secondary adjustments.

[0067] In the teaching position allocation scenario, the optimized adjustment intervention timing sequence includes multiple candidate intervention time nodes, each corresponding to a different position allocation status. For the adjustment intervention timing sequence, the conflict detection unit extracts the position allocation snapshot corresponding to each adjustment intervention time node. The position allocation snapshot is a complete record of the position allocation status at a certain time node, including the occupancy status of each teaching position and the student ID currently assigned to that position.

[0068] Specifically, the snapshots of job allocation at adjacent adjustment intervention times are compared, and the occupancy status of each teaching job is checked one by one to see if it remains consistent between the two timeframes. If a teaching job is occupied by the same student in both snapshots of adjacent timeframes, or is vacant in both, then the job is considered stable in that pair of adjacent timeframes. The job numbers whose occupancy status did not change between adjacent timeframes are counted to obtain the set of stable jobs for each pair of adjacent timeframes.

[0069] In one embodiment, the allocation of teaching positions at a teacher training college has four adjustment intervention opportunities. The conflict detection unit sequentially compares the position allocation snapshots of the first and second, second and third, and third and fourth opportunities to obtain three sets of stable positions. The positions in each set of stable positions do not change in allocation between the corresponding adjacent opportunities.

[0070] It should be noted that the conflict detection unit traverses the stable job sets of all adjacent timing pairs and extracts the job numbers that remain unchanged in occupancy across all adjacent timing pairs. This process is achieved by finding the intersection of the stable job sets; the job numbers in the intersection do not change allocation at any time point in the entire adjustment and intervention timing sequence. These job numbers are then aggregated to form a compatible job list. The matching schemes for jobs in this compatible job list remain stable across all adjustment and intervention timings and are unaffected by the choice of adjustment and intervention timing.

[0071] In one possible implementation, for the compatible job list, secondary adjustment records for each compatible job in the historical allocation cycle are obtained from the job allocation platform. Secondary adjustment records refer to historical data showing that a teaching job was adjusted again after its initial allocation due to job conflicts, priority constraints, or other reasons. The cumulative number of secondary adjustments performed on each compatible job is counted, reflecting the frequency of adjustments to that job in historical allocation. Further, the cumulative number is compared with a preset frequency threshold. The preset frequency threshold is determined based on the average number of secondary adjustments for each job in the historical allocation cycle. Compatible jobs with a cumulative number exceeding the preset frequency threshold are marked as high-frequency secondary adjustment jobs, indicating that these jobs are frequently adjusted in historical allocation; compatible jobs with a cumulative number within the preset frequency threshold are marked as low-frequency secondary adjustment jobs, indicating that these jobs are relatively stable in historical allocation.

[0072] For example, based on the low-frequency transfer positions, target positions matching the job requirements of students on the pre-selected list are selected. The matching determination is based on comparing the students' majors, job type preferences, and the job attributes of the low-frequency transfer positions. Low-frequency transfer positions with matching job attributes are included in the target position candidate range. The original job number of the student to be transferred and the job category to which the target job number belongs are obtained. Job categories include dimensions such as subject category, grade category, and campus category.

[0073] Understandably, the adjustment span is an indicator that measures the degree of change involved in a student's reassignment from their original position to their target position. The adjustment span is calculated by counting the number of category differences between the original and target position categories; the fewer the category differences, the smaller the impact of the reassignment on the student. Target positions are then sorted from smallest to largest according to the adjustment span, and the target position with the smallest adjustment span is selected as the reassignment destination. By connecting the student's original position with the reassignment destination, an adjustment path is determined that reduces the frequency of secondary reassignments. This path reassigns the student from their current position to a target position with a historically low reassignment frequency and minimal degree of change.

[0074] Step S106: Integrate the cascading data of job releases based on the adjustment path, classify the changes in job demand according to the magnitude of changes in job demand and the direction of supply and demand gaps during the high-risk window period, evaluate the improvement in the stability of the matching scheme under the priority constraint of the pre-selected list, and obtain the execution queue of the final adjustment scheme.

