A model for real-time monitoring of air microorganisms in a closed environment and a method for constructing the same

Abstract

The application discloses a kind of closed environment air microorganism real-time monitoring model and its construction method, comprising: determining the concentration of microorganism in air in closed environment, and simultaneously monitoring and collecting air environmental parameters at the same monitoring point;Microorganism concentration and air environmental parameter data set are collected;The correlation of each air environmental parameter and microorganism concentration is analyzed respectively;The absolute value of the correlation coefficient of each air environmental parameter and microorganism concentration is calculated;The polynomial fitting function of microorganism concentration and each air environmental parameter is constructed;The polynomial fitting function constructed is merged to generate the functional relationship between microorganism concentration and all air environmental parameters;The above method can be constructed to obtain the air microorganism real-time monitoring model suitable for closed environment, and has the advantages of small error, high monitoring accuracy.The application uses two-stage method to construct the principle of functional relationship, which is simple, easy to realize, and has small amount of calculation.

Description

Technical Field

[0001] This invention relates to the field of microbial monitoring technology. Specifically, it relates to a real-time monitoring model for airborne microorganisms in a closed environment and a method for constructing the model. Background Technology

[0002] Sources of indoor microbial aerosols include humans, pets, plants, ventilation and air conditioning systems, air purification equipment, mold, resuspended dust, and the outdoor environment. Indoor microbial aerosols are also affected by temperature and relative humidity, and are related to particulate matter concentration, CO2, ventilation methods, and air exchange rates. Therefore, based on extensive field monitoring data, studying the relationship between indoor microbial aerosols and environmental parameters, and using statistical methods, allows for the establishment of mathematical models for real-time prediction of microbial aerosol concentrations. Using these models, and monitoring certain environmental parameters or equipment operating parameters as independent variables, the level of indoor airborne microbial pollution can be predicted in real time.

[0003] The key to implementing the above method lies in establishing a suitable mathematical model for real-time prediction of microbial aerosol concentration. The establishment of this mathematical model depends on the data collected from on-site environmental monitoring. Existing research mainly focuses on on-site monitoring and model building in specific indoor environments within certain regions. Because on-site monitoring data varies significantly across different regions, environments, and even within the same region and environment due to meteorological variations, mathematical models based on single on-site monitoring data lack universal applicability. Therefore, some researchers have begun to establish multiple mathematical models based on on-site monitoring data from indoor environments of different climate zones and building types. In practical applications, the appropriate mathematical model is selected based on the location, thus demonstrating a degree of applicability.

[0004] Since the distribution of microbial content in indoor air is related to environmental parameters such as particulate matter, temperature, humidity, and CO2, this correlation can be studied based on a large amount of field measurement data. A mathematical model can be established using statistical methods to demonstrate the real-time correlation between environmental parameters and airborne microbial content and particle size distribution. Based on the established model, the real-time microbial content and particle size distribution can be calculated from the environmental parameters. This provides a low-cost, easy-to-implement, and widely applicable technical means for real-time online monitoring. To establish this mathematical model, existing technical approaches include... Figure 1 As shown.

[0005] from Figure 1 As can be seen, the existing technology first conducts on-site measurements and collects data, then classifies, organizes, and statistically analyzes the data, and performs correlation analysis. During the on-site measurements, PM1 and PM2.5 concentrations were collected for monitoring air particulate matter concentration. 2.5 PM 10Three types of data were collected. Airborne microbial sampling was performed using an Anderson six-stage impactor sampler. For air quality, temperature, humidity, and CO2 concentration data were collected. During actual monitoring, due to differences in human activity, ventilation and air conditioning systems, and indoor microbial pollution sources under different environmental types, researchers conducted on-site measurements for different environmental types and then performed correlation analysis to establish functional relationships between airborne microbial concentration and airborne particulate matter concentration, temperature, humidity, and CO2 concentration parameters.

[0006] From the perspective of existing mathematical models, the choice of model type depends on representative data of airborne microbial concentration: the correlation between airborne bacteria concentration and measured environmental parameters. When parameters with strong correlations exist, a linear regression mathematical model based on these strongly correlated parameters can be established. However, in real-world environments, changes in airborne microbial concentration are the result of the combined influence of multiple factors, including outdoor air pollution levels, indoor air pollution levels, air environment parameters, and human activities. In other words, it is correlated with multiple parameters. Functional relationships established based on strongly correlated environmental data will discard some moderately or weakly correlated parameters, leading to significant errors when airborne microbial concentration changes due to variations in certain factors.

