KR20110098102A - Air quality monitoring system and air quality control system - Google Patents

Abstract

The present invention relates to a method for monitoring underground indoor air quality and a system for controlling underground indoor air quality.
An indoor indoor air quality control system according to the present invention, comprising: a plurality of sensors including an indoor sensor measuring an indoor variable, an outdoor sensor measuring an outdoor variable, and an indoor air quality function module using variables by the plurality of sensors as input variables And an outdoor pollution degree function including a measured value by an outdoor sensor as an input variable, wherein the indoor air quality function value outputs an operation mode signal according to the first air quality reference range, and the outdoor air according to the operation mode signal. It characterized in that it comprises an air conditioning unit which operates in a circulation mode that operates in a circulating mode to purify the ventilation mode or indoor air after inhaling and purifying the indoor air.

Description

Indoor indoor air quality monitoring method and underground indoor air quality control system {AIR QUALITY MONITORING SYSTEM AND AIR QUALITY CONTROL SYSTEM}

The present invention relates to a method for monitoring underground indoor air quality and a system for controlling underground indoor air quality.

In today's world, where air pollution becomes severe, systems and methods for monitoring and diagnosing air quality are emerging as critical technologies in modern society.

In particular, in special spaces such as underground terminals, underground shopping centers, or subway stations, many people are gathered within a limited space, and the contaminants generated in the space are likely to be stagnant inside other spaces. And for the convenience, the situation is required to develop a technology that can more accurately and easily diagnose the air quality by monitoring the air quality in real time.

However, it is a complicated and difficult process to measure the amount and concentration change of pollutants and aggregate them, and there is a problem that it is not easy to accurately and easily diagnose the air quality composed of process variables having various correlations. .

That is, in the related art, as a method for diagnosing air quality in an underground space, such as underground history, a significant section is set for each process variable, and a process abnormality is determined according to whether a variable measured in real time is out of the significant section. The system using univariate monitoring was mainly used.

However, such a conventional short-variable air quality monitoring system collects all possible process variable values, so there is a problem that efficiency may be reduced by collecting unnecessary information, and furthermore, if there is a close correlation between each process variable. There was a limit to not consider at all.

1 is a graph showing the results obtained according to the multivariate monitoring system considering the correlation between the results obtained by the univariate monitoring system for monitoring two variables, respectively. As shown in Fig. 1, the two variables y1 and y2 are displayed on the horizontal axis y1 and the vertical axis y2 of the figure, respectively, and the upper and lower limit setting values of the monitoring are displayed.

That is, the univariate monitoring method has a rectangular monitoring area having an upper limit and a lower limit, and in reality, two variables are associated with an elliptical monitoring area as shown in FIG. 1.

However, when using such univariate statistical analysis, the air quality corresponding to point 을 is abnormal because it exists outside the elliptical monitoring area which is the normal range in the actual system, but in the univariate chart, it exists within the upper and lower limits of the normal range. So it turns out to be normal. In addition, in the case of air quality corresponding to point ◇, the actual system shows normal, but in the case of univariate chart, it turns out to be abnormal and shows wrong results. As such, any univariate chart does not effectively detect abnormal air quality and is normal in the actual process, but the univariate monitoring chart does not consider the relationship between variables, and thus has a problem of being detected as abnormal.

Therefore, the conventional air quality monitoring system using univariate statistical analysis has to consider all process variables measured in the underground space, which can increase the cumbersome monitoring and does not consider the correlation between process variables.

In addition, since indoor air of underground history is dominated by outdoor air quality, the degree of pollution is dominant. Therefore, it is necessary to monitor the air pollutants only in indoor spaces to derive accurate air conditioning methods. Development of air quality monitoring systems and methods and air control systems is required.

The present invention was created in order to meet the necessity of the above-described technology development, and can easily monitor indoor air quality by excluding the influence of outdoor air quality by using a general multivariate analysis method considering correlation of process variables. Its purpose is to provide a monitoring system and method thereof.

Another object is to provide a system for controlling the air conditioning of the indoor space monitored by the above method and system.

As the indoor indoor air quality monitoring method of the present invention for achieving the above object, the measurement data collection step of collecting the measurement value of the process variable for the air quality, and the data to exclude the outdoor air quality influence of the collected measurement data Selecting and setting a main variable by the principal component analysis method using the selected data, a modeling step of determining an air quality model in which a statistical upper limit is set according to the set main variable, and comparing the upper limit of the measured data and the air quality model. Characterized in that it comprises a step of determining the air quality.

