Clean room air purification method and clean room ion control system

Abstract

The present invention relates to the field of air purification technology, and in particular to a clean room air purification method and a clean room ion control system. The method comprises the following steps: obtaining indoor spectral data, and classifying indoor regional air components to obtain indoor air component data; obtaining indoor ion sensor data, and performing indoor air quality assessment to obtain indoor air quality assessment data; performing demand division based on indoor air quality assessment data and indoor air ion flow data to obtain indoor clean demand data; obtaining water washing parameter data, and performing strategy analysis to obtain air purification water washing strategy; obtaining temperature control parameter data, and performing strategy analysis based on indoor clean demand data and temperature control parameter data to obtain air purification temperature control strategy; performing strategy coupling based on air purification water washing strategy and air purification temperature control strategy to obtain clean room air purification strategy. The present invention can improve the air purification effect of the clean room.

Description

Technical Field

[0001] The present invention relates to the technical field of air purification, and in particular to an air purification method for a clean room and a clean room ion control system. Background Art

[0002] In the early 20th century, with the rise of industrialization and the advancement of science and technology, people began to realize the impact of particulate matter in the air on human health and industrial production. This prompted the initial research and application of air purification technology. The earliest air purification methods included simple filters and ventilation systems to remove dust, smoke and odor substances from the air. With the continuous development of science and technology, especially in the mid-20th century, the demand for clean air has increased. The demand for clean air in industries such as semiconductor industry, healthcare and biopharmaceuticals has gradually increased, which has led to the rapid development of air purification technology. In the 1970s and 1980s, with the development of microelectronics industry and biotechnology, the requirements for clean air became more stringent. Traditional filtration technology can often only remove large particles and some fine particles in the air, and has low efficiency in removing microbial pollutants such as microparticles, bacteria, and viruses. At the same time, traditional clean room air purification systems require a lot of energy to operate, especially systems that require powerful fans or high-power filters, which will increase energy consumption and operating costs. It can no longer meet the demand for efficient removal of tiny particles and microorganisms in the air. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide an air purification method for a clean room and a clean room ion control system to solve at least one of the above technical problems.

[0004] To achieve the above object, a clean room air purification method is provided, which is applied to a clean room air purification device, wherein the clean room air purification device comprises a controller, a spectrometer, an ion monitor, a temperature control device and a water washing device, wherein the spectrometer, the ion monitor, the temperature control device and the water washing device are all electrically connected to the controller, and the clean room air purification method comprises the following steps:

[0005] Step S1: Acquire indoor spectrum data through a spectrometer, and extract indoor spectrum wavelength distribution features based on the indoor spectrum data, thereby obtaining indoor spectrum wavelength distribution data; classify indoor regional air components based on the indoor spectrum wavelength distribution data, thereby obtaining indoor air component data;

[0006] Step S2: acquiring indoor ion sensor data through an ion monitor, and performing indoor air ion mobility analysis based on the indoor ion sensor data and indoor air composition data, thereby obtaining indoor air ion flow data; performing indoor air quality assessment based on the indoor air ion flow data and indoor air composition data, thereby obtaining indoor air quality assessment data;

[0007] Step S3: dividing indoor cleanliness requirements based on indoor air quality assessment data and indoor air ion flow data, thereby obtaining indoor cleanliness requirement data;

[0008] Step S4: obtaining water washing parameter data through the water washing device, and performing air purification water washing strategy analysis according to the indoor cleanliness demand data and the water washing parameter data, thereby obtaining an air purification water washing strategy;

[0009] Step S5: obtaining temperature control parameter data through the temperature control device, and performing air purification temperature control strategy analysis according to the indoor cleanliness demand data and the temperature control parameter data, thereby obtaining an air purification temperature control strategy;

[0010] Step S6: coupling the air purification strategy based on the air purification water washing strategy and the air purification temperature control strategy to obtain the clean room air purification strategy, and transmitting the clean room air purification strategy to the controller to perform the air purification task.

[0011] The present invention obtains indoor spectral data through a spectrometer, and then extracts indoor spectral wavelength distribution characteristics, which can help determine the characteristics of different components in indoor air. Such data analysis can effectively distinguish and identify various particles and pollutants in the air, which helps to more accurately understand the degree and type of air pollution. By obtaining indoor ion sensor data through an ion monitor and combining it with indoor air component data for analysis, the ion flow in indoor air can be deeply understood. This helps to evaluate air quality because ion mobility is closely related to the diffusion and propagation of particles and microorganisms in the air. Based on indoor air quality assessment data and ion flow data, indoor cleanliness requirements are divided, which helps to determine the degree of cleanliness requirements in different areas. Such division can make the air purification system respond to the needs of different areas more intelligently, thereby improving purification efficiency. Obtaining corresponding parameter data through a water washing device and a temperature control device, and performing air purification strategy analysis, helps to formulate a more accurate and effective purification strategy. Water washing and temperature control are two common air purification methods, and their reasonable use can reduce energy consumption while improving purification efficiency. Coupling the water washing strategy and the temperature control strategy to formulate a comprehensive air purification strategy can better balance the efficiency and energy consumption between different purification methods. Such a comprehensive strategy helps to improve the performance of the entire clean room air purification system and ensure that energy consumption and operating costs are reduced as much as possible while meeting the cleanliness requirements. In summary, the present invention achieves comprehensive optimization of the clean room air purification process by combining spectral analysis, ion monitoring, intelligent division of clean requirements, water washing, temperature control and other strategies, thereby improving the efficiency and energy utilization of the clean room air purification system to better meet the demand for efficient removal of tiny particles and microorganisms.

[0012] Optionally, step S1 specifically includes:

[0013] Step S11: acquiring indoor spectrum data through a spectrometer, and extracting spectrum wavelength features of the indoor spectrum data, thereby obtaining indoor spectrum wavelength data;

[0014] Step S12: performing wavelength spatial distribution statistics based on the indoor spectrum wavelength data, thereby obtaining indoor spectrum wavelength distribution data;

[0015] Step S13: performing absorption feature extraction and scattering feature extraction on the indoor spectral wavelength distribution data, thereby obtaining spectral wavelength absorption data and spectral wavelength scattering data;

[0016] Step S14: classifying the air component type based on the spectral wavelength absorption data, thereby obtaining air component type data;

[0017] Step S15: classifying the spectral wavelength scattering data into air particle types according to the air component type data, thereby obtaining air particle data;

[0018] Step S16: spatially merge the air component type data and the air particle data based on the indoor spectral wavelength data, so as to obtain indoor area air component data.

[0019] The present invention obtains indoor spectral data through a spectrometer and extracts its spectral wavelength characteristics, which helps to determine the spectral characteristics within different wavelength ranges. This feature extraction can help identify different components in indoor air and provide basic data for subsequent analysis. Wavelength spatial distribution statistics based on indoor spectral wavelength data can reveal the spectral distribution of different wavelengths in indoor air. This helps to understand the overall distribution characteristics of the indoor spectrum and provides a basis for subsequent analysis. Absorption feature extraction and scattering feature extraction are performed on the indoor spectral wavelength distribution data to obtain absorption and scattering data of spectral wavelengths. These data can help distinguish the contributions of different components to the absorption and scattering of light, and help to further understand the existence and characteristics of different components in the air. Classification of air component types based on spectral wavelength absorption data helps to determine the different component types present in indoor air. Such classification can enable the system to better distinguish different pollutants and provide a more accurate basis for subsequent analysis and processing. Classification of air particle types based on spectral wavelength scattering data according to air component type data helps to determine the different types of particulate matter present in the air. This helps to understand the characteristics of particles in the air and further guide subsequent air purification and treatment measures. By merging the air component type data and air particle data based on the indoor spectral wavelength data, we can comprehensively consider the distribution of different components and particles and provide a more comprehensive description of the indoor regional air component data. This helps to better understand the indoor air pollution and guide subsequent air purification and management work.

[0020] Optionally, step S14 is specifically:

[0021] Step S141: extracting absorption peak features from the spectral wavelength absorption data, thereby obtaining spectral absorption peak data;

[0022] Step S142: performing absorption peak similarity calculation on the spectral absorption peak data, thereby obtaining spectral absorption peak similarity data;

[0023] Step S143: performing spectral absorption peak type statistics according to the spectral absorption peak similarity data, thereby obtaining high-frequency spectral absorption peak data and low-frequency spectral absorption peak data;

[0024] Step S144: acquiring gas molecule data, and extracting gas molecule structure features from the gas molecule data, thereby obtaining gas molecule structure data;

[0025] Step S145: classifying the high-frequency spectrum absorption peak data into gas components according to the gas molecular structure data, thereby obtaining high-frequency gas component data and high-frequency non-gas component data;

[0026] Step S146: classifying the low-frequency spectrum absorption peak data into gas components according to the gas molecular structure data, thereby obtaining low-frequency gas component data and low-frequency non-gas component data;

[0027] Step S147: Merge the high-frequency gas component data, the low-frequency gas component data, the high-frequency non-gas component data, and the low-frequency non-gas component data to obtain air component type data.

[0028] The present invention extracts absorption peak features from spectral wavelength absorption data, which helps to determine the characteristics of each peak, such as the position, intensity and shape. By extracting the absorption peak features, the absorption behavior of various components in the spectrum can be more accurately represented, thereby providing a more reliable data basis for subsequent analysis. By calculating the absorption peak similarity of the spectral absorption peak data, the similarity between different absorption peaks can be quantified. This helps to identify similar absorption peaks, indicating the presence of similar components or similar absorption mechanisms, and provides guidance for subsequent classification and analysis. By statistically analyzing the spectral absorption peak similarity data, the spectral absorption peak types can be classified, including high-frequency spectral absorption peaks and low-frequency spectral absorption peaks. This helps to understand the characteristics of absorption peaks in different frequency ranges and make a preliminary distinction between different types of gas components. Obtain gas molecule data and extract gas molecule structure features, which may include information such as the chemical composition, structure, and bond length of the molecule. These features are crucial for subsequent gas component classification because different gas molecules exhibit different spectral absorption characteristics. Gas components are classified for high-frequency and low-frequency spectral absorption peak data according to the gas molecule structure data. By combining the gas molecular structure and spectral absorption characteristics, the source of each peak in the spectrum can be determined more accurately, thereby identifying high-frequency and low-frequency gas component data and non-gas component data. By merging various types of data, air component type data is obtained, including high-frequency gas components, low-frequency gas components, and non-gas component data. Such data integration can provide more comprehensive indoor air composition information and provide a more reliable basis for air quality management and regulation.

