College machine room screen broadcast teaching system and method based on behavior monitoring

Abstract

The invention discloses a college machine room screen broadcast teaching system and method based on behavior monitoring, and relates to the technical field of attendance data supervision. The college machine room screen broadcast teaching system based on behavior monitoring comprises a synchronous delay analysis module, a preloading accuracy analysis module and a rule configuration analysis module. According to the method, the synchronous delay analysis is performed on the synchronous transmission process of the screen broadcast teaching platform through the acquired synchronous delay data, then the preloading accuracy analysis is performed based on the acquired synchronous delay analysis result and the multi-thread parallel efficiency, and whether branch acquisition prediction optimization is performed or not is judged; finally, attendance adjustability analysis is carried out through the obtained attendance configuration data to judge whether to carry out rule intelligent adaptation adjustment or not, so that the flexibility is improved when the screen broadcast teaching platform synchronously processes the attendance monitoring data; the problem that in the prior art, a screen broadcast teaching platform is not high in flexibility when synchronously processing attendance monitoring data is solved.

Description

Technical Field

[0001] This invention relates to the field of attendance data monitoring technology, and in particular to a teaching system and method for screen broadcasting in university computer labs based on behavior monitoring. Background Technology

[0002] With the deep integration of computer technology and educational informatization, university computer lab screen broadcasting teaching systems have evolved from one-way demonstrations to intelligent interaction. With the upgrading of network infrastructure and the maturity of artificial intelligence technology, behavior monitoring functions have been gradually integrated into the teaching system, forming a modern teaching management platform that integrates screen broadcasting, real-time monitoring, and intelligent analysis. In the early stages, only basic screen broadcasting functions were available, using LAN broadcasting technology, where the teacher's computer sent screen images to the student's computer for one-way demonstrations. In the mid-stage, interactive teaching and centralized management were developed, introducing two-way interactive functions, combined with cloud storage, supporting courseware sharing and after-class review. In the recent stage, it has evolved into a system integrating intelligence and behavior monitoring, implementing refined management.

[0003] Existing technologies collect and centrally process student information through a unified control platform; and obtain comprehensive indicators based on the monitoring and analysis of various student behaviors to judge the student's specific performance.

[0004] For example, the invention patent with announcement number CN116596719B discloses a computer lab teaching quality management system and method, which includes: a monitoring data acquisition module, a teaching analysis module, a practice analysis module, a management analysis module, a platform database, and an execution terminal. It monitors students' abnormal behavior and screen-switching duration during teaching periods, thereby analyzing students' abnormal behavior for each teaching period.

[0005] For example, the invention patent with announcement number CN118246638A discloses a method, system, and storage medium for analyzing and evaluating student teaching and learning behaviors, including: detecting student attendance, analyzing attendance, detecting student interest, analyzing student interest, monitoring learning progress, analyzing learning efficiency, and analyzing learning behaviors, and obtaining a student behavior evaluation index for the detection period through comprehensive analysis of various detection data.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: In the existing technology, the existing system relies on manual review of attendance anomalies and cannot automatically identify special situations such as network latency and equipment failure. For example, if a group of students do not sign in due to a network failure in the computer room, the system will still mark them as absent. It is difficult to flexibly adjust the attendance judgment logic according to the actual attendance scenario, and it cannot intelligently identify attendance anomalies caused by special situations and optimize the processing flow independently. There is a problem that the screen broadcast teaching platform is not flexible enough when processing attendance monitoring data synchronously. Summary of the Invention

[0007] This application provides a screen broadcasting teaching system and method for university computer labs based on behavior monitoring, which solves the problem of low flexibility in the prior art when screen broadcasting teaching platforms are processing attendance monitoring data synchronously, and improves the flexibility of screen broadcasting teaching platforms when processing attendance monitoring data synchronously.

[0008] This application provides a screen broadcasting teaching system for university computer labs based on behavior monitoring, including: a synchronization delay analysis module, a preloading accuracy analysis module, and a rule configuration analysis module. The synchronization delay analysis module performs synchronization delay analysis on the synchronous transmission process of the screen broadcasting teaching platform based on acquired synchronization delay data, obtaining synchronization delay analysis results. It also determines whether to perform cache delay optimization based on the acquired synchronization delay analysis results. Synchronization delay analysis quantifies the synchronization efficiency of acquired attendance status change data during synchronous transmission on the screen broadcasting teaching platform. Cache delay optimization refers to improving the synchronization processing efficiency of the screen broadcasting teaching platform for attendance status change data through intelligent cache scheduling and graph computing engine reconstruction. The preloading accuracy analysis module performs preloading accuracy analysis on the intelligent cache scheduling process of the screen broadcasting teaching platform after synchronous transmission is completed, based on the acquired multi-threaded parallel efficiency, obtaining preloading accuracy analysis results. The system determines whether to perform branch acquisition prediction optimization based on the obtained preload accuracy analysis results. Branch acquisition prediction optimization refers to improving the accuracy of attendance status change data during the scheduling process through branch prediction optimization and data acquisition frequency optimization. Preload accuracy analysis is used to quantify the cache scheduling accuracy of attendance status change data in the screen broadcast teaching platform after synchronous transmission is completed. The rule configuration analysis module is used to perform attendance adjustability analysis on the attendance configuration status of the screen broadcast teaching platform based on the obtained attendance configuration data after intelligent cache scheduling is completed, and obtain the attendance adjustability analysis results. At the same time, it determines whether to perform attendance policy adjustment optimization based on the obtained attendance adjustability analysis results. Attendance policy adjustment optimization refers to improving the flexibility of the screen broadcast teaching platform in configuring attendance rules through attendance policy adjustment optimization and rule conflict detection. Attendance adjustability analysis is used to quantify the rule flexibility of attendance status change data in the identity authentication and course session activation process after intelligent cache scheduling is completed.

[0009] This application provides a screen broadcasting teaching method for university computer labs based on behavior monitoring, including the following steps: Step 1, performing synchronization delay analysis on the synchronization transmission process of the screen broadcasting teaching platform based on the acquired synchronization delay data, obtaining synchronization delay analysis results, and determining whether to perform cache delay optimization based on the acquired synchronization delay analysis results. Cache delay optimization means improving the synchronization processing efficiency of the screen broadcasting teaching platform for attendance status data changes through intelligent cache scheduling and graph computing engine reconstruction; Step 2, after the synchronization transmission is completed, performing preloading accuracy analysis on the intelligent cache scheduling process of the screen broadcasting teaching platform based on the acquired multi-threaded parallel efficiency, obtaining preloading accuracy... The accuracy analysis results are used to determine whether branch collection prediction optimization should be performed based on the obtained preload accuracy analysis results. Branch collection prediction optimization means improving the accuracy of attendance status change data during the scheduling process through branch prediction optimization and data collection frequency optimization. Step 3: After the intelligent cache scheduling is completed, the attendance configuration status of the screen broadcast teaching platform is analyzed for attendance adjustability based on the obtained attendance configuration data to obtain attendance adjustability analysis results. Attendance strategy adjustment optimization means improving the flexibility of the screen broadcast teaching platform in configuring attendance rules through attendance strategy adjustment optimization and rule conflict detection.

