Classroom environment intelligent monitoring, regulating and controlling system
The intelligent classroom environment monitoring and control system utilizes multiple sensors and edge computing modules for real-time data processing and dynamic priority sorting, solving the real-time and dynamic response issues of the classroom environment control system and achieving efficient and energy-saving classroom environment control.
Patent Information
- Application Number
- CN202511138638.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-14
- Publication Date
- 2025-11-14
AI Technical Summary
Existing intelligent monitoring and control systems for classroom environments suffer from high data transmission latency and slow response speed, failing to meet the requirements for millisecond-level real-time control. They also lack the ability to respond to dynamic changes in the environment and lack dynamic adjustment strategies for task waiting time and execution time.
The classroom environment intelligent monitoring and control system includes an environmental perception and acquisition module, a real-time edge computing module, an intelligent control execution module, an emergency early warning and response module, an energy carbon footprint tracking module, and an adaptive optimization module. Through real-time data acquisition from multiple sensors, lightweight algorithms are used for anomaly detection and dynamic priority ranking to generate control commands, which are then linked with air conditioning and lighting equipment to adjust environmental parameters. Furthermore, the system dynamically adjusts strategies based on tiered alarms and user feedback.
It achieves localized real-time processing, significantly improving system real-time performance and resource utilization, dynamically adjusting strategies to meet the needs of high-priority tasks, balancing energy saving and comfort, quickly responding to environmental changes, reducing network dependence, and optimizing carbon emissions and energy consumption.
Smart Images
Figure CN120949673A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of intelligent environmental monitoring and control technology, specifically referring to an intelligent classroom environment monitoring and control system. Background Technology
[0002] With the deepening of educational informatization and smart campus construction, creating a healthy, comfortable, and efficient learning environment has become an important goal. Traditional classroom environment management relies heavily on manual operation or simple timed and threshold controls;
[0003] However, existing intelligent monitoring and control systems for classroom environments still have certain shortcomings. The existing systems rely on centralized cloud processing of environmental data, resulting in high data transmission latency and slow response speed. Classroom air quality monitoring data needs to be uploaded to the cloud for analysis before issuing instructions, which cannot meet the millisecond-level real-time control requirements. The systems use fixed priority rules and cannot adjust strategies based on dynamic factors such as task waiting time and execution time. The systems use a single threshold to trigger device linkage and lack the ability to respond to dynamic changes in the environment. Therefore, an intelligent monitoring and control system for classroom environments is proposed. Summary of the Invention
[0004] The purpose of this invention is to provide an intelligent monitoring and control system for the classroom environment to solve the problems mentioned in the background art.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a classroom environment intelligent monitoring and control system, comprising an environmental perception and acquisition module, a real-time edge computing module, an intelligent control execution module, an emergency early warning and response module, an energy carbon footprint tracking module, an adaptive optimization module, and a remote interactive management module;
[0006] The environmental sensing and acquisition module collects real-time data on air quality, temperature and humidity, light intensity, and personnel density in the classroom through multiple sensors.
[0007] The real-time edge computing module uses data from the environmental perception and acquisition module to perform anomaly detection and dynamic priority sorting through a lightweight algorithm, and generates control instructions.
[0008] The intelligent control execution module, based on instructions from the real-time edge computing module, coordinates with air conditioning and lighting equipment to adjust environmental parameters.
[0009] The emergency early warning response module triggers a tiered alarm and coordinates with the intelligent control and execution module to initiate emergency measures based on the threshold exceeding data from the environmental perception and acquisition module.
[0010] The energy carbon footprint tracking module generates carbon emission statistics and optimizes energy-saving strategies based on the energy consumption records of the intelligent control execution module and the historical data of the environmental perception acquisition module.
[0011] The adaptive optimization module dynamically adjusts the control algorithm and threshold settings by integrating user feedback from the remote interactive management module and energy consumption data from the energy carbon footprint tracking module.
[0012] The remote interactive management module displays the real-time status of the intelligent control and execution module through a visual interface, and receives user manual control requests and feeds them back to the edge computing module.
