An information management method based on an educational administration system

Abstract

This invention relates to the field of educational resource management technology and discloses an information management method based on an academic affairs management system, including: collecting atomic logs to generate an academic affairs event sequence; comparing frequency distributions to calculate state deviation values; constructing a resource association logic graph and determining business pressure characteristics; calculating the conflict intensity coefficient of each node to identify bottleneck nodes; determining resource constraint release paths and updating node topology relationships based on logical dependency boundaries. This invention effectively avoids the risk of local congestion in static threshold judgment by establishing a mapping mechanism between business load and topology conflict, improves the allocation accuracy of teaching resources in a high-concurrency environment, and achieves stable self-healing of the business architecture by using closed-loop monitoring logic.

Description

Technical Field

[0001] This invention belongs to the field of educational resource management technology, and in particular relates to an information management method based on an academic affairs management system. Background Technology

[0002] The current academic affairs management system uses a relational database to store teaching resource data and establishes prerequisite constraint logic between resource nodes based on the preset training program. This logic is represented by a directed acyclic graph structure, which is used to limit the time path of student course selection, course scheduling and teaching progress. As the scale of teaching expands and the frequency of teaching interaction increases, the underlying logical architecture and dynamic business needs become mismatched when the system is dealing with large-scale resource scheduling.

[0003] In large-scale concurrent course selection or complex vocational education and training scenarios, the cognitive evolution of a group of users exhibits nonlinear divergent characteristics. Existing static prerequisite tables can only provide discrete and lagging management instructions. Given the limitations of the existing architecture, software-level scheduling and control logic faces challenges. For example, Chinese invention patent application CN119168301A discloses an AI-based automatic course scheduling system that introduces a game theory model to balance the interests of multiple parties and uses dynamic programming algorithms to optimize learning paths. Its core logic is based on discrete weighted evaluation of historical data and game-theoretic optimization using preset strategies. In high-concurrency, large-scale educational resource scheduling environments, business flow evolution exhibits transient bursts and logical coupling complexity. Existing technologies, based on static index weight calculation models, struggle to capture the real-time shift in load distribution of the logical network under irregular access. When the system encounters a surge in logical stress, the lack of quantitative calculation and dynamic mapping of the global topology stability leads to lag in algorithm convergence, causing misalignment between resource reconstruction actions and the actual physical state, resulting in local congestion or even systemic paralysis. The existing management logic treats the resource dependency graph as static relational data, ignoring the structural stability defects exhibited by multidimensional resource constraints when facing high-concurrency group state divergence. When massive concurrent interactions deviate from the preset benchmark, the system relies on single-point load thresholds to trigger a delayed master-slave switchover. Due to the lack of stress calculation and dynamic optimization capabilities for the global logical network under irregular access, the system cannot perceive the accumulated business pressure in the logical constraint links. This lack of logical topology modeling leads to blind reconstruction actions when the system faces complex state evolution, causing secondary resource conflicts and systemic link paralysis, and failing to meet dynamic business needs.

[0004] Therefore, how to establish a perceptual logical topology strain and realize a dynamic scheduling mechanism for adaptive constraint release has become the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention provides an information management method based on an academic affairs management system, comprising the following steps:

[0006] Step S101: Collect atomic interaction logs within the academic affairs management system and extract the sequence of academic affairs events containing timestamps, user identity identifiers, and academic affairs resource identifiers.

[0007] Step S102: Perform smoothing and denoising processing on the sequence of academic affairs events within the sampling period, and compare the frequency distribution of the sequence of academic affairs events between the preset observation period and the preset benchmark period to calculate the state deviation value that characterizes the degree of deviation in the evolution of academic affairs business.

[0008] Step S103: Obtain the logical dependencies of resource nodes within the academic affairs management system, construct a resource association logic graph, and calculate the business pressure characteristics acting on each node of the resource association logic graph based on the academic affairs event sequence.

[0009] Step S104: Based on the logical connection strength of the resource association logic graph, determine the transmission weight of the business pressure feature quantity between the nodes of the resource association logic graph, calculate the conflict intensity coefficient reflecting the degree of conflict of each node of the resource association logic graph, and identify the nodes whose conflict intensity coefficient exceeds the preset logical interference threshold as bottleneck nodes.

[0010] Step S105: Determine the resource constraint release path based on the logical dependency boundary of the bottleneck node, and update the node topology relationship in the resource association logic graph according to the resource constraint release path, so as to realize the adaptive scheduling of teaching resources.