In addition, the indoor indoor air quality monitoring method of the present invention, after determining the air quality, characterized in that it further comprises a contribution analysis step of selecting a cause variable having a large contribution from the set main variables.

In the underground indoor air quality monitoring method of the present invention, the method may further include a display step of classifying and displaying the air quality according to the determined air quality, wherein the displaying step is performed by using a face chart monitoring technique. Characterized in that the display is characterized.

On the other hand, as the indoor indoor air quality control system of the present invention, a plurality of sensors including an indoor sensor for measuring the indoor variable, an outdoor sensor for measuring the outdoor variable, and the indoor air quality using the variable by the plurality of sensors as an input variable A control module that includes a function module and an outdoor pollution degree function including a measured value by an outdoor sensor as an input variable, wherein the indoor air quality function value outputs an operation mode signal according to a first air quality reference range, and according to the operation mode signal. And an air conditioner operated in a circulation mode operated in a circulation mode in which the outdoor air is purged and introduced into the room or the suction and purification of the indoor air is circulated to the interior.

In addition, in the indoor indoor air quality control system of the present invention, the control unit, if the calculated indoor air quality function value is outside the first air quality range, if the outdoor pollution degree function value is out of the pollution degree reference range, the air conditioning unit And outputs a signal to operate in a cyclic mode.

In addition, in the underground indoor air quality control system of the present invention, when the calculated indoor air quality function value is out of the first air quality range, if the outdoor pollution degree function value is within the pollution degree reference range, the air conditioner is ventilated. And outputs a signal to operate in a mode.

In addition, in the indoor indoor air quality control system of the present invention, further comprising an alarm notification unit, the control unit, if the calculated indoor air quality function value is outside the second air quality reference range, the pollution degree function value of the outdoor air is a second When the pollution degree is within the standard, it is characterized in that for outputting a signal to notify the indoor access restriction to the warning notification unit.

In the indoor indoor air quality control system of the present invention, the plurality of sensors are a temperature sensor, a humidity sensor, a wind speed sensor, an indoor PM ₁₀ , an indoor PM _{2 . And} a dust sensor for measuring ₅ , a dust sensor for measuring outdoor PM ₁₀ , and a sensor for counting the flow population.

In addition, in the indoor indoor air quality control system of the present invention, the indoor sensor is a temperature sensor, humidity sensor, wind speed sensor, indoor PM ₁₀ , indoor PM _{2 . It} includes a dust sensor for measuring ₅ , a sensor for counting the flow population, the outdoor sensor is characterized in that it comprises a dust sensor for measuring the outdoor PM ₁₀ .

In addition, in the indoor indoor air quality control system of the present invention, the indoor variable is a temperature value, humidity value, wind speed value, indoor PM ₁₀ value, indoor PM _{2 . 5} values, Indoor PM ₁₀ and Indoor PM _{2 . And} a ratio value of ₅ , wherein the outdoor variable includes an outdoor PM ₁₀ value and an outdoor PM ₁₀ value. Characterized in that it comprises a percentage value of the indoor PM ₁₀ do.

As described above, since the present invention excludes the influence of external air quality, which affects the indoor air quality, it is possible to accurately monitor the air quality of the indoor space and to accurately find a variable that may cause a cause due to the air quality fluctuation. have.

In particular, since indoor air quality is monitored through multivariate statistical analysis (principal component analysis), it is possible to easily check the variable of air quality fluctuations of complex dimensions.

Therefore, there is an advantage that can be widely applied to the determination of the air quality of the space, such as subway station, underground shopping center, underground terminal.

1 is a graph showing results obtained according to a multivariate monitoring system in consideration of correlations between two variables and results obtained from a univariate monitoring system monitoring two variables, respectively.
Figure 2 Geometrically interpreted principal component analysis
3 is a view showing the integrated monitoring technology of underground historical air quality using a multivariate statistical model in the air quality monitoring system according to an embodiment of the present invention
4A and 4B show the correlation between the number of passengers in the underground station and the air quality. FIG. 4A is a graph showing the number of passengers according to the time in the underground station. FIG. 4B is a concentration of PM ₁₀ according to the time in the underground station. Graph shown
5 is a result of air quality analysis using multivariate air quality monitoring of the X station underground history
6 is a graph showing the results of a general multivariate statistical monitoring analysis of indoor air quality in underground stations
7 is a graph showing a multivariate statistical monitoring analysis result excluding the influence of external air quality according to an embodiment of the present invention
8 is a view for explaining a method for diagnosing air quality risk in underground history using artificial intelligence clustering technology
9 is a block diagram illustrating a system for controlling indoor indoor air quality according to an embodiment of the present invention.