[0007] Some research findings utilize multiple environmental data points affecting airborne microbial pollution under specific conditions to establish functional relationships between airborne microbial concentrations and these data. Simultaneously, actual measurements are conducted in various environments to establish corresponding functional relationships, thereby building a mathematical model system. In practical applications, the appropriate mathematical model is matched based on the environmental type to improve the model's applicability. However, establishing a relatively accurate mathematical model system requires extensive field measurements at specific locations covering seasonal changes, climate changes, and a combination of multiple environmental data points. This process is lengthy, costly, and hinders widespread application. Summary of the Invention

[0008] Therefore, the technical problem to be solved by the present invention is to provide a real-time monitoring model for airborne microorganisms in a closed environment and a method for constructing the model, so as to solve the problems of high mathematical model complexity, large workload and large error when the correlation between airborne microorganisms and environmental parameters is weak in a closed environment.

[0009] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0010] A method for constructing a real-time monitoring model for airborne microorganisms in a closed environment includes the following steps:

[0011] Step (1): Measure the concentration of microorganisms in the air under closed conditions, and simultaneously monitor and collect air environmental parameters at the same monitoring point;

[0012] Step (2): Repeat step (1) to collect the dataset of microbial concentration and air environment parameters;

[0013] Step (3): Analyze the correlation between each air environment parameter and the microbial concentration; calculate the absolute value of the correlation coefficient r between each air environment parameter and the microbial concentration, sort each air environment parameter in descending order of the absolute value of the correlation coefficient r, and select the air environment parameter with the larger absolute value of the correlation coefficient r as the fitted air environment parameter.

[0014] Step (4): Perform polynomial fitting and analysis on the microbial concentration and each fitted air environment parameter to construct a polynomial fitting function between the microbial concentration and each fitted air environment parameter.

[0015] Step (5): Combine the polynomial fitting functions constructed in step (4) to generate the functional relationship between microbial concentration and all fitted air environment parameters, and determine the weight ratio coefficient of each fitted air environment parameter, thus obtaining the real-time monitoring model of air microorganisms in a closed environment.

[0016] In-depth analysis of existing research reveals that indoor microbial contamination levels are the result of a combination of environmental factors, and different factors may dominate in different environments and at different times within the same environment. Therefore, selecting an appropriate mathematical model based on the application location is crucial for solving the problem. Some indoor environments possess a degree of airtightness, such as residential and office environments where air conditioning, fresh air systems, and air purifiers are used during hot summer weather. In such environments, the indoor microbial aerosol contamination level is significantly affected by the operation of these devices and tends to reach a relatively stable low-pollution state. Especially now that fresh air systems and air purifiers are widely available, they are equipped with the function of filtering and removing fine particulate matter and microbial aerosols, quickly controlling indoor microbial aerosol contamination levels once turned on. This invention addresses the characteristics of real-world home and office environments, focusing on the environmental parameters of residential and office environments in relatively enclosed spaces. It provides a mathematical model that is applicable, easy to establish, and highly accurate in such environments. This model avoids the problem of significant differences in on-site detection data due to weather conditions in different regions, environments, and even the same region and environment, which increases the workload and complexity of building the model system. It is conducive to establishing a relatively accurate mathematical model for predicting airborne microbial concentrations and can be commercialized and applied in the consumer market for purification and disinfection products that focus on public health.

[0017] In the above method for constructing a real-time monitoring model for airborne microorganisms in a closed environment, step (1) uses the total number of airborne bacterial colonies to represent the microbial concentration; step (3) selects temperature, relative humidity, and PM2.5. 2.5The concentrations of oxygen and CO2 were used as parameters for fitting the air environment.

[0018] In the above-mentioned method for constructing a real-time monitoring model of airborne microorganisms in a closed environment, step (1) involves determining the total number of colonies by using a six-stage Anderson impact sampler to collect airborne bacteria at the monitoring point. After culturing the six plates of the six-stage Anderson sampler according to the national standard method, the colony data of the six plates are recorded respectively, and the total number of colonies is obtained by summing them.

[0019] The above-mentioned method for constructing a real-time monitoring model for airborne microorganisms in a closed environment utilizes a real-time air quality monitor to collect various airborne environmental parameters.