[0010] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By analyzing the synchronous transmission process of the screen broadcast teaching platform using acquired synchronous delay data, the accuracy of preloading is analyzed based on the results of the synchronous delay analysis and the efficiency of multi-threaded parallelism. This determines whether branch acquisition and prediction optimization should be performed. Finally, attendance adjustability analysis is conducted using acquired attendance configuration data to determine whether intelligent rule adaptation adjustment should be performed. This improves the accuracy of preloading during intelligent cache scheduling, thereby enhancing the flexibility of the screen broadcast teaching platform in synchronously processing attendance monitoring data. This effectively solves the problem of low flexibility in synchronously processing attendance monitoring data on the screen broadcast teaching platform.

[0011] 2. By correcting the difference between the peak response latency and the peak response latency referenced in the database using the peak response latency correction value, a peak response latency score is obtained. Similarly, the cache response latency score and data throughput score are obtained through the same steps. The peak response latency score, cache response latency score and data throughput score are coupled to obtain the synchronization latency index, thereby improving the accuracy of synchronization latency index acquisition and enabling more accurate evaluation of synchronization latency data during the acquisition process.

[0012] 3. By correcting the difference between the attendance configuration quantity and the reference attendance configuration quantity in the database using the attendance configuration quantity correction value, an attendance configuration quantity score is obtained. Similarly, the attendance adjustment response time score and the flexible attendance time ratio score are obtained through the same steps. The attendance configuration quantity score, attendance adjustment response time score, and flexible attendance time ratio score are coupled to obtain the attendance policy adjustability index, thereby improving the accuracy of obtaining the attendance policy adjustability index and enabling more accurate evaluation of policy adjustments in the identity authentication and course session activation process. Attached Figure Description

[0013] Figure 1 A schematic diagram of the structure of a university computer lab screen broadcasting teaching system based on behavior monitoring provided in this application embodiment; Figure 2 A flowchart for judging the preliminary synchronization delay index corresponding to the synchronization delay analysis module provided in this application embodiment; Figure 3 This is a flowchart illustrating the cache latency optimization process in the synchronization latency analysis module provided in this application embodiment. Figure 4 The flowchart of the preloading accuracy analysis module provided in the embodiments of this application is as follows; Figure 5 The flowchart of the rule configuration analysis module provided in the embodiments of this application is as follows; Figure 6 A flowchart of a teaching method for university computer lab screen broadcasting based on behavior monitoring, provided as an embodiment of this application. Detailed Implementation

[0014] This application provides a screen broadcasting teaching system and method for university computer labs based on behavior monitoring, solving the problem of low flexibility in the synchronous processing of attendance monitoring data in existing screen broadcasting teaching platforms. The system includes: a synchronization delay analysis module, a preloading accuracy analysis module, and a rule configuration analysis module. The synchronization delay analysis module performs synchronization delay analysis on the synchronous transmission process of the screen broadcasting teaching platform based on the acquired synchronization delay data, obtaining the synchronization delay analysis result, and determining whether to perform cache delay optimization based on the obtained synchronization delay analysis result. The preloading accuracy analysis module is used to, after the synchronous transmission is completed, based on... The obtained multi-threaded parallel efficiency is used to perform preloading accuracy analysis on the intelligent cache scheduling process of the screen broadcast teaching platform, and the preloading accuracy analysis results are obtained. At the same time, based on the obtained preloading accuracy analysis results, it is determined whether to perform branch collection prediction optimization. The rule configuration analysis module is used to perform attendance adjustability analysis on the attendance configuration status of the screen broadcast teaching platform after the intelligent cache scheduling is completed, based on the obtained attendance configuration data, and the attendance adjustability analysis results are obtained. At the same time, based on the obtained attendance adjustability analysis results, it is determined whether to adjust and optimize the attendance strategy, thereby improving the flexibility of the screen broadcast teaching platform when synchronously processing attendance monitoring data.

[0015] The technical solution in this application embodiment aims to address the problem of low flexibility in the aforementioned screen broadcast teaching platform when synchronously processing attendance monitoring data. The overall approach is as follows: The system determines whether to optimize cache latency based on the synchronous latency analysis results, then determines whether to optimize branch collection prediction based on the obtained preload accuracy analysis results, and performs attendance adjustability analysis on the attendance configuration status of the screen broadcast teaching platform based on the obtained attendance configuration data. Finally, it determines whether to adjust and optimize the attendance strategy based on the attendance adjustability analysis results. This approach improves the flexibility of the screen broadcast teaching platform when processing attendance monitoring data synchronously.

[0016] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0017] like Figure 1The diagram shows the structure of a university computer lab screen broadcasting teaching system based on behavior monitoring provided in this embodiment of the application. The system includes: a synchronization delay analysis module, a preloading accuracy analysis module, and a rule configuration analysis module. The synchronization delay analysis module performs synchronization delay analysis on the synchronous transmission process of the screen broadcasting teaching platform based on acquired synchronization delay data, obtaining synchronization delay analysis results. It also determines whether to perform cache delay optimization based on the acquired synchronization delay analysis results. Synchronization delay analysis quantifies the synchronization efficiency of acquired attendance status change data during synchronous transmission on the screen broadcasting teaching platform. Cache delay optimization refers to improving the synchronization processing efficiency of the screen broadcasting teaching platform for attendance status change data through intelligent cache scheduling and graph computing engine reconstruction. The preloading accuracy analysis module preloads the intelligent cache scheduling process of the screen broadcasting teaching platform based on the acquired multi-threaded parallel efficiency after synchronous transmission is completed. Accuracy analysis is performed to obtain the pre-loading accuracy analysis results. Based on these results, it is determined whether branch acquisition prediction optimization should be performed. Branch acquisition prediction optimization improves the accuracy of attendance status change data during scheduling by optimizing branch prediction and data acquisition frequency. Pre-loading accuracy analysis quantifies the accuracy of cache scheduling of attendance status change data in the screen broadcast teaching platform after synchronous transmission. The rule configuration analysis module performs attendance adjustability analysis on the attendance configuration status of the screen broadcast teaching platform based on the obtained attendance configuration data after intelligent cache scheduling. Based on these results, it is determined whether attendance policy adjustment optimization should be performed. Attendance policy adjustment optimization improves the flexibility of attendance rule configuration on the screen broadcast teaching platform by adjusting and optimizing attendance policies and detecting rule conflicts. Attendance adjustability analysis quantifies the flexibility of rules in the identity authentication and course session activation process after intelligent cache scheduling.

[0018] In this embodiment, as Figure 2 The diagram shown is a flowchart of the preliminary synchronization delay index judgment process corresponding to the synchronization delay analysis module provided in this application embodiment. The synchronization delay index is obtained based on the acquired synchronization delay analysis results. It is then determined whether the delay index is greater than a preset index in the database. If so, further processing is performed. Figure 3 Optimize cache latency; otherwise, send data transmission instructions.