As the inventive concept allows for various changes and numerous embodiments, particular embodiments will be illustrated in the drawings and described in detail in the written description. However, this is not intended to limit the present invention to specific embodiments, it should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.

Terms including ordinal numbers such as first and second may be used to describe various components, but the components are not limited by the terms. The terms are used only for the purpose of distinguishing one component from another.

For example, without departing from the scope of the present invention, the second component may be referred to as the first component, and similarly, the first component may also be referred to as the second component.

When a component is referred to as being "connected" or "connected" to another component, it may be directly connected to or connected to that other component, but it may be understood that other components may be present in between. Should be. On the other hand, when a component is said to be "directly connected" or "directly connected" to another component, it should be understood that there is no other component in between.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting of the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present application, the terms "comprises", "having", and the like are used to specify that a feature, a number, a step, an operation, an element, a component, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

Now, the basement air quality monitoring method and the basement air quality control system according to an embodiment of the present invention will be described in detail with reference to the drawings, and the same or corresponding components are designated by the same reference numerals regardless of the reference numerals. Duplicate explanations may be omitted.

The air quality monitoring method in the basement according to the present embodiment starts with data collection to collect measured values of process variables.

Here, the process variable is a meaning encompassing all variables that may affect the air quality, and may include all variables that may directly or indirectly affect the air quality, and the process variables according to the present invention are illustrated above. It is not limited to the thing.

The above process variable may be measured by a separate sensor installed in a specific space to monitor the air quality, or may be directly input by the user, and may measure values of various process variables that may affect the air quality. All possible measuring and sensing means can be included in this.

In addition, the number of the process variables is not particularly limited, and all possible process variables may be considered to diagnose the air quality.

That is, the measurement values of all process variables that may affect the air quality can be collected and used for the air quality diagnosis. The measured value of the process variable may be collected in real time, it is also possible to use the average value of the value measured in a plurality of zones or to use the value measured only in some specific zones suitable for determining the air quality.

In addition, in collecting the measured value of the process variable, it is possible to receive and store the measured value detected by the sensor as a data signal, but is not limited to this, the process variable directly received by the user or through a variety of other data collection methods The measured value of can be collected as data.

Meanwhile, the multivariate analysis method refers to an analysis method related to identifying or analyzing a causal relationship between variables, or using a correlation between variables to reduce a variable or classify objects.

The main variable setting unit of the air quality monitoring system according to the present invention is a more efficient method by considering the correlation of each process variable by newly setting various variables having causality or correlation as the main variable according to the multivariate analysis method. To monitor and diagnose air quality.

The multivariate analysis method includes a multiple regression analysis method or a multivariate variance analysis method for identifying or analyzing a causal relationship between variables, and a principal component analysis method or a factor analysis method using correlations between variables.

In the present invention, since the correlation between various process variables is close and to monitor a large air quality, among the multivariate analysis methods, various process variables that may affect air quality can be abbreviated and can be easily and accurately monitored at a lower level. It is preferable to use a variable abbreviation method, and it is more preferable to use a principal component analysis that shortens various variables of a high dimension and analyzes them according to new variables of a low dimension among the variable reduction methods.

However, the air quality monitoring system according to the present invention is not limited to the case of using the above-described multivariate analysis method, and considering the correlation or causal relationship between various process variables that may affect the air quality according to the object of the present invention. This can include all multivariate methods that provide new key parameters for easier and more accurate air quality monitoring.

The principal component analysis (PCA) method was developed in the field of chemometrics for the purpose of conventional data compression and information extraction, and consists of a combination of variables in the direction of maximizing covariance from high-dimensional data. How to get the ingredients.

In other words, in order to discover abnormalities or outliers (different from other individuals in terms of measured variables) in multivariate data, considering the correlations of variables, It is an analysis method that makes statistical analysis easier by reducing the data of high dimension by obtaining the corresponding principal component.

