Indoor air quality automatic monitoring method

Abstract

The invention discloses an automatic indoor air quality monitoring method, and belongs to the technical field of indoor air quality detection. According to the method, a first data set and a second data set are obtained by monitoring cells, air inlets and air outlets of a communication area and an indoor area, an air state model of the communication area and each indoor area is constructed, and a state transition probability and a state input probability are predicted and substituted into the air state model; and a state vector of the next state transfer step length is generated, so that the data processing amount of multi-region air quality monitoring is reduced, and the effect of prediction in advance is achieved. Furthermore, multiple components are monitored through an air state model, a negative parameter set is generated from the aspects of germ infection, lung harm, explosion danger and the like, the air quality negative index is evaluated, a positive parameter set is generated from the aspects of proper temperature and humidity, proper O2 concentration, proper CO2 concentration and the like, the air quality positive index is evaluated, and comprehensive monitoring of the air quality is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of air quality time series data processing, and particularly to an indoor air quality automatic monitoring method. Background Art

[0002] There are multiple types of monitoring objects for indoor air quality. For example, fine particles such as PM2.5 are harmful to the lungs and respiratory tract, bacteria and fungi in the air are infectious substances, and carbon monoxide in gas is a combustible gas. These components can be included in the monitoring scope as harmful substances. Also, the levels of oxygen, carbon dioxide, etc. are directly related to the comfort of the body feeling. Chinese Patent Application with Publication No. CN110410922A discloses a natural ventilator and an indoor real-time air quality monitoring method. The method uses an air quality monitoring sensor arranged indoors to send the monitored electrical signal to a data receiving module through a data sending module, and then through a data analysis and processing module for digital-to-analog conversion and analysis and processing to obtain various indoor environment parameters and the total fresh air volume of the room and display them in real time through a display panel. The display output of the current air quality is a basic issue in air quality monitoring. In order to further improve the prediction ability of air quality, Chinese Patent Application with Publication No. CN107145737A discloses an algorithm for reverse identification of pollution sources under unsteady flow fields based on Markov chains. This method uses a Markov chain model to estimate the transfer of air pollutants, predict the concentration change of pollutants and the location of pollution sources. For large buildings with a large area and a large number of people such as office buildings, if the entire building is used as a prediction area, the amount of data of the Markov chain is very large. Buildings usually contain multiple relatively independent spaces, and pollutants are exchanged between relatively independent spaces through areas such as aisles. Therefore, it is necessary to propose a new indoor air quality automatic monitoring method to solve the monitoring of indoor multi-region air quality. Summary of the Invention

[0003] In order to solve the defects existing in the above-mentioned prior art, the present invention proposes an indoor air quality automatic monitoring method. By monitoring the cells of the connected area and the indoor area, the air inlets and outlets, the first data set and the second data set are obtained, and the air state models of the connected area and each indoor area are constructed. The state transition probability and the state input probability are predicted and substituted into the air state model, and then the state vector of the next state transition step is generated to reduce the data processing amount of multi-region air quality monitoring and achieve the effect of early prediction. Further, by monitoring multiple components in the air through the air state model, the air quality negative index and the air quality positive index are generated according to the concentrations of different components, realizing the comprehensive monitoring of indoor air quality.

[0004] The technical solution of the present invention is realized as follows: An indoor air quality automatic monitoring method, comprising the following steps: Step 1: Divide the target space into a connected area and at least two indoor areas, and set environmental monitoring nodes at the air inlets and outlets of the connected area and each indoor area; Step 2: Divide the connected area and the indoor areas into multiple cells, arrange concentration monitoring nodes in the multiple cells, and create an air state model for the connected area and each indoor area; Step 3: Collect the first data set of each concentration monitoring node and the second data set of the environmental monitoring nodes. If the sampled data in the first data set exceeds the threshold range of the corresponding component, issue a warning notice and end the task; otherwise, proceed to Step 4; Step 4: Generate the state transition probability and state transition step size of the air state model according to the first data set and the second data set of the connected area and the indoor areas respectively; Step 5: Generate the gas migration rate according to the first data set, the second data set, and the state transition step size of the same moment in different indoor areas and the connected area, and then generate the state input probability of the air state model according to the gas migration rate; Step 6: Update the air state model according to the state transition probability and the state input probability, and predict the third data set of the next state transition step according to the air state model and the first data set; Step 7: Construct response curves for different components, and generate a negative parameter set and a positive parameter set for the connected area and the indoor areas respectively according to the third data set and the response curves; Step 8: Calculate the negative air quality index for the connected area and the indoor areas respectively according to the negative parameter sets of the connected area and the indoor areas, calculate the positive air quality index for the connected area and the indoor areas respectively according to the positive parameter sets of the connected area and the indoor areas, report at least one set of negative air quality index and positive air quality index, and return to Step 3.

