KR20230042913A - Device and method for providing apartment house control service using air quality analysis - Google Patents

Abstract

The present invention relates to a method for providing an apartment house control service and a device for providing an apartment house control service performing the same. The method comprises the steps of: establishing management data for an apartment house; collecting air quality data for each unit residential space of the apartment house; generating a smoking detection model and a smoking type classification model and analyzing the collected air quality data based on machine learning results of the air quality data using the generated model; classifying the unit residential spaces of the apartment house based on analysis results of the management data and air quality data; deriving an expected damaged area for the apartment house in which the unit residential space is classified; and generating and providing apartment house control data including the classified unit residential space and the derived expected damaged area. Accordingly, inconvenience to residents can be minimized.

Description

Apparatus and method for providing apartment house control service using air quality analysis {DEVICE AND METHOD FOR PROVIDING APARTMENT HOUSE CONTROL SERVICE USING AIR QUALITY ANALYSIS}

The present invention relates to an apparatus and method for providing an apartment house control service using air quality analysis, and more particularly, to analyze management data and air quality data of the apartment house, and to control the apartment house based on the result of analyzing the air quality data. It's about technology that can do it.

Since multiple households live in an apartment complex in a densely populated space, standards are established to prevent damage among residents. In this regard, among the norms that residents must abide by, there are air quality compliance standards related to smoking or incineration. Resident smoking is a problem in that it is difficult to specify the number of people or areas, and accordingly, it is difficult to recommend compliance with the standards or carry out control measures. there is

In addition, when a situation such as fire or gas exposure occurs in an apartment building, it is difficult to immediately give warnings and guidance to neighboring households that may be damaged, and even if the situation is resolved by taking appropriate measures, all It may cause inconvenience, such as the need to evacuate the residents.

Korean Patent Publication No. 10-2021-0005765

The present invention has been devised to solve the above-mentioned problems, and collects and analyzes air quality data for each unit living space of an apartment complex, and based on the analyzed air quality data, a zone is determined so that the air quality situation of the apartment house can be intuitively checked. It is intended to provide an apparatus and method for providing an apartment house control service (hereinafter, abbreviated as a service providing apparatus and method) using air quality analysis capable of classifying and controlling an apartment house according to the classified zone.

A method for providing a multi-housing control service according to an aspect of the present invention includes the steps of establishing management data for a multi-housing; Collecting air quality data for each unit of living space in an apartment complex; generating a smoking detection model and a smoking type classification model, and analyzing the collected air quality data based on machine learning results of the air quality data using the generated models; Classifying the unit living space of the multi-unit dwelling based on the analysis results of the management data and the air quality data; Deriving a damage expected area for the multi-unit dwelling in which the unit living space is classified; and generating and providing apartment house control data including the classified unit living space and the derived damage expected area, wherein the classifying the unit living space of the apartment unit classifies the unit living space as a normal zone. , Classifying to correspond to any one of an air quality abnormal area, a smoking area, and an air quality measuring device abnormal area; includes.

In one embodiment, the step of classifying the unit living space of the multi-unit dwelling is to classify the unit living space classified as the smoking area into a first smoking area or a second smoking area according to the smoking type diagnosed by the smoking type classification model. It includes; the step of classifying;

In one embodiment, the generating and providing the multi-housing control data includes generating data visualizing the classification result of the unit living space and the derived damage prediction area and providing it to a manager terminal.

In one embodiment, the step of deriving the damage prediction area may include determining a first damage prediction area for the apartment house by using the air quality analysis big data including the collected air quality data and a history of analysis results of the air quality data. Deriving; includes.

In one embodiment, the step of deriving the damage prediction area comprises deriving a second damage prediction area based on the amount of air quality data change and real-time air quality data for the unit residential space derived from the first damage prediction area; include

In one embodiment, the step of deriving the damage expected area includes, when a report related to air quality is received for the apartment house, a third damage expected area by adding an area related to the report to the derived second damage expected area. Deriving; includes.

In one embodiment, the step of building the management data includes building the management data configured to include the number of residents, completion date, and residential space design data of the apartment house.

