Smart classroom Internet of Things system, service method, device and storage medium

Abstract

The invention provides a smart classroom Internet of Things system, a service method, equipment and a storage medium, a sensing layer, an application layer and a network layer are arranged, the sensing layer comprises a sensing control domain and a physical entity domain, and the sensing control domain is in communication connection with the physical entity domain; the application layer comprises a user domain and an application service domain, and the user domain is in communication connection with the application service domain; the application service domain and the perception control domain are in communication connection based on a network layer; wherein the user domain is used for sending a target service request to the application service domain; the physical entity domain is used for collecting classroom data of a classroom in real time; the sensing control domain is used for generating a classroom analysis result based on the classroom data; and the application service domain is used for dynamically generating a personalized teaching strategy and / or an environment regulation and control scheme according to the target service request and the classroom analysis result, so that the system structure of the smart classroom Internet of Things application system can be effectively adapted, and the formation of the smart classroom Internet of Things application ecological system is promoted.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things, and in particular to a smart classroom Internet of Things system, service method, device and storage medium. Background Art

[0002] Traditional digital teaching platforms have played a certain role in the construction of information-based teaching environment and resources, the integration of information technology and teaching process in classroom teaching, the digitization of teaching scenes and teaching processes, the improvement of teachers' information-based teaching methods and the effectiveness of knowledge transfer, online supervision and evaluation of teaching, and online teaching management.

[0003] However, there is no reference architecture in the existing IoT-based smart classrooms, which cannot effectively guide the design of IoT application systems in smart classrooms. In addition, the existing IoT-based smart classrooms basically follow the IoT three-layer architecture evolved from the Internet OSI model. Since the OSI model is designed for "people" rather than "things", the hierarchical architecture is not completely suitable for the architecture of the IoT application system in smart classrooms. As a result, it is difficult to effectively guide and promote the large-scale application of IoT-based smart classrooms, and it is difficult to form an ecological system for IoT applications in smart classrooms. Summary of the Invention

[0004] The main purpose of the embodiments of this application is to propose a smart classroom Internet of Things system, service method, equipment and storage medium, which can effectively adapt to the architecture of the smart classroom Internet of Things application system, provide effective guidance for the structural design of the Internet of Things application system of different smart classrooms, effectively promote the large-scale application of the smart classroom Internet of Things, and promote the formation of a smart classroom Internet of Things application ecosystem.

[0005] To achieve the above objectives, the first aspect of the embodiments of the present application proposes a smart classroom Internet of Things system, including: Perception layer, the perception layer includes a perception control domain and a physical entity domain, and the perception control domain and the physical entity domain are connected in communication; Application layer, the application layer includes the user domain and the application service domain, and the user domain and the application service domain are connected in communication; Network layer, the application service domain and the perception control domain are connected based on the network layer communication; Among them, the user domain is used to send target service requests to the application service domain; The physical entity domain is used to collect classroom data in real time and send the classroom data to the perception control domain; The perception control domain is used to receive classroom data and generate classroom analysis results based on the classroom data, and send the classroom analysis results to the application service domain; The application service domain is used to receive classroom analysis results and target service requests, and verify the request permissions of the target service requests. If the request permissions are approved, it dynamically generates personalized teaching strategies and / or environmental control plans based on the target service requests and classroom analysis results, and sends the personalized teaching strategies and / or environmental control plans to the perception control domain; The perception control domain is also used to generate device operation instructions based on personalized teaching strategies and / or environmental control plans, and issue device operation instructions to the physical entity domain so that the physical entity domain performs the control tasks of the corresponding devices according to the device operation instructions.

[0006] Further, in some embodiments, the user domain includes: Teacher terminal: The teacher terminal is used to provide teachers with integrated teaching resource management tools and a real-time classroom interactive interface; Student terminal: The student terminal is used to provide students with personalized learning path planning and interactive question-answering functions; Administrator terminal: The administrator terminal is used to overview the device status within the physical entity domain and issue abnormal alarms; The teacher terminal, student terminal and administrator terminal are respectively connected to the application service domain through identity token authentication.

[0007] Furthermore, in some embodiments, the user domain further includes: Evaluation agency terminal, which is used to conduct multi-dimensional analysis of classroom teaching quality; The terminal of the competent department is used to generate visual reports on classroom teaching data; The terminal of the assessment agency and the terminal of the competent department are respectively connected to the application service domain through identity token verification.

[0008] Furthermore, in some embodiments, the application service domain includes: Classroom teaching application platform, which is used to integrate virtual simulation experiment modules and classroom question-and-answer automatic response modules based on deep learning; Teaching management service platform, which is used to achieve tamper-proof storage of course schedules and grade records through blockchain technology; Supervision and evaluation service platform, which uses natural language processing technology to analyze teaching evaluation texts and generate improvement suggestions; Online learning service platform, which has a built-in adaptive learning engine that adjusts course difficulty based on student behavior data; Teaching environment monitoring platform, which uses digital twin technology to map classroom environments in real time and predict equipment failures; The classroom teaching application platform, teaching management service platform, supervision and evaluation service platform, online learning service platform and teaching environment monitoring platform are respectively connected to the network layer through standardized interface protocols.

[0009] Furthermore, in some embodiments, the perception control domain includes: Equipment detection module, which is equipped with a current sensor and a vibration sensor. The equipment detection module is used to monitor the energy consumption and mechanical status of the equipment in real time through the current sensor and the vibration sensor; Environmental monitoring module, which is equipped with a laser dust detector and an acoustic sensor. The environmental monitoring module is used to collect dust data and temperature and humidity data inside and outside the classroom through the laser dust detector, and to collect noise data inside and outside the classroom through the acoustic sensor; Personnel monitoring module, which is equipped with millimeter-wave radar and infrared thermal imager. It is used to count the density of people in the classroom through millimeter-wave radar and detect the behavior trajectory of people in the classroom through infrared thermal imager; Video surveillance module: The video surveillance module is equipped with a camera, which is used to recognize the facial expressions of classroom personnel and provide early warning of abnormal behavior; Equipment control module, which automatically adjusts the operating parameters of classroom equipment based on fuzzy logic algorithms. Classroom equipment includes air conditioning, lighting, and projection equipment; The equipment detection module, the environment monitoring module, the personnel monitoring module, the video monitoring module and the equipment control module are respectively connected to the network layer and the physical entity domain for communication.

[0010] Furthermore, in some embodiments, the physical entity domain includes: Smart classrooms are equipped with adjustable tables and chairs and AR blackboards. They provide multi-scenario teaching modes for classroom participants. Teaching equipment, including IoT printers and holographic projectors; Storage devices, which are used to store data and enable cross-campus data collaboration; Smart classrooms, teaching equipment and storage devices are respectively connected to the perception control domain.

[0011] Furthermore, in some embodiments, the network layer includes: Multi-protocol communication module, which is used for adaptive switching of multiple protocols; Blockchain security module, which is used to implement dynamic access control based on zero-trust architecture and integrate a quantum key distribution unit; Edge computing nodes, which are used to pre-process classroom data in the perception control domain to reduce the bandwidth load of the core network; The multi-protocol communication module, blockchain security module and edge computing node are respectively connected to the perception control domain and application service domain.

[0012] To achieve the above-mentioned purpose, the second aspect of the embodiment of the present application proposes a service method for a smart classroom, which uses a smart classroom Internet of Things system. The smart classroom Internet of Things system includes a perception layer, an application layer, and a network layer. The perception layer includes a perception control domain and a physical entity domain. The perception control domain and the physical entity domain are connected in communication. The application layer includes a user domain and an application service domain. The user domain and the application service domain are connected in communication. The application service domain and the perception control domain are connected in communication based on the network layer. The method includes: The user domain sends a target service request to the application service domain; The physical entity domain collects classroom data in real time and sends the classroom data to the perception control domain; The perception control domain receives classroom data and generates classroom analysis results based on the classroom data, and sends the classroom analysis results to the application service domain; The application service domain receives the classroom analysis results and target service requests, and verifies the request permissions of the target service requests. If the request permissions are approved, it dynamically generates personalized teaching strategies and / or environmental control plans based on the target service requests and classroom analysis results, and sends the personalized teaching strategies and / or environmental control plans to the perception control domain; The perception control domain generates device operation instructions based on personalized teaching strategies and / or environmental control schemes, and sends the device operation instructions to the physical entity domain so that the physical entity domain performs the control tasks of the corresponding devices according to the device operation instructions.

[0013] To achieve the above-mentioned purpose, the third aspect of the embodiment of the present application proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the smart classroom service method of the above-mentioned second aspect embodiment is implemented.

[0014] To achieve the above-mentioned purpose, the fourth aspect of the embodiment of the present application proposes a computer-readable storage medium, which stores a program executable by a processor. When the computer program is executed by the processor, the smart classroom service method of the above-mentioned second aspect embodiment is implemented.

[0015] The embodiments of the present application have the following beneficial effects: through the layered collaborative intelligent IoT architecture between the perception layer, network layer, and application layer, a closed-loop optimization of "data perception-intelligent decision-making-precise execution" is achieved in teaching scenarios, significantly improving the efficiency of the teaching system: the physical entity domain collects multi-dimensional classroom data (such as environmental parameters and device status) in real time and performs intelligent analysis through the perception control domain, ensuring low latency and high reliability of data processing; the application service domain ensures service security based on an authority verification mechanism, combines dynamic generation algorithms to achieve precise matching of personalized strategies with environmental control solutions, and then reversely controls physical entity devices through the perception control domain, forming an adaptive closed-loop adjustment of the teaching environment. At the system architecture level, the layered architecture design not only ensures the modular expansion of the functions of each domain, but also realizes the efficient flow of cross-domain data through the network layer, thereby effectively adapting to the architecture of the smart classroom IoT application system, providing effective guidance for the structural design of IoT application systems in different smart classrooms, effectively promoting the large-scale application of smart classroom IoT, and promoting the formation of a smart classroom IoT application ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is an optional architecture diagram of the smart classroom Internet of Things system provided in the embodiment of the present application; Figure 2 This is an optional flow chart of the service method of the smart classroom provided in the embodiment of the present application; Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0018] In the description of this application, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on this application.