[0005] In the present invention, in Step 2, the number of cells in the connected area is I, the number of indoor areas is N, the number of cells in each indoor area is J, and the air state model of the connected area is S t+Δt 0 = S t 0 P t 0 where S t+Δt 0 is the state vector of the connected area at time t + Δt, S t 0 is the state vector of the connected area at time t, P t 0 is the state transition matrix of the connected area at time t, and the air state model of indoor area n is S t+Δt n = S t n P tn , where S t+Δt n is the state vector of the indoor area n at the moment t + Δt, and S t n is the state vector of the indoor area n at the moment t, and P t n is the state transition matrix of the indoor area n at the moment t, and Δt is the state transition step size.

[0006] In the present invention, in step 3, the first data set at least includes the concentration of germs, the concentration of dust, the concentration of dangerous gases, the concentration of oxygen, the concentration of carbon dioxide, and the concentration of water vapor, and the second data set at least includes the inlet ventilation volume, the outlet ventilation volume, the inlet air velocity, and the inlet air velocity.

[0007] In the present invention, in step 4, the state vector S of the connected area at the moment t t 0 includes a matrix C composed of 1×I groups of sampling data 1×I and a matrix D composed of 1×N groups of output states 1×N , S t 0 = [C 1×I , D 1×N , and the state transition matrix P of the connected area at the moment t t 0 includes a matrix P' composed of I×I groups of state transition probabilities I×I and a matrix P'' composed of I×N groups of state output probabilities I×N , P t 0 = [P' I×I , P'' I×N T , and the state vector S of the indoor area n at the moment t t n includes a matrix C composed of 1×J groups of sampling data 1×J and 1×1 groups of input states, and S t n = [C 1×J , D 1×1 , and the state transition matrix P of the indoor area n at the moment t t n includes a matrix P' composed of J×J groups of state transition probabilities J×J and a matrix P'' composed of J×1 groups of state input probabilities J×1 , P t 0 = [P' J×J , P'' J×1 T . ​​

[0008] In the present invention, in step 4, when the cell i and the cell m in the connected region are adjacent, p' 1im is the diffusion probability that the substance in the i-th cell in the connected region transfers to the m-th cell, p' 2im is the convection probability that the substance in the i-th cell in the connected region transfers to the m-th cell, p' 3im is the random perturbation probability that the substance in the i-th cell in the connected region transfers to the m-th cell, α0 is the diffusion weight, β0 is the convection weight, γ0 is the random perturbation weight. When the cell i and the cell m in the connected region are not adjacent, p' im = 0, p' im is the group state transition probability of the i-th row and the m-th column in P' I×I .

[0009] In the present invention, in step 4, , , , δ is the diffusion coefficient, Δx is the distance between cells, c i is the i-th group of sampling data in C 1×I , |v im | is the wind speed magnitude of the i-th cell in the connected region towards the m-th cell, I0 is the number of cells adjacent to the i-th cell in the connected region, Δt = Δx / (|v1| + |v2|), |v1| is the magnitude of the inlet air velocity in the connected region, and |v2| is the magnitude of the inlet air velocity at the inlet of the connected region.