[0075] According to the adjustment path, the records of released positions involved in each adjustment path are obtained from the position allocation platform. These records include the position numbers and release timestamps after the originally assigned students were transferred. The cascading student lists corresponding to each released position are iterated through, and the number of students and positions involved that triggered subsequent adjustments due to the release of that position are counted. The number of students and positions are then summarized according to the released position number to obtain the cascading data set affected by the released positions. For each released position in the cascading data set, the demand change records for the corresponding positions during the high-risk adjustment window are obtained. The magnitude of the change and the direction of the supply-demand gap are extracted from these records. If the magnitude of the change is positive and the supply-demand gap direction is supply exceeding demand, the change in demand for that position is classified as a release type. If the magnitude of the change is negative and the supply-demand gap direction is supply less than demand, the change in demand for that position is classified as a shortage type, thus obtaining the classification result of the change in job demand. Based on the classification results, the number of stable positions before and after execution is counted for each adjustment path under the priority constraint of the pre-selected list for both released and scarce positions. The stability improvement is obtained by subtracting the number of stable positions before execution from the number of stable positions after execution. The adjustment paths are sorted from high to low according to the stability improvement, and the sorted adjustment paths are arranged in sequence to obtain the execution queue of the final adjustment plan.

[0076] In teaching position allocation scenarios, the execution of adjustment paths can trigger a chain reaction of position releases, involving the adjustment of multiple students' positions. Based on the adjustment paths, the system retrieves the release position records for each adjustment path from the position allocation platform. These records include the position numbers and release timestamps of the vacated positions after the originally assigned students were transferred. The system then iterates through the cascading student lists corresponding to each released position, calculating the number of students whose subsequent adjustments were triggered by the release of that position and the total number of positions involved.

[0077] Specifically, the number of students and the number of positions are summarized according to the released position number to obtain the cascading data set affected by the released positions.

[0078] In one embodiment, a certain adjustment path involves three released positions. The first released position triggers a cascading adjustment for five students and involves four subsequent positions; the second released position triggers a cascading adjustment for two students and involves two subsequent positions; and the third released position does not trigger a cascading adjustment. The above data is aggregated to form the cascading data set corresponding to this adjustment path. The starting point of the high-risk adjustment window period is determined by S102, i.e., the earliest moment when multiple conflicting positions simultaneously enter the pending release state. Demand change records are obtained from the system logs of the position allocation platform. For each released position in the cascading data set, the demand change records for the corresponding position within the high-risk adjustment window period are obtained. The demand change records reflect the demand change trend of positions within the window period, and the magnitude of the change and the direction of the supply-demand gap are extracted. The direction of the supply-demand gap represents the relative relationship between position resources and student demand; supply exceeding demand indicates sufficient position resources, and supply falling short of demand indicates tight position resources.

[0079] In one possible implementation, if the change is positive and the supply-demand gap is in the direction of supply exceeding demand, the change in demand for the position is classified as a release type, indicating that the position has released more available resources after the adjustment; if the change is negative and the supply-demand gap is in the direction of supply less than demand, the change in demand for the position is classified as a shortage type, indicating that the position faces greater resource pressure after the adjustment.

[0080] For example, based on the classification results, the number of stable positions before and after execution is counted for each adjustment path under the priority constraint of the pre-selected list for both released and scarce positions. Stable positions refer to positions whose occupancy status remains unchanged before and after the adjustment, and their number reflects the stability of the matching scheme. Subtracting the number of stable positions before execution from the number of stable positions after execution yields the stability improvement margin. A positive margin indicates that the execution of the adjustment path improved the stability of the matching scheme. Further, the adjustment paths are sorted from highest to lowest according to the stability improvement margin, with the adjustment path with the higher the stability improvement margin ranking higher. The sorted adjustment paths are arranged sequentially to obtain the execution queue of the final adjustment scheme. This queue lists the execution order of each adjustment path in descending order of stability improvement effect.

[0081] Step S107: Select the intervention point with the lowest conflict risk from the execution queue of the final reassignment plan. The reassignment timing analysis module generates a job reallocation matrix and updates the platform database. The conflict detection unit monitors the downward trend of the frequency of secondary reassignment and determines the evaluation conclusion that the matching plan is stable in the long term.

