Classroom equipment control system under three-dimensional map based on digital twinning and use method thereof

Abstract

The invention discloses a classroom equipment control system under a three-dimensional map based on digital twinning and a use method thereof, the classroom equipment control system comprises an Internet of Things module, the output end of the Internet of Things module is bidirectionally and electrically connected with a sensor module and classroom power equipment, and the output end of the Internet of Things module is bidirectionally and electrically connected with a codec. According to the invention, the sensor module is bidirectionally and electrically connected with the classroom power equipment through the Internet of Things module, intelligent control of the classroom equipment is realized, and the system can obtain environment data (such as noise, illumination and temperature) in the classroom and the operation state of the equipment in real time through the connection mode, so that automatic adjustment is carried out according to actual requirements; through the introduction of the codec, the accuracy and reliability of data transmission are ensured, control errors caused by data errors or loss are avoided, and convenience is provided for data processing and storage through the bidirectional electric connection of the control circuit, the radio frequency circuit and the memory.

Description

Technical Field

[0001] The present invention relates to the technical field of classroom equipment control, and specifically to a classroom equipment control system under a three-dimensional map based on digital twins and a method of using the same. Background Art

[0002] Classroom equipment control is an indispensable part of the modern teaching environment. It covers the operation and management of a variety of teaching equipment. Classroom equipment control refers to the remote or local operation and management of various teaching equipment in the classroom through a specific system or device. These devices include but are not limited to projectors, interactive whiteboards, sound systems, lights, air conditioners, curtains, etc.

[0003] For example, the patent document with application number: 201720279994.4, the patent name is: A multimedia classroom equipment control system, including a controller and a temperature sensor, a human body heat release sensor, a light intensity sensor and a smoke concentration sensor arranged in the multimedia classroom, the three input ends of the controller are respectively connected to the output ends of the human body heat release sensor, the light intensity sensor and the smoke concentration sensor, and the power input ends of the human body heat release sensor, the light intensity sensor and the smoke concentration sensor are connected to the output end of the power module through a voltage stabilizer and a transformer in turn, and the output end of the power module is also connected to the power input end of the controller and the temperature sensor in turn through a voltage stabilizer and a transformer. The multimedia classroom equipment control system can intelligently control the surveillance camera, LED lighting, sound and light alarm and air conditioner in the multimedia classroom, so that it has the effect of intelligent control, thereby providing convenience for users.

[0004] However, the existing control method is relatively simple, mainly relying on offline controllers for independent control. There are many classrooms in the school, and the control requirements of classrooms on different floors are different, which affects the operational control efficiency of equipment inside the classroom.

[0005] Therefore, it is necessary to design and transform the classroom equipment control system and its usage method based on the three-dimensional map of the digital twin. Summary of the invention

[0006] In order to solve the problems raised in the above background technology, the purpose of the present invention is to provide a classroom equipment control system based on a three-dimensional map of digital twins and a method of using it, which has the advantage of remote centralized control and solves the problem that the existing control method is relatively single and mainly relies on offline controllers for independent control. However, there are a large number of classrooms in the school, and the control requirements of classrooms on different floors are different, which affects the operation and control efficiency of equipment inside the classroom.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a classroom equipment control system based on a three-dimensional map of digital twins and a method of using the same, including an Internet of Things module;

[0008] The output end of the Internet of Things module is bidirectionally electrically connected to the sensor module and the classroom power equipment, the output end of the Internet of Things module is bidirectionally electrically connected to the codec, the output end of the codec is bidirectionally electrically connected to the control circuit, the output end of the control circuit is bidirectionally electrically connected to the radio frequency circuit and the memory, the output end of the memory is bidirectionally electrically connected to the input end of the codec, the output end of the radio frequency circuit is bidirectionally electrically connected to the data transceiver module, and the output end of the data transceiver module is bidirectionally electrically connected to the cloud server.