[0010] In the present invention, in step 5, the state output probability is the internal influence probability, and the state input probability refers to the external influence probability. , V n is the volume of the indoor area n, Δc n is the average concentration difference of the indoor area n in the previous state transition step length. is the average concentration of the indoor area n at time t, η is the mechanical ventilation efficiency, ΔC n is the average concentration difference between the connected region and the indoor area n at time t, C t is the average concentration of the connected region at time t, Q nt is the inlet ventilation volume of the indoor area n at time t, Q' t is the inlet ventilation volume of the connected region at time t, Q'' n is the gas exchange volume between the channel area and the indoor area n, and the gas migration rate r of the indoor area n n = Q'' n / Q nt, the state input probability p'''n = r of the indoor area n for the connected area n , P'' I×N = [p''1, p''2, …, p'' n , …, p'' N , p'' n is the state output probability of the connected area for the indoor area n, p'' n = 1 - p'''n, P'' J×1 = [p'' n1 , p'' n2 , …, p'' nj , …, p'' nJ T , p'' nj is the state input probability of the j-th cell in the indoor area n, p'' nj = r n L' nj , L' nj is the distance ratio of the j-th cell in the indoor area n.

[0011] In the present invention, in step 7, the negative parameter set U1 = {y 10 , y 20 , y 30}, where the germ infection response curve , the respiratory hazard response curve , the explosion hazard response curve , a1 is the infectivity constant, v0 is the respiratory rate, M1 is the germ infection amount threshold, is the average germ concentration, a2 is the respiratory hazard coefficient, M2 is the respiratory hazard amount threshold, is the average dust concentration, M3 is the dangerous gas concentration threshold, is the average dangerous gas concentration.

[0012] In the present invention, in step 7, the positive parameter set U2 = {y 40 , y 50 , y 60}, where the oxygen suitability response curve , the carbon dioxide suitability response curve , the humidity suitability response curve y 60 = (T0 - M6) / M6 + (R0 - M7) / M7, M4 is the oxygen concentration threshold, is the average oxygen concentration, M5 is the carbon dioxide concentration threshold, is the average carbon dioxide concentration, M6 is the temperature threshold, T0 is the average temperature, R0 is the average water vapor concentration, M7 is the water vapor concentration threshold.

[0013] In the present invention, in step 8, the air quality negative index , and the air quality positive index .

[0014] Implementing this indoor air quality automatic monitoring method of the present invention has the following beneficial effects: The present invention obtains the concentration data of various gases in the air through the concentration monitoring nodes. While judging whether the current concentration data exceeds the threshold range, it predicts the state transition probability within the independent region and the state input probability between regions to construct an air state model with a Markov chain structure, and then generates the state vector of the next state transition step size, reducing the data processing volume of multi-region air quality monitoring. Further, the present invention monitors multiple components through the air state model, generates a negative parameter set and evaluates the air quality negative index from aspects such as germ infection, lung damage, explosion risk, etc., generates a positive parameter set and evaluates the air quality positive index from aspects such as suitable temperature and humidity, suitable O2 concentration, suitable CO2 concentration, etc., and realizes the comprehensive monitoring of air quality by combining the air quality negative index, the air quality positive index, the negative parameter set, and the positive parameter set. Description of the Drawings

[0015] Figure 1 is a flowchart of the indoor air quality monitoring method of the present invention; Figure 2 is a schematic diagram of the indoor area distribution of the building of the present invention; Figure 3 is a schematic diagram of the air exchange in each indoor area of the building of the present invention; Figure 4 is a schematic diagram of the distribution of the concentration detection nodes in a preferred indoor area of the present invention; Figure 5 is a schematic diagram of the air circulation between two groups of areas in the building room of the present invention; Figure 6 is a schematic diagram of the air circulation between a preferred indoor area and the outside of the present invention; Figure 7 is a block diagram of the system for implementing the indoor air quality automatic monitoring method of the present invention. Detailed Embodiments

[0016] To more clearly understand the purpose, technical solution, and advantages of the present application, the present application will be described and explained below with reference to the drawings and embodiments. Embodiment 1

[0017] As Figures 1 to 6 shown, the indoor air quality automatic monitoring method of the present invention includes the following steps.

