Campus low-carbon energy-saving smart classroom management method and system based on virtual classroom

Abstract

The invention discloses a campus low-carbon energy-saving smart classroom management method and system based on virtual classrooms. The method comprises the following steps: constructing a basic classroom resource pool; a classroom intelligent infrastructure layer is constructed through an environment sensing device, a network transmission device and an intelligent controller; constructing a digital twinborn model of the virtual classroom by combining the attribute information of the basic classroom resources and the classroom environment parameters collected by the intelligent facility; based on a digital twinborn model, monitoring the operation condition of each piece of hardware equipment in a classroom in real time; generating an operation and maintenance scheme and forming an operation and maintenance file according to the operation condition of the equipment and the prediction analysis result; constructing an energy consumption model of the virtual classroom, performing multi-dimensional evaluation on energy consumption of the classroom in real time, and constructing a carbon effect index; corresponding classroom services are developed in a targeted manner according to attribute characteristics of different types of classrooms; for different service objects, corresponding service interfaces are provided to realize input of classroom use requirements. And a complete technical system of low-carbon energy-saving intelligent classroom management is constructed.

Description

Technical Field

[0001] The present invention relates to the technical field of smart campus management, and particularly to a management method and system for a low-carbon energy-saving smart classroom based on a virtual classroom in a campus. Background Art

[0002] At present, with the continuous development of higher education, as a key carrier for teaching and research, university classrooms face many management problems. On the one hand, with the upgrading of systems such as multimedia, air conditioners, and lighting in classrooms, the electricity consumption has risen sharply. However, prominent problems such as poor equipment usage habits, loopholes in management, and inappropriate teaching activity arrangements, such as students forgetting to turn off equipment, unclear management responsibilities for power equipment, and classroom idle time, have led to serious energy waste and become an obstacle to building a conservation-oriented university.

[0003] On the other hand, the high mobility and flexibility of the service objects in university classrooms, which are very different from the relatively fixed user groups in primary and secondary school classrooms, make the traditional management method that relies on responsible classes to implement energy conservation inapplicable. In the general environment of an energy-saving society, the management of university classrooms has fallen into two extremes: some universities fully open classrooms during non-teaching periods to fully meet the needs of teachers and students, but this has caused excessive energy waste; some universities close classrooms for energy conservation and carbon reduction, which has led to a shortage of study and discussion venues for students, seriously affecting non-teaching learning activities.

[0004] From a technical perspective, the existing classroom management technologies have many drawbacks. The traditional mode that relies on manual inspections or simple automated controls is difficult to adapt to the complex usage scenarios and personnel flow characteristics of university classrooms, and phenomena such as equipment idling and energy waste are common. The management system has a single function and isolated data, and it is impossible to achieve dynamic optimization and full-life-cycle management of classroom resources. During non-teaching periods, extreme management strategies have exacerbated the contradiction between energy and usage requirements. Moreover, the existing technologies have insufficient mining of classroom energy consumption data and lack an effective carbon efficiency evaluation system, making it difficult to meet the requirements of the sustainable development of green campuses.

[0005] At the technical implementation level, the existing management systems have obvious shortcomings. The equipment control mechanism is lagging, unable to real-time sense personnel activities and environmental changes, resulting in equipment still running continuously when there is no one or at low load. The resource scheduling lacks intelligence, and the course scheduling in academic affairs does not match the classroom attributes, and phenomena such as "using a large classroom for a small class" and "using a small classroom for a large class" occur frequently. The energy consumption monitoring system is imperfect, only able to count the total amount, lacking energy efficiency analysis and carbon emission assessment at the equipment level and scenario level. The responsibility tracing mechanism is missing, and it is difficult to hold accountable for energy waste during non-teaching periods, and it is difficult to effectively implement energy-saving measures. In addition, the existing technologies have not constructed a digital twin model that integrates the virtual and the real, and it is impossible to use virtual simulation to achieve predictive maintenance of equipment operation and dynamic deduction of energy consumption optimization. These technical bottlenecks have led to the long-term high energy consumption and low efficiency of university classroom management, and there is an urgent need to innovate management technologies to break the deadlock. Summary of the Invention

[0006] This invention addresses these technical pain points by building a comprehensive technology system encompassing environmental perception, intelligent control, digital twins, and energy efficiency assessment, thus overcoming the limitations of traditional classroom management. The system deploys intelligent terminals such as AI cameras and temperature and humidity sensors to enable real-time collection and analysis of personnel activities and environmental parameters. It leverages digital twin technology to construct a virtual classroom model, enabling dynamic assessment and predictive maintenance of equipment operating status. It combines big data analysis with artificial intelligence algorithms to optimize resource scheduling strategies and equipment operating parameters. Finally, it establishes a multi-dimensional energy consumption evaluation system to quantitatively track carbon emissions and scientifically assess carbon efficiency.

[0007] In view of the above defects or improvement needs of the prior art, the present invention provides a campus low-carbon energy-saving smart classroom management method based on a virtual classroom, comprising:

[0008] S1. Collect attribute information of various classroom resources in the corresponding universities and build a centralized resource database to form a basic classroom resource pool;

[0009] S2. Build a smart classroom infrastructure layer through environmental sensing devices, network transmission equipment, and intelligent controllers;

[0010] S3. Build the virtual smart classroom model layer. The specific steps are as follows:

[0011] Combining the attribute information of basic classroom resources and the classroom environment parameters collected by intelligent facilities, a digital twin model of the virtual classroom is constructed;

[0012] Based on the digital twin model, the operating status of each hardware device in the classroom is monitored in real time;

[0013] Generate an operation and maintenance plan based on the equipment's operating status and forecast analysis results, and form an operation and maintenance file for the entire equipment life cycle;

[0014] Build an energy consumption model for virtual classrooms, conduct multi-dimensional evaluation of classroom energy consumption in real time, and construct carbon efficiency indicators;

[0015] S4. Develop targeted classroom services based on the characteristics of different types of classrooms;

[0016] S5. For different service objects, corresponding service interfaces are provided to realize the input of classroom usage requirements.