[0020] In the above-mentioned method for constructing a real-time monitoring model for airborne microorganisms in a closed environment, step (3) uses the following formula to calculate the correlation coefficient r between airborne environmental parameters and microbial concentration:

[0021]

[0022] In formula (1), n ​​is the total number of data sets; x i y represents the microbial concentration in the i-th sample; i The data represents the air environment parameters sampled for the i-th time. This represents the average value of x. This represents the average value of y;

[0023] r = 0 indicates that the microbial concentration is not correlated with the air environment parameter; 0 < |r| < 0.2 indicates that the microbial concentration is weakly correlated with the air environment parameter; 0.2 ≤ |r| < 0.4 indicates that the microbial concentration is slightly correlated with the air environment parameter; 0.4 ≤ |r| < 0.7 indicates that the microbial concentration is moderately correlated with the air environment parameter; and 1 ≤ |r| ≤ 0.7 indicates that the microbial concentration is highly correlated with the air environment parameter.

[0024] In the above-mentioned method for constructing a real-time monitoring model for airborne microorganisms in a closed environment, step (4) uses the following polynomial fitting function formulas for the microbial concentration and each fitted air environment parameter:

[0025]

[0026] In formula (2), n is the order of the polynomial; w0, w1, ... w n denoted as polynomial coefficients; x is the fitted air environment parameter corresponding to the total number of colonies; y(x,w) is the microbial concentration calculated using the polynomial function, i.e., the fitted data of the microbial concentration.

[0027] In the above method for constructing a real-time monitoring model for airborne microorganisms in a closed environment, in formula (2), the polynomial order and polynomial coefficients w0, w1, ... w nThe method for determining it is as follows:

[0028] First, calculate the error E(w) between the fitted data and the measured data of microbial concentration, using the following formula:

[0029]

[0030] In formula (3), data represents the measured data of microbial concentration;

[0031] Then, taking the derivative of formula (3) to make it equal to 0, which satisfies the requirement of minimizing the error between the fitted data and the measured data of microbial concentration, i.e.:

[0032]

[0033] The polynomial order n and polynomial coefficients w0, w1, ..., w1 in formula (2) are calculated using formula (4). n The value of .

[0034] In the above method for constructing a real-time monitoring model for airborne microorganisms in a closed environment, step (5) shows the functional relationship between microbial concentration and all fitted air environment parameters as follows:

[0035] Y = a1 * T in C + a2*R in C + a3*CO2C + a4*PM 2.5 C (9);

[0036] In equation (9), T in C is the polynomial fitting curve of indoor temperature versus total bacterial count; R in C represents the polynomial fitting curve of indoor relative humidity versus total bacterial count; CO2C represents the polynomial fitting curve of CO2 concentration versus total bacterial count; PM 2.5 C represents PM 2.5 Polynomial fitting curve of concentration versus total bacterial count; a1 is the weighting coefficient of indoor temperature; a2 is the weighting coefficient of indoor relative humidity; a3 is the weighting coefficient of CO2 concentration; a4 is the weighting coefficient of PM2.5. 2.5 Concentration weighting coefficient.

[0037] The method for constructing the real-time monitoring model of airborne microorganisms in the above-mentioned closed environment, in equation (9), the calculation methods of a1, a2, a3 and a4 are as follows:

[0038] First, let E represent the error between the fitted function Y of microbial concentration and the measured data of microbial concentration, as shown in the following equation:

[0039]

[0040] Then, taking the derivative of formula (10) and setting it to 0, which satisfies the requirement of minimizing the error between the fitted data and the measured data of microbial concentration, we obtain the following equation:

[0041]

[0042]

[0043]

[0044]

[0045] The values ​​of a1, a2, a3 and a4 are calculated according to equations (11) to (14).

[0046] A real-time monitoring model for airborne microorganisms in a closed environment is constructed using the aforementioned method for constructing a real-time monitoring model for airborne microorganisms in a closed environment. The function of the real-time monitoring model for airborne microorganisms in a closed environment is:

[0047] Y = 0.4 * T in C + 0.3 * R in C + 0.1 * CO₂ C + 0.2 * PM 2.5 C (15);

[0048] In equation (15), Y represents the total number of airborne bacteria in a closed environment, CFU / m³. 3 ;

[0049] T in C represents the polynomial fitting curve of indoor temperature versus total bacterial count:

[0050] T in C = 0.03709T 3 -1.01T 2 + 5.804T + 24.89 (5);

[0051] In formula (5), T represents the indoor temperature, in °C;