[0009] As a preferred embodiment of the present invention, the output end of the cloud server is bidirectionally electrically connected to a data reading and writing module, the input end of the cloud server is bidirectionally electrically connected to a weather data import module, the output end of the data reading and writing module is bidirectionally electrically connected to a classroom model establishment system, the output end of the classroom model establishment system is bidirectionally electrically connected to a multidimensional convolutional neural network, the output end of the multidimensional convolutional neural network is bidirectionally electrically connected to a self-noise reduction encoder, the output end of the self-noise reduction encoder can generate a three-dimensional classroom model to be corrected, the self-noise reduction encoder can import the three-dimensional classroom model to be corrected into a prediction result verification module, the output end of the prediction result verification module can generate a classroom environment prediction model, the output end of the classroom environment prediction model is bidirectionally electrically connected to a result output module, and the output end of the result output module is bidirectionally electrically connected to the input end of the cloud server.

[0010] As a preferred embodiment of the present invention, the output end of the data reading and writing module is bidirectionally electrically connected to a backup storage server, the output end of the backup storage server is bidirectionally electrically connected to a feature engineering processing module, and the output end of the feature engineering processing module is bidirectionally electrically connected to the input ends of the classroom environment prediction model and the prediction result verification module respectively.

[0011] As a preferred embodiment of the present invention, the prediction result verification module consists of a data correction unit and a calculation analysis unit, and the output end of the prediction result verification module is bidirectionally electrically connected to the input end of the multidimensional convolutional neural network.

[0012] As a preferred embodiment of the present invention, the classroom model building system is composed of a physical environment engine, a logic model building module and a concept model building module.

[0013] As a preferred embodiment of the present invention, the sensor module consists of a noise sensor, a light sensor and a temperature sensor, and the classroom power equipment consists of a temperature-controlled air conditioner, an electric blackout curtain, an LED lighting lamp and a surveillance camera.

[0014] As a preferred embodiment of the present invention, the data transceiver module consists of a wireless connection module and a Bluetooth connection module.

[0015] A classroom equipment control system based on a three-dimensional map of digital twins and a method of using the same include the following steps:

[0016] Initialize and configure the system, configure the Internet of Things module to ensure that it can communicate normally with the sensor module and classroom power equipment, start and configure the server to ensure that it can receive, store and process data, initialize the data read and write interface, prepare for data read and write operations, initialize the physical environment engine, logical model building module and conceptual model building module, and prepare to build a three-dimensional classroom model;

[0017] Data collection and transmission: Noise sensors, light sensors, and temperature sensors collect environmental data in the classroom in real time. The collected data is transmitted to the codec through the IoT module. The codec encodes and decodes the data to ensure the accuracy and integrity of the data.

[0018] Data processing and storage: the data processed by the codec is sent to the control circuit, which operates the RF circuit and the memory according to the preset logic. The RF circuit sends the data to the data transceiver module, which sends the data to the cloud server through the wireless connection module and the Bluetooth connection module. The cloud server receives and stores the data.

[0019] Three-dimensional model construction and prediction: After the cloud server stores the data, it provides it to the data reading and writing module for reading and writing operations. The data reading and writing module passes the data to the classroom model building system. The classroom model building system uses the physical environment engine, the logical model building module and the conceptual model building module to build a three-dimensional model of the classroom. The constructed three-dimensional classroom model is input into the multi-dimensional convolutional neural network for feature extraction and pattern recognition. The self-denoising encoder performs noise reduction processing on the three-dimensional classroom model to generate a model to be corrected. The model to be corrected is imported into the prediction result verification module. The prediction result verification module verifies and optimizes the model through the data correction unit and the calculation analysis unit to generate a classroom environment prediction model.

[0020] Result output and feedback, the results of the classroom environment prediction model are output to the result output module, and the result output module returns the results to the cloud server. The cloud server stores and backs up data based on the prediction results through the data reading and writing module and the backup storage server. The cloud server feeds back the prediction results and correction suggestions to the classroom equipment control system. According to the instructions of the cloud server, the control circuit remotely or locally controls the classroom power equipment through the radio frequency circuit and memory to optimize the classroom environment.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. The present invention realizes intelligent control of classroom equipment by setting an Internet of Things module to connect the sensor module to the classroom power equipment in a bidirectional manner. This connection method enables the system to obtain real-time environmental data in the classroom (such as noise, light, temperature, etc.) and the operating status of the equipment, so as to automatically adjust according to actual needs. The introduction of the codec ensures the accuracy and reliability of data transmission and avoids control errors caused by data errors or loss. The bidirectional electrical connection between the control circuit and the radio frequency circuit and the memory facilitates data processing and storage, and also provides flexible choices for remote control and local control.