[0019] like Figure 3As shown in the diagram, the cache latency optimization workflow of the synchronization latency analysis module provided in this application embodiment is as follows: Based on the obtained synchronization latency index deviation and cache line utilization deviation, a first cache scheduling correction value corresponding to the synchronization latency index deviation and cache line utilization deviation is obtained by mapping them in the database. It is determined whether the correction value is within the preset range in the database. If it is, the cache scheduling optimization is completed. Otherwise, the index is re-obtained and a second correction value is output. It is determined whether the obtained synchronization latency index is greater than the set value in the database. If not, a preloading accuracy analysis command is sent. If it is, the synchronization latency index deviation and the second correction value are input and the cache preloading correction value is output. Computational resources are dynamically allocated. It is determined whether the preloading time is less than the preset value set in the database. If it is, the optimization is completed and a preloading analysis command is sent. Otherwise, a reconstruction warning command is issued.

[0020] like Figure 4 The diagram shows the workflow of the pre-loading accuracy analysis module provided in this application embodiment. It obtains the multi-threaded parallel efficiency, determines whether the efficiency is greater than a set value in the database, and if so, performs rule configuration analysis; otherwise, it performs branch acquisition prediction optimization. Based on the multi-threaded efficiency deviation and branch misprediction rate deviation, it maps the first branch prediction correction value corresponding to the multi-threaded efficiency deviation and branch misprediction rate deviation in the database, determines whether the reduction is within a preset range, and if so, completes branch prediction optimization; otherwise, it re-acquires the indicator and outputs the second correction value, determines whether the multi-threaded efficiency is greater than the set value, and if so, completes branch prediction optimization; otherwise, it performs data acquisition frequency optimization, maps the multi-threaded efficiency deviation and instruction throughput in the database, obtains the acquisition frequency adjustment value corresponding to the multi-threaded efficiency deviation and instruction throughput, and determines whether the multi-threaded efficiency is greater than the set value in the database. If so, it completes optimization and starts attendance adjustability analysis; otherwise, it sends an acquisition frequency warning command.

[0021] like Figure 5The diagram shows the workflow of the rule configuration analysis module provided in this application embodiment. It obtains the adjustability index of the attendance policy, determines whether the index is greater than a set value in the database, and if so, indicates that the policy adjustment is qualified and the policy is executed; otherwise, it optimizes the attendance policy adjustment. Based on the adjustability index deviation and fixed threshold, it maps the dynamic policy adjustment range corresponding to the adjustability index deviation and fixed threshold in the database. This adjustment range is then input into the identity authentication and course session activation process to output the change range. It determines whether the change range is within the preset range set in the database. If so, the attendance policy adjustment optimization is completed; otherwise, rule conflict detection is performed. Based on the adjustment change range and resource usage value, it maps the deadlock probability reduction range corresponding to the adjustment change range and resource usage value in the database, and determines whether the reduction range is greater than a preset value in the database. If so, it sends a resource pre-allocation instruction to prevent deadlock and sends a deadlock risk warning; otherwise, rule conflict detection is completed.

[0022] This system uses synchronization delay metrics to accurately quantify the latency of synchronization delay data on the screen broadcast teaching platform during the synchronous transmission period. Similarly, by acquiring attendance policy adjustability metrics, it can accurately quantify the adjustability of attendance configuration data in the identity authentication and course session activation process. This quantification provides objective and accurate data support for the data processing process, helping to understand the latency and adjustability of this process. Secondly, the acquired metrics can dynamically evaluate the optimization effect and determine whether to repeat the optimization operation or issue an alert based on the optimization effect. This closed-loop adjustment mechanism ensures the continuous optimization and improvement of the process, thereby improving the flexibility of the screen broadcast teaching platform when synchronously processing attendance monitoring data.

[0023] Furthermore, the synchronization latency data includes peak response latency, buffer response latency, and real-time data throughput. Based on the acquired synchronization latency data, a synchronization latency analysis is performed on the synchronous transmission process of the screen broadcast teaching platform. Specific steps include: First, the obtained peak response latency duration is compared with the preset peak response latency duration in the database. Simultaneously, a correction process is performed using a peak response latency duration correction value to obtain a peak response latency score. The specific expression is: In the formula, This represents the peak response latency score of the screen broadcast teaching platform at the end of the synchronous transmission period. This indicates the peak response delay correction value. This indicates the peak response latency of the screen broadcast teaching platform during the synchronous transmission period. This indicates the preset peak response delay duration.

[0024] Then, the obtained cache response latency is compared with the preset cache response latency in the database, and a correction is performed using a cache response latency correction value to obtain a cache response latency score. The specific expression is: In the formula, This represents the cache response latency score of the screen broadcast teaching platform at the end of the synchronous transmission period. This indicates the cache response latency correction value. This indicates the cache response latency of the screen broadcast teaching platform during the synchronous transmission period. This indicates the preset cache response latency.

[0025] Next, the acquired real-time data throughput is compared with the preset data throughput in the database, and a data throughput correction value is applied to obtain a data throughput score. The specific expression is: In the formula, This represents the data throughput score of the screen broadcast teaching platform at the end of the synchronous transmission period. This represents the data throughput correction value. This indicates the real-time data throughput of the screen broadcast teaching platform during the synchronous transmission period. This indicates the preset data throughput rate.

[0026] Finally, the data throughput score, after inverse proportional processing, is coupled with the peak response latency score and cache response latency score to obtain the synchronization latency metric. This metric quantifies the latency of synchronous data transmission within the screen broadcast teaching platform. The specific expression is: In the formula, This represents the synchronization delay metric of the screen broadcast teaching platform at the end of the synchronous transmission period. The inverse proportional processing here means performing a mathematical transformation on the data throughput score, so that the throughput, which was originally positively correlated with the synchronization delay (i.e., the higher the throughput, the lower the delay), becomes a value in the same direction as the synchronization delay metric.

[0027] In this embodiment, the preset peak response delay duration is represented by the sum of the historical peak response delay durations of the screen broadcast teaching platform at the end of the historical synchronous transmission period in the database; the preset cache response delay duration is represented by the sum of the historical cache response delay durations of the screen broadcast teaching platform at the end of the historical synchronous transmission period in the database; the preset data throughput is represented by the sum of the historical data throughput of the screen broadcast teaching platform at the end of the historical synchronous transmission period in the database; the peak response delay duration and cache response delay duration are obtained by time sensor monitoring, and the unit is milliseconds (ms); the data throughput is obtained by network optical splitter and traffic analyzer monitoring.

[0028] The peak response latency correction value, cache response latency correction value, and data throughput correction value represent the degree of influence of the peak response latency, cache response latency, and data throughput preset in the database on the acquisition of synchronization latency analysis indicators. Specifically, the database stores preset correction values ​​corresponding to the peak response latency, cache response latency, and data throughput. These correction values ​​have a pre-defined mapping relationship with the peak response latency, cache response latency, and data throughput. This mapping relationship can be one-to-one or many-to-one. For example, in practical applications, the real-time peak response latency, cache response latency, and data throughput can be input into this mapping relationship to quickly obtain the corresponding correction values. This provides an important quantitative indicator for evaluating the flexibility of the screen broadcast teaching platform in synchronously processing attendance monitoring data, thereby more accurately calculating the synchronization latency indicator.

