Distributed ozone anion bacteria control method for greenhouse

Abstract

This invention discloses a distributed ozone negative ion method for controlling bacteria in greenhouses, relating to the field of distributed bacterial control technology in greenhouses. It addresses the problem of increased risk of bacterial infections. The method involves detecting the number of crops planted in the greenhouse and dividing it into regions. The plant height distribution within each region is detected, and the regions are grouped and their basic levels are assessed. The time of the last irrigation of crops in each region is retrieved, and the irrigation coefficient is calculated. The transpiration rate of crops in each region is collected, and a bacterial control correction factor is generated based on the irrigation coefficient. The bacterial control level of each region is analyzed based on the correction factor and the basic level. Regions are then selected and marked according to their bacterial control levels. A bacterial control frequency is set, and the marked regions are treated with bacterial control. Furthermore, the frequency of bacterial control is optimized by comprehensively considering particulate matter size distribution and airborne spore concentration, thereby improving the adaptability of the bacterial control treatment and reducing the incidence of bacterial infections.

Description

Technical Field

[0001] This invention relates to the field of distributed bacterial control technology in greenhouses, and more specifically, to a distributed ozone negative ion bacterial control method for greenhouses. Background Technology

[0002] Currently, the growth and spread of pathogenic microorganisms during greenhouse crop cultivation are the main causes of crop yield reduction and quality decline. Existing methods for controlling pathogens mostly rely on manual experience to set irrigation frequency, pesticide spraying frequency, or environmental control parameters, lacking a precise adjustment mechanism based on the actual growth status of crops and dynamic changes in the environment.

[0003] The existing technology has the following shortcomings:

[0004] Current technologies typically implement a uniform microbial control strategy for greenhouses as a whole, ignoring the differences in crop growth in different regions. They cannot adapt to the dynamic characteristics of rapid spread of pathogenic microorganisms, making it difficult to dynamically optimize the microbial control scheme. This leads to an increased risk of microbial damage and a reduced effectiveness of microbial control treatment. Therefore, a distributed ozone negative ion microbial control method for greenhouses is proposed.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a distributed ozone negative ion bacterial control method for greenhouses, which solves the problems mentioned in the background art by employing a bacterial control zoning regulation mechanism based on dynamic correction of plant growth status and air spore concentration.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a distributed ozone negative ion method for controlling bacteria in greenhouses, comprising the following steps:

[0008] Step S1: Detect the number of crops planted in the greenhouse to be tested and divide the greenhouse into regions. Detect the plant height distribution of the crops planted in the divided regions. Use the plant height distribution to group the divided regions and evaluate the basic level.

[0009] Step S2: Retrieve the last irrigation time of the crops planted in the divided area and calculate the irrigation coefficient. Collect the transpiration rate of the crops planted in the divided area, generate a microbial control correction factor in combination with the irrigation coefficient, adjust the basic level of the divided area according to the microbial control correction factor, and analyze the microbial control level of the divided area based on the adjustment results.

[0010] Step S3: Select and mark the regions according to the bacterial control level, set the bacterial control frequency, perform bacterial control treatment on the marked regions, detect the particle size spectrum of the marked regions using an optical particle counter, and collect the airborne spore concentration in the marked regions;

[0011] Step S4: Analyze the particle size distribution based on the particle size distribution, evaluate the bacterial control efficacy characteristics by combining the airborne spore concentration and particle size distribution, calculate the correction ratio based on the bacterial control efficacy characteristics, and use the correction ratio to correct the bacterial control frequency.

[0012] In a preferred embodiment, in step S1, the number of crops planted in the greenhouse is identified by an array of cameras deployed on the top of the greenhouse to be tested;

[0013] Based on a preset quantity threshold, the number of crops planted is averaged and divided into zones. The greenhouse under test is then divided into regions based on the results of the average zoning.

[0014] The height of the top of the planted crops in the divided area is collected, and the average height of the top of each planted crop is taken as the plant height distribution of the divided area.

[0015] In a preferred embodiment, in step S1, the plant height distribution of each divided region is combined into a plant height set;

[0016] The median of the plant height set is used as the reference height. The difference between the plant height distribution and the reference height is taken as the absolute value to obtain the height deviation. The median of each height deviation is taken as the height fluctuation value.

[0017] The first altitude threshold is obtained by summing the reference altitude and the altitude fluctuation value, and the second altitude threshold is obtained by subtracting the first altitude from the altitude fluctuation value.