[0011] Preferably, step S102 specifically includes: calculating the statistical deviation of the academic affairs event sequence within the sampling period, filtering out random fluctuations in the information source noise of the academic affairs event sequence using a preset smoothing operator; statistically analyzing the business status distribution of the academic affairs event sequence between consecutive preset observation periods, and determining the probability density difference between the business status distribution and the benchmark distribution within the preset benchmark period as the status deviation value.

[0012] Preferably, step S103 specifically includes: retrieving teacher nodes, space nodes, and course nodes within the academic affairs management system; establishing a directed dependency network based on the prerequisite constraints between teacher nodes, space nodes, and course nodes; defining it as a resource association logic graph; using the number of concurrent operations and the duration of resource occupation in the atomic interaction log as components to generate business pressure feature quantities; and mapping the business pressure feature quantities to the corresponding teacher nodes, space nodes, or course nodes in the directed dependency network.

[0013] Preferably, the method for identifying bottleneck nodes in step S104 includes: calculating the load offset vectors caused by the business pressure characteristics at each teacher node, space node, and course node based on the logical topology matrix of the directed dependency network; extracting the principal component components in the load offset vectors to determine the conflict intensity coefficient; and determining the spatial location of the bottleneck node when the value of the conflict intensity coefficient exceeds a preset logical interference threshold.

[0014] Preferably, step S105 specifically includes: for the bottleneck node, retrieving a spare resource node with an idle capacity index in the resource association logic graph; using a dynamic path planning algorithm to calculate the optimal reconstruction path for migrating from the bottleneck node to the spare resource node, and confirming the optimal reconstruction path as the resource constraint release path; and adjusting the node connection weights in the directed dependency network according to the resource constraint release path.

[0015] Preferably, updating the node topology in the resource association logic graph includes: using a hash sharding algorithm to reorient the access to the academic affairs event sequence, guiding the newly generated academic affairs event sequence to the reconstructed faculty node, space node, or course node; monitoring the response frequency changes of the reconstructed nodes, and calculating the corresponding conflict decay rate.

[0016] Preferably, the method further includes step S106: establishing closed-loop monitoring rollback logic, stopping the issuance of the current resource constraint release path when the conflict decay rate is lower than the preset self-healing threshold, and restoring the resource association logic graph to the initial state before logic reconstruction based on the historical state snapshot of the bottleneck node.

[0017] Preferably, the adaptive scheduling of teaching resources includes: resetting the timetable weight parameters of teacher nodes based on the resource constraint release path, and synchronously updating the allocation priority of spatial nodes and the logical sequence of course nodes.

[0018] Preferably, the sequence of academic affairs events includes course selection requests, grade entry, resource applications, and course changes; resource nodes include teacher resources, classroom space, and course logical units.

[0019] Compared with existing technologies, the information management method based on the academic affairs management system of this invention has the following advantages:

[0020] 1. In the information management of the academic affairs management system, by translating the atomic interactive event sequence into a spatial load vector acting on the geometric network topology model, a mapping relationship between business load and logical structure displacement is established. This enables the system to directly determine the release path of resource constraints based on the feedback of the structural strain energy tensor parameters, thereby avoiding the secondary congestion risk caused by the inability of the single threshold judgment logic to take into account the global topology stability in the traditional management system.

[0021] 2. By deeply coupling the anti-shake filtering step within the feature time sliding window with the state divergence rate calculation within the academic observation period, refined preprocessing and feature extraction of heterogeneous interactive data are achieved. This ensures that the triggering of topology rewriting instructions is always based on the physical reality of business evolution, reduces the frequency of invalid scheduling actions caused by source noise, and improves the allocation accuracy of resources under complex working conditions. Attached Figure Description

[0022] Figure 1 This is a flowchart of the service load perception and resource topology adaptive scheduling process of the present invention;

[0023] Figure 2 This is a diagram of the architecture for the logical reconstruction of academic resources driven by the core scheduling kernel of this invention. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0025] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0026] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0027] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0028] An information management method based on an academic affairs management system includes the following steps:

[0029] Step S101: Collect atomic interaction logs within the academic affairs management system and extract the sequence of academic affairs events containing timestamps, user identity identifiers, and academic affairs resource identifiers.

[0030] Step S102: Perform smoothing and denoising processing on the sequence of academic affairs events within the sampling period, and compare the frequency distribution of the sequence of academic affairs events between the preset observation period and the preset benchmark period to calculate the state deviation value that characterizes the degree of deviation in the evolution of academic affairs business.

