A data access method and system applied to teaching platform

Abstract

The present invention proposes a data access method and system applied to a teaching platform. The data access method applied to the teaching platform includes: configuring a Hadoop cluster of the teaching platform and defining a data stream; when there is a user's data access request on the teaching platform, using a streaming processing tool provided by writing into the Hadoop distributed file system to provide data information in a streaming manner, and displaying the data information on the user's smart device; recommending resource data based on the user's data access content combined with the user's learning history record; real-time monitoring of the data access operating parameters of the teaching platform, and judging whether to perform data access optimization according to the optimization strategy based on the data access operating parameters. The system includes modules corresponding to the steps of the method.

Description

Technical Field

[0001] The invention proposes a data access method and system applied to a teaching platform, belonging to the technical field of data access. Background Art

[0002] The rapid development of computer network technology and the continuous improvement of educational technology have made Internet-based online teaching gradually grow and develop, and gradually become an important part of modern educational technology. The premise and foundation for the realization of online education is the construction of educational resources. At present, countries around the world are making full use of the advantages of the Internet to strengthen the construction and sharing of online educational resources. In the era of Internet big data, educational information technology is developing by leaps and bounds. The course teaching resource data is not only diverse in form, but also growing in capacity. Under this background, the teaching resource data has become more diversified, rich in project background, and big data. The teaching platform is an application system platform for applying smart classroom solutions. It is based on the advantages of the excellent content resources of the subject network and integrates big data, AI and other technologies to build it. It aims to assist teachers in precise and stratified teaching, help schools to carry out smart education and management, and enable parents to obtain their children's learning dynamics in school and in the classroom in real time, forming a smart learning environment of schools, teachers, parents and students, to solve the pain points of schools, teachers, students and parents, and enhance the competitiveness of the product matrix. However, the teaching platform in the existing technology has problems of low access efficiency and poor access response in the process of accessing a large amount of data. Summary of the invention

[0003] The present invention provides a data access method and system applied to a teaching platform to solve the technical problems existing in the above-mentioned prior art. The technical solutions adopted are as follows:

[0004] A data access method applied to a teaching platform, the data access method applied to a teaching platform comprising:

[0005] Configure the Hadoop cluster of the teaching platform and define data flows;

[0006] When there is a data access request from a user on the teaching platform, the streaming processing tool provided by the Hadoop distributed file system is used to provide data information in a streaming manner, and the data information is displayed on the user's smart device;

[0007] Recommend resource data based on the user's data access content and the user's learning history;

[0008] The data access operation parameters of the teaching platform are monitored in real time, and it is determined whether to perform data access optimization according to the optimization strategy based on the data access operation parameters.

[0009] Furthermore, configure the Hadoop cluster of the teaching platform and define the data flow, including:

[0010] Configure the Hadoop distributed file system and its ecosystem components on the teaching platform;

[0011] Determine the data type and content that need to be streamed into the Hadoop distributed file system, and set a data flow path and storage format corresponding to the data type and content.

[0012] Furthermore, when the teaching platform has a data access request from a user, a streaming processing tool provided by the Hadoop distributed file system is used to provide data information in a streaming manner, including:

[0013] The user sends a data access request to the teaching platform through a smart device, wherein the smart device includes a mobile terminal and a PC;

[0014] After receiving the data access request sent by the user through the smart device, the teaching platform verifies the identity and authority of the user;

[0015] When the user passes the identity and authority verification, the teaching platform parses the data access request sent by the user to obtain the data access requirements contained in the data access request;

[0016] According to the data access requirement sent by the user, the stream processing tool of Hadoop is called to search the Hadoop distributed file system to obtain data corresponding to the data access requirement of the user.

[0017] Furthermore, resource data is recommended based on the user's data access content and the user's learning history, including:

[0018] Collecting the user's data access content, wherein the data access content includes browsing, searching, downloading and data information corresponding to the activity;

[0019] Collect the user's learning history records; wherein the learning history records include the courses learned, knowledge points and learning material data;

[0020] Performing data analysis on the user's data access content to obtain the user's interest preference data;

[0021] Performing data analysis on the user's learning history records to obtain the user's learning knowledge information;

[0022] According to the interest preference data and learning knowledge information of the user, resource data corresponding to the interest preference data and learning knowledge information are retrieved, and the resource data is recommended to the user's smart device.

[0023] Further, real-time monitoring of data access operating parameters of the teaching platform and judging whether to perform data access optimization according to the optimization strategy according to the data access operating parameters include:

[0024] Real-time monitoring of the data access operating parameters of the teaching platform; wherein the data access operating parameters include the number of concurrent users per unit time, access response time and resource utilization;

[0025] Using the data access operation parameters of the teaching platform to obtain the operation load evaluation parameters of the teaching platform;

[0026] When the operating load evaluation parameter of the teaching platform exceeds a preset parameter threshold, data access optimization is performed according to the optimization strategy.

[0027] Further, using the data access operation parameters of the teaching platform to obtain the operation load evaluation parameters of the teaching platform includes:

[0028] Obtaining a first load evaluation factor of the teaching platform by using the number of concurrent users per unit time and the resource utilization rate per unit time of the teaching platform;

[0029] Acquiring a second load evaluation factor of the teaching platform by using the access response time of the teaching platform;

[0030] The operation load evaluation parameter is obtained by using the first load evaluation factor and the second load evaluation factor of the teaching platform, wherein the operation load evaluation parameter is obtained by the following formula:

[0031]

[0032] Among them, R z represents the operating load evaluation parameter; R0 represents the preset parameter reference value; R 01 Represents the first load evaluation factor; R 02 Represents the second load evaluation factor.

[0033] Furthermore, the first load evaluation factor of the teaching platform is obtained by using the number of concurrent users per unit time and the resource utilization rate per unit time of the teaching platform, including:

[0034] Extract the number of concurrent users per unit time of the teaching platform;

[0035] Extracting the resource utilization rate per unit time of the teaching platform; wherein the resource utilization rate includes memory utilization rate and network bandwidth utilization rate;

[0036] The resource utilization compensation coefficient is obtained by using the memory utilization and network bandwidth utilization per unit time; wherein the resource utilization compensation coefficient is obtained by the following formula:

[0037]

[0038] Among them, K x represents the resource utilization compensation coefficient; n represents the number of unit times experienced by the operation of the teaching platform, and the unit time is 1s; P ni represents the memory utilization corresponding to the i-th unit time; P bi represents the network bandwidth utilization rate corresponding to the i-th unit time; δ 01 and δ 02 Respectively represent the first adjustment coefficient and the second adjustment coefficient; wherein the first adjustment coefficient and the second adjustment coefficient are obtained by the following formula:

[0039]

[0040] Among them, P ni represents the memory utilization corresponding to the i-th unit time; P ni-1 represents the memory utilization corresponding to the i-1th unit time; P nmax and P nmin They represent the maximum and minimum memory utilization rates during the operation of the teaching platform in n unit time respectively; P np represents the average memory utilization rate during the operation of the teaching platform in n units of time; P bmax and P bmin They represent the maximum and minimum network bandwidth utilization rates during the operation of the teaching platform in n unit time, respectively; P bp It represents the average value of network bandwidth utilization during the operation of the teaching platform in n units of time;

[0041] The first load evaluation factor is obtained by using the number of concurrent users per unit time of the teaching platform and the resource utilization compensation coefficient of the teaching platform; wherein the first load evaluation factor is obtained by the following formula:

[0042]

[0043] Among them, R 01 Represents the first load evaluation factor; m i represents the number of concurrent users corresponding to the i-th unit time; m i-1represents the number of concurrent users corresponding to the i-1th unit time; K x Indicates the resource utilization compensation coefficient.

