Indoor air pollution clean room system achieving artificial intelligence green and health building standards

Abstract

This invention relates to an indoor air pollution detection and purification system for achieving AI-powered green and healthy building standards. Based on this system, and combined with efficient building design structures, renewable energy systems, water resource management systems, and a circular economy and resource recycling approach, it achieves the low energy consumption and high comfort requirements of Green Building Standards (LEED and WELL) and realizes new AI-powered green and healthy building standards. This results in near-zero indoor air pollution, near-zero water pollution, near-zero noise pollution, net-zero carbon emissions, and maximized energy conservation, thereby improving the health and comfort of the living environment.

Description

[Technical Field]

[0001] This invention relates to an indoor air pollution-free cleanroom system that achieves the standards for intelligent green and healthy buildings. In particular, it combines the goal of net-zero carbon emissions with high-efficiency air purification technology to achieve the new standards for intelligent green and healthy buildings, thereby controlling indoor air pollution to near zero, water pollution to near zero, noise pollution to near zero, carbon emissions to net zero, maximizing energy conservation, and improving the health and comfort of the living environment. [Previous Technology]

[0002] With the acceleration of urbanization and the increasing demands for environmental quality, indoor air quality and building energy consumption have received growing attention, making net-zero carbon emissions and indoor air quality crucial issues. Existing building designs and air purification technologies cannot simultaneously meet these two needs, particularly in terms of intelligent regulation and handling complex pollution sources. To address these issues, this invention introduces an intelligent control network system to achieve a holistic solution for net-zero carbon emissions and zero air pollution, meeting high standards for indoor air quality. It can be customized according to different buildings and indoor air quality requirements, not only achieving the low energy consumption and high comfort requirements of Green Building Standards (LEED and WELL) but also realizing new artificial intelligence green and healthy building standards. This results in near-zero indoor air pollution, near-zero water pollution, near-zero noise pollution, net-zero carbon emissions, and maximized energy conservation, thereby improving the health and comfort of the living environment. [Summary of the Invention]

[0003] The main objective of this invention is to provide a cleanroom system for achieving zero indoor air pollution in accordance with the standards of artificial intelligence green and healthy buildings. By integrating an intelligent indoor air pollution detection and purification system and advanced indoor and outdoor air pollution treatment equipment, this system combines efficient building design structure, renewable energy system, water resource management system, circular economy and resource recycling. Combining net-zero carbon emission targets with efficient air purification technology, it not only achieves the low energy consumption and high comfort requirements of the Green Building Standard (LEED standard) and the Healthy Building Standard (WELL standard), but also achieves the new standards of artificial intelligence green and healthy buildings. It achieves near-zero indoor air pollution, near-zero water pollution, near-zero noise pollution, net-zero carbon emissions, and maximizes energy conservation. It achieves carbon neutrality while maintaining indoor air pollutants at a level close to zero, thereby achieving a cleanroom-level cleanliness and improving the health and comfort of the living environment.

[0004] To achieve the above objectives, this invention provides an indoor air pollution-free cleanroom system that realizes the standards for intelligent green and healthy buildings. The system includes: an indoor air pollution detection and purification system comprising multiple gas detectors, at least one air pollution treatment device, at least one networked cloud computing service device, and at least one storage and data processing unit. The system comprehensively monitors indoor air pollution through multiple gas detectors and filters and purifies indoor air pollution through the air pollution treatment device. Dynamic monitoring and adjustment analysis are performed through the intelligent network system of the networked cloud computing service device and a generative artificial intelligence model. Based on the analysis results, an intelligent control command is issued to adjust the operating mode of the air pollution treatment device, achieving a purification effect on indoor air, approaching zero-pollution indoor air quality, and reaching a cleanroom-level cleanliness. The system also includes an efficient building design structure comprising a passive design structure and an airtight design structure. The passive design structure includes a natural ventilation structure (not shown), a natural lighting structure (not shown), and a thermal insulation structure (not shown), reducing the need for air conditioning and artificial lighting. This airtight design structure enhances the building's airtightness, preventing air leakage, while an intelligent ventilation system ensures air circulation and prevents pollutant accumulation. The renewable energy system combines solar photovoltaic, wind, geothermal, and energy storage systems to meet the building's electricity needs and store excess power for peak demand. The water resource management system includes a rainwater harvesting system and a high-efficiency water-saving device. The rainwater harvesting system collects rainwater. The high-efficiency water-saving device includes low-flow toilets and intelligent sprinkler systems, which utilize the rainwater collected by the rainwater harvesting system to achieve water conservation. The circular economy and resource recycling system includes a building material recycling system and a building material recycling system. The building material recycling system recycles and reuses materials from building demolition or renovation, while the building material recycling system promotes the efficient use of resources throughout the building's lifecycle.

Implementation Method

[0006] Embodiments embodying the features and advantages of the present invention will be described in detail in the following description. It should be understood that the present invention can have various variations in different forms without departing from the scope of the present invention, and the descriptions and illustrations herein are for illustrative purposes only and not intended to limit the present invention.

[0007] As shown in Figure 1A, the present invention is a cleanroom system for achieving zero indoor air pollution in accordance with the standards of artificial intelligence green and healthy buildings. It includes: an indoor air pollution detection and purification system a, an efficient building design structure b, a renewable energy system c, a water resource management system d, and a circular economy and resource recycling system e. It not only achieves the low energy consumption and high comfort requirements of the green building standard (LEED standard) and the healthy building standard (WELL standard), but also achieves the new artificial intelligence green and healthy building standard, achieving near-zero indoor air pollution, near-zero water pollution, near-zero noise pollution, net-zero carbon emissions, and maximum energy saving, thereby improving the health and comfort of the living environment.

[0008] As shown in Figures 1B and 1D, the above-mentioned indoor air pollution detection and purification system a includes: multiple gas detectors 1, at least one indoor air pollution treatment device 2, a networked cloud computing service device 3, storage and data processing 4, and a central control computer intelligent control device 5. As shown in Figures 1C and 1D, the indoor air pollution treatment equipment 2 includes a gas exchange device 2a, an air purifier 2b, a circulating filter (FFU) 2c, an exhaust device 2d, a heating and cooling system 2e, a range hood 2f, a humidity control device 2g, and a portable vacuum cleaner 2h. It can be built-in or plugged into the indoor area A. The gas exchange device 2a provides ventilation for the indoor area A and provides positive pressure air intake to prevent air pollution from entering the indoor area A. The air purifier 2b, the circulating filter (FFU) 2c, the exhaust device 2d, and the portable vacuum cleaner 2h provide near-zero air pollution purification and cleanroom treatment for the indoor area A. The heating and cooling system 2e and the humidity control device 2f provide temperature and humidity control for the indoor area A. Temperature adjustment; furthermore, the indoor air pollution treatment equipment 2 is internally equipped with at least one gas detector 1, at least one fan 21, at least one filter component 22 and at least one drive controller 23, and is equipped with an air monitoring sensor 1 electrically connected to the drive controller 23, and receives control commands through Internet of Things communication to control the start-up operation of the fan 21. Moreover, the indoor air pollution treatment equipment 2 integrates intelligent purification technologies such as HEPA, UVC, activated carbon, photocatalysis, plasma, and negative ions, as shown in Figures 1A and 12. The networked cloud computing service device 3 includes a wireless network cloud computing service module 31, a cloud control service unit 32, a device management unit 33, an application unit 34 and a generative artificial intelligence model 35 (Artificial Intelligence Generated Content, AIGC). Thus, the indoor air pollution detection and purification system a comprehensively monitors indoor air pollution through multiple gas detectors 1, and the indoor air pollution treatment equipment 2 integrates intelligent purification technologies such as HEPA, UVC, activated carbon, photocatalysis, plasma, and negative ions. It also dynamically monitors and adjusts the system through the intelligent network system of the networked cloud computing service device 3 and the generative artificial intelligence model 35, and automatically adjusts the operating mode of the indoor air pollution treatment equipment 2 based on real-time data to achieve indoor air purification effects, close to zero pollution indoor air quality, and reach the cleanliness level of a cleanroom.

[0009] The above-mentioned multiple gas detectors 1 are installed in indoor area A and outdoor area B, and are responsible for detecting air quality data such as PM1, PM2.5, CO2, VOC, temperature, and humidity. The gas detectors 1 transmit the data to the networked cloud computing service device 3 through Internet of Things technology, and the generative artificial intelligence model 35 performs data analysis to generate corresponding control commands.

[0010] The above-mentioned indoor air pollution treatment equipment 2 is internally equipped with at least one gas detector 1, at least one fan 21, at least one filter component 22 and at least one drive controller 23. The filter component 22 includes HEPA, UVC, activated carbon, photocatalysis, plasma, negative ion and other filtration and purification technologies. These indoor air pollution treatment devices 2 automatically perform air filtration, temperature and humidity adjustment and sterilization operations according to intelligent control instructions to maintain indoor air pollutants at a level close to zero, so as to achieve the air cleanliness of a cleanroom.