In one embodiment, the generating and providing of the multi-housing control data may include transmitting a control alarm corresponding to each zone in which the unit living space is classified to the residents of each unit living space and the multi-housing manager; and periodically generating indoor air quality diagnostic data for each unit living space of the multi-unit dwelling, and providing the generated indoor air quality diagnostic data to a terminal of a resident of each unit living space.

In one embodiment, the analyzing of the collected air quality data may include detecting whether or not there is smoking in the apartment house using the generated smoking detection model; Classifying a smoking type in the apartment house using the generated smoking type classification model; and diagnosing an air quality abnormality and an air quality measurement device abnormality in the apartment house.

An apparatus for providing a collective housing control service according to an aspect of the present invention includes a management data building unit configured to build management data for a collective housing; an air quality data collection unit that collects air quality data for each unit of living space in an apartment complex; an air quality data analysis unit generating a smoking detection model and a smoking type classification model, and analyzing the collected air quality data based on machine learning results of the air quality data using the generated models; a zone classification unit classifying the unit living space of the multi-unit dwelling based on the analysis results of the management data and the air quality data; a damage area derivation unit for deriving a damage expected area for the multi-unit dwelling unit in which the unit living space is classified; and a control data providing unit generating and providing control data for multi-unit housing including the classified unit living space and the derived damage expected zone, wherein the zone classifying unit classifies the unit living space into a normal zone, an abnormal air quality zone, It is classified to correspond to either a smoking area or an area with abnormal air quality measuring devices.

An apparatus and method for providing a multi-housing control service according to the present invention has an effect of being able to check all problems related to abnormal air quality occurring in a multi-housing through a multi-housing manager terminal, and to perform control thereof.

In addition, since it is easy to specify the number of people and households for problems such as smoking in apartment buildings, unnecessary disputes between residents can be prevented, and standards for setting operation policies for apartment buildings can be presented, so that the problem can be solved. There is an effect that can be solved quickly.

In addition, since it is possible to immediately identify signs of abnormal air quality by zone in the multi-unit dwelling and take appropriate measures according to the policy set for each classified zone, there is an effect of minimizing the inconvenience of residents.

1 is a reference diagram for explaining a system for providing a collective housing control service according to the present invention.
2 is a block diagram showing the configuration of a service providing device according to the present invention.
3 is a flowchart for explaining a zone classification process for a unit living space of an apartment house according to the present invention.
4 and 5 are reference views showing an example of zone classification and a process of deriving an area expected to be damaged for a unit residential space of an apartment house according to the present invention.
6 and 7 are reference views showing examples of control alarms delivered to the manager and residents of an apartment house according to the present invention.
8 is a flowchart for explaining a process of providing a joint housing control service according to the present invention.

Since the present invention can apply various transformations and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, it should be understood that this is not intended to limit the present invention to specific embodiments, and includes all transformations, equivalents, and substitutes included in the spirit and scope of the present invention. In describing the present invention, if it is determined that a detailed description of related known technologies may obscure the gist of the present invention, the detailed description will be omitted.

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

Terms used in this application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, the terms "include" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

In addition, the term "unit" described in the specification means a unit that processes one or more functions or operations, which may be implemented as hardware or software or a combination of hardware and software.

1 is a reference diagram for explaining a shared housing control service providing system (hereinafter, abbreviated as a service providing system) according to the present invention.

Referring to FIG. 1, the service providing system includes a service providing device 100, an air quality measuring device 10 that can be installed in each unit living space of an apartment house, and a manager terminal 20 for delivering apartment house control data to the manager. ). Here, the service providing device 100 may be connected to the air quality measuring device 10 and the manager terminal 20 through a network, and additionally, the network may be configured to deliver a control alarm or the like to the terminal 30 of a resident of the apartment house. can be connected through

The service providing device 100 corresponds to a computing device managed by a service company that provides an apartment management service using air quality analysis, and may be implemented as a server. Here, the service providing device 100 is connected to the air quality measuring device 10 and the manager terminal 20 through a network, and the connected air quality measuring devices 10-1, 10-2, 10-3... By analyzing the air quality data of the multi-unit dwelling collected from and transmitting the air quality data analysis result and the control data according to the air quality data analysis result to the manager terminal 20, information on each unit living space of the multi-unit dwelling to be managed by the manager It is possible to provide a collective housing control service capable of performing control in an integrated manner. Here, each of the air quality measurement devices 10-1, 10-2, 10-3... may be installed, for example, in every unit living space of each apartment house.