[0013] Preferably, the environmental sensing and acquisition module collects real-time data on air quality, temperature and humidity, light intensity, and personnel density in the classroom using multiple sensors. It deploys air quality sensors, temperature and humidity sensors, light intensity sensors, and infrared thermal imaging sensors, and uses I... 2 The C-bus connects multiple sensors, GPIO pins control relay modules, and actuates the devices. It normalizes data from different sensors, periodically corrects the baseline value of the CO2 sensor by comparing historical data, eliminates instantaneous outliers by Kalman filtering, cross-validates multi-sensor data, synchronizes the sampling frequencies of different sensors by timestamps, correlates personnel density data from thermal imaging sensors with location information from light sensors, analyzes local environmental changes, and fuses multi-sensor data using a weighted average method.
[0014] Preferably, the real-time edge computing module, based on data from the environmental perception acquisition module, performs anomaly detection and dynamic priority sorting using a lightweight algorithm, generates control instructions, receives real-time data streams from the environmental perception acquisition module, adjusts the original data in conjunction with the calibration parameters of the environmental perception acquisition module, detects univariate anomalies using EWMA, dynamically adjusts the data processing frequency according to the edge node load, prioritizes high-priority services, and performs adaptive calculation of dynamic priority, as shown in the formula:
[0015]
[0016] In the formula, P dc W(t) represents the dynamic priority of the task at time t, W(t) represents the waiting time of the task at time t, E(t) represents the estimated execution time of the task at time t, S0 represents the static priority of the task, α represents the adaptive weighting coefficient, which represents the degree of influence of dynamic factors on priority, and β represents the exponential adjustment parameter, which is used to adjust the sensitivity of waiting time and execution time.
[0017] Preferably, the real-time edge computing module, based on dynamic priority resource allocation energy consumption, is implemented using the following formula:
[0018]
[0019] In the formula, C ey (t) represents the energy consumption allocated to the task within time t, γ represents the energy consumption baseline coefficient, and Pmax T represents the maximum value of the task priority. ec (t) represents the actual execution time of the task at time t;
[0020] Specific instructions are generated based on the exception type and priority. The instructions must include the execution target, action type and parameters, and are sent to the intelligent control and execution module via the MQTT protocol.
[0021] Preferably, the intelligent control execution module, based on instructions from the real-time edge computing module, coordinates with air conditioning and lighting equipment to adjust environmental parameters. It sends instructions to the air conditioning equipment via the edge computing module to adjust temperature, fan speed, and mode. If the air quality sensor detects excessive CO2 concentration, it activates the fresh air function of the air conditioning system. It adjusts the brightness of the lights based on data from the light sensor. When air quality is abnormal, it closes the light vents and switches to a low-power mode. It continuously monitors changes in environmental parameters after the air conditioning and lighting operations, feeding the results back to the edge computing module. If the environmental parameters do not meet expectations, it recalculates priorities and adjusts the equipment operating parameters. If user intervention is detected, it automatically pauses the linkage strategy and records user preferences.
[0022] Preferably, the emergency early warning response module triggers a graded alarm and links the intelligent control execution module to start emergency measures based on the threshold exceeding data of the environmental perception acquisition module. It acquires key parameter data collected by sensors in the environment in real time, compares the collected real-time data with the preset threshold range, and if a parameter exceeds the threshold, it is marked as an abnormal event and a timestamp is recorded.
[0023] Preferably, the emergency warning response module has a tiered alarm triggering mechanism divided into primary warning, emergency alarm, and disaster-level alarm. Based on the temperature and humidity exceeding the standard, it automatically adjusts the air conditioning mode and fan speed or shuts down the equipment. When there is insufficient light, it automatically brightens the lights. When there is a smoke alarm, it closes the ventilation vents. Based on the alarm level and the urgency of the parameters, it dynamically adjusts the execution order of the equipment. It optimizes resource allocation in real time through the edge computing module. If the parameters do not recover to a safe range, it upgrades the alarm level and adjusts the linkage strategy. If the user intervenes manually, it pauses the automatic linkage and records the operation log.
