Personalized education system based on big data

Abstract

The invention discloses a personalized education system based on big data, which comprises a design processing module, a management analysis module and an acquisition and transmission module, and is characterized in that the design processing module is in network connection with the management analysis module, the management analysis module is in network connection with the acquisition and transmission module, and the design processing module is in network connection with the acquisition and transmission module. The design processing module is used for carrying out design control processing on the education system, the management analysis module is used for carrying out optimization management analysis on the education system, and the acquisition and transmission module is used for carrying out acquisition and transmission management on data information. The design processing module comprises a system design module, a service architecture module and a teaching management module, the system design module is in network connection with the service architecture module, the service architecture module is in network connection with the teaching management module, and the system design module is used for carrying out system design processing on the education system. The system has the characteristics of intelligent analysis and processing and high efficiency.

Description

Technical Field

[0001] The present invention relates to the field of educational technology, and in particular to a personalized education system based on big data. Background Art

[0002] The online education system has achieved the goals of classroom freedom, tutoring freedom, and learning freedom. Students can learn online, and teachers can comprehensively evaluate students' mastery of the course through the learning trajectory and learning progress of the platform. Students can also control their own learning progress, repeat the learning of the subject content, and deepen their impression. However, during the entire platform development and promotion process, the growth rate of user data has exceeded expectations, and the cost pressure of cloud computing resources has become the main problem that needs to be solved. On the one hand, it is the cost pressure of computing resources, and on the other hand, it is the pressure of storing data. However, many online education institutions currently only consider the crazy expansion of their business, without considering the efficiency and cost issues in the online education system, neglecting the management of cloud computing resources, and excessively wasting public cloud resources, resulting in a substantial increase in public cloud costs, so that the efficiency and cost of the entire online education system cannot meet the expected needs. Therefore, it is necessary to design a personalized education system based on big data with intelligent analysis and processing and high efficiency. Summary of the invention

[0003] The purpose of the present invention is to provide a personalized education system based on big data to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a personalized education system based on big data, comprising: a design processing module, a management analysis module and an acquisition and transmission module, the design processing module is connected to the management analysis module through a network, the management analysis module is connected to the acquisition and transmission module through a network, the design processing module is connected to the acquisition and transmission module through a network, the design processing module is used to perform design control processing of the education system, the management analysis module is used to perform optimization management and analysis of the education system, and the acquisition and transmission module is used to perform acquisition and transmission management of data information.

[0005] According to the above technical solution, the design processing module includes a system design module, a service architecture module and a teaching management module. The system design module is connected to the service architecture module through a network, and the service architecture module is connected to the teaching management module through a network. The system design module is used to perform system design processing on the education system, the service architecture module is used to perform architecture construction control processing on the education system, and the teaching management module is used to control and manage the teaching functions of the education system.

[0006] According to the above technical solution, the management and analysis module includes a teaching and research control module, a data processing module and an access analysis module. The teaching and research control module is connected to the data processing module via a network, and the data processing module is electrically connected to the access analysis module. The teaching and research control module is used to perform teaching and research control processing on the education system, the data processing module is used to perform data analysis processing on the education system, and the access analysis module is used to perform computational analysis and management on access data of the education system.

[0007] According to the above technical solution, the acquisition and transmission module includes an acquisition and sorting module and a feedback transmission module. The acquisition and sorting module is connected to the feedback transmission module through a network. The acquisition and sorting module is used to acquire and sort data information, and the feedback transmission module is used to perform feedback transmission processing on data information.

[0008] According to the above technical solution, the operation method of the personalized education system based on big data includes the following steps: Step S1: Design and manage the education system in the education system used by teachers and students for education and learning; Step S2: After completing the system design management, the service architecture and educational function management and control of the education system are carried out; Step S3: further perform operation analysis and optimization processing on the education system; Step S4: Collect teachers' and students' suggestions on the use of the education system and manage feedback transmission.

