Excitation system, method and equipment for online teaching

Abstract

The invention provides an excitation system, method and device for online teaching, the system comprises a data monitoring unit, a data processing unit, a reward configuration unit, a reward calculation unit and a reward issuing unit, binlog monitoring is used as reward driving, the excitation system is isolated from an online learning system, and decoupling of excitation and learning services is realized.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an incentive system, method, and device for online teaching. Background Art

[0002] With technological advancements, online intelligent teaching has become a common teaching method. In this area, establishing an incentive mechanism is crucial for boosting student motivation. Existing online teaching systems use incentive mechanisms that embed incentive information within the teaching process and directly reward users when they meet learning targets. For example, rewards are given within the first 5 seconds of a learning video, with the reward information popping up directly. This incentive mechanism, coupled with the business process, frequently disrupts user learning. Summary of the Invention

[0003] In view of this, the present application provides an incentive system, method and device for online teaching, the main purpose of which is to decouple incentives from business and reduce the interference of incentives on online teaching.

[0004] According to one aspect of the present application, there is provided an online teaching incentive system, comprising:

[0005] The data monitoring unit monitors the binary log information of the relational database MySQL through the data transmission service. Based on the monitored binlog information, it triggers the writing of the latest learning record data in this monitoring cycle to the data queue of the action flow processing node;

[0006] A data processing unit, which reads learning record data from the data queue of the action flow processing node, classifies and counts the learning record data, generates target learning data required for incentives, and writes the target learning data to a storage node;

[0007] The reward configuration unit pre-configures the reward types and reward data offline in the background, and stores the configured reward types and reward data in the MySQL data table;

[0008] A reward calculation unit reads the target learning data from the storage node and the reward types and reward data from the MySQL data table, performs matching calculations on the target learning data and the reward data based on pre-configured reach conditions corresponding to each reward type, determines target users eligible for reward issuance and corresponding reward information, and stores the reward information into the reward pool queue of the action flow processing node according to the reward type;

[0009] The reward issuing unit reads the reward information from the reward pool queue of the action flow processing node and issues the reward to the target user.

[0010] In one implementation, the data monitoring unit further includes:

[0011] The task monitoring module obtains the SQL statements changed in the data table by monitoring the MySQL binlog information;

[0012] The latest record determination module determines the changed data in the data table as the latest learning record data in this monitoring cycle based on the changed SQL statement in the data table;

[0013] The data writing module writes the latest learning record data to the data queue of the action stream processing node.

[0014] In one implementation, the data processing unit further includes:

[0015] The task subscription module is used to start the resident process, subscribe to the learning record data produced by the data monitoring unit and written to the data queue of the action flow processing node;

[0016] The classification and statistics module is used to perform offline classification and statistical logic processing on the learning record data to produce target learning data, which includes daily data and periodic data in the user dimension;

[0017] The data writing module is used to write the target learning data to the storage node.

[0018] In one implementation, the data writing module is specifically used to determine the target learning data format written to the storage node, and the target learning data format includes the user dimension daily data format and the user dimension periodic data format, wherein the user dimension daily data format is: Key{user id, date, business category}, value{total cumulative value}; the user dimension periodic data format is: Key{user id, business category}, value{total cumulative value}.

[0019] In one implementation, the reward calculation unit further includes:

[0020] A data reading module is used to read the target learning data from the storage node and read the reward type and reward data from the MySQL data table;

[0021] A matching calculation module is used to perform offline matching calculations on the target learning data and the reward data based on the pre-configured reach conditions corresponding to each reward type, and determine the target users who meet the requirements for awarding rewards and the corresponding reward information;

[0022] The reward writing module is used to write reward information into the reward pool queue of the action flow processing node, wherein reward information of different reward types is written into different reward pool queues corresponding to the reward types.