[0082] The conflict risk value corresponding to each adjustment path is read from the execution queue of the final adjustment plan. The adjustment paths in the execution queue are sorted from low to high according to the conflict risk value. The adjustment path with the lowest conflict risk value is selected as the current intervention point. The adjustment time sequence analysis module extracts the student ID to be adjusted, the original position ID, and the target position ID according to the adjustment path corresponding to the intervention point, and constructs a position redistribution matrix. The rows of the position redistribution matrix represent the student ID to be adjusted, the columns represent the target position ID, and the matrix elements represent the allocation relationship between the corresponding student and the position. According to the position redistribution matrix, the adjustment time sequence analysis module writes the allocation relationship in the matrix into the database of the position allocation platform one by one, updates the occupancy status of each teaching position and the allocated student ID record. After the database update is completed, the conflict detection unit reads the secondary adjustment records in the current allocation cycle, counts the cumulative number of secondary adjustments before and after the update, and subtracts the cumulative number before the update from the cumulative number after the update to obtain the change value of the secondary adjustment frequency. Regarding the change in the frequency of secondary allocation, the conflict detection unit determines whether the change is negative. If it is negative, it indicates that the cumulative number of secondary allocations after the update is lower than before the update, and the frequency of secondary allocation is determined to be decreasing. If the decreasing trend persists for multiple consecutive allocation cycles, the assessment conclusion that the matching scheme is stable in the long term is determined.

[0083] In the teaching position allocation scenario, the final adjustment plan's execution queue contains multiple adjustment paths, each corresponding to a different conflict risk value. The conflict risk value corresponding to each adjustment path is read from the execution queue, and the adjustment paths in the queue are sorted from low to high conflict risk value. The adjustment path with the lowest conflict risk value is selected as the current intervention point for execution.

[0084] Specifically, the adjustment time-series analysis module extracts the student ID to be adjusted, the original job ID, and the target job ID based on the adjustment path corresponding to the intervention point, and constructs a job redistribution matrix. The job redistribution matrix is ​​a two-dimensional data structure, where rows represent student IDs to be adjusted, columns represent target job IDs, and matrix elements represent the allocation relationship between corresponding students and jobs.

[0085] In one embodiment, if an adjustment path involves three students to be reassigned and four target positions, then the position redistribution matrix has a three-row, four-column structure. Elements with a value of one in the matrix indicate that the student corresponding to that row is assigned to the position corresponding to that column, and elements with a value of zero indicate that there is no assignment relationship.

[0086] In one possible implementation, the time-series analysis module and the job allocation platform collaborate to handle the allocation.

[0087] Specifically, the job allocation timing analysis module is used to analyze the job allocation timing, with student job matching data as input and redistribution moments as output; the job allocation platform is the database management platform in the system, and the source is the preset job resource library.

[0088] First, the job reallocation time-series analysis module writes the allocation relationships in the job reallocation matrix into the job allocation platform's database one by one. The writing process uses a loop traversal algorithm. For elements with a value of 1 in the matrix, the corresponding student ID and job ID are extracted and updated in the job allocation record table of the database. At the same time, SQL update statements are used to change the occupancy status of the original job to vacant and the occupancy status of the target job to allocated, ensuring data consistency and real-time performance.

[0089] It should be noted that the conflict detection unit is a module used to monitor resource allocation. Its function is to detect potential conflicts through algorithms. The specific process includes: after the database update is completed, firstly, querying and reading the secondary allocation records within the current allocation period; then, calculating the cumulative number of times before the update and the cumulative number of times after the update respectively; finally, subtracting N1 from N2 to obtain the change in the frequency of secondary allocation Δ=N2-N1. If Δ is negative, it indicates that the number of secondary allocations has decreased after the update. This analysis process can be used to optimize resource allocation strategies. In this embodiment, the allocation period is a one-month resource allocation period; the cumulative number of times before the update is recorded as N1, which is summed by querying historical records using SQL; the cumulative number of times after the update is recorded as N2, which is summed by querying after the update.

[0090] For example, the conflict detection unit determines whether the changed value is negative. If it is negative, it determines that the frequency of secondary adjustments is decreasing. If the decreasing trend persists for P consecutive allocation cycles, where P is a preset stability assessment cycle number determined based on the annual cycle characteristics of teaching position allocation, then the assessment conclusion that the matching scheme is stable in the long term is determined. This conclusion indicates that the implementation of the current adjustment scheme effectively reduces the adjustment frequency in the position allocation process, and the matching scheme remains stable over a relatively long period of time.

[0091] If the technical solution of this application involves the collection, processing, or application of personal information, the relevant products have strictly complied with the requirements of the "Personal Information Protection Law of the People's Republic of China" and other laws and regulations before implementing any personal information processing activities, clearly and explicitly informing individuals of the rules for personal information processing and obtaining their independent and voluntary authorization and consent. Specifically, if the information involved is sensitive personal information, the product has not only obtained the individual's separate consent before processing, but this consent is also an explicit consent made on the basis of full knowledge. For example, in areas where personal information collection devices such as cameras are deployed, prominent and eye-catching signs have been set up to clearly inform users that entering the area is considered as consenting to the collection of their personal information; or, on the personal information processing interface (such as applications, web pages, etc.), through pop-ups, checkboxes, or active uploads, the user is required to actively authorize the process after clearly displaying key rules such as the identity of the personal information processor, the purpose of processing, the processing method, and the types of information involved.