[0019] It should also be noted that, in the description of this application, "several" means more than one, "plurality" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0021] In the description of this application, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples.

[0022] Traditional digital teaching platforms have played a certain role in the construction of information-based teaching environment and resources, the integration of information technology and teaching process in classroom teaching, the digitization of teaching scenes and teaching processes, the improvement of teachers' information-based teaching methods and the effectiveness of knowledge transfer, online supervision and evaluation of teaching, and online teaching management.

[0023] However, there is no reference architecture in the existing IoT-based smart classrooms, which cannot effectively guide the design of IoT application systems in smart classrooms. In addition, the existing IoT-based smart classrooms basically follow the IoT three-layer architecture evolved from the Internet OSI model. Since the OSI model is designed for "people" rather than "things", the hierarchical architecture is not completely suitable for the architecture of the IoT application system in smart classrooms. As a result, it is difficult to effectively guide and promote the large-scale application of IoT-based smart classrooms, and it is difficult to form an ecological system for IoT applications in smart classrooms.

[0024] Based on this, the embodiments of the present application provide a smart classroom Internet of Things system, service method, equipment and storage medium, which can effectively adapt to the architecture of the smart classroom Internet of Things application system, provide effective guidance for the structural design of the Internet of Things application system of different smart classrooms, effectively promote the large-scale application of the smart classroom Internet of Things, and promote the formation of a smart classroom Internet of Things application ecosystem.

[0025] The embodiments of the present application provide a smart classroom Internet of Things system, service method, device and storage medium, which are specifically described through the following embodiments.

[0026] First, refer to Figure 1 As shown, Figure 1 This is an optional architecture diagram of the smart classroom Internet of Things system provided in an embodiment of the present application. The smart classroom Internet of Things system is provided with a perception layer 100, an application layer 200 and a network layer 300. The perception layer 100 includes a perception control domain and a physical entity domain 120, and the perception control domain and the physical entity domain 120 are communicated with each other; the application layer 200 includes a user domain 210 and an application service domain 220, and the user domain 210 and the application service domain 220 are communicated with each other; the application service domain 220 and the perception control domain are communicated with each other based on the network layer 300.

[0027] In some embodiments, the smart classroom IoT system can be deployed on campus. Specifically, sensor devices from the physical domain 120 are deployed in each classroom. For example, cameras are installed around the classroom to capture student behavior and expressions, microphones are installed at the podium to collect teacher and student voices, and temperature sensors are installed on the classroom walls to monitor ambient temperature. Furthermore, classroom teaching equipment 122, such as projectors, electronic whiteboards, and air conditioners, are brought under the management of the physical domain 120. Servers from the perception control domain can be deployed in the school's information center computer room to receive and process classroom data sent by the physical domain 120. The network layer 300 can leverage the school's campus network to ensure stable and high-speed data transmission between the application service domain 220, the perception control domain, and the physical domain 120. Servers from the application service domain 220 are also deployed in the school's information center computer room and connect to the school's teaching management system to provide users with more comprehensive teaching services.

[0028] The user domain 210 is used to send target service requests to the application service domain 220; the physical entity domain 120 is used to collect classroom data in real time and send this data to the perception control domain. In one embodiment, a camera takes a panoramic photo of the classroom every minute, a microphone records classroom audio in real time, and a temperature sensor uploads classroom temperature data every five minutes. This data is then sent to the server in the perception control domain via the campus network.

[0029] Furthermore, the perception control domain is used to receive classroom data, generate classroom analysis results based on the data, and send these results to the application service domain 220. In one embodiment, after receiving the classroom data, the perception control domain server uses image recognition algorithms to analyze the images captured by the camera to identify student behavior, such as whether they are paying attention or dozing off. It also uses voice recognition technology to analyze the audio recorded by the microphone to extract information such as the content and duration of the teacher and students' speeches. It also performs simple processing on the temperature sensor data to generate a classroom temperature curve. These analysis results are then integrated into a classroom analysis result and sent to the application service domain 220 server via the campus network.

[0030] Furthermore, the application service domain 220 is configured to receive classroom analysis results and target service requests, verify the request permissions for the target service requests, and, if the request permissions are approved, dynamically generate personalized teaching strategies and / or environmental control plans based on the target service requests and classroom analysis results, and send the personalized teaching strategies and / or environmental control plans to the perception control domain. In one embodiment, after class, a teacher logs into the teaching management platform of the application service domain 220 from their office computer and sends a target service request to obtain a teaching quality analysis report for that class. After receiving the request, the server in the application service domain 220 verifies the teacher's request permissions and confirms that the user is a legitimate teacher. Then, based on the classroom analysis results and the teacher's teaching goals and requirements, it dynamically generates personalized teaching strategies using a pre-set algorithm model. For example, it can recommend adjusting teaching methods and adding interactive elements to subsequent lessons based on periods of inattention. Simultaneously, it can generate environmental control plans based on classroom temperature fluctuations, such as suggesting lowering the air conditioning temperature by 2 degrees to improve student comfort. The personalized teaching strategies and environmental control plans are then sent to the server in the perception control domain.

[0031] Furthermore, the perception control domain is also used to generate device operation instructions based on the personalized teaching strategy and / or environmental control plan, and issue the device operation instructions to the physical entity domain 120, so that the physical entity domain 120 performs the corresponding device control tasks according to the device operation instructions. In one embodiment, after receiving the personalized teaching strategy and environmental control plan, the server in the perception control domain generates the corresponding device operation instructions, such as sending a temperature adjustment instruction to the classroom air conditioner. Upon receiving the instruction, the air conditioner in the physical entity domain 120 automatically adjusts the temperature setting and performs a cooling operation.

[0032] Furthermore, user domain 210 includes a teacher terminal 211, which is used to provide teachers with an integrated teaching resource management tool and a real-time classroom interactive interface. Teachers can use this teaching resource management tool to upload, store, categorize, manage, and retrieve resources such as teaching courseware, lesson plans, and teaching videos. For example, teachers can upload prepared PowerPoint courseware to the resource management tool in advance and directly access and display it in class through teacher terminal 211. In addition, teachers can initiate classroom activities such as questions, group discussions, and voting on the real-time classroom interactive interface and view student participation and answer results in real time. For example, when explaining a difficult math problem, the teacher can initiate a vote through the real-time interactive interface, allowing students to vote on the solution ideas. The teacher can then adjust the focus and method of the explanation in a timely manner based on the voting results.

[0033] It should be noted that the teacher terminal 211 communicates with the application service domain 220 through identity token authentication. When a teacher uses the teacher terminal 211 to send a service request or receive system feedback, the terminal device automatically verifies the validity of the identity token, ensuring that only legitimate teacher users can access the relevant functions and data of the system, thereby ensuring the security of teaching data and the stability of teaching order.

[0034] Furthermore, the user domain 210 also includes a student terminal 212, which is used to provide students with personalized learning path planning and interactive question-answering functions. The student terminal 212 can tailor a personalized learning path for students based on factors such as their learning progress, historical academic performance, and learning preferences. For example, for English subjects, the system will plan a learning path for students that includes multiple stages, such as vocabulary expansion, grammar practice, and reading comprehension improvement, based on their current vocabulary and grammar mastery, and recommend corresponding learning resources and exercises. At the same time, students can participate in classroom interactive activities initiated by teachers through the student terminal 212, such as answering questions in real time and submitting discussion points. In class, when the teacher initiates a question through the teacher terminal 211, the student terminal 212 will promptly pop up an answer interface. Students complete and submit their answers within the specified time. The answer results will be fed back to the teacher terminal 211 and the application service domain 220 in real time, allowing the teacher to understand the student's learning situation and classroom mastery.

[0035] It should be noted that student terminal 212 also communicates with application service domain 220 through identity token authentication. This identity token authentication mechanism ensures the security of communication between student terminal 212 and application service domain 220, preventing unauthorized devices and users from accessing system resources, protecting students' personal information and learning data from being leaked or misused, and maintaining the normal order of teaching activities.

[0036] Furthermore, user domain 210 also includes an administrator terminal 213, which is used to provide an overview of the device status within physical domain 120 and generate abnormality alerts. Administrators can use administrator terminal 213 to view the operating status of various devices within physical domain 120 in real time, including information such as the on / off status, operating hours, and fault conditions of projectors, electronic whiteboards, air conditioners, and cameras within the classroom. For example, on the school's device management platform, administrators can intuitively view icons and corresponding status information for each classroom device, such as "The projector in classroom 101 is operating normally, but the air conditioner in classroom 102 has a fault alarm."

[0037] At the same time, when a device within the physical entity domain 120 experiences an abnormality, such as a device failure, network interruption, or an environmental parameter exceeding a specified limit, the administrator terminal 213 will promptly receive an abnormality alert. This alert will be notified to the administrator via various forms, such as pop-up windows, text messages, and emails, allowing the administrator to quickly respond and take appropriate remedial measures. For example, if a temperature sensor in a classroom detects that the temperature exceeds a preset reasonable range, the administrator terminal 213 will immediately receive an alert, allowing the administrator to promptly arrange for personnel to inspect the air conditioning equipment or implement other cooling measures to ensure a comfortable teaching environment.

[0038] It should be noted that the administrator terminal 213 communicates with the application service domain 220 through identity token authentication. Identity token authentication ensures that only authorized administrators can access device status information and receive abnormality alerts, preventing problems such as device misuse and data loss that may result from leaking device management permissions, thereby improving the security and reliability of school device management.