[0031] Step S103: Obtain the logical dependencies of resource nodes within the academic affairs management system, construct a resource association logic graph, and calculate the business pressure characteristics acting on each node of the resource association logic graph based on the academic affairs event sequence.

[0032] Step S104: Based on the logical connection strength of the resource association logic graph, determine the transmission weight of the business pressure feature quantity between the nodes of the resource association logic graph, calculate the conflict intensity coefficient reflecting the degree of conflict of each node of the resource association logic graph, and identify the nodes whose conflict intensity coefficient exceeds the preset logical interference threshold as bottleneck nodes.

[0033] Step S105: Determine the resource constraint release path based on the logical dependency boundary of the bottleneck node, and update the node topology relationship in the resource association logic graph according to the resource constraint release path, so as to realize the adaptive scheduling of teaching resources.

[0034] Preferably, step S102 specifically includes: calculating the statistical deviation of the academic affairs event sequence within the sampling period, filtering out random fluctuations in the information source noise of the academic affairs event sequence using a preset smoothing operator; statistically analyzing the business status distribution of the academic affairs event sequence between consecutive preset observation periods, and determining the probability density difference between the business status distribution and the benchmark distribution within the preset benchmark period as the status deviation value.

[0035] Preferably, the state deviation value is calculated using the following formula: Where D is the state deviation value. For the first The frequency of occurrence of each academic affairs activity within the current preset observation period. Let i be the frequency of occurrence of the i-th academic affairs transaction within a preset baseline period. The resource sensitivity weight is preset based on the i-th academic affairs business according to the academic affairs resource identifier.

[0036] Preferably, step S103 specifically includes: retrieving teacher nodes, space nodes, and course nodes within the academic affairs management system; establishing a directed dependency network based on the prerequisite constraints between teacher nodes, space nodes, and course nodes; defining it as a resource association logic graph; using the number of concurrent operations and the duration of resource occupation in the atomic interaction log as components to generate business pressure feature quantities; and mapping the business pressure feature quantities to the corresponding teacher nodes, space nodes, or course nodes in the directed dependency network.

[0037] Preferably, the method for identifying bottleneck nodes in step S104 includes: calculating the load offset vectors caused by the business pressure characteristics at each teacher node, space node, and course node based on the logical topology matrix of the directed dependency network; extracting the principal component components in the load offset vectors to determine the conflict intensity coefficient; and determining the spatial location of the bottleneck node when the value of the conflict intensity coefficient exceeds a preset logical interference threshold.

[0038] Preferably, step S105 specifically includes: for the bottleneck node, retrieving a spare resource node with an idle capacity index in the resource association logic graph; using a dynamic path planning algorithm to calculate the optimal reconstruction path for migrating from the bottleneck node to the spare resource node, and confirming the optimal reconstruction path as the resource constraint release path; and adjusting the node connection weights in the directed dependency network according to the resource constraint release path.

[0039] Preferably, updating the node topology in the resource association logic graph includes: using a hash sharding algorithm to reorient the access to the academic affairs event sequence, guiding the newly generated academic affairs event sequence to the reconstructed faculty node, space node, or course node; monitoring the response frequency changes of the reconstructed nodes, and calculating the corresponding conflict decay rate.

[0040] Preferably, the method further includes step S106: establishing closed-loop monitoring rollback logic, stopping the issuance of the current resource constraint release path when the conflict decay rate is lower than the preset self-healing threshold, and restoring the resource association logic graph to the initial state before logic reconstruction based on the historical state snapshot of the bottleneck node.

[0041] Preferably, the adaptive scheduling of teaching resources includes: resetting the timetable weight parameters of teacher nodes based on the resource constraint release path, and synchronously updating the allocation priority of spatial nodes and the logical sequence of course nodes.

[0042] Preferably, the sequence of academic affairs events includes course selection requests, grade entry, resource applications, and course changes; resource nodes include teacher resources, classroom space, and course logical units.