[0044] Further, obtaining a second load evaluation factor of the teaching platform by using the access response time of the teaching platform includes:

[0045] Extracting the number of data accesses per unit time of the teaching platform;

[0046] The access response time corresponding to each data access per unit time of the teaching platform is used to obtain the access time parameter corresponding to the unit time of the teaching platform; wherein the access time parameter is obtained by the following formula:

[0047]

[0048] Where ξ represents the access time parameter corresponding to the unit time; m represents the number of data accesses in the unit time; T i represents the access response time corresponding to the i-th data access; T tmax Indicates the maximum allowed access response time corresponding to normal operation of data access;

[0049] Extract resource utilization compensation coefficient corresponding to unit time;

[0050] The second load evaluation factor of the teaching platform is obtained by using the access time parameter corresponding to the unit time and the resource utilization compensation coefficient corresponding to the unit time; wherein the second load evaluation factor is obtained by the following formula:

[0051]

[0052] Among them, R 02 Represents the second load evaluation factor; K x represents the resource utilization compensation coefficient; n represents the number of unit time experienced by the operation of the teaching platform; ξ i Represents the access time parameter corresponding to the i-th unit time.

[0053] Furthermore, the optimization strategy is as follows:

[0054] When the operating load evaluation parameter exceeds a preset parameter threshold, the cache space for data access of the teaching platform is adjusted, wherein the capacity of the adjusted cache space is obtained by the following formula:

[0055]

[0056] Where S represents the capacity of the cache space after adjustment; S0 represents the capacity of the cache space before adjustment; R zRepresents the operating load evaluation parameter; R y Indicates the preset parameter threshold; P nmax represents the maximum memory utilization rate during the operation of the teaching platform in n units of time; P nmb Indicates the network bandwidth utilization corresponding to the maximum memory utilization moment;

[0057] When the operating load evaluation parameter exceeds the preset parameter threshold, the expiration time of the data access of the teaching platform is adjusted, wherein the adjusted expiration time is obtained by the following formula:

[0058]

[0059] Among them, T g represents the expiration time after adjustment; T0 represents the expiration time before adjustment; S represents the capacity of the cache space after adjustment; S0 represents the capacity of the cache space before adjustment.

[0060] A data access system applied to a teaching platform, the data access system applied to a teaching platform comprising:

[0061] Configuration module, used to configure the Hadoop cluster of the teaching platform and define data flows;

[0062] A data access and display module is used to provide data information in a streaming manner using a streaming processing tool provided by the Hadoop distributed file system when there is a data access request from a user on the teaching platform, and to display the data information on the user's smart device;

[0063] A resource data recommendation module is used to recommend resource data based on the user's data access content and the user's learning history record;

[0064] The monitoring and optimization module is used to monitor the data access operation parameters of the teaching platform in real time, and determine whether to perform data access optimization according to the optimization strategy based on the data access operation parameters.

[0065] Beneficial effects of the present invention:

[0066] The data access method and system proposed by the present invention for a teaching platform utilize Hadoop cluster and streaming processing tools to efficiently process and transmit a large amount of data and meet the data access needs of the teaching platform. Through resource data recommendation, personalized learning resources related to their learning content and historical records are provided to users to improve learning efficiency and interest. The configuration of the Hadoop cluster and real-time monitoring of data access operating parameters can ensure the stability and scalability of the teaching platform and adapt to users and data volumes of different sizes. The application of streaming processing tools and data access optimization enables the teaching platform to respond to users' data access requests in real time and provide efficient and fast data services. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a flow chart of the method of the present invention;

[0068] Figure 2 The system block diagram of the system of the present invention. DETAILED DESCRIPTION

[0069] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0070] The embodiment of the present invention proposes a data access method applied to a teaching platform, such as Figure 1 As shown, the data access method applied to the teaching platform includes:

[0071] S1. Configure the Hadoop cluster of the teaching platform and define the data flow;

[0072] S2. When there is a user's data access request on the teaching platform, the streaming processing tool provided by the Hadoop distributed file system is used to provide data information in a streaming manner, and the data information is displayed on the user's smart device; wherein the streaming processing tool includes Flume and Kafka, etc.

[0073] S3, recommending resource data based on the user's data access content and the user's learning history;

[0074] S4. Monitor the data access operation parameters of the teaching platform in real time, and determine whether to perform data access optimization according to the optimization strategy based on the data access operation parameters.

[0075] The working principle of the above technical solution is as follows: First, configure the Hadoop cluster for the teaching platform. Hadoop is an open source distributed computing framework that can process large-scale data sets. Define the data flow, that is, clarify the source, transmission path and destination of the data, and ensure that the data can flow efficiently and orderly within the teaching platform.

[0076] When the teaching platform receives a data access request from a user, it uses the streaming processing tools (such as Flume and Kafka) provided by the Hadoop Distributed File System (HDFS) to provide the data to the user in a streaming manner. Flume is a distributed, reliable and highly available service for efficiently collecting, aggregating and moving large amounts of log data. Kafka is a distributed stream processing platform that can publish and subscribe to record streams, similar to a message queue or enterprise messaging system. Data information is displayed on the user's smart device (such as a mobile phone, tablet or computer) to meet the user's learning needs.

[0077] Based on the user's data access content (such as the courseware browsed, the discussions participated in, etc.), combined with the user's learning history, relevant resource data is recommended to the user. This personalized recommendation helps to improve the user's learning efficiency and interest.

[0078] Real-time monitoring of the data access operating parameters of the teaching platform, such as data transmission speed, system response time, etc. Based on the above parameters, determine whether data access optimization needs to be performed according to the optimization strategy, such as adjusting the resource allocation of the Hadoop cluster, optimizing the data flow path, etc.