[0039] Furthermore, user domain 210 also includes an evaluation agency terminal 214, which is used to conduct multi-dimensional analysis of classroom teaching quality. The evaluation agency can access classroom data and analysis results in application service domain 220 through evaluation agency terminal 214. Using professional evaluation models and indicator systems, the evaluation agency can conduct a comprehensive assessment and analysis of classroom teaching quality across multiple dimensions, including teaching content, teaching methods, teacher performance, student engagement, and achievement of teaching objectives. For example, the evaluation agency can analyze the effectiveness of teachers' use of different teaching methods in class and assess the impact of student interaction and participation on learning outcomes, thereby providing professional evaluation opinions and suggestions for schools to improve teaching quality.

[0040] It should be noted that the evaluation agency terminal 214 communicates with the application service domain 220 through identity token authentication. This identity token authentication mechanism ensures that the evaluation agency can securely and legally obtain classroom data for quality assessment, prevents data tampering or leakage, ensures the objectivity and fairness of the evaluation results, and protects the security and privacy of the school's teaching data.

[0041] Furthermore, the user domain 210 also includes a department terminal 215, which is used to generate visual reports on classroom teaching data. The department can access the teaching data in the application service domain 220 through the department terminal 215, and use data visualization tools and technologies to generate various intuitive and easy-to-understand teaching data reports, such as classroom teaching quality statistics reports, student learning situation analysis reports, and teacher teaching workload statistics reports. These reports display key indicators and trends of teaching data in the form of charts, graphs, data tables, etc., helping the department to quickly understand the overall teaching situation of the school and provide strong data support for educational decision-making, resource allocation, teaching supervision, etc. For example, the department can use the teaching data report to intuitively see the comparison of classroom teaching quality in different schools, different grades, and different subjects, so as to formulate targeted teaching improvement measures and policies.

[0042] It should be noted that the administrative department terminal 215 communicates with the application service domain 220 through identity token authentication. Identity token authentication ensures that the administrative department can securely and legally obtain teaching data for report generation and analysis, safeguarding the confidentiality and integrity of teaching data, preventing illegal access and misuse of data, and maintaining the normal teaching order and stability of the school's educational ecosystem.

[0043] Furthermore, the application service domain 220 includes a classroom teaching application platform 221, which integrates a virtual simulation experiment module and a deep learning-based classroom question-and-answer automatic response module. The classroom teaching application platform 221 uses virtual reality technologies such as 3D modeling, physics engine simulation, and graphics rendering through the virtual simulation experiment module to create a highly realistic virtual experimental environment for students. This covers common experimental scenarios across multiple disciplines, including physics, chemistry, and biology, such as "Circuit Construction and Current and Voltage Measurement" in high school physics, "Acid-Base Neutralization Titration" in high school chemistry, and "Cell Structure Observation" in high school biology. Students can wear a virtual reality headset or use a touch-enabled terminal device to immersively operate virtual experimental instruments, follow experimental steps, and observe experimental phenomena and data changes in real time. Furthermore, the classroom teaching application platform 221 also uses natural language processing through the classroom question-and-answer automatic response module to intelligently analyze and understand questions raised by teachers in class. This automatic classroom Q&A response module pre-trains a deep learning model using a large amount of classroom Q&A data, enabling it to recognize semantic meanings and generate responses to various question types (e.g., knowledge Q&A, comprehension analysis, reasoning, and judgment). When a teacher sends a question to the classroom teaching application platform 221 via the teacher terminal 211, the automatic classroom Q&A response module quickly analyzes the question, combines the knowledge content in the teaching resource library, and generates an accurate and detailed response text. This text is then fed back to the student terminal 212 to assist students in thinking and answering.

[0044] It should be noted that the classroom Q&A automatic response module relies on the network layer 300 to ensure stable data transmission when receiving teacher questions and sending text answers to student terminals 212. Furthermore, the classroom teaching application platform 221 strictly communicates with the network layer 300 according to standardized interface protocols to ensure the integrity and timeliness of question and answer data. Furthermore, the classroom teaching application platform 221 also records key information from each Q&A session (such as question type, student feedback on the answers, etc.) and transmits it via the network layer 300 to the data storage module of the application service domain 220, providing rich data resources for subsequent teaching data analysis and model optimization.

[0045] Furthermore, the application service domain 220 also includes a teaching management service platform 222, which uses blockchain technology to implement tamper-proof storage of course schedules and academic records. In one embodiment, the teaching management service platform 222 generates a data block containing each semester's course schedule information (including course title, instructor, class time, location, credit hours, etc.), and constructs a blockchain chain structure based on chronological order and course relevance. Each data block contains the hash value of the previous block (a unique identifier generated by a specific algorithm to ensure data integrity and immutability). Once course schedule information is recorded on the blockchain, any attempt to modify the course information will cause the hash value verification of subsequent blocks to fail, thus preventing tampering. For example, when a teacher needs to adjust a course schedule, the system generates a new block on the blockchain to record the adjustment information while retaining the original course schedule block, ensuring a traceable history of course schedule changes.

[0046] In another embodiment, the teaching management service platform 222 provides teachers, students, and school administrators with a convenient interface for querying grade data and course schedules. Users can quickly retrieve corresponding blockchain-stored data by entering specific query criteria (such as course name, student ID, and semester). The platform also features data verification capabilities. Leveraging blockchain's hash value verification mechanism, users can verify whether retrieved grade data and course schedule information have been tampered with, ensuring data authenticity and reliability. For example, after receiving their final exam results, students can verify the integrity and authenticity of their grade data using the platform's verification function.

[0047] It should be noted that when the teaching management service platform 222 performs storage, query, and verification operations on course arrangements and grade record data, it establishes a communication connection with the network layer 300 through a standardized interface protocol. For example, when a teacher enters new grade data on the teaching management service platform 222, the platform will package the grade data into data blocks in the format specified by the standardized interface protocol, and transmit it to the blockchain storage node for storage through the network layer 300. During the data query process, after the platform receives the user's query request, it sends a query instruction to the blockchain storage node through the network layer 300, obtains the corresponding data block, and parses it and displays it to the user in a friendly interface. During the entire data interaction process, the network layer 300 is responsible for ensuring the stability and security of data transmission, ensuring the normal operation of the teaching management service platform 222 and the accurate interaction of data.

[0048] Furthermore, the application service domain 220 also includes a supervision and evaluation service platform 223, which is used to analyze the evaluation text and generate improvement suggestions through natural language processing technology. In one embodiment, the supervision and evaluation service platform 223 uses the sentiment analysis technology in natural language processing to judge the emotional tendency in the evaluation text and identify the evaluator's positive emotions (such as satisfaction and appreciation), negative emotions (such as dissatisfaction and criticism), or neutral emotions towards the teacher's teaching. At the same time, through keyword extraction algorithms and topic model analysis methods, words, phrases, and thematic content that reflect key issues and characteristics of teaching are extracted from the evaluation text. For example, in the student's evaluation text, words and phrases such as "clear explanation", "lack of interaction", and "too many homework assignments" may be extracted as key information dimensions for analyzing the teacher's teaching quality.

[0049] In another embodiment, based on the sentiment analysis results and key information extraction content, combined with professional knowledge and experience rules in the teaching field, the supervision and evaluation service platform 223 uses natural language generation technology to generate personalized teaching quality improvement suggestions for teachers. The improvement suggestions will give affirmation and encouragement to the teacher's strengths in the teaching process, and at the same time propose specific improvement measures and directions for existing problems and shortcomings. For example, if the analysis results of the teaching evaluation text show that the teacher has deficiencies in the classroom interaction link, the improvement suggestions may include "increase group discussion activities and encourage students to speak actively" and "use a variety of interactive teaching tools, such as classroom voting, interactive question-and-answer software, etc." and other specific content to help teachers clarify improvement goals and methods.

[0050] It should be noted that the supervision and teaching evaluation service platform 223 is connected to the network layer 300 through a standardized interface protocol in the process of collecting the teaching evaluation text data and sending improvement suggestions to the teacher terminal 211. For example, when a student submits a teaching evaluation text through the student terminal 212, the supervision and teaching evaluation service platform 223 receives the data through the network layer 300 and stores it in the data storage module of the platform. After completing the analysis and processing of the teaching evaluation text and generating improvement suggestions, the platform sends the improvement suggestions to the teacher terminal 211 of the corresponding teacher through the network layer 300. In the entire data interaction process, the network layer 300 ensures the integrity and timely transmission of the teaching evaluation text data and improvement suggestions, and guarantees the normal provision of the supervision and teaching evaluation service platform 223 services and the ability of teachers to obtain teaching quality feedback information in a timely manner.

[0051] Furthermore, application service domain 220 also includes an online learning service platform 224, which has a built-in adaptive learning engine that adjusts course difficulty based on student behavior data. In one embodiment, the adaptive learning engine comprehensively understands students' learning status and characteristics by tracking and recording various student behavior data on the online learning service platform 224, such as study time, study frequency, knowledge mastery (as determined by online tests and practice test results), learning path selection (such as the order and duration of students' browsing between different course modules and learning resources), homework completion, and interactive participation (such as the frequency and quality of students' participation in online discussions and group collaboration activities).

[0052] It should be noted that the application of the adaptive learning engine enables the online learning service platform 224 to truly achieve student-centered personalized teaching, breaking the "one-size-fits-all" course design and teaching methods in the traditional online learning model. By constantly adapting to students' learning changes and dynamically adjusting the course difficulty, it helps students maintain continuous learning motivation and interest in the learning process, promotes students' independent learning ability and lifelong learning ability, and enhances the overall educational value and service quality of the online learning platform.

[0053] It should also be noted that the online learning service platform 224 establishes a communication connection with the network layer 300 via a standardized interface protocol when collecting student behavior data and pushing personalized course content and learning resources to the student terminal 212. For example, when a student watches instructional videos, submits answers to exercises, or participates in online discussions on the online learning service platform 224 through the student terminal 212, the platform transmits this behavior data in real time via the network layer 300 to the data collection module of the adaptive learning engine. After analyzing and processing, the adaptive learning engine generates new course difficulty adjustment instructions and a recommended list of learning resources, and sends the corresponding course content update information to the student terminal 212 via the network layer 300, ensuring that students can obtain personalized learning services in a timely manner.