[0018] Step 1: Divide the target space into a connected area and at least two indoor areas, and set environmental monitoring nodes at the air inlets and outlets of the connected area and each indoor area. AsFigure 2 and Figure 3 As shown, the middle space is a connected area, the surrounding space of the connected area is an indoor area, both the connected area and each indoor area have an air inlet and an air outlet, environmental monitoring nodes are set at the air inlets and air outlets of the connected area and each indoor area, a wind speed sensor is installed on each environmental monitoring node, the connected area exchanges gas with each indoor area, and the arrows represent the directions of gas in and out.

[0019] Step 2: Divide the connected area and the indoor areas into multiple cells, arrange concentration monitoring nodes in the multiple cells, and create an air state model for the connected area and each indoor area. The number of cells in the connected area is I, the number of indoor areas is N, as Figure 4 shown, the number of cells in each indoor area is J, and concentration monitoring nodes are arranged in the multiple cells. A germ sensor, a dust sensor, a hazardous gas sensor, a CO2 sensor, an O2 sensor, and a temperature and humidity sensor are installed on each concentration monitoring node. The distance between each concentration monitoring node is in the range of 5 meters to 10 meters, ensuring that the monitoring range of the concentration monitoring nodes can cover each cell.

[0020] The air state model of the connected area is S t+Δt 0 = S t 0 P t 0 , where S t+Δt 0 is the state vector of the connected area at time t + Δt, S t 0 is the state vector of the connected area at time t, and P t 0 is the state transition matrix of the connected area at time t. The air state model of indoor area n is S t+Δt n = S t n P t n , where S t+Δt n is the state vector of indoor area n at time t + Δt, S t n is the state vector of indoor area n at time t, and P t n is the state transition matrix of indoor area n at time t, and Δt is the state transition step size.

[0021] Step 3: Collect the first data set of each concentration monitoring node and the second data set of the environmental monitoring node. If the sampling data of the first data set exceeds the threshold range of the corresponding component, issue a warning notice and end the task; otherwise, proceed to Step 4. The first data set includes at least the concentration of germs, dust concentration, concentration of hazardous gases, oxygen concentration, carbon dioxide concentration, and water vapor concentration. The second data set includes at least the inlet ventilation volume, outlet ventilation volume, inlet air velocity, and inlet air velocity. The collection frequency can be set according to different real-time requirements or according to the number and volume of people in the target space. Issue a warning according to the buzzer to evacuate people in time and avoid danger.

[0022] In this embodiment, the threshold range of the germ concentration is set according to the type of germs. For example, the threshold range of Salmonella is generally [0, 10 5 , with the unit of number per cubic meter. The threshold range of the dust concentration is set according to the type of dust. For example, the threshold range of silica is generally [0, 0.05], with the unit of milligram per cubic meter. The threshold range of the concentration of hazardous gases is set according to the type of hazardous gases. For example, the threshold of the concentration of hazardous gas methane is generally [0, 327], with the unit of milligram per cubic meter. The threshold range of the oxygen concentration is generally [255000, 307000], with the unit of milligram per cubic meter. The threshold range of the carbon dioxide concentration is generally [1800, 9000], with the unit of milligram per cubic meter. The threshold range of the water vapor concentration is generally [5000, 15000], with the unit of milligram per cubic meter.

[0023] Step 4: Generate the state transition probability and state transition step size of the air state model according to the first data set and the second data set of the connected area and the indoor area respectively. The state vector S of the connected area at time t t 0 includes a matrix C composed of 1×I groups of sampling data 1×I and a matrix D composed of 1×N groups of output states 1×N , S t 0 = [C 1×I , D 1×N . The state transition matrix P of the connected area at time t t 0 includes a matrix P' composed of I×I groups of state transition probabilities I×I and a matrix P'' composed of I×N groups of state output probabilities I×N , P t 0 = [P' I×I , P'' I×N T . The state vector S of the indoor area n at time t t n ​Comprising a matrix C consisting of 1×J groups of sampled data 1×J and 1×1 group of input states, S t n =[C 1×J ,D 1×1 . The state transition matrix P of the indoor area n at time t t n Comprising a matrix P' consisting of J×J groups of state transition probabilities J×J and a matrix P'' consisting of J×1 groups of state input probabilities J×1 , P t 0 =[P' J×J ,P'' J×1 T . The state transition probability and state transition step length of the air state model are described in detail in Embodiment II.