[0011] The aforementioned indoor air pollution treatment equipment 2 further includes a central control computer intelligent control device 5, which receives intelligent control commands issued by the networked cloud computing service device 3 through Internet of Things communication, and transmits them to the gas detection module 1 of the indoor air pollution treatment equipment 2 to receive and provide control for the start-up operation of the duct fan 21; or, the central control computer intelligent control device 5 has edge computing function, which can receive and analyze the air quality data monitored by the gas detection module 1 of each indoor air pollution treatment equipment 2 through Internet of Things communication, and generate intelligent control commands based on the analysis results, directly issue intelligent control commands, transmit them to the gas detection module 1 of the indoor air pollution treatment equipment 2 through Internet of Things communication, and provide control for the start-up operation of the duct fan 21, thereby realizing the automated control and optimization of the indoor air pollution treatment equipment 2.

[0012] The aforementioned networked cloud computing service device 3 uses a generative artificial intelligence model 35 to automatically generate control commands and dynamically adjust the operating mode of the indoor air pollution treatment equipment 2, achieving energy-saving operation and fault prediction. It is worth noting that the generative artificial intelligence model 35 also includes a big data database, big data analysis, computing power calculation, capacity configuration, early warning system, air pollution flow algorithm model, site demand equivalent, equipment configuration, intelligent equipment control, and intelligent energy management system. This integrated system constitutes AI data calculation-based full-domain intelligent control, achieving automated control of the indoor air pollution treatment equipment 2.

[0013] The aforementioned generative artificial intelligence model 35 can learn user habits and environmental changes, dynamically adjusting equipment operation based on weather forecasts, external air quality, and energy consumption demands to maximize energy efficiency and optimize air quality. For example, an intelligent energy-saving system automatically adjusts the equipment's operating mode based on real-time monitoring data, thereby achieving energy conservation and emission reduction; that is, the intelligent linkage system formed by the indoor air pollution detection and purification system a instantly controls the start-up of the duct fan 21, implementing real-time monitoring of the air quality, temperature, and humidity in the indoor space A, and intelligently comparing and issuing intelligent control commands based on the monitoring status to control the start-up operation of the duct fan 21 and the adjustment of the airflow, effectively controlling the energy-saving benefits of the air conditioning unit. For example, an early warning system monitors equipment operation and predicts potential faults, and includes a self-cleaning technology, especially for the automatic cleaning of the filter components 22 and the airflow channel 24, reducing daily maintenance needs and maintaining long-term efficient equipment operation. For example, an air pollution flow algorithm model predicts future air quality changes and adjusts the equipment's operating status in advance to prevent sudden changes in air quality. For example, intelligent equipment control integrates more types of sensors (such as noise sensors) and intelligently adjusts system operation according to different environmental parameters, improving system efficiency and accuracy.

[0014] The above-mentioned storage and data processing 4: collect data from the gas detector 1 and generate a big data database, which is used by the generative artificial intelligence model 35 to retrieve data from the big data database for calculation and comparison. The model generates automatic control commands based on the calculation results to adjust the operation of the indoor air pollution treatment equipment 2.

[0015] As can be seen from the above, the indoor air pollution detection and purification system a is installed in the indoor area A. The networked cloud computing service device 3 receives the air quality data output from the gas detector 1 through the Internet of Things, and analyzes it based on the generative artificial intelligence model 35. According to the analysis results, an intelligent control command is issued to automatically adjust the operation mode of the indoor air pollution treatment equipment 2 to carry out the air pollution purification in the indoor area A to achieve a cleanroom level, thus providing the indoor area A with a cleanliness level of cleanroom.

[0016] The aforementioned efficient building design structure b includes a passive design structure and an airtight design structure. The passive design structure includes a natural ventilation structure, a natural lighting structure, and a thermal insulation structure, reducing the need for air conditioning and artificial lighting. It combines green building materials (such as low-VOC materials) to reduce pollutants released from the building materials themselves, and utilizes plant walls and roof gardens to absorb and regulate carbon dioxide in the air. The airtight design structure enhances the building's airtightness and prevents air leakage. Simultaneously, it integrates with the indoor air pollution detection and purification system a to ensure air circulation, thereby preventing the accumulation of pollutants.

[0017] The aforementioned renewable energy system c includes a combination of solar photovoltaic systems, wind power systems, geothermal energy systems, and energy storage systems (not shown), supporting the building's electricity demand and storing excess electricity to meet peak demand. It is noteworthy that the solar photovoltaic system can be installed on the building's roof, walls, etc., generating clean energy to support the building's overall electricity demand, especially the operation of air purification equipment. The energy storage system can be equipped with advanced battery energy storage systems to store excess electricity from renewable energy sources and release it during peak demand or at night, ensuring the continuous operation of air purification equipment.

[0018] The aforementioned water resource management system d includes a rainwater harvesting system (not shown) and high-efficiency water-saving equipment (not shown). The rainwater harvesting system is used to collect rainwater. The high-efficiency water-saving equipment includes low-flow toilets, intelligent sprinkler systems, and other equipment that use the rainwater collected by the rainwater harvesting system. It is worth noting that a rainwater harvesting system refers to a system that collects, stores, and reuses rainfall water resources through appropriate infrastructure. These systems typically include rooftop or ground catchments, collection pipes, filtration devices, and storage tanks. The purpose of a rainwater harvesting system is to reduce dependence on municipal water supply and, when appropriate, use the collected rainwater for non-potable purposes, such as irrigation, toilet flushing, and vehicle washing.

[0019] The aforementioned circular economy and resource recycling e includes building material recycling and reuse, as well as building material recycling. Building material recycling involves the recycling and reuse of materials from building demolition or renovation, while building material recycling promotes the efficient use of resources throughout the building's life cycle. It is worth noting that building material recycling refers to the process of recovering and reusing building materials removed during demolition or renovation. These materials include metals, concrete, bricks, wood, glass, etc. Through screening, processing, and refining, these materials can be used in new construction projects or other engineering projects, thereby reducing waste emissions and lowering the demand for raw materials, promoting sustainable development. Building material recycling is a broader concept, encompassing the rational use and regeneration of resources throughout the building's entire life cycle, including all stages from design, construction, operation, maintenance to demolition. This process not only includes the reuse of building materials but also emphasizes the efficient use of resources (such as water and energy) and the minimization of waste. The goal of recycling is to extend the service life of building materials as much as possible, reduce environmental impact, and promote the green transformation of the construction industry.

[0020] As can be seen from the above description, in specific implementation, the present invention integrates an efficient building design structure (b), a renewable energy system (c), a water resource management system (d), and a circular economy and resource recycling (e) through an intelligent controlled indoor air pollution detection and purification system (a) and an air pollution treatment device (2). Combining the net-zero carbon emission target with efficient air purification technology, it not only achieves the low energy consumption and high comfort requirements of the Green Building Standard (LEED standard) and the Healthy Building Standard (WELL standard), but also achieves the new artificial intelligence green and healthy building standard. It achieves near-zero indoor air pollution, near-zero water pollution, near-zero noise pollution, net-zero carbon emissions, and maximizes energy conservation. It achieves carbon neutrality while maintaining indoor air pollutants at a level close to zero, thereby achieving a cleanroom-level cleanliness and improving the health and comfort of the living environment.

[0021] The following describes a specific implementation example of the indoor air pollution detection and purification system a in an indoor space A:

[0022] As shown in Figure 1D, the aforementioned multiple gas detectors 1 are deployed in an indoor area A and an outdoor area B to detect air pollution and output air quality data through Internet of Things (IoT) communication. It is worth noting that the air quality data includes particulate matter (PM1, PM2.5, PM10), CO2, VOCs, temperature, humidity, etc.

[0023] The aforementioned indoor air pollution treatment equipment 2 is installed in an indoor area A. It is internally equipped with at least one gas detector 1, at least one fan 21, at least one filter assembly 22, and at least one drive controller 23. The gas detector 1 is electrically connected to the drive controller 23 and receives control commands via IoT communication to control the start-up of the fan 21, performing near-zero cleanroom treatment of circulating air pollution within the indoor area A. The gas exchange device 2a provides ventilation for the indoor area A and provides positive pressure intake to prevent air pollution from entering the indoor area A. The air purifier 2b, circulating filter (FFU) 2C, exhaust device 2d, range hood 2f, and portable vacuum cleaner 2h provide near-zero cleanroom treatment of air pollution in the indoor area A. The heating and cooling device 2e and humidity control device 2g provide temperature and humidity regulation for the indoor area A.