[0079] The effects of the above technical solution are as follows: The above technical solution of this embodiment utilizes Hadoop cluster and streaming processing tools to efficiently process and transmit large amounts of data, meeting the data access needs of the teaching platform. Through resource data recommendation, personalized learning resources related to their learning content and historical records are provided to users to improve learning efficiency and interest. The configuration of the Hadoop cluster and real-time monitoring of data access operating parameters can ensure the stability and scalability of the teaching platform and adapt to users and data volumes of different sizes. The application of streaming processing tools and data access optimization enables the teaching platform to respond to users' data access requests in real time and provide efficient and fast data services.

[0080] In one embodiment of the present invention, configuring a Hadoop cluster of a teaching platform and defining a data flow includes:

[0081] S101, configuring the Hadoop distributed file system and its ecosystem components on the teaching platform; wherein the ecosystem components of the Hadoop distributed file system include YARN, HBase, Hive, etc.;

[0082] S102, determine the data types and contents that need to be streamed into the Hadoop distributed file system, and set the data flow path and storage format corresponding to the data types and contents. The data types and contents include course materials, homework data, and user behavior logs, etc., and the storage format includes columnar storage formats such as Parquet or ORC.

[0083] The working principle of the above technical solution is as follows: In the teaching platform, first configure the Hadoop Distributed File System (HDFS), which is the core component of Hadoop and is used to store and manage large-scale data sets in a cluster environment. In addition to HDFS, Hadoop's ecosystem components are also configured, such as YARN (resource manager), HBase (distributed database), Hive (data warehouse tool), etc. The above components together constitute the complete ecosystem of Hadoop, providing a full set of solutions from data storage, management to analysis and processing.

[0084] Determine the data types and content that need to be streamed to HDFS, such as course materials, homework data, and user behavior logs. The above data is important information for the operation of the teaching platform and the user learning process. Set the corresponding data flow path and storage format for different data types and content. The data flow path defines the transmission path of data from the source to the destination, ensuring that the data can be accurately and efficiently transmitted to HDFS. The choice of storage format is crucial for efficient storage and query of data. Parquet and ORC are commonly used columnar storage formats in the Hadoop ecosystem. They improve data compression ratio and query efficiency by storing data in columns.

[0085] The effects of the above technical solutions are as follows: Hadoop Distributed File System (HDFS) provides high fault tolerance and high throughput data storage capabilities, and can process large-scale data sets. Columnar storage formats (such as Parquet and ORC) improve data compression ratio and query efficiency by storing data in columns, further enhancing data processing capabilities.

[0086] The data flow paths set for different data types and contents ensure that data can be transmitted and stored according to the predetermined paths and rules, which improves the flexibility and controllability of data management. The above solution supports streaming writing of various data types and contents such as course materials, homework data and user behavior logs, meeting the diverse needs of the teaching platform in data processing and storage. Hadoop's ecosystem components (such as YARN, HBase, Hive, etc.) provide rich functions and tools, which can be expanded and customized according to actual needs, enhancing the scalability and maintainability of the system. Through efficient data processing and storage, as well as flexible data flow management, the above solution can provide users with a smoother and more efficient learning experience. At the same time, by collecting and analyzing data such as user behavior logs, it can also provide data support for the teaching platform, help optimize teaching content and methods, and further enhance user experience.

[0087] In one embodiment of the present invention, when there is a data access request from a user on the teaching platform, a streaming processing tool provided by writing into the Hadoop distributed file system is used to provide data information in a streaming manner, including:

[0088] S201, a user sends a data access request to the teaching platform through a smart device, wherein the smart device includes a mobile terminal and a PC;

[0089] S202, after receiving the data access request sent by the user through the smart device, the teaching platform verifies the identity and authority of the user;

[0090] S203: After the user passes the identity and authority verification, the teaching platform parses the data access request sent by the user to obtain the data access requirements contained in the data access request;

[0091] S204: According to the data access requirement sent by the user, the stream processing tool of Hadoop is called to search the Hadoop distributed file system to obtain data corresponding to the data access requirement of the user.

[0092] The working principle of the above technical solution is: the user sends a data access request to the teaching platform through a smart device (such as a smart phone, tablet or PC). The above request may contain the specific data content or type that the user wants to access.

[0093] After receiving the user's data access request, the teaching platform first verifies the user's identity and permissions. This ensures that only legitimate users can access the data on the platform and can only access data within their authority.

[0094] After the user passes the identity and permission verification, the teaching platform will parse the data access request sent by the user. The above process mainly extracts the data access requirements contained in the request, such as the specific course materials, homework grades or user behavior analysis that the user wants to view.

[0095] According to the analyzed data access requirements, the teaching platform will call Hadoop's streaming processing tools (such as Flume, Kafka, etc.) to search the Hadoop Distributed File System (HDFS). The above streaming processing tools can efficiently process large amounts of data and provide data to users in real time. Through the indexing and query mechanism of HDFS, the system can quickly locate data that matches user needs and provide it to users.

[0096] The effect of the above technical solution is: using Hadoop's streaming processing tool, the teaching platform can respond to users' data access requests in real time, and efficiently retrieve and provide data. This greatly improves the user experience and enables users to quickly obtain the required information. Through identity and authority verification, the teaching platform can ensure that only legitimate users can access the data, and can only access data within their authority. This effectively protects the security and privacy of the data. Hadoop's distributed architecture and streaming processing tools make the teaching platform have good scalability and flexibility. With the increase in the number of users and the growth of data volume, the platform can easily expand hardware resources to meet demand. At the same time, the streaming processing tool can easily process various types of data and support multiple data access methods. Through the parsing of user data access requests and the precise retrieval of the Hadoop distributed file system, the teaching platform can accurately provide the data required by the user. This avoids data redundancy and interference from irrelevant information, and improves the accuracy and efficiency of data retrieval.

[0097] In summary, the above technical solution realizes the real-time, efficient, secure and accurate access to user data in the teaching platform through Hadoop's streaming processing tools and HDFS's efficient retrieval mechanism.

[0098] In one embodiment of the present invention, resource data is recommended based on the user's data access content and the user's learning history record, including:

[0099] S301, collecting data access content of the user, wherein the data access content includes browsing, searching, downloading and data information corresponding to the activity;

[0100] S302, collecting the user's learning history records; wherein the learning history records include the courses learned, knowledge points and learning material data;

[0101] S303, performing data analysis on the data access content of the user to obtain the user's interest preference data;

[0102] S304, performing data analysis on the user's learning history records to obtain the user's learning knowledge information;

[0103] S305: Retrieve resource data corresponding to the interest preference data and the learning knowledge information according to the user's interest preference data and the learning knowledge information, and recommend the resource data to the user's smart device.

[0104] The working principle of the above technical solution is to collect the user's data access content, which includes but is not limited to the user's browsing history, search history, download history and information on activities participated in on the platform. The above data can reflect the user's interests and needs.

[0105] At the same time, the user's learning history is collected, such as the courses studied, the knowledge points involved, and the data studied, etc. The above information can reveal the user's learning progress and knowledge level.

