Online educational resource dynamic adjusting system
Through the intelligent prediction body of the artificial intelligence model and the Hofitter neural network, the problem of online education institutions being difficult to accurately predict the number of students is solved, and the number of teachers is matched with the number of students is reduced, and economic costs are reduced.
Patent Information
- Application Number
- CN202411545545.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-01
- Publication Date
- 2025-02-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Online education institutions find it difficult to accurately predict the number of students, resulting in mismatch in the number of teachers, affecting the educational effect or increasing economic expenses.
Using artificial intelligence mode, based on the corresponding intelligent prediction body mapped by Hofitter neural network, a number of basic data are used to intelligently predict the number of students in the future time interval, and the total number of teachers required to be hired is determined through the parameter adjustment agency.
The number of teachers and actual number of students has been achieved, taking into account the demand for online education and economic costs.
Smart Images
Figure CN119417667A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of online education, and more specifically, to a dynamic adjustment system for online education resources. Background Art
[0002] With the development of the Internet, the education industry promoted distance education ten years ago, using Internet virtual classrooms to realize remote video teaching and electronic document sharing, allowing teachers and students to form an interactive teaching and learning experience on the Internet; and the advent of the 5G era makes learning more convenient, not just through bulky computers. As long as you have a mobile phone with large traffic, through the fast 5G network, people can more conveniently and directly participate in online education through mobile phones and other handheld tools, and the wireless network makes the interaction between teachers and students more effective.
[0003] Generally, online education institutions will simultaneously offer multiple online education courses for set subjects in a fixed city to meet the online learning needs of a large number of students in the fixed city for the set subjects. However, due to the flexibility of online learning, it is difficult for managers of online education institutions to predict the number of students who will be able to participate in online education courses for set subjects in the future time period, which makes it impossible for the number of teachers arranged in advance based on experience to match the actual number of participating students. Either the number of teachers is too small, which affects the effect of online education, or the number of teachers is too large, which brings heavy economic expenses to the online education institutions. Summary of the invention
[0004] In order to solve technical problems in related fields, the present invention provides a dynamic adjustment system for online educational resources. By adopting an artificial intelligence model, the system intelligently predicts the number of students who can participate in online education courses of set subjects in a future time period according to the past development data of online education of online education institutions in a fixed city and related configuration information, so as to match the number of teachers arranged in advance based on experience with the actual number of participating students, thereby taking into account both online education needs and economic expenditure reduction.
[0005] The present invention needs to have at least the following three important invention points: Invention point 1: Provide sufficient and comprehensive basic data for intelligent prediction of the total number of participating registered students corresponding to the online education of the course set by the online education institution in a fixed city this week, the basic data include the online education related information of the course set by the online education institution in the fixed city, the total number of participating registered students corresponding to the online education of the course set by the online education institution in the fixed city in the weeks before this week, the number of urban residents in the fixed city who have an age matching the set course, the distribution area of the fixed city and the number of other online education courses opened by the online education institution in the fixed city; Invention point 2: Based on the Hoffitt neural network mapping corresponding intelligent prediction body to perform specific intelligent prediction, based on the Hoffitt neural network mapping corresponding intelligent prediction body includes: training the Hoffitt neural network for each time to obtain the Hoffitt neural network after each training and output it as the intelligent prediction body, wherein the number of Hoffitt neural network training is proportional to the number of registered students for online education conducted in a fixed city by the online education institution setting the subject, thereby realizing the customized design of the structure of the intelligent prediction body; Invention point three: Introducing a parameter adjustment mechanism for determining the total number of hired teachers that need to be invested when the online education institution sets the subject to start online education in a fixed city this week based on the total number of registered students participating in the subject when the online education institution sets the subject to start online education in a fixed city this week, wherein the determined total number of hired teachers that need to be invested when the online education institution sets the subject to start online education in the fixed city this week is positively correlated with the total number of registered students participating in the subject when the online education institution sets the subject to start online education in the fixed city this week, thereby taking into account both the operating costs and educational effects of the online education institution.