In addition, the manager terminal 20 is a computing device that is carried, managed, or manipulated by a collective housing manager. The manager terminal 20 according to the present invention is a computing device including a display device and used as a means for providing an air quality control service to a manager. may apply. However, these examples are not intended to limit the scope of the present invention, and any computing device capable of providing visual information to a manager through a display device should be interpreted as the manager terminal 20 according to the present invention.

2 is a block diagram showing the configuration of a service providing apparatus 100 according to the present invention.

Referring to FIG. 2 , the service providing device 100 according to the present invention includes a management data construction unit 110, an air quality data collection unit 120, an air quality data analysis unit 130, a zone classification unit 140, and a damaged area. It may be configured to include a derivation unit 150 and a control data providing unit 160 .

The management data building unit 110 builds management data for the common house.

Specifically, the management data construction unit 110 may build the management data including the number of residents, completion date, and residential space design data of the apartment house. Here, the information included in the management data to be built can be used in the process of classifying the unit residential space and the process of deriving the area expected to be damaged in the present invention.

The air quality data collection unit 120 collects air quality data for each unit living space of the apartment house.

Specifically, the air quality data collection unit 120 may collect the air quality data from the air quality measurement device 10 installed in each unit living space of the multi-unit dwelling to measure air quality. Here, the data collected from the air quality measurement device 10 may include at least one of fine dust concentration, carbon dioxide concentration, and volatile organic compound concentration. In addition, the air quality measuring device 10 may include an air quality sensor (AQS). The air quality sensor is a sensor that measures the levels of harmful substances such as ECO2, TVOC, PM10, and PM2.5 contained in the air. can be used for machine learning, or used to analyze air quality data.

The air quality data analysis unit 130 generates a smoking detection model and a smoking type classification model, and analyzes the collected air quality data based on machine learning results of the air quality data using the generated models.

Specifically, the air quality data analyzer 130 detects whether or not smoking is present in the apartment building using the generated smoking detection model, classifies the smoking type in the apartment building using the generated smoking type classification model, and You can diagnose air quality abnormalities and air quality measurement device abnormalities in your home.

As a specific example of the abnormal air quality in the apartment house, the value of ECO2 measured through the air quality measuring device 10 installed in a specific unit living space of the apartment house is greater than or equal to the first reference value and the value of TVOC is greater than or equal to the second reference value case, or when the value of PM10 is greater than or equal to the third reference value and the value of PM2.5 is greater than or equal to the fourth reference value, it may be diagnosed that the air quality of the apartment building is in an abnormal state.

Specifically, the first reference value may be 1000 μg/m3, the second reference value may be 1300 μg/m3, the third reference value may be 80 μg/m3, and the fourth reference value may be 35 μg/m3. 80 μg/m3, which is an example of the third standard value of PM10, and 35 μg/m3, which is an example of the fourth standard value of PM2.5, are values selected according to air quality standards specified by the Korea Meteorological Administration. On the other hand, in the case of ECO2 and TVOC, the ratings are not separately determined according to the Korea Meteorological Administration or international standards, so when PM10 and PM2.5 are at the level of abnormal air quality in the air quality observed values collected through experiments, the observed values of TVOC and ECO2 An example of the first reference value of ECO2 and the second reference value of TVOC was determined based on the value calculated by aggregating the average of . These first to fourth reference values are not limited to the values given as examples, and the first to fourth reference values may be changed and set as needed.

In addition, the air quality data analyzer 130 may include a classification model generator 131 that generates a model for classifying air quality abnormal types based on machine learning results for the obtained air quality data, and generates a classification model. The smoking detection model or the smoking type classification model may be generated through the unit 131 .

Specifically, the classification model generation unit 131 may detect indoor smoking by learning smoking data, which is air quality data when smoking occurs indoors, and non-smoking data, which is air quality data when smoking does not occur indoors. Smoking type that can classify smoking types by creating a smoking detection model that can create a smoking detection model that can classify smoking types by learning cigarette type data, which is air quality data when tobacco cigarette smoking occurs indoors and tobacco data, which is air quality data when tobacco cigarette smoking occurs indoors. A classification model can be created.