[0106] Conduct in-depth analysis of the collected user data access content, and identify and extract user interest preference data through data mining, machine learning and other technologies. The above data may include the user's favorite course type, subject or learning method.

[0107] At the same time, similar data analysis is performed on the user's learning history records to obtain the user's learning knowledge information. The above information can depict the user's learning trajectory, knowledge mastery level, and possible knowledge blind spots.

[0108] Based on the user interest preference data and learning knowledge information obtained from the above analysis, the system will retrieve relevant resource data from the resource library. The above resource data may include courses, learning materials, exercises, video tutorials, etc. The system presents the above resource data in the form of recommendations to the user's smart devices, such as mobile phones, tablets or PCs. The recommended resources are designed to meet the user's interests and needs, while helping users improve their learning efficiency and effectiveness.

[0109] The effect of the above technical solution is: by deeply analyzing the user's data access content and learning history, the system can provide users with personalized resource data recommendations. This recommendation method is more in line with the user's interests and needs, and improves the user's learning satisfaction. The recommended resource data is closely related to the user's learning history and interest preferences, which can help users find the required learning resources faster, reduce invalid search and browsing time, and thus improve learning efficiency. Personalized resource data recommendations not only improve learning efficiency, but also bring users a better learning experience. Users can feel the intelligent and humanized design of the system, and enhance their loyalty and satisfaction with the platform. By continuously collecting and analyzing user data, the system can continuously optimize the recommendation algorithm and model, and provide more accurate and intelligent learning assistance services. This helps to cultivate users' autonomous learning ability and improve learning effects.

[0110] In one embodiment of the present invention, data access operation parameters of a teaching platform are monitored in real time, and judging whether to perform data access optimization according to an optimization strategy based on the data access operation parameters includes:

[0111] S401, real-time monitoring of the data access operation parameters of the teaching platform; wherein the data access operation parameters include the number of concurrent users per unit time, access response time and resource utilization;

[0112] S402, using the data of the teaching platform to access the operating parameters to obtain the operating load evaluation parameters of the teaching platform;

[0113] S403: When the operation load evaluation parameter of the teaching platform exceeds a preset parameter threshold, data access optimization is performed according to an optimization strategy.

[0114] The working principle of the above technical solution is: the system collects the data access operation parameters of the teaching platform in real time, including but not limited to the number of concurrent users per unit time, access response time and resource utilization. The above parameters can intuitively reflect the real-time operation status and performance of the platform.

[0115] By using the collected data to access the operating parameters, the system calculates the operating load evaluation parameters of the teaching platform through a certain algorithm or model. The above evaluation parameters are a comprehensive indicator that can reflect the current overall operating status of the platform.

[0116] When the operating load evaluation parameters of the teaching platform exceed the preset parameter threshold, the system considers that the platform is currently in a high load or performance bottleneck state and needs to be optimized. At this time, the system will optimize data access according to the preset optimization strategy. The optimization strategy may include load balancing, cache strategy adjustment, limiting concurrency, optimizing database queries, etc., aiming to improve the response speed and stability of the platform and ensure user experience.

[0117] The effect of the above technical solution is: by real-time monitoring and evaluation of the operating load of the teaching platform, the system can promptly discover and deal with potential performance bottlenecks, avoid platform crashes or performance degradation due to overload, and thus improve the stability and reliability of the system. The implementation of the optimization strategy can reduce user waiting time and improve response speed, thereby improving user experience. Users can access and use the teaching platform more smoothly, improve learning efficiency and satisfaction. Through reasonable optimization strategies, the system can more effectively utilize hardware and software resources and improve resource utilization. This helps to reduce operating costs and improve the overall performance of the teaching platform. The above technical solution provides quantifiable indicators and strategies for the performance optimization of the teaching platform, so that the platform can be more easily expanded and upgraded when facing more users or more complex scenarios to meet changing business needs.

[0118] In one embodiment of the present invention, the operation load evaluation parameters of the teaching platform are obtained by using the data access operation parameters of the teaching platform, including:

[0119] S4021, obtaining a first load evaluation factor of the teaching platform by using the number of concurrent users per unit time and the resource utilization rate per unit time of the teaching platform;

[0120] S4022, obtaining a second load evaluation factor of the teaching platform by using the access response time of the teaching platform;

[0121] S4023. Obtain an operation load evaluation parameter using the first load evaluation factor and the second load evaluation factor of the teaching platform, wherein the operation load evaluation parameter is obtained by the following formula:

[0122]

[0123] Among them, R z represents the operating load evaluation parameter; R0 represents the preset parameter reference value; R 01 Represents the first load evaluation factor; R 02 Represents the second load evaluation factor.

[0124] The working principle of the above technical solution is as follows: First, the first load evaluation factor is calculated using the number of concurrent users of the teaching platform per unit time and the resource utilization per unit time. The number of concurrent users reflects the ability of the platform to process requests simultaneously, while the resource utilization reflects the utilization of the platform's hardware and software resources. The combination of these two parameters can more comprehensively reflect the load status of the platform.

[0125] Next, the access response time of the teaching platform is used to calculate the second load evaluation factor. The access response time is directly related to the user experience. A longer response time usually means that the platform is under high load or has a performance bottleneck. Therefore, the access response time is an important evaluation indicator.

[0126] Finally, the first load evaluation factor and the second load evaluation factor are substituted into the formula, and a preset parameter reference value is considered to calculate the operation load evaluation parameter. The above parameter is a comprehensive evaluation index that can quantitatively reflect the overall load status of the teaching platform.

[0127] The effect of the above technical solution is: by calculating the operation load evaluation parameters, the above technical solution can quantitatively evaluate the current operation status and load level of the teaching platform, and provide data support for subsequent optimization and decision-making. Taking into account multiple data access operation parameters such as the number of concurrent users, resource utilization and access response time, it can comprehensively reflect the load status of the platform from multiple dimensions, and improve the accuracy and reliability of the evaluation. The preset parameter benchmark values ​​can be adjusted according to actual conditions to adapt to the evaluation requirements of different teaching platforms or different operating environments. When the operation load evaluation parameters exceed the preset threshold, the corresponding optimization strategy can be triggered, such as increasing server resources, optimizing code logic, etc., thereby improving the stability of the platform and user experience. At the same time, the future load status can also be predicted based on the changing trend of the evaluation parameters, providing a reference for advance planning and adjustment.