[0029] In this example, the values ​​for peak response latency correction, cache response latency correction, and data throughput correction are all limited to between 0 and 1, and the sum of the three is 1.

[0030] It's important to note the interrelationship among peak response latency, cache response latency, and data throughput. Peak response latency directly reflects the overall system performance under high load, while cache response latency is a key component. A successful cache response significantly reduces request processing time, while a cache failure increases latency due to origin server lookups. Increased data throughput typically enhances system processing capacity, but exceeding resource limits (such as CPU and network bandwidth) can lead to task backlog and higher latency. Caching efficiency is crucial for balancing throughput and latency: a high cache hit rate reduces backend pressure, allowing the system to support higher throughput while maintaining low latency; conversely, cache misses or hotspot conflicts exacerbate I / O contention, simultaneously reducing throughput and increasing latency.

[0031] By considering the above-mentioned interaction mechanisms, we can gain a more comprehensive understanding of the relationship between synchronization delay indicators and various variables. These relationships are crucial for the flexibility assessment of the screen broadcast teaching platform when processing attendance monitoring data synchronously. By optimizing peak response latency, cache response latency, and data throughput, we have improved the flexibility of the screen broadcast teaching platform when processing attendance monitoring data synchronously, effectively solving the problem of low flexibility in the screen broadcast teaching platform when processing attendance monitoring data synchronously.

[0032] Furthermore, based on the obtained synchronization delay analysis results, it is determined whether to perform cache delay optimization. The specific steps include: comparing the obtained synchronization delay index with the synchronization delay index set in the database; if the obtained synchronization delay index is greater than the synchronization delay index set in the database, the obtained synchronization delay analysis result is recorded as unqualified synchronization transmission and cache delay optimization is performed; if the obtained synchronization delay index is not greater than the synchronization delay index set in the database, the obtained synchronization delay analysis result is recorded as qualified synchronization transmission and a preload accuracy analysis command is sent.

[0033] Specifically, the steps for cache latency optimization are as follows: First, based on the obtained synchronization latency deviation and cache line utilization deviation, a first cache scheduling correction value corresponding to the synchronization latency deviation and cache line utilization deviation is mapped in the database to improve cache synchronization response. After one cache scheduling optimization, it is determined whether the reduction in the obtained synchronization latency deviation is within the preset reduction range in the database. If so, the cache scheduling optimization is completed; otherwise, a second cache scheduling correction value corresponding to the synchronization latency deviation and cache line utilization deviation is mapped in the database based on the newly obtained synchronization latency deviation and cache line utilization deviation to improve cache synchronization response. The preset reduction range represents the database... The range corresponding to the maximum and minimum values ​​of the reduction in the historical synchronization delay index deviation; if the synchronization delay index re-acquired within the cache scheduling optimization count is greater than the synchronization delay index set in the database, then the graph computing engine reconstruction optimization is performed; otherwise, a preloading accuracy analysis command is sent; the first cache scheduling correction value refers to the compensation step size in the buffer adaptive adjustment, which improves the synchronization transmission frequency of the screen broadcast teaching platform by eliminating time deviation, thereby improving cache response; the second cache scheduling correction value refers to the preloading window size in the dynamic window adjustment, which improves the data processing volume of the screen broadcast teaching platform by expanding the amount of preloaded data, thereby improving cache response.

[0034] The specific steps for graph computing engine reconstruction and optimization are as follows: Based on the synchronization latency index deviation and the second cache scheduling correction value obtained after cache scheduling optimization and mapped in the database, the corresponding cache preloading correction value is dynamically allocated to the change range of computing resources; After graph computing engine reconstruction and optimization, it is determined whether the obtained cache preloading duration is less than the preset cache preloading duration in the database. If so, a cache scheduling optimization completion instruction is sent and a preloading accuracy analysis instruction is sent; otherwise, a graph computing engine reconstruction warning instruction is sent.

[0035] In this embodiment, the cache preload correction value refers to the data compression rate adjusted in the graph computing engine. This data compression rate improves the data transmission efficiency of the screen broadcast teaching platform by compressing topological features, thereby reducing the cache preload time. The synchronization latency index deviation represents the difference between the obtained synchronization latency index and the preset synchronization latency index. The cache line utilization deviation represents the difference between the obtained cache line utilization and the preset cache line utilization. The reduction in the synchronization latency index deviation represents the difference between the obtained synchronization latency index and the synchronization latency index re-obtained after a cache scheduling optimization.

[0036] Optimization is triggered by comparing the measured synchronization latency with a database-defined threshold: if the latency exceeds the standard, it is marked as unqualified and cache latency optimization is initiated; otherwise, it is marked as qualified and proceeds to preloading analysis. Cache optimization consists of two stages: the first stage handles latency deviation and cache line utilization deviation, generates correction values ​​to improve response speed, and then verifies whether the latency improvement meets the standard. If it does not meet the standard, it iterates until a preset number of optimizations is reached or the latency is qualified. If it still does not meet the standard after multiple optimizations, it enters the second stage, which combines latency deviation and correction values ​​to dynamically adjust resource allocation to optimize preloading performance. Finally, it verifies whether the preloading duration meets the standard. If it does, optimization is completed and preloading analysis is triggered; otherwise, a refactoring warning is issued.

[0037] The entire process forms a closed-loop control, gradually improving system performance through progressive optimization strategies. Its core lies in the dynamic feedback mechanism and multi-stage collaborative optimization, which not only ensures the rapid response of basic cache scheduling, but also addresses deep performance bottlenecks in complex scenarios through graph computing engine reconstruction.

[0038] Furthermore, based on the obtained preload accuracy analysis results, it is determined whether to perform branch acquisition prediction optimization. The specific steps include: if the obtained multi-threaded parallel efficiency is not greater than the multi-threaded parallel efficiency set in the database, the preload accuracy analysis result is recorded as unqualified and branch acquisition prediction optimization is performed; if the obtained multi-threaded parallel efficiency is greater than the multi-threaded parallel efficiency set in the database, the preload accuracy analysis result is recorded as qualified and rule configuration analysis is performed; the multi-threaded parallel efficiency is used to quantify the processing speed of the screen broadcast teaching platform when handling multi-threaded tasks.

[0039] Specifically, the steps for branch acquisition prediction optimization are as follows: Based on the acquired multi-threaded parallel efficiency deviation and branch misprediction rate deviation, a first branch prediction correction value corresponding to the multi-threaded parallel efficiency deviation and branch misprediction rate deviation is mapped in the database to improve branch parallel response; after one branch prediction optimization, it is determined whether the reduction in the acquired multi-threaded parallel efficiency deviation is within the preset reduction range in the database. If so, the branch prediction optimization is completed; otherwise, a second branch prediction correction value corresponding to the multi-threaded parallel efficiency deviation and branch misprediction rate deviation is mapped in the database based on the re-acquired multi-threaded parallel efficiency deviation and branch misprediction rate deviation to improve branch parallel response; if the re-acquired multi-threaded parallel efficiency within the number of branch prediction optimizations is greater than the multi-threaded parallel efficiency set in the database, the branch prediction optimization is completed; otherwise, data acquisition frequency optimization is performed; the first branch prediction correction value refers to the partition size in task partitioning adjustment. This partition size increases the overall computational load by increasing the partition size, thereby improving the branch calculation rate in the screen broadcast teaching platform; the second branch prediction refers to the queue capacity in pipeline parallelism. This queue capacity increases the branch running rate of the screen broadcast teaching platform by increasing the output queue, thereby improving the branch parallel response.

