Method for realizing benefit of indoor cleaning system via generative artificial intelligence (AIGC)

Abstract

To provide a method for realizing a benefit of an indoor cleaning system via a generative artificial intelligence (AIGC).SOLUTION: A method includes the steps of: supplying special generation data of an indoor cleaning system; supplying user generation data; inputting the special generation data and the user generation data via an application (APP) software and storing in a storage center; supplying a generative artificial intelligence (AIGC) model, and generating automatic generation data based on the special generation data and the user generation data; and integrating the automatic generation data, optimizing data correction by deep learning processing, comparing learning of the automatic generation data with improvements, guiding so that the generative artificial intelligence (AIGC) model quickly converges, and regressing the automatic generation data to the most accurate one and correcting so as to optimize the benefit of the indoor cleaning system and implement the indoor cleaning system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method for realizing the benefits of an indoor cleaning system by combining it with artificial intelligence and generative computing (AIGC) to detect, identify, circulate, and filter air contamination within an indoor area to meet the requirements of a clean room (ZAPClean room level). [Background technology]

[0002] Modern people are increasingly concerned about the quality of the air around them. Exposure to gases such as carbon monoxide, carbon dioxide, volatile organic compounds (VOCs), PM2.5, nitrogen monoxide, and sulfur monoxide, as well as the fine particles contained in these gases, can all affect human health and, in severe cases, even be fatal. Environmental gas quality has therefore attracted international attention, and the question of how to detect gas quality and avoid or avoid areas with poor gas quality has become a hot topic. Indoor air quality is difficult to grasp, and in addition to outdoor air quality, indoor air conditioning conditions and pollution sources are all major factors that affect indoor air quality. Intelligent and rapid detection of indoor air pollution sources in various indoor areas, effective removal of indoor air pollution, and the creation of clean, safe-to-breath gas conditions are needed, along with real-time monitoring of indoor air quality anywhere and at any time. To this end, we provide an indoor air purification system that can be installed in indoor areas to detect, identify, circulate, and filter air pollution. The entire system is equipped with gas sensors and an intelligent cloud, forming an intelligent interconnected system that transmits control commands via communication to control the processing operation of the air purifier and filter air pollution, ensuring that the air pollution status in the indoor area meets the requirements of a clean room (ZAPClean room).

[0003] In addition, the main challenge of this invention is how to implement this indoor purification system in an indoor area, optimize the number of purification devices, specification layout, optimize performance control, implement noise reduction control, and minimize installation costs (all of which require verifying a huge amount of data and calculating the optimal planning settings through repeated experiments), and how to apply such a system to general indoor domestic life. Summary of the Invention [Problem to be solved by the invention]

[0004] The main objective of the present invention is to provide a method for realizing the benefits of an indoor cleaning system through artificial intelligence (AIGC). The method for realizing the benefits of an indoor cleaning system by combining an indoor cleaning system that can be implemented in indoor home life with an artificial intelligence (AIGC) module can optimize the number and specification layout of cleaning devices, optimize performance control, implement noise reduction control, and minimize installation costs. The indoor cleaning system can be applied to indoor areas to detect, identify, circulate, and filter air pollution to meet the cleanroom (ZAPClean room) level requirements. [Means for solving the problem]

[0005] To achieve the above object, the present invention provides a method for realizing the benefits of an indoor cleaning system through artificial intelligence generative computing (AIGC), which includes the steps of: providing expert-generated data for an indoor cleaning system, the expert-generated data including outdoor and indoor air pollution data for a building, indoor area data for the building, clean room level data, hardware specifications for the air cleaning system, and software specifications for the air cleaning system; providing user-generated data for the indoor cleaning system, the user-generated data including indoor area parameter data for the user's building, experimentally measured indoor area air pollution parameter data for the user's building, and air exchange rate parameter data for the indoor area heating, ventilation, and air conditioning (HVAC) for the user's building; and providing an AIGC model, which inputs the expert-generated data and the user-generated data to perform calculations, comparisons, and discrimination to generate automatically generated data. The method includes the steps of: providing an integrated benefit correction, where the automatically generated data includes the number of air purification hardware optimizations, performance control of air purification hardware optimizations, noise reduction control of air purification hardware optimizations, information on the minimum one-time installation cost of the air purification system, and information on the minimum operating cost of the air purification system; and finally integrating the automatically generated data generated by the generative artificial intelligence (AIGC) model, optimizing data correction by new actual deep learning processing, comparing the learning and improvement of the automatically generated data, guiding the generative artificial intelligence (AIGC) model to quickly converge in a correct and applicable direction, and regressing and correcting the automatically generated data to the most accurate one, thereby optimizing the benefits of the indoor purification system and implementing the indoor purification system. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a schematic diagram illustrating how the benefits of an indoor cleaning system can be realized through the artificial intelligence generative computing (AIGC) of the present invention. [Figure 2] 1 is a schematic diagram showing the configuration of an indoor cleaning system of the present invention. [Figure 3]1 is a comparison table of cleanliness levels of the indoor cleaning system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0007] The following description will provide detailed descriptions of embodiments illustrating the features and advantages of the present invention. It should be understood that the present invention may have various modifications in different aspects, all without departing from the scope of the present invention, and that the description and drawings are illustrative in nature and are not intended to limit the present invention.