[0128] In one embodiment of the present invention, the first load evaluation factor of the teaching platform is obtained by using the number of concurrent users per unit time and the resource utilization rate per unit time of the teaching platform, including:

[0129] Step 1, extracting the number of concurrent users per unit time of the teaching platform;

[0130] Step 2: extracting the resource utilization rate per unit time of the teaching platform; wherein the resource utilization rate includes memory utilization rate and network bandwidth utilization rate;

[0131] Step 3: Obtain a resource utilization compensation coefficient using the memory utilization and network bandwidth utilization per unit time; wherein the resource utilization compensation coefficient is obtained by the following formula:

[0132]

[0133] Among them, K x represents the resource utilization compensation coefficient; n represents the number of unit times experienced by the operation of the teaching platform, and the unit time is 1s; P ni represents the memory utilization corresponding to the i-th unit time; P birepresents the network bandwidth utilization rate corresponding to the i-th unit time; δ 01 and δ 02 Respectively represent the first adjustment coefficient and the second adjustment coefficient; wherein the first adjustment coefficient and the second adjustment coefficient are obtained by the following formula:

[0134]

[0135]

[0136] Among them, P ni represents the memory utilization corresponding to the i-th unit time; P ni-1 represents the memory utilization corresponding to the i-1th unit time; P nmax and P nmin They represent the maximum and minimum memory utilization rates during the operation of the teaching platform in n unit time respectively; P np represents the average memory utilization rate during the operation of the teaching platform in n units of time; P bmax and P bmin They represent the maximum and minimum network bandwidth utilization rates during the operation of the teaching platform in n unit time, respectively; P bp It represents the average value of network bandwidth utilization during the operation of the teaching platform in n units of time;

[0137] Step 4: Obtain a first load evaluation factor using the number of concurrent users per unit time of the teaching platform and the resource utilization compensation coefficient of the teaching platform; wherein the first load evaluation factor is obtained by the following formula:

[0138]

[0139] Among them, R 01 Represents the first load evaluation factor; m i represents the number of concurrent users corresponding to the i-th unit time; m i-1 represents the number of concurrent users corresponding to the i-1th unit time; K x Indicates the resource utilization compensation coefficient.

[0140] The working principle of the above technical solution is: extracting the number of concurrent users per unit time (such as per second) and the corresponding resource utilization rate from the teaching platform in real time, wherein the resource utilization rate includes memory utilization rate and network bandwidth utilization rate.

[0141] A compensation coefficient is used to reflect the impact of resource utilization on load evaluation. The compensation coefficient is determined by calculating the changes in memory utilization and network bandwidth utilization per unit time and combining the first adjustment coefficient and the second adjustment coefficient.

[0142] The first adjustment coefficient and the second adjustment coefficient are calculated based on historical data (including maximum value, minimum value and average value) of memory utilization and network bandwidth utilization to reflect the fluctuation and stability of resource utilization.

[0143] Combining the number of concurrent users per unit time and the resource utilization compensation coefficient, the first load evaluation factor is calculated through a specific mathematical formula. The above factor comprehensively considers the dual impact of user concurrency and resource utilization, and provides a quantitative indicator for the load evaluation of the teaching platform.

[0144] The effect of the above technical solution is: the above technical solution not only takes into account the number of concurrent users, but also introduces a resource utilization compensation coefficient to comprehensively evaluate the load status of the teaching platform. This helps to more accurately reflect the actual operation of the platform. By calculating the resource utilization compensation coefficient, the above technical solution can take into account the fluctuation of resource utilization and adjust the results of load evaluation accordingly. This helps to improve the accuracy and reliability of the evaluation. By calculating the first load evaluation factor, the above technical solution provides a quantitative indicator for the load evaluation of the teaching platform. This helps administrators or decision makers to understand the load status of the platform more intuitively and take corresponding optimization measures accordingly. Based on the results of the load evaluation, the administrator can adjust the resource configuration of the teaching platform according to actual conditions, such as increasing the number of servers, improving network bandwidth, etc., to optimize the platform's performance and user experience.

[0145] On the other hand, by extracting the number of concurrent users and resource utilization (including memory utilization and network bandwidth utilization) per unit time, the operation status of the teaching platform can be reflected in real time or quasi-real time. The resource utilization compensation coefficient is used to comprehensively consider the relative importance and change trend of memory utilization and network bandwidth utilization, making the load evaluation more comprehensive and accurate. The introduction of the first adjustment coefficient and the second adjustment coefficient can dynamically adjust the resource utilization compensation coefficient according to the fluctuation and extreme value of memory utilization and network bandwidth utilization. This dynamic adjustment can make the load evaluation more sensitive to the sudden change or abnormality of resource utilization, so as to reflect the actual load status of the teaching platform more timely. At the same time, the number of concurrent users is a direct indicator of the activity of the teaching platform, while the resource utilization reflects the ability of the platform to handle these active users. Combining the number of concurrent users with the resource utilization compensation coefficient can obtain a more comprehensive first load evaluation factor, which can consider both user activity and system resource utilization efficiency. Statistics such as average, maximum, and minimum are used in the formula, which can reflect the overall situation and fluctuation range of resource utilization. The adjustment coefficient is calculated by the difference between resource utilization rates of adjacent unit times, which can capture the short-term change trend of resource utilization.

[0146] In one embodiment of the present invention, obtaining a second load evaluation factor of the teaching platform by using the access response time of the teaching platform includes:

[0147] Step 1, extracting the number of data accesses per unit time of the teaching platform;

[0148] Step 2: using the access response time corresponding to each data access per unit time of the teaching platform to obtain the access time parameter corresponding to the unit time of the teaching platform; wherein the access time parameter is obtained by the following formula:

[0149]

[0150] Where ξ represents the access time parameter corresponding to the unit time; m represents the number of data accesses in the unit time; T i represents the access response time corresponding to the i-th data access; T tmax Indicates the maximum allowed access response time corresponding to normal operation of data access;

[0151] Step 3: Extract the resource utilization compensation coefficient corresponding to unit time;

[0152] Step 4: Use the access time parameter corresponding to the unit time and the resource utilization compensation coefficient corresponding to the unit time to obtain the second load evaluation factor of the teaching platform; wherein the second load evaluation factor is obtained by the following formula:

[0153]

[0154] Among them, R 02 Represents the second load evaluation factor; K x represents the resource utilization compensation coefficient; n represents the number of unit time experienced by the operation of the teaching platform; ξ i Represents the access time parameter corresponding to the i-th unit time.

[0155] The working principle of the above technical solution is: extract the number of data accesses per unit time (such as per second) from the teaching platform in real time, which reflects the frequency of users' access to the platform within the above time period. For each data access within each unit time, extract its corresponding access response time. Use the formula to calculate the access time parameter corresponding to the above unit time. The above parameter comprehensively considers the response time of all accesses and is compared with the maximum allowed access response time corresponding to the normal operation of data access to quantify the degree of deviation of the access response time.

[0156] The resource utilization compensation coefficient is calculated based on the resource utilization of the teaching platform (such as memory utilization and network bandwidth utilization). The above coefficient reflects the fluctuation of resource utilization and its impact on load evaluation.

[0157] Combining the access time parameter corresponding to each unit time and the resource utilization compensation coefficient, the second load evaluation factor is calculated through a specific mathematical formula. The above factors comprehensively consider the impact of access response time and resource utilization, and provide quantitative indicators for the load evaluation of the teaching platform.