[0023] In one implementation, the attainment conditions corresponding to each reward type include:

[0024] Reaching a predetermined number of learning star points every day will reward you with a chance to draw a treasure box;

[0025] Completing a predetermined number of puzzles will reward you with medals of master puzzle solvers of different levels;

[0026] After completing a predetermined number of learning units, you will be awarded the Unit Learning Star Medal.

[0027] In one implementation, the reward issuing unit further includes:

[0028] The task subscription module is used to start the resident process, subscribe to the data produced by the reward calculation unit, and read the reward information from the reward pool queue;

[0029] The reward distribution module is used to explicitly or implicitly distribute reward information based on the user asset table.

[0030] In one implementation, the reward distribution module is specifically configured to:

[0031] Explicit reward distribution: Reward information of different types is distributed to different waiting queues, displayed on the online teaching platform page and waiting for users to claim. After claiming, it is written to the user's asset table;

[0032] Implicit distribution of rewards: Reward information is directly distributed to the user's asset table and displayed on the personal center achievement wall of the online teaching platform.

[0033] According to one aspect of the present application, a method for motivating online teaching is provided, comprising:

[0034] Through the data transmission service, the binary log information of the relational database MySQL is monitored. Based on the monitored binlog information, the latest learning record data in this monitoring cycle is triggered to be written to the data queue of the action flow processing node;

[0035] Reading learning record data from the data queue of the action flow processing node, classifying and counting the learning record data, generating target learning data required for incentives, and writing the target learning data to a storage node;

[0036] Read the target learning data from the storage node and read the reward types and reward data from the MySQL data table, perform matching calculations on the target learning data and the reward data according to the pre-configured reach conditions corresponding to each reward type, determine the target users who meet the requirements for awarding rewards and the corresponding reward information, and store the reward information into the reward pool queue of the action flow processing node according to the reward type, wherein the reward types and reward data are pre-configured offline in the background, and the configured reward types and reward data are stored in the MySQL data table;

[0037] Reward information is read from the reward pool queue of the action flow processing node, and rewards are distributed to target users.

[0038] According to one aspect of the present application, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the above-mentioned online teaching motivation method.

[0039] By leveraging the above technical solution, this application provides an online teaching incentive system, method, and device. Unlike existing solutions that monitor online teaching services in real time and couple reward information with the service, this application does not perform real-time reward calculation and distribution within the service. Instead, it triggers incentive calculation and distribution based on MySQL binlog, decoupling incentive information from the service. Using offline real-time calculation, it makes incentive distribution and automatic collection convenient, autonomous, and controllable. This application ensures the learning experience brought by incentives without disturbing users' learning, and autonomously and controllably completes incentive distribution and collection.

[0040] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0042] Figure 1 A logical diagram of an online teaching incentive system provided by an embodiment of the present application is shown;

[0043] Figure 2 A schematic diagram of the structure of an online teaching incentive system provided by an embodiment of the present application is shown;

[0044] Figure 3 An example diagram of an online teaching incentive system provided by an embodiment of the present application is shown;

[0045] Figure 4 A schematic diagram of learning monitoring provided by an embodiment of the present application is shown;

[0046] Figure 5 A schematic diagram of learning data processing provided by an embodiment of the present application is shown;

[0047] Figure 6 A schematic diagram of the reward configuration provided in an embodiment of the present application is shown;

[0048] Figure 7 A schematic diagram of reward calculation provided in an embodiment of the present application is shown;

[0049] Figure 8 A schematic diagram of reward distribution provided in an embodiment of the present application is shown;

[0050] Figure 9 A schematic diagram of automatically claiming rewards provided in an embodiment of the present application is shown;

[0051] Figure 10 A flowchart of an online teaching motivation method provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of this application. It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0053] As previously analyzed, existing incentive schemes embed incentive information within the business. When a user reaches the target learning condition, the reward is directly issued to the user, disrupting the user's learning. For example, a reward is issued to the user in the 5th second of a learning video, and the reward information pops up directly. To avoid disrupting user learning due to incentives, the incentive system proposed in the embodiment of the present application triggers the calculation and issuance of incentives based on MySQL binlog, decoupling the incentive information from the business. Using offline real-time calculation, it makes the issuance and automatic collection of incentives convenient, autonomous, and controllable.