[0023] 2. The present invention introduces a cloud server, so that the system can remotely store and process a large amount of data, improving the efficiency and accuracy of data processing. The weather data import module provides an external data source for the system, so that the system can more comprehensively consider the impact of environmental factors on the classroom environment, thereby making more accurate predictions and controls. The combined use of the classroom model building system, multidimensional convolutional neural network and self-noise reduction encoder enables the system to construct an accurate three-dimensional classroom model, and perform noise reduction and optimization processing on the model, thereby improving the accuracy and practicality of the model.

[0024] 3. The present invention provides the system with data backup and recovery functions through the use of a backup storage server, thereby ensuring the security and integrity of the data. The introduction of the feature engineering processing module enables the system to pre-process and extract features of the data, thereby improving the quality and availability of the data. The bidirectional electrical connection between the feature engineering processing module and the classroom environment prediction model and the prediction result verification module provides rich data support for the training and verification of the model, thereby improving the accuracy and generalization ability of the model.

[0025] 4. The prediction result verification module of the present invention is composed of a data correction unit and a calculation analysis unit, so that the system can perform detailed analysis and correction on the prediction results, thereby improving the accuracy and reliability of the prediction results. The bidirectional electrical connection between the prediction result verification module and the multidimensional convolutional neural network provides a feedback mechanism for the iteration and optimization of the model, so that the system can continuously learn and improve.

[0026] 5. The classroom model building system of the present invention is composed of a physical environment engine, a logical model building module and a conceptual model building module. This structure enables the system to comprehensively and systematically build a three-dimensional model of the classroom, providing a solid foundation for subsequent prediction and control.

[0027] 6. The sensor module of the present invention is composed of a noise sensor, a light sensor and a temperature sensor. This combination enables the system to comprehensively monitor the environmental data in the classroom and provide rich information support for the formulation of control strategies. The classroom power equipment is composed of a temperature-controlled air conditioner, an electric blackout curtain, an LED lighting fixture and a surveillance camera. This configuration enables the system to fully adjust and control the classroom environment according to actual conditions.

[0028] 7. The data transceiver module of the present invention is composed of a wireless connection module and a Bluetooth connection module, so that the system can flexibly select the communication mode, thereby improving the flexibility and reliability of data transmission. At the same time, the wireless connection and Bluetooth connection also provide convenience for the remote control and local control of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] like Figure 1 As shown, the present invention provides a classroom equipment control system based on a three-dimensional map of digital twins and a method of using the same, including an Internet of Things module; The output end of the Internet of Things module is bidirectionally electrically connected to the sensor module and the classroom power equipment, the output end of the Internet of Things module is bidirectionally electrically connected to the codec, the output end of the codec is bidirectionally electrically connected to the control circuit, the output end of the control circuit is bidirectionally electrically connected to the radio frequency circuit and the memory, the output end of the memory is bidirectionally electrically connected to the input end of the codec, the output end of the radio frequency circuit is bidirectionally electrically connected to the data transceiver module, and the output end of the data transceiver module is bidirectionally electrically connected to the cloud server.

[0032] refer to Figure 1The output end of the cloud server is bidirectionally electrically connected to a data reading and writing module, the input end of the cloud server is bidirectionally electrically connected to a weather data import module, the output end of the data reading and writing module is bidirectionally electrically connected to a classroom model establishment system, the output end of the classroom model establishment system is bidirectionally electrically connected to a multidimensional convolutional neural network, the output end of the multidimensional convolutional neural network is bidirectionally electrically connected to a self-noise reduction encoder, the output end of the self-noise reduction encoder can generate a three-dimensional classroom model to be corrected, the self-noise reduction encoder can import the three-dimensional classroom model to be corrected into the prediction result verification module, the output end of the prediction result verification module can generate a classroom environment prediction model, the output end of the classroom environment prediction model is bidirectionally electrically connected to a result output module, and the output end of the result output module is bidirectionally electrically connected to the input end of the cloud server.

[0033] As a technical optimization solution of the present invention, through the introduction of cloud servers, the system can remotely store and process large amounts of data, thereby improving the efficiency and accuracy of data processing. The weather data import module provides the system with an external data source, enabling the system to more comprehensively consider the impact of environmental factors on the classroom environment, thereby making more accurate predictions and controls. The combined use of the classroom model building system, multidimensional convolutional neural network and self-denoising encoder enables the system to construct an accurate three-dimensional classroom model, and perform denoising and optimization processing on the model, thereby improving the accuracy and practicality of the model.