[0052] R in C represents the polynomial fitting curve of indoor relative humidity versus total bacterial count:

[0053] R in C = 2.052 * 10 -6 R 7 –0.0007659R 6 +0.1213R 5 -10.56R 4 +545.6R 3 –1.673*10 4

[0054] R 2 +2.819*10 -5 R – 2.012 * 10 -6 (6);

[0055] In formula (6), R represents indoor relative humidity, %rh;

[0056] CO2C is a polynomial fitted curve of CO2 concentration versus total bacterial count:

[0057] CO2C = 1.871 * 10 -13 CO2 5 –1.652*10 -10 CO2 4 –6.938*10 -7 CO2 3 +0.001047

[0058] *CO2 2 –0.4369*CO2+108.9(7);

[0059] In formula (7), CO2 is expressed as CO2 concentration, ppm;

[0060] PM 2.5 C represents PM 2.5 Polynomial fitting curve of concentration versus total bacterial count:

[0061] PM 2.5 C = 9.929 * 10 -8 pm 5 –2.222*10 -5 pm 4 +0.001016pm 3 –0.05304pmx 2 +

[0062] 3.957pm+99.3(8);

[0063] In formula (8), pm is represented as PM 2.5 Concentration, μg / m 3 .

[0064] The technical solution of the present invention achieves the following beneficial technical effects:

[0065] 1. This invention addresses the characteristics of real-world home and office environments, focusing on the environmental parameters of residential and office environments in relatively enclosed spaces. It provides a mathematical model that is applicable, easy to establish, and highly accurate in such environments. This model avoids the problem of significant differences in on-site detection data due to weather conditions in different regions, environments, and even the same region and environment, which increases the workload and complexity of establishing a model system. It is conducive to establishing a relatively accurate mathematical model for predicting airborne microbial concentrations and can be commercialized and applied in the consumer market for purification and disinfection products that focus on public health.

[0066] 2. This invention involves extensive on-site data collection of airborne microorganisms and environmental parameters in enclosed environments, and establishes a suitable mathematical model using a two-stage method. The model-building method of this invention takes into account the significant characteristic that enclosed environments easily achieve a relatively stable low-pollution state, reducing the workload and time span of data collection, and lowering the complexity of mathematical model construction when the correlation between airborne bacteria concentration and measured environmental parameters is weak. This establishes a practical and easily integrated real-time airborne microorganism monitoring model for enclosed environments. Attached Figure Description

[0067] Figure 1 A schematic diagram of the technical route of the prior art solution in the background art of this invention;

[0068] Figure 2 In the embodiments of the present invention, the total number of colonies and PM 2.5 Scatter plot;

[0069] Figure 3 A scatter plot of total bacterial count versus temperature in an embodiment of the present invention;

[0070] Figure 4 Scatter diagram of total bacterial count and relative humidity in an embodiment of the present invention;

[0071] Figure 5 A scatter plot of total bacterial count and CO2 concentration in an embodiment of the present invention;

[0072] Figure 6 A polynomial fitting curve of indoor temperature versus total bacterial count in an embodiment of the present invention;

[0073] Figure 7 A polynomial fitting curve of indoor relative humidity and total bacterial count in an embodiment of the present invention;

[0074] Figure 8 A polynomial fitting curve of CO2 concentration versus total bacterial count in an embodiment of the present invention;

[0075] Figure 9 PM in the embodiments of the present invention 2.5Polynomial fitting curve of concentration versus total bacterial count;

[0076] Figure 10 A schematic diagram of the fitting curve corresponding to the mathematical model constructed in this embodiment of the invention. Detailed Implementation

[0077] This embodiment describes a closed environment [Environmental conditions: an enclosed indoor space in an office building, equipped with air conditioning, air purifiers, fresh air systems, etc., and the PM2.5 level in the space after the equipment is running]. 2.5 The main steps in constructing a real-time airborne microbial monitoring model are as follows: [Temperature and relative humidity have reached a basically stable state]

[0078] Step (1): First, using a six-stage Anderson impactor sampler, monitor and collect airborne bacteria at a specific location. After culturing the six plates of the six-stage Anderson sampler according to national standards, record the colony data for each of the six plates and sum them to obtain the total colony count. The total colony count represents the concentration of microorganisms in the sampled air. Simultaneously, at the same time and location, use a real-time air quality monitoring instrument to collect PM2.5 concentrations in the air. 2.5 The concentration of oxygen, CO2 concentration, temperature and relative humidity were monitored and recorded, and the average value was taken.