[0040] The specific steps for optimizing the data acquisition frequency are as follows: Based on the multi-threaded parallel efficiency deviation and instruction throughput re-acquired after branch prediction optimization, the data acquisition frequency adjustment values ​​corresponding to the multi-threaded parallel efficiency deviation and instruction throughput are mapped in the database to improve the data acquisition frequency of the screen broadcast teaching platform; if the multi-threaded parallel efficiency re-acquired after one data acquisition frequency optimization is greater than the multi-threaded parallel efficiency set in the database, the data acquisition frequency optimization is completed and attendance adjustability analysis is performed; otherwise, a collection frequency warning command is sent; the multi-threaded parallel efficiency deviation is used to quantify the degree of difference between the acquired multi-threaded parallel efficiency and the preset multi-threaded parallel efficiency in the database; the branch misprediction rate deviation is used to quantify the degree of difference between the acquired branch misprediction rate and the preset branch misprediction rate in the database; the decrease in the multi-threaded parallel efficiency deviation represents the difference between the acquired multi-threaded parallel efficiency and the multi-threaded parallel efficiency re-acquired after one branch prediction optimization.

[0041] In this embodiment, when the multi-threaded parallel efficiency is not up to standard, the system first dynamically adjusts the branch prediction parameters. It generates correction values ​​by quantitatively analyzing the parallel efficiency deviation and the misprediction rate deviation, directly improving the branch parallel response capability. If the branch prediction optimization still cannot achieve the target efficiency, the system will switch to the data acquisition frequency optimization stage. Based on the dynamic algorithm, it balances instruction throughput and resource consumption. By intelligently adjusting the acquisition frequency, the overall parallel processing speed can be further improved. By establishing a precise mapping relationship between efficiency deviation and correction measures, it ensures that the platform can quickly respond to real-time requirements and maintain stable performance output when processing multi-threaded tasks. Ultimately, this improves the concurrent processing capability of the teaching platform and reduces the probability of branch misprediction, allowing the system to adapt to different load conditions and ensuring the smooth operation of the attendance process.

[0042] Furthermore, the attendance configuration data includes the number of attendance configurations, the response time for attendance adjustments, and the percentage of flexible attendance time. Based on the obtained attendance configuration data, an attendance adjustability analysis is performed on the attendance configuration status of the screen broadcast teaching platform. The specific steps include: First, the obtained attendance configuration quantity is compared with the preset attendance configuration quantity in the database. Simultaneously, an attendance configuration quantity correction value is applied to obtain an attendance configuration quantity score. The specific expression is: In the formula, This indicates the attendance score for the screen broadcast teaching platform at the end of the configured time period. This indicates the adjustment value for the number of attendance settings. This indicates the number of attendance records configured on the screen broadcast teaching platform at the end of the configured time period. This indicates the number of preset attendance configurations.

[0043] Secondly, the obtained attendance adjustment response time is compared with the preset attendance adjustment response time in the database, and a correction is performed based on the attendance adjustment response time correction value to obtain an attendance adjustment response time score. The specific expression is: In the formula, This indicates the attendance adjustment response time score of the screen broadcast teaching platform at the end of the configured time period. This indicates the adjustment value for the response time of attendance adjustments. This indicates the response time for attendance adjustments on the screen broadcast teaching platform at the end of the configured time period. This indicates the preset response time for attendance adjustments.

[0044] Next, the obtained flexible attendance time percentage is compared with the preset flexible attendance time percentage in the database, and a correction is performed based on the flexible attendance time percentage correction value to obtain the flexible attendance time percentage score. The specific expression is: In the formula, This indicates the percentage of flexible attendance time at the end of the configured time period for the screen broadcast teaching platform. This indicates the adjustment value for the proportion of flexible attendance time. This indicates the percentage of flexible attendance time at the end of the configured time period for the screen broadcast teaching platform. This indicates the preset percentage of flexible attendance time.

[0045] Finally, the results of inversely proportionalizing the obtained attendance configuration quantity score and attendance adjustment response time score, along with the flexible attendance time percentage score, are coupled to calculate the attendance strategy adjustability index. The specific expression is: In the formula, This indicates the adjustability metric for configuring the attendance policy at the end of the time period during the identity authentication and course session activation process.

[0046] In this embodiment, the inverse proportional processing refers to a mathematical transformation of the attendance configuration quantity score and the attendance adjustment response time score, so that the attendance configuration quantity score and the attendance adjustment response time score, which were originally positively correlated with the adjustability of the attendance strategy, are transformed into values ​​consistent with the direction of the adjustability of the attendance strategy. The preset attendance configuration quantity is represented by the sum and average of the historical attendance configuration quantities of the screen broadcast teaching platform at the end of the historical configuration period in the database. The preset attendance adjustment response time is represented by the sum and average of the historical attendance adjustment response times of the screen broadcast teaching platform at the end of the historical configuration period in the database. The preset flexible attendance time percentage is represented by the sum and average of the historical flexible attendance time percentages of the screen broadcast teaching platform at the end of the historical configuration period in the database. The attendance configuration quantity is obtained by a counter, the attendance adjustment response time is monitored by a time sensor, and the flexible attendance time percentage represents the ratio of the flexible attendance time obtained by the time detector to the total duration of the screen broadcast teaching platform at the end of the historical configuration period.

[0047] The correction values ​​for attendance configuration quantity, attendance adjustment response time, and flexible attendance duration percentage are respectively the degree of influence of the pre-set attendance configuration quantity, attendance adjustment response time, and flexible attendance duration percentage in the database on the acquisition of attendance policy adjustability indicators. Specifically, the database stores the preset correction values ​​corresponding to the attendance configuration quantity, attendance adjustment response time, and flexible attendance duration percentage. These correction values ​​have a pre-defined mapping relationship with the attendance configuration quantity, attendance adjustment response time, and flexible attendance duration percentage. This mapping relationship can be one-to-one or many-to-one. For example, in practical applications, the real-time attendance configuration quantity, attendance adjustment response time, and flexible attendance duration percentage can be input into this mapping relationship to quickly obtain the corresponding correction values. This provides an important quantitative indicator for evaluating the flexibility of the screen broadcast teaching platform in synchronously processing attendance monitoring data, thereby more accurately calculating the synchronization delay indicator.

[0048] In this example, the values ​​for the attendance configuration quantity correction, attendance adjustment response time correction, and flexible attendance duration percentage correction are all limited to between 0 and 1, and the sum of the three is 1.

[0049] There is a dynamic balance among the number of attendance configurations, the response time for attendance adjustments, and the proportion of flexible attendance periods. The number of attendance configurations directly affects the system's processing complexity. The more configuration rules there are (such as multiple shifts or multiple attendance points), the longer the response time for attendance adjustments is usually, because the system needs to handle more logical judgments and anomaly detection.

