Generative artificial intelligence indoor air cleaning system

Abstract

To provide a generative artificial intelligence indoor air cleaning system.SOLUTION: The system includes a memory unit, an air quality detector, application (APP) software, an operation unit, and gas purification device hardware. The operation unit includes a generative artificial intelligence model, which obtains stored dedicated generated data and user generated data through the Internet, and performs deep learning processing to form automatically generated data. The gas purification device hardware receives control commands sent via the Internet on the basis of the automatically generated data from the operation unit, and performs induction, circulation, and filtration to purify the indoor environment to cleanroom quality.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an indoor air purification system, and more particularly to a generative artificial intelligence indoor air purification system that is installed indoors and uses generative artificial intelligence to detect, locate, and circulate and filter air pollution in the indoor environment, thereby meeting the clean room ZAPClean room 1 to 9 grade requirements. [Background technology]

[0002] Modern people are increasingly concerned about the quality of the air around them. For example, carbon monoxide, carbon dioxide, volatile organic compounds (VOCs), PM2.5, nitrogen monoxide, sulfur monoxide, and other airborne particles are all present in the environment and can affect human health and, in severe cases, even death. Therefore, environmental air quality has attracted worldwide attention, and how to monitor air quality and avoid harmful gases in the environment is a hot topic. Understanding indoor air quality is difficult. In addition to outdoor air quality, indoor air conditioning conditions and pollution sources are also major factors affecting indoor air quality. Intelligently and quickly detecting pollution sources in indoor environments can effectively remove air pollution, creating a clean, safe, and breathable atmosphere. This paper proposes an indoor air purification system to detect indoor air quality in real time. The indoor air purification system is installed in an indoor environment to detect, locate and circulate air pollution. Furthermore, the system is combined with a gas detection sensor and artificial intelligence cloud computing technology to form an artificial intelligence linked system, which communicates and transmits control commands to operate the pollution filtering operation of the purification device, so that the air pollution in the indoor environment can be purified to meet the clean room ZAPClean room 1 to 9 grade requirements.

[0003] A huge amount of data is required to implement and verify how to introduce an indoor air purification system into an indoor environment, optimize the number and specification layout of purification devices, optimize efficiency control, implement noise reduction control, and minimize installation costs. Repeated experiments are necessary to calculate the optimal planning settings. The main research and development theme of this invention is how to apply an indoor air purification system with such performance to typical indoor household life. Summary of the Invention [Problem to be solved by the invention]

[0004] The main objective of the present invention is to provide a generative artificial intelligence indoor air purification system that can be applied to indoor home life by combining an indoor air purification system with a generative artificial intelligence module, thereby realizing an indoor air purification system that optimizes the quantity and specification layout of gas purification device hardware, optimizes efficiency control, implements noise reduction control, and proposes a minimum installation cost plan, and detects, locates, and circulates and filters air pollution in the indoor environment to meet the clean room ZAPClean room 1 to 9 grade requirements. [Means for solving the problem]

[0005] To achieve the above objective, the present invention provides a generative artificial intelligence (GAI) indoor air purification system. The generative artificial intelligence (GAI) indoor air purification system includes a memory unit, at least one air quality detector, application (APP) software, a computing unit, and at least one air purification device hardware. The memory unit collects and stores information data from the indoor purification system, forming a big data database consisting of dedicated generation data and user-generated data. The air quality detector detects air pollution in the building's outdoor and indoor environments, outputs pollution data, and transmits the pollution data to the memory unit via a network, which generates the user-generated data. The application (APP) software inputs information data from the indoor purification system and transmits it to the memory unit via a network, where it is stored. The computing unit includes a generative artificial intelligence (GAI) model, which acquires the dedicated generation data and user-generated data stored in the memory unit via the network, and performs deep learning processing and analysis on the data to generate automatically generated data. The gas purification device hardware is installed in the indoor environment of a building and comprises at least one fan and at least one filtering element, and the gas purification device hardware intelligently selects and sends control commands via the Internet based on automatically generated data formed by the computing unit to control the operation of the fan of the gas purification device hardware, induces and circulates air pollution in the indoor environment to pass through the filtering element, and purifies the air pollution in the indoor environment to meet the clean room class requirements. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a diagram showing the configuration relationship of the generative artificial intelligence indoor air purification system of the present invention. [Figure 2] FIG. 1 is a conceptual diagram illustrating how the generative artificial intelligence model of the present invention evaluates the effectiveness of an indoor purification system. DETAILED DESCRIPTION OF THE INVENTION

[0007] The following detailed description of the preferred embodiments embodying the features and advantages of the present invention will be given. The present invention can be modified in various ways without departing from the scope of the claims. It should be understood that the detailed description and drawings are for illustrative purposes only and are not intended to limit the scope of the present invention.