[0079] Step (2): Repeat the above two detection processes to obtain data; collect a large amount of data and create a table to form a multidimensional data table. The results are shown in Table 1 below. This embodiment involved a large amount of data sampling. Due to space limitations, Table 1 only lists a portion of the data.

[0080]

[0081] Step (3), Correlation Analysis: According to the national standard method, the flow rate of the impact sampler is 28.3 L / min, and the duration of one impact sampling is set to 15 min. Therefore, the air volume collected in one impact sampling is 28.3 * 15 = 424.5 L = 0.4245 m³. 3 Divide the colony count in the table by its corresponding air volume, which is 0.4245m³. 3 The corresponding airborne bacteria concentration can then be obtained, in units of CFU / m³. 3 Therefore, in this embodiment, the total number of bacterial colonies represents the level of airborne microbial contamination in the environment.

[0082] For the multidimensional data in Table 1, we first analyzed the relationship between the total number of colonies and PM2.5. 2.5 The correlation between concentration, CO2 concentration, temperature, and relative humidity is calculated using correlation coefficients, denoted by r. Strictly speaking, it should be called the "linear correlation coefficient." This is because the correlation coefficient only describes the degree of "linear" relationship between the two sets of variables, X and Y. The formula for calculating r is as follows:

[0083]

[0084] In formula (1), n is the total number of data groups; x i and y i are respectively the total number of colonies and environmental parameter data for the i-th sampling. Among them, the environmental parameter data is one of PM 2.5 concentration, CO2 concentration, temperature, and relative humidity. and respectively represent the average values of x and y.

[0085] The value range of the calculated correlation coefficient r is [-1, 1]. A positive r value indicates a positive correlation between two variables, a negative r value indicates a negative correlation between two variables, and 0 indicates no correlation between two variables. The closer the absolute value of r is to 1, the stronger the correlation; the closer it is to 0, the weaker the correlation. The correlation degree between variables can be estimated roughly according to the following criteria: r < 0.2 is a weak correlation, 0.2 < r < 0.4 is a low correlation, 0.4 < r < 0.7 is a medium correlation, and r > 0.7 is a high correlation.

[0086] For the data of this embodiment, calculate the correlation coefficients between the total number of colonies and PM 2.5 concentration, CO2 concentration, temperature, and relative humidity respectively. The results are shown in Table 2:

[0087] Table 2

[0088]

[0089] As can be seen from Table 2, the total number of colonies has a low correlation with PM 2.5 concentration and weak correlations with CO2 concentration, temperature, and relative humidity (the absolute value of r is < 0.5 for all). That is, there are other functional relationships between the total number of colonies and these variables rather than simple direct linear relationships.

[0090] Step (4), the first stage of mathematical model construction: Perform polynomial fitting and analysis on the total number of colonies and each environmental parameter. According to the results of the correlation analysis in step (3), the total number of colonies has a low correlation with PM 2.5 concentration and weak correlations with CO2 concentration, temperature, and relative humidity (the absolute value of r is < 0.5 for all). At this time, if the linear regression analysis method is directly used to establish the functional relationship between the total number of colonies and environmental data, the error will be large. Therefore, in this embodiment, the method of using polynomial functions for data curve fitting and multi-factor decoupling is selected to establish the functional relationship between the total number of colonies and environmental data.

[0091] The polynomial function of the total number of colonies y is as follows:

[0092]

[0093] In formula (2), n is the order of the polynomial, and W is (w0, w1, ..., w n ) represents the coefficients of the polynomial, x is the environmental parameter corresponding to the total colony count y, and y(x,w) is the total colony count calculated using the polynomial function, i.e., the fitted data. data is the dataset of total colony counts obtained from actual sampling using the impaction method.

[0094] Let E(w) represent the error between the fitted data and the measured data:

[0095]

[0096] To minimize the error, we first differentiate E(w). When it is 0, the requirement of minimizing the error is met, and the following equation can be established.

[0097]

[0098] Finding w and n that satisfy the above equation determines the polynomial function y(x,w) of the total colony count y, which is the fitting result with the smallest error.