[0024] As shown in Figure 12A, the aforementioned networked cloud computing service device 3 includes a wireless network cloud computing service module 31 that receives air quality data from outdoor area B and indoor area A, receives communication information from the indoor air pollution treatment device 2, and transmits control commands. The wireless network cloud computing service module 31 receives air quality data from indoor area A and outdoor area B and transmits it to the cloud control service unit 32 for storage to form an air pollution big data database. It performs intelligent calculations and compares the data with the air pollution database, and sends control commands to the wireless network cloud computing service module 31. The wireless network cloud computing service module 31 then transmits the control start operation to the indoor air pollution treatment device 2. The device management unit 33 receives the communication information from the indoor air pollution treatment device 2 through the wireless network cloud computing service module 31 for user login management and device binding management, and can provide the indoor air pollution treatment device 2 with... The system provides management information such as maintenance and management, automated anomaly detection, analysis, handling and improvement, control and inspection measurement to ensure compliance with cleanroom cleanliness requirements, customer feedback and hardware and software technology improvement correction mechanisms to the application unit 34 for system control and management. The application unit 34 also displays and notifies users of the air quality data obtained from the cloud control service unit 32, allowing users to understand the real-time status of air pollution removal through mobile phones or communication devices. Users can also control the operation of the indoor air pollution detection and purification system a, which meets the standards of artificial intelligence green and healthy buildings, through the application unit 34 on their mobile phones or communication devices. The generative artificial intelligence model 35 calculates, compares and identifies automatically generated data through generated data, forming AI data calculation full-domain intelligent control, integrating the indoor air pollution treatment equipment 2 to achieve automated control, dynamically adjusting the operating mode of the indoor air pollution treatment equipment 2, and achieving energy-saving operation and fault prediction.

[0025] It is worth noting that the aforementioned networked cloud computing service device 3 receives air quality data from indoor area A and outdoor area B through Internet of Things communication to form an air pollution big data database. Based on the detected air quality data, it intelligently compares and selects to issue a control command to the gas detector 1 of the indoor air pollution treatment equipment 2, which in turn connects to the control drive controller 23 to actuate the start-up and control operation of the duct fan 21. The networked cloud computing service device 3 receives air quality data output from the gas detector 1 through the Internet of Things and analyzes it based on the generative artificial intelligence model 35 technology. Based on the analysis results, it issues intelligent control commands to automatically adjust the operating mode of the indoor air pollution treatment equipment 2, and performs near-zero cleanroom treatment of indoor area A through internal circulation air pollution purification, providing indoor area A with a cleanliness level of cleanroom.

[0026] It is worth noting that the gas detector 1 described above is equipped with a gas detection module. Please refer to Figures 3A and 3B. The gas detector 1 can be configured with an external power supply terminal. It can be directly plugged into the power interface in the indoor area A to start operation and detect the pressure of air pollution and carbon dioxide (CO2) and the temperature and humidity of the gas. Alternatively, as shown in Figure 3C, it can be a gas detection module without an external power supply terminal. It can be directly connected to the indoor air pollution treatment equipment 2 and receive a control command to control the power supply of the indoor air pollution treatment equipment 2 to start operation.

[0027] It is worth noting that the air pollution mentioned above refers to one or a combination of suspended particulates, carbon monoxide, carbon dioxide, ozone, sulfur dioxide, nitrogen dioxide, lead, total volatile organic compounds, formaldehyde, bacteria, fungi, and viruses.

[0028] It is worth noting that the aforementioned Internet of Things (IoT) communication refers to a collective network connecting various devices and a technology that helps devices communicate with the cloud and with each other. This IoT communication can be a wired communication, allowing connection to the networked cloud computing service device 3 via a wired line. Alternatively, it can be a wireless communication, allowing connection to the networked cloud computing service device 3 via a wireless connection, and this wireless communication can be one of a Wi-Fi module, a Bluetooth module, a radio frequency identification (RFID) module, or a near-field communication (NFC) module.

[0029] It is worth noting that, as shown in Figure 1D, the indoor air pollution treatment equipment 2 is installed in indoor area A. Indoor area A is provided with at least one air intake C1 and at least one exhaust port C2. As shown in Figures 1D and 2A, the gas exchange device 2a includes a flow channel 24. The flow channel 24 has an air intake port 24a corresponding to the air intake port C1 in indoor area A, a recirculation return air port 24b connecting to indoor area A, and a filter duct 24c connecting to indoor area A. A gas exchange fan 25 is provided in the recirculation return air port 24b section, and a guide fan 21 and a filter assembly 22 are provided in the filter duct 24c. The networked cloud computing service device 3 intelligently calculates and compares the carbon dioxide (CO2) pressure detection information of indoor area A and outdoor area B. The carbon dioxide (CO2) pressure of indoor area A... The safe value of the pressure detection information must be maintained between 400 and 600 PPM. As shown in Figure 2G, when the networked cloud computing service device 3 receives the detection information from the gas exchange device 2a through IoT communication and compares the pressure difference of carbon dioxide (CO2) in indoor area A and outdoor area B, it checks whether the pressure detection information of carbon dioxide (CO2) in indoor area A and outdoor area B is equal. If the pressure difference is not equal, a control command is sent to the gas detector 1 of the gas exchange device 2a to control the start-up of the fan 21. This allows the gas from outdoor area B to be introduced into the filter duct 24c through the air inlet C1 and filtered by the filter component 22 before entering indoor area A. At the same time, the gas in indoor area A enters the filter duct 24c again through the recirculation return air inlet 24b for circulation filtration and temperature adjustment to achieve ventilation. This ventilation is performed to achieve a zero balance between the pressure detection difference of carbon dioxide (CO2) in indoor area A and outdoor area B. It is worth noting that when the gas exchange device 2a is started and put into operation to carry out ventilation, the space in the indoor area A must be maintained at a positive pressure of 0 Pa or above to prevent air pollution from the outdoor area B from entering the indoor area A. In this system, the internal gas detector 1 of the indoor air pollution treatment equipment 2 continuously receives control commands from the networked cloud computing service device 3, which in turn controls the drive controller 23 to start the operation of the duct fan 21. This continuously provides air pollution to the indoor area A for internal circulation purification and near-zero cleanroom treatment, as well as temperature and humidity regulation. When the networked cloud computing service device 3 compares the pressure difference of carbon dioxide (CO2) between the indoor area A and the outdoor area B and finds that it has reached zero equilibrium, the networked cloud computing service device 3 sends a control command to the internal gas detector 1 of the indoor air pollution treatment equipment 2. This command then controls the drive controller 23 to adjust the speed of the duct fan 21 to reduce the airflow volume, effectively controlling the energy-saving benefits of the equipment operation and effectively suppressing the noise generated by the airflow. This achieves real-time detection and near-zero cleanroom treatment of air pollution, reaching the cleanliness level of a cleanroom.It is worth noting that, as shown in Figure 1C, the gas exchange device 2a is a fresh air unit, or a total heat exchanger, or a heating, ventilation and air conditioning (HVAC) unit, but is not limited thereto.

[0030] As shown in Figure 1D, Figure 2B and Figure 2C, the air purifier 2b is placed in the space of indoor area A. The networked cloud computing service device 3 sends control commands to the gas detector 1 inside the air purifier 2b through Internet of Things communication. The controller 23 controls the start-up of the air guide fan 21 to draw air pollutants from the space of indoor area A and filter them through the filter component 22. The purified air is then introduced into the space of indoor area A, causing the air pollutants in the space of indoor area A to be drawn through the filter component 22 multiple times for air pollution purification and cleanroom treatment to approach zero.

[0031] As shown in Figures 1D and 2D, the aforementioned fan-filter unit (FFU) 2c is built-in in indoor area A. The fan-filter unit (FFU) 2c includes a flow channel 24, which has a recirculation return air inlet 24b connecting to indoor area A and a filter duct 24c connecting to indoor area A. The filter duct 24c is equipped with a fan 21 and a filter assembly 22. The networked cloud computing service device 3 issues control commands through the material. The network communication receives the gas detector 1 inside the fan filter unit (FFU) 2c and controls the drive controller 23 to start the fan 21. This draws air pollution from indoor area A into the guide channel 24 through the recirculation return air inlet 24b, through the filter duct 24c, and through the filter assembly 22 for filtration and purification before being introduced into the space of indoor area A. This causes the air pollution in the space of indoor area A to be drawn through the guide channel 24 multiple times, effectively suppressing the gas backflow effect of the recirculation filtration and achieving air pollution purification to zero cleanroom treatment.

[0032] As shown in Figure 1D, the exhaust device 2d is built-in in the indoor area A and corresponds to the exhaust port C2 to guide the airflow to the outdoor area B. The networked cloud computing service device 3 sends control commands to the gas detector 1 inside the exhaust device 2d through Internet of Things communication. The controller 23 controls the start-up of the guide fan 21 to guide the air pollution in the indoor area A into the air by the guide fan 21, filter it through the filter component 22, and discharge it to the outdoor area B to achieve near-zero air pollution cleanroom treatment in the indoor area A.

[0033] As shown in Figure 1D, the aforementioned heating and cooling device 2e is installed in indoor area A. The heating and cooling device 2e includes a temperature regulating exchanger 26. The networked cloud computing service device 3 sends control commands via IoT communication to the gas detector 1 inside the heating and cooling device 2e. The controller 23 then controls the start-up of the fan 21, directing gas through the temperature regulating exchanger 26 to regulate the gas temperature and humidity in indoor area A. The gas detector 1 also transmits the gas temperature and humidity information of indoor area A to the outside world. It is noteworthy that the heating and cooling device 2e maintains the temperature in indoor area A at 25°C ± 3°C and the humidity at 50% ± 10%. It is also noteworthy that, as shown in Figure 1C, the heating and cooling device 2e can be a single air conditioner, or a single heating unit, or a single cooling and heating unit, but is not limited to these limitations.