[0043] Example 1: In a specific application scenario of the information management method based on the academic affairs management system provided by this invention, the academic affairs management system is deployed in a comprehensive vocational education park management environment with 20,000 students and a peak concurrent course selection request rate of 5,000 times per second. The students in this environment generate non-linear course selection needs based on their individual learning progress. The academic affairs management system dynamically schedules educational resources through the following steps to solve the resource mismatch problem caused by static pre-allocation: The academic affairs management system collects internally generated atomic interaction logs and extracts academic affairs event sequences containing timestamps, user identification identifiers, and academic affairs resource identifiers. The academic affairs event sequences cover time-series data streams such as course selection requests, grade entry, resource applications, and course changes. The academic affairs management system uses a preset smoothing operator to smooth and denoise the academic affairs event sequences within the sampling period. By calculating the statistical deviation of the academic affairs event sequences within the sampling period, random fluctuations in source noise are filtered out. The frequency distribution of the academic affairs event sequences between the preset observation period and the preset baseline period is compared, and the state deviation value, representing the degree of deviation in the evolution of academic affairs business, is calculated according to the following formula. : Where D is the state deviation value. Let be the frequency of occurrence of the i-th academic affairs transaction within the current preset observation period. Let i be the frequency of occurrence of the i-th academic affairs transaction within a preset baseline period. To determine the resource sensitivity weight corresponding to the i-th academic affairs business based on the preset academic affairs resource identifier, where n is the total number of academic affairs businesses; the state deviation value D can quantify the degree to which the current interactive behavior deviates from the baseline trajectory. In step S102, the system selects the exponentially weighted moving average algorithm as the preset smoothing operator, extracts the original concurrent observation value of each academic affairs business according to the sampling period, sets the time decay factor, and numerically extracts the inverse of the network response delay variance from the historical operation log for calibration; to ensure that the time decay factor meets the strict requirements of the exponentially weighted moving average algorithm for the parameter interval, the system extracts the first ten historical sampling windows including the current time to calculate the dynamic variance of the response delay, and compresses its inverse variance to an effective weight range greater than 0 and less than 1 through the normalization mapping function. The system multiplies the original concurrent observation value of the current period by the time decay factor, and linearly sums it with the smoothed output value of the previous period multiplied by the remaining weight ratio, outputting the frequency of occurrence after smoothing and denoising in the current period. Based on the historical inertia of the time series, the system objectively suppresses the instantaneous concurrent acquisition mutation caused by the underlying database lock contention.

[0044] The academic affairs management system retrieves faculty nodes, space nodes, and course nodes within the system. Based on the preconditions between these nodes, it establishes a directed dependency network, defining it as a resource association logic graph. The system uses the operation concurrency and resource occupancy duration from the atomic interaction logs as components to generate business pressure characteristics, mapping them to the corresponding resource nodes in the directed dependency network. Based on the logical connection strength of the resource association logic graph, the system determines the transmission weight of the business pressure characteristics between nodes and calculates a conflict intensity coefficient reflecting the degree of conflict at each node. Using the logical topology matrix of the directed dependency network, the system calculates the load offset vectors caused by the business pressure characteristics at each faculty node, space node, and course node, and extracts the principal components from these load offset vectors to determine the conflict intensity coefficient. When the conflict intensity coefficient exceeds a preset logical interference threshold, the system identifies the corresponding node as a bottleneck. In step S104, during the determination of the conflict intensity coefficient, the system extracts the weights passed between nodes in the resource association logic graph to construct a logical topology matrix. The set containing the product of the number of concurrent operations and the resource occupation time of each node is extracted as a column vector of business pressure features. The system executes the instruction to multiply the logical topology matrix and the column vector matrix of business pressure features to calculate and generate the load offset vector corresponding to each node dimension. The system calls the singular value decomposition algorithm to process the covariance matrix of the load offset vector, extracts the largest principal component with an eigenvalue contribution rate exceeding 85%, and outputs the magnitude of the principal component as the conflict intensity coefficient reflecting the queuing congestion degree of a specific node. Before executing the above singular value decomposition algorithm, the system does not process isolated column vectors at a single moment, but extracts multiple load offset column vectors within a continuous preset number of sampling periods, and combines and splices them horizontally according to the time series to form a multi-dimensional historical load tracking matrix. The covariance matrix corresponding to the tracking matrix is ​​calculated and used as the compliance input parameter of the algorithm.

