Healthy indoor environment edge computing system

Abstract

The invention discloses a healthy indoor environment edge computing system. The edge computing system comprises a sensing equipment layer, an edge node computing layer, a communication layer, a cloud platform layer, a control execution layer, a user interaction layer and an artificial intelligence and machine learning layer. The method has the beneficial effects that the indoor environment is monitored in an edge monitoring, calculation and analysis manner, so that the real-time processing of monitoring data at a monitoring place is realized, the data transmission path is shortened, the data transmission cost and time are saved, the detection efficiency of the indoor environment is higher, and the detection efficiency of the indoor environment is improved. The method has the advantages that the data transmission requirement is reduced, the defect of data loss caused by network delay or interruption is avoided, the detection result is more accurate, the edge calculation detection mode is adopted, the data transmission requirement is reduced, and the limitation of the central server on the system expansibility and the response speed is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of indoor environmental monitoring, and particularly to a health indoor environment edge computing system. Background Art

[0002] Indoor environmental monitoring refers to the process of collecting, analyzing, and evaluating indoor environmental quality in real time through various sensors and devices. Its goal is to ensure the comfort and health of the indoor environment and to make adjustments when necessary. Common monitoring contents include temperature, humidity, air quality, noise, light, etc.

[0003] Currently, when monitoring the indoor environment, it mainly relies on multiple sensors deployed indoors to monitor the indoor environmental quality, and after monitoring, the information is uniformly processed by the control center to obtain the detection results of the indoor environment. Although the above detection method can achieve the monitoring of the indoor environment, since the above environmental monitoring system is mainly based on a centralized architecture, this means that all data, regardless of its source, needs to be transmitted to the central server for processing and analysis. This not only increases the time and cost of data transmission, resulting in relatively low detection efficiency of the indoor environment, but also easily causes data loss due to network latency or interruption, making the detection results have large errors; in addition, the central server is prone to becoming a bottleneck, restricting the scalability and response speed of the system. Summary of the Invention

[0004] The purpose of the present invention is to provide a health indoor environment edge computing system to solve the above problems.

[0005] The present invention achieves the above purpose through the following technical solutions:

[0006] A health indoor environment edge computing system, which includes: a sensing device layer, an edge node computing layer, a communication layer, a cloud platform layer, a control execution layer, a user interaction layer, and an artificial intelligence and machine learning layer.

[0007] Furthermore, the sensing device layer is the basis of the entire system and is responsible for collecting various types of data of the indoor environment in real time; these sensors usually include: an air quality sensor: used to monitor the concentration of pollutants such as PM2.5, PM10, CO2, TVOCs, etc. in the air; a temperature and humidity sensor: used to detect the temperature and humidity indoors to ensure a comfortable environment; a noise sensor: monitors the noise level indoors to ensure a quiet environment; a light sensor: measures the indoor light intensity to adjust lighting equipment; a carbon dioxide sensor: used to detect the concentration of carbon dioxide to ensure fresh indoor air.

[0008] Furthermore, the edge node computing layer is where the sensor data is preliminarily processed and analyzed; its functions are as follows: Data acquisition and preprocessing: Obtain data from various sensors and perform preliminary cleaning, filtering, and integration; Data storage and analysis: The edge node can locally store the collected environmental data and perform real-time analysis to provide immediate feedback; Decision-making and control: Based on the data analysis results, the edge node can make real-time judgments and control indoor devices (such as air conditioners, air purifiers, lighting devices, etc.) for adjustment.

[0009] Furthermore, the communication layer is responsible for ensuring effective connections and data transmission between layers; common communication technologies include: Wireless communication: such as Wi-Fi, Zigbee, LoRa, etc., for connecting sensors to edge computing nodes; Wired communication: such as Ethernet, for stable data transmission between devices; Cloud connection: Upload, analyze, and backup data to a remote cloud via the Internet.

[0010] Furthermore, the cloud platform layer is used to store, backup, and further analyze data from edge computing nodes; its functions include: Data integration and big data analysis: Centralize environmental data from various locations to the cloud for in-depth analysis and provide higher-level predictions and decisions; Remote monitoring and management: Through the cloud platform, users can remotely view the indoor environmental conditions and make adjustments and controls; Historical data storage: The cloud storage system will save long-term historical data, facilitating users to view the environmental change trends and optimize.

[0011] Furthermore, the control execution layer is responsible for controlling the actual environmental regulation devices based on the feedback from the edge computing nodes or the cloud platform, including: Air conditioner and temperature and humidity control system: Adjust devices such as air conditioners, humidifiers, and dehumidifiers according to indoor temperature and humidity data; Air purification equipment: Automatically adjust the working state of air purifiers according to air quality data; Lighting control system: Adjust the indoor lighting brightness according to the light sensor data to ensure a comfortable lighting environment; Noise control equipment: Start noise reduction or sound insulation equipment when needed to maintain a quiet indoor environment.