[0024] Step 5: Generate the gas migration rate according to the first data set and the second data set at the same time of different indoor areas and connected areas and the state transition step length, and then generate the state input probability of the air state model according to the gas migration rate. As Figure 5 and Figure 6 shown, gas exchange occurs between the indoor area and the connected area, and at the same time, the indoor area and the connected area respectively exchange gas with the outside through the air inlet and the air outlet. A fan and an air purifier are installed between the air inlet and the air outlet to achieve the mechanical ventilation mode. The state output probability is the internal influence probability, and the state input probability refers to the external influence probability. The gas migration rate is the ratio of the gas exchange amount to the inlet ventilation volume. The calculation of the gas migration rate, the state input probability and the state output probability of the air state model is described in detail in Embodiment III.

[0025] Step 6: Update the air state model according to the state transition probability and the state input probability, and predict the third data set of the next state transition step length according to the air state model and the first data set. The third data set includes the state vector of the connected area at the next state transition step length and the state vector of each indoor area at the next state transition step length.

[0026] Step 7: Construct response curves of different components, and generate a negative parameter set and a positive parameter set for the connected area and the indoor area respectively according to the third data set and the response curves. The negative parameter set U1={y 10 、y 20 、y 30}, where the pathogen infection response curve , the respiratory hazard response curve , the explosion hazard response curve ​a1 is the infectious constant, without unit. v0 is the breathing rate, in cubic meters per hour. M1 is the threshold of the amount of germ infection, which is set according to different types of germs. For example, the threshold of the amount of Salmonella infection is taken as 10 5 pieces. is the average concentration of germs, in pieces per cubic meter. a2 is the breathing hazard coefficient, without unit. M2 is the threshold of the breathing hazard amount, which is set according to the type of dust. For example, the threshold of the breathing hazard amount of silica is 0.05 mg. is the average concentration of dust, in mg per cubic meter. M3 is the threshold of the concentration of hazardous gas, which is set according to the type of hazardous gas. For example, the threshold of the concentration of methane as a hazardous gas is generally 3571 mg. is the average concentration of hazardous gas, in mg per cubic meter.

[0027] The positive parameter set U2 = {y 40 、y 50 、y 60}, where the oxygen suitability response curve , the carbon dioxide suitability response curve , and the humidity suitability response curve y 60 =(T0 - M6) / M6+(R0 - M7) / M7. M4 is the oxygen concentration threshold, generally 273000 mg per cubic meter. M5 is the carbon dioxide concentration threshold, generally 1800 mg per cubic meter. M6 is the temperature threshold, generally taken as 28 °C. M7 is the water vapor concentration threshold, generally taken as 8650 mg per cubic meter. T0 is the average temperature, and R0 is the average water vapor concentration. is the average oxygen concentration, is the average carbon dioxide concentration, in mg per cubic meter. The average concentrations of various gases in the response curves of the negative parameter set and the positive parameter set can be obtained from the state vector of the third data set.

[0028] Step 8: Calculate the air quality negative index according to the negative parameter set of the connected area and the indoor area respectively, calculate the air quality positive index according to the positive parameter set of the connected area and the indoor area respectively, report at least one set of air quality negative index and air quality positive index, and return to Step 3. The air quality negative index , and the air quality positive index . The smaller the air quality negative index, the worse the air quality, and the larger the air quality positive index, the worse the air quality. The air quality is fed back through the air quality negative index and the air quality positive index, clearly reflecting the real-time air state of the target space. Embodiment 2

[0029] This embodiment further discloses a preferred method for updating the state transition probability and the state transition step size.

[0030] The state transition probability is the probability that the substance in a certain cell in the same region is transferred to another cell due to diffusion, convection, and random perturbation, and the state transition step length is the time for the substance in a certain cell in the same region to be transferred to another cell.