[0158] The effect of the above technical solution is: the above technical solution not only takes into account the access response time, but also introduces the resource utilization compensation coefficient, which can more comprehensively evaluate the load status of the teaching platform. This helps to more accurately reflect the actual operation of the platform. By calculating the resource utilization compensation coefficient, the above technical solution can take into account the fluctuation of resource utilization and adjust the results of load evaluation accordingly. This helps to improve the accuracy and reliability of the evaluation. By calculating the access time parameter, the above technical solution can quantitatively evaluate the access response time of the teaching platform, so as to more intuitively understand the response speed of user access. By calculating the second load evaluation factor, the above technical solution provides a quantitative indicator for the load evaluation of the teaching platform. This helps administrators or decision makers to more intuitively understand the load status of the platform and take corresponding optimization measures accordingly. Based on the results of the load evaluation, the administrator can adjust the resource configuration or optimization strategy of the teaching platform according to the actual situation to improve the response speed and stability of the platform, thereby optimizing the user experience.

[0159] On the other hand, by extracting the access response time of the teaching platform and calculating the second load evaluation factor based on it, the actual experience of users when using the teaching platform can be directly reflected. Access response time is one of the important indicators of user perception of platform performance. Therefore, the evaluation based on access response time can more directly reflect the user's satisfaction with the performance of the teaching platform.

[0160] When calculating the second load evaluation factor, not only the access response time (reflected by the access time parameter ξ) is taken into account, but also the resource utilization compensation coefficient is combined. This means that the evaluation factor not only takes into account the utilization efficiency of system resources, but also comprehensively considers the actual experience of users during use, thereby providing a more comprehensive performance evaluation. The calculation of the access time parameter ξ takes into account the relationship between the response time of each data access and the maximum allowed access response time. This calculation method makes the evaluation factor more sensitive to fluctuations and anomalies in the access response time, and can dynamically reflect changes in the performance of the teaching platform.

[0161] By defining access time parameters and using weighted average to comprehensively consider the response time of all data accesses within a unit time, the evaluation factor is made more reasonable and accurate. The introduction of resource utilization compensation coefficients can dynamically adjust the access time parameters according to the actual resource utilization, further improving the accuracy and practicality of the evaluation factor. At the same time, it has high flexibility and scalability, and can be adjusted and optimized according to actual needs.

[0162] In summary, this technical solution can directly reflect the user experience and comprehensively consider the relationship between resource utilization and user experience by extracting the access response time of the teaching platform and calculating the second load evaluation factor based on it. The mathematical calculation formula plays a key role in this, making the evaluation factor more reasonable, accurate and practical.

[0163] In one embodiment of the present invention, the optimization strategy is as follows:

[0164] When the operating load evaluation parameter exceeds a preset parameter threshold, the cache space for data access of the teaching platform is adjusted, wherein the capacity of the adjusted cache space is obtained by the following formula:

[0165]

[0166] Where S represents the capacity of the cache space after adjustment; S0 represents the capacity of the cache space before adjustment; R z Represents the operating load evaluation parameter; R y Indicates the preset parameter threshold; P nmax represents the maximum memory utilization rate during the operation of the teaching platform in n units of time; P nmb Indicates the network bandwidth utilization corresponding to the maximum memory utilization moment;

[0167] When the operating load evaluation parameter exceeds the preset parameter threshold, the expiration time of the data access of the teaching platform is adjusted, wherein the adjusted expiration time is obtained by the following formula:

[0168]

[0169] Among them, T g represents the expiration time after adjustment; T0 represents the expiration time before adjustment; S represents the capacity of the cache space after adjustment; S0 represents the capacity of the cache space before adjustment.

[0170] The working principle of the above technical solution is: when the operating load evaluation parameter exceeds the preset parameter threshold, the system will calculate the adjusted cache space capacity based on a series of parameters.

[0171] The adjustment of cache space capacity may be to increase (if the current load evaluation parameters show that the system needs more cache space to cope with high concurrency or high resource utilization) or decrease (if the load evaluation parameters decrease, the system may release some cache space to save resources).

[0172] Similarly, when the operating load evaluation parameter exceeds a preset threshold, the system will also calculate a new expiration time based on the adjusted cache space capacity, the expiration time before the adjustment, and the cache space capacity before the adjustment.

[0173] Adjusting the expiration time may involve reducing the expiration time (to increase the update frequency of the cache and ensure the freshness of the data) or increasing the expiration time (if the system load is low, the cache time of the data can be extended to reduce the access pressure on the backend server).

[0174] The effect of the above technical solution is: the above technical solution can dynamically adjust the cache space and expiration time based on the real-time operation load evaluation parameters, so as to ensure that the teaching platform can maintain optimal performance under different load conditions. By increasing the cache space in a timely manner, the system can reduce the number of accesses to the back-end database, thereby improving the data reading speed and the overall response speed of the system. When the load is low, by reducing the cache space or increasing the expiration time, the system can release unnecessary memory resources and improve resource utilization. By ensuring the freshness of data and fast response, the optimization strategy can significantly improve the user experience, especially in high concurrency or high resource utilization. The above technical solution provides clear optimization strategies and adjustment formulas, so that administrators can easily implement and manage the above optimization measures without complex configuration or adjustment.

[0175] On the other hand, when the operating load evaluation parameter exceeds the preset parameter threshold, the data access cache space of the teaching platform is dynamically adjusted according to the mathematical formula. This dynamic adjustment can ensure that the size of the cache space can match the current system requirements under high load conditions, thereby improving the efficiency and performance of data access. The maximum memory utilization and network bandwidth utilization are introduced into the cache space adjustment formula to reflect the actual use of system resources. In this way, the adjustment of the cache space is not only based on the current load evaluation parameters, but also takes into account the bottlenecks and limitations of system resources, so as to more accurately meet the system requirements. In addition to the cache space, the expiration time of data access is also adjusted according to the changes in the load evaluation parameters and cache space. This helps to optimize the use efficiency of the cache by adjusting the expiration time when the cache space is limited, ensure that important data can be stored in the cache for a longer time, and reduce unnecessary data access delays. The introduction of mathematical formulas makes the adjustment of cache space and expiration time more scientific, reasonable and predictable. The results calculated by the formula can accurately reflect the current load and resource utilization of the system, thereby providing a strong basis for cache optimization. The parameters in the formula are all configurable and can be adjusted and optimized according to actual needs. This makes the technical solution highly flexible and scalable, and can adapt to teaching platforms of different sizes and needs.

[0176] In summary, this technical solution can effectively improve the data access performance and user experience of the teaching platform by dynamically adjusting the cache space and expiration time. Mathematical calculation formulas play a key role in this, making the adjustment process more scientific, reasonable and predictable.