[0018] In a preferred embodiment, in step S1, if the plant height distribution exceeds a first height threshold, the area is divided into a high-growth group and the basic level of the area is set to 1.

[0019] If the plant height distribution is between the first height threshold and the second height threshold, the area will be divided into the medium growth group and the basic level of the area will be set to 2.

[0020] If the plant height distribution is below the first height threshold, the area will be divided into a low growth group and the basic level of the area will be set to 3.

[0021] In a preferred embodiment, in step S2, the time of the last irrigation of crops in the divided area is retrieved from the irrigation database, and the difference between the last irrigation time and the current time is used to obtain the irrigation time interval.

[0022] The irrigation time interval is used to calculate the irrigation coefficient using an exponential function;

[0023] Multiple sampling points were evenly distributed on the leaves of the crops planted in the divided areas. The transpiration rate of the sampling points was monitored by a photosynthesis meter, and the median of the transpiration rate of each sampling point was selected as the transpiration rate of the divided areas.

[0024] In a preferred embodiment, in step S2, the combined transpiration coefficient and irrigation coefficient are used to calculate the bacterial control correction factor using a harmonic averaging algorithm;

[0025] The product of the bacterial control correction factor and the basic level of the region is used as the bacterial control level of the region.

[0026] In a preferred embodiment, in step S3, each divided region is screened and marked based on its bacterial control level using a preset level threshold:

[0027] When the bacterial control level of a defined area is greater than or equal to a preset level threshold, the defined area is identified as a high-risk area and marked.

[0028] Conversely, if the area is not classified as a low-risk area, it will not be marked.

[0029] After completing the area division and marking, the frequency of bacterial control is set according to the bacterial control level.

[0030] In a preferred embodiment, in step S3, the marked and divided regions are subjected to bacterial control treatment according to the bacterial control frequency;

[0031] The particle size distribution of airborne particulate matter is detected in real time by an optical particle counter deployed in the marked area, and the particle size spectrum of the marked area is obtained, including the particle size spectrum before and after bacterial control.

[0032] The concentration of airborne spores was detected using an airborne spore collector and the impact culture method.

[0033] In a preferred embodiment, in step S4, the particle size distribution before and after bacterial control is integrated to obtain the particle change.

[0034] The changes in particle size and airborne spore concentration were combined for calculation to evaluate the characteristics of bacterial control efficacy.

[0035] The correction ratio is calculated based on the characteristics of bacterial control efficacy.

[0036] The frequency of bacterial control is dynamically adjusted using a correction ratio to obtain the corrected frequency of bacterial control.

[0037] The technical effects and advantages of this invention are as follows:

[0038] This invention detects the number of crops planted in a greenhouse and divides the greenhouse into regions. It detects the plant height distribution of the crops in the divided regions, groups the divided regions and evaluates their basic levels, retrieves the last irrigation time of the crops in each region and calculates the irrigation coefficient, collects the transpiration rate of the crops in each region, and generates a microbial control correction factor based on the irrigation coefficient. Based on the microbial control correction factor and the basic level, it analyzes the microbial control level of each region, selects and marks the regions according to the microbial control level, sets the microbial control frequency, and applies microbial control treatment to the marked regions. Furthermore, it optimizes the microbial control frequency by comprehensively considering particulate matter size spectrum and airborne spore concentration. This achieves refined classification of microbial control levels and dynamic optimization of microbial control strategies, improves the adaptability of microbial control treatment, and reduces the incidence of microbial diseases. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the implementation of a distributed ozone negative ion bacterial control method for greenhouses according to the present invention.

[0040] Figure 2 This is a schematic diagram illustrating the steps of a distributed ozone negative ion method for controlling bacteria in greenhouses according to the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] This invention detects the number of crops planted in a greenhouse and divides the greenhouse into regions. It detects the plant height distribution of the crops in each region, groups the regions, and evaluates their basic levels. It retrieves the last irrigation time of the crops in each region and calculates the irrigation coefficient. It collects the transpiration rate of the crops in each region and generates a microbial control correction factor based on the irrigation coefficient. It analyzes the microbial control level of each region based on the microbial control correction factor and the basic level. It then selects and marks the regions based on their microbial control levels, sets the microbial control frequency, and applies microbial control treatment to the marked regions. Furthermore, it optimizes the microbial control frequency by comprehensively considering particulate matter size distribution and airborne spore concentration. This achieves refined classification of microbial control levels and dynamic optimization of microbial control strategies, improving the adaptability of microbial control treatment.