[0008] Referring to FIG. 1, the present invention provides a method for realizing the benefits of an indoor cleaning system through artificial intelligence generated content (AIGC). The method includes the steps of: providing expert-generated data for an indoor cleaning system, the expert-generated data including outdoor and indoor air pollution data of a building, indoor area data of the building, clean room level data, hardware specifications of the air cleaning system, and software specifications of the air cleaning system; providing user-generated data for the indoor cleaning system, the user-generated data including indoor area parameter data of the user's building, experimentally measured indoor area air pollution parameter data of the user's building, and air exchange rate parameter data of the indoor area heating, ventilation, and air conditioning (HVAC) of the user's building; and providing an artificial intelligence generated content (AIGC) model 4, which inputs the expert-generated data and the user-generated data to calculate, compare, and discriminate between the expert-generated data and the user-generated data to generate automatically generated data. The data includes the number of air purification hardware optimizations, performance control of air purification hardware optimization, noise reduction control of air purification hardware optimization, information on the minimum one-time installation cost of the air purification system, and information on the minimum operating cost of the air purification system; and the steps of providing an integrated benefit correction for the generative artificial intelligence (AIGC) model 4, finally integrating the automatically generated data generated by the generative artificial intelligence (AIGC) model 4, optimizing data correction by new actual deep learning processing, comparing the learning and improvement of the automatically generated data, guiding the generative artificial intelligence (AIGC) model 4 to quickly converge in a correct applicable direction, and regressing and correcting the automatically generated data to the most accurate one, thereby optimizing the benefits of the indoor purification system and implementing the indoor purification system.

[0009] As shown in FIG. 2, the indoor purification system A includes a storage center 1, an air quality detector 2, an application (APP) software 3, an artificial intelligence generative computing (AIGC) model 4, and an air purification device hardware 5.

[0010] The storage center 1 collects and stores information data for indoor purification systems, forming a big database of expert-generated data and user-generated data. The information data for indoor purification systems refers to reference data such as spatial parameters of the systems implemented in the environmental area, data parameters for air pollution detection, standard safety value parameters, cleanroom level comparison parameters, software and hardware specifications of the purification equipment, and control parameters. It also includes, but is not limited to, all system-related application data, such as research data for the AI ​​machine learning model and historical data. The expert-generated data includes outdoor and indoor air pollution standard data for buildings (e.g., air pollution characteristics, concentration safety values, etc.), indoor area spatial data for buildings (e.g., building size and ratio, usage functions, environmental ventilation, temperature control comfort parameters, etc.), cleanroom level standard data (e.g., a comparison table of ZAPClean room levels 1 to 12, Figure 3), and required software and hardware specifications for the air purification system (e.g., fan clean air supply rate CADR, filter specifications, etc.). The user-generated data includes indoor area air pollution data of the user's building (e.g., real-time detection data of air quality detector 2), indoor area laboratory measurement air pollution data of the user's building (actual air pollution data of the user's indoor area detected by the testing unit), and indoor area heating, ventilation, and air conditioning (HVAC) air exchange rate parameter data of the user's building (e.g., comparison data of indoor and outdoor carbon dioxide detected in real time by air quality detector 2 in the user's environmental area).

[0011] The air quality detector 2 detects air pollution in the outdoor area and indoor area of ​​the building, outputs air pollution data, and transmits the air pollution data to the storage center 1 via the Internet of Things to form user-generated data.

[0012] The application (APP) software 3 allows the input of relevant indoor cleaning system information data, which is then transmitted to the storage center 1 via the Internet of Things for storage.

[0013] The AIGC model 4 may 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 expert-generated data and user-generated data stored in the storage center 1 are captured by the AIGC model 4 via the Internet of Things (IoT), and the AIGC model 4 processes and analyzes the data using deep learning to generate automatically generated data (as shown in Figure 1). Specifically, the application software 3 inputs expert-generated data (including information data such as outdoor and indoor air pollution standard data for the building, indoor area space data for the building, cleanroom level standard data, and required software and hardware specifications for the air purification system) and related indoor purification system information data (including information data such as indoor area air pollution data for the user's building, experimentally measured indoor area air pollution data for the user's building, and air exchange rate data for the heating, ventilation, and air conditioning (HVAC) system for the user's building). These data are then stored in the storage center 1, forming a big database of air pollution data. The user inputs system requirement information, such as the number of air purifying device optimizations, specification layout, optimization performance control, noise reduction control implementation, and minimum installation cost plan, via application software (APP) 3. The generative artificial intelligence (AIGC) model 4 collects information data on indoor purifying systems stored in the storage center 1 via the Internet of Things, and can form expert-generated data and user-generated data in basic generation. It also processes and analyzes the data using deep learning to generate and compare an actual classifier with the generated trained classifier, obtaining automatically generated data, such as the number of air purifying hardware optimizations, optimization performance control of air purifying hardware, optimization noise reduction control of air purifying hardware, minimum one-time installation cost information for the air purifying system, and minimum operating cost information for the air purifying system.The AIGC model 4 itself is equipped with an autoregression correction analysis mechanism, which provides integrated benefit correction for the AIGC model 4 and performs a final integrated evaluation of the automatically generated data generated by the AIGC model 4. This allows it to predict and generate new actual deep learning processed data, optimally correct it, compare the reliability of the automatically generated data, guide the AIGC model 4 to quickly converge in the "correct" applicable direction, and regress and correct the automatically generated data to the most accurate one. In this way, by combining the AIGC platform with the AIGC model 4 before implementing the indoor air purification system, it is possible to obtain the optimal pre-operation layout and application optimization benefits, obtain an indoor air purification system with the required installation specifications and cost, and then apply it to the indoor area to detect, identify, circulate, and filter air pollution.

