High-concurrency student course selection queuing processing system

Abstract

The invention relates to the technical field of student course selection queuing processing systems, and discloses a high-concurrency student course selection queuing processing system which comprises a main controller module, a user management module, a course management module, a high-concurrency processing module and a course notification module. The main controller module controls the user management module, the course management module, the high-concurrency processing module and the course notification module, so that cooperative work is achieved, it is guaranteed that students can rapidly complete the operation in the high-concurrency time period, and the user management module supports student identity verification and access authority control and prevents malicious users from occupying system resources. According to the high-concurrency student course selection queuing processing system, graduation class students, specialized class students, hot courses and multiple times of course selection failure students can quickly complete course selection through dynamic weight adjustment, the throughput of the system is increased through a multi-stage scheduling module, course selection of core courses can be quickly completed, and course selection efficiency is improved. And meanwhile, the influence of non-core courses in a high-concurrency period is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of student course selection queuing processing systems, and in particular to a high-concurrency student queuing processing system. Background Art

[0002] The student queuing processing system is an online platform designed specifically for students to select courses. It aims to achieve automated management and efficient queuing processing of the course selection process through computer technology and network platforms.

[0003] The system usually has the following core functions: online course selection, queue processing, course information management, etc.

[0004] In colleges and universities, in addition to completing required courses, students can also choose elective courses based on their interests and hobbies. Course selection is a highly concurrent system, with tens of thousands of students logging into the system within a few minutes. This can easily cause the academic network to crash. Existing systems usually expand the system by adding external hardware, which is not only expensive but also easily leads to uneven student distribution and even affects student graduation. Therefore, a highly concurrent student queuing processing system is proposed to solve the above problems. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides a high-concurrency student course selection queuing processing system, which has the advantages of better system stability and ensuring that most students can complete their courses. It solves the problem that the existing system is prone to crash and the existing system usually expands the system by adding external hardware, which is not only expensive but also easily leads to uneven distribution of students and even affects students' graduation.

[0007] (2) Technical solution

[0008] To achieve the above-mentioned goal of improving system stability and ensuring that most students can complete the course, the present invention provides the following technical solutions: a high-concurrency student course selection queuing processing system, comprising a main controller module, a user management module, a course management module, a high-concurrency processing module, and a course notification module. The main controller module controls the user management module, the course management module, the high-concurrency processing module, and the course notification module, thereby achieving collaborative work and ensuring that students can complete the course quickly during high-concurrency periods;

[0009] The user management module supports student identity verification, single sign-on (SSO) and access rights control to prevent malicious users from occupying system resources;

[0010] The course management module manages basic course information, such as course number, enrollment limit, and course start time, and updates course availability in real time;

[0011] The high-concurrency processing module responds to instantaneous high-concurrency requests, implements orderly request processing, and ensures that most students can complete course selection;

[0012] The course notification module displays a successful course selection page and sends relevant information such as the course start time to students.

[0013] Preferably, the high concurrency processing module also includes a dynamic weight calculation module, a priority decision module, a multi-level queue scheduling module, a fuse self-healing protection module and an elastic resource management module.

[0014] Preferably, the dynamic weight calculation module dynamically adjusts the weight of course selection based on grade, major matching, history course selection success rate and course popularity, and at the same time cooperates with the attenuation factor, and the weight decays by 40% after 30 seconds.

[0015] Preferably, the priority decision module diverts requests to different processing channels to ensure the response speed of high-priority requests and achieve optimal allocation of system resources.

[0016] Preferably, the channels are divided into VIP channels, fast channels and buffer channels, wherein the VIP channel is a compulsory course for graduating classes with a response time of less than 100ms, the fast channel is a key elective course with a response time of less than 300 seconds, and the buffer channel is an ordinary general education course with a response time of less than 500 seconds.

[0017] The VIP channel utilizes an FPGA for protocol parsing acceleration and a dedicated network card (DPDK) to improve throughput. The VIP channel also features a queue isolation design: independent thread pools handle requests from different channels (10 threads for VIP and 50 threads for Express). Buffered channel requests are asynchronously transferred to Kafka for processing. A dynamic downgrade mechanism automatically reduces traffic by 30% to the Express channel when VIP channel latency exceeds 150ms. When the overall system load exceeds 80%, non-essential buffered channel functions are disabled.

[0018] Preferably, the fuse self-healing protection module includes five-layer protection of flow control, resource isolation and three-level fuse, and the three-level fuse mechanism is divided into: service level → course level → regional level and takes effect step by step.