[0017] Furthermore, in S2, the intelligent classroom infrastructure layer is constructed through environmental sensing devices, network transmission equipment and intelligent controllers. The specific steps are as follows:

[0018] The environmental sensing device is responsible for collecting real-time data including the distribution of people in the classroom, ambient temperature, humidity, and light intensity;

[0019] Test the connectivity and stability of the data transmission link;

[0020] Transmit the collected data to the campus control center through the network transmission device;

[0021] Deploy AI algorithms on the server of the campus control center, including personnel distribution recognition algorithm, environmental parameter analysis algorithm and control strategy generation algorithm;

[0022] Conduct system integration testing to verify the collaborative working ability between the AI system and intelligent devices, and ensure that the system can automatically adjust the classroom environment according to real-time data;

[0023] Analyze the collected data and generate control instructions;

[0024] The intelligent controller adjusts the operating state of the equipment in the classroom according to the control instructions.

[0025] Furthermore, the environment perception devices include: AI cameras, temperature and humidity sensors, and brightness sensors; the AI cameras are installed in the corners of the classroom to monitor the position and number of people in the classroom in real time, and identify the activity status of people through deep learning algorithms and feedback it to the AI system. The AI system calculates the personnel density and activity area of the current classroom according to the personnel distribution data collected by the cameras and the seat layout of the classroom; according to the personnel distribution, the intelligent lighting system only turns on the lighting equipment in the area where there are people and adjusts the light brightness according to the personnel activity status.

[0026] Furthermore, the temperature and humidity sensors and brightness sensors monitor the temperature, humidity and light intensity in the classroom in real time and feedback it to the AI system. The AI system judges whether the current environment meets the comfort standard according to the environmental parameter data and the preset comfort range; when the indoor temperature exceeds the preset range, automatically adjust the air conditioner temperature setting value; when the humidity exceeds the preset range, the air conditioner automatically turns on the dehumidification function; according to the light intensity, the intelligent lighting system automatically adjusts the light brightness.

[0027] Furthermore, in step S3, combine the attribute information of the basic classroom resources and the classroom environment parameters collected by the intelligent facilities to construct a digital twin model of the virtual classroom. The specific method is as follows:

[0028] Collect equipment energy consumption and operation status data by the minute through intelligent electricity meters, equipment operation status monitoring modules, sensors and communication interfaces;

[0029] Based on 3D modeling software, construct a real-time operation status visualization model to display the changes in equipment operation and environmental parameters;

[0030] Improve the parameter settings related to energy consumption. In the equipment energy consumption sub-model, refine the relationship parameters between equipment energy consumption, operating status, and environmental parameters.

[0031] Develop a real-time monitoring interface, synchronize data with the digital twin model in real time, and set up a warning mechanism.

[0032] Integrate an energy consumption analysis tool in the digital twin model platform to support energy consumption statistical analysis according to different time dimensions and different classroom types.

[0033] Furthermore, in step S3, generate an operation and maintenance plan based on the operating conditions of the equipment and the prediction and analysis results, and form an operation and maintenance file for the entire life cycle of the equipment. The specific method is as follows:

[0034] According to the equipment data collected in constructing the digital twin model of the virtual classroom, integrate multi-source data, establish a unified format standard, classify by equipment type and time, store it in the database, and formulate storage and backup strategies.

[0035] Include the historical simulation operation data of the equipment in the digital twin model in the scope of statistical analysis, and calculate the statistical indicators of various parameters together with the actual historical operation data.

[0036] Use time series analysis methods to model and predict the integrated actual and simulated equipment operation parameters.

[0037] Utilize the visualization function of the digital twin model to formulate intuitive maintenance operation guides for different fault types.

[0038] Based on the long-term simulation analysis of the equipment operation status by the digital twin model, combined with the actual operation characteristics of the equipment and historical fault data, optimize the equipment regular maintenance plan.

[0039] Establish an archive query system that interacts with the digital twin model, supporting querying of the operation and maintenance archive through various methods such as equipment name, model, time range, and relevant parameters of the digital twin model.

[0040] Furthermore, the method for constructing the energy consumption model of the virtual classroom in step S3 is as follows:

[0041] Establish an equipment energy consumption sub-model based on the formula in the dimension of equipment energy consumption:

[0042]

[0043] Individually model the energy consumption of each type of equipment. In the formula, P i represents the power of the current equipment, and t i represents the running time of the current equipment;

[0044] Establish a personnel activity energy consumption sub-model. For per capita energy consumption, based on the formula:

[0045]

[0046] Combined with the number of people N in the classroom and the total equipment energy consumption E monitored in real time total-device , the per capita energy consumption at different times can be calculated;

[0047] For the energy consumption adjustment of different activity types, the corresponding adjustment is made according to the following formula:

[0048] E activity-adjusted = E total-device × k activity

[0049] The energy consumption adjustment coefficient k corresponding to different activity types is preset activity ; By identifying the current activity type through the AI camera, the total equipment energy consumption can be adjusted accordingly, so as to reflect the energy consumption changes in different activity scenarios;

[0050] Establish a comprehensive energy consumption model integration, comprehensively consider the interaction of equipment energy consumption, personnel activities and environmental parameters, and form the final virtual classroom energy consumption model; Calculate through the total energy consumption comprehensive evaluation formula:

[0051]

[0052] In the formula, T represents the actually monitored temperature, T standard represents the standard temperature, and α is the temperature influence coefficient; H represents the actually monitored humidity, H standard represents the standard humidity, and β is the humidity influence coefficient; I represents the actually monitored light intensity, I standard represents the standard light intensity, and γ is the light intensity influence coefficient.

[0053] Furthermore, in step S4, corresponding classroom services are developed specifically according to the attribute characteristics of different types of classrooms. The specific method is:

[0054] Ordinary classrooms are divided into teaching and self-study uses according to the usage form:

[0055] In the teaching mode, the system automatically docks with the course arrangement of the educational administration system, matches the number of students in the course, the resources required for teaching, the teacher's class schedule and various educational administration information with the attributes of the virtual classroom, arranges the verified classroom and distributes it to the corresponding teachers and students;

[0056] In the self-study mode, the system can open separate self-study classrooms for reservation; According to the reservation needs of teachers and students, adjust the open classroom resources and classroom types, and guide the self-study teachers and students to the centralized open self-study classrooms through guidance.

[0057] For small seminar rooms, the system automatically reserves seminar room resources within the corresponding range based on the number of participants. It also provides direct feedback to the users of the seminar rooms through energy consumption monitoring and energy efficiency evaluation, and encourages energy conservation and reduces waste through relevant management systems.

[0058] For large lecture halls that are mainly used for large-scale reports from various units on campus, the campus control center reasonably turns on relevant equipment through appointment times to reduce energy waste during non-use periods. During the use period, it monitors energy consumption and cooperates with corresponding management systems to urge direct-using units to fulfill their energy-saving responsibilities.