[0043] Example 1

[0044] Please see Figures 1 to 2 A distributed ozone negative ion method for bacterial control in greenhouses, the specific operation process is as follows:

[0045] Step S1: Detect the number of crops planted in the greenhouse to be tested and divide the greenhouse into regions. Detect the plant height distribution of the crops planted in the divided regions. Use the plant height distribution to group the divided regions and evaluate the basic level.

[0046] Step S2: Retrieve the last irrigation time of the crops planted in the divided area and calculate the irrigation coefficient. Collect the transpiration rate of the crops planted in the divided area, generate a microbial control correction factor in combination with the irrigation coefficient, adjust the basic level of the divided area according to the microbial control correction factor, and analyze the microbial control level of the divided area based on the adjustment results.

[0047] Step S3: Select and mark the regions according to the bacterial control level, set the bacterial control frequency, perform bacterial control treatment on the marked regions, detect the particle size spectrum of the marked regions using an optical particle counter, and collect the airborne spore concentration in the marked regions;

[0048] Step S4: Analyze the particle size distribution based on the particle size distribution, evaluate the bacterial control efficacy characteristics by combining the airborne spore concentration and particle size distribution, calculate the correction ratio based on the bacterial control efficacy characteristics, and use the correction ratio to correct the bacterial control frequency.

[0049] In step S1, the number of crops planted in the greenhouse is identified by a camera array deployed on the top of the greenhouse to be tested. The number of crops is divided into average zones based on a preset quantity threshold. The greenhouse to be tested is then divided into regions based on the results of the zone division.

[0050] It should be explained that the camera array includes multiple image acquisition devices for acquiring images of crops grown in the greenhouse under test, and counting the number of crops grown based on image recognition; the preset quantity threshold is used to construct a segmented reference standard for the number of crops grown, which can be set according to the type and density of the crops grown. For example, the quantity threshold is set too low for crops with large leaf area or dense planting.

[0051] The height of the top of the crops in the divided area is collected by a camera array, and the average height of the top of each crop is taken as the plant height distribution of the divided area.

[0052] The plant height distribution of each divided area is combined into a plant height set. The median of the plant height set is used as the reference height. The difference between the plant height distribution and the reference height is taken as the absolute value to obtain the height deviation. The median of each height deviation is taken as the height fluctuation value. The reference height and the height fluctuation value are summed to obtain the first height threshold. The difference between the first height and the height fluctuation value is taken to obtain the second height threshold.

[0053] The regions are grouped based on a first height threshold and a second height threshold:

[0054] If the plant height distribution exceeds the first height threshold, the area will be divided into a high-growth group and the basic level of the area will be set to 1.

[0055] If the plant height distribution is between the first height threshold and the second height threshold, the area will be divided into the medium growth group and the basic level of the area will be set to 2.

[0056] If the plant height distribution is below the first height threshold, the area will be divided into a low growth group and the basic level of the area will be set to 3.

[0057] The lower the plant height distribution, the closer the crop grows to the ground, making it easier to create a poorly ventilated bottom environment for the microorganisms to reproduce, resulting in a higher basic grade; the higher the plant height distribution, the better it is for moisture diffusion and air circulation, resulting in a lower basic grade.

[0058] By statistically analyzing the number of crops planted and dividing the area into regions, and combining the plant height distribution to group the regions and assess their basic levels, a differentiated division and initial risk perception based on the number and height of crops planted was achieved, laying a stratified foundation for the dynamic allocation and frequency regulation of subsequent fungal control measures.

[0059] In step S2, the time of the last irrigation of crops in the divided area is retrieved from the irrigation database, and the difference between the last irrigation of crops and the current time is used to obtain the irrigation time interval.

[0060] The irrigation time interval is used to calculate the irrigation coefficient using an exponential function: ,in, For irrigation intervals, For the preset reference interval, This is the irrigation coefficient;

[0061] It should be explained that the irrigation database is a data set used to store the historical irrigation records of the greenhouse under test, including information such as the last irrigation time of the divided area; the preset benchmark interval is a reference value used to evaluate the irrigation time interval, which can be adaptively set based on historical irrigation record data. For example, the median of the multiple irrigation time intervals recorded in the divided area in the last 30 days can be taken as the preset benchmark interval.

[0062] The irrigation interval reflects the moisture level of the soil and air in the divided area. The shorter the irrigation interval, the greater the irrigation coefficient and the higher the moisture level, which is more conducive to the reproduction of microorganisms.