[0009] According to the above technical solution, step S1 further includes the following steps: A layered design is adopted for the education system. The system includes a display layer, an application logic layer, a database layer, a high-performance distributed file layer and a common distributed file layer. The display layer is the interface layer for the interaction between the system and the user. The system is displayed to the outside through the web interface of the online education system. The page is mainly implemented externally using a browser. The application layer is used for data interaction and logic processing between the front-end and back-end. On the one hand, it accepts requests from the display layer and performs authority identification operations. On the other hand, it displays data provided by the data layer. The data layer mainly formats table data of the system and performs data construction.

[0010] According to the above technical solution, step S2 further includes the following steps: Step S21: When the education system is running, the education service architecture is processed through the cloud computing network, wherein the access layer deploys the Node nodes of the access system, and each Node node deploys the application corresponding to the HTTP RESTful interface, which can provide Http protocol services to the outside world. The access layer is only responsible for request forwarding, which is forwarded to the corresponding Node node of the microservice logic application layer according to the set requirements. The Node of the access layer and the logic application layer communicates through the RPC remote control protocol; Step S22: Manage and control the live broadcast of teaching videos in the education system. After entering the login account and password information, the teacher sends a login() method request. At this time, the database verifies whether the information entered by the user is correct. When the login() method returns false, the system will prompt the teacher that the information entered is incorrect. When the login() method returns true, the teacher enters the system. After the teacher sends a teaching video live broadcast management request to the live broadcast and teaching video on-demand management service, the system first sends a request to the service center according to the live broadcast application, and then the service center will send a request to the data processing layer at the same time. At this time, the system will show the current application status to the teacher. After the application is approved, the teacher can view the live broadcast list and conduct the corresponding teaching live broadcast.

[0011] According to the above technical solution, step S3 further includes the following steps: Step S31: When conducting teaching and research interaction management, the teaching and research staff sends a findResearch() request to the teaching and research interaction service InfoServer to view the current teaching and research information, and then releases the teaching and research interaction information through the release() method. After receiving the request, the system will prompt the teaching and research staff to enter the information to be released. After submission, the database processing layer uses the isExist() method to determine whether the same teaching and research activity has been released. If it already exists, the system prompts the teaching and research staff that the current teaching and research information already exists. If not, the database information will be updated, and finally the teaching and research interaction is completed; Step S32: In the education system, fixed striping of data is performed using the RSC algorithm. Since dynamic striping requires storing the size of each calculated stripe, saving and obtaining stripe information will cause performance loss in storage and calculation, increase the complexity of data loss recovery, and reduce the reliability of the overall system. Therefore, by summarizing the processing steps, a fixed stripe size mode is used for data cutting, which effectively improves the stability and security of data processing in the education system. Step S33: Using a hierarchical storage mode, the hot data in the education system that meets the settings is stored in a high-performance disk, and the remaining data is stored in a low-performance disk. Since the capacity of the high-performance disk is limited, the data needs to be replaced to complete the hot and cold replacement of the data. Therefore, the hit rate P of the data cache is calculated by the LRU algorithm, and the high-performance disk data with a cache hit rate P lower than the set threshold is preferentially selected for client access to reduce user access delay. The calculation formula is as follows: In the formula, N is the hit data, and M is the non-hit data. When a client user accesses high-performance disk data, if the accessed data is in the cache, it is called a hit. Otherwise, the data is obtained from the disk, and the data retrieval and user access are completed synchronously.

[0012] According to the above technical solution, step S4 further includes the following steps: Step S41: collecting teacher or student suggestion data information on the use of the education system, and arranging the suggestion data information collected within a set time period into a table form; Step S42: After the obtained and sorted table data is transmitted to the backend management end, it provides a reference basis for further optimization of the subsequent system, so that the optimization can effectively meet user needs.