[0019] Preferably, the

[0020] Level 1 circuit breaker (QPS>50,000): non-core services are downgraded and 30% of resources are released; Level 2 circuit breaker (error rate>5%): queuing mode is enabled and a 503 status code is returned; Level 3 circuit breaker (response time>2s): disaster recovery nodes are automatically switched, with an RTO<30s.

[0021] Preferably, the elastic resource management module automatically triggers the resource pool pre-allocation mechanism during the course enrollment peak, reserves 20% redundant resources in advance, and dynamically expands the database connection pool, increasing the maximum number of connections from 500 to 2000.

[0022] (3) Beneficial effects

[0023] Compared with the existing technology, the present invention provides a high-concurrency student queuing processing system with the following beneficial effects:

[0024] 1. This high-concurrency student course selection queue processing system enables graduating students, students taking professional courses, students taking popular courses, and students who have failed to select courses multiple times to quickly complete course selection through dynamic weight adjustment.

[0025] 2. This high-concurrency student course selection queuing system increases system throughput through a multi-level scheduling module, enabling core courses to be quickly selected while reducing the impact of non-core courses during high-concurrency periods.

[0026] 3. The high-concurrency student queuing processing system uses a circuit breaker mechanism to ensure stable operation during high-concurrency course selection periods, preventing system downtime and enabling smooth course selection during peak periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a schematic diagram of the high-concurrency student course selection queuing processing system of the present invention. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0029] See also Figure 1 A high-concurrency student course selection queue processing system includes a main controller module, a user management module, a course management module, a high-concurrency processing module, and a course notification module. The main controller module controls the user management module, the course management module, the high-concurrency processing module, and the course notification module to achieve collaborative work and ensure that students can complete their tasks quickly during high-concurrency periods.

[0030] User management module supports student identity verification, single sign-on (SSO) and access control to prevent malicious users from occupying system resources;

[0031] The course management module manages basic course information, such as course number, maximum number of students, and course start time, and updates the course availability in real time;

[0032] High-concurrency processing module, which can handle instantaneous high-concurrency requests and process them in an orderly manner, ensuring that most students can complete their course selection.

[0033] The course notification module displays the successful course selection page and sends relevant information such as the course start time to students.

[0034] Specifically, through the cooperation of the above modules, the problem of the existing system being prone to crash and the existing system usually expanding the system by adding external hardware is solved, which is not only expensive but also easily leads to uneven distribution of students and even affects students' graduation.

[0035] exist Figure 1 The medium and high concurrency processing modules also include a dynamic weight calculation module, a priority decision module, a multi-level queue scheduling module, a circuit breaker and self-healing protection module, and an elastic resource management module.

[0036] Specifically, the system is guaranteed to run stably during high-concurrency periods to ensure that students can complete their studies. Based on factors such as time urgency, course importance, course selection success rate, and course popularity, the weights are dynamically adjusted to ensure that students can complete their studies.

[0037] exist Figure 1 In the dynamic weight calculation module, the weight of course selection is dynamically adjusted based on grade, major matching, history course selection success rate and course popularity. At the same time, with the attenuation factor, the weight decays by 40% after 30 seconds.

[0038] Specifically, in the time dimension, an exponential decay function is used to process the request time difference, accurate to nanosecond timestamps. The earlier the request arrives, the slower the score decays.

[0039] Academic weighting: Establish a course type matrix (weight of required graduation courses 1.8 > elective courses 0.8), and dynamically adjust based on student grades;

[0040] Compensation mechanism: A logarithmic function processes the number of historical failures to prevent high-frequency retries from excessively occupying resources.

[0041] Network compensation: Give weighted bonuses to high-latency users (>300ms) to reduce the impact of regional differences;

[0042] Dynamic adjustment strategy:

[0043] Automatically adjust the calculation complexity according to the real-time load (the simplified algorithm is enabled when the load is >80%), update the weight coefficient every 5 minutes, and automatically optimize the parameters through monitoring data.

[0044] Implementation steps

[0045] Step 1: Data collection layer: connect to the student registration system to obtain user identity tags and record the user's historical success rate;

[0046] Step 2: Deploy the Flink stream processing engine in the real-time computing layer, update the weights every 50ms, and use a sliding window (5-second window) to filter out abnormal weight fluctuations.

[0047] Step 3: Weight storage, write the calculation results into RedisSortedSet (sorted by weight value).