[0050] When the flexible attendance percentage is increased, the system's response speed to attendance adjustments improves because it can automatically absorb some deviations without triggering manual intervention. However, when the increased flexible percentage exceeds the preset range, it may lead to lax attendance management, requiring an increase in the number of attendance configurations to strengthen control, thus creating a reverse constraint. Attendance adjustment response time, as a key performance indicator, is positively affected by the number of configurations (more rules mean longer calculation time) and negatively affected by the flexible percentage (the larger the flexibility range, the less adjustment is needed).

[0051] By considering the above interrelationships, we can gain a more comprehensive understanding of the relationship between the attendance strategy adjustability indicators and various variables. These relationships are crucial for evaluating the flexibility of the screen broadcast teaching platform when processing attendance monitoring data synchronously. By optimizing the number of attendance configurations, the response time for attendance adjustments, and the proportion of flexible attendance time, we have improved the flexibility of the screen broadcast teaching platform when processing attendance monitoring data synchronously, thus solving the problem of low flexibility in the screen broadcast teaching platform when processing attendance monitoring data synchronously.

[0052] Furthermore, based on the obtained attendance adjustability analysis results, it is determined whether to adjust and optimize the attendance strategy. The specific steps include: comparing the obtained attendance strategy adjustability indicators with the attendance strategy adjustability indicators set in the database; if the obtained attendance strategy adjustability indicators are not greater than the attendance strategy adjustability indicators set in the database, the obtained attendance adjustability analysis results are marked as unqualified for strategy adjustment and attendance strategy adjustment and optimization are carried out; if the obtained attendance strategy adjustability indicators are greater than the attendance strategy adjustability indicators set in the database, the obtained attendance adjustability analysis results are marked as qualified for strategy adjustment and strategy implementation is carried out.

[0053] Specifically, the steps for adjusting and optimizing the attendance strategy are as follows: First, based on the obtained deviation of the attendance strategy adjustability index and the fixed threshold of the attendance strategy, a first strategy adjustment correction value corresponding to the deviation of the attendance strategy adjustability index and the fixed threshold of the attendance strategy is mapped in the database to improve the strategy adjustment response. After one attendance strategy adjustment and optimization, it is determined whether the reduction in the obtained deviation of the attendance strategy adjustability index is within the preset reduction range in the database. If so, the attendance strategy adjustment and optimization is completed; otherwise, a second strategy adjustment correction value corresponding to the deviation of the attendance strategy adjustability index and the fixed threshold of the attendance strategy is mapped in the database based on the newly obtained deviation of the attendance strategy adjustability index and the fixed threshold of the attendance strategy. The value is used to improve the response to strategy adjustments. If the adjustable index of the attendance strategy is re-acquired within the number of attendance strategy adjustment optimizations, it is greater than the adjustable index of the attendance strategy set in the database. Then, rule conflict detection is performed. Otherwise, the attendance strategy adjustment optimization is completed and the strategy implementation instruction is sent. The first strategy adjustment correction value refers to the fault tolerance time in the sliding tolerance time window adjustment. This fault tolerance time improves the policy adaptability of the screen broadcast teaching platform by increasing the tolerance time range, thereby improving the strategy adjustment response. The second strategy adjustment correction value refers to the correction step size in the hierarchical control adjustment. This correction step size improves the response speed of the screen broadcast teaching platform by eliminating processing speed deviation, thereby improving the strategy adjustment response.

[0054] The specific steps for rule conflict detection are as follows: Based on the deviation of the adjustable attendance policy index obtained after the attendance policy adjustment and optimization and the second policy adjustment correction value, the deadlock probability reduction value corresponding to the deviation of the adjustable attendance policy index and the second policy adjustment correction value is mapped in the database to reduce the probability of attendance rule configuration failure of the screen broadcast teaching platform; if the adjustable attendance policy index obtained after rule conflict detection is not greater than the adjustable attendance policy index set in the database, the rule conflict detection is completed and the policy implementation instruction is sent based on the result of the rule conflict detection; otherwise, a deadlock risk warning instruction is sent.

[0055] In this embodiment, the deviation of the attendance strategy adjustability index is used to quantify the degree of difference between the obtained attendance strategy adjustability index and the preset attendance strategy adjustability index in the database; the fixed threshold of the attendance strategy represents the static judgment standard set in the database; the reduction of the deviation of the attendance strategy adjustability index represents the difference between the obtained attendance strategy adjustability index and the attendance strategy adjustability index re-obtained after an attendance strategy adjustment and optimization.

[0056] When attendance adjustability fails to meet standards, the system dynamically generates correction values, optimizing operations to significantly improve the response speed of strategy adjustments and enhance the adjustability of attendance strategies. If conflict risks still exist after optimization, deadlock detection effectively identifies and marks risky rules and mitigates risks, reducing the failure rate of attendance configuration. By driving strategy adjustments with quantitative indicators, the system ensures both the flexibility of attendance rules and the stability of configuration, enabling the teaching platform to intelligently adapt to the changing attendance needs of different teaching scenarios and provide reliable and adaptive attendance management support.

[0057] like Figure 6 The diagram shows a flowchart of a screen broadcast teaching method for university computer labs based on behavior monitoring, provided in an embodiment of this application. The method includes: Step 1, performing synchronization delay analysis on the synchronization transmission process of the screen broadcast teaching platform based on acquired synchronization delay data, obtaining synchronization delay analysis results, and simultaneously determining whether to perform cache delay optimization based on the acquired synchronization delay analysis results. Cache delay optimization refers to improving the synchronization processing efficiency of the screen broadcast teaching platform for attendance status data changes through intelligent cache scheduling and graph computing engine reconstruction; Step 2, after synchronization transmission is completed, performing intelligent cache scheduling on the screen broadcast teaching platform based on the acquired multi-threaded parallel efficiency. Step 1: Perform preloading accuracy analysis to obtain the preloading accuracy analysis results. Based on these results, determine whether branch acquisition prediction optimization should be performed. Branch acquisition prediction optimization refers to improving the accuracy of attendance status change data during the scheduling process through branch prediction optimization and data acquisition frequency optimization. Step 2: After intelligent cache scheduling is completed, perform attendance adjustability analysis on the attendance configuration status of the screen broadcast teaching platform based on the obtained attendance configuration data. Obtain attendance adjustability analysis results. Based on these results, determine whether attendance policy adjustment optimization should be performed. Attendance policy adjustment optimization refers to improving the flexibility of the screen broadcast teaching platform in configuring attendance rules through attendance policy adjustment optimization and rule conflict detection.

[0058] In this embodiment, firstly, the timeliness of data transmission is evaluated based on synchronization latency analysis. Latency data is used to quantify synchronization transmission efficiency, and cache optimization needs are determined to improve data synchronization latency. Secondly, preloading accuracy analysis is performed. Multi-threaded parallel efficiency analysis is used to assess cache scheduling accuracy, determining whether branch prediction optimization should be initiated to improve preloading performance and ensure the accuracy of subsequent data preloading. Finally, the flexibility of attendance configuration data rules is analyzed. By analyzing the rules of configuration data, the adjustability of identity authentication and course activation processes is evaluated. Based on the attendance adjustability analysis results, it is determined whether attendance strategy adjustments and optimizations are needed, thereby enhancing the system's adaptability to different teaching scenarios and ultimately achieving efficient processing and response to attendance status change data throughout the entire process.