[0024] Preferably, the energy carbon footprint tracking module obtains real-time energy consumption records of the equipment from the intelligent control execution module and integrates historical energy consumption data. It also obtains historical environmental data from the environmental perception acquisition module. Combining the environmental status during equipment operation, it calculates direct carbon emissions based on equipment energy consumption data and corresponding activity data, using the emission factor method. Based on indirect activity data from the supply chain or production process, it calculates indirect carbon emissions. It performs full life-cycle carbon footprint tracking for key equipment, ranks the energy-saving potential of equipment according to carbon emission statistics, adjusts equipment operation modes according to environmental parameters, optimizes equipment operating periods using peak-valley electricity pricing strategies, and achieves multi-device collaborative energy saving by combining environmental perception data. When air quality is good, it reduces the operating frequency of the fresh air system. The optimized energy-saving strategy is sent to the intelligent control execution module to automatically adjust equipment operating parameters. It triggers alarms and enforces energy-saving measures in high-carbon emission scenarios, continuously monitors changes in energy consumption and carbon emissions after strategy execution, and dynamically adjusts strategy priority if the strategy is ineffective.
[0025] Preferably, the adaptive optimization module dynamically adjusts the control algorithm and threshold settings by integrating user feedback from the remote interactive management module and energy consumption data from the energy carbon footprint tracking module; it obtains real-time user feedback on equipment operating status from the remote interactive management module, integrates historical user behavior data, and obtains equipment energy consumption records, real-time environmental parameters, and carbon emission statistics from the energy carbon footprint tracking module. It identifies user group characteristics through cluster analysis, mines the relationship between user feedback and equipment operating parameters using association rules, constructs a time series model based on historical energy consumption data, identifies high-energy-consumption periods and inefficient operating scenarios, establishes a causal model between user feedback and carbon emissions, optimizes carbon emission attribution by combining environmental parameters, dynamically adjusts parameters based on user feedback and energy consumption data, sets priority weights, generates the optimal strategy combination through Pareto front analysis, dynamically adjusts the threshold range based on historical data and user feedback, optimizes thresholds by combining carbon emission data, sets equipment start-up and shutdown thresholds according to energy consumption patterns, and sends the optimized algorithm and thresholds to the intelligent control execution module.
[0026] Preferably, the remote interactive management module displays the real-time status of the intelligent control execution module through a visual interface and receives user manual control requests, which are then fed back to the edge computing module. The interactive interface is designed using a graphical interface framework. It obtains real-time data from the intelligent control execution module, subscribes to the real-time data stream through the edge computing module's API or message queue, parses the received real-time data into a format recognizable by the visual interface, dynamically updates the device status in the interface, and returns the result to the edge computing module after the intelligent control execution module completes its execution.
[0027] Compared with the prior art, the beneficial effects of the present invention are:
[0028] 1. This invention achieves localized real-time processing through lightweight algorithms and dynamic priority sorting. It dynamically adjusts the data processing frequency based on the load of edge nodes, prioritizes high-priority tasks, dynamically adjusts strategies based on task waiting time and execution time, and efficiently sends instructions to the execution module through the MQTT protocol, avoiding cloud communication delays. It achieves millisecond-level decision-making on low-power devices, significantly improving system real-time performance and resource utilization, and reducing dependence on network stability.
[0029] 2. This invention uses the edge computing module to link air conditioning and lighting equipment to achieve dynamic parameter adjustment. When the CO2 concentration exceeds the standard, the fresh air function is automatically activated; when the light is insufficient, the lights are brightened; and when the air quality is abnormal, a low-power mode is switched to balance energy saving and comfort. The invention continuously monitors changes in environmental parameters after execution and feeds them back to the edge computing module. If the expected results are not achieved, the priority is recalculated and the equipment parameters are adjusted to form a closed-loop optimization. At the same time, the automatic strategy can be paused by user intervention, and preference data is recorded for use by the adaptive optimization module. Attached Figure Description
[0030] Figure 1 This is a schematic diagram of the intelligent classroom environment monitoring and control system of the present invention;
[0031] Figure 2 The operating flow of the intelligent classroom environment monitoring and control system of the present invention. Figure 1 ;
[0032] Figure 3 The operating flow of the intelligent classroom environment monitoring and control system of the present invention. Figure 2 . Detailed Implementation
[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0034] Example
[0035] Please see Figures 1-3 As shown, the present invention provides a technical solution including an environmental perception and acquisition module, a real-time edge computing module, an intelligent control and execution module, an emergency early warning and response module, an energy carbon footprint tracking module, an adaptive optimization module, and a remote interactive management module;
[0036] The environmental sensing and acquisition module collects real-time data on air quality, temperature and humidity, light intensity, and personnel density in the classroom through multiple sensors.