[0029] Optionally, step S15 is specifically:

[0030] Step S151: extracting non-gas component data from the air component type data, thereby obtaining indoor air non-gas component data;

[0031] Step S152: performing high-value scattering amplitude statistics on the spectral wavelength scattering data, thereby obtaining high-value scattering amplitude data; performing high-value scattering intensity statistics on the spectral wavelength scattering data, thereby obtaining high-value scattering intensity data;

[0032] Step S153: performing a spectral intersection operation on the high-value scattering amplitude data and the high-value scattering intensity data, thereby obtaining the scattering data of the air particles to be classified;

[0033] Step S154: acquiring air particle molecular data, and extracting particle molecular structure features from the air particle molecular data, thereby obtaining air particle molecular structure data;

[0034] Step S155: classifying the air particle types of the air particle scattering data to be classified according to the air particle molecular structure data, thereby obtaining air particle classification data;

[0035] Step S156: spatially correlate the air particle classification data according to the indoor air non-gas component data to obtain air particle data.

[0036] The present invention extracts non-gas component data from air component type data, aiming to separate non-gas component data from overall air component data. These non-gas components include dust, particulate matter, microparticles, etc., and their presence will affect air quality. The high-value scattering amplitude and intensity statistics are performed on the spectral wavelength scattering data to obtain data related to particle scattering. The statistics of scattering amplitude and intensity help to understand the scattering behavior of particles within the spectral wavelength range and provide basic data for subsequent particle analysis. The high-value scattering amplitude and intensity data are subjected to spectral intersection operations to obtain air particle scattering data to be classified. Information related to air particles is extracted from the scattering data to prepare for further particle classification and analysis. Air particle molecular data is obtained and particle molecular structure features are extracted, which include information such as chemical composition, shape, size, etc. of the particles. These structural features are crucial for subsequent particle classification because particles of different structures exhibit different scattering behaviors. The particle type classification is performed on the air particle scattering data to be classified according to the air particle molecular structure data, which helps to identify and distinguish different types of particles. By combining the particle molecular structure and scattering data, the type and source of the particles can be determined more accurately. The particle classification data were spatially correlated with the indoor air non-gaseous component data. This means that correlating the particle data with the non-gaseous component data helps to understand the relationship between particle matter and other air components, providing more complete information for comprehensive analysis.

[0037] Optionally, step S2 specifically includes:

[0038] Step S21: Acquire indoor ion sensing data through an ion monitor;

[0039] Step S22: performing indoor air ion association on the indoor ion sensing data and the indoor air composition data, thereby obtaining indoor air ion data;

[0040] Step S23: performing indoor air flow simulation based on the indoor air ion data, thereby obtaining indoor air flow simulation data;

[0041] Step S24: performing ion flow time series statistics based on the indoor air ion flow data, thereby obtaining the indoor air ion flow data;

[0042] Step S25: Perform indoor air quality assessment based on the indoor air ion flow data and the indoor air composition data, thereby obtaining indoor air quality assessment data.

[0043] The present invention obtains indoor ion sensor data through an ion monitor. The ion sensor data can provide information about the ion concentration in the indoor environment, which is of great significance for evaluating air quality and understanding pollutants in the indoor environment. The indoor ion sensor data is associated with the indoor air component data to obtain the indoor air ion data. This can link the ion data with other air components (such as particulate matter, gas, etc.) to provide a more comprehensive data basis for subsequent analysis. Indoor air flow simulation is performed based on the indoor air ion data. The flow of indoor air including the propagation and diffusion process of ions in the air is simulated. By simulating the air flow, the quality distribution of indoor air and the propagation path of pollutants can be evaluated. Performing ion flow time series statistics based on indoor air ion flow data helps to understand the changing trend of ions in different time periods and identify air quality abnormalities or pollution sources. Indoor air quality assessment is performed based on indoor air ion flow data and indoor air component data. By comprehensively considering the ion flow data and other air component data, the indoor air quality can be comprehensively evaluated, potential air pollution problems can be identified, and corresponding measures can be taken to improve the air quality.

[0044] Optionally, step S25 is specifically:

[0045] Step S251: extracting air particle flow characteristics according to indoor air ion flow data, thereby obtaining air particle flow data;

[0046] Step S252: performing particle fluidity evaluation on the air particle fluidity data to obtain air particle fluidity data, and performing time series analysis on the air particle fluidity data to obtain particle fluidity change data;

[0047] Step S253: performing air turbidity evaluation according to the indoor air composition data to obtain air turbidity data, and performing time series analysis on the air turbidity data to obtain air turbidity change data;

[0048] Step S254: constructing an air quality assessment system according to the air turbidity change data and the particle mobility change data;

[0049] Step S255: Perform indoor regional air quality assessment on the indoor air ion flow data through an air quality assessment system, thereby obtaining indoor air quality assessment data.

[0050] The present invention can obtain data on the movement, diffusion and distribution of indoor particles by extracting the particle flow characteristics from the indoor air ion flow data. This helps to understand the behavior and distribution of particles in indoor air. Evaluating the air particle flowability and performing time series analysis can help identify the changing trend of particle flowability. Such analysis can reveal the movement characteristics of particles in the indoor environment and then evaluate the impact of particles on air quality. Evaluating air turbidity based on indoor air composition data is another consideration of indoor air quality. By analyzing air turbidity data through time series, the changing trend of air quality can be found, which helps to identify the existing pollution sources or the reasons for the deterioration of air quality. Combining air turbidity change data with particle flowability change data to construct an air quality evaluation system. Such an evaluation system comprehensively considers multiple factors such as particle flowability and air turbidity, and can more accurately reflect the overall situation of indoor air quality. The indoor air ion flow data is evaluated by the air quality evaluation system to obtain indoor air quality evaluation data. The obtained data are integrated to provide a comprehensive and objective indoor air quality evaluation result, which provides a basis and direction for improving the indoor environment.

[0051] Optionally, step S3 specifically includes:

[0052] Step S31: performing regional air quality statistics based on the indoor air quality assessment data, thereby obtaining high air quality regional data and low air quality regional data;

[0053] Step S32: dividing the air particle flow area according to the indoor air ion flow data, thereby obtaining low air particle flow area data and high air particle flow area data;

[0054] Step S33: performing intersection operation on the low air quality area data and the low air particle flow area data, so as to obtain the high indoor cleanliness demand area data;

[0055] Step S34: performing intersection operation on the high air quality area data and the high air particle flow area data, so as to obtain the low indoor cleanliness requirement area data;

[0056] Step S35: spatially merging the data of the area with high indoor cleanliness requirement and the data of the area with low indoor cleanliness requirement to obtain indoor cleanliness requirement data.

[0057] The present invention can obtain the air quality conditions of different regions by performing regional air quality statistics based on indoor air quality assessment data. This helps to identify high air quality areas and low air quality areas, and provides basic data for subsequent clean demand area division. According to the indoor air ion flow data, the air particle flow area division can understand the flow of particles in different areas. This helps to identify high air particle flow areas and low air particle flow areas, and provides more detailed information for subsequent clean demand area division. By performing intersection operations on low air quality area data and low air particle flow area data, high indoor clean demand areas are determined. These areas are areas with poor air quality and high particle mobility, and more clean measures are needed to improve the indoor environment. By performing intersection operations on high air quality area data and high air particle flow area data, low indoor clean demand areas are determined. These areas are areas with good air quality and low particle mobility, and relatively few clean measures are needed. The high indoor clean demand area data and the low indoor clean demand area data are spatially merged to obtain overall indoor clean demand data. These data can guide indoor environment managers to take targeted cleaning and ventilation measures to improve indoor air quality and protect residents' health.

[0058] Optionally, step S4 is specifically:

[0059] Step S41: obtaining water washing parameter data through the water washing device, and classifying the water washing modes according to the water washing parameter data, thereby obtaining a high water flow clean water washing mode parameter combination and a low water flow clean water washing mode parameter combination;

[0060] Step S42: extracting spatial features of indoor cleanliness demand data, thereby obtaining spatial data of high cleanliness demand areas and spatial data of low cleanliness demand areas;

[0061] Step S43: Calculate the water washing flow demand of the target area according to the spatial data of the high cleanliness demand area and the water washing parameter data, so as to obtain the water washing flow data of the main target area;

[0062] Step S44: performing a high indoor clean area water washing parameter combination according to the water washing flow data of the main target area and the high water flow clean water washing mode parameter combination, thereby obtaining a high cleanliness requirement area water washing parameter combination;

[0063] Step S45: Calculate the water washing flow demand of the target area according to the spatial data of the low cleanliness demand area and the water washing parameter data, so as to obtain the water washing flow data of the secondary target area;

[0064] Step S46: performing a low indoor clean area water washing parameter combination according to the water washing flow data of the secondary target area and the low water flow clean water washing mode parameter combination, thereby obtaining a low cleanliness requirement area water washing parameter combination;

[0065] Step S47: Perform water washing strategy analysis based on the water washing parameter combination of the high cleanliness requirement area and the water washing parameter combination of the low cleanliness requirement area, so as to obtain the air purification water washing strategy.