In one embodiment, the classification model generation unit 131 may include one or more selected from learning models including a decision tree, a random forest, extreme gradient boosting (XGBOOST), and a support vector machine (SVM). A smoking detection model or a smoking type classification model may be created using the learning model. In addition, the classification model generation unit 131 selects an optimal parameter for the hyper parameter applied to the learning model by using one or more of grid search and cross validation. can

In addition, the air quality data analysis unit 130 may determine that the residents of the multi-unit dwelling are smoking when smoking of a second set number of times is detected within a second set time by the smoking detection model. For example, the air quality data analyzer 130 may detect smoking by using a smoking detection model. When smoking is detected three or more times within two minutes, it may be determined that the residents of the apartment house smoke. Here, the second set time or the second set number of times may be set to an appropriate value in order to determine whether the customer smokes or not, and may be changed and set as needed.

In addition, when it is determined that the residents of the apartment house smoke, the air quality data analyzer 130 may not detect smoking for a third set time from the first smoking detection point after detecting smoking for a second set number of times. For example, if smoking is detected 3 or more times within 2 minutes and it is determined that the customer is smoking, smoking may not be detected for 6 minutes from the first smoking detection point. The reason for not detecting smoking for a certain period of time is to prevent repeated detection of smoking for a single smoking activity. In the above example, the third set time is set to 6 minutes, which is set considering the average smoking time of 4 minutes and the normal air quality recovery time of 2 minutes. However, the third set time is not limited thereto, and may be set to an appropriate value or changed if necessary.

In addition, the air quality data analyzer 130 diagnoses the final smoking type based on the smoking type classified through the smoking type classification model, and determines the smoking type representing the higher number among the number of classifications of tobacco cigarettes and the number of classifications of electronic cigarettes. It can be diagnosed by the final smoking type.

The zone classification unit 140 classifies the unit living space of the multi-unit dwelling based on the analysis results of the management data and air quality data. This classification is based on air quality diagnosis results such as classification of smoking type and detection of smoking by the air quality data analyzer 130 .

Specifically, the zone classification unit 140 may classify the unit living space into one of a normal zone, an air quality zone, a smoking zone, and an air quality measuring instrument zone. Here, the zone classification unit 140 may subdivide the unit living space classified into the smoking zone into a first smoking zone or a second smoking zone according to the smoking type diagnosed by the smoking type classification model.

In this regard, FIG. 3 is a flowchart for explaining a zone classification process for unit living space of an apartment house according to the present invention.

Referring to FIG. 3 , the zone classification process according to the air quality diagnosis for each unit living space of the apartment house is as follows.

First, it is possible to determine whether there is an abnormality in the collected air quality data, and in this process, it is determined whether an abnormality has occurred in the air quality measuring device 10 .

Next, it is possible to determine whether the air quality is abnormal based on the collected air quality data. Here, the air quality abnormality may mean an air quality abnormality related to an increase in fine dust levels, fire occurrence, and gas leakage, excluding smoking-related air quality abnormalities. It can be classified as an abnormal zone.

Next, smoking is detected based on the collected air quality data, and if smoking is detected, the type of smoking may be classified to classify the unit dwelling space into a first smoking area or a second smoking area.

Next, unit living spaces that do not correspond to all of the above-mentioned types of zones can be tentatively classified as normal zones, and when a zone expected to be damaged is derived, zones already classified as other types can be reclassified as zones expected to be damaged. can

In addition, the result of the zone classification process described above with reference to FIG. 3 can be confirmed through the example of FIG. 4 . 4 is a reference diagram showing an example of zone classification for unit living space of an apartment house according to the present invention.

The damage area derivation unit 150 derives an expected damage area for the multi-unit dwelling in which the unit living space is classified.

Specifically, the damage area derivation unit 150 may derive a first damage expected area for the multi-family house by using air quality analysis big data including the collected air quality data and a history of analysis results of the air quality data. there is.

In addition, when the first damage prediction area is derived, the damage area derivation unit 150 predicts second damage based on the amount of air quality data change and real-time air quality data for the unit residential space derived from the first damage prediction area. area can be derived.