[0037] The real-time edge computing module uses data from the environmental perception and acquisition module to perform anomaly detection and dynamic priority sorting through a lightweight algorithm, and generates control instructions.
[0038] The intelligent control execution module, based on instructions from the real-time edge computing module, coordinates with air conditioning and lighting equipment to adjust environmental parameters.
[0039] The emergency early warning response module triggers a tiered alarm and coordinates with the intelligent control and execution module to initiate emergency measures based on the threshold exceeding data from the environmental perception and acquisition module.
[0040] The energy carbon footprint tracking module generates carbon emission statistics and optimizes energy-saving strategies based on the energy consumption records of the intelligent control execution module and the historical data of the environmental perception acquisition module.
[0041] The adaptive optimization module dynamically adjusts the control algorithm and threshold settings by integrating user feedback from the remote interactive management module and energy consumption data from the energy carbon footprint tracking module.
[0042] The remote interactive management module displays the real-time status of the intelligent control and execution module through a visual interface, and receives user manual control requests and feeds them back to the edge computing module.
[0043] Preferably, the environmental sensing and acquisition module collects real-time data on air quality, temperature and humidity, light intensity, and personnel density in the classroom using multiple sensors. It deploys air quality sensors, temperature and humidity sensors, light intensity sensors, and infrared thermal imaging sensors, and uses I... 2 The C-bus connects multiple sensors, GPIO pins control relay modules, and actuates the devices. It normalizes data from different sensors, periodically corrects the baseline value of the CO2 sensor by comparing historical data, eliminates instantaneous outliers by Kalman filtering, cross-validates multi-sensor data, synchronizes the sampling frequencies of different sensors by timestamps, correlates personnel density data from thermal imaging sensors with location information from light sensors, analyzes local environmental changes, and fuses multi-sensor data using a weighted average method.
[0044] Preferably, the real-time edge computing module, based on data from the environmental perception acquisition module, performs anomaly detection and dynamic priority sorting using a lightweight algorithm, generates control instructions, receives real-time data streams from the environmental perception acquisition module, adjusts the original data in conjunction with the calibration parameters of the environmental perception acquisition module, detects univariate anomalies using EWMA, dynamically adjusts the data processing frequency according to the edge node load, prioritizes high-priority services, and performs adaptive calculation of dynamic priority, as shown in the formula:
[0045]
[0046] In the formula, P dcW(t) represents the dynamic priority of the task at time t, W(t) represents the waiting time of the task at time t, E(t) represents the estimated execution time of the task at time t, S0 represents the static priority of the task, α represents the adaptive weighting coefficient, which represents the degree of influence of dynamic factors on priority, and β represents the exponential adjustment parameter, which is used to adjust the sensitivity of waiting time and execution time.
[0047] Preferably, the real-time edge computing module, based on dynamic priority resource allocation energy consumption, is implemented using the following formula:
[0048]
[0049] In the formula, C ey (t) represents the energy consumption allocated to the task within time t, γ represents the energy consumption baseline coefficient, and P max T represents the maximum value of the task priority. ec (t) represents the actual execution time of the task at time t;
[0050] Specific instructions are generated based on the exception type and priority. The instructions must include the execution target, action type and parameters, and are sent to the intelligent control and execution module via the MQTT protocol.
[0051] Preferably, the intelligent control execution module, based on instructions from the real-time edge computing module, coordinates with air conditioning and lighting equipment to adjust environmental parameters. It sends instructions to the air conditioning equipment via the edge computing module to adjust temperature, fan speed, and mode. If the air quality sensor detects excessive CO2 concentration, it activates the fresh air function of the air conditioning system. It adjusts the brightness of the lights based on data from the light sensor. When air quality is abnormal, it closes the light vents and switches to a low-power mode. It continuously monitors changes in environmental parameters after the air conditioning and lighting operations, feeding the results back to the edge computing module. If the environmental parameters do not meet expectations, it recalculates priorities and adjusts the equipment operating parameters. If user intervention is detected, it automatically pauses the linkage strategy and records user preferences.
