CN111523646A - Remote education learning center intelligent perception network based on Internet of things and management method - Google Patents
Remote education learning center intelligent perception network based on Internet of things and management method Download PDFInfo
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Abstract
The invention discloses a remote education digital learning center intelligent perception network system based on the Internet of things and a management method. The intelligent perception network layer can perceive the data of the user side and the data of the smart campus and conduct interaction and comprehensive analysis. And different users at the user side perform the next teaching, learning and management according to the feedback data. Therefore, the invention is beneficial to the integration and application of resources, enables the offline learning center to become an offline digital learning environment support of an open education system, provides an auxiliary means for teachers to manage students, saves teaching management resources to a certain extent, and promotes the quality improvement of teaching and learning.
Description
Technical Field
The invention relates to the field of Internet of things, in particular to a remote education learning center intelligent perception network based on the Internet of things and a management method.
Background
With the rapid development and innovation of the internet of things, the current intelligent learning technology is deeply applied to the education industry, and the intelligent learning brings benefits to educators and learners and is accompanied with some problems, however, the design of the digital chemistry learning environment of the learning center at the current stage lacks scientific planning, the online entity teaching business does not interact with the learning data of cloud teachers and students, the change of the entity teaching environment is not designed, the teaching aid for students is not in place, and the education workers and managers lack of active response and effective management modes, so that the teaching efficiency is not high and the resources are wasted.
In order to solve the problems in the prior art, the invention provides an intelligent perception network of a remote education learning center based on the internet of things and a management method. The system and method provide an intelligent learning system that incorporates the internet of things, particularly the perception network. Therefore, the intelligent learning system and the intelligent learning method can solve the problems that the intelligent learning center has huge data, low response efficiency, insufficient intelligence in management and even errors.
Disclosure of Invention
The invention aims to solve the problem that the entity learning center in the prior art is not efficient in operation. The invention provides a learning center intelligent perception network system based on the Internet of things and a management method. The method is provided in the system, which comprises a user side, an intelligent campus application layer, an intelligent application support platform and the Internet of things; the user side comprises school managers, teachers, students, social public and various devices; the campus application layer comprises an intelligent teaching service subsystem, an intelligent management subsystem, an intelligent learning subsystem, an intelligent life service subsystem, an intelligent safety subsystem, a teaching quality monitoring and evaluating subsystem and an education and teaching panoramic display subsystem; the intelligent application support platform comprises a data analysis subsystem, a cloud computing platform and a data mining and displaying subsystem; the Internet of things comprises an intelligent information acquisition subsystem, an intelligent information management subsystem, a network communication layer and an intelligent sensing network layer. Therefore, the invention can solve the problem of imperfect intelligent learning center in the prior art. According to the remote education digital learning center intelligent perception network system based on the Internet of things and the management method, the Internet of things is arranged, particularly perception network training is performed, resource integration and application are facilitated, the learning center teaching environment based on-line and off-line integration is constructed, the learning center becomes an intelligent teaching application model, auxiliary means are provided for teachers to manage students, teaching management resources are saved to a certain extent, and quality improvement of teaching and learning is promoted.
In order to solve the above technical problems, a first aspect of the present invention provides an internet-of-things-based intelligent perception network system for a digital learning center for distance education, comprising:
the system comprises a user side, a smart campus application layer, a smart application support platform and the Internet of things;
the user side comprises school managers, teachers, students, social public and various devices;
the campus application layer comprises an intelligent teaching service subsystem, an intelligent management subsystem, an intelligent learning subsystem, an intelligent life service subsystem, an intelligent safety subsystem, a teaching quality monitoring and evaluating subsystem and an education and teaching panoramic display subsystem;
the intelligent application support platform comprises a data analysis subsystem, a cloud computing platform and a data mining and displaying subsystem;
the Internet of things comprises an intelligent information acquisition subsystem, an intelligent information management subsystem, a network communication layer and an intelligent sensing network layer;
the intelligent information acquisition subsystem comprises an intelligent information acquisition module, an intelligent information acquisition gateway and an Internet of things data/metadata collection/storage module;
the intelligent information management subsystem comprises an Internet of things intercommunication management center and an Internet of things operation equipment management subsystem;
the network communication layer comprises a 3G/4G/5G, WiFi, an Ipv/Ipv6 network, Bluetooth and/or an infrared interface;
the intelligent sensing network layer comprises a smart phone, wearable sensing equipment, an environment sensor, a camera, a microphone, a collector and RFID equipment;
the intelligent campus application layer, the intelligent application support platform and the Internet of things are connected through the network communication layer;
the user side forms a main source of the perception network data;
the smart campus application layer is used for receiving and collecting data generated by the user side;
data generated by the user side are transmitted to the intelligent application support platform through the Internet of things to be analyzed, and the intelligent application support platform feeds the analyzed data back to the intelligent campus application layer.