[0054] Furthermore, the application service domain 220 also includes a teaching environment monitoring platform 225, which uses digital twin technology to map the classroom environment in real time and predict equipment failures. In one embodiment, the teaching environment monitoring platform 225 deploys various sensor devices (such as temperature and humidity sensors, light sensors, carbon dioxide concentration sensors, noise sensors, etc.) and video surveillance equipment within the classroom to comprehensively collect environmental data and physical status information. This real-time collected data is then used to construct a digital twin model corresponding to the real classroom environment in a virtual space. This model not only accurately reflects the real-time changes in environmental parameters such as temperature, humidity, light intensity, and air quality within the classroom, but also displays detailed information such as the layout of classroom equipment, its operating status (such as whether the projector is on, the air conditioning temperature setting, and the status of doors and windows), and the distribution of personnel. Teachers, school administrators, and logistics support personnel can view the digital twin model of the classroom environment anytime and anywhere through the terminal interface of the teaching environment monitoring platform 225 (such as computer monitoring software, mobile phone applications, etc.), and grasp the real-time environmental conditions in the classroom as if they were there in person, providing strong environmental information support for the smooth development of teaching activities.

[0055] In another embodiment, the teaching environment monitoring platform 225 can also conduct deep learning and analysis on historical operating data of classroom equipment (such as equipment usage time, fault repair records, and performance parameter change trends). Combined with real-time collected equipment operating status data, it uses machine learning algorithms (such as time series prediction algorithms and fault diagnosis algorithms) to establish an equipment failure prediction model. By continuously monitoring and analyzing equipment operating data, this model can predict the type and time of possible classroom equipment failures in advance, providing a scientific basis for preventive maintenance and repair of equipment. For example, based on the historical usage time and performance degradation curve of a classroom projector, combined with recent changes in performance parameters such as brightness and color saturation, the digital twin model predicts that the projector may experience insufficient brightness due to bulb aging within the next two weeks. The system will then send an equipment failure warning message to the administrator's terminal in advance, reminding the administrator to arrange for repair or bulb replacement in a timely manner to avoid disrupting normal teaching procedures due to sudden equipment failures.

[0056] It should be noted that the Teaching Environment Monitoring Platform 225 uses digital twin technology to achieve real-time, accurate mapping of classroom environments, providing schools with intuitive and convenient environmental monitoring tools. This helps promptly identify and resolve classroom environmental issues, creating a comfortable and healthy teaching and learning environment for teachers and students. Furthermore, the equipment failure prediction function effectively changes the passive "after-the-fact repair" model of traditional equipment maintenance, enabling proactive, preventative maintenance management of equipment. This reduces the impact and frequency of equipment failures on teaching activities, improves the efficiency and quality of the school's teaching support services, reduces equipment maintenance costs and downtime, and extends the lifespan of equipment, ensuring the smooth operation of school teaching.

[0057] The teaching environment monitoring platform 225 establishes communication connections with various sensor devices deployed in the classroom and the perception control domain via the network layer 300. Environmental data and device status data collected by the sensor devices are encapsulated according to the format specified by the standardized interface protocol and transmitted to the data processing module of the teaching environment monitoring platform 225 via the network layer 300. After real-time processing and analysis of this data, the platform updates the relevant parameters of the digital twin model and sends the latest model status information to the relevant user's terminal devices via the network layer 300. Furthermore, when the digital twin model predicts a device failure, the platform sends a fault warning message to the administrator's terminal via the network layer 300. Furthermore, the teaching environment monitoring platform 225 transmits environmental control commands (such as adjusting classroom temperature or light intensity) sent by users via their terminal devices to the perception control domain via the network layer 300. The perception control domain generates specific device operation commands and sends them to the corresponding devices in the physical domain 120 to execute the control tasks, thus achieving remote intelligent control of the classroom environment.

[0058] Overall, the integration and collaboration of the various platforms within Application Service Domain 220 have produced significant synergistic effects, significantly enhancing the overall effectiveness of the smart school teaching system. On the one hand, through data sharing and interaction, the various platforms can complement and improve each other's information, providing more comprehensive and accurate data support for the school's teaching decisions. On the other hand, the coordinated operation of business processes optimizes the allocation and utilization of teaching resources, reduces manual intervention and information silos between various teaching links, and improves the consistency and fluidity of teaching operations. For example, teachers can more easily access information about students' learning status and the teaching environment, allowing them to adjust teaching strategies in a targeted manner. Administrators can gain real-time insights into the school's teaching operations and promptly identify and resolve problems that arise during teaching management. Students enjoy a consistent learning experience across various learning platforms, better meeting their diverse learning needs. Overall, the collaborative operation of the various platforms within Application Service Domain 220 creates an efficient, intelligent, personalized, and continuously improving teaching environment for smart schools, effectively promoting the in-depth development of educational informatization and the overall improvement of teaching quality.

[0059] Furthermore, the perception control domain includes a device detection module 111, which is equipped with current sensors and vibration sensors. Device detection module 111 uses these sensors to monitor device energy consumption and mechanical status in real time. In one embodiment, current sensors are installed on the power lines of classroom equipment (such as air conditioners, projectors, and electronic whiteboards) to collect real-time operating current data. The perception control domain's data processing unit calculates the device's real-time power consumption based on the current data and the device's rated voltage, and then calculates the device's energy consumption over different time periods. For example, by monitoring the air conditioner's operating current, the air conditioner's hourly power consumption can be accurately calculated. The energy efficiency ratio of the air conditioner can be evaluated based on the energy consumption data, providing data support for the school's energy conservation management. Furthermore, when a device's operating current exceeds its normal operating current range (e.g., due to an internal fault causing the current to be excessive or insufficient), the current sensor immediately detects the abnormality and sends an alarm signal to the perception control domain's abnormality processing unit. The perception control domain will further analyze the characteristics of the abnormal current, determine the type of equipment failure that may be caused (such as motor stall, line short circuit, etc.), and send detailed equipment failure warning information to the application service domain 220 and the administrator terminal 213 through the network layer 300, reminding management personnel to perform equipment maintenance in a timely manner to avoid equipment damage and interruption of teaching activities.

[0060] In another embodiment, vibration sensors are installed at key locations on mechanical equipment in the classroom (such as projector cooling fans and air conditioner compressors) to monitor the equipment's vibration status in real time. By analyzing the characteristics of the vibration signal, it is possible to determine whether the equipment's mechanical components are loose, worn, or unbalanced. For example, if the bearings of a projector cooling fan are worn, the vibration sensor will detect abnormal changes in vibration frequency and amplitude. Based on these changes, the data analysis unit in the perception control domain accurately identifies the bearing wear fault and issues a timely warning, allowing for early repairs and extending the equipment's service life.

[0061] Furthermore, the perception control domain also includes an environmental monitoring module 112, which is equipped with a laser dust detector and an acoustic sensor. This module is used to collect dust and temperature and humidity data inside and outside the classroom using the laser dust detector, and noise data inside and outside the classroom using the acoustic sensor. In one embodiment, the laser dust detector is installed on walls or vents inside and outside the classroom to monitor airborne dust concentration in real time. It can detect the concentration levels of dust particles of varying sizes (such as PM2.5 and PM10), and simultaneously collects ambient temperature and humidity data using built-in temperature and humidity sensors. The data collection unit in the perception control domain collects dust concentration, temperature, and humidity data at a high frequency (e.g., once per minute) and stores it in a local data storage unit. For example, during class time, the laser dust detector collects dust concentration, temperature, and humidity data within the classroom every minute, providing rich real-time data for subsequent environmental quality analysis. The data analysis unit in the perception control domain then conducts a real-time assessment of classroom environmental quality based on the collected dust concentration, temperature, and humidity data, combined with relevant environmental quality standards (such as the national indoor air quality standards for PM2.5 and PM10 concentration limits and the appropriate temperature and humidity range for teaching environments). When dust concentration exceeds the standard or temperature and humidity exceed the comfortable range, the environmental monitoring module 112 sends environmental control suggestions to the application service domain 220 via the network layer 300, such as activating air purification equipment to reduce dust concentration or starting air conditioning to adjust temperature or humidity. Based on the suggestions, the application service domain 220 generates corresponding device control instructions and sends them to the device control module 115 in the physical entity domain 120 via the network layer 300 and the perception control domain, enabling automatic control of the classroom environment and ensuring it remains suitable for teaching.

[0062] In another embodiment, acoustic sensors are deployed at various locations inside and outside the classroom, such as the four corners and hallways, to monitor ambient noise levels in real time. They can detect noise across various frequency ranges, including low-frequency noise (such as air conditioning equipment), mid-frequency noise (such as students chatting), and high-frequency noise (such as the rubbing of a blackboard eraser). The data acquisition unit in the perception control domain processes the noise data collected by the acoustic sensors in real time, calculating the ambient noise sound pressure level (in decibels) and its spectral characteristics. The noise data is then stored in a local database at set intervals (e.g., every 5 minutes). For example, during normal school hours, acoustic sensors continuously monitor noise levels inside and outside the classroom, recording noise changes over time, providing data support for analyzing noise sources and assessing their impact on teaching activities. The data analysis unit in the perception control domain then uses the noise data to perform noise pollution analysis. By comparing the noise data with pre-set noise standard limits (e.g., classroom noise levels should be controlled below 50 decibels), if the noise exceeds the standard limit, the environmental monitoring module 112 immediately sends a noise exceedance alarm to the exception handling unit in the perception control domain. The perception control domain further analyzes the noise's spectral characteristics and duration, attempting to determine the likely type and location of the noise source. It then sends the noise pollution alert and related analysis results to the application service domain 220 and administrator terminal 213 via the network layer 300. Administrators can take timely action based on the alert, such as checking classroom equipment for noise sources and reminding students to remain quiet, to minimize noise interference with teaching activities and maintain good teaching order and environmental quality.