[0052] Preferably, the emergency early warning response module triggers a graded alarm and links the intelligent control execution module to start emergency measures based on the threshold exceeding data of the environmental perception acquisition module. It acquires key parameter data collected by sensors in the environment in real time, compares the collected real-time data with the preset threshold range, and if a parameter exceeds the threshold, it is marked as an abnormal event and a timestamp is recorded.
[0053] Preferably, the emergency warning response module has a tiered alarm triggering mechanism divided into primary warning, emergency alarm, and disaster-level alarm. Based on the temperature and humidity exceeding the standard, it automatically adjusts the air conditioning mode and fan speed or shuts down the equipment. When there is insufficient light, it automatically brightens the lights. When there is a smoke alarm, it closes the ventilation vents. Based on the alarm level and the urgency of the parameters, it dynamically adjusts the execution order of the equipment. It optimizes resource allocation in real time through the edge computing module. If the parameters do not recover to a safe range, it upgrades the alarm level and adjusts the linkage strategy. If the user intervenes manually, it pauses the automatic linkage and records the operation log.
[0054] Preferably, the energy carbon footprint tracking module obtains real-time energy consumption records of the equipment from the intelligent control execution module and integrates historical energy consumption data. It also obtains historical environmental data from the environmental perception acquisition module. Combining the environmental status during equipment operation, it calculates direct carbon emissions based on equipment energy consumption data and corresponding activity data, using the emission factor method. Based on indirect activity data from the supply chain or production process, it calculates indirect carbon emissions. It performs full life-cycle carbon footprint tracking for key equipment, ranks the energy-saving potential of equipment according to carbon emission statistics, adjusts equipment operation modes according to environmental parameters, optimizes equipment operating periods using peak-valley electricity pricing strategies, and achieves multi-device collaborative energy saving by combining environmental perception data. When air quality is good, it reduces the operating frequency of the fresh air system. The optimized energy-saving strategy is sent to the intelligent control execution module to automatically adjust equipment operating parameters. It triggers alarms and enforces energy-saving measures in high-carbon emission scenarios, continuously monitors changes in energy consumption and carbon emissions after strategy execution, and dynamically adjusts strategy priority if the strategy is ineffective.
[0055] Preferably, the adaptive optimization module dynamically adjusts the control algorithm and threshold settings by integrating user feedback from the remote interactive management module and energy consumption data from the energy carbon footprint tracking module; it obtains real-time user feedback on equipment operating status from the remote interactive management module, integrates historical user behavior data, and obtains equipment energy consumption records, real-time environmental parameters, and carbon emission statistics from the energy carbon footprint tracking module. It identifies user group characteristics through cluster analysis, mines the relationship between user feedback and equipment operating parameters using association rules, constructs a time series model based on historical energy consumption data, identifies high-energy-consumption periods and inefficient operating scenarios, establishes a causal model between user feedback and carbon emissions, optimizes carbon emission attribution by combining environmental parameters, dynamically adjusts parameters based on user feedback and energy consumption data, sets priority weights, generates the optimal strategy combination through Pareto front analysis, dynamically adjusts the threshold range based on historical data and user feedback, optimizes thresholds by combining carbon emission data, sets equipment start-up and shutdown thresholds according to energy consumption patterns, and sends the optimized algorithm and thresholds to the intelligent control execution module.
[0056] Preferably, the remote interactive management module displays the real-time status of the intelligent control execution module through a visual interface and receives user manual control requests, which are then fed back to the edge computing module. The interactive interface is designed using a graphical interface framework. It obtains real-time data from the intelligent control execution module, subscribes to the real-time data stream through the edge computing module's API or message queue, parses the received real-time data into a format recognizable by the visual interface, dynamically updates the device status in the interface, and returns the result to the edge computing module after the intelligent control execution module completes its execution.