[0045] For bottleneck nodes, the academic affairs management system retrieves backup resource nodes with idle capacity indicators from the resource association logic graph. It then uses a dynamic path planning algorithm to calculate the optimal reconstruction path for migrating from the bottleneck node to the backup resource node, and confirms this optimal reconstruction path as the resource constraint release path. The academic affairs management system updates the node topology in the resource association logic graph based on this resource constraint release path. During this process, the academic affairs management system uses a hash sharding algorithm to perform access reset on the academic affairs event sequence. This access reset, while maintaining the top-level business constraint logic of the academic affairs system completely unchanged, is a specific control process in the underlying data flow network that dynamically rewrites and relocates the addressing path of the business request that causes congestion. Specifically, when the original... When a resource node is identified as a bottleneck due to a surge in high concurrency, the system utilizes dynamically planned resource constraint release paths. By overwriting the underlying communication routing table or updating hash mapping relationships, it forcibly intercepts access requests originally directed to the bottleneck node at the transport or application layer of the network protocol stack and redirects them in real time to homogeneous backup resource nodes with available capacity. This mechanism breaks the limitations of traditional static single mapping in databases, achieving seamless and smooth switching of business access paths and physical load balancing. Through the aforementioned access reset mechanism, newly generated academic affairs event sequences are guided to the reconstructed faculty nodes, space nodes, or course nodes, and the response frequency changes of the reconstructed nodes are monitored to calculate the corresponding conflict decay rate. The management system, based on resource constraint release paths, resets the timetable weight parameters of teacher nodes and synchronously updates the allocation priority of spatial nodes and the logical sequence of course nodes to achieve adaptive scheduling of teaching resources. In step S105, updating node topology relationships, the system uses Dijkstra's algorithm to perform dynamic path planning, scans and retrieves the resource association logic graph, extracts the current available connections of backup resource nodes and the logical step size between nodes, and determines the reciprocal of the ratio of the two as the travel resistance weight of the directed dependency network edge. The system uses the identified bottleneck node as the starting optimization coordinate, calculates the cumulative path loss to each backup resource node hop by hop according to the travel resistance weight, and selects the link sequence with the minimum cumulative loss value as the resource constraint release path. According to the sequence overwrite system hash reset routing table, subsequent new academic affairs event sequence access requests are directly routed to the reconstructed idle nodes. In this reset process, the system only rewrites the request routing at the data flow level of the microservice architecture, so that database operations or concurrent queuing instructions pointing to congested nodes are seamlessly diverted to the backup mirror server pool with homogeneous business logic, rather than tampering with students' elective course entities or violating prerequisite constraints in the top-level academic affairs scenario. The academic affairs management system establishes closed-loop monitoring and rollback logic. When the conflict decay rate is lower than the preset self-healing threshold, the current resource constraint release path is stopped, and the resource association logic graph is restored to the initial state before the logic reconstruction based on the historical state snapshot of the bottleneck node.Through the above steps, the academic affairs management system, without changing the underlying database architecture, improves the accuracy of teaching resource allocation in high-concurrency environments by dynamically reconstructing logical constraints, and maintains the stability of business operations by utilizing closed-loop monitoring logic.

[0046] Example 2: The experiment constructs a distributed stress test environment simulating a comprehensive vocational education park to verify the scheduling stability of an information management method based on an academic affairs management system when dealing with massive concurrent business. This environment simulates the concurrent interaction behavior of 20,000 independent users. The experimental data source is collected from historical academic affairs operation records stored in the academic affairs management system, and the data has undergone anonymization processing. The core objective of the experiment is to quantitatively analyze the reconstruction efficiency of the resource association logic graph under logical stress through continuous monitoring of academic affairs event sequences. In the initialization phase of the experiment, the system determines the sampling period as a core process parameter. The value of this parameter determines the balance between the real-time performance of data acquisition and the system's computational load. If the sampling period is too large, the system cannot identify logical conflicts caused by transient pulses; if the sampling period is too small, redundant computational overhead will occur. The logic for determining the sampling period is as follows: the system reads the frequency of course selection interactions per unit time; calculates the first derivative of the interaction frequency to characterize the rate of business fluctuation; establishes a negative correlation mapping relationship between the sampling period and the first derivative. When the rate of change of the interaction frequency is detected to increase, the system reduces the sampling period. Under the typical working conditions of this experiment, when the interaction frequency is in the preset stable range, the sampling period is set to 100ms. When the rate of change of the interaction frequency exceeds the preset sudden change threshold, the sampling period is automatically adjusted to 20ms to ensure the accuracy of state perception.