[0059] As a second aspect of the present invention, a campus low-carbon energy-saving smart classroom management system based on a virtual classroom is provided, comprising:

[0060] The basic classroom resource pool unit is used to collect attribute information of various classroom resources in the corresponding university, build a centralized resource database to form a basic classroom resource pool;

[0061] The classroom smart infrastructure unit is used to build the classroom smart infrastructure layer through environmental sensing devices, network transmission equipment and intelligent controllers;

[0062] The virtual smart classroom model unit is used to build the virtual smart classroom model layer. The specific steps are as follows:

[0063] Combining the attribute information of basic classroom resources and the classroom environment parameters collected by intelligent facilities, a digital twin model of the virtual classroom is constructed;

[0064] Based on the digital twin model, the operating status of each hardware device in the classroom is monitored in real time;

[0065] Generate an operation and maintenance plan based on the equipment's operating status and forecast analysis results, and form an operation and maintenance file for the entire equipment life cycle;

[0066] Build an energy consumption model for virtual classrooms, conduct multi-dimensional evaluation of classroom energy consumption in real time, and construct carbon efficiency indicators;

[0067] Application service unit, used to develop corresponding classroom services according to the characteristics of different types of classrooms;

[0068] The external service unit is used to provide corresponding service interfaces for different service objects to realize the input of classroom usage requirements.

[0069] As a third aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, and the computer program is executed by a processor to perform any step of the above-mentioned campus low-carbon and energy-saving smart classroom management method based on a virtual classroom.

[0070] Generally speaking, compared with the prior art, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0071] 1. The campus low-carbon energy-saving intelligent classroom management method based on virtual classrooms of the present invention realizes the dynamic evaluation and predictive maintenance of the operating status of equipment by constructing a digital twin platform that integrates virtual and real, and deeply coupling the real-time data of physical classrooms and intelligent sensing devices. The system conducts multi-dimensional modeling and analysis of classroom energy consumption through virtual simulation technology, establishes an operation and maintenance file for the entire life cycle, and effectively improves the refined level of equipment management. This technology breaks through the time and space limitations of traditional management models, realizes the digital mapping and dynamic optimization of classroom resources, and provides traceable and predictable intelligent support for campus energy management.

[0072] 2. The campus low-carbon energy-saving intelligent classroom management method based on virtual classrooms of the present invention integrates data on personnel distribution recognition, temperature and humidity perception, and light intensity monitoring through an AI-driven multi-modal environment regulation mechanism, and dynamically optimizes the operating parameters of equipment such as air conditioners and lighting. It realizes the real-time analysis of environmental parameters and the rapid response of equipment instructions, significantly reducing energy consumption while ensuring the comfort of teachers and students. This technology breaks through the passivity of traditional equipment control and realizes the dual improvement of energy utilization efficiency and usage experience through an adaptive regulation strategy, providing an intelligent solution for the construction of smart campuses.

[0073] 3. The campus low-carbon energy-saving intelligent classroom management method based on virtual classrooms of the present invention constructs a differential management model through a hierarchical and classified responsibility system and an intelligent access control system. For different scenarios such as ordinary classrooms, lecture halls, and seminar rooms, strategies such as intelligent regulation, reservation and pre-opening, and energy consumption feedback are adopted respectively, deeply binding the energy-saving responsibility to the usage scenario. The system realizes the precise tracking and energy efficiency evaluation of resource usage through the linkage of intelligent access control data and reservation management, effectively reducing equipment idling and resource mismatching. This technology breaks through the limitations of the traditional one-size-fits-all management model and constructs a collaborative energy-saving mechanism of technical constraints + institutional guidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is a flowchart of the campus low-carbon energy-saving intelligent classroom management method based on virtual classrooms according to an embodiment of the present invention;

[0075] Figure 2 is a schematic diagram of the architecture of the low-carbon energy-saving intelligent classroom management system according to an embodiment of the present invention;

[0076] Figure 3 is a configuration diagram of a typical low-carbon energy-saving intelligent classroom (ordinary classroom) according to an embodiment of the present invention;

[0077] Figure 4 is a configuration diagram of a typical low-carbon energy-saving intelligent classroom (large lecture hall) according to an embodiment of the present invention;

[0078] Figure 5 Configuration diagram of a typical low-carbon energy-saving smart classroom (small seminar room) in an embodiment of the present invention;

[0079] Figure 6 Unit diagram of a campus low-carbon energy-saving smart classroom management system based on a virtual classroom in an embodiment of the present invention. Detailed implementation manners

[0080] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0081] Embodiment 1

[0082] Please refer to Figure 1 , this Embodiment 1 provides a campus low-carbon energy-saving smart classroom management method based on a virtual classroom, including:

[0083] S1. Collect various classroom resource attribute information within the corresponding university, and construct a centralized resource database to form a basic classroom resource pool;

[0084] S2. Construct a classroom intelligent infrastructure layer through environmental perception devices, network transmission devices, and intelligent controllers;

[0085] S3. Construct a virtual smart classroom model layer, and the specific steps are as follows:

[0086] Combine the attribute information of the basic classroom resources and the classroom environmental parameters collected by the intelligent facilities to construct a digital twin model of the virtual classroom;

[0087] Based on the digital twin model, monitor the operation status of each hardware device in the classroom in real time;

[0088] Generate an operation and maintenance plan according to the operation status of the device and the prediction and analysis results, and form an operation and maintenance file for the entire life cycle of the device;

[0089] Construct an energy consumption model of the virtual classroom, evaluate the energy consumption of the classroom in multiple dimensions in real time, and build a carbon efficiency index;

[0090] S4. Develop corresponding classroom services according to the attribute characteristics of different types of classrooms;

[0091] S5. Provide corresponding service interfaces for different service objects to realize the input of classroom usage requirements.

[0092] The implementation steps of the present invention will be specifically elaborated as follows:

[0093] Please refer to Figure 2 , a campus low-carbon energy-saving intelligent classroom management method based on a virtual classroom, and its system architecture is as follows Figure 2 as shown. The entire system architecture is divided into 5 layers. The bottom layer is the basic classroom resource pool, which mainly involves all types of classroom resources owned by colleges and universities, including ordinary classrooms, lecture halls, seminar rooms, etc. Incorporate them into the low-carbon energy-saving intelligent classroom management system to realize the management of the whole school's resources by one system and open up the resource sharing channel.