FIG. 2 is a graph in which the principal component analysis is geometrically analyzed. When the principal component analysis is used, the process variable data of the three-dimensional image is transformed into two-dimensional principal components that are orthogonal to the orthogonal component by the principal component analysis. It can be seen that the information processing and interpretation can be done easily.

Furthermore, since the dimension of the original variable is reduced, it is possible to diagnose the air quality more quickly and easily by monitoring according to the new principal component without monitoring the data of all process variables daily. It can be used more effectively in monitoring.

In other words, by using the characteristics of the highly correlated process variable data, we find a reduced model space that reduces the process variable dimension while minimizing the loss of information of the process variable. Air quality can be monitored.

Therefore, when the principal component analysis method is used, the principal variable is the principal component consisting of a combination of process variables with maximum covariance.

Here, the data matrix X ^∈ R ^{n ⅹ m} consisting of n process variable samples measured by m sensors may be decomposed into a model estimate and a residual by principal component analysis, which may be expressed as Equation 1 below.

[Equation 1]

Here, T∈R ^{n ⅹp} is a score matrix, P∈R ^{m ⅹp} is a loading matrix, E∈R ^{n ⅹm} is a residual matrix, and p is a dimension of a reduced model space.

Therefore, it is possible to reduce the dimension of the original process variable to a simpler dimension through principal component analysis. Here, an air quality model in which a statistical upper limit is set according to the main variable is established.

The air quality model refers to a model in which a statistical upper limit is set as a standard for determining air quality in order to monitor and diagnose air quality, and preferably includes a global model and a local model.

The global model is a comprehensive air quality model established through major variables. It is a model that diagnoses air quality measured within a certain period of time without being classified according to air quality or separately according to time. On the other hand, the changed data in the global model may be divided into a certain number of clusters, for example, it may be classified as a local model that determines the air quality differently according to a certain region.

Each local model is used to monitor the air quality conditions in the area, and the local air model can be used to select the local model that represents the closest state by comparing the air quality condition diagnosed according to the local model with the air pollution obtained from the global model.

The statistical upper limit of the established air quality model is 100 (1-α)% of reliability which can be shown in the steady state of air quality as shown in Equation 2 below and 100 (Q) as shown in Equation 3 A statistical upper limit with confidence of 1-α)% is set.

[Equation 2]

&Quot; (3) "

이며,

는 샘플 공분 행렬의 i 번째 대각원소,

는 n이 매우 큰 값을 가질 경우 p 값이 α일 때의 t 값이다. here,

,

Is the i-th diagonal element of the sample conjugate matrix,

Is the value of t when p is α when n has a very large value.

The sum of squared weighted scores of Equation 4 and the residual sum of squares of Equation 5 is calculated on the new measured value x measured from the previously selected measurement variables.

&Quot; (4) "

[Equation 5]

It is represented as

The upper limit is compared with a statistical upper limit having a confidence level of 100 (1-α)% that can appear in the steady state air quality of Equation 2 and a statistical upper limit having a reliability of 100 (1-α)% of Q in Equation 3. If is greater than the measured value, it can be confirmed that the current process is steady at a confidence level of 100 (1-α)%.

However, the above multivariate statistical monitoring system requires an important step in building reliable data for developing statistical monitoring models. However, as mentioned above, since indoor air quality in the underground history is heavily influenced by external air quality, it is urgent to develop a technology for excluding this external air quality, which is a goal that must be achieved before the development of a multivariate statistical monitoring system. have.

In order to exclude the influence of external air quality, the external information technique proposed by Kano et al. Was applied. Process variables can be divided into two main categories: external variables that greatly affect the process, and main variables that are affected by the set external variables. The main variable is divided into two parts: one affected by the external variable and one not affected by the external variable. From a monitoring point of view, the external variable that was previously set up is not a fault or anomalous behavior. Therefore, it is necessary to remove the part of the main variable that is affected by the external variable. More specifically, the k samples of the data matrix X∈R ^{n ⅹm} is contaminated indoor air quality parameters that are affected by the _g m of external variables and external air quality, such as the outer bulb air quality parameters related contamination consisting of sensors obtained through m m _h ( mm _g ) divided by the main variable. That is, the original data X is divided into an H matrix and a G matrix as shown in Equation 6.

&Quot; (6) "

Here, the matrix H consists of main variables, and the matrix G is part of an external variable. The H matrix is divided into parts affected by an external variable and parts not affected, and the part affected by the external variable is calculated by multiplying the G part and the regression coefficient C. The regression coefficient C is calculated by Equation 7 below, and the portion E which is not affected by external variables is calculated by Equation 8.