[0063] Furthermore, the perception control domain also includes a occupancy monitoring module 113, which is equipped with a millimeter-wave radar and an infrared thermal imager. This module is used to count classroom occupancy density using the millimeter-wave radar and to detect occupant movement using the infrared thermal imager. In one embodiment, the millimeter-wave radar is mounted on the ceiling or wall of the classroom, and its emitted millimeter-wave signals can cover the entire classroom area. By analyzing the reflected signals in real time, the millimeter-wave radar can accurately detect the number of occupants in the classroom and their distribution within the classroom. The data processing unit in the perception control domain uses data analysis methods such as clustering algorithms based on the occupant location information returned by the millimeter-wave radar to divide the classroom into multiple areas (such as the podium area, student seating area, and aisle area). It then counts the number of occupants in each area, thereby generating a occupancy density map for the entire classroom. For example, during classroom instruction, the millimeter-wave radar can provide real-time statistics on student attendance and monitor student location changes (such as when a student leaves their seat). This crowd density data is sent to the application service domain 220 via the network layer 300, providing accurate data support for teachers' teaching management and school staff scheduling, such as adjusting classroom ventilation frequency and seating arrangements based on crowd density. Furthermore, millimeter-wave radar can monitor the flow of people in and out of the classroom in real time. By analyzing the Doppler effect of millimeter-wave signals (i.e., determining the speed and direction of an object based on the frequency changes of the reflected signal), it can capture the movements of people entering and exiting the classroom, as well as their movements within the classroom. The data analysis unit in the perception and control domain can perform statistical analysis on crowd flow data, calculating information such as the number of people entering and exiting the classroom per unit time and the average length of time people stay in the classroom. For example, by monitoring crowd flow during breaks, school administrators can understand students' activity patterns and optimize break schedules. They can also promptly identify abnormal crowd flow (such as the potential safety hazards caused by a large number of students concentrating in a certain area of ​​the classroom) and implement appropriate safety measures to ensure student safety.

[0064] In another embodiment, infrared thermal imagers are installed at key locations in the classroom, such as the classroom entrance and near the podium, to capture infrared thermal images of people in real time. By analyzing the thermal image sequence, the image processing unit in the perception and control domain can identify the outline and position changes of people, thereby tracking their behavior within the classroom. For example, it can monitor changes in students' sitting posture (such as whether they are fidgeting), whether they stand up and move around, and their movement routes within the classroom. Furthermore, by combining target recognition and behavior analysis technologies in deep learning algorithms, the infrared thermal imager can identify and classify specific behaviors (such as students raising their hands to answer questions or standing up suddenly and quickly), record this behavior information, and transmit it to the application service domain 220 via the network layer 300. Teachers can view students' classroom behavior trajectory data through the application service domain 220 interface to understand their participation and behavioral performance in class, providing a reference for teaching evaluation and classroom management. For example, it can promptly identify and remind students of their inattention in class, or optimize teaching interaction methods based on their behavioral patterns.

[0065] Furthermore, the perception control domain also includes a video surveillance module 114, which is equipped with cameras. This module is used to recognize classroom expressions and provide warnings for abnormal behavior. In one embodiment, cameras are installed at key locations in the classroom, such as the front (covering the podium and front row of students), the middle (covering the middle student area), the back (covering the back row of students), and the left and right sides (covering the student seating areas on both sides of the classroom). This ensures comprehensive, no-blind-angle image capture of classroom personnel and teaching activities. The cameras utilize high-definition, wide-angle lenses and feature autofocus and low-light photography. They can clearly capture classroom images under various lighting conditions (such as natural light and indoor lighting), ensuring the quality and usability of video surveillance data. Furthermore, the cameras capture classroom video images in real time at a set frame rate (e.g., 25 frames per second) and digitally encode and compress the captured video data to reduce data transmission bandwidth and storage space usage. The processed video data is transmitted via the network layer 300 to the video data processing unit in the perception control domain. For example, during classroom teaching, the camera continuously records the teaching scene in the classroom, capturing detailed information such as the teacher's teaching movements and expressions, as well as the students' classroom performance, and transmits the video data to the perception control domain in real time, providing raw data support for subsequent image analysis and behavior recognition.

[0066] In another embodiment, the video monitoring module 114 can evaluate the students' classroom learning status through real-time recognition and analysis of students' classroom expressions. For example, when students are found to frequently show confused expressions in class, it may mean that the students have difficulty understanding the current teaching content; when students show expressions such as fatigue and yawning, it may imply that the teaching pace is too fast or the students lack sleep, affecting the learning effect. These expression recognition results are sent to the application service domain 220 through the network layer 300. Teachers can view students' classroom expression statistics in real time through the interface of the application service domain 220, and adjust teaching methods and content in a timely manner to better meet students' learning needs and improve teaching effectiveness. At the same time, students' learning status assessment data can also serve as an important reference for teaching evaluation and teaching quality improvement, helping schools to gain an in-depth understanding of problems in the teaching process and optimize teaching management and teaching resource allocation.

[0067] Furthermore, the perception control domain also includes a device control module 115, which automatically adjusts the operating parameters of classroom equipment, including air conditioners, lighting, and projectors, based on fuzzy logic algorithms. Fuzzy logic algorithms are a control method based on fuzzy set theory and fuzzy inference rules, used to address system control problems involving uncertainty and ambiguity. In device control module 115, fuzzy sets of equipment operating parameters (such as air conditioner temperature, lighting brightness, and projection brightness) are first defined. Each fuzzy set contains multiple fuzzy linguistic variables (such as "high temperature," "moderate temperature," and "low temperature"). Membership functions are used to describe the degree to which each input variable (such as ambient temperature and classroom occupancy density) belongs to one of these fuzzy linguistic variables. The shape (e.g., triangular, trapezoidal, etc.) and parameters of the membership functions are set based on the actual equipment characteristics and teaching environment requirements, reflecting the fuzzy relationship between the input variables and the fuzzy linguistic variables. Then, based on an understanding of the teaching environment and equipment operating patterns, a fuzzy rule base is established. Fuzzy rules use an "if-then" form to describe the fuzzy causal relationship between the input variables and the output variables (adjustments to the equipment operating parameters). For example, "If the classroom temperature is high and the occupancy density is high, then the air conditioning cooling capacity should be increased significantly," "If the ambient brightness is moderate and the projection device brightness is high, then the projection device brightness should be reduced appropriately," etc. The number and content of fuzzy rules are continuously optimized and adjusted based on actual control requirements and device performance to ensure that the device control module 115 can make reasonable control decisions based on different environmental conditions and teaching scenarios.

[0068] In one embodiment, the device control module 115, based on the inference results of the fuzzy logic algorithm, sends specific control instructions to the air conditioner to adjust the cooling or heating capacity. These control instructions include adjusting parameters such as the air conditioner's set temperature, fan speed, and compressor operating frequency, enabling precise control of the air conditioner. For example, when increasing cooling capacity, the device control module 115 can lower the air conditioner's set temperature (e.g., from 26°C to 24°C), increase the fan speed, and increase the compressor operating frequency. This allows the air conditioner to quickly and effectively lower the classroom temperature, providing a comfortable teaching environment for teachers and students. In heating mode, the fuzzy logic algorithm also adjusts the air conditioner's heating output based on factors such as ambient temperature and occupancy density, ensuring that the classroom remains warm and comfortable in cold weather. Throughout the adjustment process, the device control module 115 continuously monitors the air conditioner's operating status and ambient temperature changes, dynamically adjusting the control strategy based on this feedback to ensure stable and efficient operation of the air conditioner, achieving the dual goals of energy conservation and comfort.

[0069] In another embodiment, the device control module 115 combines the lighting data from the environment monitoring module 112 and the occupancy distribution data from the occupancy monitoring module 113 to use a fuzzy logic algorithm to assess the actual lighting needs within the classroom. For example, in areas with high occupancy density and low ambient light (such as seating areas near the interior of the classroom), light brightness needs to be increased to ensure students can clearly engage in learning activities. In areas with low occupancy density or near windows with ample natural light, light brightness can be appropriately reduced to avoid energy waste. The device control module 115 uses a fuzzy logic algorithm to determine the light brightness adjustment range and on / off control strategy, and sends corresponding control instructions to the classroom lighting devices. Control instructions include adjusting the light brightness level (e.g., from 50% to 80%) or turning lights on / off in specific areas. For example, if the back row of seats in the classroom is far from a window and has insufficient ambient light, the device control module 115 can send a command to turn on the lights in that area and adjust the brightness to an appropriate level. On sunny days, the lights in the front row of seats near the window can be automatically turned off, fully utilizing natural light and reducing artificial lighting energy consumption. At the same time, the device control module 115 can also preset corresponding lighting control scene modes according to different scenarios of teaching activities (such as teaching mode, multimedia playback mode, examination mode, etc.), realize one-click switching of lighting status, meet diverse teaching needs, and improve the adaptability and flexibility of the teaching environment.

[0070] Furthermore, the device detection module 111, environmental monitoring module 112, personnel monitoring module 113, video monitoring module 114, and device control module 115 are respectively connected to the network layer 300 and the physical entity domain 120. Furthermore, the device detection module 111, environmental monitoring module 112, personnel monitoring module 113, video monitoring module 114, and device control module 115 within the perception control domain are tightly integrated and work collaboratively via standardized interface protocols. Collected data and analysis results are shared between the modules in real time. For example, the device energy consumption data from the device detection module 111 is combined with the temperature and humidity data from the environmental monitoring module 112 to provide the device control module 115 with more comprehensive environmental and equipment operation information, enabling more precise adjustment of operating parameters for equipment such as air conditioners. The personnel density and behavior trajectory data from the personnel monitoring module 113 complement the classroom facial expression recognition results from the video monitoring module 114, providing the teaching management service platform 222 with richer information on student learning status, helping teachers better understand their students and adjust their teaching strategies. The dust data and noise data from the environmental monitoring module 112 are integrated with the classroom image data from the video monitoring module 114 to generate a comprehensive classroom environment quality report for the application service domain 220, helping the school to comprehensively evaluate and improve the quality of the teaching environment.