[0008] As shown in Figure 1, the present invention provides a generative artificial intelligence indoor air purification system A. The generative artificial intelligence indoor air purification system A includes a memory unit 1, at least one air quality detector 2, application (APP) software 3, a calculation unit 4, and at least one air purification device hardware 5.

[0009] The storage unit 1 collects information data of the indoor purification system and forms and stores a big data database consisting of professionally generated data and user-generated data. The information data of the indoor purification system includes information data for reference, such as spatial information parameters of the implementation environment, pollution detection data parameters, reference safety numerical parameters, clean room grade comparison parameters, purification device software and hardware specifications, and control parameters. However, the present invention is not limited to these and may also include application data such as learning data and historical data of all system-related generated AI machine learning models. The professionally generated data includes outdoor and indoor air pollution standard data for buildings (e.g., air pollution characteristics, safety concentration values, etc.), indoor space information data for buildings (e.g., building dimensions and proportions, intended use functions, environmental ventilation, temperature-comfort control parameters, etc.), clean room grade specification data (e.g., a comparison table of ZAPClean room grades 1 to 9), and software and hardware specifications required for the indoor air purification system (e.g., the clean air supply rate CADR of the air purification fan, filter specifications, etc.). The user-generated data includes information data on indoor air pollution in the building used by the user (e.g., real-time detection data from the air quality detector 2, etc.), actual measurement information data on indoor air pollution in the building used by the user (pollution information data on indoor air pollution detected by a detection agency), and air exchange rate data on the indoor heating, ventilation, and air conditioning (HVAC) in the building used by the user (e.g., comparison data on indoor and outdoor carbon dioxide in the user's environment detected in real time by the air quality detector 2, etc.). The air quality detector 2 detects pollution in the building's outdoor and indoor environments, outputs pollution data, and transmits the pollution data to the storage unit 1 via the Internet of Things (which may be a combination of sensor technology such as sensors and electronic tags with an information and communication network, or may be the Internet) to form user-generated data.The application (APP) software 3 inputs related indoor purification system information data and transmits it to the storage unit 1 via the Internet (or the Internet) for storage. The computing unit 4 includes a generative artificial intelligence (GAI) model. The GAI model can be an OpenAI API AI model, an Azure AI model, a Gemini AI model, an AWS AI model, or an IBM Watson AI model. The dedicated and user-generated data stored in the storage unit 1 are acquired via the Internet by the GAI model, which processes and analyzes the data using deep learning to generate automatically generated data. As shown in FIG. 2, the application (APP) software 3 inputs related indoor purification system information data. For example, the dedicated generated data includes information data such as outdoor and indoor air pollution standard data for the building, indoor space information data for the building, cleanroom classification data, and software and hardware specifications required for the indoor air purification system. The user-generated data also includes information such as indoor environmental pollution data for the building used by the user, actual measured indoor air pollution data for the building used by the user, and air exchange rate data for the indoor heating, ventilation, and air conditioning (HVAC) system for the building used by the user. This information data is stored in memory 1 to form a big data database of pollution data. Through application software 3, the user inputs data such as the optimal number of purification devices required for the system, specification layout, optimized efficiency control, noise reduction control implementation, and installation cost minimization. The generative artificial intelligence (GAI) model obtains the dedicated generated data and user-generated data generated by collecting information data for the indoor purification system stored in memory 1 via the Internet, and can be formed based on this data. The GAI model then processes and analyzes the data using deep learning to form an actual learned classifier and a trained classifier, and then compares them to obtain automatically generated data.The automatically generated data may include, for example, information on the optimized quantity of gas purification device hardware, optimized gas purification device hardware efficiency control, optimized gas purification device hardware noise reduction control, minimized one-time installation costs (installation costs, temporary installation costs) of the indoor air purification system, and minimized operating costs of the indoor air purification system. The generative artificial intelligence (GAI) model itself has an autoregression correction analysis function, which predicts the automatically generated data, generates new actual deep learning processed data, and optimally corrects the automatically generated data. That is, by comparing the reliability of the automatically generated data, it can help the GAI model quickly converge in the "right" direction (accurate convergence) and return to the most accurate automatically generated data. In this way, before the indoor air purification system is installed, the combination of the generative artificial intelligence platform can obtain optimal pre-operational layout and application optimization, realizing an indoor air purification system with the required installation specifications and cost, and applying and installing it to detect, locate, and circulate air pollution in the indoor environment. The gas purification device hardware 5 is installed in the indoor environment of a building. The gas purification device hardware 5 may be a fresh air blower 5a, a total heat exchanger 5b, a circulation filter unit (FFU) 5c, a range hood 5d, a bathroom exhaust fan 5e, or a negative pressure exhaust fan 5f, but the present invention is not limited to these. Each gas purification device hardware 5 includes at least one fan and at least one filter. The gas purification device hardware 5 can intelligently select control commands based on automatically generated data generated by the computing unit 4 via the Internet, thereby controlling the operation of the fan in the gas purification device hardware 5 and guiding and circulating the polluted air in the indoor environment through the filter. This allows the air pollution level in the indoor environment to meet the clean room ZAPClean room grades 1 to 9.