[0063] Multiple sampling points were evenly distributed on the leaves of the crops planted in the divided areas. The transpiration rate of the sampling points was monitored by a photosynthesis meter, and the median of the transpiration rate of each sampling point was selected as the transpiration rate of the divided areas.

[0064] The higher the transpiration rate, the more water vapor is released from the leaves of the planted crops, and the higher the air humidity in the divided area, which is more conducive to the reproduction of fungi.

[0065] The transpiration coefficient is obtained by standardizing the transpiration rate of the divided regions.

[0066] The combined transpiration coefficient and irrigation coefficient were used to calculate the microbial control correction factor using a harmonic averaging algorithm. ,in, This is the irrigation coefficient. The evapotranspiration coefficient, As a bacterial control correction factor;

[0067] When both the transpiration coefficient and the irrigation coefficient are high, it indicates that the division of areas is conducive to the propagation of microorganisms, and the demand for microbial control is higher.

[0068] The product of the bacterial control correction factor and the basic level of the region is used as the bacterial control level of the region.

[0069] It should be explained that a photosynthesis meter is a gas exchange analysis device used to monitor photosynthesis and transpiration in crop leaves. It uses a built-in infrared gas analyzer, temperature and humidity probe, and flow control module to detect the gas exchange process in real time and output the transpiration rate of the leaves. The standardization methods include, but are not limited to, standard linear transformation based on interval scaling, statistical Z-Score standardization method, or normalization method based on nonlinear mapping function. The application methods of standardization will not be elaborated here.

[0070] By integrating irrigation coefficient and transpiration rate to generate microbial control correction factor, and combining it with basic level to complete the adjustment analysis of microbial control level, the microbial control demand intensity of the divided area under the current environment is reflected, effectively improving the sensitivity of microbial control level assessment and realizing dynamic adaptation of microbial control strategy.

[0071] In step S3, based on the bacterial control level of each divided region, each divided region is screened and marked using a preset level threshold:

[0072] When the bacterial control level of a defined area is greater than or equal to a preset level threshold, the defined area is identified as a high-risk area and marked.

[0073] Conversely, the area is identified as a low-risk area and is not marked.

[0074] It should be noted that the preset risk level threshold is a criterion used to distinguish different risk levels. Its value is determined by a combination of experimental statistical results of the susceptibility conditions of pathogenic microorganisms in the crop growth environment and historical greenhouse environmental monitoring data. For example, by long-term monitoring of the correlation between temperature and humidity, plant transpiration rate and airborne spore concentration in the greenhouse, it was determined that when the control level is 2, the airborne spore concentration is significantly higher than the safe range. Therefore, the control level of 2 is used as the preset risk level threshold. Taking this as an example, when the control level of a certain area is ≥2, the area is automatically identified as a high-risk area and marked, while when the control level is <2, it is considered a low-risk area.

[0075] After completing the area marking, the frequency of bacterial control is set according to the bacterial control level. The specific calculation formula is as follows:

[0076] ;

[0077] in, For antibacterial frequency, As the base frequency, For bacterial control level, This represents the maximum value for the bacterial control level.

[0078] It should be noted that the baseline frequency is the basic number of times the control treatment is administered. It is set based on the routine disease control needs of crop growth and the half-life of ozone and negative ions in the air. This value is preset through experiments. For example, in a greenhouse environment, by monitoring changes in airborne spore concentration at different application frequencies, the minimum application frequency that can maintain a stable spore concentration when there is no external pathogen invasion is determined as the baseline frequency. If experimental results show that, without additional control intervention, performing the control treatment once every 24 hours can maintain an airborne spore concentration below 100 CFU / m³, then the baseline frequency is established. 3 If 1 time / day is used as the baseline frequency, then the frequency is adjusted by multiple based on the change in the control level, so as to obtain the control frequency of each marked area.

[0079] The marked areas were subjected to bacterial control treatment according to the frequency of bacterial control.

[0080] Specifically, the distributed bacteria control devices deployed within the marked areas are activated according to the bacteria control frequency, releasing ozone gas and negative ion streams quantitatively into the marked areas. Ozone gas has a high efficiency of oxidation and can destroy the cell membrane structure of bacteria, thereby achieving sterilization. Negative ions can adsorb suspended particles and biological carriers in the air, enhancing the air purification effect.

[0081] The particle size distribution of airborne particulate matter is detected in real time by an optical particle counter deployed in the marked area, and the particle size spectrum of the marked area is obtained, including the particle size spectrum before and after bacterial control.