In addition, the damage area derivation unit 150 adds an area related to the report to the derived second damage expected area when an air quality related report is received for the apartment house in a state where the second damage expected area is derived. Thus, a third damage expected area can be derived. Here, the report related to air quality may include a complaint about smoking from another resident and a report about a fire occurring in the vicinity of an apartment building.

The damage area derivation unit 150 may derive the second damage expected area or the third damage expected area as the final damage expected area based on the above-described damage expected area derivation process, and the damage expected area derivation result continuously air quality It can be used to update analytics big data.

The control data providing unit 160 generates and provides apartment house control data including the classified unit living space and the derived damage expected area.

Specifically, the control data providing unit 160 may generate data visualizing the result of classification of the unit living space and the derived damage prediction area, and provide the generated data to the manager terminal 20 . Here, examples of the data visualizing the derived damage prediction area are shown in FIGS. 4 and 5, and the apartment house manager can receive the visualized data through the manager terminal 20 and intuitively control the apartment house. be able to

In addition, the control data providing unit 160 may deliver a control alarm corresponding to each zone in which the unit living space is classified to the residents of each unit living space and the apartment house manager. In this regard, FIGS. 6 and 7 are reference diagrams illustrating examples of control alarms transmitted to the manager and residents of the apartment house according to the present invention. Referring to FIGS. 6 and 7, a control alarm transmitted to the manager terminals 20-1 and 20-2 of the apartment house and smoking in the apartment house are detected, and a dwelling unit living in a unit living space classified as a smoking area is detected. An example of a control alarm transmitted to the terminal 30 is shown. Here, in the case of the control alarm transmitted to the resident terminal 30, a preliminary control alarm delivered by the service providing device 100 may be included before a direct control alarm is delivered from the manager, through which the control alarm is transmitted. It can induce people to comply with the norms.

In addition, the control data providing unit 160 periodically generates indoor air quality diagnostic data for each unit living space of the apartment house, and transmits the generated indoor air quality diagnostic data to the resident terminal 30 of each unit living space. can provide Here, the provided indoor air quality diagnostic data may be provided for the purpose of improving the air quality interest of apartment dwelling residents and raising awareness of air quality deterioration. In addition, the provided indoor air quality diagnosis data includes an average indoor fine dust and ultrafine dust level for each specifically set period, TVOC concentration, number of smoking detections, number of abnormal air quality diagnoses, and an index of expected damage due to neighboring residents smoking. Evaluation data may be included.

In addition, the provided indoor air quality diagnosis data may include air quality trend data including concentrations of fine dust and ultrafine dust per day and number of detections of smoking per day.

In addition, the provided indoor air quality diagnosis data may include air quality comparison data including a comparison status of air quality by month, a comparison status of air quality by residential space on the same floor, and a comparison status of air quality by unit residential space in the same apartment house. .

In addition, the provided indoor air quality diagnosis data may include comprehensive air quality diagnosis result data including comprehensive analysis results of indicators measured for air quality.

8 is a flowchart for explaining a process of providing a joint housing control service according to the present invention.

Referring to FIG. 8 , management data for an apartment house subject to air quality control is established (S801). Here, the management data may include the number of residents of the multi-unit dwelling, completion date, and residential space design data.

Air quality data for each unit living space of the apartment house is collected (S802).

A smoking detection model and a smoking type classification model are generated, and the collected air quality data is analyzed based on a machine learning result of the air quality data using the generated model (S803).

Based on the analysis results of the management data and air quality data, unit living spaces of the multi-unit dwelling are classified (S804). Here, the unit dwelling space may be classified to correspond to any one of a normal area, an air quality area with abnormal air quality, a smoking area, and an air quality measurement device abnormal area.

A damage expected area for the multi-family house in which the unit living space is classified is derived (S805).

Collective housing control data including the classified unit living space and the derived damage expected area is generated and provided (S806). Here, the multi-housing control data is visualized and provided so that the multi-housing manager can intuitively check and control the air quality of the multi-housing.