[0059] In summary, this application embodiment analyzes the synchronization latency of the screen broadcast teaching platform's synchronous transmission process using acquired synchronization latency data. Then, based on the obtained synchronization latency analysis results and multi-threaded parallel efficiency, it performs preloading accuracy analysis to determine whether branch acquisition prediction optimization is necessary. Finally, it analyzes attendance adjustability using acquired attendance configuration data to determine whether rule intelligent adaptation adjustment is required. This improves the preloading accuracy during intelligent cache scheduling, thereby enhancing the flexibility of the screen broadcast teaching platform in synchronously processing attendance monitoring data and effectively solving the problem of low flexibility in synchronously processing attendance monitoring data.

[0060] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0064] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0065] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A screen broadcasting teaching system for university computer labs based on behavior monitoring, characterized in that: include: Synchronization delay analysis module, preloading accuracy analysis module, and rule configuration analysis module; The synchronization delay analysis module is used to perform synchronization delay analysis on the synchronization transmission process of the screen broadcast teaching platform based on the acquired synchronization delay data, and obtain the synchronization delay analysis results. At the same time, it determines whether to perform cache delay optimization based on the acquired synchronization delay analysis results. The cache delay optimization means improving the synchronization processing efficiency of the screen broadcast teaching platform for attendance status data change data through intelligent cache scheduling and graph computing engine reconstruction. The preloading accuracy analysis module is used to perform preloading accuracy analysis on the intelligent cache scheduling process of the screen broadcast teaching platform based on the obtained multi-threaded parallel efficiency after the synchronous transmission is completed, and obtain the preloading accuracy analysis result. At the same time, it determines whether to perform branch acquisition prediction optimization based on the obtained preloading accuracy analysis result. The branch acquisition prediction optimization means improving the accuracy of attendance status change data in the scheduling process through branch prediction optimization and data acquisition frequency optimization. The rule configuration analysis module is used to perform attendance adjustability analysis on the attendance configuration status of the screen broadcast teaching platform based on the acquired attendance configuration data after the intelligent cache scheduling is completed, and to obtain the attendance adjustability analysis results. At the same time, it determines whether to adjust and optimize the attendance strategy based on the acquired attendance adjustability analysis results. The attendance strategy adjustment and optimization means improving the flexibility of the screen broadcast teaching platform in configuring attendance rules through attendance strategy adjustment and optimization and rule conflict detection.

2. The university computer lab screen broadcasting teaching system based on behavior monitoring as described in claim 1, characterized in that, The synchronization delay data includes peak response delay duration, cache response delay duration, and real-time data throughput. The step of performing a synchronization delay analysis on the synchronous transmission process of the screen broadcast teaching platform based on the acquired synchronization delay data includes: The peak response latency duration is compared with the preset peak response latency duration in the database, and then corrected by combining the peak response latency duration correction value to obtain the peak response latency score. The obtained cache response latency is compared with the preset cache response latency in the database, and then corrected using a cache response latency correction value to obtain a cache response latency score. The real-time data throughput obtained is compared with the preset data throughput in the database, and then corrected by combining the data throughput correction value to obtain the data throughput score. The data throughput score is processed inversely and coupled with the peak response latency score and the cache response latency score to obtain the synchronization latency index. The synchronization latency index is used to quantify the latency of synchronous data transmission in the screen broadcast teaching platform.

3. The university computer lab screen broadcasting teaching system based on behavior monitoring as described in claim 1, characterized in that, The specific steps for determining whether to perform cache latency optimization based on the obtained synchronization latency analysis results include: The obtained synchronization delay metric is compared with the synchronization delay metric set in the database: If the obtained synchronization delay index is greater than the synchronization delay index set in the database, the obtained synchronization delay analysis result will be recorded as unqualified synchronization transmission and cache delay optimization will be performed. If the obtained synchronization delay index is not greater than the synchronization delay index set in the database, the obtained synchronization delay analysis result will be recorded as qualified for synchronization transmission and a preload accuracy analysis command will be sent.

4. The university computer lab screen broadcasting teaching system based on behavior monitoring as described in claim 3, characterized in that, The specific steps for optimizing cache latency are as follows: Based on the obtained synchronization delay index deviation and cache line utilization deviation, the first cache scheduling correction value corresponding to the synchronization delay index deviation and cache line utilization deviation is obtained by mapping in the database to improve cache synchronization response. After a cache scheduling optimization, it is determined whether the reduction in the obtained synchronization latency index deviation is within the preset reduction range in the database. If so, the cache scheduling optimization is completed. Otherwise, the second cache scheduling correction value corresponding to the synchronization latency index deviation and cache line utilization deviation is mapped in the database based on the newly obtained synchronization latency index deviation and cache line utilization deviation to improve cache synchronization response. If the synchronization latency metric re-acquired within the cache scheduling optimization count is greater than the synchronization latency metric set in the database, then the graph computing engine reconstruction optimization is performed; otherwise, a preloading accuracy analysis command is sent. The synchronization delay index deviation is used to quantify the degree of difference between the obtained synchronization delay index and the preset synchronization delay index in the database. The cache line utilization deviation is used to quantify the degree of difference between the obtained cache line utilization and the preset cache line utilization in the database.

5. The university computer lab screen broadcasting teaching system based on behavior monitoring as described in claim 4, characterized in that, The specific steps for the graph computing engine reconstruction and optimization are as follows: Based on the synchronization delay index deviation and the second cache scheduling correction value re-acquired after cache scheduling optimization, the cache preloading correction value corresponding to the synchronization delay index deviation and the second cache scheduling correction value is mapped in the database to dynamically allocate the change range of computing resources. After the graph computing engine is refactored and optimized, it is determined whether the obtained cache preloading time is less than the preset cache preloading time in the database. If so, a cache scheduling optimization completion instruction is sent and a preloading accuracy analysis instruction is sent; otherwise, a graph computing engine refactoring warning instruction is sent.

6. The university computer lab screen broadcasting teaching system based on behavior monitoring as described in claim 1, characterized in that, The specific steps for determining whether to perform branch acquisition prediction optimization based on the obtained preload accuracy analysis results include: If the obtained multi-threaded parallel efficiency is not greater than the multi-threaded parallel efficiency set in the database, the preload accuracy analysis result will be marked as unqualified and branch collection prediction optimization will be performed. If the obtained multi-threaded parallel efficiency is greater than the multi-threaded parallel efficiency set in the database, the preload accuracy analysis result will be recorded as qualified and rule configuration analysis will be performed. The multi-threaded parallel efficiency is used to quantify the processing speed of the screen broadcast teaching platform when handling multi-threaded tasks.