[0047] To simulate abnormal interference in a real communication environment, 15% of invalid records and random channel jitter noise were injected into the collected atomic interaction logs. The system used a preset smoothing operator to denoise the academic affairs event sequence and eliminated fluctuation anomalies by calculating the statistical deviation of the sequence within the sampling period. Experimental data showed that in the unprocessed raw data segment, the state deviation value D oscillated at high frequencies due to noise, fluctuating between 0.42 and 0.65, leading to misjudgments by the system. However, after processing by the smoothing operator, the state deviation value D stabilized below 0.08 during the stable business phase. Here, D is the state deviation value, and its calculation logic follows the formula defined above. The processed data can reflect the distribution characteristics of academic affairs business within the preset observation period and is compared with the probability distribution of the preset baseline period to determine the degree of evolution deviation of academic affairs business.

[0048] This experiment set up three sample groups for comparative verification, including a control group 1 using static pre-allocation logic, a partial missing control group 2 removing the topology reconstruction step, and an experimental group using the method of this invention. As the concurrent requests increased from 1000 requests per second to 5000 requests per second, the control group 1 experienced a sudden increase in average response latency from 15.4ms to 1450.8ms when the concurrency reached 2200 requests, due to the bottleneck node's inability to absorb logical stress, causing the system to enter a blocked state. The control group 2, which only had noise reduction sensing and lacked dynamic topology reconstruction functionality, saw its conflict intensity coefficient saturate at the core course node when the concurrency reached 3100 requests, causing the resource allocation success rate to drop from 98.5% to 62.4%. The experimental group, by calculating the transmission weight of business pressure characteristics between resource association logic graph nodes and issuing resource constraint release paths based on the conflict intensity coefficient determined by the load offset vector, maintained a stable average response latency under a peak load of 5000 requests per second. At approximately 28.6ms, with a resource allocation success rate exceeding 99.2%, the experiment further tested the boundary effect of the logical interference threshold to verify the rationality of the invention's defined scope. When the logical interference threshold was set at more than 2.5 times the normal conflict intensity, the system experienced lag in identifying bottleneck nodes, causing the conflict attenuation rate to drop from 0.85 to 0.12, exhibiting significant response degradation. However, when the logical interference threshold was set within the optimal working window of [1.3, 1.6], the system was able to balance scheduling frequency and stability. At this point, the calculated state deviation value D and the conflict intensity coefficient showed a high degree of linear coupling. Through comparison of multiple sets of gradient data, it was demonstrated that the method of this invention, by establishing a dynamic mapping logic between business pressure and resource topology, successfully transformed the static logical constraints in the academic affairs management system into a flexible reconstruction path. Under the premise that the total amount of physical resources remained unchanged, the method achieved precise monitoring and stable scheduling of complex business evolution states through real-time adjustment of the management logical path.

[0049] Example 3: In the scenario of resource scheduling during the final exam week of a comprehensive vocational education park, the academic affairs management system faces a concentrated scheduling task involving 120 courses across three major professional categories. The conflict of teacher node occupancy and the capacity constraints of space nodes form a highly coupled nonlinear stress. The academic affairs management system determines the control parameters for resource scheduling and implements constraint release through the following procedures: The academic affairs management system retrieves the logical dependencies of resource nodes, determines the scheduling availability of teacher nodes, the physical carrying capacity of space nodes, and the exam sequence requirements of course nodes as the initial technical state, and transforms the above prerequisite constraints into logical edges in a directed dependency network; when two courses share teachers or student groups, the system establishes a logical edge between the nodes of the corresponding two courses; when the exam of one course must be conducted after the exam of another course, the system establishes a directional logical edge.

[0050] The academic affairs management system takes the number of concurrent invigilation applications and the examination room occupancy time in the academic affairs event sequence as input to generate business pressure characteristics affecting each resource node. To determine the transmission weight, the system establishes a weight allocation model based on logical step size. That is, the academic affairs management system identifies the logical distance from node a to node b in the resource association logical graph. This logical distance is determined by the minimum number of logical edges between the two nodes. The system calculates the reciprocal of this logical distance as the transmission weight between nodes. Based on this, the academic affairs management system constructs a logical topology matrix of a directed dependency network to reflect the coupling strength between nodes. According to the logical topology matrix, the academic affairs management system calculates the load offset vector caused by the business pressure characteristics at each node and extracts the principal component components in the load offset vector to determine the conflict intensity coefficient. To ensure that the determination of the logical interference threshold has an engineering basis, the system performs the following calibration procedure: retrieves historical conflict intensity data of the academic affairs management system in a stable state within a preset operating cycle, and calculates the sample mean of the historical data. with sample standard deviation The logical interference threshold is determined according to the following formula. : Where T is the logical interference threshold. This represents the sample mean of the conflict intensity. The standard deviation of the conflict intensity is given by the Laida criterion, which eliminates the subjective influence of parameter settings.