Optionally, the intelligent sensing network layer can sense data of the user side and data of the offline learning center, the data of the user side comprises sound data, video data, image data, text data, teaching interaction data and emotion data, and/or the data of the smart campus comprises environment data, attendance data, temperature data, illumination data and teaching resource data.
Optionally, the analysis of the sound data, the video data, the image data, the text data and the emotion data of the user side by the sensing network satisfies the following formula, and the correspondence (X) of the N samplesp，Yp) (p is 1,2, … …, n), and the input sum of the data of the user side of j of the Mth sample cell is set as INmjThe sum of the outputs of unit i is set to OUTmiThen, then
INmj＝ΣWijOUTmi＝Σ1/[1+exp(-INmj)][1-exp(INmj)]
Where Wji is the weight between neurons i, j.
Optionally, data synthesis of the smart campus is set as S;
the perception network integrates the data of the user side and the data of the smart campus of the remote education digital learning center to obtain a decision function satisfying the following conditions:
where y is the y sample, Q (x)iX) is kernel function, the optimal value is obtained by training the kernel function, which is threshold value, αkIs a correction factor.
In order to solve the above technical problems, a second aspect of the present invention provides a management method for an internet of things-based intelligent perception network system for a remote education digital learning center, which is applied to the internet of things-based intelligent perception network system for a remote education digital learning center, and includes the following steps:
(1) sending out a course arrangement instruction, and enabling the system to enter a class-taking mode;
(2) checking whether all the environments of the smart campus are in class mode, and if yes, executing the step (3); otherwise, executing the step (4);
(3) monitoring various situations and data of the whole intelligent learning center through the campus application layer; the data obtained by monitoring the campus application layer are sorted and analyzed through the intelligent application support platform;
(4) adjusting and correcting the environment of the smart campus, and returning to the step (2);
(5) feeding back the data obtained by analysis to the campus application layer through the Internet of things;
(6) the campus application layer feeds back to different users of the user side according to feedback data attributes
(7) And different users of the user side perform the next intelligent learning according to the feedback data.
In order to solve the above technical problem, a third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, the program being executable by a processor to implement the above method.
By the system or the method, the transmission of the Internet of things, big data sorting, sensing network analysis data and directional distribution of the data to different users form an intelligent learning center, and the next step of teaching is implemented according to different data analysis. Therefore, the intelligent learning center can solve the problem of management of the traditional intelligent learning center, so that the management function of the intelligent learning center based on the Internet of things is more diversified, the connection and communication between terminals/systems are more convenient, the data transmission is more reliable and safer, the data analysis efficiency can be improved, the integration and application of resources are facilitated, and powerful guarantee is provided for comprehensive system teaching and personalized teaching.
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In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects obtained more clear, the following will describe in detail the embodiments of the present invention with reference to the accompanying drawings. It should be noted, however, that the drawings described below are only illustrations of exemplary embodiments of the invention, from which other embodiments can be derived by those skilled in the art without inventive step.
Fig. 1 is a schematic structural diagram illustrating an internet of things-based remote education digital learning center intelligent perception network system according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating a management method of a remote education digital learning center intelligent perception network system based on the internet of things according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of a computer-readable medium according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the invention may be embodied in many specific forms, and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art.
The structures, properties, effects or other characteristics described in a certain embodiment may be combined in any suitable manner in one or more other embodiments, while still complying with the technical idea of the invention.
In describing particular embodiments, specific details of structures, properties, effects, or other features are set forth in order to provide a thorough understanding of the embodiments by one skilled in the art. However, it is not excluded that a person skilled in the art may implement the invention in a specific case without the above-described structures, performances, effects or other features.
The flow chart in the drawings is only an exemplary flow demonstration, and does not represent that all the contents, operations and steps in the flow chart are necessarily included in the scheme of the invention, nor does it represent that the execution is necessarily performed in the order shown in the drawings. For example, some operations/steps in the flowcharts may be divided, some operations/steps may be combined or partially combined, and the like, and the execution order shown in the flowcharts may be changed according to actual situations without departing from the gist of the present invention.