[0034] Furthermore, as shown in Figure 1D, when cooking food in the kitchen of indoor space A, serious air pollution will be generated relatively quickly. In order to avoid the air pollution generated in indoor space A from affecting human health and causing harm, the indoor air pollution treatment equipment 2 of the indoor air pollution detection and purification system a, which realizes the artificial intelligence green and healthy building standard, can be set as a range hood 2f, which is set in the kitchen of indoor space A. The range hood 2f includes an exhaust channel 2fa, which corresponds to the exhaust port C2 connecting to the outdoor space B, and is set above the cooking appliance H. The exhaust channel 2fa is equipped with a fan 21, a filter component 22 and a drive controller 23, and the range hood 2f includes a row of oil fume body 2fb, which corresponds to the exhaust port C2 connecting to the outdoor space B. In area B, located in front of cooking appliance H, a fan 21, a filter assembly 22, and a drive controller 23 are installed in the main exhaust unit 2fb. A gas detector 1 is installed in both the exhaust duct 2fa and the main exhaust unit 2fb, and is electrically connected to the drive controller 23. The networked cloud computing service device 3 sends control commands to the gas detectors 1 inside the exhaust duct 2fa and the main exhaust unit 2fb via IoT communication, which in turn control the drive controller 23 to start the fan 21. This draws air pollution from the kitchen area of ​​indoor area A into the exhaust duct 2fa and the main exhaust unit 2fb, where it is filtered and purified by the filter assembly 22 before being discharged to outdoor area B for near-zero air pollution cleanroom treatment.

[0035] As shown in Figures 1D and 2E, the humidity control device 2g is plug-in placed in the indoor space A. The networked cloud computing service device 3 sends control commands via IoT communication to the gas detector 1 inside the humidity control device 2g, which in turn controls the controller 23 to start the fan 21. This allows air pollution in the indoor space A to be purified by the filter assembly 22, resulting in a near-zero cleanroom environment. It also regulates the temperature and humidity within the indoor space A. It is noteworthy that the humidity control device 2g maintains the set safe temperature and humidity values ​​at 25°C ± 3°C and 50% ± 10%. It is also noteworthy that, as shown in Figure 1C, the humidity control device 2g can be a dehumidifier, a humidifier, or a combination of dehumidifier and humidifier, but is not limited to these specific types.

[0036] As shown in Figures 1D and 2F, the aforementioned portable vacuum cleaner 2h is plug-in positioned in the indoor space A. The networked cloud computing service device 3 sends control commands via IoT communication to the gas detector 1 inside the portable vacuum cleaner 2h, which in turn controls the controller 23 to activate the fan 21. This draws air pollution from the indoor space A, which is then purified by the filter assembly 22 to achieve near-zero cleanroom conditions. It is worth noting that the portable vacuum cleaner 2h is a robotic vacuum cleaner.

[0037] Furthermore, it is worth noting that, as shown in Figure 1A, the aforementioned storage and data processing 4 collects information and data from the system to form a large database of professionally generated data and user-generated data. Through the generative artificial intelligence model 35, it calculates, compares, and identifies automatically generated data, automatically generates control commands, dynamically adjusts the equipment operation mode, and achieves energy-saving operation and fault prediction.

[0038] As shown in Figure 12B, the aforementioned generative artificial intelligence model 35 includes an intelligent energy control system that automatically adjusts the operating mode of the equipment based on real-time monitoring data, thereby achieving energy conservation and emission reduction. Specifically, it realizes the intelligent linkage system formed by the indoor air pollution detection and purification system a, which meets the standards for artificial intelligence green and healthy buildings. This system instantly controls the start-up of the air duct 21, monitors the air quality, temperature, and humidity in the indoor space A at any time, and intelligently compares the monitored status to issue drive control commands, thereby controlling the start-up operation of the air duct 21 and adjusting the airflow, effectively controlling the energy-saving benefits of the air conditioning unit. The aforementioned generative artificial intelligence model 35 includes a system maintenance diagnostic system that monitors equipment operation and predicts potential faults. It also includes a self-cleaning technology, particularly for the automatic cleaning of the filter components 22 and the airflow channels 24, reducing daily maintenance requirements and maintaining long-term efficient equipment operation. The aforementioned generative artificial intelligence model 35 includes an air quality prediction model that predicts future air quality changes and adjusts the equipment's operating status in advance to prevent sudden changes in air quality. The aforementioned generative artificial intelligence model 35 includes an intelligent environmental sensing system that integrates more types of sensors (such as noise sensors) and intelligently adjusts the system operation according to different environmental parameters to improve the system's operating efficiency and accuracy.

[0039] As can be seen from the above description, the present invention provides an indoor air pollution-free cleanroom system that achieves the standards of artificial intelligence green and healthy buildings. It combines the net-zero carbon emission target with high-efficiency air purification technology to achieve carbon neutrality while maintaining indoor air pollutants at a level close to zero. Through an intelligently controlled indoor air pollution detection and purification system a, an integrated high-efficiency building design structure b, a renewable energy system c, a water resource management system d, a circular economy and resource recycling e, and advanced indoor and outdoor indoor air pollution treatment equipment 2, it achieves high-efficiency energy utilization and comprehensive air purification, maintaining indoor air pollutants at a level close to zero, so as to achieve a cleanroom-level cleanliness.

[0040] It is worth noting that indoor area A requires a cleanroom class of ZAPClean room 1~12. The following explains the classification of ZAPClean room classes 1~12:

[0041] The air pollution status of indoor space A is based on the cumulative detection of 21,000 suspended particulate matter particles with a diameter between 1 nm and 2.5 μm over 24 hours. The required output air pollution data for indoor space calibration is as follows: PM2.5 ≤ 0.000000012 μg / m3 average, PM10 ≤ 0.00000019 μg / m3 average, bacteria ≤ 0 CFU (colony count) / m3 per cubic meter, fungi ≤ 0 CFU (colony count) / m3 per cubic meter, formaldehyde ≤ 0.00028 ppm average per hour, and volatile organic compounds (TVOC) ≤ 0.00094 ppm per hour. The average ppm values ​​are as follows: carbon dioxide is detected at an average value of 500-650 ppm every 8 hours; carbon monoxide is detected at an average value of ≤0.03149 ppm every 8 hours; and ozone is detected at an average value of ≤0.00021 ppm every 8 hours, meeting the requirements of ZAPClean room level 1.

[0042] For indoor space A, the air pollution status is based on the cumulative detection of 210,000 suspended particulate matter particles with a diameter between 1 nm and 2.5 μm over 24 hours. The required output air pollution data for indoor space calibration is as follows: PM2.5 ≤ 0.00000012 μg / m³ average, PM10 ≤ 0.0000019 μg / m³ average, bacteria ≤ 0 CFU (colony count) / m³, fungi ≤ 0 CFU / m³, formaldehyde ≤ 0.00047 ppm average per hour, and volatile organic compounds (TVOC) ≤ 0.00157 ppm per hour. The average ppm values ​​are as follows: carbon dioxide is detected at an average of 500-650 ppm every 8 hours; carbon monoxide is detected at an average of ≤0.05249 ppm every 8 hours; and ozone is detected at an average of ≤0.00035 ppm every 8 hours, meeting the requirements of ZAPClean room level 2.

[0043] For indoor space A, the required output air pollution data for the 24-hour cumulative detection of suspended particulate matter with a diameter between 1 nm and 2.5 μm is 2,100,000. This data is based on the following: PM2.5 ≤ 0.00000124 μg / m³ average, PM10 ≤ 0.000019 μg / m³ average, bacteria ≤ 0 CFU / m³, fungi ≤ 0 CFU / m³, and formaldehyde ≤ 0.00078 per hour. The average ppm values ​​for volatile organic compounds (TVOC) are ≤0.00261 ppm per hour, for carbon dioxide ≤500~650 ppm per 8 hours, for carbon monoxide ≤0.08748 ppm per 8 hours, and for ozone ≤0.00058 ppm per 8 hours, meeting the ZAPClean room level 3 requirements.

[0044] For the air pollution status of indoor space A, based on the cumulative detection of 21,000,000 suspended particulate matter particles with a diameter between 1 nm and 2.5 μm over 24 hours, the required output air pollution data for indoor space calibration is as follows: PM2.5 ≤ 0.00001235 μg / m³ average, PM10 ≤ 0.000185 μg / m³ average, bacteria ≤ 0 CFU (colony count) / m³ per cubic meter, fungi ≤ 0 CFU (colony count) / m³ per cubic meter, and formaldehyde ≤ 0.00130 per hour. The average ppm values ​​are as follows: volatile organic compounds (TVOC) with an average value of ≤0.00435 ppm per hour; carbon dioxide with an average value of 500~650 ppm per 8 hours; carbon monoxide with an average value of ≤0.14580 ppm per 8 hours; and ozone with an average value of ≤0.00097 ppm per 8 hours, meeting the ZAPClean room level 4 requirements.