[0066] The washing parameter data obtained by the washing device of the present invention is helpful to determine different washing modes. After classifying the high water flow clean washing mode and the low water flow clean washing mode, the appropriate washing method can be selected for different cleaning needs, so as to achieve efficient indoor air cleaning. The spatial feature extraction of the indoor clean demand data is helpful to distinguish the high clean demand area and the low clean demand area. Such spatial feature extraction can provide a basis for the subsequent calculation of the washing flow demand. By combining the spatial data and washing parameter data of the high clean demand area, the washing flow demand of the target area is calculated, which helps to determine the washing flow required by the main target area to meet the cleanliness requirements of the indoor environment. Based on the high water flow clean washing mode parameter combination, the washing parameters of the high clean demand area are determined to ensure that the high clean demand area is fully cleaned and purified. Similarly, according to the spatial data and washing parameter data of the low clean demand area, the washing flow demand of the secondary target area is calculated to meet the cleaning requirements of the low clean demand area. On the basis of determining the low water flow clean washing mode parameter combination, the washing parameters of the low clean demand area are set to save water resources and ensure the moderate cleanliness of the indoor environment. By analyzing the water washing parameter combinations in areas with high cleanliness requirements and areas with low cleanliness requirements, an overall water washing strategy is formulated to effectively purify indoor air and improve indoor environmental quality.

[0067] Optionally, step S5 specifically includes:

[0068] Step S51: obtaining temperature control parameter data through a temperature control device;

[0069] Step S52: spatially correlating the indoor cleanliness requirement data and the indoor air composition data, thereby obtaining the air composition data of the high cleanliness requirement area and the air composition data of the low cleanliness requirement area;

[0070] Step S53: acquiring air pollution component growth rules, and performing temperature growth rule extraction on the air pollution component growth rules, thereby obtaining air pollution component temperature growth rules;

[0071] Step S54: combining the temperature control parameter data according to the temperature growth rule of the air pollution component, thereby obtaining a decontamination temperature control parameter combination;

[0072] Step S55: Calculate the air pollutant component ratios of the air composition data of the high cleanliness requirement area and the air composition data of the low cleanliness requirement area, so as to obtain the air pollutant component ratio data of the high cleanliness requirement area and the air pollutant component ratio data of the low cleanliness requirement area;

[0073] Step S56: analyzing the temperature control strategy of the high cleanliness demand area according to the decontamination temperature control parameter combination and the air pollutant component proportion data of the high cleanliness demand area, so as to obtain the temperature control strategy of the high cleanliness demand area;

[0074] Step S57: analyzing the temperature control strategy of the low cleanliness demand area according to the decontamination temperature control parameter combination and the air pollutant component proportion data of the low cleanliness demand area, so as to obtain the temperature control strategy of the low cleanliness demand area;

[0075] Step S58: coupling the temperature control strategy of the high cleanliness requirement area and the temperature control strategy of the low cleanliness requirement area to obtain an air purification temperature control strategy.

[0076] The present invention obtains temperature control parameter data through a temperature control device. It is conducive to real-time monitoring and control of indoor temperature, and provides a basis for subsequent air purification strategies. By spatially correlating indoor clean demand data and indoor air composition data, high clean demand areas and low clean demand areas are distinguished, and corresponding air composition data are obtained. This helps the system to more accurately regulate the air quality of different areas. The growth rules of air pollution components are obtained, and the temperature growth rules therein are extracted. It is helpful to understand the growth laws of different pollutants at different temperatures, and provide guidance for subsequent temperature control. According to the temperature growth rules of air pollution components, the temperature control parameters are adjusted to obtain a temperature control parameter combination suitable for air purification. It is helpful to control the temperature while suppressing the growth and diffusion of air pollutants to the greatest extent. By calculating the proportion of air pollutant components in high clean demand areas and low clean demand areas, the system can more accurately understand the degree of pollution in different areas, and provide data support for formulating targeted temperature control strategies. Based on the decontamination temperature control parameter combination and the air pollutant component proportion data of each area, the temperature control strategy analysis of high clean demand areas and low clean demand areas is performed respectively. These strategies can adjust the temperature to purify the air to the maximum extent according to the needs of different areas. The temperature control strategies of high cleanliness demand areas and low cleanliness demand areas are coupled to form a comprehensive air purification temperature control strategy. This comprehensive strategy takes into account the needs and air quality conditions of different areas and can achieve comprehensive indoor air purification and comfort control.

[0077] Optionally, the present specification also provides a clean room ion control system for executing the above-mentioned clean room air purification method, the clean room ion control system comprising:

[0078] The air composition classification module is used to obtain indoor spectral data through a spectrometer, and extract indoor spectral wavelength distribution characteristics based on the indoor spectral data, so as to obtain indoor spectral wavelength distribution data; classify indoor regional air components based on the indoor spectral wavelength distribution data, so as to obtain indoor air composition data;

[0079] An indoor air quality assessment module is used to obtain indoor ion sensor data through an ion monitor, and perform indoor air ion mobility analysis based on the indoor ion sensor data and indoor air composition data, thereby obtaining indoor air ion flow data; perform indoor air quality assessment based on the indoor air ion flow data and indoor air composition data, thereby obtaining indoor air quality assessment data;

[0080] An indoor cleanliness demand classification module is used to classify indoor cleanliness demands based on indoor air quality assessment data and indoor air ion flow data, thereby obtaining indoor cleanliness demand data;

[0081] The water washing strategy analysis module is used to obtain water washing parameter data through the water washing device, and perform air purification water washing strategy analysis based on the indoor cleanliness demand data and the water washing parameter data, so as to obtain the air purification water washing strategy;

[0082] The temperature control strategy analysis module is used to obtain temperature control parameter data through the temperature control device, and perform air purification temperature control strategy analysis based on indoor cleanliness demand data and temperature control parameter data, so as to obtain the air purification temperature control strategy;

[0083] The air purification strategy analysis module is used to couple the air purification strategy based on the air purification water washing strategy and the air purification temperature control strategy to obtain the clean room air purification strategy, and transmit the clean room air purification strategy to the controller to perform the air purification task.

[0084] The clean room ion control system of the present invention can implement any clean room air purification method of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the clean room air purification method. The internal modules of the system cooperate with each other, thereby improving the air purification effect and energy utilization efficiency of the clean room. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0086] Figure 1 It is a schematic flow chart of the steps of the clean room air purification method of the present invention;

[0087] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0088] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0089] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0090] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0091] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0092] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0093] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for purifying air in a clean room, the method comprising the following steps:

[0094] Step S1: Acquire indoor spectrum data through a spectrometer, and extract indoor spectrum wavelength distribution features based on the indoor spectrum data, thereby obtaining indoor spectrum wavelength distribution data; classify indoor regional air components based on the indoor spectrum wavelength distribution data, thereby obtaining indoor air component data;

[0095] In this embodiment, the spectrometer is started, scanned, and spectral data is recorded. With respect to the spectral data, an optical analysis algorithm is used to identify spectral features of different wavelengths, such as peaks and troughs in the visible and ultraviolet regions. Next, the spectral data is analyzed based on known optical properties and gas absorption spectra to identify spectral features of different gases and classify them. For example, volatile organic compounds (VOCs) such as formaldehyde and benzene are identified based on absorption peaks within a specific wavelength range. Finally, based on these identification results, indoor air composition data is generated to describe the concentration distribution of different components in the indoor air.

[0096] Step S2: acquiring indoor ion sensor data through an ion monitor, and performing indoor air ion mobility analysis based on the indoor ion sensor data and indoor air composition data, thereby obtaining indoor air ion flow data; performing indoor air quality assessment based on the indoor air ion flow data and indoor air composition data, thereby obtaining indoor air quality assessment data;

[0097] In this embodiment, the ion monitor is started to record the concentration data of positive and negative ions in the room and transmit it to the analysis system in real time. At the same time, combined with the previously acquired indoor air composition data, the calculation model is used to analyze the diffusion and migration behavior of ions in the air, as well as their interaction with other gas components. Based on these analysis results, the indoor air quality is evaluated, including the source and propagation path of ions, and the impact of ions on air quality.

[0098] Step S3: dividing indoor cleanliness requirements based on indoor air quality assessment data and indoor air ion flow data, thereby obtaining indoor cleanliness requirement data;

[0099] In this embodiment, the indoor space is divided into different cleanliness demand areas according to the indoor air quality assessment data and ion flow data. For example, in the high cleanliness demand area, the air quality is required to meet higher standards, and the investment and operation frequency of air purification equipment need to be increased; while in the low cleanliness demand area, the air quality requirements are relatively low, and the use of purification equipment can be appropriately reduced to save energy and maintain equipment.

[0100] Step S4: obtaining water washing parameter data through the water washing device, and performing air purification water washing strategy analysis according to the indoor cleanliness demand data and the water washing parameter data, thereby obtaining an air purification water washing strategy;

[0101] In this embodiment, a water washing device is used to collect indoor water washing parameter data, such as water washing flow rate, spray intensity, etc. According to the cleanliness demand data and water washing parameter data of different indoor areas, a corresponding air purification water washing strategy is formulated, such as increasing the water washing frequency or increasing the water washing intensity in areas with high cleanliness demand.

[0102] Step S5: obtaining temperature control parameter data through the temperature control device, and performing air purification temperature control strategy analysis according to the indoor cleanliness demand data and the temperature control parameter data, thereby obtaining an air purification temperature control strategy;

[0103] In this embodiment, the parameters of the temperature control device are adjusted to control the indoor temperature within a suitable range according to the cleanliness requirement data and the temperature control algorithm. For example, in areas with high cleanliness requirements, the temperature is kept low to improve air purification efficiency; while in areas with low cleanliness requirements, the temperature can be appropriately increased to improve comfort and save energy.

[0104] Step S6: coupling the air purification strategy based on the air purification water washing strategy and the air purification temperature control strategy to obtain the clean room air purification strategy, and transmitting the clean room air purification strategy to the controller to perform the air purification task.

[0105] In this embodiment, the air purification water washing strategy and the air purification temperature control strategy are coupled to form a comprehensive clean room air purification strategy. The air purification water washing strategy and the air purification temperature control strategy can be divided into strategy execution optimization levels by setting priority rules. For example, when the temperature is too high, the air purification temperature control strategy is executed first; when the air quality deteriorates, the air purification water washing strategy is executed first. The strategy is transmitted to the controller to perform air purification tasks, such as automatically adjusting the water washing equipment and the temperature control device to achieve continuous purification of indoor air and maintain a clean environment.