[0006] According to the present invention, a system for dynamically adjusting online educational resources is provided, the system comprising: The first capture device is used to obtain online education related information of the subjects set by the online education institution in a fixed city, and the online education related information of the subjects set by the online education institution in the fixed city is the number of weekly course hours, the number of parallel classes, the number of employed teachers, and the number of registered students of the online education of the subjects set by the online education institution in the fixed city; The second crawling device is used to obtain the number of urban residents in the fixed city whose age matches the set subject, the distribution area of the fixed city, and the number of other online education subjects opened by the online education institution in the fixed city; The third capture device is used to obtain the total number of registered students participating in each of the online education courses set by the online education institution in the fixed city in the weeks before the current week; The object mapping mechanism is used to map the corresponding intelligent prediction body based on the Hoffet neural network, and the mapping of the corresponding intelligent prediction body based on the Hoffet neural network includes: training the Hoffet neural network for each time to obtain the Hoffet neural network after each training and output it as the intelligent prediction body, wherein the number of Hoffet neural network trainings is proportional to the number of registered students for online education conducted in a fixed city under the set subject of the online education institution; A prediction execution mechanism is connected to the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively, and is used to use an intelligent prediction body to intelligently predict the total number of registered students participating in the online education subject set by the online education institution in the fixed city when the online education is carried out this week according to the online education related information of the subject set by the online education institution in the fixed city, the total number of registered students participating in the online education subject set by the online education institution in the fixed city in the weeks before this week, the number of urban residents in the fixed city with an age matching the set subject, the distribution area of the fixed city and the number of other online education subjects opened by the online education institution in the fixed city. A parameter adjustment mechanism, connected to the prediction execution mechanism, is used to determine the total number of teachers required to be employed when the online education institution sets the subject and conducts online education in the fixed city this week based on the total number of registered students participating in the subject when the online education institution sets the subject and conducts online education in the fixed city this week; Among them, determining the total number of hired teachers that need to be invested in the online education institution's set subjects when the online education is conducted in a fixed city this week based on the total number of registered students participating in the online education institution's set subjects when the online education is conducted in the fixed city this week includes: the determined total number of hired teachers that need to be invested in the online education institution's set subjects when the online education is conducted in the fixed city this week is positively correlated with the total number of registered students participating in the online education institution's set subjects when the online education is conducted in the fixed city this week.
[0007] The online education resource dynamic adjustment system of the present invention is intelligent in operation and compact in design. Since it can use an artificial intelligence model to intelligently predict the number of students who can participate in online education courses of set subjects in the future time interval based on multiple basic data selected in a targeted manner, the number of teachers arranged in advance based on experience matches the number of actual participating students, thereby improving the intelligent level of online education management. BRIEF DESCRIPTION OF THE DRAWINGS
[0008] Those skilled in the art may better understand the numerous advantages of the present invention by referring to the accompanying drawings, in which: Figure 1 It is a structural diagram of an online educational resource dynamic adjustment system according to the primary embodiment of the present invention.
[0009] Figure 2 It is a structural diagram of an online educational resource dynamic adjustment system according to a secondary embodiment of the present invention.
[0010] Figure 3 It is a structural diagram of an online educational resource dynamic adjustment system according to a further embodiment of the present invention. DETAILED DESCRIPTION
[0011] Figure 1 1 is a schematic diagram of the structure of a system for dynamically adjusting online educational resources according to a primary embodiment of the present invention, the system comprising: The first capture device is used to obtain online education related information of the subjects set by the online education institution in a fixed city, and the online education related information of the subjects set by the online education institution in the fixed city is the number of weekly course hours, the number of parallel classes, the number of employed teachers, and the number of registered students of the online education of the subjects set by the online education institution in the fixed city; Specifically, the first capture device is used to obtain the online education related information of the online education institution setting the subject in a fixed city, and the online education related information of the online education institution setting the subject in the fixed city is the number of weekly course hours, the number of parallel classes, the number of employed teachers and the number of registered students of the online education of the online education institution setting the subject in the fixed city, including: in the first capture device, a plurality of different capture elements are used to respectively obtain the number of weekly course hours, the number of parallel classes, the number of employed teachers and the number of registered students of the online education of the online education institution setting the subject in the fixed city; The second crawling device is used to obtain the number of urban residents in the fixed city whose age matches the set subject, the distribution area of the fixed city, and the number of other online education subjects opened by the online education institution in the fixed city; The third capture device is used to obtain the total number of registered students participating in each of the online education courses set by the online education institution in the fixed city in the weeks before the current week; The object mapping mechanism is used to map the corresponding intelligent prediction body based on the Hoffet neural network, and the mapping of the corresponding intelligent prediction body based on the Hoffet neural network includes: training the Hoffet neural network for each time to obtain the Hoffet neural network after each training and output it as the intelligent prediction body, wherein the number of Hoffet neural network trainings is proportional to the number of