[Equation 7]

C = (G ^T G) ^-1 G ^T H

[Equation 8]

E = H-GC

That is, the main data matrix H is divided into GC and E parts, where E part is related to indoor air pollutant parameters excluding the influence of external air quality.

Figure 3 is a simplified illustration of the integrated monitoring technology of the indoor air quality excluding the influence of the external air quality, after excluding the variables affecting the external air quality, the current part of the air quality is identified using the multivariate statistical monitoring technology do.

On the other hand, the air quality monitoring system according to the present invention preferably further comprises a contribution analysis step of selecting a cause variable having a high contribution to the abnormal state of the air quality.

In other words, the contribution analysis makes it easy to observe which variables are abnormal by determining the cause variables that contribute to the abnormal state of the air quality when the air quality is abnormal by judging the air quality. For example, when it is determined that the air quality is abnormal, the measured value of each process variable may be checked and a process variable having a large degree exceeding a predetermined statistical upper limit may be selected as a cause variable having a high contribution to air pollution. Here, the criterion for determining the degree beyond the statistical upper limit can be arbitrarily determined by the user, and can be set to an appropriate range according to each process variable.

Therefore, if the cause variable is selected according to the contribution analysis, the user can draw a conclusion by comparing the normal state while looking at the change in the process situation. In addition to this contribution analysis step, it does not directly show accurate results of air quality contamination, but provides information about possible causes, greatly reducing the time and effort involved in diagnosing the process. There is an advantage.

In addition, the air quality monitoring method according to the present invention preferably further includes a display step of classifying and displaying the grade according to the air quality. In other words, it is possible to model the comfort in the subway by linking multivariate statistical monitoring technology and emotion model. Emotion is a structure of experience that each individual has, and it is expressed as a high level of experience that is expressed by combining various emotion elements in combination with one image, so that modeling of data relating to air quality and emotion is possible. The display step may use face chart monitoring technology to match the air quality to the human face condition. As such, when the current air quality is displayed through the human face state, convenience may be provided to the user to more easily grasp the indicator management and the operating conditions for the current air quality.

[Example]

Hereinafter, the air quality monitoring method according to the present invention will be described in more detail with reference to the following examples.

Using the air quality monitoring method according to an embodiment of the present invention was analyzed the air pollution data measured in the domestic X zone. The air pollution data forms a database of about 7 months from October 2007 to April 2008, and in this embodiment, as a measurement variable, seven variables (outside air PM ₁₀ , temperature, humidity, wind speed, platform PM) _10, the landing PM _2.5, number of passengers) has been selected, according to the present invention was applied to a total of 9 variables including the outside air quality PM _10, PM ₁₀ and the landing ratio value and the PM ₁₀ and the landing platform of the ratio PM _{2 .5.}

4 PM ₁₀ according to a multivariate air quality passenger to indicate the need for monitoring the number of passengers in accordance with the change in time of a week illustrates the correlation between the concentration of air quality and the time of underground stations Indicates concentration. As you can see, there is a similar trend between the number of passengers and the air quality (PM ₁₀ concentration) in the underground history, which is very correlated. The PM _{2 .5} different air quality levels, which showed a similar relationship also CO _2, NOx. These correlations indicate that efficient air quality monitoring in underground stations requires multivariate monitoring rather than traditional univariate monitoring.

5 is a variance of the training data by the multivariate model in the 95% tolerance range, and about 2/3 or more of the measured values can be explained by the model. Dispersion also indicates the need for a local model before and after the installation of the screen door, and the 95% statistical upper limit is set, allowing monitoring using this dispersion. If you look at the 95% statistical upper limit of dispersion, you can see that the data before and after the installation of the screen door is divided into two, and the points outside the 95% statistical limit indicate a contaminated state.

6 is one of the results of a general multivariate statistical monitoring analysis conducted to investigate the influence of the outside air quality on the indoor air quality in the underground history. (a) Referring to the correlation pre-screen door installed, a high correlation between the temperature and humidity parameters, in particular, the correlation between the outside air and the landing PM ₁₀ PM _10, PM _{2 .5} variable, it can be seen that very deep. However, when examining the correlation after the screen door installation in (b), the correlation with the outside air quality was not shown by the installation effect of the screen door, and only the PM ₁₀ and PM _2.5 variables in the platform showed a correlation _. . Contrary to common knowledge, however, there was no influence of passenger behavior on indoor air quality.