[0054] See also Figure 1, shows a schematic diagram of the structure of an online teaching incentive system provided by an embodiment of the present application. The incentive system includes a data monitoring unit, a data processing unit, a reward configuration unit, a reward calculation unit, and a reward distribution unit, wherein,

[0055] The data monitoring unit monitors the binary log information of the relational database MySQL through the data transmission service DTS. Based on the monitored binlog information, it triggers the writing of the latest learning record data in this monitoring cycle to the data queue of the action stream processing node (such as Kafka);

[0056] The data processing unit reads the learning record data from the data queue of the action flow processing node, classifies and counts the learning record data, produces the target learning data required for the incentive, and writes the target learning data to the storage node (such as Redis);

[0057] The reward configuration unit pre-configures the reward types and reward data offline in the background, and stores the configured reward types and reward data in the MySQL data table;

[0058] The reward calculation unit reads the target learning data from the storage node and the reward types and reward data from the MySQL data table. Based on the pre-configured reach conditions corresponding to each reward type, it matches the target learning data with the reward data, determines the target users who meet the requirements for reward issuance and the corresponding reward information, and stores the reward information into the reward pool queue of the action flow processing node according to the reward type.

[0059] The reward distribution unit reads the reward information from the reward pool queue of the action flow processing node and distributes the reward to the target user.

[0060] MySQL is a relational database management system and one of the most popular open source databases currently used in web applications. It uses structured query language (SQL) for database management and operations, and supports multiple storage engines and transaction processing. In the embodiment of the present application, the online teaching platform stores business data in MySQL, and MySQL serves as the data source for the incentive system to provide learning data as the basis for incentives. The incentive system obtains the latest learning data within the monitoring period by monitoring MySQL.

[0061] Among them, the binlong information of MySQL can be monitored through data transmission services (such as DTS) to obtain the latest learning data.

[0062] DTS (Data Transmission Service) enables data migration, subscription, and real-time synchronization between different data sources (including but not limited to relational databases, NoSQL databases, and big data services). DTS can be understood as a database ecosystem tool that can meet requirements such as database migration to the cloud without interrupting business operations. Binary logs (Binary Logs) are binary log files in MySQL databases that record all SQL statements that modify the database, including insert, update, and delete operations.

[0063] The data monitoring unit monitors the binlog information of MySQL through DTS, and based on the monitored binlog information, triggers the writing of the latest learning record data in this monitoring cycle into the data queue of the action stream processing node (such as Kafka).

[0064] In one implementation, see Figure 2 The data monitoring unit may further include: a task monitoring module, which monitors the MySQL binlog information through DTS and obtains the changed SQL statements in the data table; a latest record determination module, which determines the changed data in the data table as the latest learning record data in this monitoring cycle based on the changed SQL statements in the data table; and a data writing module, which writes the latest learning record data to the data queue of the action flow processing node.

[0065] Among them, the action stream processing node can adopt Kafka, for example. Kafka is a high-throughput distributed publish-subscribe messaging system that can process all action stream data of consumers on the website. Kafka can process all action stream data of users on the platform. This action (user action) data is usually solved by processing logs and log aggregation due to throughput requirements. For log data and offline analysis systems, but requiring real-time processing restrictions, Kafka is a feasible solution. Its purpose is to unify online and offline message processing through a parallel loading mechanism, and to provide real-time messages through clusters. In an embodiment of the present application, two types of queues can be maintained in Kafka, one is a data queue, which is used to store the latest learning record data monitored by the data monitoring unit, and the other is a reward pool queue, which is used to store the reward information calculated by the reward calculation unit.

[0066] The data processing unit is mainly used to read learning record data from the action stream processing node (Kafka), classify and count the learning record data, output the target learning data required for incentives, and write the target learning data to the storage node (such as Redis). In the embodiment of the present application, in addition to reading learning record data from Kafka, the data processing unit also supports reading learning record data from the data monitoring unit.