[0034] refer to Figure 1 The output end of the data reading and writing module is bidirectionally electrically connected to the backup storage server, the output end of the backup storage server is bidirectionally electrically connected to the feature engineering processing module, and the output end of the feature engineering processing module is bidirectionally electrically connected to the input end of the classroom environment prediction model and the prediction result verification module respectively.

[0035] As a technical optimization solution of the present invention, the use of a backup storage server provides the system with data backup and recovery functions, ensuring the security and integrity of the data. The introduction of the feature engineering processing module enables the system to pre-process and extract features of the data, thereby improving the quality and availability of the data. The bidirectional electrical connection between the feature engineering processing module and the classroom environment prediction model and the prediction result verification module provides rich data support for the training and verification of the model, thereby improving the accuracy and generalization ability of the model.

[0036] refer to Figure 1 ,The prediction result verification module is composed of a data correction unit and a calculation and analysis unit, and the output end of the prediction result verification module is bidirectionally electrically connected with the input end of the multi-dimensional convolutional neural network.

[0037] As a technical optimization solution of the present invention, the prediction result verification module is composed of a data correction unit and a calculation analysis unit, so that the system can perform detailed analysis and correction on the prediction results, thereby improving the accuracy and reliability of the prediction results. The bidirectional electrical connection between the prediction result verification module and the multidimensional convolutional neural network provides a feedback mechanism for the iteration and optimization of the model, so that the system can continuously learn and improve.

[0038] refer to Figure 1 ,The classroom model building system is composed of the physical environment engine, ,logical model building module and conceptual model building module.

[0039] As a technical optimization solution of the present invention, the classroom model building system consists of a physical environment engine, a logical model building module and a conceptual model building module. This structure enables the system to comprehensively and systematically construct a three-dimensional model of the classroom, providing a solid foundation for subsequent prediction and control.

[0040] refer to Figure 1 The sensor module consists of noise sensors, light sensors and temperature sensors, and the classroom power equipment consists of temperature-controlled air conditioners, electric blackout curtains, LED lighting and surveillance cameras.

[0041] As a technical optimization solution of the present invention, the sensor module is composed of a noise sensor, a light sensor and a temperature sensor. This combination enables the system to comprehensively monitor the environmental data in the classroom and provide rich information support for the formulation of control strategies. The classroom power equipment is composed of a temperature-controlled air conditioner, an electric blackout curtain, an LED lighting fixture and a surveillance camera. This configuration enables the system to comprehensively adjust and control the classroom environment according to actual conditions.

[0042] refer to Figure 1 ,The data transceiver module is composed of a wireless connection module and a Bluetooth connection module.

[0043] As a technical optimization solution of the present invention, the data transceiver module is composed of a wireless connection module and a Bluetooth connection module, so that the system can flexibly select the communication method, thereby improving the flexibility and reliability of data transmission. At the same time, the wireless connection and Bluetooth connection also provide convenience for the remote control and local control of the system.

[0044] refer to Figure 1 , a classroom equipment control system based on a three-dimensional map of digital twins and a method of using the same, comprising the following steps:

[0045] Initialize and configure the system, configure the Internet of Things module to ensure that it can communicate normally with the sensor module and classroom power equipment, start and configure the server to ensure that it can receive, store and process data, initialize the data read and write interface, prepare for data read and write operations, initialize the physical environment engine, logical model building module and conceptual model building module, and prepare to build a three-dimensional classroom model;

[0046] Data collection and transmission: Noise sensors, light sensors, and temperature sensors collect environmental data in the classroom in real time. The collected data is transmitted to the codec through the IoT module. The codec encodes and decodes the data to ensure the accuracy and integrity of the data.

[0047] Data processing and storage: the data processed by the codec is sent to the control circuit, which operates the RF circuit and the memory according to the preset logic. The RF circuit sends the data to the data transceiver module, which sends the data to the cloud server through the wireless connection module and the Bluetooth connection module. The cloud server receives and stores the data.