[0031] , when cell i and cell m in the connected region are adjacent, , when cell i and cell m in the connected region are not adjacent, p' im = 0. p' im is the probability that the substance in the i-th cell in the connected region is transferred to the m-th cell, that is, the state transition probability of the i-th row and m-th column group in P' I×I . α0 is the diffusion weight, β0 is the convection weight, and γ0 is the random perturbation weight. α0 + β0 + γ0 = 1.

[0032] , p' 1im is the diffusion probability that the substance in the i-th cell in the connected region is transferred to the m-th cell. δ is the diffusion coefficient, which is related to the specific gas. For example, the diffusion coefficient of carbon dioxide in air is 16 square millimeters per second. Δx is the distance between cells, and c i is the i-th group of sampling data in C 1×I . , p' 2im is the convection probability that the substance in the i-th cell in the connected region is transferred to the m-th cell, and |v im | is the wind speed modulus of the i-th cell in the connected region towards the m-th cell. , p' 3im is the random perturbation probability that the substance in the i-th cell in the connected region is transferred to the m-th cell, and I0 is the number of cells adjacent to the i-th cell in the connected region.

[0033] Δt = Δx / (|v1| + |v2|), where |v1| is the modulus of the inlet air velocity of the connected region, and |v2| is the modulus of the outlet air velocity of the connected region. The state transition probability of the indoor region can be calculated according to the above method. Example 3

[0034] This example further discloses a preferred method for updating the gas migration rate, state input probability, and state output probability.

[0035] First, calculate the gas exchange amount through the following formula, and then calculate the gas migration rate. , V n is the volume of the indoor region n, in cubic meters. Δc n is the average concentration difference of the indoor region n in the previous state transition step, in milligrams per cubic meter. Q ntThe inlet ventilation volume of the indoor area n at time t, with the unit of cubic meters per hour. η is the mechanical ventilation efficiency, dimensionless. C t is the average concentration of the connected area at time t, Q' t is the inlet ventilation volume of the connected area at time t, is the average concentration of the indoor area n at time t, ΔC n is the average concentration difference between the connected area and the indoor area n at time t, Q'' n is the gas exchange volume between the passage area and the indoor area n. Among them, , is the average concentration of the indoor area n at time t - Δt, , c nj is the j - th group of sampling data in C 1×J , , . The gas migration rate r of the indoor area n n = Q'' n / Q nt .

[0036] Then, calculate the state input probability according to the gas migration rate, and finally calculate the state output probability according to the state input probability. The state input probability p'''n of the indoor area n to the connected area = r n , P'' I×N = [p''1, p''2, …, p'' n , …, p'' N , p'' n is the state output probability of the connected area to the indoor area n, p'' n = 1 - p'''n, P'' J×1 = [p'' n1 , p'' n2 , …, p'' nj , …, p'' nJ T , p'' nj is the state input probability of the j - th cell in the indoor area n, p'' nj = r n L' nj , L' nj is the distance ratio of the j - th cell in the indoor area n, , L nj is the distance between the j - th cell in the indoor area n and the connected area. Example 4

[0037] Such as Figure 7 ​As shown in the figure, the system for implementing the indoor air quality automatic monitoring method of the present invention includes: a germ sensor, a dust sensor, a hazardous gas sensor, a CO2 sensor, an O2 sensor, a temperature and humidity sensor, a wind speed sensor, a data acquisition unit, a data error correction unit, a data analysis unit, a control unit, and a buzzer. The germ sensor is used to measure the germ concentration. The dust sensor is used to measure the dust concentration. The hazardous gas sensor is used to measure the concentration of flammable gases. The CO2 sensor is used to measure the CO2 concentration. The O2 sensor is used to measure the O2 concentration. The temperature and humidity sensor is used to measure the temperature and water vapor concentration. The wind speed sensor is used to measure the inlet ventilation volume, the outlet ventilation volume, the inlet air flow rate, and the inlet air flow rate. The data acquisition unit is used to collect the first data set of each concentration monitoring node and the second data set of each environmental monitoring node and send them to the data error correction unit and the data analysis unit. The data error correction unit is used to identify abnormal data in the first data set and the second data set. The data analysis unit is used to analyze whether the sampled data in the first data set exceeds the threshold range of the corresponding component. If it exceeds, a warning notice is issued. The control unit is used to receive the warning notice and control the buzzer to turn on, and send a collection signal to the data acquisition unit. The buzzer is used for alarm to indicate danger.