[0106] The present invention obtains indoor spectral data through a spectrometer, and then extracts indoor spectral wavelength distribution characteristics, which can help determine the characteristics of different components in indoor air. Such data analysis can effectively distinguish and identify various particles and pollutants in the air, which helps to more accurately understand the degree and type of air pollution. By obtaining indoor ion sensor data through an ion monitor and combining it with indoor air component data for analysis, the ion flow in indoor air can be deeply understood. This helps to evaluate air quality because ion mobility is closely related to the diffusion and propagation of particles and microorganisms in the air. Based on indoor air quality assessment data and ion flow data, indoor cleanliness requirements are divided, which helps to determine the degree of cleanliness requirements in different areas. Such division can make the air purification system respond to the needs of different areas more intelligently, thereby improving purification efficiency. Obtaining corresponding parameter data through a water washing device and a temperature control device, and performing air purification strategy analysis, helps to formulate a more accurate and effective purification strategy. Water washing and temperature control are two common air purification methods, and their reasonable use can reduce energy consumption while improving purification efficiency. Coupling the water washing strategy and the temperature control strategy to formulate a comprehensive air purification strategy can better balance the efficiency and energy consumption between different purification methods. Such a comprehensive strategy helps to improve the performance of the entire clean room air purification system and ensure that energy consumption and operating costs are reduced as much as possible while meeting the cleanliness requirements. In summary, the present invention achieves comprehensive optimization of the clean room air purification process by combining spectral analysis, ion monitoring, intelligent division of clean requirements, water washing, temperature control and other strategies, thereby improving the efficiency and energy utilization of the clean room air purification system to better meet the demand for efficient removal of tiny particles and microorganisms.

[0107] Optionally, step S1 specifically includes:

[0108] Step S11: acquiring indoor spectrum data through a spectrometer, and extracting spectrum wavelength features of the indoor spectrum data, thereby obtaining indoor spectrum wavelength data;

[0109] In this embodiment, the spectrum data of the indoor environment is obtained by using a high-resolution spectrometer, such as an X-spectrometer. The spectrum data can cover the visible light and infrared light range. Then, the spectrum data is processed using Fourier transform and other technologies to extract the light intensity characteristics at different wavelengths, such as the peak or valley value at a specific wavelength. For example, for a certain light source, its radiation intensity at different wavelengths is extracted to form indoor spectrum wavelength data.

[0110] Step S12: performing wavelength spatial distribution statistics based on the indoor spectrum wavelength data, thereby obtaining indoor spectrum wavelength distribution data;

[0111] In this embodiment, the obtained indoor spectrum wavelength data is used to perform distribution statistics on the spectrum wavelength data by using a statistical analysis method, such as histogram analysis or a Gaussian mixture model. This can reveal the distribution of the indoor spectrum in different wavelength ranges, such as the frequency distribution of the spectrum intensity in certain wavelength ranges, and form indoor spectrum wavelength distribution data.

[0112] Step S13: performing absorption feature extraction and scattering feature extraction on the indoor spectral wavelength distribution data, thereby obtaining spectral wavelength absorption data and spectral wavelength scattering data;

[0113] In this embodiment, based on the indoor spectral wavelength distribution data, the characteristics of the absorption spectrum and the scattering spectrum are used for extraction. By using spectroscopy methods, such as the Lambert-Beer law, the absorption characteristics of various gases or particles are derived from the spectral data. At the same time, scattering theories, such as the Mie scattering theory, are used to extract scattering characteristics from the spectral data. In this way, indoor spectral wavelength absorption data and spectral wavelength scattering data can be obtained.

[0114] Step S14: classifying the air component type based on the spectral wavelength absorption data, thereby obtaining air component type data;

[0115] In this embodiment, the absorption spectrum is classified using a machine learning algorithm, such as a support vector machine (SVM) or a deep learning network, using the obtained spectral wavelength absorption data. By training the model, the absorption spectra of different gases or compounds are associated with known air component types, thereby achieving the classification of air component types. For example, the absorption spectrum of carbon dioxide is associated with the carbon dioxide concentration to achieve the classification of carbon dioxide.

[0116] Step S15: classifying the spectral wavelength scattering data into air particle types according to the air component type data, thereby obtaining air particle data;

[0117] In this embodiment, based on the air component type data, a similar machine learning method is used to classify the spectral wavelength scattering data. According to the air component type data, the scattering characteristics of different gases or particles are associated with known air particle types to achieve classification of air particle data. For example, the scattering characteristics of PM2.5 are associated with PM2.5 concentration to achieve classification of PM2.5 particles.

[0118] Step S16: spatially merge the air component type data and the air particle data based on the indoor spectral wavelength data, so as to obtain indoor area air component data.

[0119] In this embodiment, the classified air component type data and air particle data are spatially merged using indoor spectral wavelength data. By matching the absorption data and scattering data with the spectral wavelength data, the indoor spectral wavelength data can be spatially associated with the air component and particle data to obtain indoor area air component data. For example, the CO2 concentration and PM2.5 concentration are spatially associated with the corresponding spectral wavelength data to form the air component data of the indoor area.

[0120] The present invention obtains indoor spectral data through a spectrometer and extracts its spectral wavelength characteristics, which helps to determine the spectral characteristics within different wavelength ranges. This feature extraction can help identify different components in indoor air and provide basic data for subsequent analysis. Wavelength spatial distribution statistics based on indoor spectral wavelength data can reveal the spectral distribution of different wavelengths in indoor air. This helps to understand the overall distribution characteristics of the indoor spectrum and provides a basis for subsequent analysis. Absorption feature extraction and scattering feature extraction are performed on the indoor spectral wavelength distribution data to obtain absorption and scattering data of spectral wavelengths. These data can help distinguish the contributions of different components to the absorption and scattering of light, and help to further understand the existence and characteristics of different components in the air. Classification of air component types based on spectral wavelength absorption data helps to determine the different component types present in indoor air. Such classification can enable the system to better distinguish different pollutants and provide a more accurate basis for subsequent analysis and processing. Classification of air particle types based on spectral wavelength scattering data according to air component type data helps to determine the different types of particulate matter present in the air. This helps to understand the characteristics of particles in the air and further guide subsequent air purification and treatment measures. By merging the air component type data and air particle data based on the indoor spectral wavelength data, we can comprehensively consider the distribution of different components and particles and provide a more comprehensive description of the indoor regional air component data. This helps to better understand the indoor air pollution and guide subsequent air purification and management work.

[0121] Optionally, step S14 is specifically:

[0122] Step S141: extracting absorption peak features from the spectral wavelength absorption data, thereby obtaining spectral absorption peak data;

[0123] In this embodiment, the collected spectral wavelength absorption data is preprocessed, including denoising and data smoothing. Then, the absorption peak features are extracted from the preprocessed data using a peak detection algorithm (such as a method based on first-order derivatives or second-order derivatives). This can be achieved by identifying local maxima or using the slope and width of the peak. Finally, the extracted absorption peak features are converted into data structures, such as the position, intensity, and width of the peak, to obtain spectral absorption peak data.

[0124] Step S142: performing absorption peak similarity calculation on the spectral absorption peak data, thereby obtaining spectral absorption peak similarity data;

[0125] In this embodiment, the absorption peak similarity calculation is performed on the spectral absorption peak data to evaluate the similarity between different peaks. Various similarity measurement methods can be used, such as Euclidean distance, cosine similarity or correlation coefficient, etc. By comparing the characteristics of the absorption peaks, such as position, shape and intensity, etc., the similarity between the peaks is calculated, thereby obtaining the spectral absorption peak similarity data.

[0126] Step S143: performing spectral absorption peak type statistics according to the spectral absorption peak similarity data, thereby obtaining high-frequency spectral absorption peak data and low-frequency spectral absorption peak data;

[0127] In this embodiment, the spectrum absorption peak type statistics are performed based on the spectrum absorption peak similarity data. This can be done by setting a similarity threshold to divide the types of each peak, for example, peaks with similarity higher than a certain threshold are divided into the same type. According to the statistical results, the spectrum absorption peak data is divided into high-frequency spectrum absorption peak data and low-frequency spectrum absorption peak data.

[0128] Step S144: acquiring gas molecule data, and extracting gas molecule structure features from the gas molecule data, thereby obtaining gas molecule structure data;

[0129] In this embodiment, gas molecule data is obtained, for example, spectral characteristic data of various gas molecules are obtained from literature. Then, feature extraction is performed on these gas molecule data, for example, structural information of molecules is extracted, including atomic species, bond lengths, bond angles, etc. This can be achieved using chemical information processing tools or computational chemistry methods, such as quantum chemical calculation software or database query.

[0130] Step S145: classifying the high-frequency spectrum absorption peak data into gas components according to the gas molecular structure data, thereby obtaining high-frequency gas component data and high-frequency non-gas component data;

[0131] In this embodiment, the high-frequency spectral absorption peak data is classified into gas components according to the gas molecular structure data. This can be done by comparing the spectral absorption peak characteristics and the gas molecular structure data to determine the gas components corresponding to each peak. At the same time, the peaks that fail to match the gas molecules are regarded as high-frequency non-gas component data.

[0132] Step S146: classifying the low-frequency spectrum absorption peak data into gas components according to the gas molecular structure data, thereby obtaining low-frequency gas component data and low-frequency non-gas component data;

[0133] In this embodiment, the gas components are classified based on the low-frequency spectral absorption peak data according to the gas molecular structure data. The gas components corresponding to each peak are determined by comparing the spectral absorption peak characteristics and the gas molecular structure data. Peaks that are not matched to gas molecules are regarded as low-frequency non-gas component data.

[0134] Step S147: Merge the high-frequency gas component data, the low-frequency gas component data, the high-frequency non-gas component data, and the low-frequency non-gas component data to obtain air component type data.