[0082] Meanwhile, the concentration of airborne spores was detected using an airborne spore collector and the impact culture method.

[0083] The sampling pump draws in air at a set flow rate. After passing through a narrow slit nozzle, the air impacts the surface of a rotating culture medium plate at high speed. During the collision, spores in the air are deposited onto the culture medium. After a set incubation period, the number of colonies formed is counted using an image recognition algorithm. The ratio of the number of colonies to the sampling volume is then calculated to obtain the air spore concentration.

[0084] By screening and marking the divided areas according to the bacterial control level, the distribution and scheduling of bacterial control resources are optimized. While performing bacterial control treatment, the changes in environmental factors are detected in real time using optical particle counters and air spore collectors, realizing a rapid feedback mechanism for the effect of bacterial control treatment and ensuring the coverage and accuracy of bacterial control measures.

[0085] It should be noted that an optical particle counter is a device that detects the concentration and size distribution of particulate matter in the air based on the principle of optical scattering; an air spore collector is a device used to collect microbial spores in the air, separating and enriching spores in the air through methods such as impact, filtration, or centrifugation; impact culture is an airborne microbial detection method, the core principle of which is to use high-speed airflow to impact airborne microbial particles onto the surface of a solid culture medium, causing the particles to form visible colonies during the culture process; and an image recognition algorithm is an analysis method based on digital image processing and computer vision technology, used to identify and count colonies formed on culture medium plates.

[0086] In step S4, the particle size distribution before and after bacterial control is integrated to obtain the particle size change. The specific calculation formula is as follows:

[0087] ;

[0088] in, For the first The amount of particle change in each marked region For the particle size distribution after bacterial control, For particle size distribution before bacterial control, The particle size variable represents the range of diameters of airborne particulate matter. This is the minimum particle size value. This represents the maximum particle size.

[0089] The changes in particle size and airborne spore concentration were combined for calculation to evaluate the antibacterial efficacy characteristics. The specific calculation formula is as follows:

[0090] ;

[0091] in, For the first The antibacterial efficacy characteristics of each marked region For the first The amount of particle change in each marked region For the first Air spore concentration in each marked region and These are the weight parameters.

[0092] It should be noted that the weighting parameters are determined based on the relative importance of different detection indicators in the evaluation of bacterial control efficacy, through experimental statistics and multi-indicator correlation analysis. Long-term monitoring was conducted in multiple sample areas within a greenhouse environment, collecting data on changes in particulate matter size and airborne spore concentration. Principal component analysis was used to assess the contribution of each to the reduction in disease incidence. The weighting parameters were then normalized according to the contribution ratio. For example, if experiments show that changes in airborne spore concentration contribute 70% to the disease inhibition rate and changes in particulate matter contribute 30%, then the weighting parameters can be set as follows: , Principal component analysis is a data dimensionality reduction and feature extraction method. Its core principle is to transform multiple related variables into a set of independent variables ordered by variance, i.e., principal components, while keeping the original data information as complete as possible.

[0093] The correction ratio is calculated based on the characteristics of bacterial control efficacy, as shown in the following expression:

[0094] ;

[0095] in, To correct the ratio, For the first The antibacterial efficacy characteristics of each marked region.

[0096] The frequency of bacterial control is dynamically adjusted using a correction ratio to obtain the corrected frequency of bacterial control, as expressed below:

[0097] ;

[0098] in, This is the corrected frequency of bacterial control. The correction ratio for marking the division of regions.

[0099] The frequency of bacterial control in the marked and divided regions has been changed to the revised frequency of bacterial control.

[0100] By combining the trend of particulate matter size distribution with the concentration of airborne spores, the actual efficacy of bacterial control is evaluated. Furthermore, based on the characteristics of bacterial control efficacy, a correction ratio is calculated to dynamically adjust the frequency of bacterial control, avoiding over- or under-control.