[0067] In one implementation, see Figure 2 The data processing unit may further include: a task subscription module for starting a resident process, subscribing to the learning record data produced by the data monitoring unit and written into the data queue of the action flow processing node; a classification and statistics module for performing offline classification and statistical logic processing on the learning record data to produce target learning data, wherein the target learning data includes daily data and periodic data of the user dimension; and a data writing module for writing the target learning data into a storage node (such as Redis). The data writing module can be specifically used to determine the target learning data format written to the storage node, wherein the target learning data format includes the daily data format of the user dimension and the periodic data format of the user dimension, wherein the daily data format of the user dimension is: Key{user id, date, business category}, value{total cumulative value}; the periodic data format of the user dimension is: Key{user id, business category}, value{total cumulative value}.

[0068] For example, the storage node can use Redis. Redis (Remote Dictionary Server) is an open source log-type key-value database written in ANSI C language, supports networking, can be based on memory or persisted, and supports a variety of data structures, such as strings, hash tables, lists, sets, and ordered sets. It is based on memory operations, provides fast read and write access speeds, and supports functions such as data persistence, master-slave replication, and transaction processing. In the embodiment of the present application, the target learning data is written to Redis for the reward calculation unit to call when performing reward calculations.

[0069] The reward configuration unit pre-configures the reward types and reward data offline in the background, and stores the configured reward types and reward data in a MySQL data table. The reward calculation unit can then read the MySQL data to obtain the reward types and reward data. Alternatively, the reward calculation unit can directly read the reward-related data from the reward configuration unit.

[0070] The reward calculation unit reads the target learning data from the storage node (Redis) and the reward types and reward data from the MySQL data table. Based on the pre-configured reach conditions corresponding to each reward type, it matches and calculates the target learning data and reward data, determines the target users who meet the requirements for awarding rewards and the corresponding reward information, and stores the reward information in the reward pool queue of the action flow processing node according to the reward type. In addition, it is understood that the embodiments of the present application also support the reward calculation unit reading the target learning data from the data processing unit and reading the reward types and reward data from the reward configuration unit.

[0071] In one implementation, see Figure 2 , the reward calculation unit may further include: a data reading module for reading the target learning data from the storage node and reading the reward types and reward data from the MySQL data table; a matching calculation module for performing offline matching calculations on the target learning data and the reward data according to the pre-configured reach conditions corresponding to each reward type, and determining the target users who meet the requirements for awarding rewards and the corresponding reward information; and a reward writing module for writing the reward information to the reward pool queue of the action flow processing node, wherein the reward information of different reward types is written to different reward pool queues corresponding to the reward types. Among them, the reach conditions corresponding to each reward type include: reaching a predetermined number of learning star points every day, and being awarded a treasure box lottery opportunity; completing a predetermined number of difficult problems, and being awarded medals for mastering difficult problems of different levels; completing a predetermined number of learning units, and being awarded the unit learning star medal.

[0072] The reward issuing unit reads the reward information from the reward pool queue of the action stream processing node (kafka) and issues the reward to the target user. In addition, it can be understood that the embodiment of the present application also supports the reward issuing unit to read the reward information from the reward calculation unit.

[0073] In one implementation, see Figure 2 The reward distribution unit may further include: a task subscription module for starting a resident process, subscribing to the data produced by the reward calculation unit, and reading reward information from the reward pool queue; and a reward distribution module for explicitly or implicitly distributing reward information based on the user's asset table. Specifically, the reward distribution module can be used for: explicit distribution of rewards: distributing reward information of different types to different waiting queues, displaying them on the online teaching platform page and waiting for users to claim them. After claiming them, they are written to the user's asset table; implicit distribution of rewards: directly distributing reward information to the user's asset table and displaying them on the personal center achievement wall of the online teaching platform.