[0094] The upper layer is the classroom intelligent facility layer, which is composed of various environmental perception devices such as AI cameras, temperature and humidity sensors, and brightness sensors, network transmission devices such as Bluetooth gateways, wireless APs, and management networks, and various intelligent switches. The control of various electrical equipment in the classroom is unified and converged to the campus control center of the classroom management to facilitate remote resource scheduling and regulation, and reduce equipment energy consumption and operation and maintenance losses based on methods such as data analysis and artificial intelligence.

[0095] The next upper layer is the virtual intelligent classroom model layer, which mainly includes two types of models. One type is various classroom models. In the model layer, the basic classroom resources and classroom environment parameters collected by the intelligent facilities are modeled to form a virtual classroom digital twin platform. The virtual classroom is a data modeling and virtual simulation platform for real classrooms on campus.

[0096] Based on the digital twin platform, the operating conditions of each hardware in the classrooms of the whole school can be dynamically evaluated and predicted and analyzed, an operation and maintenance plan can be generated, and the operation parameters after operation and maintenance are automatically fed back to the system simulation level for correction and update to form an operation and maintenance file for the whole life cycle. Based on the virtual classroom, an energy consumption model of the virtual classroom is constructed in this layer to evaluate the energy consumption of the classroom in multiple dimensions in real time and build a carbon efficiency index.

[0097] Based on a virtual classroom, an energy consumption model is constructed, and energy consumption is evaluated from the following three dimensions: Equipment energy consumption dimension: including the energy consumption of lighting equipment, air conditioning equipment, multimedia equipment, and other electrical equipment. The power, switch status, and usage duration of each device are monitored in real time through sensors. Personnel activity dimension: The number of people (personnel density) in the classroom is monitored in real time through an AI camera, and different activity types such as teaching, self-study, and seminars are distinguished. Different activity types have different usage requirements for equipment, thus affecting energy consumption. Environmental parameter dimension: The temperature, humidity, and light intensity in the classroom are monitored in real time through temperature and humidity sensors and light sensors. These parameters directly affect the operating efficiency and energy consumption of equipment. Energy consumption evaluation indicators: Total energy consumption, energy consumption per unit area, energy consumption per capita, proportion of equipment energy consumption, energy consumption efficiency, and carbon efficiency indicators: Total carbon emissions, carbon emissions per unit area, carbon emissions per capita, carbon efficiency ratio, etc. are used to evaluate the low-carbon operating efficiency of the classroom. The evaluation results can be used internally for the operation and maintenance warning of various electrical facilities in the classroom, and equipment in abnormal working conditions can be detected and repaired in a timely manner; externally, they can be used to construct a campus carbon footprint model, connect to future carbon trading platforms, and contribute to energy conservation and carbon reduction in all scenarios. The virtual intelligent classroom model layer is an abstraction of various hardware resources, the key to the informatization and intelligence of the management system, and the basis for the management system to provide external services.

[0098] In a preferred embodiment, the specific method for constructing a digital twin model of a virtual classroom by combining the attribute information of basic classroom resources and the classroom environment parameters collected by intelligent facilities is as follows:

[0099] (1) Collect equipment energy consumption and operation status data by the minute through an intelligent electricity meter, equipment operation status monitoring module, sensors, and communication interfaces; on the basis of installing an intelligent electricity meter on the power supply line of classroom equipment to collect energy consumption data, an equipment operation status monitoring module is added. For the intelligent lighting system, monitor the switch status and dimming level of the lamps; for the air conditioning system, monitor key parameters such as the operation status of the compressor, fan, and refrigerant pressure. Obtain data through sensors or the built-in communication interfaces of the equipment, and also summarize and store it by the minute to accurately grasp the real-time operation of the equipment and provide a detailed basis for equipment operation status for energy consumption analysis.

[0100] In addition to temperature, humidity, and light intensity sensors, air quality sensors are added to monitor indicators such as carbon dioxide and particulate matter concentration in the classroom. These parameters not only affect personnel comfort but are also related to equipment energy consumption. For example, too high a carbon dioxide concentration may affect human heat dissipation and indirectly affect air conditioning energy consumption. The sensor layout is further optimized, and denser arrangements are made at key locations such as crowded areas and equipment heat dissipation outlets to ensure that the data can comprehensively and accurately reflect the classroom environment and provide richer environmental data support for energy consumption analysis.

[0101] (2) Based on 3D modeling software, construct a visual model of the real-time operating state to display the changes in equipment operation and environmental parameters; on the basis of the geometric model and physical model, focus on constructing a visual model of the real-time operating state. Utilize the dynamic display function of 3D modeling software to present the equipment operating state in an intuitive manner. For example, the on / off of intelligent lighting fixtures is shown with real effects, and the operation of the air-conditioning compressor is simulated through animation. At the same time, visualize the real-time changes in environmental parameters in the model. For example, the temperature change is displayed by color gradient in the classroom space. Through this model, users can clearly understand the real-time operating state of the classroom at a glance without complex data interpretation.

[0102] (3) Improve the parameter settings of the energy consumption-related models. In the equipment energy consumption sub-model, refine the relationship parameters between equipment energy consumption, operating state, and environmental parameters; improve the parameter settings of the energy consumption-related models. In the equipment energy consumption sub-model, refine the relationship parameters between equipment energy consumption, operating state, and environmental parameters. For example, in the air-conditioning energy consumption model, consider the influence coefficient of heat exchange efficiency on energy consumption under different air quality conditions. In the personnel activity energy consumption sub-model, further distinguish the differences in personnel activity patterns under different course types (experimental courses, theoretical courses, etc.) on energy consumption, making the model more in line with the actual energy consumption scenario of the classroom and providing more accurate model support for accurate energy consumption analysis.

[0103] (4) Develop a real-time monitoring interface to synchronize data with the digital twin model in real time. Display information such as the operating state of classroom equipment, environmental parameters, and energy consumption data on the interface in real time. Set up a warning mechanism. When the equipment shows abnormal operating states (such as air-conditioning compressor failure), environmental parameters exceed the comfortable range (high temperature, high carbon dioxide concentration), or there are abnormal fluctuations in energy consumption, send warnings in a timely manner through pop-up windows, text messages, etc., facilitating managers to handle in a timely manner to ensure the normal operation of the classroom and also providing clues for the analysis of abnormal energy consumption.

[0104] (5) Integrate an energy consumption analysis tool in the digital twin model platform. Support energy consumption statistical analysis according to different time dimensions (hours, days, weeks, etc.) and different classroom types (ordinary classrooms, seminar rooms, etc.). Generate visual charts such as energy consumption trend charts and pie charts of equipment energy consumption ratios to intuitively display the energy consumption distribution and change rules. At the same time, combined with real-time operating state data, deeply analyze the correlation between energy consumption, equipment operation, and environmental factors, providing a data-driven basis for formulating energy-saving strategies and meeting the requirements of energy consumption analysis based on real-time operating states.