[0012] Furthermore, the user interaction layer is the bridge between the system and users, including: Smart phone applications or PC interfaces: Through these devices, users can view real-time data, set personal preferences, and control indoor environmental devices; Voice control system: Control through voice assistants (such as Alexa, Google Assistant, etc.) to make operations more convenient; Smart dashboard: Users can view the working status of the system through the dashboard and monitor various environmental indicators in real time.

[0013] Furthermore, the artificial intelligence and machine learning layer enables the system to gradually learn and optimize control strategies by integrating artificial intelligence technology: Machine learning algorithms: By analyzing historical data, optimize indoor environment control strategies to achieve more intelligent management; Predictive analysis: Based on trend analysis of data, predict environmental changes in advance and make adjustments, such as predicting whether to turn on the air conditioner or humidifier according to temperature and humidity changes.

[0014] The beneficial effects of the present invention are as follows:

[0015] The present invention monitors the indoor environment by means of edge monitoring, calculation and analysis, which not only realizes the real-time processing of monitoring data at the monitoring site, shortens the data transmission path, saves data transmission costs and time, makes the detection efficiency of the indoor environment higher, but also avoids the disadvantages of data loss caused by network delay or interruption, makes the detection results more accurate. At the same time, the edge computing detection method is adopted to reduce the data transmission requirements and avoid the limitations of the central server on the system scalability and response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a structural block diagram of the edge computing system for a healthy indoor environment according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The edge computing system for a healthy indoor environment includes: a sensing device layer, an edge node computing layer, a communication layer, a cloud platform layer, a control execution layer, a user interaction layer, and an artificial intelligence and machine learning layer.

[0018] In this embodiment, the sensing device layer is the foundation of the entire system and is responsible for real-time collection of various data of the indoor environment; these sensors usually include: an air quality sensor: used to monitor the concentration of pollutants such as PM2.5, PM10, CO2, TVOCs, etc. in the air; a temperature and humidity sensor: used to detect the indoor temperature and humidity to ensure a comfortable environment; a noise sensor: monitors the indoor noise level to ensure a quiet environment; a light sensor: measures the indoor light intensity to adjust lighting equipment; a carbon dioxide sensor: used to detect the concentration of carbon dioxide to ensure fresh indoor air.

[0019] In this embodiment, the edge node computing layer is where the sensor data is preliminarily processed and analyzed; its functions are: data collection and preprocessing: obtain data from each sensor and perform preliminary cleaning, filtering and integration; data storage and analysis: the edge node can locally store the collected environmental data and perform real-time analysis to provide immediate feedback; decision-making and control: according to the data analysis results, the edge node can make real-time judgments and control indoor devices (such as air conditioners, air purifiers, lighting equipment, etc.) for adjustment.

[0020] In this embodiment, the communication layer is responsible for ensuring effective connections and data transmission between layers; common communication technologies include: wireless communication such as Wi-Fi, Zigbee, LoRa, etc., which are used to connect sensors to edge computing nodes; wired communication such as Ethernet, which is used for stable data transmission between devices; cloud connection, through which data is uploaded, analyzed, and backed up to a remote cloud via the Internet.

[0021] In this embodiment, the cloud platform layer is used to store, back up, and further analyze data from edge computing nodes; its functions include: data integration and big data analysis: centralizing environmental data from various locations to the cloud for in-depth analysis to provide higher-level predictions and decisions; remote monitoring and management: through the cloud platform, users can remotely view the indoor environmental conditions and make adjustments and controls; historical data storage: the cloud storage system will save long-term historical data to facilitate users to view the environmental change trends and optimize them.

[0022] In this embodiment, the control and execution layer is responsible for controlling actual environmental adjustment devices based on the feedback from edge computing nodes or the cloud platform, including: air conditioners and temperature and humidity control systems: adjusting devices such as air conditioners, humidifiers, and dehumidifiers according to indoor temperature and humidity data; air purification devices: automatically adjusting the working status of air purifiers according to air quality data; lighting control systems: adjusting the indoor lighting brightness according to light sensor data to ensure a comfortable lighting environment; noise control devices: starting noise reduction or sound insulation devices when needed to maintain a quiet indoor environment.