registered students for online education conducted in a fixed city under the set subject of the online education institution; A prediction execution mechanism is connected to the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively, and is used to use an intelligent prediction body to intelligently predict the total number of registered students participating in the online education subject set by the online education institution in the fixed city when the online education is carried out this week according to the online education related information of the subject set by the online education institution in the fixed city, the total number of registered students participating in the online education subject set by the online education institution in the fixed city in the weeks before this week, the number of urban residents in the fixed city with an age matching the set subject, the distribution area of the fixed city and the number of other online education subjects opened by the online education institution in the fixed city. A parameter adjustment mechanism, connected to the prediction execution mechanism, is used to determine the total number of teachers required to be employed when the online education institution sets the subject and conducts online education in the fixed city this week based on the total number of registered students participating in the subject when the online education institution sets the subject and conducts online education in the fixed city this week; Among them, determining the total number of employed teachers that need to be invested in the online education institution's set subject when the online education is carried out in a fixed city this week based on the total number of registered students participating in the online education institution's set subject when the online education is carried out in a fixed city this week includes: the determined total number of employed teachers that need to be invested in the online education institution's set subject when the online education is carried out in the fixed city this week is positively correlated with the total number of registered students participating in the online education institution's set subject when the online education is carried out in the fixed city this week; Among them, the total number of registered students participating in the online education of subjects set by online education institutions in fixed cities in each week before this week includes: the number of weeks before this week is positively correlated with the number of weekly course hours of online education of subjects set by online education institutions in fixed cities.
[0012] Figure 2 It is a structural diagram of an online educational resource dynamic adjustment system according to a secondary embodiment of the present invention.
[0013] and Figure 1 different, Figure 2 The online educational resource dynamic adjustment system in the system may also include the following components: a voltage conversion device, arranged near the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism and connected to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively; Among them, the voltage conversion device is arranged near the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism and is respectively connected to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism, including: the voltage conversion device is used to provide the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism with their respective required working voltages.
[0014] Figure 3 It is a structural diagram of an online educational resource dynamic adjustment system according to a further embodiment of the present invention.
[0015] and Figure 1 different, Figure 3 The online educational resource dynamic adjustment system in the system may also include the following components: a quartz oscillator device, arranged near the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism and connected to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively; Among them, the quartz oscillator device is arranged near the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism and is respectively connected to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism, including: the quartz oscillator device is used to provide the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism with the reference clock pulses they need respectively.
[0016] Next, the specific structure of the online educational resource dynamic adjustment system of the present invention will be further described.
[0017] In the online educational resource dynamic adjustment system according to various embodiments of the present invention: Using a programmable logic device to perform image data processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively; Wherein, using a programmable logic device to perform image data processing on the output data of the prediction execution mechanism, the first grabbing device, the second grabbing device, the third grabbing device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grabbing device, the second grabbing device, the third grabbing device and the object mapping mechanism respectively includes: performing image sharpening processing on the output data of the prediction execution mechanism, the first grabbing device, the second grabbing device, the third grabbing device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grabbing device, the second grabbing device, the third grabbing device and the object mapping mechanism respectively; Wherein, using a programmable logic device to perform image data processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively includes: performing image enhancement processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively; Wherein, using a programmable logic device to perform image data processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively includes: performing image filtering processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively; And wherein, a programmable logic device is used to perform image data processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively, including: performing distortion correction processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively.
[0018] In addition, in the online education resource dynamic adjustment system, an intelligent prediction body is used to intelligently predict the total number of registered students participating in the online education subject set by the online education institution in the fixed city when the online education is carried out this week according to the online education related information of the subject set by the online education institution in the fixed city, the total number of registered students participating in the online education subject set by the online education institution in the fixed city in each week before this week, the number of urban residents with an age matching the set subject in the fixed city, the distribution area of the fixed city, and the number of other online education subjects opened by the online education institution in the fixed city. The method includes: using a synchronous drive device to realize the online education related information of the subject set by the online education institution in the fixed city, the total number of registered students participating in the online education subject set by the online education institution in the fixed city in each week before this week, the number of urban residents with an age matching the set subject in the fixed city, the distribution area of the fixed city, and the number of other online education subjects opened by the online education institution in the fixed city to the intelligent prediction body.