[0092] The above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the invention. Those skilled in the art will understand that implementing all or part of the above-described embodiments and making equivalent changes in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A method for matching and allocating homogeneous teaching position resources, characterized in that, include: The adjustment time sequence analysis module obtains the current matching round progress, the loading schedule of the batch of internal list, the job demand for the internal list to be loaded, and the number of people in the cascade rearrangement of released teaching positions from the job allocation platform. The conflict risk index of each teaching position is calculated based on the matching round progress and the demand for positions. The conflict risk index is dynamically calibrated by combining the batch loading schedule and the number of people in the cascade reshuffle. The period when the risk index exceeds the preset range is determined as a high-risk adjustment window period. During the high-risk adjustment window period, the K-means clustering algorithm is used to group the occupancy status and demand changes of each teaching position, and to divide the position occupancy conflict cluster under the priority constraint of the pre-selected list. Extract the subset of people affected by the cascade rearrangement from the job occupancy conflict cluster, and simulate the job release scenario by combining the adjustment timing analysis module with the subsequent batch loading schedule. The release node with the lowest secondary adjustment frequency is determined as the optimized adjustment intervention timing sequence. For the aforementioned adjustment and intervention timing sequence, the conflict detection unit performs a timing consistency verification on the job allocation snapshots of each intervention timing, extracts a list of compatible jobs with stable matching schemes, and determines an adjustment path to reduce the frequency of secondary adjustments; Based on the adjustment path and the compatible job list, the cascading data of the released jobs are integrated and released. The changes in demand are classified according to the direction of the job supply and demand gap during the high-risk adjustment window period. The improvement of the stability of the matching scheme is evaluated, and the execution queue of the final adjustment scheme is obtained. The intervention point with the lowest conflict risk is selected from the execution queue. The adjustment timing analysis module generates a job reallocation matrix and updates the job allocation platform database to determine the long-term stability of the matching scheme.

2. The method for matching and allocating homogeneous teaching position resources according to claim 1, characterized in that, The process of obtaining the current matching round progress, the batch loading schedule of the pre-selected list, the job demand for the pre-selected list to be loaded, and the number of people in the cascading reshuffle of released teaching positions from the job allocation platform through the adjustment time sequence analysis module includes: The job allocation records of each round are traversed from the job occupancy conflict cluster. The student IDs involved when the cascading rearrangement is triggered by the priority constraint of the pre-determined list are extracted. The students are grouped and statistically analyzed according to the round affiliation to obtain the subset of the number of people affected by the cascading rearrangement. The adjustment time sequence analysis module reads the size value of the subset of the number of people affected and compares it with a preset threshold. Based on the comparison result, the current conflict cluster is marked as a high-pressure state or a normal state. For the conflict clusters under high pressure or normal conditions, obtain the distribution pattern of the secondary adjustment frequency and the subsequent batch loading schedule. Based on the sorting position of the high-frequency adjustment rounds in the distribution pattern, and combined with the expected arrival node of each batch in the subsequent batch loading schedule, traverse the job release records corresponding to each batch after loading, count the number of job releases under each batch loading time node, and obtain the job release list corresponding to each time node. Based on the job release list, each time node is sorted from most to least number of job releases. Candidate nodes whose job releases exceed the preset supply threshold and are not related to the time nodes of the high-frequency adjustment round are selected. The candidate nodes are arranged in chronological order to obtain the optimized adjustment intervention timing sequence.