7. The university computer lab screen broadcasting teaching system based on behavior monitoring as described in claim 6, characterized in that, The specific steps for optimizing branch acquisition prediction are as follows: Based on the obtained multi-threaded parallel efficiency deviation and branch misprediction rate deviation, the first branch prediction correction value corresponding to the multi-threaded parallel efficiency deviation and branch misprediction rate deviation is mapped in the database to improve the branch parallel response. After a branch prediction optimization, it is determined whether the reduction in the obtained multi-threaded parallel efficiency deviation is within the preset reduction range in the database. If so, the branch prediction optimization is completed. Otherwise, the second branch prediction correction value corresponding to the multi-threaded parallel efficiency deviation and the branch misprediction rate deviation is mapped in the database based on the newly obtained multi-threaded parallel efficiency deviation and the branch misprediction rate deviation to improve the branch parallel response. If the multi-threaded parallel efficiency re-acquired within the number of branch prediction optimization iterations is greater than the multi-threaded parallel efficiency set in the database, then the branch prediction optimization is completed; otherwise, the data acquisition frequency optimization is performed. The specific steps for optimizing the data acquisition frequency are as follows: Based on the multi-threaded parallel efficiency deviation and instruction throughput re-acquired after branch prediction optimization, the data acquisition frequency adjustment value corresponding to the multi-threaded parallel efficiency deviation and instruction throughput is mapped in the database to improve the data acquisition frequency of the screen broadcast teaching platform. If the multi-threaded parallel efficiency of the data acquisition after optimization is greater than the multi-threaded parallel efficiency set in the database, the data acquisition frequency optimization is completed and attendance adjustability analysis is performed; otherwise, an acquisition frequency warning command is sent. The multi-threaded parallel efficiency deviation is used to quantify the degree of difference between the obtained multi-threaded parallel efficiency and the preset multi-threaded parallel efficiency in the database; The branch misprediction rate deviation is used to quantify the degree of difference between the obtained branch misprediction rate and the preset branch misprediction rate in the database.

8. The university computer lab screen broadcasting teaching system based on behavior monitoring as described in claim 1, characterized in that, The attendance configuration data includes the number of attendance configurations, the response time for attendance adjustments, and the percentage of flexible attendance time. The step of performing attendance adjustability analysis on the attendance configuration status of the screen broadcast teaching platform based on the acquired attendance configuration data includes the following steps: The obtained attendance configuration quantity is compared with the preset attendance configuration quantity in the database, and then corrected by combining the attendance configuration quantity correction value to obtain the attendance configuration quantity score. The obtained attendance adjustment response time is compared with the preset attendance adjustment response time in the database, and then the difference is corrected by combining the attendance adjustment response time correction value to obtain the attendance adjustment response time score. The obtained flexible attendance time percentage is compared with the preset flexible attendance time percentage in the database, and then the flexible attendance time percentage correction value is used for correction to obtain the flexible attendance time percentage score. The results of inversely proportionally processing the obtained attendance configuration quantity score and attendance adjustment response time score, along with the flexible attendance time percentage score, are coupled and calculated to obtain the attendance strategy adjustability index.

9. The university computer lab screen broadcasting teaching system based on behavior monitoring as described in claim 1, characterized in that, The specific steps for determining whether to adjust and optimize the attendance strategy based on the obtained attendance adjustability analysis results include: The obtained attendance strategy adjustability indicators are compared with the attendance strategy adjustability indicators set in the database: If the obtained attendance strategy adjustability index is not greater than the attendance strategy adjustability index set in the database, the obtained attendance adjustability analysis result will be marked as unqualified and the attendance strategy will be adjusted and optimized. If the obtained attendance strategy adjustability index is greater than the attendance strategy adjustability index set in the database, the obtained attendance adjustability analysis result will be marked as qualified for strategy adjustment and the strategy will be implemented. The specific steps for adjusting and optimizing the attendance strategy are as follows: Based on the obtained attendance policy adjustability index deviation and attendance policy fixed threshold, the first policy adjustment correction value corresponding to the attendance policy adjustability index deviation and attendance policy fixed threshold is obtained in the database to improve the policy adjustment response. After an attendance strategy adjustment and optimization, it is determined whether the reduction in the deviation of the adjustable attendance strategy indicator is within the preset reduction range in the database. If so, the attendance strategy adjustment and optimization is completed. Otherwise, the second strategy adjustment correction value corresponding to the deviation of the adjustable attendance strategy indicator and the fixed threshold of the attendance strategy is obtained by mapping the newly obtained deviation of the adjustable attendance strategy indicator and the fixed threshold of the attendance strategy in the database to improve the strategy adjustment response. If the attendance policy adjustability index re-obtained within the number of attendance policy adjustment and optimization cycles is greater than the attendance policy adjustability index set in the database, then a rule conflict detection is performed; otherwise, the attendance policy adjustment and optimization is completed and a policy implementation instruction is sent. The specific steps for rule conflict detection are as follows: Based on the attendance strategy adjustment and optimization, the deviation of the attendance strategy adjustability index and the second strategy adjustment correction value are re-acquired and mapped in the database to obtain the deadlock probability reduction value corresponding to the deviation of the attendance strategy adjustability index and the second strategy adjustment correction value, so as to reduce the failure probability of attendance rule configuration of the screen broadcast teaching platform. If the attendance policy adjustability index re-obtained after rule conflict detection is not greater than the attendance policy adjustability index set in the database, then the rule conflict detection is completed and a policy implementation instruction is sent based on the result of the rule conflict detection; otherwise, a deadlock risk warning instruction is sent. The deviation of the attendance strategy adjustability index is used to quantify the degree of difference between the obtained attendance strategy adjustability index and the preset attendance strategy adjustability index in the database.

10. A teaching method for screen broadcasting in university computer labs based on behavior monitoring, characterized in that: Includes the following steps: Step 1: Perform synchronization delay analysis on the synchronization transmission process of the screen broadcast teaching platform based on the obtained synchronization delay data to obtain the synchronization delay analysis results. At the same time, determine whether to perform cache delay optimization based on the obtained synchronization delay analysis results. The cache delay optimization means improving the synchronization processing efficiency of the screen broadcast teaching platform for attendance status data change data through intelligent cache scheduling and graph computing engine reconstruction. Step 2: After the synchronous transmission is completed, the preloading accuracy analysis of the intelligent cache scheduling process of the screen broadcast teaching platform is performed based on the obtained multi-threaded parallel efficiency to obtain the preloading accuracy analysis results. At the same time, based on the obtained preloading accuracy analysis results, it is determined whether to perform branch acquisition prediction optimization. The branch acquisition prediction optimization means improving the accuracy of attendance status change data in the scheduling process through branch prediction optimization and data acquisition frequency optimization. Step 3: After the intelligent cache scheduling is completed, the attendance configuration status of the screen broadcast teaching platform is analyzed for attendance adjustability based on the acquired attendance configuration data. The results of the attendance adjustability analysis are obtained. At the same time, it is determined whether to adjust and optimize the attendance strategy based on the acquired attendance adjustability analysis results. The attendance strategy adjustment and optimization means improving the flexibility of the screen broadcast teaching platform in configuring attendance rules through attendance strategy adjustment and optimization and rule conflict detection.

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