[0099] In this embodiment, x represents indoor temperature, relative humidity, CO2 concentration, and PM2.5 concentration, respectively. 2.5 The concentration was then fitted with a fourth-order polynomial using the method described above, resulting in four polynomials and their fitted curves, as shown below:

[0100] ① Polynomial fitting curve of indoor temperature and total bacterial count T in C is:

[0101] T in C = 0.03709T 3 - 1.01T 2 + 5.804T + 24.89 (5);

[0102] In formula (5), T represents the indoor temperature, °C.

[0103] ② Polynomial fitting curve R between indoor relative humidity and total bacterial count in C is:

[0104] R in C = 2.052 * 10 -6 R 7 -0.0007659R 6 +0.1213R 5 -10.56R 4 +545.6R 3 -1.673*10 4

[0105] R2 +2.819*10 -5 R-2.012*10 -6 (6);

[0106] In formula (6), R represents the indoor relative humidity, %rh.

[0107] ③The polynomial fitting curve of CO2 concentration versus total bacterial count (CO2C) is:

[0108] CO2C = 1.871 * 10 -13 CO2 5 -1.652*10 -10 CO2 4 -6.938*10 -7 CO2 3 +0.001047

[0109] *CO2 2 -0.4369*CO2+108.9(7);

[0110] In formula (7), CO2 is expressed as CO2 concentration, ppm.

[0111] ④PM 2.5 Polynomial fitting curve of concentration versus total bacterial count PM 2.5 C is:

[0112] PM 2.5 C = 9.929 * 10 -8 pm 5 –2.222*10 -5 pm 4 +0.001016pm 3 -0.05304pmx 2 +

[0113] 3.957pm+99.3(8);

[0114] In formula (8), pm is represented as PM 2.5 Concentration, μg / m 3 .

[0115] Step (4) The first stage of mathematical model construction is to decompose the air environment parameter data into multiple two-dimensional data related to the total number of colonies. That is, first fit them pairwise, and use the error minimization to construct an equation to obtain a local optimal solution, that is, the functional relationship between the total number of colonies and any parameter.

[0116] Step (5), the second stage of mathematical model construction: merging the multiple functional relationships obtained in step (4) to generate a functional relationship between the total number of colonies and all air environment parameters:

[0117] Y = a1 * T in C + a2*R in C + a3*CO2C + a4*PM 2.5 C (9);

[0118] To obtain the optimal solutions for a1, a2, a3, and a4, the error between the function Y representing the total colony count and the actual measured value (data) of the total colony count is expressed by the following equation:

[0119]

[0120] To minimize the error, we first differentiate E and establish the following equation. When the value of E is 0, the requirement of minimizing the error is met.

[0121]

[0122]

[0123]

[0124]

[0125] The closest solutions for a1, a2, a3 and a4 can be obtained from equations (11) to (14).

[0126] Based on the data in this example, the optimal solutions for a1, a2, a3, and a4 are found to be 0.4, 0.3, 0.1, and 0.2, respectively. This yields a combined optimization mathematical model, the function of which is as follows:

[0127] Y = 0.4 * T in C + 0.3*R in C + 0.1*CO2C + 0.2*PM 2.5 C (15);

[0128] Step (5) uses the correlation analysis results in step (3) and the maximum and minimum values ​​of the error function (gradient optimization) to determine the weight ratio of any air environment parameter. This ratio is used as the coefficient of the corresponding function relationship in step (4). Multiple function relationships are merged to generate the final colony count data prediction model.

[0129] The fitting curve corresponding to the final mathematical model constructed in this embodiment is as follows: Figure 10 As shown. Verification has shown that using this mathematical model for real-time monitoring of airborne microorganisms in enclosed environments yields monitoring data that closely matches measured values, exhibiting advantages such as small error and high monitoring accuracy. The principle behind constructing the functional relationship using the two-stage method is simple, easy to implement, and computationally inexpensive.

[0130] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of the claims of this patent application.