The block diagrams in the figures generally represent functional entities and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The same reference numerals denote the same or similar elements, components, or parts throughout the drawings, and thus, a repetitive description thereof may be omitted hereinafter. It will be further understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these elements, components, or sections should not be limited by these terms. That is, these phrases are used only to distinguish one from another. For example, a first device may also be referred to as a second device without departing from the spirit of the present invention. Furthermore, the term "and/or", "and/or" is intended to include all combinations of any one or more of the listed items.
Fig. 1 is a schematic structural diagram illustrating an internet of things-based remote education digital learning center intelligent perception network system according to an embodiment of the present invention.
As shown in fig. 1, a client 1, a smart campus application layer 2, a smart application support platform 3 and an internet of things 4;
the user side 1 comprises a school manager, a teacher, students, social public and various devices;
the campus application layer 2 comprises an intelligent teaching service subsystem, an intelligent management subsystem, an intelligent learning subsystem, an intelligent life service subsystem, an intelligent safety subsystem, a teaching quality monitoring and evaluating subsystem and an education and teaching panoramic display subsystem;
the intelligent application support platform 3 comprises a data analysis subsystem, a cloud computing platform and a data mining and displaying subsystem;
the Internet of things 4 comprises an intelligent information acquisition subsystem, an intelligent information management subsystem, a network communication layer and an intelligent sensing network layer;
the intelligent information acquisition subsystem comprises an intelligent information acquisition module, an intelligent information acquisition gateway and an Internet of things data/metadata collection/storage module;
the intelligent information management subsystem comprises an Internet of things intercommunication management center and an Internet of things operation equipment management subsystem;
the network communication layer comprises a 3G/4G/5G, WiFi, an Ipv/Ipv6 network, Bluetooth and/or an infrared interface;
the intelligent sensing network layer comprises a smart phone, wearable sensing equipment, an environment sensor, a camera, a microphone, a collector and RFID equipment. Wherein, the wearable sensing device may be a wearable smart device. The sensor device can sense the roll call response, the monitoring of the temperature in the classroom, carbon dioxide and PM2.5 related data in the teaching process, the cleaning of the classroom after class, the automatic power off of related devices and the like. The environment of the lecture place, for example, a classroom, can be controlled based on a predetermined threshold.
The system comprises a user side 1, a smart campus application layer 2, a smart application support platform 3 and the Internet of things 4 which are connected through a network communication layer;
the user side forms a main source of the perception network data;
the smart campus application layer is used for receiving and collecting data generated by the user side;
data generated by the user side are transmitted to the intelligent application support platform through the Internet of things to be analyzed, and the intelligent application support platform feeds the analyzed data back to the intelligent campus application layer.
The user side comprises terminal equipment used by school managers, teachers, students, social public and various personnel. The school managers can perform comprehensive management of the intelligent learning center based on the Internet of things according to data collected by teachers, students and social public.
Optionally, the smart sensor network layer can sense data of the user side and data of the smart campus, the data of the user side comprises sound data, video data, image data, character data and emotion data, and/or the data of the smart campus comprises environment data, attendance data, temperature data, illumination data and teaching resource data.
Optionally, the analysis of the sound data, the video data, the image data, the text data and the emotion data of the user side by the sensing network satisfies the following formula, and the correspondence (X) of the N samplesp，Yp) (p is 1,2, … …, n), and the input sum of the data of the user side of j of the Mth sample cell is set as INmjThe sum of the outputs of unit i is set to OUTmiThen, then
INmj＝ΣWijOUTmi＝Σ1/[1+exp(-INmj)][1-exp(INmj)]
Where Wji is the weight between neurons i, j. The perception network analysis method provided by the invention has strong information processing capability. All data input by the user side can be integrated, such as sound, video, images, characters, emotion data and the like, wherein the emotion data can be data sent by a student perception teacher, and can also be data fed back by the student perception teacher. Thus, the output is not only a comprehensive analysis of the input, but also includes the user's perception information.