[0177] The embodiment of the present invention provides a data access system applied to a teaching platform, such as Figure 2 As shown, the data access system applied to the teaching platform includes:

[0178] Configuration module, used to configure the Hadoop cluster of the teaching platform and define data flows;

[0179] The data access and display module is used to provide data information in a streaming manner using the streaming processing tool provided by the Hadoop distributed file system when there is a user's data access request on the teaching platform, and display the data information on the user's smart device; wherein the streaming processing tools include Flume and Kafka, etc.

[0180] A resource data recommendation module is used to recommend resource data based on the user's data access content and the user's learning history record;

[0181] The monitoring and optimization module is used to monitor the data access operation parameters of the teaching platform in real time, and determine whether to perform data access optimization according to the optimization strategy based on the data access operation parameters.

[0182] The working principle of the above technical solution is as follows: First, configure the Hadoop cluster for the teaching platform. Hadoop is an open source distributed computing framework that can process large-scale data sets. Define the data flow, that is, clarify the source, transmission path and destination of the data, and ensure that the data can flow efficiently and orderly within the teaching platform.

[0183] When the teaching platform receives a data access request from a user, it uses the streaming processing tools (such as Flume and Kafka) provided by the Hadoop Distributed File System (HDFS) to provide the data to the user in a streaming manner. Flume is a distributed, reliable and highly available service for efficiently collecting, aggregating and moving large amounts of log data. Kafka is a distributed stream processing platform that can publish and subscribe to record streams, similar to a message queue or enterprise messaging system. Data information is displayed on the user's smart device (such as a mobile phone, tablet or computer) to meet the user's learning needs.

[0184] Based on the user's data access content (such as the courseware browsed, the discussions participated in, etc.), combined with the user's learning history, relevant resource data is recommended to the user. This personalized recommendation helps to improve the user's learning efficiency and interest.

[0185] Real-time monitoring of the data access operating parameters of the teaching platform, such as data transmission speed, system response time, etc. Based on the above parameters, determine whether data access optimization needs to be performed according to the optimization strategy, such as adjusting the resource allocation of the Hadoop cluster, optimizing the data flow path, etc.

[0186] At the same time, a data access system applied to the teaching platform can cooperate with multiple units such as the Education Bureau, schools, and training institutions. At the same time, the teaching platform access framework adopts a streaming data access method, adopts a data access method of writing once and collecting multiple times, and writes the course teaching resource data to the distributed file storage system of Hadoo in an appending manner, and calls the Hadoop distributed file system through an imperative or API interface. On the other hand, the system framework can be specifically divided into a terminal access layer, a business application layer, and an underlying architecture layer. The terminal access layer is responsible for the access of terminal users, which can be smart devices such as PCs and mobile terminals. The business application layer provides various resource recommendation services and data statistics for the terminal. At the business application layer, the user access teaching resource statistics service is to build a distributed file system based on virtualization. The underlying architecture layer shields files and effectively screens resources. Perfect resource construction in shared question banks, famous school test papers, and famous teacher videos.

[0187] The effects of the above technical solution are as follows: The above technical solution of this embodiment utilizes Hadoop cluster and streaming processing tools to efficiently process and transmit large amounts of data, meeting the data access needs of the teaching platform. Through resource data recommendation, personalized learning resources related to their learning content and historical records are provided to users to improve learning efficiency and interest. The configuration of the Hadoop cluster and real-time monitoring of data access operating parameters can ensure the stability and scalability of the teaching platform and adapt to users and data volumes of different sizes. The application of streaming processing tools and data access optimization enables the teaching platform to respond to users' data access requests in real time and provide efficient and fast data services.

[0188] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if the above-mentioned modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include the above-mentioned modifications and variations.

Claims

1. A data access method applied to a teaching platform, characterized in that: The data access method applied to the teaching platform includes: Configure the Hadoop cluster of the teaching platform and define data flows; When there is a data access request from a user on the teaching platform, the streaming processing tool provided by the Hadoop distributed file system is used to provide data information in a streaming manner, and the data information is displayed on the user's smart device; Recommend resource data based on the user's data access content and the user's learning history; Monitor the data access operation parameters of the teaching platform in real time, and determine whether to perform data access optimization according to the optimization strategy based on the data access operation parameters; The method of monitoring the data access operation parameters of the teaching platform in real time and determining whether to perform data access optimization according to the optimization strategy according to the data access operation parameters includes: Real-time monitoring of the data access operating parameters of the teaching platform; wherein the data access operating parameters include the number of concurrent users per unit time, access response time and resource utilization; Using the data access operation parameters of the teaching platform to obtain the operation load evaluation parameters of the teaching platform; When the operation load evaluation parameter of the teaching platform exceeds a preset parameter threshold, data access optimization is performed according to the optimization strategy; Wherein, using the data access operation parameters of the teaching platform to obtain the operation load evaluation parameters of the teaching platform includes: Obtaining a first load evaluation factor of the teaching platform by using the number of concurrent users per unit time and the resource utilization rate per unit time of the teaching platform; Acquiring a second load evaluation factor of the teaching platform by using the access response time of the teaching platform; The operation load evaluation parameter is obtained by using the first load evaluation factor and the second load evaluation factor of the teaching platform, wherein the operation load evaluation parameter is obtained by the following formula: Among them, R z represents the operating load evaluation parameter; R0 represents the preset parameter reference value; R 01 Represents the first load evaluation factor; R 02 represents the second load evaluation factor; At the same time, the first load evaluation factor is obtained by the following formula: Among them, R 01 Represents the first load evaluation factor; m i represents the number of concurrent users corresponding to the i-th unit time; m i-1 represents the number of concurrent users corresponding to the i-1th unit time; K x Indicates the resource utilization compensation coefficient; Furthermore, the second load evaluation factor is obtained by the following formula: Among them, R 02 Represents the second load evaluation factor; K x represents the resource utilization compensation coefficient; n represents the number of unit time experienced by the operation of the teaching platform; ξ i Represents the access time parameter corresponding to the i-th unit time.

2. The data access method applied to the teaching platform according to claim 1, characterized in that: Configure the Hadoop cluster of the teaching platform and define the data flow, including: Configure the Hadoop distributed file system and its ecosystem components on the teaching platform; Determine the data type and content that need to be streamed into the Hadoop distributed file system, and set a data flow path and storage format corresponding to the data type and content.

3. The data access method applied to the teaching platform according to claim 1, characterized in that: When there is a data access request from a user on the teaching platform, the streaming processing tool provided by the Hadoop distributed file system is used to provide data information in a streaming manner, including: The user sends a data access request to the teaching platform through a smart device, wherein the smart device includes a mobile terminal and a PC; After receiving the data access request sent by the user through the smart device, the teaching platform verifies the identity and authority of the user; When the user passes the identity and authority verification, the teaching platform parses the data access request sent by the user to obtain the data access requirements contained in the data access request; According to the data access requirement sent by the user, the stream processing tool of Hadoop is called to search the Hadoop distributed file system to obtain data corresponding to the data access requirement of the user.