[0045] For indoor space A, the required air pollution data for this air pollution state is based on the cumulative detection of 210,000,000 suspended particulate matter particles with a diameter between 1 nm and 2.5 μm over 24 hours. The output air pollution data for indoor space calibration is as follows: PM2.5 ≤ 0.00012353 μg / m³ average, PM10 ≤ 0.0001853 μg / m³ average, bacteria ≤ 1 CFU (colony count) / m³, and fungi ≤ 1 CFU / m³. The system detects formaldehyde at an average value of ≤0.00216 ppm per hour, volatile organic compounds (TVOC) at an average value of ≤0.00726 ppm per hour, carbon dioxide at an average value of 500~650 ppm per 8 hours, carbon monoxide at an average value of ≤0.24300 ppm per 8 hours, and ozone at an average value of ≤0.00162 ppm per 8 hours, meeting the ZAPClean room level 5 requirements.

[0046] For indoor space A, the air pollution status is based on a 24-hour cumulative detection of 21,000,000,000 suspended particulate matter particles with a diameter between 1 nm and 2.5 μm. The required output air pollution data for indoor space calibration is as follows: PM2.5 ≤ 0.01235294 μg / m³ average, PM10 ≤ 0.0185294 μg / m³ average, bacteria ≤ 3 CFU (colony count) / m³, and fungi ≤ 3 CFU / m³. The system detects formaldehyde at an average value of ≤0.00360 ppm per hour, volatile organic compounds (TVOC) at an average value of ≤0.01210 ppm per hour, carbon dioxide at an average value of 500~650 ppm per 8 hours, carbon monoxide at an average value of ≤0.40500 ppm per 8 hours, and ozone at an average value of ≤0.00270 ppm per 8 hours, meeting the ZAPClean room level 6 requirements.

[0047] For indoor space A, the required air pollution data for this air pollution state is based on the cumulative detection of 21,000,000,000 suspended particulate matter particles with a diameter between 1 nm and 2.5 μm over 24 hours. The output air pollution data for this indoor space calibration is as follows: PM2.5 ≤ 0.01235294 μg / m³ average, PM10 ≤ 0.0185294 μg / m³ average, bacteria ≤ 8 CFU (colony count) / m³, and fungi ≤ 8 CFU / m³. The system detects formaldehyde at an average value of ≤0.00600 ppm per hour, volatile organic compounds (TVOC) at an average value of ≤0.02016 ppm per hour, carbon dioxide at an average value of 500~650 ppm per 8 hours, carbon monoxide at an average value of ≤0.67500 ppm per 8 hours, and ozone at an average value of ≤0.00450 ppm per 8 hours, meeting the ZAPClean room level 7 requirements.

[0048] For indoor space A, the air pollution status is determined by accumulating 105,000,000,000 suspended particulate matter particles with a diameter between 1 nm and 2.5 μm over 24 hours. The required output air pollution data for indoor space calibration is as follows: PM2.5 ≤ 0.06176471 μg / m³ average, PM10 ≤ 0.0926471 μg / m³ average, bacteria ≤ 15 CFU (colony count) / m³, and fungi ≤ 15 CFU / m³. The system detects formaldehyde at an average value of ≤0.009 ppm per hour, volatile organic compounds (TVOC) at an average value of ≤0.02688 ppm per hour, carbon dioxide at 500~800 ppm per 8 hours, carbon monoxide at ≤1.0125 ppm per 8 hours, and ozone at an average value of ≤0.00675 ppm per 8 hours, meeting the ZAPClean room level 8 requirements.

[0049] For indoor space A, the required air pollution data for this air pollution state is based on the cumulative detection of 210,000,000,000 suspended particulate matter particles with a diameter between 1 nm and 2.5 μm over 24 hours. The output air pollution data for indoor space calibration is as follows: average value of PM2.5 ≤ 0.12 μg / m³, average value of PM10 ≤ 0.1852941 μg / m³, bacterial detection at a sampling rate of ≤ 20 CFU (colony count) / m³ per cubic meter, and fungal detection at a sampling rate of ≤ 20 CFU / m³ per cubic meter. The system detects formaldehyde at an average value of ≤0.012 ppm per hour, volatile organic compounds (TVOC) at an average value of ≤0.0336 ppm per hour, carbon dioxide at an average value of 500~800 ppm per 8 hours, carbon monoxide at an average value of ≤1.35 ppm per 8 hours, and ozone at an average value of ≤0.009 ppm per 8 hours, meeting the ZAP Cleanroom Level 9 requirements. For indoor space A, the required air pollution data for calibrating the space, based on a 24-hour cumulative detection of 1,050,000,000,000 suspended particulate matter with a diameter between 1 nm and 2.5 μm, is as follows: PM2.5 ≤ 0.62 μg / m³ average, PM10 ≤ 0.9264706 μg / m³ average, bacteria ≤ 100 CFU (colony count) / m³, and fungi ≤ 80 CFU / m³. The system detects formaldehyde at an average value of ≤0.018 ppm per hour, volatile organic compounds (TVOC) at an average value of ≤0.0728 ppm per hour, carbon dioxide at an average value of 500~800 ppm per 8 hours, carbon monoxide at an average value of ≤2.025 ppm per 8 hours, and ozone at an average value of ≤0.0135 ppm per 8 hours, meeting the ZAPClean room level 10 requirements.

[0050] For indoor space A, the required air pollution data for the 24-hour cumulative detection of suspended particulate matter with a particle size between 1 nm and 2.5 μm is 2,100,000,000,000. The output air pollution data for the indoor space calibration is as follows: PM2.5 ≤ 1.24 μg / m³ average, PM10 ≤ 1.85 μg / m³ average, bacteria ≤ 200 CFU (colony count) / m³, and fungi ≤ 150 CFU / m³. The system detects formaldehyde at an average value of ≤0.024 ppm per hour, volatile organic compounds (TVOC) at an average value of ≤0.112 ppm per hour, carbon dioxide at an average value of 500~800 ppm per 8 hours, carbon monoxide at an average value of ≤2.7 ppm per 8 hours, and ozone at an average value of ≤0.018 ppm per 8 hours, meeting the requirements of ZAPClean room level 11.

[0051] For the air pollution status of indoor area A, based on the cumulative detection of 21,000,000,000,000 suspended particulate matter with a particle size between 1 nm and 2.5 μm over 24 hours, the required output air pollution data for indoor area spatial calibration is as follows: PM2.5 ≤ 12.35 μg / m³ average, PM10 ≤ 18.53 μg / m³ average, bacteria ≤ 1500 CFU (colony count) / m³, and fungi ≤ 750 CFU / m³. The system detects formaldehyde at an average value of ≤0.08 ppm per hour, volatile organic compounds (TVOC) at an average value of ≤0.156 ppm per hour, carbon dioxide at an average value of 800~1000 ppm per 8 hours, carbon monoxide at an average value of ≤9 ppm per 8 hours, and ozone at an average value of ≤0.06 ppm per 8 hours, meeting the ZAPClean room level 12 requirements.

[0052] To understand the specific implementation of the cleanroom system for achieving the standards of intelligent green and healthy buildings provided by this invention, the following is a detailed description of the gas detection module structure of the gas detector 1 of this invention. Please refer to Figures 3A to 11. The gas detection module includes: a control circuit board 11, a gas detection body 12, a microprocessor 13, and a communicator 14. The gas detection body 12, the microprocessor 13, and the communicator 14 are packaged in the control circuit board 11 and are electrically connected to each other. The microprocessor 13 and the communicator 14 are disposed on the control circuit board 11, and the microprocessor 13 controls the drive signal of the gas detection body 12 to start the detection operation. Thus, the gas detection body 12 detects air pollution and outputs detection information, which is processed by the microprocessor 13 and provided to the communicator 14 for external transmission to the networked cloud computing service device 3 through Internet of Things (IoT) communication.

[0053] Referring again to Figures 4A to 9A, the gas detection body 12 includes a base 121, a piezoelectric actuator 122, a drive circuit board 123, a laser assembly 124, a particle sensor 125, and an outer cover 126. The base 121 has a first surface 1211, a second surface 1212, a laser mounting area 1213, an air inlet groove 1214, an air guide assembly support area 1215, and an air outlet groove 1216. The first surface 1211 and the second surface 1212 are two surfaces arranged opposite to each other. The laser mounting area 1213 is formed by hollowing out from the first surface 1211 towards the second surface 1212. The outer cover 126 covers the base 121 and has a side plate 1261, which has an air inlet frame 1261a and an air outlet frame 1261b. The air intake groove 1214 is formed by a recess in the second surface 1212 and is adjacent to the laser mounting area 1213. The air intake groove 1214 has an air intake port 1214a, which connects to the outside of the base 121 and corresponds to the air outlet port 1216a of the outer cover 126. The two side walls of the air intake groove 1214 penetrate through the light-transmitting window 1214b of the piezoelectric actuator 122 and communicate with the laser mounting area 1213. Therefore, the first surface 1211 of the base 121 is covered by the outer cover 126, and the second surface 1212 is covered by the drive circuit board 123, so that the air intake groove 1214 defines an air intake path. The air guide component bearing area 1215 is formed by a recess in the second surface 1212 and connects to the air inlet groove 1214. A vent 1215a is passed through the bottom surface, and each of the four corners of the air guide component bearing area 1215 has a positioning protrusion 1215b. The aforementioned air outlet groove 1216 is provided with an air outlet 1216a, which is correspondingly arranged with the air outlet frame opening 1261b of the outer cover 126. The venting groove 1216 includes a first section 1216b formed by the recess of the first surface 1211 into the vertical projection area of ​​the air guide component support area 1215, and a second section 1216c formed by hollowing out from the first surface 1211 to the second surface 1212. The first section 1216b and the second section 1216c are connected to form a step, and the first section 1216b of the venting groove 1216 communicates with the vent hole 1215a of the air guide component support area 1215, and the second section 1216c of the venting groove 1216 communicates with the vent outlet 1216a. Therefore, when the first surface 1211 of the base 121 is covered by the outer cover 126 and the second surface 1212 is covered by the drive circuit board 123, the venting groove 1216 and the drive circuit board 123 together define an venting path.