Optionally, data synthesis of the smart campus is set as S;
the perception network integrates the data of the user side and the data of the smart campus to obtain a decision function, and the decision function satisfies the following conditions:
where y is the y sample, Q (x)iX) is kernel function, the optimal value is obtained by training the kernel function, which is threshold value, αkIs a correction factor;
and by the decision function, the field data of the user side is integrated, and accurate teaching can be performed. Specifically, the teaching resource data of the smart campus is pushed to the corresponding user side. For example, a teacher can perform audio, video, image data, text data and audio-visual teaching, a student end can perform feedback through a perception network, for example, a wearable intelligent device, and the wearable intelligent device can detect the reaction, emotion, brain reaction and the like of the student after the student obtains teaching information sent by the teacher. The decision function can carry out personalized teaching analysis on the student side through a model established by data operation, machine learning and data training, such as the degree of mastering knowledge and skill, whether the class is interested or not, whether the class has knowledge reserve or not, and the like. And after the information is synthesized, the personalized teaching resources are pushed to different students. And the teacher obtains the data of all students in the classroom simultaneously, adjusts the teaching and realizes intelligent teaching.
Optionally, the student can feed back voice, video, image and text data through the wearable intelligent device, segment decomposition is carried out on the data, each small segment is scored, different scoring rules are set for different courses, a data model is formed, data training and machine learning are carried out, and the optimal feedback data model of different courses is obtained. The teaching mode and the teaching strategy are communicated through the data, recombination of the teaching process and innovation of the teaching mode are promoted, and the teaching quality is improved.
Fig. 2 is a flowchart illustrating a management method of a remote education digital learning center intelligent perception network system based on the internet of things according to an embodiment of the present invention.
As shown in fig. 2, a management method of a remote education digital learning center intelligent perception network system based on the internet of things is applied to the remote education digital learning center intelligent perception network system based on the internet of things, and includes the following steps:
(1) sending out a course arrangement instruction, and enabling the system to enter a class-taking mode;
(2) checking whether all the environments of the smart campus are in class mode, and if yes, executing the step (3); otherwise, executing the step (4);
(3) monitoring various situations and data of the whole intelligent learning center through the campus application layer; the data obtained by monitoring the campus application layer are sorted and analyzed through the intelligent application support platform;
(4) adjusting and correcting the environment of the smart campus, and returning to the step (2);
(5) feeding back the data obtained by analysis to the campus application layer through the Internet of things;
(6) the campus application layer feeds back to different users of the user side according to feedback data attributes
(7) And different users of the user side perform the next intelligent learning according to the feedback data.
In the next step of learning, for example, a teacher can adjust a teaching scheme and push personalized course materials of courses to students, or an intelligent learning center automatically pushes teaching resources to the teacher, the students can provide teaching improvement schemes to the teacher, the public can supervise teaching or provide suggestions, and a teaching manager can monitor learning and obtain teaching assessment. Because the comprehensive data obtained by each student through the perception network are different and personalized, the next intelligent learning is also personalized, diversified and intelligent.
FIG. 3 is a schematic diagram of a computer-readable medium of an embodiment of the invention. As shown in fig. 3, the computer program may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. The computer program, when executed by one or more data processing devices, enables the computer-readable medium to implement the above-described methods of the present invention.
The intelligent energy consumption management and control system can be used for establishing an intelligent energy consumption management and control system of a learning center and butting with a system for class opening arrangement and the like by implanting sensing equipment in a physical environment or providing wearable sensing equipment for learners, so that appointed classroom access control opening, automatic opening of a power supply and teaching equipment, wireless network association opening in a classroom after a course is opened on time, monitoring of roll call, temperature in the classroom, carbon dioxide and PM2.5 related data in the course of teaching, classroom cleaning after class, automatic power off of the related equipment and the like are realized. On the administrative level, by virtue of efficient utilization of space, the smart learning center can realize reservation of mobile office space, renting of furniture and equipment with RFID, correspondence of telephone numbers and employee cards and intelligent message pushing. Of course, if necessary, the system can also collect and early warn the health data such as the body temperature of the user, and realize humanized intelligent teaching service.
Compared with the prior art, the invention has the beneficial effects that:
the intelligent perception network system is arranged in a remote education digital learning center based on the Internet of things, the structure and the model of the intelligent perception network system are known, data perception and analysis are the basis of intelligent learning center 'intelligence', data in multiple aspects are gathered through the intelligent learning center perception network system based on the Internet, emerging technologies such as big data analysis and optimization models are introduced, and intelligent learning, intelligent teaching and intelligent management are achieved.
Compared with the prior art, the invention has the following improved technical points:
1. the invention provides a user side, a smart campus application layer, a smart application support platform and the Internet of things, which form a remote education digital learning center smart sensing network system based on the Internet of things.