[0105] In a preferred embodiment, generate an operation and maintenance plan according to the operating conditions of the equipment and the prediction and analysis results, and form an operation and maintenance file for the entire life cycle of the equipment. The specific method is as follows:

[0106] (1) Integrate multi-source data based on the device data collected in building the digital twin model of the virtual classroom, establish a unified format standard, classify it by device type and time, store it in the database, and formulate storage and backup strategies. Optimize the database storage structure, set up a separate storage partition for the data related to the digital twin model, and adopt an efficient storage algorithm such as columnar storage to improve the storage and reading efficiency of large-scale simulation data. At the same time, utilize the backup and recovery functions of the database to regularly back up the digital twin model data and related operation data to ensure data security.

[0107] (2) Incorporate the historical simulation operation data of the devices in the digital twin model into the scope of statistical analysis, and calculate the statistical indicators (such as mean, standard deviation, etc.) of various parameters together with the actual historical operation data. By comparing the changing trends of the statistical values of the actual and simulated data in different time periods, more accurately define the normal fluctuation range of the device operation parameters and improve the accuracy of anomaly detection.

[0108] (3) Use time series analysis methods (such as the ARIMA model) to model and predict the integrated actual and simulated device operation parameters (such as energy consumption, temperature). Utilize the forward-looking simulation ability of the digital twin model to predict in advance the possible changes in the operation status of the device under future complex working conditions, providing a basis for formulating more forward-looking operation and maintenance strategies.

[0109] (4) Utilize the visualization function of the digital twin model to formulate intuitive maintenance operation guides for different fault types. For example, for air conditioning system failures, in the digital twin model, display the detailed steps of repairing or replacing components such as compressors and refrigerant pipes in the form of 3D animations, as well as the commissioning methods and acceptance criteria after maintenance, improving the operation accuracy and efficiency of maintenance personnel.

[0110] Estimate the time and cost required for maintenance through the digital twin model, and correct it in combination with actual maintenance experience. At the same time, track the maintenance progress in the model, real-time update the operation status of the device in the virtual environment, and keep it synchronized with the actual maintenance site to ensure that the device can return to normal operation in a timely manner.

[0111] (5) Based on the long-term simulation analysis of the device operation status by the digital twin model, combined with the actual operation characteristics of the device and historical fault data, optimize the device's regular maintenance plan. For example, according to the model's simulation of the wear conditions of device components under different usage frequencies, reasonably adjust the maintenance cycle. For example, for the frequently used lighting system, the cleaning and inspection cycle can be appropriately shortened.

[0112] Simulate the process of regular maintenance operations in the digital twin model, and clarify the specific content of each maintenance (equipment cleaning, component inspection, parameter calibration, replacement of vulnerable parts, etc.). Through the visualization display of the model, provide maintenance personnel with standardized operation procedures and quality standards. For example, display the ideal state after the air conditioner filter is cleaned in the model to ensure the standardization of maintenance work.

[0113] Use the digital twin model to record the details of each maintenance work in the virtual environment, including maintenance time, maintenance personnel, maintenance content, and changes in equipment status, etc., which are mutually verified with the actual maintenance records to facilitate subsequent analysis and summary of maintenance experience.

[0114] (6) Establish an archive query system that interacts with the digital twin model, supporting querying of operation and maintenance archives in multiple ways, such as by equipment name, model, time range, and relevant parameters of the digital twin model (such as simulated working conditions, model prediction results, etc.). Managers and maintenance personnel can quickly obtain comprehensive information on the equipment in the actual operation and virtual simulation environments, providing more comprehensive support for equipment management, fault diagnosis, and maintenance decision-making.

[0115] In a preferred embodiment, the method for building an energy consumption model of a virtual classroom is as follows:

[0116] Establish an equipment energy consumption sub-model, based on the formula for the equipment energy consumption dimension:

[0117]

[0118] Individually model the energy consumption of each type of equipment. In the formula, P i represents the power of the current equipment, and t i represents the running time of the current equipment;

[0119] Establish a personnel activity energy consumption sub-model. For per capita energy consumption, according to the formula:

[0120]

[0121] Combined with the number of people N in the classroom and the total equipment energy consumption E total-device monitored in real time, the per capita energy consumption at different times can be calculated;

[0122] For the energy consumption adjustment of different activity types, make corresponding adjustments according to the following formula:

[0123] E activity-adjusted = E total-device × k activity

[0124] Preset the energy consumption adjustment coefficient k corresponding to different activity types activityBy using the AI camera to identify the current activity type, the total device energy consumption can be adjusted accordingly to reflect the energy consumption changes in different activity scenarios;

[0125] Establish a comprehensive energy consumption model integration, comprehensively consider the interaction of equipment energy consumption, personnel activities and environmental parameters, and form the final virtual classroom energy consumption model; calculate it through the comprehensive evaluation formula of total energy consumption:

[0126]

[0127] Where, T represents the actual monitoring temperature, T standard represents the standard temperature, α is the temperature influence coefficient; H represents the actual monitored humidity, H standard represents the standard humidity, β is the humidity influence coefficient; I represents the actual monitored light intensity, I standard represents the standard light intensity, and γ is the light intensity influence coefficient.

[0128] Furthermore, above the model layer is the application service layer. In this layer, corresponding classroom services are developed according to the attributes of different types of classrooms to facilitate the provision of services to teachers and students. For example, ordinary classrooms can be divided into teaching and self-study according to the form of use. In the teaching mode, the system automatically connects to the class scheduling of the academic affairs system. It can match various academic affairs information with the attributes of the virtual classroom based on the number of students in the course, the resources required for teaching (such as multimedia, smart drawing boards, AR / VR, experimental facilities), the class schedule of the teaching teacher, etc., arrange the verified classroom and issue it to the corresponding teachers and students. Avoid unreasonable arrangements such as small classroom students occupying large classrooms and large classroom students occupying small classrooms.

[0129] In self-study mode, the system can open individual self-study rooms for reservation. Based on the reservation needs of teachers and students, the system flexibly adjusts the available classroom resources and classroom types, and guides self-study teachers and students to more centralized development self-study classrooms through reasonable guidance, avoiding the waste of resources and energy caused by the excessive dispersion of self-study teachers and students, resulting in multiple classrooms serving only a small number of people.