[0101] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0102] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0103] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0104] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0105] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A distributed ozone negative ion method for bacterial control in greenhouses, characterized in that: Includes the following steps: Step S1: Detect the number of crops planted in the greenhouse to be tested and divide the greenhouse into regions. Detect the plant height distribution of the crops planted in the divided regions. Use the plant height distribution to group the divided regions and evaluate the basic level. Step S2: Retrieve the last irrigation time of the crops planted in the divided area and calculate the irrigation coefficient. Collect the transpiration rate of the crops planted in the divided area, generate a microbial control correction factor in combination with the irrigation coefficient, adjust the basic level of the divided area according to the microbial control correction factor, and analyze the microbial control level of the divided area based on the adjustment results. Step S3: Select and mark the regions according to the bacterial control level, set the bacterial control frequency, perform bacterial control treatment on the marked regions, detect the particle size spectrum of the marked regions using an optical particle counter, and collect the airborne spore concentration in the marked regions; In step S3, the marked areas are subjected to bacterial control treatment according to the frequency of bacterial control. The particle size distribution of airborne particulate matter is detected in real time by an optical particle counter deployed in the marked area, and the particle size spectrum of the marked area is obtained, including the particle size spectrum before and after bacterial control. The concentration of airborne spores was detected using an airborne spore collector and the impact culture method. Step S4: Analyze the particle size distribution based on particulate matter size distribution, evaluate the bacterial control efficacy characteristics by combining airborne spore concentration and particle size distribution, calculate the correction ratio based on the bacterial control efficacy characteristics, and use the correction ratio to correct the bacterial control frequency. In step S4, the particle size distribution before and after bacterial control is integrated to obtain the particle size change. The changes in particle size and airborne spore concentration were combined for calculation to evaluate the antibacterial efficacy characteristics. The specific calculation formula is as follows: ； in, For the first The antibacterial efficacy characteristics of each marked region For the first The amount of particle change in each marked region For the first Air spore concentration in each marked region and These are weight parameters; The correction ratio is calculated based on the characteristics of bacterial control efficacy. The frequency of bacterial control is dynamically adjusted using a correction ratio to obtain the corrected frequency of bacterial control.

2. The distributed ozone negative ion bacterial control method for greenhouses according to claim 1, characterized in that: In step S1, the number of crops planted in the greenhouse is identified by an array of cameras installed on the top of the greenhouse. Based on a preset quantity threshold, the number of crops planted is averaged and divided into zones. The greenhouse under test is then divided into regions based on the results of the average zoning. The height of the top of the planted crops in the divided area is collected, and the average height of the top of each planted crop is taken as the plant height distribution of the divided area.

3. The distributed ozone negative ion bacterial control method for greenhouses according to claim 2, characterized in that: In step S1, the plant height distribution of each divided region is combined into a plant height set; The median of the plant height set is used as the reference height. The difference between the plant height distribution and the reference height is taken as the absolute value to obtain the height deviation. The median of each height deviation is taken as the height fluctuation value. The first altitude threshold is obtained by summing the reference altitude and the altitude fluctuation value, and the second altitude threshold is obtained by subtracting the first altitude from the altitude fluctuation value.

4. The distributed ozone negative ion bacterial control method for greenhouses according to claim 3, characterized in that: In step S1, if the plant height distribution exceeds the first height threshold, the area is divided into a high-growth group and the basic level of the area is set to 1. If the plant height distribution is between the first height threshold and the second height threshold, the area will be divided into the medium growth group and the basic level of the area will be set to 2. If the plant height distribution is below the second height threshold, the area will be classified as a low-growth group and the basic level of the area will be set to 3.

5. The distributed ozone negative ion bacterial control method for greenhouses according to claim 1, characterized in that: In step S2, the time of the last irrigation of crops in the divided area is retrieved from the irrigation database, and the difference between the last irrigation of crops and the current time is used to obtain the irrigation time interval. The irrigation time interval is used to calculate the irrigation coefficient using an exponential function; Multiple sampling points were evenly distributed on the leaves of the crops planted in the divided areas. The transpiration rate of the sampling points was monitored by a photosynthesis meter, and the median of the transpiration rate of each sampling point was selected as the transpiration rate of the divided areas.

6. The distributed ozone negative ion bacterial control method for greenhouses according to claim 5, characterized in that: In step S2, the combined transpiration coefficient and irrigation coefficient are used to calculate the bacterial control correction factor using a harmonic averaging algorithm; The product of the bacterial control correction factor and the basic level of the region is used as the bacterial control level of the region.

7. The distributed ozone negative ion bacterial control method for greenhouses according to claim 1, characterized in that: In step S3, based on the bacterial control level of each divided region, each divided region is screened and marked using a preset level threshold: When the bacterial control level of a defined area is greater than or equal to a preset level threshold, the defined area is identified as a high-risk area and marked. Conversely, if the area is not classified as a low-risk area, it will not be marked. After completing the area division and marking, the frequency of bacterial control is set according to the bacterial control level.

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