[0051] When the conflict intensity coefficient of a certain examination room node exceeds the logical interference threshold T, the system identifies it as a bottleneck node and searches for a classroom with available capacity in the resource association logic graph as a backup resource node. A dynamic path planning algorithm is used to calculate the resource constraint release path from the bottleneck node to the backup resource node, and the node topology in the resource association logic graph is updated based on this path. The academic affairs management system uses a hash sharding algorithm to redirect subsequent examination scheduling instructions to the reconstructed spatial nodes and continuously monitors the response frequency of the reconstructed nodes to calculate the conflict decay rate. If the conflict decay rate is lower than a preset self-healing threshold, the system uses stored node state snapshots to restore the resource association logic graph to its initial state before logical reconstruction to prevent cascading failures caused by scheduling actions. Through the above operations on the resource topology matrix and dynamic threshold calibration, the academic affairs management system maintains the conflict intensity coefficient within a preset range under conditions of limited examination room resources, achieving stable scheduling of educational resources in complex constraint environments.

[0052] Example 4: During the deployment of the newly established smart campus academic affairs central system, the academic affairs management system determines the resource sensitivity weights corresponding to each academic affairs business. The academic affairs management system simulates single-item stress on n standard academic affairs services, such as course selection and grade entry, under a controlled offline testing environment. It records the CPU cycle usage, peak memory usage, and database lock holding time for each service during processing. The system calculates the weighted sum of these indicators and generates a normalized result to determine the basic resource consumption coefficient for that service. Finally, the system multiplies this coefficient by the reciprocal of the corresponding resource node's concurrency capacity limit to determine the resource sensitivity weight for that service. .

[0053] When the academic affairs management system is initially deployed in a new teaching unit, the frequency of occurrence within the preset benchmark period required to calculate the state deviation value D is determined. The system employs a 14-day silent observation procedure to eliminate the impact of differences in operational habits among specific institutions on the baseline distribution. During the silent observation period, the academic affairs management system maintains its original management logic, collects atomic interaction logs, extracts academic affairs event sequences, and calculates the average daily frequency of the i-th academic affairs transaction across different time periods. At the end of the observation period, the academic affairs management system calculates the variance of this frequency across all observation days. When the variance is lower than the preset convergence index, the academic affairs management system confirms the average frequency of occurrence within the silent observation period as the frequency of occurrence within the preset baseline period. At this point, the academic affairs management system completes the in-situ modeling of the business characteristics of the new environment and starts the real-time status monitoring logic based on the determined baseline frequency distribution.

[0054] Example 5: In an application scenario where the integration of newly added teaching buildings into the academic affairs management system leads to changes in the size of spatial nodes, the system initiates a process targeting the frequency of occurrence within a preset baseline period. The system employs a dynamic calibration procedure to eliminate deviations in business statistical benchmarks caused by physical resource expansion; it retrieves atomized interaction logs after new buildings are put into use, calculates the average daily frequency of each academic affairs activity over five consecutive working days, and compares the currently observed average activity frequency with the original... The statistical variance between them; when the statistical variance exceeds a preset drift threshold, the system uses a sliding window moving average algorithm to adjust the value. Numerical overlay is performed, and the updated values ​​are used as the baseline distribution for subsequent state deviation value D calculations, so that the measurement logic of business evolution deviation is kept in sync with the current physical resource boundaries.

[0055] When the academic affairs management system integrates heterogeneous server nodes with different processing performance specifications as logical carriers of faculty nodes or space nodes, the system implements resource sensitivity weights. The in-situ calibration process involves sending simulated service requests of preset intensity to newly added resource nodes via a stress simulation module, synchronously collecting the database input / output waiting time and logic lock holding time of the resource nodes while processing the requests, and establishing a resource consumption feature vector composed of waiting time and logic lock holding time. The system calculates the magnitude of this feature vector and maps it to the ratio of its physical rated throughput to determine the sensitivity weight of new resources for the corresponding academic affairs business under this specific hardware environment. The calculation results are then injected into the weight matrix to update the transmission weights in the resource association logic graph. The system continuously monitors the conflict intensity coefficient calculated under the influence of this weight until its fluctuation range converges to the preset steady-state range. The adaptive scheduling capability of academic resources in a heterogeneous environment has thus completed the hardware-level alignment verification.