[0135] In this embodiment, high-frequency gas component data, low-frequency gas component data, high-frequency non-gas component data, and low-frequency non-gas component data are merged. This can be achieved by merging data sets and marking them according to data attributes, thereby obtaining a data set containing various air component types.

[0136] The present invention extracts absorption peak features from spectral wavelength absorption data, which helps to determine the characteristics of each peak, such as the position, intensity and shape. By extracting the absorption peak features, the absorption behavior of various components in the spectrum can be more accurately represented, thereby providing a more reliable data basis for subsequent analysis. By calculating the absorption peak similarity of the spectral absorption peak data, the similarity between different absorption peaks can be quantified. This helps to identify similar absorption peaks, indicating the presence of similar components or similar absorption mechanisms, and provides guidance for subsequent classification and analysis. By statistically analyzing the spectral absorption peak similarity data, the spectral absorption peak types can be classified, including high-frequency spectral absorption peaks and low-frequency spectral absorption peaks. This helps to understand the characteristics of absorption peaks in different frequency ranges and make a preliminary distinction between different types of gas components. Obtain gas molecule data and extract gas molecule structure features, which may include information such as the chemical composition, structure, and bond length of the molecule. These features are crucial for subsequent gas component classification because different gas molecules exhibit different spectral absorption characteristics. Gas components are classified for high-frequency and low-frequency spectral absorption peak data according to the gas molecule structure data. By combining the gas molecular structure and spectral absorption characteristics, the source of each peak in the spectrum can be determined more accurately, thereby identifying high-frequency and low-frequency gas component data and non-gas component data. By merging various types of data, air component type data is obtained, including high-frequency gas components, low-frequency gas components, and non-gas component data. Such data integration can provide more comprehensive indoor air composition information and provide a more reliable basis for air quality management and regulation.

[0137] Optionally, step S15 is specifically:

[0138] Step S151: extracting non-gas component data from the air component type data, thereby obtaining indoor air non-gas component data;

[0139] In this embodiment, relevant data marked as non-gas components in the air component type data are extracted to obtain indoor air non-gas component data.

[0140] Step S152: performing high-value scattering amplitude statistics on the spectral wavelength scattering data, thereby obtaining high-value scattering amplitude data; performing high-value scattering intensity statistics on the spectral wavelength scattering data, thereby obtaining high-value scattering intensity data;

[0141] In this embodiment, statistics of high-value scattering amplitude and high-value scattering intensity are respectively performed for the spectral wavelength scattering data. Amplitude statistical analysis is performed on the preprocessed scattering data to calculate the scattering amplitude at each wavelength point, and obtain statistical characteristics of amplitude distribution, such as mean value, median, standard deviation, etc. Intensity statistical analysis is performed on the preprocessed scattering data to calculate the scattering intensity at each wavelength point, and obtain statistical characteristics of intensity distribution.

[0142] Step S153: performing a spectral intersection operation on the high-value scattering amplitude data and the high-value scattering intensity data, thereby obtaining the scattering data of the air particles to be classified;

[0143] In this embodiment, the high-value scattering amplitude data and the high-value scattering intensity data are subjected to spectral intersection operation to determine the air particle scattering data to be classified. This can be done by performing an intersection operation on the two sets of data to screen out the spectral wavelength data points that exist in both sets, i.e., the air particle scattering data points to be classified.

[0144] Step S154: acquiring air particle molecular data, and extracting particle molecular structure features from the air particle molecular data, thereby obtaining air particle molecular structure data;

[0145] In this embodiment, the molecular data of air particles are obtained, for example, the molecular structure information of various air particles is obtained from a database, and then the molecular data are subjected to feature extraction, such as molecular size, chemical composition, etc., to obtain the molecular structure data of air particles.

[0146] Step S155: classifying the air particle types of the air particle scattering data to be classified according to the air particle molecular structure data, thereby obtaining air particle classification data;

[0147] In this embodiment, the molecular structure data of air particles is used to classify the scattering data of air particles to be classified. This can be done by comparing the scattering data with the scattering characteristics of different particle molecular structures and using a classification algorithm (such as a support vector machine, a neural network, etc.) to perform classification. In this way, the classification data of air particles can be obtained, including information such as the type and concentration of the particles.

[0148] Step S156: spatially correlate the air particle classification data according to the indoor air non-gas component data to obtain air particle data.

[0149] In this embodiment, spatial association is performed with the air particle classification data based on the indoor air non-gas component data. This involves spatially corresponding the air particle data with the indoor non-gas component data to obtain the distribution of particles within the same spatial range. For example, the spatial information of the two can be associated using geographic information system (GIS) technology to obtain the spatial distribution of the air particle data.

[0150] The present invention extracts non-gas component data from air component type data, aiming to separate non-gas component data from overall air component data. These non-gas components include dust, particulate matter, microparticles, etc., and their presence will affect air quality. The high-value scattering amplitude and intensity statistics are performed on the spectral wavelength scattering data to obtain data related to particle scattering. The statistics of scattering amplitude and intensity help to understand the scattering behavior of particles within the spectral wavelength range and provide basic data for subsequent particle analysis. The high-value scattering amplitude and intensity data are subjected to spectral intersection operations to obtain air particle scattering data to be classified. Information related to air particles is extracted from the scattering data to prepare for further particle classification and analysis. Air particle molecular data is obtained and particle molecular structure features are extracted, which include information such as chemical composition, shape, size, etc. of the particles. These structural features are crucial for subsequent particle classification because particles of different structures exhibit different scattering behaviors. The particle type classification is performed on the air particle scattering data to be classified according to the air particle molecular structure data, which helps to identify and distinguish different types of particles. By combining the particle molecular structure and scattering data, the type and source of the particles can be determined more accurately. The particle classification data were spatially correlated with the indoor air non-gaseous component data. This means that correlating the particle data with the non-gaseous component data helps to understand the relationship between particle matter and other air components, providing more complete information for comprehensive analysis.

[0151] Optionally, step S2 specifically includes:

[0152] Step S21: Acquire indoor ion sensing data through an ion monitor;

[0153] In this embodiment, indoor ion sensing data is obtained by a highly sensitive ion monitor, such as a charge coupled device (CCD) or an ion counter. These sensors can capture the number and type of positive and negative ions in the indoor air, as well as their concentration changes. The sensitivity and sampling frequency of the sensor ensure the accuracy and comprehensiveness of the data.

[0154] Step S22: performing indoor air ion association on the indoor ion sensing data and the indoor air composition data, thereby obtaining indoor air ion data;

[0155] In this embodiment, the indoor ion sensor data is correlated with the indoor air composition data. Data integration and correlation analysis software, such as Python's Pandas library or MATLAB, can be used. Through statistical analysis and data visualization, the correlation and change trend between ions and other air components can be identified. This correlation analysis can reveal the correlation and interaction between ions and other air components, providing a basis for subsequent air quality assessment.

[0156] Step S23: performing indoor air flow simulation based on the indoor air ion data, thereby obtaining indoor air flow simulation data;

[0157] In this embodiment, a three-dimensional model of indoor air flow is established using professional computational fluid dynamics software, such as ANSYS Fluent or OpenFOAM. Appropriate boundary conditions and initial conditions are set in the model, including the geometry of the indoor space, the location and size of the vents, etc. By numerically solving the fluid dynamics equations, the flow of indoor air can be simulated, and the transmission path and concentration distribution of ions in space can be obtained.

[0158] Step S24: performing ion flow time series statistics based on the indoor air ion flow data, thereby obtaining the indoor air ion flow data;

[0159] In this embodiment, the simulated indoor air flow data is subjected to a time series statistical analysis of ion flow, which can be performed with the aid of statistical software such as R or Python's NumPy and SciPy libraries. The change of ion concentration over time is statistically described, such as calculating the mean, variance, and spectrum of the ion concentration. Through time series statistical analysis, periodic changes and emergencies in ion concentration can be identified, providing a basis for subsequent air quality assessment.

[0160] Step S25: Perform indoor air quality assessment based on the indoor air ion flow data and the indoor air composition data, thereby obtaining indoor air quality assessment data.

[0161] In this embodiment, the indoor air quality is evaluated based on the ion flow data and other air component data, and a comprehensive evaluation method, such as a multi-index comprehensive evaluation model, can be used. The ion flow data is comprehensively analyzed with parameters such as gas concentration and humidity to obtain a comprehensive evaluation result of the indoor air quality. According to the evaluation results, corresponding air quality management measures can be formulated, such as increasing ventilation, purifying the air, etc., to improve the indoor environmental quality.

[0162] The present invention obtains indoor ion sensor data through an ion monitor. The ion sensor data can provide information about the ion concentration in the indoor environment, which is of great significance for evaluating air quality and understanding pollutants in the indoor environment. The indoor ion sensor data is associated with the indoor air component data to obtain the indoor air ion data. This can link the ion data with other air components (such as particulate matter, gas, etc.) to provide a more comprehensive data basis for subsequent analysis. Indoor air flow simulation is performed based on the indoor air ion data. The flow of indoor air including the propagation and diffusion process of ions in the air is simulated. By simulating the air flow, the quality distribution of indoor air and the propagation path of pollutants can be evaluated. Performing ion flow time series statistics based on indoor air ion flow data helps to understand the changing trend of ions in different time periods and identify air quality abnormalities or pollution sources. Indoor air quality assessment is performed based on indoor air ion flow data and indoor air component data. By comprehensively considering the ion flow data and other air component data, the indoor air quality can be comprehensively evaluated, potential air pollution problems can be identified, and corresponding measures can be taken to improve the air quality.

[0163] Optionally, step S25 is specifically:

[0164] Step S251: extracting air particle flow characteristics according to indoor air ion flow data, thereby obtaining air particle flow data;

[0165] In this embodiment, the indoor air ion flow data can be used to extract the characteristics of air particle flow, such as the number, size and distribution of particles. For example, by using the change in the number of ions recorded by the ion monitor over time, combined with particle motion models such as diffusion models and discrete element models, the motion trajectory and velocity distribution of particles in the air can be inferred, and then the air particle flow data can be obtained.