However, referring to the case hayeoteul excluding the external air quality, prior to installation of the screen doors on the drawing left analysis result as shown in FIG. 7, similarly to the result before the external air quality rule, and the correlation between the PM ₁₀ and PM platform _{2 .5} variable exists, External PM ₁₀ The variable was shown to affect the platform PM concentration. However, after the screen door installed on the right side on the drawing In the analysis results, the general results of multivariate analysis, and is the correlation between the PM ₁₀ and the landing platform PM _{2 .5} variable exists, and otherwise, the concentration PM of the platform is also affected by the passenger Appeared.

Therefore, the results of the case of not excluding outside air quality differed from those of excluding outside air quality, and the results of the analysis showed that the correlation was more accurate when the outside air quality was excluded, resulting in accurate management of indoor air quality monitoring in underground history. Can be. Therefore, it can be used to derive a control law suitable for the current driving state using the monitoring information as shown in FIG. This method is the development of management control technology that derives the best control method for the current operating state based on process monitoring information and artificial intelligence technology. It diagnoses abrupt process abnormalities and kinds of abnormal conditions using multivariate clustering techniques in statistical space conditions. It can be actively used in methodologies that can be done. By providing the convenience of delivering the current subway air quality in real time to the driver, the subway and underground history, and the passengers who manage the air quality by the above-described effect of the present invention, to provide safety and confidence to the passengers using the subway It is believed to be able.

Meanwhile, as an embodiment of the present invention, the air quality control system in the basement as shown in FIG. 9 is disclosed.

Underground air quality control system according to this embodiment is for measuring the dust temperature sensor 110, humidity sensor 120, a wind speed sensor 130, an indoor PM _10, PM _{2 .5} indoor room sensor for measuring the indoor variable Sensor 140, 150, flow population counter 170, dust sensor 160 for measuring the outdoor PM ₁₀ as an outdoor sensor, and indoor air quality function calculation module using the ratio between the plurality of sensors and the sensing data as input variables And an outdoor pollution function calculation module using the measured value by the outdoor sensor 160 as an input variable, and outputting an operation mode signal of the air conditioner 400 to be described later according to the first air quality reference range. The controller 200 and the air conditioner 400 operated in the ventilation mode or the circulation mode according to the operation mode signal of the controller 200. The ventilation mode, which is an operation mode of the air conditioner 400, is a mode in which outdoor air is purged and introduced into the room, and a circulation mode is a mode in which indoor air is sucked and purified to circulate into the room.

In addition, the indoor indoor air quality control system of the present embodiment further includes an alarm notification unit 300 for receiving a signal from the control unit 200 to display the sound and / or display to the outside.

The indoor air quality function calculation module of the controller 200 includes the measured values of the indoor sensors, indoor PM ₁₀ and indoor PM _{2 .} The ratio of ₅ is used as an input variable, and the outdoor pollution function calculation module determines the measured value of the outdoor sensor and the outdoor PM ₁₀ and The ratio value of the indoor PM ₁₀ is used as an input variable.

Each function calculation module as described above may be expressed in the form of a linear linear combination for each input variable. Individual coefficients may be weighted for each input variable, and each coefficient may vary depending on various situation conditions such as a measurement location, a season, and an area of an indoor space. Therefore, the indoor air quality value and the outdoor pollution degree value can be calculated as a single index by the respective function calculation modules.

The control unit 200 controls the air conditioner 400 and the alarm notification unit 300 using the values calculated by the respective function calculation modules, wherein the calculated indoor air quality function value is the first air quality. If it is out of the range, the outdoor pollution degree function value is checked, and if the pollution degree is outside the reference range, the control signal for purifying and circulating indoor air is output to the air conditioner 400. In other words, if the indoor air quality is not good, if the outdoor air also has a high degree of pollution, the indoor air is purged internally to be circulated again.

In addition, the control unit 200 checks the outdoor pollution degree function value when the calculated indoor air quality function value is outside the first air quality range, and controls the air conditioning control signal to introduce outdoor air into the room when the pollution degree is within the reference range. The output to the unit 400. That is, when the quality of indoor air is not good, when the outdoor air is in good condition (contamination degree falls within the reference range), outdoor air is purged and ventilated to the room.