[0074] It can be seen that, unlike the existing scheme in which the online teaching business monitors in real time and couples the reward information in the business, in the embodiment of the present application, the reward calculation and issuance are not performed in real time in the business, but an incentive system triggered by MySQL binlog is used. It is based on not disturbing the user's learning, but also ensures the learning experience brought by the incentive, and automatically completes the incentive issuance and automatically completes the incentive collection. Those skilled in the art can understand that when the business system stores the learning data in MySQL, the current teaching has been completed. At this time, the learning incentive (reward calculation and issuance, etc.) is triggered based on the MySQL binlog information, which will not disturb the students' learning and can improve the learning effect. In addition, the explicit issuance or implicit issuance of rewards, where explicit issuance is a way for users to actively receive them after class, will not interfere with the user's class, and implicit issuance is to issue the reward directly to the user's asset table, so that the user is unaware, which is a way that will not interfere with the user's learning at all.

[0075] See also Figure 3 , shows an example diagram of an online teaching incentive system provided in an embodiment of the present application.

[0076] The incentive system in this example mainly includes a data monitoring unit, a data processing unit, a reward configuration unit, a reward calculation unit, and a reward distribution unit. The data monitoring unit can collect various learning data of users (students); the data processing unit performs pre-processing such as classification and statistics on students' learning data; the reward information configuration unit configures the types of rewards and various reward information; the reward calculation unit calculates the reward information that meets the requirements for distribution based on the classification of learning data and the access conditions of various reward information, and stores them in different reward pool queues; the reward distribution unit explicitly or implicitly distributes rewards to students based on the requirements of the reward pool.

[0077] The following is an exemplary description of the implementation of each unit.

[0078] 1. Data Monitoring Unit

[0079] See also Figure 4 , shows a schematic diagram of learning monitoring.

[0080] The learning monitoring process is:

[0081] a. Users generate learning record data through learning devices (such as learning machines) and answering questions;

[0082] b. Store the learning record data generated by students into the MySQL data table;

[0083] c. Use DTS to monitor the binlog information of the MySQL learning data table and write the learning record data to the Kafka queue.

[0084] 2. Learning Data Processing Unit

[0085] See also Figure 5 , which is a schematic diagram of learning data processing.

[0086] In the embodiment of the present application, after the user generates learning data, the learning data is monitored from MySQL, and then offline real-time calculation is adopted to process the target data required for issuing rewards. Therefore, it will not cause interference to normal business processing.

[0087] The learning data processing process is:

[0088] a. Start the resident process job to subscribe to the Kafka data produced by the learning data monitoring unit;

[0089] b. Logical processing of classification and statistics of consumed learning record data;

[0090] c. Produce daily and periodic data at the user level; these data may include various categorized data, such as knowledge point data, unit completion data, problem solving data, plan classification data, and graded level practice data.

[0091] d. Write the data to Redis.

[0092] For example, the data format is as follows:

[0093] Redis Key {user talid + date + business category} value {today's cumulative value};

[0094] Key{user talid+business category}value{total accumulated value}.

[0095] 3. Reward Information Configuration Unit

[0096] See also Figure 6 , which is a schematic diagram of reward information configuration.

[0097] The reward configuration process is:

[0098] a. Manually enter reward data into the reward management platform, and use the reward management platform to conveniently and efficiently maintain reward information;

[0099] b. Store the reward data in the MySQL table.

[0100] For example, the types of reward information configured specifically may include: achievement medals, gold coins, star points, treasure box lottery opportunities, learning level props, Avatar components, etc.

[0101] 4. Reward Calculation Unit

[0102] See also Figure 7 , which is a schematic diagram of the reward calculation process.

[0103] The present embodiment of the application abandons the existing method of directly distributing rewards after calculating reward data because writing reward data into different reward pool queues can improve data processing throughput and avoid consumption delays. The reward pool queues can include achievement medal queues, prop queues, virtual component queues, and star value queues.