[0166] Step S252: performing particle fluidity evaluation on the air particle fluidity data to obtain air particle fluidity data, and performing time series analysis on the air particle fluidity data to obtain particle fluidity change data;

[0167] In this embodiment, when evaluating the particle fluidity of air particle flow data, indicators such as the diffusion rate, aggregation degree, and diffusion path of the particles can be considered. By performing time series analysis on the particle fluidity data, the changing trend of particle fluidity over time, such as the increase or decrease in diffusion rate, the change in aggregation degree, etc., can be understood, thereby obtaining particle fluidity change data. For example, the diffusion coefficient of the particles is calculated using the Gaussian diffusion model, and then the diffusivity of the particles in the air is evaluated. At the same time, the K-means clustering algorithm is used to perform cluster analysis on the particle distribution to evaluate the aggregation degree of the particles in the air.

[0168] Step S253: performing air turbidity evaluation according to the indoor air composition data to obtain air turbidity data, and performing time series analysis on the air turbidity data to obtain air turbidity change data;

[0169] In this embodiment, the air turbidity can be evaluated using air composition data, such as particle concentration, gas concentration, etc. For example, the PM2.5 and PM10 concentrations of the air are calculated based on the particle concentration data, and the concentration of harmful gases in the air is calculated based on the gas concentration data. Through time series analysis, the change of air turbidity over time can be understood, thereby obtaining air turbidity change data.

[0170] Step S254: constructing an air quality assessment system according to the air turbidity change data and the particle mobility change data;

[0171] In this embodiment, an air quality assessment system can be constructed based on the air turbidity change data and the particle mobility change data. The system includes the weight setting of various air quality indicators, the establishment of an evaluation method, and the interpretation and application of the evaluation results. For example, a multi-indicator comprehensive evaluation method can be used to comprehensively consider particle mobility, air turbidity and other air quality indicators to establish an air quality assessment model.

[0172] Step S255: Perform indoor regional air quality assessment on the indoor air ion flow data through an air quality assessment system, thereby obtaining indoor air quality assessment data.

[0173] In this embodiment, the constructed air quality evaluation system is used to evaluate the indoor air ion flow data. For example, the ion flow data is combined with the air quality evaluation model, and the comprehensive score of the indoor air quality is calculated according to the weight of each indicator. Through this score, the quality of the indoor air quality can be understood, and corresponding improvement measures can be taken.

[0174] The present invention can obtain data on the movement, diffusion and distribution of indoor particles by extracting the particle flow characteristics from the indoor air ion flow data. This helps to understand the behavior and distribution of particles in indoor air. Evaluating the air particle flowability and performing time series analysis can help identify the changing trend of particle flowability. Such analysis can reveal the movement characteristics of particles in the indoor environment and then evaluate the impact of particles on air quality. Evaluating air turbidity based on indoor air composition data is another consideration of indoor air quality. By analyzing air turbidity data through time series, the changing trend of air quality can be found, which helps to identify the existing pollution sources or the reasons for the deterioration of air quality. Combining air turbidity change data with particle flowability change data to construct an air quality evaluation system. Such an evaluation system comprehensively considers multiple factors such as particle flowability and air turbidity, and can more accurately reflect the overall situation of indoor air quality. The indoor air ion flow data is evaluated by the air quality evaluation system to obtain indoor air quality evaluation data. The obtained data are integrated to provide a comprehensive and objective indoor air quality evaluation result, which provides a basis and direction for improving the indoor environment.

[0175] Optionally, step S3 specifically includes:

[0176] Step S31: performing regional air quality statistics based on the indoor air quality assessment data, thereby obtaining high air quality regional data and low air quality regional data;

[0177] In this embodiment, statistical methods, such as cluster analysis or spatial interpolation methods, are used to process these data and divide the regions into high air quality regions and low air quality regions. For example, the data can be divided into two clusters using a K-means clustering algorithm, each cluster representing an air quality region. Finally, air quality statistical data for each region is obtained.

[0178] Step S32: dividing the air particle flow area according to the indoor air ion flow data, thereby obtaining low air particle flow area data and high air particle flow area data;

[0179] In this embodiment, according to the indoor air ion flow data, a spatial analysis method is used, such as GIS spatial analysis technology or a spatial statistical method based on raster data, to divide the indoor area into a low air particle flow area and a high air particle flow area. For example, the spatial interpolation method of raster data can be used to infer the particle flow conditions in different areas.

[0180] Step S33: performing intersection operation on the low air quality area data and the low air particle flow area data, so as to obtain the high indoor cleanliness demand area data;

[0181] In this embodiment, the low air quality area data and the low air particle flow area data are intersected. For example, using geographic information system software, the two data sets are spatially superimposed to obtain the areas with low air particle flow in the indoor low air quality area.

[0182] Step S34: performing intersection operation on the high air quality area data and the high air particle flow area data, so as to obtain the low indoor cleanliness requirement area data;

[0183] In this embodiment, the intersection operation is performed on the high air quality area data and the high air particle flow area data. Similarly, the high air quality area data and the high air particle flow area data are superimposed using the geographic information system software to obtain the area with high air particle flow in the indoor high air quality area.

[0184] Step S35: spatially merging the data of the area with high indoor cleanliness requirement and the data of the area with low indoor cleanliness requirement to obtain indoor cleanliness requirement data.

[0185] In this embodiment, the data of the high indoor cleanliness demand area and the low indoor cleanliness demand area are spatially merged. GIS software or spatial statistical analysis tools are used to merge the data of the high cleanliness demand area and the low cleanliness demand area to obtain the final indoor cleanliness demand data to guide the design and optimization of the indoor air purification and ventilation system.

[0186] The present invention can obtain the air quality conditions of different regions by performing regional air quality statistics based on indoor air quality assessment data. This helps to identify high air quality areas and low air quality areas, and provides basic data for subsequent clean demand area division. According to the indoor air ion flow data, the air particle flow area division can understand the flow of particles in different areas. This helps to identify high air particle flow areas and low air particle flow areas, and provides more detailed information for subsequent clean demand area division. By performing intersection operations on low air quality area data and low air particle flow area data, high indoor clean demand areas are determined. These areas are areas with poor air quality and high particle mobility, and more clean measures are needed to improve the indoor environment. By performing intersection operations on high air quality area data and high air particle flow area data, low indoor clean demand areas are determined. These areas are areas with good air quality and low particle mobility, and relatively few clean measures are needed. The high indoor clean demand area data and the low indoor clean demand area data are spatially merged to obtain overall indoor clean demand data. These data can guide indoor environment managers to take targeted cleaning and ventilation measures to improve indoor air quality and protect residents' health.

[0187] Optionally, step S4 is specifically:

[0188] Step S41: obtaining water washing parameter data through the water washing device, and classifying the water washing modes according to the water washing parameter data, thereby obtaining a high water flow clean water washing mode parameter combination and a low water flow clean water washing mode parameter combination;

[0189] In this embodiment, a water washing device is used to collect water washing parameter data including water flow rate, nozzle angle, cleaning time, etc. Then, through data analysis and classification algorithms, such as a classifier based on a decision tree or a support vector machine, the water washing mode is divided into a high water flow clean water washing mode and a low water flow clean water washing mode. For example, according to the parameter combination when the water flow rate is greater than a certain threshold, it is divided into a high water flow clean water washing mode, otherwise it is divided into a low water flow clean water washing mode.

[0190] Step S42: extracting spatial features of indoor cleanliness demand data, thereby obtaining spatial data of high cleanliness demand areas and spatial data of low cleanliness demand areas;

[0191] In this embodiment, spatial features of indoor cleanliness demand data can be extracted by using spatial data mining technology, such as spatial clustering or spatial interpolation methods. For example, spatial features of high cleanliness demand areas, such as area, shape, surrounding environment, etc., can be extracted by GIS software and quantitatively described.

[0192] Step S43: Calculate the water washing flow demand of the target area according to the spatial data of the high cleanliness demand area and the water washing parameter data, so as to obtain the water washing flow data of the main target area;

[0193] In this embodiment, the water washing flow demand of the target area is calculated based on the spatial data of the high cleanliness demand area and the water washing parameter data. This can be achieved by establishing a water washing flow demand model, combining the spatial characteristics of the high cleanliness demand area and the influencing factors of the water washing parameters for quantitative analysis, so as to determine the water washing flow demand of the main target area.

[0194] Step S44: performing a high indoor clean area water washing parameter combination according to the water washing flow data of the main target area and the high water flow clean water washing mode parameter combination, thereby obtaining a high cleanliness requirement area water washing parameter combination;

[0195] In this embodiment, the high water flow clean water washing mode parameter combination is used in combination with the water washing flow data of the main target area to determine the water washing parameter combination of the high cleanliness demand area. For example, according to the parameter setting of the high water flow mode, the nozzle angle and water flow are adjusted to meet the water washing requirements of the high cleanliness demand area.

[0196] Step S45: Calculate the water washing flow demand of the target area according to the spatial data of the low cleanliness demand area and the water washing parameter data, so as to obtain the water washing flow data of the secondary target area;

[0197] In this embodiment, the water washing flow demand of the target area is calculated based on the spatial data of the low cleanliness demand area and the water washing parameter data. This can also be achieved by establishing a water washing flow demand model, combining the spatial characteristics and water washing parameters of the low cleanliness demand area to determine the water washing flow data of the secondary target area.

[0198] Step S46: performing a low indoor clean area water washing parameter combination according to the water washing flow data of the secondary target area and the low water flow clean water washing mode parameter combination, thereby obtaining a low cleanliness requirement area water washing parameter combination;

[0199] In this embodiment, the washing parameter combination of the low water flow clean water washing mode is determined in combination with the washing flow data of the secondary target area. For example, the nozzle angle and water flow are adjusted to meet the washing needs of the low clean demand area, while ensuring the effective use of water resources.

[0200] Step S47: Perform water washing strategy analysis based on the water washing parameter combination of the high cleanliness requirement area and the water washing parameter combination of the low cleanliness requirement area, so as to obtain the air purification water washing strategy.