Claims

1. A method for constructing a model for real-time monitoring of air microorganisms in a closed environment, characterized by, Comprising the following steps: Step (1), measure the concentration of microorganisms in the air in a closed environment, and monitor and collect air environmental parameters at the same monitoring point at the same time; Step (2), repeat step (1) to collect data sets of microorganism concentration and air environmental parameters; Step (3), analyze the correlation between each air environment parameter and the microorganism concentration respectively; calculate the correlation coefficient of each air environment parameter and the microorganism concentration r , sort the air environment parameters according to the absolute values of the correlation coefficients r from large to small, and select the air environment parameter with a larger absolute value of the correlation coefficient r as the fitting air environment parameter; Step (4), perform polynomial fitting and analysis on the microorganism concentration and each fitted air environmental parameter respectively, and construct a polynomial fitting function of the microorganism concentration and each fitted air environmental parameter; Step (5), combine the polynomial fitting functions constructed in step (4) to generate a functional relationship between the microorganism concentration and all fitted air environmental parameters, and determine the weight proportion coefficient of each fitted air environmental parameter, i.e. obtain the air microorganism real-time monitoring model in a closed environment; In step (4), the polynomial fitting function formula of the microorganism concentration and each fitted air environmental parameter is as follows: （2）； In formula (2), n is a polynomial order; w 0, w 1… w n respectively represent polynomial coefficients; x is a fitting air environmental parameter corresponding to the total number of colonies; y x w is the concentration of microorganisms calculated using a polynomial function, i.e., the fitting data of the concentration of microorganisms;​​ In Equation (2), the polynomial order and polynomial coefficients w 0, w 1… w n The determination method is as follows: First, the error between the fitted data and the measured data of the microbial concentration is calculated E ( w ), as follows: （3）； In formula (3), data is the measured data of the concentration of microorganisms; Then, take the derivative of formula (3) to make it zero, i.e. meet the requirement that the error between the fitted data and the measured data of the microorganism concentration is minimized, i.e. （4）； The values of the polynomial order n and the polynomial coefficients w 0, w 1... w n in equation (2) are calculated according to equation (4) In step (5), the functional relationship between the microorganism concentration and all fitted air environmental parameters is as follows: Y= a 1* T in C+ a 2* R in C + a 3*CO2C + a 4*PM 2.5 C (9); In equation (9), T in C is the polynomial fitting curve of indoor temperature versus total bacterial count; R in C represents the polynomial fitting curve of indoor relative humidity versus total bacterial count; CO2C represents the polynomial fitting curve of CO2 concentration versus total bacterial count; PM 2.5 C represents PM 2.5 Polynomial fitting curve of concentration versus total bacterial count; a 1 represents the weighting coefficient for indoor temperature; a 2 represents the weighting factor for indoor relative humidity; a 3 represents the weighting coefficient for CO2 concentration; a 4 represents PM 2.5 Concentration weighting coefficient; In formula (9), a 1、 a 2、 a 3 and a 4 are calculated as follows: First, the fitting function Y representing the microorganism concentration is calculated using E The error between the fitting function Y representing the microorganism concentration and the measured data of the microorganism concentration is represented by the following equation: （10）； Then, take the derivative of formula (10) and make it zero, i.e. meet the requirement that the error between the fitted data and the measured data of the microorganism concentration is minimized, to obtain the following equation: （11）； （12）； （13）； （14）； According to equations (11) to (14), the values of a1, a2, a3 and a4 are calculated.

2. The method of claim 1, wherein the method is performed in a closed environment. In step (1), the total number of colonies of airborne bacteria was used to represent the concentration of microorganisms; in step (3), the temperature, relative humidity, concentration of PM 2.5 and CO2 concentration were selected as fitting air environment parameters.

3. The method of claim 2, wherein the method is performed in a closed environment. In step (1), the method for measuring the total number of colonies is as follows: use a six-stage Anderson impact sampler to collect planktonic bacteria in the air at the monitoring point, culture the six-stage six-petri dishes according to the national standard method, and record the colony data of the six dishes respectively, and then sum up to obtain the total number of colonies.

4. The method for constructing a model for monitoring air microorganisms in real time in a closed environment according to claim 2, characterized in that, Use a real-time air quality monitor to monitor and collect various air environmental parameters.

5. The method for constructing a model for monitoring air microorganisms in a closed environment in real time according to claim 1, characterized in that, In step (3), the correlation coefficient between the air environment parameters and the microorganism concentration r is calculated by the following formula: （1）； In equation (1), n is the total number of data sets; x i is the microorganism concentration of the nth sample; i y i is the air environmental parameter data of the nth sample; i represents the average value of x represents the average value of y ;​​​ r =0 indicates that the microbial concentration is not correlated with air environment parameters; 0<| r |<0.2 indicates a weak correlation between microbial concentration and air environmental parameters, and 0.2≤| r |<0.4 indicates a low correlation between microbial concentration and air environmental parameters; 0.4≤| r |<0.7 indicates a moderate correlation between microbial concentration and air environmental parameters, 1≤| r |≤0.7 indicates that the microbial concentration is highly correlated with air environmental parameters.

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