[0104] The reward calculation process is:

[0105] a. Get the configured reward information from the reward information configuration unit;

[0106] b. Match and calculate based on the user dimension data and reward configuration award condition information produced by the learning data processing unit;

[0107] c. Determine whether the award conditions are met. If so, write the user information and reward information into Kafka.

[0108] Among them, the reward conditions may be as follows:

[0109] 1. Reach X star points every day and receive 1 chance to draw a treasure box;

[0110] 2. Complete Y puzzles and be rewarded with different levels of puzzle master medals (generally, medals are divided into five levels, each with different triggering conditions);

[0111] 3. Complete Z units and be awarded the Unit Learning Star Medal.

[0112] 5. Reward Distribution Unit

[0113] See also Figure 8 , which is a schematic diagram of reward distribution.

[0114] The reward distribution process is as follows:

[0115] a. Start the resident process job to subscribe to the Kafka data produced by the reward calculation unit;

[0116] b. Based on the reward data consumed, rewards are distributed to the user's asset table and stored in MySQL.

[0117] This application embodiment supports different awarding methods:

[0118] 1. Explicit distribution: Different reward information is distributed to different waiting queues, and children can collect them one by one on the homepage of the learning machine, allowing children to intuitively feel the achievements brought by learning. After collection, the rewards are written into the user asset table;

[0119] 2. Implicit distribution: Directly distributed to the user's asset table, no need to claim, and displayed on the achievement wall of the personal center.

[0120] The explicit distribution method can be understood as the automatic method of receiving rewards. Figure 9 , a diagram of automatic reward claiming. Explicitly issued rewards must be claimed in a centralized location. For example, each time a user accesses the Luancher page on the learning machine, the list of pending rewards is read, the reward animation is played sequentially, and the rewards are automatically claimed and written to the user's asset table.

[0121] In the above example, the data calculation unit adopts offline real-time calculation to avoid disturbing users during the learning process. In addition, for the learning motivation experience and perception, rewards can be automatically collected on the homepage, completely isolating the incentive system from online business, facilitating an autonomous and controllable incentive system, and avoiding the impact of incentive system problems on learning business.

[0122] Therefore, the embodiments of the present application have the following features and advantages:

[0123] 1. Transform from traditional online incentives to offline real-time computing;

[0124] 2. Binlog monitoring as a reward driver;

[0125] 3. Isolate the incentive system from the online learning system to decouple the incentive and learning businesses;

[0126] 4. Offline calculation automatically matches reward target information and automatically distributes rewards, eliminating the need to calculate whether rewards can be distributed in the C-end business;

[0127] 5. In order not to lose the motivational experience brought by the incentive, rewards are automatically collected on the homepage so that users can feel it.

[0128] See also Figure 10 , which shows a flowchart of an online teaching incentive method provided in an embodiment of the present application.

[0129] The motivational method for online teaching includes the following steps:

[0130] S1001: Monitor the binary log information of the relational database MySQL through the data transmission service, and based on the monitored binlog information, trigger the writing of the latest learning record data in this monitoring cycle into the data queue of the action flow processing node;

[0131] S1002: Read learning record data from the data queue of the action flow processing node, classify and count the learning record data, generate target learning data required for incentives, and write the target learning data to the storage node;

[0132] S1003: Read the target learning data from the storage node and the reward types and reward data from the MySQL data table. According to the pre-configured reach conditions corresponding to each reward type, match the target learning data and reward data to determine the target users who meet the reward requirements and the corresponding reward information. Then, store the reward information into the reward pool queue of the action flow processing node according to the reward type. The reward types and reward data are pre-configured offline in the background and stored in the MySQL data table.

[0133] S1004: Read reward information from the reward pool queue of the action flow processing node and distribute rewards to target users.

[0134] In one implementation, the specific process of S1001 is:

[0135] By monitoring MySQL binlog information, we can obtain the SQL statements that have been changed in the data table.

[0136] According to the changed SQL statements in the data table, the changed data in the data table is determined as the latest learning record data in this monitoring cycle;

[0137] Write the latest learning record data to the data queue of the action stream processing node.