[0054] The aforementioned laser assembly 124 and particle sensor 125 are both mounted on the drive circuit board 123 and located within the base 121. To clearly illustrate the positions of the laser assembly 124 and particle sensor 125 relative to the base 121, the drive circuit board 123 is deliberately omitted. The laser assembly 124 is housed within the laser mounting area 1213 of the base 121, and the particle sensor 125 is housed within the air intake groove 1214 of the base 121 and aligned with the laser assembly 124. Furthermore, the laser assembly 124 corresponds to the light-transmitting window 1214b, through which the laser light emitted by the laser assembly 124 passes, illuminating the air intake groove 1214. The beam path emitted by the laser assembly 124 passes through the light-transmitting window 1214b and forms an orthogonal direction with the air intake groove 1214. The laser assembly 124 emits a beam of light through the light-transmitting window 1214b into the air intake groove 1214. The detection data in the gas in the air intake groove 1214 is illuminated. When the beam of light comes into contact with the gas, it will scatter and generate a projected light spot, so that the particle sensor 125 is positioned in its orthogonal direction and receives the projected light spot generated by the scattering to perform calculations to obtain the gas detection data.

[0055] The piezoelectric actuator 122 described above is housed in the square gas guide assembly support area 1215 of the base 121. Furthermore, the gas guide assembly support area 1215 communicates with the air inlet groove 1214. When the piezoelectric actuator 122 is actuated, gas is drawn from the air inlet groove 1214 into the piezoelectric actuator 122, and the gas is supplied through the vent hole 1215a of the gas guide assembly support area 1215 into the air outlet groove 1216. Additionally, the drive circuit board 123 described above covers the second surface 1212 of the base 121. The laser assembly 124 is disposed on the drive circuit board 123 and electrically connected. The particle sensor 125 is also disposed on the drive circuit board 123 and electrically connected. When the outer cover 126 covers the base 121, the air outlet 1216a corresponds to the air inlet 1214a of the base 121, and the air outlet frame 1261b corresponds to the air outlet 1216a of the base 121.

[0056] The piezoelectric actuator 122 described above includes an air jet plate 1221, a cavity frame 1222, an actuator 1223, an insulating frame 1224, and a conductive frame 1225. The air jet plate 1221 is made of a flexible material and has a suspension plate 1221a and a hollow hole 1221b. The suspension plate 1221a is a sheet-like structure that bends and vibrates, and its shape and size correspond to the inner edge of the air-guiding component bearing area 1215. The hollow hole 1221b penetrates the center of the suspension plate 1221a to allow gas flow. In a preferred embodiment of the present invention, the shape of the suspension plate 1221a can be one of a square, a graphic, an ellipse, a triangle, or a polygon.

[0057] The aforementioned cavity frame 1222 is stacked on the jet orifice plate 1221, and its appearance corresponds to that of the jet orifice plate 1221. The actuator 1223 is stacked on the cavity frame 1222, and defines a resonant cavity 1226 between itself, the jet orifice plate 1221, and the suspension plate 1221a. The insulating frame 1224 is stacked on the actuator 1223, and its appearance is similar to that of the cavity frame 1222. The conductive frame 1225 is stacked on the insulating frame 1224, and its appearance is similar to that of the insulating frame 1224. The conductive frame 1225 has a conductive pin 1225a and a conductive electrode 1225b extending outward from the outer edge of the conductive pin 1225a, and the conductive electrode 1225b extending inward from the inner edge of the conductive frame 1225. Furthermore, the actuator 1223 further includes a piezoelectric carrier plate 1223a, an adjusting resonance plate 1223b, and a piezoelectric plate 1223c. The piezoelectric carrier plate 1223a is stacked on the cavity frame 1222. The adjusting resonance plate 1223b is stacked on the piezoelectric carrier plate 1223a. The piezoelectric plate 1223c is stacked on the adjusting resonance plate 1223b. The adjusting resonance plate 1223b and the piezoelectric plate 1223c are housed within an insulating frame 1224. The piezoelectric plate 1223c is electrically connected to the conductive plate 1225b of the conductive frame 1225. In a preferred embodiment of the present invention, both the piezoelectric carrier plate 1223a and the adjusting resonance plate 1223b are made of conductive materials. The piezoelectric carrier plate 1223a has a piezoelectric pin 1223d, which is connected to the drive circuit (not shown) on the drive circuit board 123 via a conductive pin 1225a to receive drive signals (which may be drive frequency and drive voltage). The drive signal forms a circuit through the piezoelectric pin 1223d, the piezoelectric carrier plate 1223a, the adjusting resonant plate 1223b, the piezoelectric plate 1223c, the conductive electrode 1225b, the conductive frame 1225, and the conductive pin 1225a. An insulating frame 1224 isolates the conductive frame 1225 from the actuator 1223 to prevent short circuits, allowing the drive signal to be transmitted to the piezoelectric plate 1223c. After receiving the drive signal, the piezoelectric plate 1223c deforms due to the piezoelectric effect, further driving the piezoelectric carrier plate 1223a and the adjusting resonant plate 1223b to reciprocate bending vibrations.

[0058] To further explain, the adjusting resonant plate 1223b is located between the piezoelectric plate 1223c and the piezoelectric carrier plate 1223a, serving as a buffer between the two, and can adjust the vibration frequency of the piezoelectric carrier plate 1223a. Basically, the thickness of the adjusting resonant plate 1223b is greater than that of the piezoelectric carrier plate 1223a, and the vibration frequency of the actuator 1223 is adjusted by changing the thickness of the adjusting resonant plate 1223b.

[0059] Referring to Figures 7A, 7B, 8A, 8B, and 9A, the jet nozzle 1221, cavity frame 1222, actuator 1223, insulating frame 1224, and conductive frame 1225 are sequentially stacked and positioned within the air guide assembly support area 1215, causing the piezoelectric actuator 122 to be positioned within the air guide assembly support area 1215. The piezoelectric actuator 122 defines a gap 1221c between the suspension plate 1221a and the inner edge of the air guide assembly support area 1215 for gas flow. An airflow chamber 1227 is formed between the jet nozzle 1221 and the bottom surface of the air guide assembly support area 1215. The airflow chamber 1227 is connected to the resonant chamber 1226 between the actuator 1223, the jet orifice 1221, and the suspension plate 1221a through the hollow hole 1221b in the jet orifice 1221. By using the vibration frequency of the gas in the resonant chamber 1226 to make it close to the vibration frequency of the suspension plate 1221a, the resonant chamber 1226 and the suspension plate 1221a can generate a Helmholtz resonance effect, thereby improving the gas transmission efficiency. When the piezoelectric plate 1223c moves away from the bottom surface of the air guide assembly bearing area 1215, the piezoelectric plate 1223c drives the suspension plate 1221a of the jet nozzle plate 1221 to move away from the bottom surface of the air guide assembly bearing area 1215, causing the volume of the airflow chamber 1227 to expand rapidly, the internal pressure drops and a negative pressure is generated, which attracts the gas outside the piezoelectric actuator 122 to flow in through the gap 1221c, and enter the resonant chamber 1226 through the hollow hole 1221b, increasing the air pressure in the resonant chamber 1226 and thus generating a pressure gradient. When the piezoelectric plate 1223c drives the suspension plate 1221a of the jet nozzle plate 1221 to move toward the bottom surface of the air guide assembly support area 1215, the gas in the resonant chamber 1226 flows out rapidly through the hollow hole 1221b, compresses the gas in the airflow chamber 1227, and causes the converged gas to be ejected rapidly and in large quantities into the vent hole 1215a of the air guide assembly support area 1215 in an ideal gas state close to Bernoulli's law.