[0130] In addition, in the smart classroom, an AI camera is used to sense the distribution of people in the classroom in real time, identify changes in the classroom scene, and optimize the operating parameters of various electrical equipment in the classroom, such as air conditioner temperature and lighting, through AI algorithms in combination with the environmental data collected by environmental sensors, so as to achieve a seamless regulation for teachers and students and provide a comfortable and energy-saving classroom environment for them. The AI system collects data in real time through a variety of sensors integrated in the classroom (such as AI cameras, temperature and humidity sensors, brightness sensors, etc.), and analyzes and makes decisions based on this data to achieve automated and intelligent control of the classroom environment. The architecture of the AI system is as follows: Data acquisition layer: It consists of an AI camera, temperature and humidity sensors, brightness sensors, etc., and is responsible for collecting data such as the distribution of people in the classroom, environmental temperature, humidity, and light intensity in real time. Data transmission layer: The collected data is transmitted to the campus control center through network transmission devices such as Bluetooth gateways and wireless APs. Data analysis and decision-making layer: It is deployed on the server in the campus control center, runs AI algorithms to analyze the collected data, and generates control instructions. Execution layer: It consists of intelligent switches, intelligent air conditioner controllers, intelligent lighting controllers, etc., and adjusts the operating status of the equipment in the classroom according to the control instructions.

[0131] For the perception and control of the distribution of people, AI cameras are installed in the corners of the classroom to monitor the position and number of people in the classroom in real time. The cameras use deep learning algorithms and can accurately identify the activity status of people (such as standing, sitting, walking, etc.). The AI system calculates the current personnel density and activity area of the classroom based on the personnel distribution data collected by the cameras in combination with the seat layout of the classroom. According to the personnel distribution, the intelligent lighting system only turns on the lighting equipment in the areas where there are people and adjusts the light brightness according to the activity status of people. For example, when it is detected that there is no one in a certain area, the lights in that area are automatically turned off; when people enter, the lights are automatically turned on. According to the personnel density and activity area, the air volume and temperature of the air conditioner are adjusted. For example, when the personnel density is high, the cooling or heating power of the air conditioner is appropriately increased; when people are concentrated on one side of the classroom, the air supply direction of the air conditioner is adjusted to ensure comfort while reducing energy consumption.

[0132] For the perception and control of environmental parameters, temperature and humidity sensors and brightness sensors monitor the temperature, humidity, and light intensity in the classroom in real time.

[0133] The AI system determines whether the current environment meets the comfort standard based on the environmental parameter data and in combination with the preset comfort range (such as temperature 18°C - 26°C, humidity 40% - 60%, light intensity 300 - 500 lux). When the indoor temperature exceeds the preset range, the air conditioner temperature setting value is automatically adjusted. For example, when the temperature is higher than 26°C, the air conditioner automatically turns on the cooling mode and dynamically adjusts the cooling power according to the temperature change. When the humidity exceeds the preset range, the air conditioner automatically turns on the dehumidification function. For example, when the humidity is higher than 60%, the air conditioner dehumidification function starts and continues until the humidity drops to the comfort range. According to the light intensity, the intelligent lighting system automatically adjusts the light brightness. For example, when the natural light intensity is higher than 500 lux, some or all of the artificial lighting is turned off; when the natural light intensity is lower than 300 lux, the lights are automatically turned on and the brightness is adjusted to the comfort level.

[0134] To achieve AI intelligent control, the support of software and hardware is required. Hardware deployment includes cameras, sensors, network devices, and intelligent devices, etc. Install multiple AI cameras on the ceiling or walls of the classroom to ensure coverage of the entire classroom area. The installation location of the cameras should avoid direct sunlight and reflection interference. Deploy Bluetooth gateways and wireless APs to ensure the stability and real-time nature of data transmission. Install temperature and humidity sensors and brightness sensors at different locations in the classroom to ensure accurate collection of environmental data. Install intelligent switches, intelligent air conditioner controllers, and intelligent lighting controllers to ensure that the devices can receive control instructions and execute them. The integration of software and system debugging includes calibrating and debugging the cameras and sensors to ensure the accuracy and stability of data collection. Test the connectivity and stability of the data transmission link to ensure that data can be transmitted to the campus control center in real time. Deploy AI algorithms on the server of the campus control center, including personnel distribution recognition algorithms, environmental parameter analysis algorithms, and control strategy generation algorithms. Conduct system integration testing to verify the collaborative working ability between the AI system and intelligent devices, and ensure that the system can automatically adjust the classroom environment according to real-time data. At the same time, it is necessary to perform operation and maintenance and continuous optimization on the software and hardware systems, so as to achieve real-time monitoring and adjustment, timely and effective collection and analysis of data, and adjustment according to user feedback.

[0135] In addition, the classroom adopts intelligent access control. In the teaching mode, it can be used to count the sign-in situation of teachers and students, which can be synchronized to the educational administration system for teacher and student attendance and learning situation analysis, promptly discover students with multiple absences, and initiate relevant assistance. In the self-study mode, it can be used to reserve the feedback of teachers and students, avoiding the waste of classroom resources caused by large-scale false reservations. For large lecture halls, only open to secondary units within the school for large report reservations, reasonably turn on relevant facilities in advance through the reservation time, and monitor energy consumption.

[0136] For small seminar rooms, which are frequently used and mainly for small meetings, teacher-student seminars, etc., the energy-saving responsibility can be assigned to the relevant bookers. The system can automatically allocate seminar room resources within the corresponding range according to the number of people planning to participate in the seminar, and directly feedback to the users of the seminar room through energy consumption monitoring and energy efficiency evaluation. Energy conservation can be encouraged through relevant management systems to reduce waste.

[0137] For the above three types of classroom resources, the system energy-saving strategy and the configuration strategy of the corresponding intelligent hardware are as follows: For the multi-person use scenarios in classrooms, the usage requirements and classroom resources are best matched through the management system to reduce waste. Inside the classroom, a large number of AI cameras and environmental sensors are used to achieve automatic optimization for energy conservation. The energy-saving responsibility is implemented in the centralized campus control center, and comfortable and energy-saving services are provided for all teachers and students in the school through intelligent facilities.

[0138] For large lecture halls, which mainly undertake large reports of various units within the school, the energy-saving responsibility is mainly assigned to the using units. The campus control center reasonably turns on relevant equipment according to the reserved time to reduce energy waste during the non-use stage. During the use stage, energy consumption monitoring is combined with corresponding management systems to urge the direct using units to implement the energy-saving responsibility.