[0048] Three-dimensional model construction and prediction: After the cloud server stores the data, it provides it to the data reading and writing module for reading and writing operations. The data reading and writing module passes the data to the classroom model building system. The classroom model building system uses the physical environment engine, the logical model building module and the conceptual model building module to build a three-dimensional model of the classroom. The constructed three-dimensional classroom model is input into the multi-dimensional convolutional neural network for feature extraction and pattern recognition. The self-denoising encoder performs noise reduction processing on the three-dimensional classroom model to generate a model to be corrected. The model to be corrected is imported into the prediction result verification module. The prediction result verification module verifies and optimizes the model through the data correction unit and the calculation analysis unit to generate a classroom environment prediction model.

[0049] Result output and feedback, the results of the classroom environment prediction model are output to the result output module, and the result output module returns the results to the cloud server. The cloud server stores and backs up data based on the prediction results through the data reading and writing module and the backup storage server. The cloud server feeds back the prediction results and correction suggestions to the classroom equipment control system. According to the instructions of the cloud server, the control circuit remotely or locally controls the classroom power equipment through the radio frequency circuit and memory to optimize the classroom environment.

[0050] The working principle and use process of the present invention are as follows: the system is first initialized, and the connection and parameter settings of hardware devices such as the Internet of Things module, sensor module, and classroom power equipment are configured. Software modules such as the cloud server, data reading and writing module, and classroom model establishment system are also initialized and ready to receive and process data. The sensor module (including noise sensor, light sensor, and temperature sensor) collects environmental data in the classroom in real time. These data are transmitted to the codec through the Internet of Things module for encoding and decoding to ensure the accuracy and integrity of the data. The data processed by the codec is sent to the control circuit. The control circuit operates the radio frequency circuit and the memory according to the preset logic. The radio frequency circuit sends the data to the data transceiver module, which sends the data to the cloud server through the wireless connection module and the Bluetooth connection module. The cloud server receives and stores the data, and provides the data to the data reading and writing module for reading and writing operations. The data reading and writing module passes the data to the classroom model establishment system. The system uses the physical environment The three-dimensional model of the classroom is constructed by the environment engine, the logical model building module and the conceptual model building module. The three-dimensional classroom model generated by the classroom model building system is input into the multidimensional convolutional neural network for feature extraction and pattern recognition. The self-denoising encoder performs noise reduction on the three-dimensional classroom model to generate a model to be corrected. The model to be corrected is imported into the prediction result verification module. The module verifies and optimizes the model through the data correction unit and the calculation analysis unit to generate a classroom environment prediction model. The result of the classroom environment prediction model is output to the result output module, which returns the result to the cloud server. The cloud server stores and backs up data through the data reading and writing module and the backup storage server based on the prediction results. At the same time, the cloud server can also feed back the prediction results and correction suggestions to the classroom equipment control system to optimize the classroom environment. According to the instructions of the cloud server, the control circuit remotely or locally controls the classroom power equipment (such as temperature control air conditioners, electric blackout curtains, LED lighting and surveillance cameras) through the radio frequency circuit and memory.

[0051] To sum up: the classroom equipment control system based on the three-dimensional map of digital twins and its use method, by setting the Internet of Things module to bidirectionally electrically connect the sensor module with the classroom power equipment, realizes the intelligent control of classroom equipment. This connection method enables the system to obtain real-time environmental data in the classroom (such as noise, light, temperature, etc.) and the operating status of the equipment, so as to automatically adjust according to actual needs. The introduction of the codec ensures the accuracy and reliability of data transmission and avoids control errors caused by data errors or loss. The bidirectional electrical connection between the control circuit and the radio frequency circuit and memory facilitates data processing and storage, and also provides flexible choices for remote control and local control.

[0052] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0053] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A classroom equipment control system based on a three-dimensional map of digital twins, including an Internet of Things module; Features: The output end of the Internet of Things module is bidirectionally electrically connected to the sensor module and the classroom power equipment, the output end of the Internet of Things module is bidirectionally electrically connected to the codec, the output end of the codec is bidirectionally electrically connected to the control circuit, the output end of the control circuit is bidirectionally electrically connected to the radio frequency circuit and the memory, the output end of the memory is bidirectionally electrically connected to the input end of the codec, the output end of the radio frequency circuit is bidirectionally electrically connected to the data transceiver module, and the output end of the data transceiver module is bidirectionally electrically connected to the cloud server.