[0057] Working principle: The system collects multi-dimensional data about the classroom environment in real time using multiple sensors. The sensors transmit data via I... 2 The C-bus connection and GPIO pin control relay module to link and execute devices. After data acquisition, normalization processing is performed to eliminate instantaneous outliers. The baseline value of the CO2 sensor is corrected by historical data. Multi-sensor data cross-validation is combined with timestamp synchronization. The personnel density data of the thermal imaging sensor is correlated with the position information of the light sensor to analyze local environmental changes. Multi-source data is fused by weighted averaging to generate high-precision environmental status input. Real-time data streams from the environmental perception module are received and the original data is adjusted by combining calibration parameters. Environmental anomalies are identified by lightweight algorithms, and task priorities are calculated based on dynamic priorities to prioritize high-priority services. The data processing frequency is dynamically adjusted according to the edge node load to reduce resource waste. After anomaly detection, an instruction containing execution target, action type and parameters is generated and sent to the intelligent control execution module via MQTT protocol. At the same time, energy consumption allocation is optimized based on the resource allocation energy consumption formula of dynamic priority.
[0058] The intelligent control execution module, based on instructions from the edge computing module, coordinates with devices such as air conditioners and lights to regulate the environment. When CO2 concentration exceeds the standard, it activates the air conditioner's fresh air function; when lighting is insufficient, it brightens the lights; and when air quality is abnormal, it switches devices to low-power mode and closes unnecessary vents. After execution, it continuously monitors changes in environmental parameters. If the expected results are not met, it feeds back to the edge computing module to recalculate priorities and adjust parameters. When the user intervenes manually, the automatic strategy is paused and preference data is recorded, forming a closed-loop control. It dynamically adjusts device operating modes, acquires sensor data in real time and compares it with preset thresholds, marks abnormal events and records timestamps, and triggers tiered alarms based on the severity of the anomaly. When temperature exceeds the standard, it adjusts the air conditioner mode; when smoke alarms are triggered, vents are closed; when lighting is insufficient, lights are brightened; it dynamically adjusts the device execution sequence and optimizes resource allocation. If parameters do not recover, the alarm level is escalated. When the user intervenes manually, automatic linkage is paused and operation logs are recorded, ensuring the flexibility and safety of emergency measures. Through a tiered alarm mechanism, it quickly responds to potential risks, integrating real-time energy consumption records from the intelligent control execution module with historical data from the environmental sensing module, and combining the emission factor method to calculate direct and indirect emissions. By tracking carbon emissions and the entire lifecycle carbon footprint of key equipment through supply chain data, mandatory energy-saving measures are triggered for high-carbon emission scenarios. Peak-valley electricity pricing strategies are used to optimize equipment operating times, and the frequency of fresh air systems is reduced when air quality is good, achieving collaborative energy saving across multiple devices. Energy consumption and carbon emission changes after strategy implementation are continuously monitored, and if the effect is unsatisfactory, strategy priorities are dynamically adjusted to drive the system toward carbon neutrality. Cluster analysis is used to identify user group characteristics, and association rules are used to mine the relationship between user feedback and equipment parameters. A time series model is built based on historical energy consumption data to identify high-energy-consumption periods and inefficient operating scenarios. A causal model between user feedback and carbon emissions is established, and the parameters and priority weights of the control algorithm are dynamically adjusted. Pareto front analysis is used to generate the optimal strategy combination, and carbon emission attribution is optimized based on environmental parameters. The adjusted algorithm and thresholds are then sent to the intelligent control execution module to achieve personalized and dynamic system optimization. The real-time status of the intelligent control execution module is displayed through a graphical interface framework. Users can send manual control requests through the interface, and the instructions are fed back to the edge computing module through API or message queues. The interface dynamically updates the equipment status.
[0059] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their likenesses.
[0060] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.