[0060] By repeating the actions shown in Figures 9B and 9C, the piezoelectric plate 1223c vibrates reciprocally. According to the principle of inertia, the gas pressure inside the resonant chamber 1226 after exhaust is lower than the equilibrium gas pressure, which guides the gas to re-enter the resonant chamber 1226. In this way, the vibration frequency of the gas in the resonant chamber 1226 is controlled to be similar to the vibration frequency of the piezoelectric plate 1223c, so as to generate the Helmholtz resonance effect and realize the high-speed and large-volume transmission of gas. All gas enters through the air inlet 1214a of the outer cover 126, enters the air inlet groove 1214 of the base 121 through the air inlet 1214a, and flows to the position of the particle sensor 125. Furthermore, the piezoelectric actuator 122 continuously drives the intake gas through the intake path, facilitating the rapid and stable flow of external gas. The gas passes above the particle sensor 125. At this time, the laser assembly 124 emits a beam of light through the light-transmitting window 1214b into the intake groove 1214. The intake groove 1214 passes above the particle sensor 125. When the beam of light from the particle sensor 125 irradiates the suspended particles in the gas, scattering and projection light spots are generated. The particle sensor 125 receives the projection light spots generated by the scattering and calculates to obtain information such as the particle size and concentration of the suspended particles in the gas. The gas above the particle sensor 125 is also continuously driven by the piezoelectric actuator 122 and guided into the vent 1215a of the air guide assembly bearing area 1215, and into the exhaust groove 1216. Finally, when the gas enters the outlet groove 1216, the gas is continuously supplied into the outlet groove 1216 by the piezoelectric actuator 122. Therefore, the gas in the outlet groove 1216 is pushed and discharged to the outside through the outlet port 1216a and the outlet frame port 1261b.

[0061] The gas detector 1 of the present invention can not only detect suspended particles in the gas, but also further detect the characteristics of the introduced gas, such as formaldehyde, ammonia, carbon monoxide, carbon dioxide, oxygen, ozone, etc. Therefore, the gas detector 1 of the present invention further includes a gas sensor 127, which is positioned and electrically connected to the drive circuit board 123 and housed in the gas outlet groove 1216 to detect the characteristics of the introduced gas. The gas sensor 127 can be a volatile organic compound sensor to detect carbon dioxide or total volatile organic compound gas information; a formaldehyde sensor to detect formaldehyde gas information; a bacteria sensor to detect bacteria or fungi; a virus sensor to detect virus gas information; or a temperature and humidity sensor to detect gas temperature and humidity information.

[0062] Referring again to Figure 2H, the air guide fan 21 of the aforementioned indoor air pollution treatment equipment 2 is activated under control to guide air pollution through the filter assembly 22 for filtration. The filter assembly 22 includes filtration and purification technologies such as HEPA, UVC, activated carbon, photocatalysis, plasma, and negative ions. The filter assembly 22 can be a filter with an MREV 8 or higher (minimum filtration efficiency value), or it can be a high-efficiency particulate air filter (HEPA). It adsorbs chemical fumes, bacteria, dust particles, and pollen contained in the air pollution, thus achieving the effect of filtration and purification. It is worth noting that the high-efficiency particulate air filter (HEPA) in this case is indeed a high-efficiency particulate air filter (HEPA). A filter with a dust holding capacity greater than 12000mg or higher, or a more efficient LPA14 filter grade, further enhances filtration efficiency and meets higher cleanliness requirements. The filter assembly 22 can be further combined with physical or chemical materials to provide sterilization of air pollutants. The airflow path of the fan 21 is as indicated by the arrow. The filter assembly 22 utilizes a chemical method involving the application of a decomposition layer to sterilize and remove air pollutants. This decomposition layer can be activated carbon 22a, which removes organic and inorganic substances from the air, as well as colored and odorous substances. Notably, the activated carbon 22a in this case has a formaldehyde absorption capacity greater than 1500mg / m³. g. The decomposition layer can be a chlorine dioxide purification factor 22b, which inhibits viruses, bacteria, fungi, influenza A virus, influenza B virus, enterovirus, and norovirus in air pollution with an inhibition rate of over 99%, helping to reduce cross-infection of viruses. The decomposition layer can be a herbal protective layer 22c of ginkgo and Japanese sumac, which effectively resists allergies and destroys the surface proteins of influenza viruses (e.g., H1N1). The decomposition layer can be a silver ion 22d, which inhibits viruses, bacteria, and fungi introduced into the air pollution. The decomposition layer can be a zeolite 22e, which removes ammonia nitrogen, heavy metals, organic pollutants, E. coli, phenol, chloroform, and anionic surfactants.In some embodiments, the filter component 22 can also be combined with a photochemical method to sterilize and remove air pollution. The photochemical irradiation is a photocatalyst unit consisting of a photocatalyst 22f and an ultraviolet lamp 22g, to further improve the removal efficiency of air pollutants and allergens. When the photocatalyst 22f is irradiated by the ultraviolet lamp 22g, it can convert light energy into electrical energy, decompose harmful substances in the air pollution, and disinfect and sterilize to achieve a filtration and sterilization effect. It is worth noting that the power of the ultraviolet lamp 22g in this case is above 120mw. The photochemical irradiation can be a photoplasma unit consisting of a nano-tube 22h. When the air pollution is irradiated by the nano-tube 22h, the oxygen and water molecules in the air pollution are decomposed into highly oxidizing photoplasma, forming an ion gas flow that destroys organic molecules, thereby removing volatile organic compounds (VOCs) such as formaldehyde, toluene, and volatile organic compounds from the air pollution. VOCs and other gas molecules are decomposed into water and carbon dioxide to further improve the removal efficiency of air pollutants and allergens, achieving the effect of filtration and sterilization. In some embodiments, the filter component 22 can also be combined with a decomposition unit to chemically remove air pollution through sterilization. The decomposition unit can be a negative ion unit 22i, which causes the particles contained in the introduced air pollution to attach to the negatively charged particles, thereby further improving the removal efficiency of air pollutants and allergens, achieving the effect of filtration and sterilization of the introduced air pollution. The decomposition unit can be a plasma ion unit 22j, through which oxygen molecules and water molecules contained in the air pollution are ionized to generate cations (H+) and anions (O2-). After the substances with water molecules attached to the ions attach to the surface of viruses and bacteria, they are converted into highly oxidizing active oxygen (hydroxyl, OH group) under the action of chemical reaction, thereby taking away hydrogen from the surface proteins of viruses and bacteria, oxidizing and decomposing them. This can decompose and eliminate air pollutants, allergens and microorganisms, improve air cleanliness, and achieve the effect of filtration and sterilization of the introduced air pollution.

[0063] In summary, the present invention provides an indoor air pollution-free cleanroom system that achieves the standards of artificial intelligence green and healthy buildings. By integrating an intelligently controlled indoor air pollution-free cleanroom system and advanced indoor and outdoor air pollution treatment equipment, it integrates efficient building design structure, renewable energy system, water resource management system, circular economy and resource recycling. Combining net-zero carbon emission targets with efficient air purification technology, it not only achieves the low energy consumption and high comfort requirements of green building standards (LEED standards) and healthy building standards (WELL standards), but also achieves the new artificial intelligence green and healthy building standards. It achieves near-zero indoor air pollution, near-zero water pollution, near-zero noise pollution, net-zero carbon emissions, and maximizes energy conservation. It achieves carbon neutrality while maintaining indoor air pollutants at a level close to zero, thereby achieving cleanroom-level cleanliness and improving the health and comfort of the living environment. [Simplified Explanation of the Diagram]

[0005] Figure 1A is a schematic diagram of the indoor air pollution-free cleanroom system architecture for achieving the standards of artificial intelligence green and healthy buildings according to the present invention. Figure 1B is a schematic diagram of the indoor air pollution detection and purification system architecture according to the present invention. Figure 1C is a schematic diagram of the indoor air pollution treatment equipment architecture according to the present invention. Figure 1D is an embodiment diagram of the indoor air pollution-free cleanroom system of the present invention in an indoor field. Figure 2A is a schematic diagram of the gas exchange device of the indoor air pollution treatment equipment according to the present invention. Figure 2B is a schematic diagram of the air purifier of the indoor air pollution treatment equipment according to the present invention. Figure 2C is a cross-sectional schematic diagram of the air purifier of the indoor air pollution treatment equipment in Figures 1D and 2B of the present invention. Figure 2D is a schematic diagram of the fan filter unit (FFU) of the indoor air pollution treatment equipment according to the present invention. Figure 2E is a cross-sectional schematic diagram of the humidity control device of the indoor air pollution treatment equipment in Figure 1D of the present invention. Figure 2F is a cross-sectional schematic diagram of the portable vacuum cleaner of the indoor air pollution treatment equipment in Figure 1D of the present invention. Figure 2G is a schematic diagram illustrating the process of the gas exchange device of the present invention controlling positive pressure air intake by comparing the carbon dioxide (CO2) pressure difference between the indoor and outdoor areas through a networked cloud computing service device. Figure 2H is a schematic diagram illustrating the assembly relationship of the filter components of the indoor air pollution treatment equipment of the present invention. Figure 3A is a three-dimensional appearance schematic diagram of the gas detector of the present invention. Figure 3B is a three-dimensional appearance schematic diagram of the gas detector of the present invention from another angle. Figure 3C is a three-dimensional appearance schematic diagram of the gas detection module installed inside the gas detector of the present invention. Figure 4A is a three-dimensional assembly schematic diagram (I) of the gas detection main body of the present invention. Figure 4B is a three-dimensional assembly schematic diagram (II) of the gas detection main body of the present invention. Figure 4C is a three-dimensional exploded schematic diagram of the gas detector of the present invention. Figure 5A is a three-dimensional schematic diagram (I) of the base of the present invention. Figure 5B is a three-dimensional schematic diagram (II) of the base of the present invention. Figure 6 is a three-dimensional schematic diagram (III) of the base of the present invention. Figure 7A is an exploded three-dimensional schematic diagram of the piezoelectric actuator and base of the present invention. Figure 7B is a three-dimensional schematic diagram of the piezoelectric actuator and base of the present invention assembled. Figure 8A is an exploded perspective view (I) of the piezoelectric actuator of the present invention. Figure 8B is an exploded perspective view (II) of the piezoelectric actuator of the present invention. Figure 9A is a cross-sectional view of the piezoelectric actuator of the present invention (I) in operation. Figure 9B is a cross-sectional view of the piezoelectric actuator of the present invention (II) in operation. Figure 9C is a cross-sectional view of the piezoelectric actuator of the present invention (III) in operation. Figure 10A is a cross-sectional view (I) of the gas detection main assembly. Figure 10B is a cross-sectional view (II) of the gas detection main assembly. Figure 10C is a cross-sectional view (III) of the gas detection main assembly. Figure 11 is a schematic diagram of the transmission of the gas detector of the present invention. Figure 12A is a schematic diagram of the architecture of the networked cloud computing service device of the present invention. Figure 12B is a schematic diagram of the generative artificial intelligence model (AIGC) technical architecture of the networked cloud computing service device of the present invention.