[0048] exist Figure 1 In the process, the priority decision module diverts requests to different processing channels to ensure the response speed of high-priority requests and achieve optimal allocation of system resources.

[0049] In addition, the channels are divided into VIP channels, fast channels and buffer channels. The VIP channel is a compulsory course for graduating classes with a response time of less than 100ms, the fast channel is a key elective course with a response time of less than 300 seconds, and the buffer channel is a general general education course with a response time of less than 500 seconds.

[0050] Specifically, the VIP channel handles requests for compulsory courses for graduating classes and uses hardware acceleration cards to achieve microsecond responses; the fast channel is for high-priority regular requests and achieves millisecond delays based on memory queues; the buffered channel asynchronously processes non-critical requests and batch merges database operations.

[0051] It also includes an anti-starvation mechanism: the priority of regular requests is automatically upgraded when the waiting timeout (default 30 seconds), and a low-priority task is forcibly inserted for every 1,000 requests processed.

[0052] Specific implementation steps:

[0053] Step 1: The VIP channel uses FPGA to implement protocol parsing acceleration and configures a dedicated network card (DPDK technology) to improve throughput;

[0054] Step 2: Queue isolation design: Independent thread pools handle different channel requests (VIP: 10 threads, fast: 50 threads), and buffer channel requests and transfer them to Kafka for asynchronous processing;

[0055] Step 3: Dynamic degradation mechanism: When the VIP channel delay is greater than 150ms, 30% of the traffic is automatically reduced to the fast channel. When the overall system load is greater than 80%, unnecessary functions of the buffer channel are turned off.

[0056] exist Figure 1 The circuit breaker self-healing protection module includes five-layer protection of flow control, resource isolation and three-level circuit breaker. The three-level circuit breaker mechanism is divided into: service level → course level → regional level, which takes effect step by step.

[0057] Specifically, it includes the following five layers of protection:

[0058] At the edge layer, global CDN node traffic cleaning can intercept 50% of invalid requests;

[0059] At the access layer, dynamic token bucket algorithm + request fingerprint verification, when QPS exceeds 2 million

[0060] Gateway layer, adaptive circuit breaker strategy (error rate > 30% trigger), response < 5ms

[0061] Service layer, service grid dynamic drainage, fault switching < 3 seconds

[0062] Data layer, hot data sharding + multi-level cache, cache hit rate > 95%

[0063] Level 1 circuit breaker (QPS>50,000): non-core services are downgraded and 30% of resources are released; Level 2 circuit breaker (error rate>5%): queuing mode is enabled and a 503 status code is returned; Level 3 circuit breaker (response time>2s): disaster recovery nodes are automatically switched, with an RTO<30s.

[0064] Specifically, by monitoring QPS, error rate, and response time, a three-level circuit breaker strategy is triggered. At this time, the page will display content such as "Page Busy, Please Refresh". Through the circuit breaker mechanism, the system can be guaranteed to run stably during high-concurrency course selection periods, prevent downtime, and ensure that course selection can be completed smoothly during peak periods.

[0065] The elastic resource management module automatically triggers the resource pool pre-allocation mechanism during course enrollment peaks, reserving 20% redundant resources in advance. The database connection pool is dynamically expanded, and the maximum number of connections is increased from 500 to 2000.

[0066] Specifically, time series analysis (ARIMA model) is used to predict the load in the next 5 minutes.

[0067] Pre-launched container instances maintain a 20% redundant resource pool.

[0068] In summary, the high-concurrency student course selection queuing processing system enables graduating students, professional course students, popular courses, and students who have failed to select courses multiple times to complete course selection quickly through dynamic weight adjustment. The system throughput is increased through a multi-level scheduling module, so that core courses can be quickly completed. At the same time, the impact of non-core courses is reduced during high-concurrency periods. The fuse mechanism ensures that the system runs stably during high-concurrency course selection periods, prevents downtime, and enables smooth course selection during peak periods. This solves the problem that existing systems are prone to crashes and that existing systems usually expand the system by adding external hardware, which is not only expensive but also easily leads to uneven student distribution and even affects student graduation.

[0069] It also includes continuous optimization of weight calculation and scheduling strategies through closed-loop control to deal with sudden traffic and abnormal scenarios.

[0070] Correction mechanism, weight balance detection

[0071] Calculate the standard deviation of the weight distribution σ every 10 seconds. If σ>15 and lasts for 10 seconds: trigger a weight reset. Reset range = maximum 20% weight request × random decay factor (0.6-0.9).