[0013] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, by providing a design processing module, a management analysis module and an acquisition and transmission module, can effectively improve the network analysis and processing efficiency of the education system, while improving the analysis and processing efficiency of live teaching, and simplifying the live broadcast processing operation requests, so that older teachers can also conduct live teaching quickly and efficiently, greatly improving the system's user satisfaction, avoiding repeated data information upload processing, and effectively reducing the system's storage and analysis operation load, thereby improving the analysis and search efficiency of data information for teachers and students in the education system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0016] See also Figure 1 The present invention provides a technical solution: a personalized education system based on big data, including: a design processing module, a management analysis module and a collection and transmission module. The design processing module is connected to the management analysis module through a network, the management analysis module is connected to the collection and transmission module through a network, the design processing module is connected to the collection and transmission module through a network, the design processing module is used to perform design control processing on the education system, the management analysis module is used to perform optimization management and analysis on the education system, and the collection and transmission module is used to perform collection and transmission management of data information.

[0017] The design processing module includes a system design module, a service architecture module and a teaching management module. The system design module and the service architecture module are connected to the network, and the service architecture module and the teaching management module are connected to the network. The system design module is used to perform system design processing on the education system, the service architecture module is used to perform architecture construction control processing on the education system, and the teaching management module is used to control and manage the teaching functions of the education system.

[0018] The management and analysis module includes a teaching and research control module, a data processing module and an access analysis module. The teaching and research control module and the data processing module are connected to the network, and the data processing module and the access analysis module are electrically connected. The teaching and research control module is used to perform teaching and research control processing on the education system, the data processing module is used to perform data analysis processing on the education system, and the access analysis module is used to perform computational analysis and management on access data of the education system.

[0019] The collection and transmission module includes a collection and sorting module and a feedback transmission module. The collection and sorting module and the feedback transmission module are connected to the network. The collection and sorting module is used to collect and sort data information, and the feedback transmission module is used to perform feedback transmission processing of data information.

[0020] The operation method of the personalized education system based on big data includes the following steps: Step S1: Design and manage the education system in the education system used by teachers and students for education and learning; Step S2: After completing the system design management, the service architecture and educational function management and control of the education system are carried out; Step S3: further perform operation analysis and optimization processing on the education system; Step S4: Collect teachers' and students' suggestions on the use of the education system and manage feedback transmission.

[0021] Step S1 further comprises the following steps: The education system adopts a layered design. The system includes a display layer, an application logic layer, a database layer, a high-performance distributed file layer and a general distributed file layer. The display layer is the interface layer for the interaction between the system and the user. The system is displayed to the outside world as the web interface of the online education system. The page is mainly implemented externally using a browser. The application layer is used for data interaction and logic processing between the front-end and back-end. On the one hand, it accepts requests from the display layer and performs operations for authority identification. On the other hand, it displays data provided by the data layer. The data layer mainly formats table data of the system and builds data. In this step, the high-performance distributed file layer is built using the open source distributed file system CephFS, which is mainly used to store database files and hot data files. The data backup of these files is configured according to the file type. The database file uses three copies of redundancy. Since the hot data files are redundantly stored in the general file layer, they are stored in a single copy mode. The general distributed file system layer is the same as the high-performance file system software, but the data storage medium and data storage redundancy method are different. It uses erasure coding for storage, which effectively improves the reliability of education file data.

[0022] Step S2 further comprises the following steps: Step S21: When the education system is running, the education service architecture is processed through the cloud computing network, wherein the access layer deploys the Node nodes of the access system, and each Node node deploys the application corresponding to the HTTP RESTful interface, which can provide Http protocol services to the outside world. The access layer is only responsible for request forwarding, which is forwarded to the corresponding Node node of the microservice logic application layer according to the set requirements. The Node of the access layer and the Node of the logic application layer communicate through the RPC remote control protocol; in this step, the microservices with corresponding functions are deployed in the Node nodes of the logic application layer, and then the corresponding database system is accessed through the Redis cache layer (optional). The Node is controlled by Kubernetes, which can start and delete the corresponding Node nodes according to the configuration requirements, effectively improving the network analysis and processing efficiency of the education system; Step S22: Manage and control the live broadcast of teaching videos in the education system. After entering the login account and password information, the teacher sends a login() method request. At this time, the database verifies whether the information entered by the user is correct. When the login() method returns false, the system will prompt the teacher that the information entered is incorrect. When the login() method returns true, the teacher enters the system. After the teacher sends a teaching video live broadcast management request to the live broadcast and teaching video on-demand management service, the system first sends a request to the service center according to the live broadcast application, and then the service center will send a request to the data processing layer at the same time. At this time, the system will show the current application status to the teacher. After the application is approved, the teacher can view the live broadcast list and conduct the corresponding teaching live broadcast; through this step, the teacher uses the VideoServer service to send a videoRecording() request, and then the service uses the storage() method to determine and parse the EC code. After the parsing is completed, the address information will be obtained, and then through Crush The engine can obtain the storage address, and finally complete the live broadcast information storage through the dataEngine engine, effectively improving the analysis and processing efficiency of live teaching, and simplifying the live broadcast processing operation requests, so that older teachers can also conduct live teaching quickly and efficiently, greatly improving the system's user satisfaction.