[0014] The air purifier hardware 5 is installed in the indoor area of ​​a building. The air purifier hardware 5 may be, but is not limited to, a ventilation system 5a, a total heat exchanger 5b, a range hood 5c, a circulation filtration unit (FFU) 5d, a negative pressure exhaust fan 5e, a bathroom exhaust fan 5f, a mobile circulation filtration unit, or a central control unit (computer). Each air purifier hardware 5 includes at least one fan and at least one filtration component. The air purifier hardware 5 is connected to the storage center 1 via the Internet of Things and intelligently and selectively issues control commands based on automatically generated data to control the operation of the fan in the air purifier hardware 5, thereby guiding the air pollution in the indoor area to be filtered by the filtration component, and ensuring that the air pollution level in the indoor area meets the requirements of clean rooms (ZAPClean room levels 1 to 12).

[0015] As described above, the present invention combines an indoor purification system that can be implemented in indoor home life with a generative artificial intelligence (AIGC) model to realize the benefits of the indoor purification system, optimizing the number of purification devices, arranging the specification layout, optimizing performance control, implementing noise reduction control, and building an indoor purification system that minimizes installation costs. In this way, by combining a generative artificial intelligence platform before the implementation of the indoor purification system, it is possible to obtain the benefits of optimal pre-operation layout and application optimization, and to obtain an indoor purification system with the required installation specifications and cost. Furthermore, it can be applied in indoor areas to detect, identify, circulate, and filter air pollution, meeting the requirements of a clean room (ZAPClean room), which has great industrial applicability. [Explanation of symbols]

[0016] A: Indoor purification system 1: Memory Center 2: Air quality detector 3: Application (APP) software 4: Artificial Intelligence Generator (AIGC) Model 5: Air purifier hardware 5a: Ventilation equipment 5b: Total heat exchanger 5c: Range hood 5d: Circulating filtration unit (FFU) 5e: Negative pressure exhaust fan 5F: Bathroom exhaust fan

Claims

1. A method for realizing the benefits of an indoor cleaning system through generative artificial intelligence, comprising: Providing expert-generated data for the indoor cleaning system, the expert-generated data including outdoor and indoor air pollution data for the building, indoor area data for the building, clean room level data, hardware specifications for the air cleaning system, and software specifications for the air cleaning system; providing user-generated data for the indoor cleaning system, the user-generated data including: indoor area parameter data for the user's building; indoor area laboratory-measured air pollution parameter data for the user's building; and indoor area heating, ventilation, and air conditioning air exchange rate parameter data for the user's building; providing a generative artificial intelligence model, inputting the expert-generated data and the user-generated data to calculate, compare, and discriminate to generate automatically generated data, wherein the automatically generated data includes the number of optimizations of the air purification hardware, the performance control of the optimization of the air purification hardware, the noise reduction control of the optimization of the air purification hardware, the minimum one-time installation cost information of the air purification system, and the minimum operating cost information of the air purification system; Providing an integrated benefit correction, finally integrating the automatically generated data generated by the generative artificial intelligence model, optimizing data correction by deep learning processing, comparing the learning and improvement of the automatically generated data, guiding the generative artificial intelligence (AIGC) model to quickly converge in a correct applicable direction, and regressing and correcting the automatically generated data to the most accurate one, thereby optimizing the benefit of the indoor cleaning system and implementing the indoor cleaning system; A method comprising:

2. The method for realizing the benefits of an indoor cleaning system through generative artificial intelligence as described in claim 1, wherein the generative artificial intelligence model includes any 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.

3. The method for realizing the benefits of an indoor purification system through generative artificial intelligence as described in claim 1, wherein the indoor purification system includes air purification device hardware, and the specifications of the air purification device hardware include air quality detector specifications, ventilation device specifications, total heat exchanger specifications, range hood specifications, circulating filtration device specifications, mobile circulating filtration device specifications, bathroom exhaust fan specifications, central control device (computer) specifications, negative pressure exhaust fan specifications, and filtration component specifications.

4. The method for realizing the benefits of an indoor cleaning system through generative artificial intelligence as described in claim 3, wherein the professional generated data includes clean room level reference data, and the clean room level reference data is ZAPClean room level 1 to 12 standard.

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