4. The data access method applied to the teaching platform according to claim 1, characterized in that: Recommend resource data based on the user's data access content and the user's learning history, including: Collecting the user's data access content, wherein the data access content includes browsing, searching, downloading and data information corresponding to the activity; Collect the user's learning history records; wherein the learning history records include the courses learned, knowledge points and learning material data; Performing data analysis on the user's data access content to obtain the user's interest preference data; Performing data analysis on the user's learning history records to obtain the user's learning knowledge information; According to the interest preference data and learning knowledge information of the user, resource data corresponding to the interest preference data and learning knowledge information are retrieved, and the resource data is recommended to the user's smart device.

5. The data access method applied to the teaching platform according to claim 1, characterized in that: The first load evaluation factor of the teaching platform is obtained by using the number of concurrent users per unit time and the resource utilization rate per unit time of the teaching platform, including: Extract the number of concurrent users per unit time of the teaching platform; Extracting the resource utilization rate per unit time of the teaching platform; wherein the resource utilization rate includes memory utilization rate and network bandwidth utilization rate; The resource utilization compensation coefficient is obtained by using the memory utilization and network bandwidth utilization per unit time; wherein the resource utilization compensation coefficient is obtained by the following formula: Among them, K x represents the resource utilization compensation coefficient; n represents the number of unit times experienced by the operation of the teaching platform, and the unit time is 1s; P ni represents the memory utilization corresponding to the i-th unit time; P bi represents the network bandwidth utilization rate corresponding to the i-th unit time; δ 01 and δ 02 Respectively represent the first adjustment coefficient and the second adjustment coefficient; wherein the first adjustment coefficient and the second adjustment coefficient are obtained by the following formula: Among them, P ni represents the memory utilization corresponding to the i-th unit time; P ni-1 represents the memory utilization corresponding to the i-1th unit time; P nmax and P nmin They represent the maximum and minimum memory utilization rates during the operation of the teaching platform in n unit time respectively; P np represents the average memory utilization rate during the operation of the teaching platform in n units of time; P bmax and P bmin They represent the maximum and minimum network bandwidth utilization rates during the operation of the teaching platform in n unit time, respectively; P bp It represents the average value of network bandwidth utilization during the operation of the teaching platform in n units of time; The first load evaluation factor is obtained by using the number of concurrent users of the teaching platform per unit time and the resource utilization compensation coefficient of the teaching platform.

6. The data access method applied to the teaching platform according to claim 1, characterized in that: The second load evaluation factor of the teaching platform is obtained by using the access response time of the teaching platform, including: Extracting the number of data accesses per unit time of the teaching platform; The access response time corresponding to each data access per unit time of the teaching platform is used to obtain the access time parameter corresponding to the unit time of the teaching platform; wherein the access time parameter is obtained by the following formula: Where ξ represents the access time parameter corresponding to the unit time; m represents the number of data accesses in the unit time; T i represents the access response time corresponding to the i-th data access; T tmax Indicates the maximum allowed access response time corresponding to normal operation of data access; Extract resource utilization compensation coefficient corresponding to unit time; The second load evaluation factor of the teaching platform is obtained by using the access time parameter corresponding to the unit time and the resource utilization compensation coefficient corresponding to the unit time.

7. The data access method applied to the teaching platform according to claim 1, characterized in that: The optimization strategy is as follows: When the operating load evaluation parameter exceeds a preset parameter threshold, the cache space for data access of the teaching platform is adjusted, wherein the capacity of the adjusted cache space is obtained by the following formula: Where S represents the capacity of the cache space after adjustment; S0 represents the capacity of the cache space before adjustment; R z Represents the operating load evaluation parameter; R y Indicates the preset parameter threshold; P nmax represents the maximum memory utilization rate during the operation of the teaching platform in n units of time; P nmb Indicates the network bandwidth utilization corresponding to the maximum memory utilization moment; When the operating load evaluation parameter exceeds the preset parameter threshold, the expiration time of the data access of the teaching platform is adjusted, wherein the adjusted expiration time is obtained by the following formula: Among them, T g represents the expiration time after adjustment; T0 represents the expiration time before adjustment; S represents the capacity of the cache space after adjustment; S0 represents the capacity of the cache space before adjustment.

8. A data access system applied to a teaching platform, characterized in that: The data access system applied to the teaching platform includes: Configuration module, used to configure the Hadoop cluster of the teaching platform and define data flows; A data access and display module is used to provide data information in a streaming manner using a streaming processing tool provided by the Hadoop distributed file system when there is a data access request from a user on the teaching platform, and to display the data information on the user's smart device; A resource data recommendation module is used to recommend resource data based on the user's data access content and the user's learning history record; A monitoring and optimization module is used to monitor the data access operation parameters of the teaching platform in real time, and determine whether to perform data access optimization according to the optimization strategy based on the data access operation parameters; The method of monitoring the data access operation parameters of the teaching platform in real time and determining whether to perform data access optimization according to the optimization strategy according to the data access operation parameters includes: Real-time monitoring of the data access operating parameters of the teaching platform; wherein the data access operating parameters include the number of concurrent users per unit time, access response time and resource utilization; Using the data access operation parameters of the teaching platform to obtain the operation load evaluation parameters of the teaching platform; When the operation load evaluation parameter of the teaching platform exceeds a preset parameter threshold, data access optimization is performed according to the optimization strategy; Wherein, using the data access operation parameters of the teaching platform to obtain the operation load evaluation parameters of the teaching platform includes: Obtaining a first load evaluation factor of the teaching platform by using the number of concurrent users per unit time and the resource utilization rate per unit time of the teaching platform; Acquiring a second load evaluation factor of the teaching platform by using the access response time of the teaching platform; The operation load evaluation parameter is obtained by using the first load evaluation factor and the second load evaluation factor of the teaching platform, wherein the operation load evaluation parameter is obtained by the following formula: Among them, R z represents the operating load evaluation parameter; R0 represents the preset parameter reference value; R 01 Represents the first load evaluation factor; R 02 represents the second load evaluation factor; At the same time, the first load evaluation factor is obtained by the following formula: Among them, R 01 Represents the first load evaluation factor; m i represents the number of concurrent users corresponding to the i-th unit time; m i-1 represents the number of concurrent users corresponding to the i-1th unit time; K x Indicates the resource utilization compensation coefficient; Furthermore, the second load evaluation factor is obtained by the following formula: Among them, R 02 Represents the second load evaluation factor; K x represents the resource utilization compensation coefficient; n represents the number of unit time experienced by the operation of the teaching platform; ξ i Represents the access time parameter corresponding to the i-th unit time.

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