Claims
1. A classroom environment intelligent monitoring and control system, characterized in that: It includes an environmental perception and acquisition module, a real-time edge computing module, an intelligent control and execution module, an emergency early warning and response module, an energy carbon footprint tracking module, an adaptive optimization module, and a remote interactive management module; The environmental sensing and acquisition module collects real-time data on air quality, temperature and humidity, light intensity, and personnel density in the classroom through multiple sensors. The real-time edge computing module uses data from the environmental perception and acquisition module to perform anomaly detection and dynamic priority sorting through a lightweight algorithm, and generates control instructions. The intelligent control execution module, based on instructions from the real-time edge computing module, coordinates with air conditioning and lighting equipment to adjust environmental parameters. The emergency early warning response module triggers a tiered alarm and coordinates with the intelligent control and execution module to initiate emergency measures based on the threshold exceeding data from the environmental perception and acquisition module. The energy carbon footprint tracking module generates carbon emission statistics and optimizes energy-saving strategies based on the energy consumption records of the intelligent control execution module and the historical data of the environmental perception acquisition module. The adaptive optimization module dynamically adjusts the control algorithm and threshold settings by integrating user feedback from the remote interactive management module and energy consumption data from the energy carbon footprint tracking module. The remote interactive management module displays the real-time status of the intelligent control and execution module through a visual interface, and receives user manual control requests and feeds them back to the edge computing module.
2. The intelligent classroom environment monitoring and control system according to claim 1, characterized in that: The environmental sensing and acquisition module collects real-time data on air quality, temperature and humidity, light intensity, and personnel density in the classroom using multiple sensors. It deploys air quality sensors, temperature and humidity sensors, light intensity sensors, and infrared thermal imaging sensors. 2 The C-bus connects multiple sensors, GPIO pins control relay modules, and actuates the devices. It normalizes data from different sensors, periodically corrects the baseline value of the CO2 sensor by comparing historical data, eliminates instantaneous outliers by Kalman filtering, cross-validates multi-sensor data, synchronizes the sampling frequencies of different sensors by timestamps, correlates personnel density data from thermal imaging sensors with location information from light sensors, analyzes local environmental changes, and fuses multi-sensor data using a weighted average method.
3. The intelligent classroom environment monitoring and control system according to claim 1, characterized in that: The real-time edge computing module, based on data from the environmental perception acquisition module, performs anomaly detection and dynamic priority ranking using a lightweight algorithm, generates control instructions, receives real-time data streams from the environmental perception acquisition module, adjusts the original data in conjunction with the calibration parameters of the environmental perception acquisition module, detects univariate anomalies using EWMA, dynamically adjusts the data processing frequency according to the edge node load, prioritizes high-priority services, and performs adaptive dynamic priority calculation, implemented using the following formula: In the formula, P dc W(t) represents the dynamic priority of the task at time t, W(t) represents the waiting time of the task at time t, E(t) represents the estimated execution time of the task at time t, S0 represents the static priority of the task, α represents the adaptive weighting coefficient, which represents the degree of influence of dynamic factors on priority, and β represents the exponential adjustment parameter, which is used to adjust the sensitivity of waiting time and execution time.
4. The intelligent classroom environment monitoring and control system according to claim 3, characterized in that: The real-time edge computing module, based on dynamic priority resource allocation energy consumption, implements the following formula: In the formula, C ey (t) represents the energy consumption allocated to the task within time t, γ represents the energy consumption baseline coefficient, and P max T represents the maximum value of the task priority. ec (t) represents the actual execution time of the task at time t; Specific instructions are generated based on the exception type and priority. The instructions must include the execution target, action type and parameters, and are sent to the intelligent control and execution module via the MQTT protocol.
5. The intelligent classroom environment monitoring and control system according to claim 1, characterized in that: The intelligent control execution module, based on instructions from the real-time edge computing module, coordinates with air conditioning and lighting equipment to adjust environmental parameters. It sends instructions to the air conditioning equipment via the edge computing module to adjust temperature, fan speed, and mode. If the air quality sensor detects excessive CO2 concentration, it activates the air conditioning's fresh air function. It adjusts lighting brightness based on light sensor data. In cases of abnormal air quality, it closes light vents and switches to low-power mode. It continuously monitors changes in environmental parameters after the air conditioning and lighting operations, feeding the results back to the edge computing module. If environmental parameters do not meet expectations, it recalculates priorities and adjusts equipment operating parameters. If user intervention is detected, it automatically pauses the linkage strategy and records user preferences.