[0139] For small seminar rooms reserved by teachers and students, since the number of people participating in each seminar is small and internal communication is convenient, the use of various equipment inside the small seminar room is communicated by the participants themselves. The classroom management system only conducts energy consumption monitoring and energy efficiency evaluation, and then feeds back the energy efficiency to the users. On campus, various management systems such as energy efficiency points can be adopted to guide teachers and students to actively save energy and use greenly. By classifying classroom resources and making reasonable differential intelligent upgrades according to the target usage scenarios, the construction cost of the entire intelligent classroom management system can be effectively reduced, and energy conservation in classroom use can be maximized, realizing the parallel development of a green campus and a comfortable campus.

[0140] The top layer of the system architecture is the external service layer. For different service objects, the low-carbon and energy-saving intelligent classroom management system provides multiple service interfaces to realize the input of classroom usage requirements. Ordinary teachers and students as individual users can view available classroom seats, seminar rooms, etc. through web pages, WeChat mini-programs, official accounts, enterprise accounts, etc. for reservation and use; secondary units within the school use OA or offline application methods to reserve classrooms, lecture halls, seminar rooms, etc. for use; the teaching affairs system directly imports teaching affairs data into the classroom management system through API software interfaces for automatic matching and course scheduling. The administrator logs in to the classroom management system through the administrator account to conduct operations such as resource inventory, operating energy consumption analysis, and necessary system setting and operation maintenance.

[0141] Please refer to Figures 3 - 5 For a more detailed introduction to the intelligent classroom management system proposed in the present invention, the following Figure 3 、 Figure 4 、Figure 5 Typical configurations of ordinary classrooms, large lecture halls, and small seminar rooms are given respectively according to different application scenarios for reference. In the actual process, on the premise of ensuring the integrity of the entire system architecture and the perfection of functions, corresponding adjustments can be made to the corresponding classroom equipment, intelligent control equipment, etc. according to actual applications to make classroom management more intelligent.

[0142] Example 2

[0143] Please refer to Figure 6 , this Example 2 provides a campus low-carbon energy-saving intelligent classroom management system based on virtual classrooms, including:

[0144] The basic classroom resource pool unit is used to collect various classroom resource attribute information within the corresponding universities, construct a centralized resource database to form a basic classroom resource pool;

[0145] The classroom intelligent infrastructure unit is used to construct a classroom intelligent infrastructure layer through environmental perception devices, network transmission devices, and intelligent controllers;

[0146] The virtual intelligent classroom model unit is used to construct a virtual intelligent classroom model layer, and the specific steps are as follows:

[0147] Combining the attribute information of basic classroom resources and the classroom environmental parameters collected by intelligent facilities, construct a digital twin model of the virtual classroom;

[0148] Based on the digital twin model, real-time monitor the operation status of each hardware device in the classroom;

[0149] Generate an operation and maintenance plan according to the operation status of the equipment and the prediction and analysis results, and form an operation and maintenance file for the entire life cycle of the equipment;

[0150] Construct an energy consumption model of the virtual classroom, conduct multi-dimensional evaluation of the classroom energy consumption in real time, and build a carbon efficiency index;

[0151] The application service unit is used to develop corresponding classroom services specifically according to the attribute characteristics of different types of classrooms;

[0152] The external service unit is used to provide corresponding service interfaces for different service objects to realize the input of classroom use requirements.

[0153] Example 3

[0154] This Example 3 also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, any step of the campus low-carbon energy-saving intelligent classroom management method based on virtual classrooms can be realized.

[0155] The computer-readable storage medium may include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0156] It is easy for those skilled in the art to understand that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A management method for a low-carbon and energy-saving intelligent classroom on campus based on a virtual classroom, characterized in that, include: S1. Collect attribute information of various classroom resources in the corresponding universities and build a centralized resource database to form a basic classroom resource pool; S2. Build a smart classroom infrastructure layer through environmental sensing devices, network transmission equipment, and intelligent controllers; S3. Build the virtual smart classroom model layer. The specific steps are as follows: Combining the attribute information of basic classroom resources and the classroom environment parameters collected by intelligent facilities, a digital twin model of the virtual classroom is constructed; Based on the digital twin model, the operating status of each hardware device in the classroom is monitored in real time; Generate an operation and maintenance plan based on the equipment's operating status and forecast analysis results, and form an operation and maintenance file for the entire equipment life cycle; Build an energy consumption model for virtual classrooms, conduct multi-dimensional evaluation of classroom energy consumption in real time, and construct carbon efficiency indicators; S4. Develop targeted classroom services based on the characteristics of different types of classrooms; S5. For different service objects, corresponding service interfaces are provided to realize the input of classroom usage requirements.

2. The campus low-carbon energy-saving smart classroom management method based on virtual classroom according to claim 1 is characterized in that: In S2, the intelligent classroom infrastructure layer is constructed through environmental sensing devices, network transmission equipment, and intelligent controllers. The specific steps are as follows: The environmental sensing device is responsible for collecting real-time data including the distribution of people in the classroom, ambient temperature, humidity, and light intensity; Test the connectivity and stability of data transmission links; Transmit the collected data to the campus control center through network transmission equipment; Deploy AI algorithms on the campus control center's servers, including algorithms for identifying personnel distribution, analyzing environmental parameters, and generating control strategies; Conduct system integration testing to verify the interoperability between the AI system and smart devices, ensuring that the system can automatically adjust the classroom environment based on real-time data; Analyze the collected data and generate control instructions; The intelligent controller adjusts the operating status of the equipment in the classroom according to the control instructions.

3. The campus low-carbon energy-saving intelligent classroom management method based on a virtual classroom according to claim 2, characterized in that, The environmental sensing device includes: AI cameras, temperature and humidity sensors, and brightness sensors; the AI cameras are installed in various corners of the classroom to monitor the location and number of people in the classroom in real time, and identify the activity status of people through deep learning algorithms and feed it back to the AI system. The AI system calculates the current classroom population density and activity area based on the population distribution data collected by the camera and the classroom seating layout; based on the population distribution, the intelligent lighting system only turns on lighting equipment in areas with people and adjusts the light brightness according to the activity status of people.