2. According to claim 1, a classroom equipment control system based on a three-dimensional map of digital twins is characterized by: The output end of the cloud server is bidirectionally electrically connected to a data reading and writing module, the input end of the cloud server is bidirectionally electrically connected to a weather data importing module, the output end of the data reading and writing module is bidirectionally electrically connected to a classroom model establishing system, the output end of the classroom model establishing system is bidirectionally electrically connected to a multidimensional convolutional neural network, the output end of the multidimensional convolutional neural network is bidirectionally electrically connected to a self-noise reduction encoder, the output end of the self-noise reduction encoder can generate a three-dimensional classroom model to be corrected, the self-noise reduction encoder can import the three-dimensional classroom model to be corrected into a prediction result verification module, the output end of the prediction result verification module can generate a classroom environment prediction model, the output end of the classroom environment prediction model is bidirectionally electrically connected to a result output module, and the output end of the result output module is bidirectionally electrically connected to the input end of the cloud server.

3. According to claim 2, a classroom equipment control system based on a three-dimensional map of digital twins is characterized by: The output end of the data reading and writing module is bidirectionally electrically connected to a backup storage server, the output end of the backup storage server is bidirectionally electrically connected to a feature engineering processing module, and the output end of the feature engineering processing module is bidirectionally electrically connected to the input end of the classroom environment prediction model and the prediction result verification module respectively.

4. According to claim 3, a classroom equipment control system based on a three-dimensional map of digital twins is characterized in that: The prediction result verification module consists of a data correction unit and a calculation analysis unit, and the output end of the prediction result verification module is bidirectionally electrically connected to the input end of the multidimensional convolutional neural network.

5. According to claim 4, a classroom equipment control system based on a three-dimensional map of digital twins is characterized in that: The classroom model building system consists of a physical environment engine, a logic model building module and a concept model building module.

6. According to claim 5, a classroom equipment control system based on a three-dimensional map of digital twins is characterized in that: The sensor module consists of a noise sensor, a light sensor and a temperature sensor, and the classroom power equipment consists of a temperature-controlled air conditioner, an electric blackout curtain, an LED lighting fixture and a surveillance camera.

7. According to claim 6, a classroom equipment control system based on a three-dimensional map of digital twins is characterized by: The data transceiver module is composed of a wireless connection module and a Bluetooth connection module.

8. The method for using a classroom equipment control system based on a three-dimensional map of digital twins according to claim 7 is characterized in that: The following steps are involved: Initialize and configure the system, configure the Internet of Things module to ensure that it can communicate normally with the sensor module and classroom power equipment, start and configure the server to ensure that it can receive, store and process data, initialize the data read and write interface, prepare for data read and write operations, initialize the physical environment engine, logical model building module and conceptual model building module, and prepare to build a three-dimensional classroom model; Data collection and transmission: Noise sensors, light sensors, and temperature sensors collect environmental data in the classroom in real time. The collected data is transmitted to the codec through the IoT module. The codec encodes and decodes the data to ensure the accuracy and integrity of the data. Data processing and storage: the data processed by the codec is sent to the control circuit, which operates the RF circuit and the memory according to the preset logic. The RF circuit sends the data to the data transceiver module, which sends the data to the cloud server through the wireless connection module and the Bluetooth connection module. The cloud server receives and stores the data. Three-dimensional model construction and prediction: After the cloud server stores the data, it provides it to the data reading and writing module for reading and writing operations. The data reading and writing module passes the data to the classroom model building system. The classroom model building system uses the physical environment engine, the logical model building module and the conceptual model building module to build a three-dimensional model of the classroom. The constructed three-dimensional classroom model is input into the multi-dimensional convolutional neural network for feature extraction and pattern recognition. The self-denoising encoder performs noise reduction processing on the three-dimensional classroom model to generate a model to be corrected. The model to be corrected is imported into the prediction result verification module. The prediction result verification module verifies and optimizes the model through the data correction unit and the calculation analysis unit to generate a classroom environment prediction model. Result output and feedback, the results of the classroom environment prediction model are output to the result output module, and the result output module returns the results to the cloud server. The cloud server stores and backs up data based on the prediction results through the data reading and writing module and the backup storage server. The cloud server feeds back the prediction results and correction suggestions to the classroom equipment control system. According to the instructions of the cloud server, the control circuit remotely or locally controls the classroom power equipment through the radio frequency circuit and memory to optimize the classroom environment.

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