[0072] Channel capacity prediction, based on the LSTM model, predicts the number of requests in the next 30 seconds

[0073] Expansion threshold = current capacity × predicted growth rate × safety factor (1.2).

[0074] Implementation steps

[0075] S1 model training, using historical data to train prediction models (TensorFlow framework)

[0076] S2 feature extraction, where features include: time period, course type, user behavior pattern

[0077] Deploy the S3 feedback loop to write prediction results to the Kafka message bus in real time. The scheduling module consumes the prediction data to dynamically adjust parameters.

[0078] S4 abnormal circuit breaking, configure three-level circuit breaking strategy (single course / single user / region level), abnormal traffic automatically switches to the backup data center.

[0079] Also includes: Fault self-healing

[0080] Real-time monitoring of node health status. When a downed instance is discovered, the faulty node is automatically isolated within 30 seconds, and a new instance is started to fill the gap (image preloading technology reduces the startup time to less than 15 seconds). Traffic is automatically migrated and traffic weights are dynamically adjusted through the load balancer. The business impact time during the failure is ≤3 seconds.

[0081] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0082] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A high-concurrency student course selection queuing processing system, comprising a main controller module, a user management module, a course management module, a high-concurrency processing module, and a course notification module, characterized in that: The main controller module controls the user management module, course management module, high concurrency processing module and course notification module to achieve collaborative work and ensure that students can complete the tasks quickly during high concurrency periods; The user management module supports student identity verification, single sign-on (SSO) and access rights control to prevent malicious users from occupying system resources; The course management module manages basic course information, such as course number, enrollment limit, and course start time, and updates course availability in real time; The high-concurrency processing module responds to instantaneous high-concurrency requests, implements orderly request processing, and ensures that most students can complete course selection; The course notification module displays a successful course selection page and sends relevant information such as the course start time to students.

2. A high-concurrency student course selection queuing processing system according to claim 1, characterized in that: The high concurrency processing module also includes a dynamic weight calculation module, a priority decision module, a multi-level queue scheduling module, a fuse self-healing protection module and an elastic resource management module.

3. A high-concurrency student course selection queuing processing system according to claim 2, characterized in that: The dynamic weight calculation module dynamically adjusts the weight of course selection based on grade, major matching, history course selection success rate and course popularity, and at the same time uses an attenuation factor, with the weight decaying by 40% after 30 seconds.

4. A high-concurrency student course selection queuing processing system according to claim 2, characterized in that: The priority decision module diverts requests to different processing channels to ensure the response speed of high-priority requests and achieve optimal allocation of system resources.

5. The high-concurrency student queue processing system according to claim 1, characterized in that: The channels are divided into VIP channels, fast channels and buffer channels. The VIP channel is a compulsory course for graduating classes with a response time of less than 100ms, the fast channel is a key elective course with a response time of less than 300 seconds, and the buffer channel is an ordinary general education course with a response time of less than 500 seconds.

6. A high-concurrency student course selection queuing processing system according to claim 1, characterized in that: The VIP channel uses an FPGA for protocol parsing acceleration and a dedicated network card (DPDK technology) to improve throughput. It also features a queue isolation design: independent thread pools handle requests from different channels (VIP: 10 threads, Express: 50 threads). Buffered channel requests are transferred to Kafka for asynchronous processing. A dynamic downgrade mechanism automatically reduces 30% of traffic to the Express channel when VIP channel latency exceeds 150ms. When the overall system load exceeds 80%, non-essential buffered channel functions are disabled.

7. A high-concurrency student course selection queuing processing system according to claim 1, characterized in that: The circuit breaker self-healing protection module includes five-layer protection of flow control, resource isolation and three-level circuit breaker. The three-level circuit breaker mechanism is divided into: service level → course level → regional level, which takes effect step by step.

8. The high-concurrency student course selection queuing processing system according to claim 1 is characterized by: described Level 1 circuit breaker (QPS>50,000): non-core services are downgraded and 30% of resources are released; Level 2 circuit breaker (error rate>5%): queuing mode is enabled and a 503 status code is returned; Level 3 circuit breaker (response time>2s): disaster recovery nodes are automatically switched, with an RTO<30s.

9. The high-concurrency student course selection queuing processing system according to claim 1 is characterized by: The elastic resource management module automatically triggers the resource pool pre-allocation mechanism during course enrollment peaks, reserving 20% redundant resources in advance, and dynamically expanding the database connection pool, increasing the maximum number of connections from 500 to 2000.

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