[0071] Furthermore, the physical entity domain 120 includes a smart classroom 121, which is equipped with adjustable tables and chairs and an AR blackboard. The smart classroom 121 is used to provide multi-scenario teaching modes for classroom personnel. Among them, the adjustable tables and chairs are not only convenient for individual students to adjust, but also can quickly adapt to the needs of different teaching scenarios. In the traditional lecture-style teaching scenario, the tables and chairs can be adjusted to a standard height and neatly arranged facing the podium, which is convenient for teachers to conduct centralized lectures; in the group collaborative learning scenario, students can easily adjust the height of the tables and chairs, and arrange the tables and chairs into a circular or square layout required for group discussions, promoting communication and interaction between students; in teaching scenarios such as project practice and experimental operations that require standing operation or free movement, the tables and chairs can be adjusted to the highest height or temporarily stored to one side to provide students with sufficient operating space and activity venues. By flexibly adjusting the height and layout of tables and chairs, the smart classroom 121 can meet the needs of diverse teaching modes, stimulate students' learning enthusiasm and creativity, and improve classroom teaching effectiveness. The AR blackboard is also suitable for a variety of teaching scenarios, such as experimental simulations in science courses (using AR technology to present virtual experimental scenes on the blackboard, allowing students to visually observe experimental phenomena and operational processes) and the reproduction of historical and cultural sites in liberal arts courses (using AR blackboards to display 3D models of historical buildings and artifacts, allowing students to experience the historical and cultural atmosphere firsthand). Furthermore, the AR blackboard is integrated with the school's teaching resource management system, allowing teachers to easily download a rich library of AR teaching resources from the cloud and customize and edit them based on teaching progress and student needs. For example, teachers can search the resource library for AR teaching materials, virtual experimental cases, and other resources related to the course topic. After simple modification, they can apply them to their own classroom teaching, enriching the teaching content, expanding the breadth and depth of teaching resources, and providing students with a more vivid, interesting, and efficient classroom teaching experience.

[0072] Furthermore, physical entity domain 120 also includes teaching equipment 122, which includes IoT printers and holographic projectors. IoT printers are widely used in teaching activities. For example, teachers can use them to promptly print exercises, test papers, and learning materials needed for classroom distribution to meet students' learning needs. Students can also, through specific authorization, independently print their own learning works and research reports when needed, thereby enhancing their learning autonomy. In exam scenarios, IoT printers can quickly and accurately print test papers, supporting large-scale exam organization. At the same time, schools can use the IoT platform to uniformly manage and schedule printers across the entire school, optimize the allocation of printing resources, reduce school operating costs, improve the responsiveness and quality of teaching support services, and provide efficient, stable, and secure printing solutions for daily teaching activities.

[0073] It should also be noted that the holographic projector is deeply integrated with other devices in the smart classroom 121 (such as the AR blackboard, adjustable tables and chairs, etc.), as well as the systems in the perception and control domain, achieving a comprehensive expansion of the teaching space and an intelligent integration of the teaching environment. For example, the holographic projector can work in conjunction with the AR blackboard to project holographic images onto the blackboard for interactive display with AR teaching content, enriching the classroom visual experience. The adjustable tables and chairs can be flexibly arranged and adjusted according to the needs of holographic teaching activities, providing students with ample observation space and interactive areas. Furthermore, the holographic projector can automatically adjust the projection position, size, and angle of the holographic image based on classroom space data (such as classroom dimensions and occupancy distribution) provided by the environmental monitoring module 112 in the perception and control domain and instructions sent by the control module 115 of the teaching equipment 122 to ensure optimal viewing of the holographic image. This creates an immersive and intelligent teaching environment for teachers and students, breaking the spatial limitations of traditional classrooms, opening up new teaching models and learning experiences, and promoting innovation and development in school education and teaching.

[0074] Furthermore, the physical entity domain 120 also includes a storage device 123, which is used to store data and realize cross-campus data collaboration. Among them, the storage device 123 adopts a distributed storage architecture to store data in a dispersed manner on multiple storage nodes. These storage nodes can be distributed in different campuses or different physical locations of the school. Each storage node has independent storage capacity and data processing capabilities, and forms a unified storage resource pool through a high-speed network connection. The advantage of a distributed storage architecture is that it can improve the reliability, availability and scalability of data storage. For example, when a storage node fails, other storage nodes can quickly take over its storage tasks to ensure uninterrupted access to data and continuity of storage services; at the same time, as the amount of data in the school grows, the storage system can be linearly expanded by simply adding new storage nodes to meet the school's growing storage needs.

[0075] It should also be noted that the storage device 123 realizes data sharing and real-time synchronization between multiple campuses through the cross-campus data collaboration function. Different campuses of the school can be connected to a unified storage platform via a high-speed wide area network. Teachers, students and administrators of each campus can access and share data resources stored in the cloud within the scope of authorization. For example, teachers at the school headquarters can upload their own teaching courseware, teaching videos and other resources to the shared data area of ​​the storage device 123. Teachers at branch schools can obtain these resources in a timely manner and make personalized adjustments and applications based on the actual situation of the branch students, realizing the cross-campus sharing and dissemination of high-quality teaching resources and promoting the balanced development of the school's overall teaching quality. At the same time, the storage device 123 will automatically synchronize cross-campus data access and modification operations in real time to ensure data consistency and accuracy. For example, when a teacher at a branch school updates a student's academic performance data, the storage device 123 will immediately synchronize the updated data to the storage node at the headquarters, ensuring that the school management and teachers at the headquarters can obtain the latest student performance information in a timely manner, providing timely and accurate data support for teaching management and decision-making.

[0076] Furthermore, the adjustable desks and chairs, AR blackboard, teaching equipment 122 (IoT printers, holographic projectors), and storage devices 123 within smart classroom 121 communicate with the perception control domain via the campus's wired network (e.g., Ethernet) and wireless network (e.g., Wi-Fi 6 or 5G campus private network). Wired network connections are primarily used for devices with high bandwidth and stringent data transmission requirements (e.g., holographic projectors and storage devices 123), ensuring high-speed and stable data transmission. Wireless network connections facilitate flexible classroom device placement and mobility (e.g., IoT printers can be placed in various locations for easy access by teachers and students), while reducing cabling costs and construction complexity. For example, the AR blackboard connects to the perception control domain via a wired network to ensure stable transmission of high-definition teaching content and real-time interactive data. Meanwhile, the IoT printer maintains communication with the perception control domain via a wireless network, allowing teachers to send print jobs from anywhere in the classroom, and the printers to receive and execute them promptly.

[0077] It's worth noting that devices in the physical entity domain 120 proactively report device status data to the perception control domain periodically (e.g., every minute) or when their status changes (e.g., adjusting the height of adjustable tables and chairs, replacing ink cartridges in IoT printers, etc.). After receiving the device status data, the perception control domain analyzes and processes it to determine whether the device is operating properly. If a device anomaly is detected (e.g., a holographic projector fault alarm), the perception control domain immediately sends fault handling instructions to the corresponding device and simultaneously sends a device fault warning to the application service domain 220 and the administrator terminal 213, notifying relevant personnel to perform repairs. For example, if the AR blackboard's built-in camera malfunctions, the AR blackboard immediately sends fault status data to the perception control domain. After analysis and confirmation, the perception control domain sends an instruction to suspend the AR blackboard's teaching function to prevent the fault from escalating. It also sends detailed fault information and repair recommendations to the administrator terminal 213, ensuring timely and effective device maintenance.

[0078] At the same time, the perception control domain can also generate corresponding device control instructions based on the personalized teaching strategies, environmental control plans, and user operation instructions sent by the application service domain 220 (such as the teacher's instruction to turn on the holographic projector sent via the teacher terminal 211, or the student's instruction to print learning materials sent via the student terminal 212). These instructions are then sent to the corresponding devices in the physical entity domain 120 via the communication network. Upon receiving the control instructions, the devices in the physical entity domain 120 immediately perform the corresponding operations (such as the holographic projector projecting the specified teaching content, the IoT printer starting a print task, etc.) and, after completion, provide feedback to the perception control domain on the execution results (such as whether the device successfully executed the instruction or whether any errors occurred during execution). The perception control domain processes and records the feedback results. If the instruction execution fails, it will resend the instruction or perform other processing operations according to the preset fault handling strategy, and promptly provide feedback on the instruction execution status to the application service domain 220 and relevant users. For example, a teacher sends an instruction via the teacher terminal 211 to adjust the adjustable tables and chairs in the classroom to a group discussion mode. After receiving the instruction, the perception control domain converts it into table and chair control instructions and sends them to the corresponding devices. After receiving the command, the adjustable desk and chair automatically adjust their height and position, and upon completion, they send a successful feedback message to the perception control domain. The perception control domain then sends this feedback message to the teacher terminal 211, allowing the teacher to monitor the execution of the command in real time, ensuring that classroom teaching activities proceed smoothly as expected.

[0079] Devices in physical entity domain 120 and the perception control domain not only exchange control instructions and status data for a single device but also enable data collaboration and integrated applications across multiple devices. For example, teaching resource data in storage device 123 interacts with the resource management module of the perception control domain. Based on the teaching task schedule of teaching device 122 (such as a holographic projector or AR blackboard) and user resource requests, the perception control domain retrieves the corresponding teaching resources from storage device 123 and transmits them to teaching device 122 via the network for display and use. Simultaneously, the perception control domain provides feedback to storage device 123 on the resource usage of teaching device 122 (such as resource access frequency and usage duration). Storage device 123 uses this data to optimize resource storage strategies and caching mechanisms, improving resource access efficiency and availability. In the teaching scenario of the smart classroom 121, the layout adjustment data of the adjustable tables and chairs, the operating status data of the teaching equipment 122, and the teaching resource data of the storage device 123 together constitute a complete teaching environment information system. The perception control domain provides the application service domain 220 with comprehensive and accurate teaching environment and service quality evaluation data through the fusion analysis and collaborative processing of these multi-source data, assisting the school in making teaching decisions and management optimization, and realizing the overall function maximization and value optimization of the smart school Internet of Things system.