[0201] In this embodiment, the water washing strategy analysis is performed by comprehensively considering the water washing parameter combination of the high cleanliness demand area and the water washing parameter combination of the low cleanliness demand area. For example, according to the cleanliness demand and water washing resource availability of different areas, a reasonable water washing area priority plan and strategy are formulated to achieve the best indoor air purification effect.

[0202] The washing parameter data obtained by the washing device of the present invention is helpful to determine different washing modes. After classifying the high water flow clean washing mode and the low water flow clean washing mode, the appropriate washing method can be selected for different cleaning needs, so as to achieve efficient indoor air cleaning. The spatial feature extraction of the indoor clean demand data is helpful to distinguish the high clean demand area and the low clean demand area. Such spatial feature extraction can provide a basis for the subsequent calculation of the washing flow demand. By combining the spatial data and washing parameter data of the high clean demand area, the washing flow demand of the target area is calculated, which helps to determine the washing flow required by the main target area to meet the cleanliness requirements of the indoor environment. Based on the high water flow clean washing mode parameter combination, the washing parameters of the high clean demand area are determined to ensure that the high clean demand area is fully cleaned and purified. Similarly, according to the spatial data and washing parameter data of the low clean demand area, the washing flow demand of the secondary target area is calculated to meet the cleaning requirements of the low clean demand area. On the basis of determining the low water flow clean washing mode parameter combination, the washing parameters of the low clean demand area are set to save water resources and ensure the moderate cleanliness of the indoor environment. By analyzing the water washing parameter combinations in areas with high cleanliness requirements and areas with low cleanliness requirements, an overall water washing strategy is formulated to effectively purify indoor air and improve indoor environmental quality.

[0203] Optionally, step S5 specifically includes:

[0204] Step S51: obtaining temperature control parameter data through a temperature control device;

[0205] In this embodiment, temperature control parameter data including ventilation parameters, air conditioning heating and cooling parameters, etc. are collected by the temperature control device.

[0206] Step S52: spatially correlating the indoor cleanliness requirement data and the indoor air composition data, thereby obtaining the air composition data of the high cleanliness requirement area and the air composition data of the low cleanliness requirement area;

[0207] In this embodiment, spatial data analysis technology, such as geographic information system (GIS), is used to associate indoor cleanliness demand data with indoor air composition data. For example, the cleanliness demand area and the air composition monitoring point are spatially superimposed through GIS software to obtain air composition data of each area.

[0208] Step S53: acquiring air pollution component growth rules, and performing temperature growth rule extraction on the air pollution component growth rules, thereby obtaining air pollution component temperature growth rules;

[0209] In this embodiment, the air pollution component growth rules are obtained by consulting the data. The air pollution component growth rules include the generation and decomposition rules of various pollutants. For example, the release of volatile organic compounds (VOCs) is greatly affected by temperature. Temperature growth rule extraction can be based on experimental data or model prediction to determine the generation rate of different pollutants at different temperatures.

[0210] Step S54: combining the temperature control parameter data according to the temperature growth rule of the air pollution component, thereby obtaining a decontamination temperature control parameter combination;

[0211] In this embodiment, according to the temperature growth rule of air pollution components, the temperature control parameters are adjusted, such as increasing the ventilation volume, adjusting the air conditioning cooling or heating mode, to control the indoor temperature, thereby reducing the generation rate of pollutants.

[0212] Step S55: Calculate the air pollutant component ratios of the air composition data of the high cleanliness requirement area and the air composition data of the low cleanliness requirement area, so as to obtain the air pollutant component ratio data of the high cleanliness requirement area and the air pollutant component ratio data of the low cleanliness requirement area;

[0213] In this embodiment, the proportion of various pollutants in the high cleanliness requirement area and the low cleanliness requirement area is calculated through air composition monitoring data. For example, the concentration ratio of harmful substances such as formaldehyde and benzene in the two areas is calculated.

[0214] Step S56: analyzing the temperature control strategy of the high cleanliness demand area according to the decontamination temperature control parameter combination and the air pollutant component proportion data of the high cleanliness demand area, so as to obtain the temperature control strategy of the high cleanliness demand area;

[0215] In this embodiment, a temperature control strategy for areas with high cleanliness requirements is formulated by combining the decontamination temperature control parameter combination and the air pollutant composition ratio data of areas with high cleanliness requirements. For example, the ventilation frequency is increased in areas with high pollutant concentrations, and the air conditioning temperature setting is adjusted to accelerate the removal of pollutants.

[0216] Step S57: analyzing the temperature control strategy of the low cleanliness demand area according to the decontamination temperature control parameter combination and the air pollutant component proportion data of the low cleanliness demand area, so as to obtain the temperature control strategy of the low cleanliness demand area;

[0217] In this embodiment, a temperature control strategy for low cleanliness demand areas is formulated based on the decontamination temperature control parameter combination and the air pollutant component ratio data of low cleanliness demand areas. For example, a more energy-saving temperature control mode is adopted, and the air conditioning operation time is reduced in low pollutant concentration areas to reduce energy consumption.

[0218] Step S58: coupling the temperature control strategy of the high cleanliness requirement area and the temperature control strategy of the low cleanliness requirement area to obtain an air purification temperature control strategy.

[0219] In this embodiment, the temperature control strategies of high cleanliness demand areas and low cleanliness demand areas are comprehensively considered, and the air purification temperature control strategy is coupled through the intelligent temperature control system. For example, according to real-time monitoring data and preset control algorithms, the temperature control parameters of each area are dynamically adjusted to maximize the indoor air quality and comfort.

[0220] The present invention obtains temperature control parameter data through a temperature control device. It is conducive to real-time monitoring and control of indoor temperature, and provides a basis for subsequent air purification strategies. By spatially correlating indoor clean demand data and indoor air composition data, high clean demand areas and low clean demand areas are distinguished, and corresponding air composition data are obtained. This helps the system to more accurately regulate the air quality of different areas. The growth rules of air pollution components are obtained, and the temperature growth rules therein are extracted. It is helpful to understand the growth laws of different pollutants at different temperatures, and provide guidance for subsequent temperature control. According to the temperature growth rules of air pollution components, the temperature control parameters are adjusted to obtain a temperature control parameter combination suitable for air purification. It is helpful to control the temperature while suppressing the growth and diffusion of air pollutants to the greatest extent. By calculating the proportion of air pollutant components in high clean demand areas and low clean demand areas, the system can more accurately understand the degree of pollution in different areas, and provide data support for formulating targeted temperature control strategies. Based on the decontamination temperature control parameter combination and the air pollutant component proportion data of each area, the temperature control strategy analysis of high clean demand areas and low clean demand areas is performed respectively. These strategies can adjust the temperature to purify the air to the maximum extent according to the needs of different areas. The temperature control strategies of high cleanliness demand areas and low cleanliness demand areas are coupled to form a comprehensive air purification temperature control strategy. This comprehensive strategy takes into account the needs and air quality conditions of different areas and can achieve comprehensive indoor air purification and comfort control.

[0221] Optionally, the present specification also provides a clean room ion control system for executing the above-mentioned clean room air purification method, the clean room ion control system comprising:

[0222] The air composition classification module is used to obtain indoor spectral data through a spectrometer, and extract indoor spectral wavelength distribution characteristics based on the indoor spectral data, so as to obtain indoor spectral wavelength distribution data; classify indoor regional air components based on the indoor spectral wavelength distribution data, so as to obtain indoor air composition data;

[0223] An indoor air quality assessment module is used to obtain indoor ion sensor data through an ion monitor, and perform indoor air ion mobility analysis based on the indoor ion sensor data and indoor air composition data, thereby obtaining indoor air ion flow data; perform indoor air quality assessment based on the indoor air ion flow data and indoor air composition data, thereby obtaining indoor air quality assessment data;

[0224] An indoor cleanliness demand classification module is used to classify indoor cleanliness demands based on indoor air quality assessment data and indoor air ion flow data, thereby obtaining indoor cleanliness demand data;

[0225] The water washing strategy analysis module is used to obtain water washing parameter data through the water washing device, and perform air purification water washing strategy analysis based on the indoor cleanliness demand data and the water washing parameter data, so as to obtain the air purification water washing strategy;

[0226] The temperature control strategy analysis module is used to obtain temperature control parameter data through the temperature control device, and perform air purification temperature control strategy analysis based on indoor cleanliness demand data and temperature control parameter data, so as to obtain the air purification temperature control strategy;

[0227] The air purification strategy analysis module is used to couple the air purification strategy based on the air purification water washing strategy and the air purification temperature control strategy to obtain the clean room air purification strategy, and transmit the clean room air purification strategy to the controller to perform the air purification task.

[0228] The clean room ion control system of the present invention can implement any clean room air purification method of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the clean room air purification method. The internal modules of the system cooperate with each other, thereby improving the air purification effect and energy utilization efficiency of the clean room.

[0229] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0230] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A clean room air purification method, characterized in that: Applied to a clean room air purification device, the clean room air purification device includes a controller, a spectrometer, an ion monitor, a temperature control device and a water washing device, wherein the spectrometer, the ion monitor, the temperature control device and the water washing device are all electrically connected to the controller, and the clean room air purification method includes the following steps: Step S1: Acquire indoor spectrum data through a spectrometer, and extract indoor spectrum wavelength distribution features based on the indoor spectrum data, thereby obtaining indoor spectrum wavelength distribution data; classify indoor regional air components based on the indoor spectrum wavelength distribution data, thereby obtaining indoor air component data; Step S2: acquiring indoor ion sensor data through an ion monitor, and performing indoor air ion mobility analysis based on the indoor ion sensor data and indoor air composition data, thereby obtaining indoor air ion flow data; performing indoor air quality assessment based on the indoor air ion flow data and indoor air composition data, thereby obtaining indoor air quality assessment data; Step S3: dividing indoor cleanliness requirements based on indoor air quality assessment data and indoor air ion flow data, thereby obtaining indoor cleanliness requirement data; Step S4: obtaining water washing parameter data through the water washing device, and performing air purification water washing strategy analysis according to the indoor cleanliness demand data and the water washing parameter data, thereby obtaining an air purification water washing strategy; Step S5: obtaining temperature control parameter data through the temperature control device, and performing air purification temperature control strategy analysis according to the indoor cleanliness demand data and the temperature control parameter data, thereby obtaining an air purification temperature control strategy; Step S6: coupling the air purification strategy based on the air purification water washing strategy and the air purification temperature control strategy to obtain the clean room air purification strategy, and transmitting the clean room air purification strategy to the controller to perform the air purification task.