4. The campus low-carbon energy-saving intelligent classroom management method based on a virtual classroom according to claim 3, wherein, The temperature and humidity sensors and brightness sensors monitor the temperature, humidity and light intensity in the classroom in real time and feed back to the AI system. The AI system determines whether the current environment meets the comfort standard based on the environmental parameter data and the preset comfort range. When the indoor temperature exceeds the preset range, the air conditioning temperature setting value is automatically adjusted; when the humidity exceeds the preset range, the air conditioning automatically turns on the dehumidification function; according to the light intensity, the intelligent lighting system automatically adjusts the light brightness.

5. The campus low-carbon energy-saving smart classroom management method based on virtual classroom according to claim 1 is characterized in that: In S3, the attribute information of basic classroom resources and the classroom environment parameters collected by intelligent facilities are combined to build a digital twin model of the virtual classroom. The specific method is as follows: Collect device energy consumption and operation status data by the minute through smart meters, device operation status monitoring modules, sensors, and communication interfaces; Based on 3D modeling software, build a real-time operation status visualization model to display changes in device operation and environmental parameters; Improve the parameter settings of the energy consumption-related model. In the device energy consumption sub-model, refine the relationship parameters between device energy consumption, operation status, and environmental parameters; Develop a real-time monitoring interface, synchronize data with the digital twin model in real time, and set up a warning mechanism; Integrate an energy consumption analysis tool in the digital twin model platform to support energy consumption statistical analysis according to different time dimensions and different classroom types.

6. The campus low-carbon energy-saving intelligent classroom management method based on a virtual classroom according to claim 1, characterized in that, In S3, generate an operation and maintenance plan according to the operation status of the device and the prediction and analysis results, and form an operation and maintenance file for the entire life cycle of the device. The specific method is as follows: According to the device data collected in building the digital twin model of the virtual classroom, integrate multi-source data, establish a unified format standard, classify by device type and time, store it in the database, and formulate storage and backup strategies; Incorporate the historical simulation operation data of the devices in the digital twin model into the scope of statistical analysis, and calculate the statistical indicators of various parameters together with the actual historical operation data; Use time series analysis methods to model and predict the integrated actual and simulated device operation parameters; Utilize the visualization function of the digital twin model to formulate intuitive maintenance operation guides for different fault types; Based on the long-term simulation analysis of the device operation status by the digital twin model, combined with the actual operation characteristics of the device and historical fault data, optimize the device's regular maintenance plan; Establish an archive query system that interacts with the digital twin model, supporting querying of operation and maintenance archives in various ways such as device name, model, time range, and relevant parameters of the digital twin model.

7. The campus low-carbon energy-saving intelligent classroom management method based on a virtual classroom according to claim 1, characterized in that The method for building the energy consumption model of the virtual classroom in S3 is as follows: Establish a device energy consumption sub-model based on the formula for the device energy consumption dimension: Model the energy consumption of each type of device separately. In the formula, P i represents the power of the current device, and t i represents the operating time of the current device; Establish a personnel activity energy consumption sub-model. For per capita energy consumption, based on the formula: Combined with the number of people N in the classroom and the total equipment energy consumption E monitored in real time total-device , the per capita energy consumption at different times can be calculated; For the adjustment of energy consumption of different activity types, make corresponding adjustments according to the following formula: E activity-adjusted = E total-device × k activity Preset the energy consumption adjustment coefficient k corresponding to different activity types activity ; By using an AI camera to identify the current activity type, the total energy consumption of the equipment can be adjusted accordingly, thus reflecting the energy consumption changes in different activity scenarios; Establish an integrated energy consumption model, comprehensively consider the interaction of device energy consumption, personnel activities, and environmental parameters, and form the final virtual classroom energy consumption model; Calculate through the total energy consumption comprehensive evaluation formula: where, T represents the actual monitored temperature, T standard represents the standard temperature, and α is the temperature influence coefficient; H represents the actual monitored humidity, H standard represents the standard humidity, and β is the humidity influence coefficient; I represents the actual monitored light intensity, I standard represents the standard light intensity, and γ is the light intensity influence coefficient.

8. The campus low-carbon energy-saving intelligent classroom management method based on a virtual classroom according to claim 1, characterized in that, In S4, develop corresponding classroom services according to the attribute characteristics of different types of classrooms. The specific method is as follows: Ordinary classrooms are divided into teaching and self-study uses according to their usage forms: In the teaching mode, the system automatically docks with the course scheduling arrangement of the educational administration system, matches the number of students in the course, teaching resources required, the teaching schedule of the teaching teacher, and various educational administration information with the attributes of the virtual classroom, arranges the verified classroom, and distributes it to the corresponding teachers and students; In the self-study mode, the system can open separate self-study classrooms for reservation; according to the reservation needs of teachers and students, adjust the open classroom resources and classroom types, and guide the self-study teachers and students to the centralized open self-study classrooms through guidance; For small seminar rooms, the system automatically reserves the seminar room resources within the corresponding range according to the number of people intending to participate in the seminar, and directly feedbacks to the users of the seminar room in combination with energy consumption monitoring and energy efficiency evaluation, and encourages energy conservation and reduces waste through relevant management systems; For large lecture halls that are mainly used for large-scale reports from various units on campus, the campus control center reasonably turns on relevant equipment through appointment times to reduce energy waste during non-use periods. During the use period, it monitors energy consumption and cooperates with corresponding management systems to urge direct-using units to fulfill their energy-saving responsibilities.

9. A campus low-carbon energy-saving intelligent classroom management system based on a virtual classroom, characterized in that, include: The basic classroom resource pool unit is used to collect attribute information of various classroom resources in the corresponding university, build a centralized resource database to form a basic classroom resource pool; The classroom smart infrastructure unit is used to build the classroom smart infrastructure layer through environmental sensing devices, network transmission equipment and intelligent controllers; The virtual smart classroom model unit is used to build the virtual smart classroom model layer. The specific steps are as follows: Combining the attribute information of basic classroom resources and the classroom environment parameters collected by intelligent facilities, a digital twin model of the virtual classroom is constructed; Based on the digital twin model, the operating status of each hardware device in the classroom is monitored in real time; Generate an operation and maintenance plan based on the equipment's operating status and forecast analysis results, and form an operation and maintenance file for the entire equipment life cycle; Build an energy consumption model for virtual classrooms, conduct multi-dimensional evaluation of classroom energy consumption in real time, and construct carbon efficiency indicators; Application service unit, used to develop corresponding classroom services according to the characteristics of different types of classrooms; The external service unit is used to provide corresponding service interfaces for different service objects to realize the input of classroom usage requirements.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to implement the campus low-carbon and energy-saving smart classroom management method based on a virtual classroom as described in any one of claims 1 to 8.

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