[0019] Although the present invention has been disclosed in detail with various preferred embodiments as above, the present invention should not be interpreted as only covering the above embodiments, which are only exemplary embodiments. Any person skilled in the art may make some changes and improvements without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the claims and their equivalents.
Claims
1. A dynamic adjustment system for online educational resources, characterized in that: The system comprises: The first capture device is used to obtain online education related information of the subjects set by the online education institution in a fixed city, and the online education related information of the subjects set by the online education institution in the fixed city is the number of weekly course hours, the number of parallel classes, the number of employed teachers, and the number of registered students of the online education of the subjects set by the online education institution in the fixed city; The second crawling device is used to obtain the number of urban residents in the fixed city whose age matches the set subject, the distribution area of the fixed city, and the number of other online education subjects opened by the online education institution in the fixed city; The third capture device is used to obtain the total number of registered students participating in each of the online education courses set by the online education institution in the fixed city in the weeks before the current week; The object mapping mechanism is used to map the corresponding intelligent prediction body based on the Hoffet neural network, and the mapping of the corresponding intelligent prediction body based on the Hoffet neural network includes: training the Hoffet neural network for each time to obtain the Hoffet neural network after each training and output it as the intelligent prediction body, wherein the number of Hoffet neural network trainings is proportional to the number of registered students for online education conducted in a fixed city under the set subject of the online education institution; A prediction execution mechanism is connected to the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively, and is used to use an intelligent prediction body to intelligently predict the total number of registered students participating in the online education subject set by the online education institution in the fixed city when the online education is carried out this week according to the online education related information of the subject set by the online education institution in the fixed city, the total number of registered students participating in the online education subject set by the online education institution in the fixed city in the weeks before this week, the number of urban residents in the fixed city with an age matching the set subject, the distribution area of the fixed city and the number of other online education subjects opened by the online education institution in the fixed city. A parameter adjustment mechanism, connected to the prediction execution mechanism, is used to determine the total number of teachers required to be employed when the online education institution sets the subject and conducts online education in the fixed city this week based on the total number of registered students participating in the subject when the online education institution sets the subject and conducts online education in the fixed city this week; Among them, determining the total number of hired teachers that need to be invested in the online education institution's set subjects when the online education is conducted in a fixed city this week based on the total number of registered students participating in the online education institution's set subjects when the online education is conducted in the fixed city this week includes: the determined total number of hired teachers that need to be invested in the online education institution's set subjects when the online education is conducted in the fixed city this week is positively correlated with the total number of registered students participating in the online education institution's set subjects when the online education is conducted in the fixed city this week.
2. The online education resource dynamic adjustment system according to claim 1, characterized in that: Obtaining the total number of registered students participating in each week before this week when online education was conducted in a fixed city under a subject set by an online education institution includes: the number of weeks before this week is positively correlated with the number of weekly course hours for online education conducted in a fixed city under a subject set by an online education institution.
3. The online education resource dynamic adjustment system according to claim 2, characterized in that: The system further comprises: a voltage conversion device, arranged near the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism and connected to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively; Among them, the voltage conversion device is arranged near the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism and is respectively connected to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism, including: the voltage conversion device is used to provide the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism with their respective required working voltages.
4. The online education resource dynamic adjustment system according to claim 2, characterized in that: The system further comprises: a quartz oscillator device, arranged near the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism and connected to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively; Among them, the quartz oscillator device is arranged near the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism and is respectively connected to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism, including: the quartz oscillator device is used to provide the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism with the reference clock pulses they need respectively.
5. The online educational resource dynamic adjustment system according to any one of claims 2 to 4, characterized in that: A programmable logic device is used to perform image data processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively.
6. The online education resource dynamic adjustment system according to claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively includes: performing image sharpening processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively.
7. The online educational resource dynamic adjustment system according to claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively includes: performing image enhancement processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively.
8. The online education resource dynamic adjustment system according to claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively includes: performing image filtering processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively.
9. The online education resource dynamic adjustment system according to claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively includes: performing distortion correction processing on the output data of the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism to obtain the output processing data corresponding to the prediction execution mechanism, the first grasping device, the second grasping device, the third grasping device and the object mapping mechanism respectively.