3. The method for matching and allocating homogeneous teaching position resources according to claim 1, characterized in that, The conflict risk index for each teaching position is calculated based on the matching round progress and the demand for positions. This index is then dynamically calibrated using the batch loading schedule and the number of positions to be reassigned. Periods where the risk index exceeds a preset range are designated as high-risk adjustment windows, including: An initial threshold is set based on historical job allocation data. A batch loading schedule for the internal list is obtained from the job allocation platform. The schedule records the expected loading time node and the corresponding job demand for each batch of internal list. The time nodes are arranged in chronological order. The time interval between adjacent batches is extracted. The initial threshold is reduced or increased according to the relationship between the time interval and the initial threshold to obtain a calibrated dynamic threshold. The conflict detection unit reads the calibrated dynamic threshold, traverses the current allocation record of each teaching position and the position requirement list to be loaded into the internal list, compares whether there is overlap between the assigned students and the students to be assigned in the internal list for each teaching position, marks the teaching positions with overlap as conflict positions, counts the number of conflict positions, and obtains the conflict position set. Based on the set of conflicting positions, the conflict detection unit assesses the occupancy status of each teaching position and classifies the occupancy status into three categories: idle, allocated, and pending release. For teaching positions that are pending release and are within the set of conflicting positions, the corresponding batch loading time node is extracted, and the earliest time when conflicting positions are released in a concentrated manner is determined as the starting point of the high-risk adjustment window period.

4. The method for matching and allocating homogeneous teaching position resources according to claim 3, characterized in that, Also includes: Extract the expected arrival time of the subsequent pre-selected list from the loading schedule of each batch, and compare the arrival time with the starting point of the high-risk adjustment window period in time sequence; Select batches that arrive after the starting point, and for the job requirements corresponding to the batches, check the records in the currently assigned positions that overlap with the internal list, and summarize them to obtain a list of newly added conflicting positions. For the newly added conflicting job list, obtain the release time nodes of the released teaching jobs, compare the time records of each released job being occupied by other rounds after release, mark the overlapping jobs as lost jobs, and summarize the interval from the release time node to the occupied time node corresponding to the lost jobs to obtain the adjustable resource loss interval. Based on the newly added conflict position list and the range of available resource loss, the number of newly added conflict positions and the number of lost positions are compared. The time point when the number of newly added conflict positions and the number of lost positions are both within the preset allowable range is selected to determine the optimal intervention starting boundary that takes into account both the completeness of batch information and the timeliness of the adjustment window.

5. The method for matching and allocating homogeneous teaching position resources according to claim 1, characterized in that, During the high-risk adjustment window period, the K-means clustering algorithm is used to group the occupancy status and demand changes of each teaching position, and to divide the position occupancy conflict clusters under the priority constraint of the pre-selected list, including: Based on the starting point of the high-risk adjustment window period, the progress data of each matching round is obtained from the job allocation platform. The progress data includes the number of students who have been allocated and the number of students who are to be allocated in each round, as well as the priority constraint flag of the internal list corresponding to each round. The progress data is arranged according to the round number and time order to construct the matching round progress sequence. For the matching round progress sequence, the K-means clustering algorithm is used to group the people with the ratio of the number of people who have been assigned to the number of people who are to be assigned, using the clustering feature. Rounds with similar ratios are grouped into the same group to obtain several round progress groups. From the round progress groups, round records that contain the priority constraint mark of the pre-selected list and have overlapping job occupancy are selected and aggregated to obtain the job occupancy conflict cluster. For the aforementioned job occupancy conflict cluster, the adjustment records of each round within the cluster are read, and the number of secondary adjustments occurring in each round within the current allocation cycle is counted. The secondary adjustment frequency list for each round within the cluster is obtained by summarizing according to the round number. The secondary adjustment frequency list is sorted from high to low according to the frequency value, and rounds with frequency values ​​exceeding a preset threshold are marked as high-frequency adjustment rounds. The proportion and sorting position of high-frequency adjustment rounds within the cluster are counted to identify the distribution pattern of secondary adjustment frequency within the cluster.

6. The method for matching and allocating homogeneous teaching position resources according to claim 1, characterized in that, Also includes: For each batch loading schedule, the loading time nodes of each batch are traversed one by one. The changes in the number of job occupancy before and after the node are statistically analyzed from the job occupancy conflict cluster. The number of job vacancies after the node is subtracted from the number of job vacancies before the node to obtain the change in job vacancies under each batch loading time node. Based on the change range of the job vacancies, extract the time node corresponding to each adjustment and intervention opportunity from the optimized adjustment and intervention opportunity sequence, obtain the number of people in the cascade rearrangement and the number of people in the internal list to be assigned under the time node, divide the number of people in the cascade rearrangement by the number of people in the internal list to be assigned to obtain the matching ratio, and mark it as a high matching state or a low matching state according to the comparison result of the matching ratio and the preset matching interval. For each marked adjustment intervention opportunity, obtain its corresponding conflict risk value and job supply surplus, filter out adjustment intervention opportunities marked as high matching status with conflict risk value lower than preset risk threshold and job supply surplus exceeding preset supply threshold, and obtain a set of candidate intervention windows; The intervention windows are sorted from low to high according to the conflict risk value. The corresponding range of executable conditions is marked for each sorted intervention window to obtain an optimized intervention timing list.