Claims

1. A cleanroom system for achieving zero indoor air pollution in accordance with AI-powered green and healthy building standards, comprising: an indoor air pollution detection and purification system, including multiple gas detectors, at least one indoor air pollution treatment device, at least one networked cloud computing service device, and at least one storage and data processing unit, wherein the multiple gas detectors comprehensively monitor indoor air pollution, the indoor air pollution treatment device filters and purifies indoor air pollution, and the networked cloud computing service device performs dynamic monitoring and adjustment analysis through an intelligent network system and a generative AI model, issuing an intelligent control command based on the analysis results to adjust the operating mode of the indoor air pollution treatment device; an efficient building design structure, including a passive design structure and an airtight design structure, wherein the passive design structure includes a natural ventilation structure, a natural lighting structure, and a thermal insulation structure, wherein the airtight design structure is used to improve the building's airtightness; and a renewable energy system, including a solar photovoltaic system, a wind power system, a geothermal power system, and an energy storage system, for providing electricity to meet the building's needs and storing excess electricity. A water resource management system includes a rainwater harvesting system and a high-efficiency water-saving device. The rainwater harvesting system is used to collect rainwater, and the high-efficiency water-saving device includes a low-flow toilet and a smart sprinkler system, which respectively use the rainwater collected by the rainwater harvesting system. A circular economy and resource recycling system includes a building material recycling system and a building material recycling system. The building material recycling system recycles and reuses materials from building demolition or renovation, and the building material recycling system promotes the efficient use of resources throughout the building's life cycle. The networked cloud computing service device of the indoor air pollution detection and purification system includes a wireless network cloud computing service module, a cloud control service unit, a device management unit, an application unit, and a generative artificial intelligence model. The system also includes a storage and data processing system that collects information and stores it to form a big data database. The generative artificial intelligence model retrieves data from the big data database and performs calculations, comparisons, and identifications to generate automatically generated data. The indoor air pollution treatment equipment includes a gas exchange device, which includes a fan. When the gas exchange device is activated to perform air exchange, it maintains a positive pressure of 0 Pa or higher in an indoor area to prevent air pollution from an outdoor area from entering the indoor area. The networked cloud computing service device compares the pressure difference of carbon dioxide (CO₂) between the indoor and outdoor areas. When the pressure difference reaches zero equilibrium, the networked cloud computing service device issues an intelligent control command to control the fan of the gas exchange device to adjust and reduce the airflow.

2. The indoor air pollution zero-pollution cleanroom system for realizing the AI ​​green and healthy building standard as described in claim 1, wherein the indoor air pollution detection and purification system's indoor air pollution treatment equipment is installed in the indoor area, and is internally equipped with at least one gas detector, at least one filter component and at least one drive controller. The gas detector is electrically connected to the drive controller and receives the intelligent control command through an Internet of Things communication to control the start-up and operation of the duct fan to perform indoor air pollution purification and zero-pollution cleanroom treatment.

3. The cleanroom system for achieving the standards of intelligent green and healthy buildings as described in claim 2, wherein the indoor air pollution treatment equipment includes a purifier, a circulating filter (FFU), an exhaust fan, a heating and cooling system, a range hood, a humidity control device, and a portable vacuum cleaner.

4. The cleanroom system for achieving zero air pollution in indoor air as described in Request 1, wherein the generative artificial intelligence model technology includes a big data database, big data analysis, computing power calculation, capacity configuration, early warning system, air pollution flow algorithm model, site demand equivalent, equipment configuration, equipment intelligent control, and intelligent energy management system, which integrates the system to form AI data calculation full-domain intelligent control, and integrates the indoor air pollution treatment equipment to achieve automated control.

5. The cleanroom system for achieving zero indoor air pollution as described in claim 3, wherein the indoor air pollution detection and purification system includes a central control computer intelligent control device that receives intelligent control commands issued by the networked cloud computing service device through the Internet of Things (IoT) communication, transmits them to the gas detector of the indoor air pollution treatment equipment to receive and control the start-up operation of the duct fan, and the central control computer intelligent control device has edge computing capabilities, which can receive and analyze the air quality data monitored by the gas detector of each indoor air pollution treatment equipment through the IoT communication, generate an intelligent control command based on the analysis results, directly issue the intelligent control command, transmit it through the IoT communication to the gas detector of the indoor air pollution treatment equipment to receive and control the start-up operation of the duct fan, thereby realizing the automated control and optimization of the indoor air pollution treatment equipment.

6. The cleanroom system for achieving the standards of intelligent green and healthy buildings as described in claim 3, wherein the filter component is a filter with an MREV (Minimum Filtration Efficiency Value) of 8 or higher.

7. The cleanroom system for achieving AI-powered green and healthy building standards as described in claim 3, wherein the filter component is a high-efficiency particulate air filter (HEPA).

8. The cleanroom system for achieving zero air pollution in the indoor environment as described in claim 7, wherein the high-efficiency particulate air filter (HEPA) is of grade 10 or above and has a dust holding capacity of more than 12,000 mg.

9. The cleanroom system for achieving AI-powered green and healthy building standards as described in claim 3, wherein the filter component is of ULPA14 filter grade.

10. The cleanroom system for achieving AI-powered green and healthy building standards as described in claim 3, wherein the filter assembly incorporates a chemical method of removing air pollution by applying a decomposition layer through a permeation coating.

11. The cleanroom system for achieving the standards of intelligent green and healthy buildings as described in claim 10, wherein the decomposition layer is an activated carbon with a formaldehyde absorption capacity greater than 1500 mg.

12. The cleanroom system for achieving AI-powered green and healthy building standards as described in claim 3, wherein the filter component is combined with a light-irradiated chemical method to sterilize and remove the air pollution.

13. The cleanroom system for achieving AI-powered green and healthy building standards as described in claim 12, wherein the light irradiation is a photocatalyst unit consisting of a photocatalyst and an ultraviolet lamp.

14. The cleanroom system for achieving the standards of intelligent green and healthy buildings as described in claim 13, wherein the ultraviolet lamp has a power of 120mw or more.

15. The cleanroom system for achieving AI-powered green and healthy building standards as described in claim 12, wherein the light irradiation is a photoplasma unit with a nanometer light tube.

16. The cleanroom system for achieving AI-powered green and healthy building standards as described in claim 3, wherein the filter assembly, in conjunction with a decomposition unit, chemically removes the air pollution through sterilization.

17. The cleanroom system for achieving AI-powered green and healthy building standards as described in claim 16, wherein the decomposition unit is a negative ion unit.

18. The cleanroom system for achieving AI-powered green and healthy building standards as described in claim 16, wherein the decomposition unit is a plasma ion unit.

19. The cleanroom system for achieving the standards of intelligent green and healthy buildings as described in claim 2, wherein the Internet of Things communication is a wireless communication for wireless communication with the networked cloud computing service device, wherein the wireless communication may be one of a Wi-Fi module, a Bluetooth module, a radio frequency identification module, or a near-field communication module.

20. The cleanroom system for achieving AI-powered green and healthy building standards as described in claim 2, wherein the Internet of Things communication is a wired communication for communication with the networked cloud computing service device via a wired connection.

21. The cleanroom system for achieving zero indoor air pollution as described in claim 2, wherein the gas detector includes a control circuit board, a gas detection body, a microprocessor, and a communicator, wherein the control circuit board is electrically connected to the drive controller, and the gas detection body, the microprocessor, and the communicator are packaged in the control circuit board and electrically connected, and the microprocessor controls the detection operation of the gas detection body, causing the gas detection body to detect the air pollution, and the microprocessor processes the detected air pollution and outputs the air pollution information to the communicator for external communication transmission.

22. The indoor air pollution-free cleanroom system that achieves the standards for intelligent green and healthy buildings as described in claim 1, wherein the cleanroom level achieved by the indoor air pollution-free cleanroom system is ZAPClean room 1 to 12.

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