[0038] In addition, the system further includes a data processing unit. The data processing unit is used to update and generate the state transition probability and state transition step size of the air state model according to the first data set and the second data set, calculate the gas migration rate, and then generate the state input probability of the air state model according to the gas migration rate. Update the air state model according to the state transition probability and the state input probability. Predict the third data set of the next state transition step according to the air state model and the first data set. Generate a negative parameter set and a positive parameter set for the connected area and the indoor area respectively according to the third data set and the response curve. Calculate the negative air quality index according to the negative parameter set, and calculate the positive air quality index according to the positive parameter set.

[0039] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An indoor air quality automatic monitoring method, characterized in that, It includes the following steps: Step 1: Divide the target space into a connected area and at least two indoor areas, and set environmental monitoring nodes at the air inlets and outlets of the connected area and each indoor area; Step 2: Divide the connected area and the indoor areas into multiple cells, arrange concentration monitoring nodes in the multiple cells, and create an air state model for the connected area and each indoor area; Step 3: Collect the first data set of each concentration monitoring node and the second data set of the environmental monitoring nodes. If the sampling data in the first data set exceeds the threshold range of the corresponding component, issue a warning notice and end the task. Otherwise, proceed to Step 4; Step 4: Generate the state transition probability and state transition step size of the air state model according to the first data set and the second data set of the connected area and the indoor areas respectively; Step 5: Generate the gas migration rate according to the first data set, the second data set and the state transition step size of different indoor areas and the connected area at the same moment, and then generate the state input probability of the air state model according to the gas migration rate; Step 6: Update the air state model according to the state transition probability and the state input probability, and predict the third data set of the next state transition step according to the air state model and the first data set; Step 7: Construct response curves for different components, and generate negative parameter sets and positive parameter sets for the connected area and the indoor areas respectively according to the third data set and the response curves; Step 8: Calculate the negative air quality index according to the negative parameter sets of the connected area and the indoor areas respectively, calculate the positive air quality index according to the positive parameter sets of the connected area and the indoor areas respectively, report at least one set of negative air quality index and positive air quality index, and return to Step 3.

2. The indoor air quality automatic monitoring method according to claim 1, wherein In step 2, the number of cells in the connected area is I, the number of indoor areas is N, the number of cells in each indoor area is J, and the air state model of the connected area is S t+Δt 0 =S t 0 P t 0 , where S t+Δt 0 is the state vector of the connected region at time t+Δt, S t 0 is the state vector of the connected region at time t, P t 0 is the state transfer matrix of the connected area at time t, and the air state model of indoor area n is S t+Δt n = S t n P t n , where S t+Δt n is the state vector of indoor area n at time t+Δt, S t n is the state vector of indoor area n at time t, P t n is the state transfer matrix of indoor area n at time t, and Δt is the state transfer step size.

3. The indoor air quality automatic monitoring method according to claim 2, characterized in that, In Step 3, the first data set includes at least germ concentration, dust concentration, hazardous gas concentration, oxygen concentration, carbon dioxide concentration, and water vapor concentration, and the second data set includes at least inlet ventilation volume, outlet ventilation volume, inlet air velocity, and inlet air velocity.