[0010] As described above, the present invention provides a generative artificial intelligence indoor air purification system. The present invention utilizes a generative artificial intelligence indoor air purification system composed of a home indoor air purification system and a generative artificial intelligence module to optimally design the quantity and specifications of the gas purification device hardware, optimizing efficiency control and noise reduction control, and minimizing installation costs. By incorporating a generative artificial intelligence platform prior to installation, optimal layout and practical optimization can be achieved before installation, resulting in an indoor air purification system with the required specifications and cost. Furthermore, by utilizing indoor area detection, location identification, and air pollution circulating filtration, the system meets the requirements for clean rooms (ZAPClean Room 1 to 9), making it highly valuable for industrial application. [Explanation of symbols]

[0011] A: Generative AI indoor air purification system 1: Storage part 2:Air Quality Detector 3: Application (APP) software 4: Arithmetic section 5: Gas Purification Equipment Hardware 5a: Fresh air blower (fan mechanism) 5b: Total heat exchanger 5c: Circulating Filtration Unit (FFU) 5d: Range hood 5e: Bathroom exhaust fan 5f: Negative pressure exhaust fan

Claims

1. A generative artificial intelligence indoor air purification system comprising: a memory unit, at least one air quality detector, application (APP) software, a computing unit, and at least one air purification device hardware, The storage unit collects information data of the indoor purification system, forms a big data database including dedicated generated data and user generated data, and stores the data; The at least one air quality detector detects air pollution in the outdoor environment and the indoor environment of the building, outputs pollution data, and transmits the pollution data to the storage unit via a mutual network to form the user-generated data; The application (APP) software inputs information data of the indoor purification system and transmits the data to the storage unit for storage via the Internet; The computing unit includes a generative artificial intelligence model, and acquires the dedicated generated data and the user generated data stored in the storage unit through a network, and performs deep learning processing and analysis of the data to form automatically generated data; The at least one air purification device hardware is installed in the indoor environment of the building and comprises at least one fan and at least one filter component, and the air purification device hardware receives the automatically generated data formed by the computing unit via the internet and intelligently selects and sends control commands to control the operation of the fan of the air purification device hardware, so as to induce and circulate the air pollution in the indoor environment to pass through the filter component, and purify the air pollution in the indoor environment to meet clean room grade requirements.

2. 2. The generative artificial intelligence indoor air purification system of claim 1, wherein the air purification device hardware includes one selected from the group consisting of a fresh air blower, a total heat exchanger, a circulation filtration unit (FFU), a range hood, a bathroom exhaust fan, and a negative pressure exhaust fan.

3. The generative artificial intelligence indoor air purification system of claim 1, wherein the dedicated generated data includes outdoor and indoor air pollution standard data of the building, the indoor space information data of the building, clean room classification standard data, and software and hardware specifications required for the indoor air purification system.

4. The generative artificial intelligence indoor air purification system of claim 1, wherein the user-generated data includes indoor environmental pollution data of the building used by the user, actual measurement information data of indoor air pollution of the building used by the user, and air exchange rate data of the indoor heating, ventilation, and air conditioning (HVAC) of the building used by the user.

5. 2. The generative artificial intelligence indoor air purification system of claim 1, wherein the automatically generated data includes information on an optimized quantity of air purification device hardware, an optimized efficiency control of the air purification device hardware, an optimized noise reduction control of the air purification device hardware, information on minimized installation costs of the indoor air purification system, and information on minimized operation costs of the indoor air purification system.

6. 2. The generative artificial intelligence indoor air purification system of claim 1, wherein the generative artificial intelligence (GAI) model comprises one selected from the group consisting of an OpenAI API artificial intelligence model, an Azure artificial intelligence model, a Gemini artificial intelligence model, an AWS artificial intelligence model, and an IBM Watson artificial intelligence model.

7. The generative artificial intelligence (GAI) indoor air purification system according to claim 1, characterized in that the GAI model itself has an autoregression correction analysis function, uses the automatically generated data for prediction, generates new actual deep learning processing data, and modifies it for optimization, thereby guiding the GAI model to quickly converge in the correct and appropriate direction.

8. The generative artificial intelligence indoor air purification system according to claim 3, wherein the clean room grade standard data is ZAP Clean room grade 1 to 9 standard.

Images