On the other hand, if the calculated indoor air quality function value is outside the second air quality reference range, and the pollution degree function value of the outdoor air is within the second pollution degree standard, the control unit 200 enters the interior through the warning notification unit 300. By outputting a warning message to restrict access, it is possible to restrict access to indoors according to indoor and outdoor air quality conditions.

The first air quality range and the first pollution degree range are reference ranges for driving control of the air conditioner 400, and the second air quality range and the second pollution degree range are respectively limited to indoor access through the alarm notification unit 300. Preferably, the first and second values are set to be different from each other as a reference range for performing the operation.

One embodiment of the present invention described above should not be construed as limiting the technical spirit of the present invention. The protection scope of the present invention is limited only by the matters described in the claims, and those skilled in the art can change and change the technical idea of the present invention in various forms. Therefore, such improvements and modifications will fall within the protection scope of the present invention as long as it will be apparent to those skilled in the art.

200; Control unit 300; Alarm notification unit
400; Air conditioner

Claims (10)

As underground indoor air quality monitoring method,
A measurement data collection step of collecting measurement values of process variables for air quality;
Selecting data to exclude the influence of outdoor air quality among the collected measurement data, and setting main variables by the principal component analysis method using the selected data;
A modeling step of determining an air quality model in which a statistical upper limit is set according to the set main variable;
And an air quality determining step of determining air quality by comparing the measured data with an upper limit value of the air quality model. The method of claim 1,
And a contribution analysis step of selecting a cause variable having a large contribution from the set main variables after determining the air quality. The method of claim 1,
The method may further include a display step of classifying and displaying the grade according to the determined air quality.
The displaying step is the indoor indoor air quality monitoring method, characterized in that by using a face chart monitoring technology to match the air quality to the human face. As underground indoor air control system,
A plurality of sensors including an indoor sensor measuring an indoor variable, an outdoor sensor measuring an outdoor variable,
An indoor air quality function calculation module using the variables of the plurality of sensors as input variables, and an outdoor pollution function calculation module including the measured value by the outdoor sensor as an input variable, wherein the indoor air quality function value is the first air quality reference range. A control unit for outputting a driving mode signal according to
Underground characterized in that it comprises an air conditioning unit operated in a circulation mode that operates in a circulation mode for purifying the outdoor air to enter the indoors by purifying the outdoor air in accordance with the operation mode signal or by inhaling and purifying indoor air. Indoor air control system. The method of claim 4, wherein
The control unit,
If the calculated indoor air quality function value is out of the first air quality range,
If the outdoor pollution degree function value is out of the first pollution degree reference range, the indoor indoor air control system, characterized in that for outputting a signal to operate in the circulation mode in the air conditioning unit. The method of claim 4, wherein
The control unit,
If the calculated indoor air quality function value is out of the first air quality range,
And when the outdoor pollution degree function value is within the first pollution degree reference range, outputting a signal to operate in the ventilation mode to the air conditioning unit. The method of claim 4, wherein
Further includes an alarm notification unit,
The control unit,
If the calculated indoor air quality function value is outside the second air quality reference range,
The indoor indoor air control system, when the pollution degree function value of the outdoor air is within a second pollution degree standard, outputting a signal for notifying indoor access restriction to the warning notification unit. The method of claim 4, wherein
The plurality of sensors
Temperature sensor, humidity sensor, wind speed sensor, indoor PM ₁₀ , indoor PM _{2 .} An indoor indoor air control system, comprising: a dust sensor for measuring ₅ , a dust sensor for measuring outdoor PM ₁₀ , and a sensing sensor for counting the flow population. The method of claim 8,
The indoor sensor is a temperature sensor, humidity sensor, wind speed sensor, indoor PM ₁₀ , indoor PM _{2 .} Dust sensor for measuring ₅ , detection sensor for counting the floating population,
The outdoor sensor is an indoor indoor air control system, characterized in that it comprises a dust sensor for measuring the outdoor PM ₁₀ . 10. The method of claim 9,
The indoor variable is a temperature value, a humidity value, a wind speed value, an indoor PM ₁₀ value, an indoor PM _{2 . 5} values, Indoor PM ₁₀ and Indoor PM _{2 .} Contains a ratio value of ₅ ,
The outdoor variable includes an outdoor PM ₁₀ value and an outdoor PM ₁₀ value. Characterized in that it comprises a percentage value of the indoor PM ₁₀ Underground indoor air control system.

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