6. The intelligent classroom environment monitoring and control system according to claim 1, characterized in that: The emergency early warning response module triggers a tiered alarm based on the threshold exceeding data from the environmental perception and acquisition module, and links the intelligent control and execution module to initiate emergency measures. It acquires key parameter data collected in real time by sensors in the environment, compares the collected real-time data with the preset threshold range, and if a parameter exceeds the threshold, it is marked as an abnormal event and a timestamp is recorded.
7. The intelligent classroom environment monitoring and control system according to claim 6, characterized in that: The emergency warning response module has a tiered alarm triggering mechanism, which includes primary warning, emergency alarm, and disaster-level alarm. Based on excessive temperature and humidity, it automatically adjusts the air conditioning mode and fan speed or shuts down the equipment. When there is insufficient light, it automatically brightens the lights. When a smoke alarm is triggered, it closes the ventilation vents. Based on the alarm level and the urgency of the parameters, it dynamically adjusts the execution order of the equipment. It optimizes resource allocation in real time through the edge computing module. If the parameters have not recovered to a safe range, it upgrades the alarm level and adjusts the linkage strategy. If the user intervenes manually, it pauses the automatic linkage and records the operation log.
8. The intelligent classroom environment monitoring and control system according to claim 1, characterized in that: The energy carbon footprint tracking module obtains real-time energy consumption records of equipment from the intelligent control and execution module and integrates historical energy consumption data. It also obtains historical environmental data from the environmental perception and acquisition module. Combining the environmental status during equipment operation, it calculates direct carbon emissions based on equipment energy consumption data and corresponding activity data, using the emission factor method. It calculates indirect carbon emissions based on indirect activity data from the supply chain or production process. It tracks the carbon footprint of key equipment throughout its entire life cycle, ranks equipment energy-saving potential based on carbon emission statistics, adjusts equipment operating modes according to environmental parameters, optimizes equipment operating periods using peak-valley electricity pricing strategies, and achieves collaborative energy saving of multiple devices by combining environmental perception data. It reduces the operating frequency of the fresh air system when air quality is good, sends the optimized energy-saving strategy to the intelligent control and execution module, automatically adjusts equipment operating parameters, triggers alarms and enforces energy-saving measures in high-carbon emission scenarios, continuously monitors changes in energy consumption and carbon emissions after strategy execution, and dynamically adjusts strategy priority if the strategy is ineffective.
9. The intelligent classroom environment monitoring and control system according to claim 1, characterized in that: The adaptive optimization module dynamically adjusts the control algorithm and threshold settings by integrating user feedback from the remote interactive management module and energy consumption data from the energy carbon footprint tracking module. It obtains real-time user feedback on equipment operating status from the remote interactive management module, integrates historical user behavior data, and obtains equipment energy consumption records, real-time environmental parameters, and carbon emission statistics from the energy carbon footprint tracking module. Through cluster analysis, it identifies user group characteristics, utilizes association rules to mine the relationship between user feedback and equipment operating parameters, constructs a time series model based on historical energy consumption data, identifies high-energy-consumption periods and inefficient operating scenarios, establishes a causal model between user feedback and carbon emissions, optimizes carbon emission attribution based on environmental parameters, dynamically adjusts parameters based on user feedback and energy consumption data, sets priority weights, generates optimal strategy combinations through Pareto front analysis, dynamically adjusts threshold ranges based on historical data and user feedback, optimizes thresholds based on carbon emission data, sets equipment start-up and shutdown thresholds according to energy consumption patterns, and sends the optimized algorithm and thresholds to the intelligent control execution module.
10. The intelligent monitoring and control system for classroom environment according to claim 1, characterized in that: The remote interactive management module displays the real-time status of the intelligent control execution module through a visual interface and receives user manual control requests, which are then fed back to the edge computing module. The interactive interface is designed using a graphical interface framework. It obtains real-time data from the intelligent control execution module, subscribes to real-time data streams through the edge computing module's API or message queue, parses the received real-time data into a format recognizable by the visual interface, dynamically updates the device status in the interface, and returns the results to the edge computing module after the intelligent control execution module completes its execution.