[0138] In one implementation, the specific process of S1002 is:

[0139] Start the resident process, subscribe to the learning record data produced by the data monitoring unit and written to the data queue of the action flow processing node;

[0140] Perform offline classification and statistical logic processing on learning record data to produce target learning data, which includes daily data and periodic data in the user dimension;

[0141] Write the target learning data to the storage node.

[0142] In one implementation, the process of writing the target learning data to the storage node is as follows:

[0143] Determine the target learning data format to be written to the storage node, wherein the target learning data format includes the user dimension daily data format and the user dimension periodic data format, wherein the user dimension daily data format is: Key{user id, date, business category}, value{cumulative value of the day}; the user dimension periodic data format is: Key{user id, business category}, value{total cumulative value}.

[0144] In one implementation, the specific process of S1003 is:

[0145] Read the target learning data from the storage node and read the reward type and reward data from the MySQL data table;

[0146] According to the pre-configured reach conditions corresponding to each reward type, the target learning data and the reward data are matched offline to determine the target users who meet the reward requirements and the corresponding reward information;

[0147] The reward information is written into the reward pool queue of the action flow processing node, wherein the reward information of different reward types is written into different reward pool queues corresponding to the reward types.

[0148] In one implementation, the attainment conditions corresponding to each reward type include:

[0149] Reaching a predetermined number of learning star points every day will reward you with a chance to draw a treasure box;

[0150] Completing a predetermined number of puzzles will reward you with medals of master puzzle solvers of different levels;

[0151] After completing a predetermined number of learning units, you will be awarded the Unit Learning Star Medal.

[0152] In one implementation, the specific process of S1004 includes:

[0153] Start the resident process, subscribe to the data produced by the reward calculation unit, and read the reward information from the reward pool queue;

[0154] Reward information is issued explicitly or implicitly based on the user's asset table.

[0155] In one implementation, performing explicit or implicit issuance includes:

[0156] Explicit reward distribution: Reward information of different types is distributed to different waiting queues, displayed on the online teaching platform page and waiting for users to claim. After claiming, it is written to the user's asset table;

[0157] Implicit distribution of rewards: Reward information is directly distributed to the user's asset table and displayed on the personal center achievement wall of the online teaching platform.

[0158] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0159] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0160] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0161] Through the data transmission service, the binary log information of the relational database MySQL is monitored. Based on the monitored binlog information, the latest learning record data in this monitoring cycle is triggered to be written to the data queue of the action flow processing node;

[0162] Read learning record data from the data queue of the action flow processing node, classify and count the learning record data, generate target learning data required for incentives, and write the target learning data to the storage node;

[0163] Read the target learning data from the storage node and the reward types and reward data from the MySQL data table. According to the pre-configured reach conditions for each reward type, match the target learning data and reward data, determine the target users who meet the reward requirements and the corresponding reward information, and store the reward information in the reward pool queue of the action flow processing node according to the reward type. The reward types and reward data are pre-configured offline in the background and stored in the MySQL data table.

[0164] Read reward information from the reward pool queue of the action flow processing node and distribute rewards to target users.

[0165] Optionally, specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0166] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0167] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0168] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0169] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0170] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0171] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0172] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An online teaching incentive system, characterized in that: include: The data monitoring unit monitors the binary log binlog information of the relational database MySQL through the data transmission service, and based on the monitored binlog information, triggers the writing of the latest learning record data in this monitoring cycle into the data queue of the action flow processing node; A data processing unit, which reads the learning record data from the data queue of the action flow processing node, classifies and counts the learning record data, generates target learning data required for the incentive, and writes the target learning data to the storage node; The reward configuration unit configures the reward types and reward data offline in the background in advance, and stores the configured reward types and reward data in the MySQL data table; A reward calculation unit reads the target learning data from the storage node and the reward type and reward data from the MySQL data table, performs matching calculation on the target learning data and the reward data according to the pre-configured access conditions corresponding to each reward type, determines the target user who meets the reward issuance requirements and the corresponding reward information, and stores the reward information into the reward pool queue of the action flow processing node according to the reward type; The reward issuing unit reads the reward information from the reward pool queue of the action flow processing node and issues rewards to the target user.