[0056] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. An information management method based on an academic affairs management system, characterized in that, Includes the following steps: Step S101: Collect atomic interaction logs within the academic affairs management system and extract the sequence of academic affairs events containing timestamps, user identity identifiers, and academic affairs resource identifiers. Step S102: Perform smoothing and denoising processing on the sequence of academic affairs events within the sampling period, and compare the frequency distribution of the sequence of academic affairs events between the preset observation period and the preset benchmark period to calculate the state deviation value that characterizes the degree of deviation in the evolution of academic affairs business. Step S103: Obtain the logical dependencies of resource nodes within the academic affairs management system, construct a resource association logic graph, and calculate the business pressure characteristics acting on each node of the resource association logic graph based on the academic affairs event sequence. Step S104: Based on the logical connection strength of the resource association logic graph, determine the transmission weight of the business pressure feature quantity between the nodes of the resource association logic graph, calculate the conflict intensity coefficient reflecting the degree of conflict of each node of the resource association logic graph, and identify the nodes whose conflict intensity coefficient exceeds the preset logical interference threshold as bottleneck nodes. Step S105: Determine the resource constraint release path based on the logical dependency boundary of the bottleneck node, and update the node topology relationship in the resource association logic graph according to the resource constraint release path, so as to realize the adaptive scheduling of teaching resources.

2. The information management method based on the academic affairs management system according to claim 1, characterized in that, Step S102 specifically includes: calculating the statistical deviation of the academic affairs event sequence within the sampling period, using a preset smoothing operator to filter out the source noise of random fluctuations in the academic affairs event sequence; statistically analyzing the business status distribution of the academic affairs event sequence between consecutive preset observation periods, and determining the probability density difference between the business status distribution and the benchmark distribution within the preset benchmark period as the status deviation value.

3. The information management method based on the academic affairs management system according to claim 1, characterized in that, Step S103 specifically includes: retrieving teacher nodes, space nodes, and course nodes from the academic affairs management system; establishing a directed dependency network based on the prerequisite constraints between teacher nodes, space nodes, and course nodes; defining it as a resource association logic graph; using the number of concurrent operations and the duration of resource occupation in the atomic interaction log as components to generate business pressure feature quantities; and mapping the business pressure feature quantities to the corresponding teacher nodes, space nodes, or course nodes in the directed dependency network.

4. The information management method based on the academic affairs management system according to claim 3, characterized in that, The method for identifying bottleneck nodes in step S104 includes: calculating the load offset vectors caused by the business pressure characteristics at each teacher node, space node, and course node based on the logical topology matrix of the directed dependency network; extracting the principal component components in the load offset vectors to determine the conflict intensity coefficient; and determining the spatial location of the bottleneck node when the value of the conflict intensity coefficient exceeds the preset logical interference threshold.

5. The information management method based on the academic affairs management system according to claim 1, characterized in that, Step S105 specifically includes: for the bottleneck node, searching for a backup resource node with an idle capacity index in the resource association logic graph; using a dynamic path planning algorithm to calculate the optimal reconstruction path for migrating from the bottleneck node to the backup resource node, and confirming the optimal reconstruction path as the resource constraint release path; adjusting the node connection weights in the directed dependency network according to the resource constraint release path.

6. The information management method based on the academic affairs management system according to claim 5, characterized in that, Updating the node topology in the resource association logic graph includes: using a hash sharding algorithm to reorient the access of the academic affairs event sequence, guiding the newly generated academic affairs event sequence to the reconstructed faculty node, space node, or course node; monitoring the response frequency changes of the reconstructed nodes, and calculating the corresponding conflict decay rate.

7. The information management method based on the academic affairs management system according to claim 6, characterized in that, The method also includes step S106: establishing closed-loop monitoring rollback logic, stopping the issuance of the current resource constraint release path when the conflict decay rate is lower than the preset self-healing threshold, and restoring the resource association logic graph to the initial state before logic reconstruction based on the historical state snapshot of the bottleneck node.

8. The information management method based on the academic affairs management system according to claim 1, characterized in that, To achieve adaptive scheduling of teaching resources, the following steps are taken: based on the resource constraint release path, the timetable weight parameters of the teacher nodes are reset, and the allocation priority of the space nodes and the logical order of the course nodes are updated synchronously.

9. The information management method based on the academic affairs management system according to claim 1, characterized in that, The sequence of academic affairs events includes course selection requests, grade entry, resource applications, and course changes; resource nodes include teacher resources, classroom space, and course logical units.

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