[0023] In this embodiment, the user interaction layer is the bridge between the system and users, including: smartphone applications or PC interfaces: through these devices, users can view real-time data, set personal preferences, and control indoor environmental devices; voice control systems: control through voice assistants (such as Alexa, Google Assistant, etc.) to make operations more convenient; intelligent dashboards: users can view the working status of the system through the dashboards and monitor various environmental indicators in real time.

[0024] In this embodiment, the artificial intelligence and machine learning layer enables the system to gradually learn and optimize control strategies by integrating artificial intelligence technologies: machine learning algorithms: optimizing indoor environmental control strategies by analyzing historical data to achieve more intelligent management; predictive analysis: based on trend analysis of data, predicting environmental changes in advance and making adjustments, such as predicting whether to turn on the air conditioner or humidifier according to temperature and humidity changes.

[0025] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A health indoor environment edge computing system, characterized in that: The edge computing system includes: a sensing device layer, an edge node computing layer, a communication layer, a cloud platform layer, a control execution layer, a user interaction layer, and an artificial intelligence and machine learning layer.

2. The edge computing system for a healthy indoor environment according to claim 1, wherein: The sensing device layer is the foundation of the entire system and is responsible for collecting various types of data of the indoor environment in real time; these sensors usually include: an air quality sensor: used to monitor the concentrations of pollutants such as PM2.5, PM10, CO2, TVOCs, etc. in the air; a temperature and humidity sensor: used to detect the indoor temperature and humidity to ensure a comfortable environment; a noise sensor: monitors the indoor noise level to ensure a quiet environment; a light sensor: measures the indoor light intensity to adjust lighting equipment; a carbon dioxide sensor: used to detect the concentration of carbon dioxide to ensure fresh indoor air.

3. The edge computing system for a healthy indoor environment according to claim 1, wherein: The edge node computing layer is where the sensor data is initially processed and analyzed; its functions are: data collection and preprocessing: obtaining data from each sensor and performing initial cleaning, filtering, and integration; data storage and analysis: the edge node can locally store the collected environmental data and perform real-time analysis to provide immediate feedback; Decision-making and control: Based on the data analysis results, the edge node can make real-time judgments and control indoor devices (such as air conditioners, air purifiers, lighting equipment, etc.) for adjustment.

4. The edge computing system for a healthy indoor environment according to claim 1, wherein: The communication layer is responsible for ensuring effective connections and data transmission between layers; common communication technologies include: wireless communication: such as Wi-Fi, Zigbee, LoRa, etc., for connecting sensors to edge computing nodes; wired communication: such as Ethernet, for stable data transmission between devices; cloud connection: uploading, analyzing, and backing up data to a remote cloud via the Internet.

5. The edge computing system for a healthy indoor environment according to claim 1, wherein: The cloud platform layer is used to store, back up, and further analyze data from edge computing nodes; Its functions include: data integration and big data analysis: centralizing environmental data from various locations to the cloud for in-depth analysis to provide higher-level prediction and decision-making; remote monitoring and management: through the cloud platform, users can remotely view the indoor environment conditions and make adjustments and controls; historical data storage: the cloud storage system will save long-term historical data, facilitating users to view the environmental change trends and optimize.

6. The edge computing system for a healthy indoor environment according to claim 1, wherein: The control execution layer is responsible for controlling actual environmental adjustment devices based on the feedback from edge computing nodes or the cloud platform, including: an air conditioner and temperature and humidity control system: adjusting devices such as air conditioners, humidifiers, and dehumidifiers according to indoor temperature and humidity data; air purification equipment: automatically adjusting the working state of air purifiers according to air quality data; lighting control system: adjusting the indoor lighting brightness according to light sensor data to ensure a comfortable lighting environment; noise control equipment: starting noise reduction or sound insulation equipment when necessary to maintain a quiet indoor environment.

7. The edge computing system for a healthy indoor environment according to claim 1, wherein: The user interaction layer is the bridge between the system and the user, including: smartphone applications or PC interfaces: through these devices, users can view real-time data, set personal preferences, and control indoor environmental devices; voice control systems: controlled through voice assistants (such as Alexa, Google Assistant, etc.), making operations more convenient; intelligent dashboards: users can view the working status of the system through the dashboards and monitor various environmental indicators in real time.

8. The edge computing system for a healthy indoor environment according to claim 1, wherein: The artificial intelligence and machine learning layer enables the system to gradually learn and optimize control strategies by integrating artificial intelligence technologies: machine learning algorithms: by analyzing historical data, optimizing indoor environmental control strategies to achieve more intelligent management; predictive analysis: based on trend analysis of data, predicting environmental changes in advance and making adjustments, such as predicting whether to turn on the air conditioner or humidifier according to temperature and humidity changes.

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