2. The clean room air purification method according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: acquiring indoor spectrum data through a spectrometer, and extracting spectrum wavelength features of the indoor spectrum data, thereby obtaining indoor spectrum wavelength data; Step S12: performing wavelength spatial distribution statistics based on the indoor spectrum wavelength data, thereby obtaining indoor spectrum wavelength distribution data; Step S13: performing absorption feature extraction and scattering feature extraction on the indoor spectral wavelength distribution data, thereby obtaining spectral wavelength absorption data and spectral wavelength scattering data; Step S14: classifying the air component type based on the spectral wavelength absorption data, thereby obtaining air component type data; Step S15: classifying the spectral wavelength scattering data into air particle types according to the air component type data, thereby obtaining air particle data; Step S16: spatially merge the air component type data and the air particle data based on the indoor spectral wavelength data, so as to obtain indoor area air component data.

3. The clean room air purification method according to claim 2, characterized in that: Step S14 is specifically as follows: Step S141: extracting absorption peak features from the spectral wavelength absorption data, thereby obtaining spectral absorption peak data; Step S142: performing absorption peak similarity calculation on the spectral absorption peak data, thereby obtaining spectral absorption peak similarity data; Step S143: performing spectral absorption peak type statistics according to the spectral absorption peak similarity data, thereby obtaining high-frequency spectral absorption peak data and low-frequency spectral absorption peak data; Step S144: acquiring gas molecule data, and extracting gas molecule structure features from the gas molecule data, thereby obtaining gas molecule structure data; Step S145: classifying the high-frequency spectrum absorption peak data into gas components according to the gas molecular structure data, thereby obtaining high-frequency gas component data and high-frequency non-gas component data; Step S146: classifying the low-frequency spectrum absorption peak data into gas components according to the gas molecular structure data, thereby obtaining low-frequency gas component data and low-frequency non-gas component data; Step S147: Merge the high-frequency gas component data, the low-frequency gas component data, the high-frequency non-gas component data, and the low-frequency non-gas component data to obtain air component type data.

4. The clean room air purification method according to claim 2, characterized in that: Step S15 is specifically as follows: Step S151: extracting non-gas component data from the air component type data, thereby obtaining indoor air non-gas component data; Step S152: performing high-value scattering amplitude statistics on the spectral wavelength scattering data, thereby obtaining high-value scattering amplitude data; Perform high-precision scattering intensity statistics on the spectral wavelength scattering data to obtain high-precision scattering intensity data; Step S153: performing a spectral intersection operation on the high-value scattering amplitude data and the high-value scattering intensity data, thereby obtaining the scattering data of the air particles to be classified; Step S154: acquiring air particle molecular data, and extracting particle molecular structure features from the air particle molecular data, thereby obtaining air particle molecular structure data; Step S155: classifying the air particle types of the air particle scattering data to be classified according to the air particle molecular structure data, thereby obtaining air particle classification data; Step S156: spatially correlate the air particle classification data according to the indoor air non-gas component data to obtain air particle data.

5. The clean room air purification method according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: Acquire indoor ion sensing data through an ion monitor; Step S22: performing indoor air ion association on the indoor ion sensing data and the indoor air composition data, thereby obtaining indoor air ion data; Step S23: performing indoor air flow simulation based on the indoor air ion data, thereby obtaining indoor air flow simulation data; Step S24: performing ion flow time series statistics based on the indoor air ion flow data, thereby obtaining the indoor air ion flow data; Step S25: Perform indoor air quality assessment based on the indoor air ion flow data and the indoor air composition data, thereby obtaining indoor air quality assessment data.

6. The clean room air purification method according to claim 5, characterized in that: Step S25 is specifically as follows: Step S251: extracting air particle flow characteristics according to indoor air ion flow data, thereby obtaining air particle flow data; Step S252: performing particle fluidity evaluation on the air particle fluidity data to obtain air particle fluidity data, and performing time series analysis on the air particle fluidity data to obtain particle fluidity change data; Step S253: performing air turbidity evaluation according to the indoor air composition data to obtain air turbidity data, and performing time series analysis on the air turbidity data to obtain air turbidity change data; Step S254: constructing an air quality assessment system according to the air turbidity change data and the particle mobility change data; Step S255: Perform indoor regional air quality assessment on the indoor air ion flow data through an air quality assessment system, thereby obtaining indoor air quality assessment data.

7. The clean room air purification method according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: performing regional air quality statistics based on the indoor air quality assessment data, thereby obtaining high air quality regional data and low air quality regional data; Step S32: dividing the air particle flow area according to the indoor air ion flow data, thereby obtaining low air particle flow area data and high air particle flow area data; Step S33: performing intersection operation on the low air quality area data and the low air particle flow area data, so as to obtain the high indoor cleanliness demand area data; Step S34: performing intersection operation on the high air quality area data and the high air particle flow area data, so as to obtain the low indoor cleanliness requirement area data; Step S35: spatially merging the data of the area with high indoor cleanliness requirement and the data of the area with low indoor cleanliness requirement to obtain indoor cleanliness requirement data.

8. The clean room air purification method according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: obtaining water washing parameter data through the water washing device, and classifying the water washing modes according to the water washing parameter data, thereby obtaining a high water flow clean water washing mode parameter combination and a low water flow clean water washing mode parameter combination; Step S42: extracting spatial features of indoor cleanliness demand data, thereby obtaining spatial data of high cleanliness demand areas and spatial data of low cleanliness demand areas; Step S43: Calculate the water washing flow demand of the target area according to the spatial data of the high cleanliness demand area and the water washing parameter data, so as to obtain the water washing flow data of the main target area; Step S44: performing a high indoor clean area water washing parameter combination according to the water washing flow data of the main target area and the high water flow clean water washing mode parameter combination, thereby obtaining a high cleanliness demand area water washing parameter combination; Step S45: Calculate the water washing flow demand of the target area according to the spatial data of the low cleanliness demand area and the water washing parameter data, so as to obtain the water washing flow data of the secondary target area; Step S46: performing a low indoor clean area water washing parameter combination according to the water washing flow data of the secondary target area and the low water flow clean water washing mode parameter combination, thereby obtaining a low cleanliness requirement area water washing parameter combination; Step S47: Perform water washing strategy analysis based on the water washing parameter combination of the high cleanliness requirement area and the water washing parameter combination of the low cleanliness requirement area, so as to obtain the air purification water washing strategy.

9. The clean room air purification method according to claim 1, characterized in that: Step S5 is specifically as follows: Step S51: obtaining temperature control parameter data through a temperature control device; Step S52: spatially correlating the indoor cleanliness requirement data and the indoor air composition data, thereby obtaining the air composition data of the high cleanliness requirement area and the air composition data of the low cleanliness requirement area; Step S53: obtaining air pollution component growth rules, and performing temperature growth rule extraction on the air pollution component growth rules, thereby obtaining air pollution component temperature growth rules; Step S54: combining the temperature control parameter data according to the temperature growth rule of the air pollution component, thereby obtaining a decontamination temperature control parameter combination; Step S55: Calculate the air pollutant component ratios of the air composition data of the high cleanliness requirement area and the air composition data of the low cleanliness requirement area, so as to obtain the air pollutant component ratio data of the high cleanliness requirement area and the air pollutant component ratio data of the low cleanliness requirement area; Step S56: analyzing the temperature control strategy of the high cleanliness demand area according to the decontamination temperature control parameter combination and the air pollutant component proportion data of the high cleanliness demand area, so as to obtain the temperature control strategy of the high cleanliness demand area; Step S57: analyzing the temperature control strategy of the low cleanliness demand area according to the decontamination temperature control parameter combination and the air pollutant component proportion data of the low cleanliness demand area, so as to obtain the temperature control strategy of the low cleanliness demand area; Step S58: coupling the temperature control strategy of the high cleanliness requirement area and the temperature control strategy of the low cleanliness requirement area to obtain an air purification temperature control strategy.

10. A clean room ion control system, characterized in that: For executing the clean room air purification method according to claim 1, the clean room ion control system comprises: The air composition classification module is used to obtain indoor spectral data through a spectrometer, and extract indoor spectral wavelength distribution characteristics based on the indoor spectral data, so as to obtain indoor spectral wavelength distribution data; classify indoor regional air components based on the indoor spectral wavelength distribution data, so as to obtain indoor air composition data; An indoor air quality assessment module is used to obtain indoor ion sensor data through an ion monitor, and perform indoor air ion mobility analysis based on the indoor ion sensor data and indoor air composition data, thereby obtaining indoor air ion flow data; perform indoor air quality assessment based on the indoor air ion flow data and indoor air composition data, thereby obtaining indoor air quality assessment data; An indoor cleanliness demand classification module is used to classify indoor cleanliness demands based on indoor air quality assessment data and indoor air ion flow data, thereby obtaining indoor cleanliness demand data; The water washing strategy analysis module is used to obtain water washing parameter data through the water washing device, and perform air purification water washing strategy analysis based on the indoor cleanliness demand data and the water washing parameter data, so as to obtain the air purification water washing strategy; The temperature control strategy analysis module is used to obtain temperature control parameter data through the temperature control device, and perform air purification temperature control strategy analysis based on indoor cleanliness demand data and temperature control parameter data, so as to obtain the air purification temperature control strategy; The air purification strategy analysis module is used to couple the air purification strategy based on the air purification water washing strategy and the air purification temperature control strategy to obtain the clean room air purification strategy, and transmit the clean room air purification strategy to the controller to perform the air purification task.

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