[0080] Furthermore, the network layer 300 includes a multi-protocol communication module, which is used for adaptive switching between multiple protocols. The module supports a variety of common communication protocols, including TCP / IP, UDP, MQTT (Message Queuing Telemetry Transport Protocol), CoAP (Constrained Application Protocol), HTTP / HTTPS, LoRaWAN (Long Range Wireless Communication Protocol), and ZigBee. During data transmission, the module dynamically adjusts the communication protocol used based on network conditions and transmission task requirements. For example, if a large-scale event on campus causes Wi-Fi signal congestion and bandwidth limitations, while the module is transmitting configuration update data for teaching device 122 via Wi-Fi using HTTP, the intelligent protocol selection algorithm detects a significant increase in network latency and packet loss. It immediately triggers a protocol switching process, switching the transmission protocol to a stable 4G LTE protocol or TCP / IP on the wired network, ensuring smooth data transmission and avoiding data transmission interruptions or errors caused by network fluctuations. The entire dynamic protocol switching process is transparent to users and devices and requires no human intervention, ensuring the continuity and reliability of data transmission in the smart school IoT system and improving the overall stability and robustness of the system.

[0081] Furthermore, the network layer 300 also includes a blockchain security module, which implements dynamic access control based on a zero-trust architecture and integrates a quantum key distribution unit. Within the network layer 300, the blockchain security module employs a zero-trust architecture to perform strict identity authentication and authorization for every user, device, and application attempting to access system resources (including devices, data, services, etc.), regardless of whether they are located on the campus internal network or external networks. This ensures that only legitimate and trusted entities are granted access rights. For example, when a teacher terminal 211 attempts to access the classroom teaching application platform 221 in the application service domain 220 to obtain virtual simulation experiment resources, the blockchain security module will request multiple identity credentials (such as username and password, biometric authentication information, dynamic password, etc.) from the teacher terminal 211 and perform real-time verification of these credentials. Furthermore, the blockchain security module will conduct a comprehensive assessment based on the terminal device's fingerprint information (such as the device MAC address, hardware signature code, etc.) and current network environment information (such as access location and time) to determine whether the access request complies with predefined trust policies.

[0082] Furthermore, based on a zero-trust architecture, the blockchain security module establishes a dynamic access control mechanism. It dynamically adjusts access rights and authorization levels based on real-time user behavior, device status, network environment, and changes in system security policies. For example, during normal teaching hours, teacher terminal 211 is granted full access to the classroom teaching application platform 221, including access to virtual simulation experiment resources and configuration of the classroom Q&A automatic response module. However, outside of class time or when teacher terminal 211's network access location changes (e.g., switching from the campus internal network to an off-campus public Wi-Fi network), the blockchain security module automatically tightens access rights, allowing teacher terminal 211 to perform only non-sensitive operations (such as viewing teaching notifications). Sensitive operations such as modifying teaching resources and controlling devices require additional, high-level authentication (e.g., using a one-time password generated by a quantum key distribution unit). This dynamic adjustment of access control policies effectively addresses the uncertainties of the network environment, minimizes security risks associated with static trust relationships, and ensures that system resources are always securely and controllably accessible.

[0083] Furthermore, the network layer 300 also includes edge computing nodes, which are used to pre-process classroom data from the perception control domain to reduce the bandwidth load on the core network. For example, various sensors and devices in the perception control domain (such as the laser dust detector and acoustic sensor in the environmental monitoring module 112, the millimeter-wave radar and infrared thermal imager in the personnel monitoring module 113, and the camera in the video surveillance module 114) generate massive amounts of raw data. Edge computing nodes, deployed at the edge of the network layer 300 (such as small servers in the smart classroom 121 or regional network aggregation points on campus), are able to receive this raw data in real time and perform preliminary analysis and filtering on it. For example, the laser dust detector in the environmental monitoring module 112 collects a large number of dust concentration data points per second. The edge computing nodes perform real-time statistical analysis on this data, calculating statistical indicators such as the average dust concentration value, maximum value, and minimum value per minute, and determine whether the current dust concentration exceeds the standard based on preset environmental quality thresholds. For normal data that does not exceed the standard, the node can perform appropriate data compression and aggregation processing to reduce the data volume before transmitting it to the application service domain 220 in the core network; for data that exceeds the standard, it will be immediately marked and prioritized to ensure that it can be transmitted to the application service domain 220 quickly and completely, so as to trigger environmental control measures in a timely manner.

[0084] Furthermore, by preprocessing classroom data in the perception control domain at edge computing nodes, the bandwidth load on the core network can be significantly reduced. For example, without edge computing nodes, all raw data in the perception control domain (such as high-definition video streams and high-frequency sensor data sampling points) must be transmitted directly to the application service domain 220 in the core network for processing. This consumes a significant amount of network bandwidth, especially during peak hours when multiple smart classrooms 121 are simultaneously teaching and multiple teaching devices 122 are operating simultaneously. This can easily lead to network congestion and data transmission delays, impacting the real-time and stability of the system. However, after edge computing nodes complete preliminary data processing and compression at the edge, only filtered and extracted key data (such as statistical indicators, feature information, and anomaly alerts) needs to be transmitted to the core network, significantly reducing data transmission volume. For example, after being processed by the edge computing node, the transmission bandwidth requirement of video surveillance data can be reduced from the original 1080p high-definition video stream (occupying approximately 5-10 Mbps of bandwidth) to a low-bandwidth mode for transmitting key frames and abnormal fragments (occupying approximately 1-2 Mbps of bandwidth), thereby releasing more core network bandwidth resources to ensure the smooth progress of other key businesses (such as the course schedule data synchronization of the teaching management service platform 222, the interactive teaching data transmission of the online learning service platform 224, etc.), and optimizing the allocation and utilization efficiency of the entire network resources.

[0085] Furthermore, the multi-protocol communication module, blockchain security module, and edge computing nodes communicate with the perception control domain and application service domain 220, respectively. The network layer 300 utilizes a layered communication architecture, with the multi-protocol communication module serving as the underlying foundational communication component. This module provides diverse communication links for data transmission between various devices in the perception control domain (e.g., the environmental monitoring module 112, the equipment detection module 111, the personnel monitoring module 113), the edge computing nodes, and the application service domain 220. Devices in the perception control domain select appropriate communication protocols based on their characteristics and the network environment they operate in (e.g., using the ZigBee protocol for short-range devices connected to the edge computing node deployed in the smart classroom 121, and using the 4G / 5G protocol for long-range devices connected directly to the multi-protocol communication module in the network layer 300) to establish communication connections with the network layer 300. Edge computing nodes, acting as mid-layer data processing units, receive raw data from the perception control domain. After completing data preprocessing, they transmit the processed data to the application service domain 220 via the communication link provided by the multi-protocol communication module in the protocol format required by the application service domain 220 (such as HTTP / HTTPS, MQTT, etc.). The blockchain security module runs throughout the entire network layer 300 communication architecture, providing security management and assurance for authentication requests for perception control domain devices accessing the network layer 300, encryption and decryption operations during data transmission, and sensitive data exchange between the application service domain 220 and the perception control domain. This ensures the security, integrity, and trustworthiness of data within the network layer 300 and during cross-layer transmission.

[0086] Taking the example of adjustable tables and chairs in smart classroom 121 sending device status data to application service domain 220, the sensors built into the adjustable tables and chairs first collect height adjustment status data and pressure sensor data, and transmit this raw data to the edge computing node deployed in smart classroom 121 according to the ZigBee protocol. After receiving the data, the edge computing node performs preliminary analysis and filtering, extracting key status indicators (such as the current height of the tables and chairs and the average sitting pressure distribution of the students), and encrypts the data (using the quantum key encryption algorithm provided by the blockchain security module). The edge computing node then selects an appropriate communication protocol (such as TCP / IP in a campus internal network environment) through the multi-protocol communication module and transmits the encrypted data to the device management service platform in application service domain 220. Upon receiving the data, application service domain 220 decrypts it using the decryption key provided by the blockchain security module. It then further analyzes and processes the device status data, such as assessing the usage of the tables and chairs and generating equipment maintenance recommendations. The processing results are then fed back to the teacher terminal 211 and the administrator terminal 213. At the same time, based on actual needs and data sensitivity, the application service domain 220 may use the blockchain security module to send back control instructions (such as instructions for adjusting the height of tables and chairs) for the adjustable tables and chairs to perform digital signature and authentication operations to ensure the legitimacy and security of the instructions. After receiving the instructions, the edge computing node forwards them to the adjustable tables and chairs through the ZigBee protocol to complete the control operation of the device.

[0087] It is worth noting that the multi-protocol communication module, blockchain security module, and edge computing nodes work together in network layer 300 to maximize data transmission efficiency and overall system performance while ensuring data transmission security. The multi-protocol communication module's adaptive switching function ensures fast and reliable data transmission under diverse network environments and device conditions. The blockchain security module's zero-trust architecture and quantum key distribution technology provide comprehensive, high-intensity security protection for data transmission, preventing data theft, tampering, and malicious attacks. The edge computing nodes' data preprocessing capabilities effectively reduce the burden on the core network and improve the real-time and targeted nature of data processing. This collaborative working mechanism enables the smart school IoT system to ensure the secure transmission and storage of critical information such as teaching data and device control instructions in complex and changing network environments, while also promptly responding to various data requests and control instructions in teaching activities. This provides efficient, stable, and secure network support for school teaching management and teaching practice, thereby improving the school's educational informatization level and the quality of teaching services.