The above-described method for providing the collective housing control service has been described with reference to the flow chart presented in the drawings. For simplicity, the method is shown and described as a series of blocks, but the invention is not limited to the order of the blocks, and some blocks may occur in a different order or concurrently with other blocks than shown and described herein. and various other branches, flow paths, and sequences of blocks that achieve the same or similar results may be implemented. Also, not all blocks shown may be required for implementation of the methods described herein.

Although the above has been described with reference to preferred embodiments of the present invention, those skilled in the art will variously modify and change the present invention within the scope not departing from the spirit and scope of the present invention described in the claims below. You will understand that it can be done.

10: air quality measurement device
20: manager terminal
30: Resident terminal
100: shared housing control service providing device
110: management data building unit
120: air quality data collection unit
130: air quality data analysis unit
131: classification model generation unit
140: zone classification unit
150: damage area extraction unit
160: control data provision unit

Claims (10)

building management data for shared housing;
Collecting air quality data for each unit of living space in an apartment complex;
generating a smoking detection model and a smoking type classification model, and analyzing the collected air quality data based on machine learning results of the air quality data using the generated models;
Classifying the unit living space of the multi-unit dwelling based on the analysis results of the management data and the air quality data;
Deriving a damage expected area for the multi-unit dwelling in which the unit living space is classified; and
Generating and providing collective housing control data including the classified unit residential space and the derived damage expected area; Including,
The step of classifying the unit living space of the apartment house is
Classifying the unit living space to correspond to any one of a normal area, an air quality abnormal area, a smoking area, and an air quality measurement device abnormal area; including
How to provide joint housing control services.
According to claim 1,
The step of classifying the unit living space of the apartment house is
Classifying the unit living space classified as the smoking area into a first smoking area or a second smoking area according to the smoking type diagnosed by the smoking type classification model; including
How to provide joint housing control services.
According to claim 1,
The step of generating and providing the multi-household control data
Generating data visualizing the classification result of the unit living space and the derived damage expected area and providing it to an administrator terminal; including
How to provide joint housing control services.
According to claim 1,
The step of deriving the damage expected area is
Deriving a first damage expected area for the multi-unit dwelling by using air quality analysis big data including the collected air quality data and a history of analysis results of the air quality data;
How to provide joint housing control services.
According to claim 4,
The step of deriving the damage expected area is
Deriving a second damage prediction area based on the amount of air quality data change and real-time air quality data for the unit residential space derived from the first damage prediction area;
How to provide joint housing control services.
According to claim 5,
The step of deriving the damage expected area is
Deriving a third damage expected area by adding an area related to the report to the derived second damage expected area when a report related to air quality is received for the apartment house; including
How to provide joint housing control services.
According to claim 1,
The step of building the management data is
Building the management data including the number of residents, completion date and residential space design data of the apartment house; comprising
How to provide joint housing control services.
According to claim 1,
The step of generating and providing the multi-household control data
transmitting a control alarm corresponding to each zone in which the unit living space is classified to the residents of each unit living space and the apartment house manager; and
Periodically generating indoor air quality diagnostic data for each unit living space of the multi-unit dwelling, and providing the generated indoor air quality diagnostic data to a resident terminal of each unit living space.
How to provide joint housing control services.
According to claim 1,
Analyzing the collected air quality data
detecting whether or not there is smoking in the apartment house using the generated smoking detection model;
Classifying a smoking type in the apartment house using the generated smoking type classification model; and
Diagnosing an air quality abnormality and an air quality measurement device abnormality in the apartment house; comprising
How to provide joint housing control services.
a management data building unit that builds management data for the collective housing;
an air quality data collection unit that collects air quality data for each unit of living space in an apartment building;
an air quality data analysis unit generating a smoking detection model and a smoking type classification model, and analyzing the collected air quality data based on machine learning results of the air quality data using the generated models;
a zone classification unit classifying the unit living space of the multi-unit dwelling based on the analysis results of the management data and the air quality data;
a damage area derivation unit for deriving a damage expected area for the multi-unit dwelling unit where the unit living space is classified; and
A control data providing unit for generating and providing multi-unit housing control data including the classified unit residential space and the derived damage expected area;
The district classification unit
Classifying the unit living space to correspond to any one of a normal area, an air quality abnormal area, a smoking area, and an air quality measurement device abnormal area
A device for providing collective housing control services.

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