7. The method for matching and allocating homogeneous teaching position resources according to claim 1, characterized in that, For the aforementioned adjustment and intervention timing sequence, the conflict detection unit performs a time-series consistency verification on the job allocation snapshots for each intervention timing, extracts a list of compatible jobs with stable matching schemes, and determines adjustment paths to reduce the frequency of secondary adjustments, including: For the optimized adjustment and intervention timing sequence, the conflict detection unit compares the job allocation snapshots of adjacent adjustment and intervention timings, counts the job numbers whose job occupancy status has not changed between adjacent timings, and obtains the stable job set for each adjacent timing pair. Based on the set of stable positions, extract the position numbers that remain in an occupied state in all adjacent time pairs, and aggregate them to form a list of compatible positions; For the list of compatible positions, obtain the secondary adjustment records of each compatible position in the historical allocation cycle, count the cumulative number of times each compatible position has been subject to secondary adjustment, and mark it as a high-frequency adjustment position or a low-frequency adjustment position based on the comparison result of the cumulative number of times with the preset frequency threshold. Based on the low-frequency adjustment positions, target positions that match the job requirements of students on the pre-selected list are selected. The number of category differences between the original position category and the target position category is counted as the adjustment span. The target positions are sorted from smallest to largest according to the adjustment span, and the target position with the smallest adjustment span is selected as the adjustment destination. The adjustment path that reduces the frequency of secondary adjustments is determined.

8. The method for matching and allocating homogeneous teaching position resources according to claim 1, characterized in that, The process involves integrating and releasing cascading data related to job impact based on the adjustment path and the compatible job list, classifying demand changes according to the direction of job supply and demand gaps during the high-risk adjustment window, evaluating the improvement in the stability of the matching scheme, and obtaining the execution queue of the final adjustment scheme, including: According to the adjustment path, obtain the release job records involved in each adjustment path from the job allocation platform, traverse the cascading rearrangement student list corresponding to each release job, count the number of students and the number of jobs involved that trigger subsequent adjustments due to the release of the job, and summarize the cascading data set affected by the release job according to the release job number. For each released position in the cascaded data set, obtain the demand change record of the corresponding position during the high-risk adjustment window period, extract the change magnitude and the direction of the supply and demand gap, and classify the change in job demand into release type or shortage type according to the combination of the change magnitude and the direction of the supply and demand gap. Based on the type of change in job demand, the number of stable jobs before and after implementation is calculated separately for both released and scarce jobs under the priority constraint of the pre-selected list for each adjustment path. The stability improvement is calculated by subtracting the number of stable positions before execution from the number of stable positions after execution. The adjusted paths are then sorted from highest to lowest according to the stability improvement to obtain the execution queue of the final adjustment plan.

9. The method for matching and allocating homogeneous teaching position resources according to claim 1, characterized in that, The step involves selecting the intervention point with the lowest conflict risk from the execution queue, generating a job reallocation matrix using the time-series analysis module, updating the job allocation platform database, and determining a long-term stable evaluation conclusion for the matching scheme, including: Sort the execution queue of the final adjustment scheme from low to high according to the conflict risk value, and select the adjustment path with the lowest conflict risk value as the current execution intervention point; The adjustment time sequence analysis module extracts the student ID, original position ID, and target position ID based on the adjustment path corresponding to the intervention point, and constructs a position redistribution matrix. The rows of the position redistribution matrix represent the student IDs to be adjusted, the columns represent the target position IDs, and the matrix elements represent the allocation relationship between the corresponding students and positions. Based on the job redistribution matrix, the allocation relationships in the matrix are written into the database of the job allocation platform one by one, and the occupancy status and student number records of each teaching job are updated. The conflict detection unit reads the secondary adjustment records within the current allocation cycle, counts the cumulative number of secondary adjustments before and after the update, calculates the change value obtained by subtracting the cumulative number before the update from the cumulative number after the update, and determines whether the frequency of secondary adjustments shows a downward trend based on the change value. If the downward trend continues to exist in multiple consecutive allocation cycles, the assessment conclusion that the matching scheme is stable in the long term is determined.

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