[0088] Secondly, refer to Figure 2 As shown, Figure 2This is an optional flowchart of the service method of the smart classroom provided in an embodiment of the present application. The method applies a smart classroom Internet of Things system. The smart classroom Internet of Things system includes a perception layer, an application layer and a network layer. The perception layer includes a perception control domain and a physical entity domain. The perception control domain and the physical entity domain are communicated with each other. The application layer includes a user domain and an application service domain. The user domain and the application service domain are communicated with each other. The application service domain and the perception control domain are communicated with each other based on the network layer. The method may include but is not limited to steps S101 to S105.

[0089] Step S101: The user domain sends a target service request to the application service domain.

[0090] Step S102: The physical entity domain collects classroom data in real time and sends the classroom data to the perception control domain.

[0091] Step S103: The perception control domain receives the classroom data and generates a classroom analysis result based on the classroom data, and sends the classroom analysis result to the application service domain.

[0092] Step S104: The application service domain receives the classroom analysis results and the target service request, and verifies the request authority of the target service request. If the request authority is passed, it dynamically generates personalized teaching strategies and / or environmental control plans based on the target service request and the classroom analysis results, and sends personalized teaching strategies and / or environmental control plans to the perception control domain.

[0093] Step S105: The perception control domain generates device operation instructions according to the personalized teaching strategy and / or environmental control scheme, and sends the device operation instructions to the physical entity domain, so that the physical entity domain performs the control task of the corresponding device according to the device operation instructions.

[0094] It should be noted that in steps S101 to S105 of this application, a layered, collaborative intelligent IoT architecture between the perception, network, and application layers achieves a closed-loop optimization of "data perception - intelligent decision-making - precise execution" in teaching scenarios, significantly improving the effectiveness of the teaching system: the physical entity domain collects multi-dimensional classroom data (such as environmental parameters and device status) in real time and performs intelligent analysis through the perception and control domain, ensuring low latency and high reliability of data processing; the application service domain ensures service security based on a permission verification mechanism, combines dynamic generation algorithms to accurately match personalized strategies with environmental control solutions, and then reversely controls physical devices through the perception and control domain, forming an adaptive closed-loop adjustment loop for the teaching environment. At the system architecture level, the layered architecture design not only ensures the modular expansion of each domain's functions, but also enables efficient cross-domain data flow through the network layer, effectively adapting to the architecture of the smart classroom IoT application system, providing effective guidance for the structural design of IoT application systems in different smart classrooms, effectively promoting the large-scale application of the smart classroom IoT, and promoting the formation of a smart classroom IoT application ecosystem.

[0095] The present application also provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the smart classroom service method of the second embodiment. The electronic device can be any smart terminal, including a mobile phone, a tablet computer, and an in-vehicle computer.

[0096] See also Figure 3 , Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present application, the electronic device comprising: The processor 301 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the service method of the smart classroom of the second embodiment provided in the embodiment of the present application; The memory 302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 302, and the processor 301 calls and executes the service method of the smart classroom of the second embodiment provided in the embodiments of this application. Input / output interface 303, used to implement information input and output; Communication interface 304, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.); bus 305 , which transmits information between the various components of the device (e.g., processor 301 , memory 302 , input / output interface 303 , and communication interface 303 ); The processor 301 , the memory 302 , the input / output interface 303 and the communication interface 304 are connected to each other in communication within the device via the bus 305 .

[0097] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the service method of the smart classroom in the above-mentioned second aspect of the embodiment provided in the embodiment of the present application is provided.

[0098] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0099] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0100] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0101] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0102] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0103] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0104] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0105] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the above units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0106] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0108] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store programs.

[0109] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A smart classroom Internet of Things system, characterized by: include: A perception layer, the perception layer including a perception control domain and a physical entity domain, wherein the perception control domain and the physical entity domain are communicatively connected; An application layer, the application layer including a user domain and an application service domain, wherein the user domain and the application service domain are communicatively connected; Network layer, the application service domain and the perception control domain are connected based on the network layer communication; Wherein, the user domain is used to send a target service request to the application service domain; The physical entity domain is used to collect classroom data of the classroom in real time and send the classroom data to the perception control domain; The perception control domain is used to receive the classroom data and generate a classroom analysis result based on the classroom data, and send the classroom analysis result to the application service domain; The application service domain is used to receive the classroom analysis results and the target service request, and verify the request authority of the target service request. If the request authority is passed, the personalized teaching strategy and / or environmental regulation plan is dynamically generated according to the target service request and the classroom analysis results, and the personalized teaching strategy and / or environmental regulation plan is sent to the perception control domain; The perception control domain is also used to generate device operation instructions based on the personalized teaching strategy and / or environmental control plan, and send the device operation instructions to the physical entity domain so that the physical entity domain performs the control tasks of the corresponding device according to the device operation instructions.

2. The smart classroom Internet of Things system according to claim 1, characterized in that: The user domain includes: Teacher terminal, which is used to provide teachers with integrated teaching resource management tools and a real-time classroom interactive interface; Student terminals, which are used to provide students with personalized learning path planning and interactive question-answering functions; An administrator terminal, which is used to provide an overview of the device status within the physical entity domain and issue abnormal alarms; The teacher terminal, the student terminal and the administrator terminal are respectively connected to the application service domain through identity token authentication.

3. The smart classroom Internet of Things system according to claim 1, characterized in that: The user domain also includes: An evaluation agency terminal, which is used to conduct a multi-dimensional analysis of the teaching quality of the classroom; A terminal of the competent department, which is used to generate visual reports on classroom teaching data; The evaluation agency terminal and the competent department terminal are respectively connected to the application service domain through identity token authentication.

4. The smart classroom Internet of Things system according to claim 1, characterized in that: The application service domain includes: A classroom teaching application platform, which is used to integrate a virtual simulation experiment module and a classroom question-and-answer automatic response module based on deep learning; A teaching management service platform, which uses blockchain technology to implement tamper-proof storage of course schedules and performance records; A teaching supervision and evaluation service platform, which is used to analyze teaching evaluation texts using natural language processing technology and generate improvement suggestions; An online learning service platform with a built-in adaptive learning engine for adjusting course difficulty based on student behavior data; A teaching environment monitoring platform, which uses digital twin technology to map classroom environments in real time and predict equipment failures; The classroom teaching application platform, the teaching management service platform, the teaching supervision and evaluation service platform, the online learning service platform and the teaching environment monitoring platform are respectively connected to the network layer through standardized interface protocols.

5. The smart classroom Internet of Things system according to claim 1, characterized in that: The perception control domain includes: An equipment detection module, wherein the equipment detection module is configured with a current sensor and a vibration sensor, and is used to monitor the energy consumption and mechanical status of the equipment in real time through the current sensor and the vibration sensor; An environmental monitoring module, which is equipped with a laser dust detector and an acoustic sensor. The environmental monitoring module is used to collect dust data and temperature and humidity data inside and outside the classroom through the laser dust detector, and to collect noise data inside and outside the classroom through the acoustic sensor; A personnel monitoring module, which is equipped with a millimeter-wave radar and an infrared thermal imager. The module is used to count the density of people in the classroom using the millimeter-wave radar and to detect the behavior of people in the classroom using the infrared thermal imager. A video monitoring module, which is equipped with a camera and is used to recognize facial expressions of classroom participants and provide early warning of abnormal behavior. An equipment control module, which automatically adjusts operating parameters of classroom equipment based on a fuzzy logic algorithm, including air conditioning, lighting, and projection equipment; The equipment detection module, the environment monitoring module, the personnel monitoring module, the video monitoring module, and the equipment control module are respectively communicatively connected to the network layer and the physical entity domain.

6. The smart classroom Internet of Things system according to claim 1, characterized in that: The physical entity domain includes: Smart classrooms, equipped with adjustable tables and chairs and AR blackboards, provide multi-scenario teaching modes for classroom participants; Teaching equipment, including an IoT printer and a holographic projector; Storage devices, which are used to store data and enable cross-campus data collaboration; The smart classroom, the teaching equipment and the storage device are respectively communicatively connected to the perception control domain.

7. The smart classroom Internet of Things system according to claim 1, characterized in that: The network layer includes: A multi-protocol communication module, wherein the multi-protocol communication module is used to perform adaptive switching of multiple protocols; A blockchain security module, which is used to implement dynamic access control based on a zero-trust architecture and integrate a quantum key distribution unit; An edge computing node, wherein the edge computing node is used to pre-process the classroom data of the perception control domain to reduce the bandwidth load of the core network; The multi-protocol communication module, the blockchain security module and the edge computing node are respectively communicatively connected to the perception control domain and the application service domain.

8. A smart classroom service method, using a smart classroom Internet of Things system, characterized in that: The smart classroom Internet of Things system includes a perception layer, an application layer, and a network layer. The perception layer includes a perception control domain and a physical entity domain. The perception control domain and the physical entity domain are communicatively connected. The application layer includes a user domain and an application service domain. The user domain and the application service domain are communicatively connected. The application service domain and the perception control domain are communicatively connected based on the network layer. The method includes: The user domain sends a target service request to the application service domain; The physical entity domain collects classroom data of the classroom in real time and sends the classroom data to the perception control domain; The perception control domain receives the classroom data and generates a classroom analysis result based on the classroom data, and sends the classroom analysis result to the application service domain; The application service domain receives the classroom analysis result and the target service request, and verifies the request authority of the target service request. If the request authority is passed, the personalized teaching strategy and / or environmental regulation plan is dynamically generated according to the target service request and the classroom analysis result, and the personalized teaching strategy and / or environmental regulation plan is sent to the perception control domain; The perception control domain generates device operation instructions according to the personalized teaching strategy and / or environmental control scheme, and sends the device operation instructions to the physical entity domain so that the physical entity domain performs the control tasks of the corresponding device according to the device operation instructions.

9. An electronic device, characterized in that: It includes a memory and a processor, the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the service method of the smart classroom as described in claim 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program executable by a processor, and when the program executable by the processor is executed by the processor, the service method of the smart classroom as claimed in claim 8 is implemented.

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