2. The system according to claim 1, characterized in that The data monitoring unit further comprises: The task monitoring module obtains the SQL statements changed in the data table by monitoring the MySQL binlog information; The latest record determination module determines the changed data in the data table as the latest learning record data in this monitoring cycle according to the changed SQL statement in the data table; The data writing module writes the latest learning record data to the data queue of the action stream processing node.

3. The system according to claim 1, characterized in that The data processing unit further comprises: The task subscription module is used to start the resident process and subscribe to the learning record data produced by the data monitoring unit and written into the data queue of the action flow processing node; The classification and statistics module is used to perform offline classification and statistical logic processing on the learning record data to produce target learning data, which includes daily data and periodic data of the user dimension; The data writing module is used to write the target learning data to the storage node.

4. The system according to claim 3, characterized in that The data writing module is specifically used to determine the target learning data format to be written to the storage node, and the target learning data format includes the user dimension daily data format and the user dimension periodic data format, wherein the user dimension daily data format is: Key{user id, date, business category}, value{total cumulative value}; the user dimension periodic data format is: Key{user id, business category}, value{total cumulative value}.

5. The system according to claim 1, characterized in that The reward calculation unit further comprises: A data reading module, used to read the target learning data from the storage node and read the reward type and reward data from the MySQL data table; A matching calculation module, used to perform offline matching calculation on the target learning data and the reward data according to the pre-configured reach conditions corresponding to each reward type, and determine the target users who meet the reward issuance requirements and the corresponding reward information; The reward writing module is used to write reward information into the reward pool queue of the action flow processing node, wherein reward information of different reward types is written into different reward pool queues corresponding to the reward types.

6. The system according to claim 5, characterized in that The access conditions corresponding to each reward type include: If you reach a predetermined number of learning star points every day, you will be awarded a chance to draw a treasure box; After completing a predetermined number of puzzles, you will be awarded with medals of master of puzzle solving of different levels; After completing a predetermined number of learning units, you will be awarded the Unit Learning Star Medal.

7. The system according to claim 1, characterized in that The reward issuing unit further comprises: The task subscription module is used to start the resident process, subscribe to the data produced by the reward calculation unit, and read the reward information from the reward pool queue; The reward distribution module is used to explicitly or implicitly distribute reward information based on the user asset table.

8. The system according to claim 7, characterized in that The reward distribution module is specifically used to: Explicit distribution of rewards: Distribute reward information of different types of rewards to different waiting queues, display them on the online teaching platform page and wait for users to claim them. After claiming, they will be written into the user's asset table; Implicit distribution of rewards: The reward information is directly distributed to the user's asset table and displayed on the personal center achievement wall of the online teaching platform.

9. A method for motivating online teaching, characterized in that: include: Through the data transmission service, the binary log binlog information of the relational database MySQL is monitored. Based on the monitored binlog information, the latest learning record data in this monitoring cycle is triggered to be written into the data queue of the action flow processing node; Reading learning record data from the data queue of the action flow processing node, classifying and counting the learning record data, generating target learning data required for incentives, and writing the target learning data to a storage node; Read the target learning data from the storage node and read the reward type and reward data from the MySQL data table, perform matching calculations on the target learning data and the reward data according to the pre-configured reach conditions corresponding to each reward type, determine the target user who meets the award criteria and the corresponding reward information, and store the reward information into the reward pool queue of the action flow processing node according to the reward type, wherein the reward type and reward data are configured offline in the background in advance, and the configured reward type and reward data are stored in the MySQL data table; Reward information is read from the reward pool queue of the action flow processing node, and rewards are distributed to target users.

10. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to run the computer program to perform the method of claim 9.

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