4. The indoor air quality automatic monitoring method according to claim 3, characterized in that, In step 4, the state vector S of the connected region at time t t 0 includes a matrix C composed of 1×I groups of sampled data 1×I and a matrix D composed of 1×N groups of output states 1×N , S t 0 = [C 1×I , D 1×N , and the state transition matrix P of the connected region at time t t 0 includes a matrix P' composed of I×I groups of state transition probabilities I×I and a matrix P'' composed of I×N groups of state output probabilities I×N , P t 0 = [P' I×I , P'' I×N T , and the state vector S of indoor area n at time t t n includes a matrix C composed of 1×J groups of sampled data 1×J and 1×1 group of input states, S t n = [C 1×J , D 1×1 , and the state transition matrix P of indoor area n at time t t n includes a matrix P' composed of J×J groups of state transition probabilities J×J and a matrix P'' composed of J×1 groups of state input probabilities J×1 , P t 0 = [P' J×J , P'' J×1 T .​​ 5. The indoor air quality automatic monitoring method according to claim 4, characterized in that, In step 4, when cell i and cell m in the connected region are adjacent, , p' 1im is the diffusion probability that the substance in the i-th cell in the connected region transfers to the m-th cell, p' 2im is the convection probability that the substance in the i-th cell in the connected region transfers to the m-th cell, p' 3im is the random perturbation probability that the substance in the i-th cell in the connected region transfers to the m-th cell, α0 is the diffusion weight, β0 is the convection weight, γ0 is the random perturbation weight. When cell i and cell m in the connected region are not adjacent, p' im = 0, p' im is the group state transition probability of the i-th row and m-th column in P' I×I .

6. The indoor air quality automatic monitoring method according to claim 5, characterized in that, In step 4, , , , where δ is the diffusion coefficient, Δx is the distance between cells, c i is the i-th set of sampling data in C 1×I , |v im | is the wind speed magnitude of the i-th cell in the connected region towards the m-th cell, I0 is the number of cells adjacent to the i-th cell in the connected region, Δt = Δx / (|v1| + |v2|), |v1| is the magnitude of the inlet air velocity in the connected region, and |v2| is the magnitude of the inlet air velocity at the inlet of the connected region.

7. The indoor air quality automatic monitoring method according to claim 6, characterized in that, In step 5, the state output probability is the internal influence probability, and the state input probability refers to the external influence probability. , V n is the volume of indoor area n, and Δc n is the average concentration difference of indoor area n in the previous state transition step length. is the average concentration of indoor area n at time t, η is the mechanical ventilation efficiency, and ΔC n is the average concentration difference between the connected area and indoor area n at time t, and C t is the average concentration of the connected area at time t, and Q nt is the inlet ventilation volume of indoor area n at time t, and Q' t is the inlet ventilation volume of the connected area at time t, and Q'' n is the gas exchange volume between the channel area and indoor area n, and the gas migration rate r of indoor area n n = Q'' n / Q nt , the state input probability p'''n of indoor area n to the connected area = r n , P'' I×N = [p''1, p''2, …, p'' n , …, p'' N , p'' n is the state output probability of the connected area to indoor area n, and p'' n = 1 - p'''n, P'' J×1 = [p'' n1 , p'' n2 , …, p'' nj , …, p'' nJ T , p'' nj is the state input probability of the j-th cell in indoor area n, and p'' nj = r n L' nj , L' nj is the distance ratio of the j-th cell in indoor area n.​ 8. The indoor air quality automatic monitoring method according to claim 7, characterized in that, In step 7, the negative parameter set U1 = {y 10 , y 20 , y 30}, where the germ infection response curve , the respiratory hazard response curve , the explosion hazard response curve , a1 is the infectivity constant, v0 is the respiratory rate, M1 is the germ infection amount threshold, is the average germ concentration, a2 is the respiratory hazard coefficient, M2 is the respiratory hazard amount threshold, is the average dust concentration, M3 is the hazardous gas concentration threshold, is the average hazardous gas concentration.

9. The indoor air quality automatic monitoring method according to claim 8, characterized in that, In step 7, the positive parameter set U2 = {y 40 , y 50 , y 60}, where the oxygen suitability response curve , the carbon dioxide suitability response curve , and the humidity suitability response curve y 60 = (T0 - M6) / M6 + (R0 - M7) / M7, M4 is the oxygen concentration threshold, is the average oxygen concentration, M5 is the carbon dioxide concentration threshold, is the average carbon dioxide concentration, M6 is the temperature threshold, T0 is the average temperature, R0 is the average water vapor concentration, and M7 is the water vapor concentration threshold.

10. The automatic indoor air quality monitoring method according to claim 9, characterized in that, In step 8, the negative air quality index , and the positive air quality index .

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