[0023] Step S3 further comprises the following steps: Step S31: When conducting teaching and research interaction management, the teaching and research staff sends a findResearch() request to the teaching and research interaction service InfoServer to view the current teaching and research information, and then releases the teaching and research interaction information through the release() method. After receiving the request, the system will prompt the teaching and research staff to enter the information to be released. After submission, the database processing layer uses the isExist() method to determine whether the same teaching and research activity has been released. If it already exists, the system prompts the teaching and research staff that the current teaching and research information already exists. If not, the database information will be updated, and finally the teaching and research interaction will be prompted to be released. Through this step, the upload processing of duplicate data information can be avoided, the storage and analysis operation load of the system can be effectively reduced, and the analysis and search efficiency of data information in the education system can be improved; Step S32: In the education system, fixed striping of data is performed using the RSC algorithm. Since dynamic striping requires storing the size of each calculated stripe, saving and obtaining stripe information will cause performance loss in storage and calculation, increase the complexity of data loss recovery, and reduce the reliability of the overall system. Therefore, by summarizing the processing steps, a fixed stripe size mode is used for data cutting, which effectively improves the stability and security of data processing in the education system. Step S33: Using a hierarchical storage mode, the hot data in the education system that meets the settings is stored in a high-performance disk, and the remaining data is stored in a low-performance disk. Since the capacity of the high-performance disk is limited, the data needs to be replaced to complete the hot and cold replacement of the data. Therefore, the hit rate P of the data cache is calculated by the LRU algorithm, and the high-performance disk data with a cache hit rate P lower than the set threshold is preferentially selected for client access to reduce user access delay. The calculation formula is as follows: In the formula, N is the hit data, and M is the hit data. When a client user accesses high-performance disk data, if the accessed data is in the cache, it is called a hit, otherwise the data is obtained from the disk, and the data retrieval and user access are completed synchronously. Since Non-Volatile Memory Express (NVMe for short) defines the transmission specification of solid-state drives, it can maximize the read and write performance at a lower hardware cost, thereby reducing the high latency caused by the AHCI (Advanced Host Controller Interface) interface, and completely liberating the ultimate performance of solid-state drives in the SATA era. However, the cost of NVMe SSDs and ordinary disks is several times different. Educational information systems need to store student data for a long time. Therefore, a tiered storage mode is adopted through this step to store hot data on high-performance disks and store low-frequency access data on low-performance disks, effectively improving the efficiency of data storage and access, saving data storage costs, and avoiding unnecessary waste of system resources.

[0024] Step S4 further comprises the following steps: Step S41: collecting teacher or student suggestion data information on the use of the education system, and arranging the suggestion data information collected within a set time period into a table form; Step S42: After the obtained and sorted table data is transmitted to the backend management end, it provides a reference basis for further optimization of the subsequent system, so that the optimization can effectively meet user needs.