2. And (4) analyzing the data by a system of the perception network, and establishing an optimization model.
3. And (4) formulating or pushing a personalized teaching scheme.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
Claims (6)
1. A distance education digital learning center intelligent perception network system based on the Internet of things, the system comprises:
the system comprises a user side, a smart campus application layer, a smart application support platform and the Internet of things;
the user side comprises school managers, teaching staff, students, social public and various devices;
the campus application layer comprises an intelligent teaching service subsystem, an intelligent management subsystem, an intelligent learning subsystem, an intelligent life service subsystem, an intelligent safety subsystem, a teaching quality monitoring and evaluating subsystem and an education and teaching panoramic display subsystem;
the intelligent application support platform comprises a data analysis subsystem, a cloud computing platform and a data mining and displaying subsystem;
the Internet of things comprises an intelligent information acquisition subsystem, an intelligent information management subsystem, a network communication layer and an intelligent sensing network layer;
the intelligent information acquisition subsystem comprises an intelligent information acquisition module, an intelligent information acquisition gateway and an Internet of things data/metadata collection/storage module;
the intelligent information management subsystem comprises an Internet of things intercommunication management center and an Internet of things operation equipment management submodule;
the network communication layer comprises a 3G/4G/5G, WiFi, an Ipv/Ipv6 network, Bluetooth and/or an infrared interface;
the intelligent sensing network layer comprises a smart phone, wearable sensing equipment, an environment sensor, a camera, a microphone, a collector and RFID equipment;
the user side, the smart campus application layer, the smart application support platform and the Internet of things are connected through the network communication layer;
the user side forms a main source of the perception network data;
the smart campus application layer is used for receiving and collecting data generated by the user side;
and data generated by the user side is transmitted to the intelligent application support platform through the Internet of things to analyze the data, and the intelligent application support platform feeds the analyzed data back to the intelligent campus application layer.
2. The system of claim 1, wherein:
the intelligent perception network layer can perceive the data of the user side and the data of the smart campus, the data of the user side comprises sound data, video data, image data, character data, teaching interaction data and emotion data, and/or the data of the smart campus comprises environment data, attendance data, temperature data, illumination data and teaching resource data.
3. The system of claim 2, wherein:
the perception network analyzes the sound data, the video data, the image data, the character data and the emotion data of the user side to satisfy the following formula, and the correspondence (X) of N samplesp，Yp) (p is 1,2, … …, n), and the input sum of the data of the user side of j of the Mth sample cell is set as INmjThe sum of the outputs of unit i is set to OUTmiThen, then
INmj＝ΣWijOUTmi＝Σ1/[1+exp(-INmj)][1-exp(INmj)]
Where Wji is the weight between neurons i, j.
4. The system of claim 3, wherein:
setting the data synthesis of the smart campus as S;
the perception network integrates the data of the user side and the data of the smart campus to obtain a decision function, and the decision function satisfies the following conditions:
where y is the y sample, Q (x)iX) is kernel function, the optimal value is obtained by training the kernel function, which is threshold value, αkIs a correction factor.
5. A management method of a remote education digital learning center intelligent perception network system based on the internet of things, which is characterized in that the remote education digital learning center intelligent perception network system based on the internet of things as claimed in one of claims 1-4 is applied, and comprises the following steps:
(1) sending out a course arrangement instruction, and starting a class-taking mode by the system;
(2) checking whether all the environments of the smart campus are in class mode, and if yes, executing the step (3); otherwise, executing the step (4);
(3) monitoring various situations and data of the whole learning center through the campus application layer; the data obtained by monitoring the campus application layer are sorted and analyzed through the intelligent application support platform;
(4) adjusting and correcting the environment of the smart campus, and returning to the step (2);
(5) feeding back the data obtained by analysis to the campus application layer for processing and display through the Internet of things;
(6) the campus application layer feeds back the data to different users of the user side according to the feedback data attribute;
(7) and different users of the user side perform the next intelligent learning according to the feedback data.
6. A computer-readable storage medium, on which a computer program is stored which is executable by a processor to implement the method as claimed in claim 5.
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| CN112700353A (en) * | 2020-12-31 | 2021-04-23 | 南方科技大学 | Smart classroom system, student health data management method, server, and medium |
| CN112929404A (en) * | 2020-12-29 | 2021-06-08 | 四川格瑞特科技有限公司 | Campus building automation thing networking system |
| CN116938986A (en) * | 2023-09-19 | 2023-10-24 | 深圳市爱为物联科技有限公司 | Intelligent campus management method and system based on Internet of things |
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