[0025] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0026] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A personalized education system based on big data, comprising: The design processing module, the management and analysis module and the collection and transmission module are characterized in that: the design processing module is connected to the management and analysis module through a network, the management and analysis module is connected to the collection and transmission module through a network, the design processing module is connected to the collection and transmission module through a network, the design processing module is used to perform design control processing of the education system, the management and analysis module is used to perform optimization management and analysis of the education system, and the collection and transmission module is used to perform collection and transmission management of data information; The operation method of the personalized education system based on big data includes the following steps: Step S1: Design and manage the education system in the education system used by teachers and students for education and learning; Step S2: After completing the system design management, the service architecture and educational function management and control of the education system are carried out; Step S3: further perform operation analysis and optimization processing on the education system; Step S4: collecting teachers' and students' suggestions on the use of the education system and managing feedback transmission; The step S3 further comprises the following steps: Step S31: When conducting teaching and research interaction management, the teaching and research staff sends a findResearch() request to the teaching and research interaction service InfoServer to view the current teaching and research information, and then releases the teaching and research interaction information through the release() method. After receiving the request, the system will prompt the teaching and research staff to enter the information to be released. After submission, the database processing layer uses the isExist() method to determine whether the same teaching and research activity has been released. If it already exists, the system prompts the teaching and research staff that the current teaching and research information already exists. If not, the database information will be updated, and finally the teaching and research interaction is completed; Step S32: In the education system, fixed striping of data is performed using the RSC algorithm. Since dynamic striping requires storing the size of each calculated stripe, saving and obtaining stripe information will cause performance loss in storage and calculation, increase the complexity of data loss recovery, and reduce the reliability of the overall system. Therefore, by summarizing the processing steps, a fixed stripe size mode is used for data cutting, which effectively improves the stability and security of data processing in the education system. Step S33: Using a hierarchical storage mode, the hot data in the education system that meets the settings is stored in a high-performance disk, and the remaining data is stored in a low-performance disk. Since the capacity of the high-performance disk is limited, the data needs to be replaced to complete the hot and cold replacement of the data. Therefore, the hit rate P of the data cache is calculated by the LRU algorithm, and the high-performance disk data with a cache hit rate P lower than the set threshold is preferentially selected for client access to reduce user access delay. The calculation formula is as follows: In the formula, N is the hit data, and M is the missed data. When a client user accesses high-performance disk data, if the accessed data is in the cache, it is called a hit. Otherwise, the data is retrieved from the disk, and the data retrieval and user access are completed synchronously. The step S4 further comprises the following steps: Step S41: collecting teacher or student suggestion data information on the use of the education system, and arranging the suggestion data information collected within a set time period into a table form; Step S42: After the obtained table data is sorted, it is transmitted to the backend management end to provide a reference basis for further optimization of the subsequent system so that the optimization can effectively meet user needs; The step S1 further comprises the following steps: The education system adopts a layered design. The system includes a display layer, an application logic layer, a database layer, a high-performance distributed file layer, and a common distributed file layer. The display layer is the interface layer for the interaction between the system and the user. The system is displayed to the outside through the web interface of the online education system. The page is implemented externally using a browser. The application layer is used for data interaction and logic processing between the front-end and back-end. On the one hand, it accepts requests from the display layer and performs operations for authority identification. On the other hand, it displays data provided by the data layer. The data layer is the system's formatted table data and performs data construction. The step S2 further comprises the following steps: Step S21: When the education system is running, the education service architecture is processed through the cloud computing network, wherein the access layer deploys the Node nodes of the access system, and each Node node deploys the application corresponding to the HTTP RESTful interface, which can provide Http protocol services to the outside world. The access layer is only responsible for request forwarding, which is forwarded to the corresponding Node node of the microservice logic application layer according to the set requirements. The Node of the access layer and the logic application layer communicates through the RPC remote control protocol; Step S22: Manage and control the live broadcast of teaching videos in the education system. After entering the login account and password information, the teacher sends a login() method request. At this time, the database verifies whether the information entered by the user is correct. When the login() method returns false, the system will prompt the teacher that the information entered is incorrect. When the login() method returns true, the teacher enters the system. After the teacher sends a teaching video live broadcast management request to the live broadcast and teaching video on-demand management service, the system first sends a request to the service center according to the live broadcast application, and then the service center will send a request to the data processing layer at the same time. At this time, the system will show the current application status to the teacher. After the application is approved, the teacher can view the live broadcast list and conduct the corresponding teaching live broadcast.

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