KR102468888B1 - A system for monitoring the condition of a building based on IoT - Google Patents

Abstract

The present invention relates to a system for monitoring building conditions based on IoT. The system for monitoring building conditions based on IoT according to a preferred embodiment of the present invention may include: a database server (20) for collecting, storing, and managing sensing values from a sensor unit (1) installed in a building to be monitored (B); and a monitoring device (30) for analyzing and alerting on the sensing values using the sensing values stored in the database server (20). The present invention allows real-time monitoring and analysis of parameters related to cracking and collapse of buildings.

Description

IoT based building condition monitoring system {A system for monitoring the condition of a building based on IoT}

The present invention relates to a system for monitoring building conditions based on IoT.

A person who conducts a safety inspection, etc. must designate a safety level corresponding to FIG. 1 according to the result of the safety inspection, etc. (Appendix 8 of the Enforcement Decree of the Special Act on Safety and Maintenance of Facilities, Article 16 (1) of the Special Act on Safety and Maintenance of Facilities)

The timing of the safety inspection of the building according to Table 3 of the Enforcement Decree of the Special Act on Safety and Maintenance of Facilities is shown in FIG. 2.

And, 1) A person who causes serious damage to facilities by not conducting safety inspections, detailed safety inspections, or emergency safety inspections or does not conduct them in good faith, thereby causing public danger 2) Failure to conduct emergency safety inspections without justifiable reasons or necessary A person who causes serious damage to facilities and creates public danger by failing to comply with an order to take measures A person who falls under any of the following shall be sentenced to between one year and ten years in prison. (Special Act on Safety and Maintenance of Facilities Article 63 Paragraph 1 Subparagraphs 1 through 3)

As above, safety inspection of buildings is regulated, but its effectiveness is inevitably low. During a precise safety inspection, it is said that the condition of the facility is judged, whether the facility satisfies the requirements for use at the time of inspection, and the external inspection and measurement/testing equipment are conducted at a level that can check the condition of the main members of the facility. The precision of the test is bound to be low. In addition, it is not possible to prevent accidental accidents due to secular changes in buildings to the extent of conducting precise safety inspections about once every two years. In addition, when there is a risk of a disaster or a disaster, although an emergency safety check is performed, there is a limit to predicting a disaster or a risk of a disaster in advance. In addition, when a disaster or a building crack or collapse occurs due to a disaster, data to evaluate the impact of the disaster or disaster cannot be secured in the existing system. In addition, even if construction is carried out around the building and the building around the construction area is exposed to a risk situation, there is no technical and legal solution in the existing system to determine the crack or collapse state of the building during the construction period.

1. Article 16 (1) of the Special Act on the Safety and Maintenance of Facilities and Attached Table 8 of the Enforcement Decree of the Special Act on the Safety and Maintenance of Facilities 2. Attached Table 3 of the Enforcement Decree of the Special Act on Safety and Maintenance of Facilities 3. Special Act on Safety and Maintenance of Facilities Article 63 (1) 1 through 3

An object of the present invention is to provide an IoT-based building condition monitoring system capable of monitoring and analyzing parameters related to cracks and collapses of buildings in real time.

In addition, an object of the present invention is to provide an IoT-based building condition monitoring system capable of providing an alarm in a dangerous situation based on a result of real-time monitoring of parameters related to cracks and collapse of a building.

Other purposes may be interpreted in various ways by the following description.

An IoT-based building condition monitoring system according to a preferred embodiment of the present invention includes a database server 20 for collecting, storing, and managing sensed values from a sensor unit 1 installed in a building to be monitored (B); and a monitoring device 30 that analyzes and alerts on the sensed values using the sensed values stored in the database server 20 .

Here, the database server 20 may store building slope distribution information for each region and change trend information of building slope for each building.

In addition, the database server 20 may store risk information for each building determined according to risk determination criteria.

Also, the database server 20 may store data related to alerts.

In addition, when the monitoring device 30 determines that an anomaly occurs in any one of the plurality of monitored buildings B, the monitoring device 30 selects a wide-area event diagnosis mode, a local event diagnosis mode, and an individual event diagnosis mode. Detailed diagnosis can be performed in any one of the diagnosis modes.

In addition, the monitoring device 30 may determine that a wide-area event has occurred when vibration frequencies of a plurality of monitored buildings distributed in a wide-area area are within a standard deviation range.

In addition, the monitoring device 30 may determine that a local event has occurred if there are a plurality of buildings subject to monitoring in which signs of anomalies have been detected and the plurality of objects to be monitored buildings in which signs of abnormalities have occurred are located within a preset reference radius.

The present invention allows real-time monitoring and analysis of parameters related to cracking and collapse of buildings.

The present invention can provide alarms in dangerous situations from various viewpoints based on the results of real-time monitoring of parameters related to cracks and collapse of buildings in real time from various viewpoints.

1 is a table showing state information of facilities by safety level according to the Special Act on Safety and Maintenance of Facilities.
2 is a table showing safety inspection cycles according to safety level/type of safety inspection/safety level according to the Enforcement Decree of the Special Act on Safety and Maintenance of Facilities.
Figure 3 is an aerial photograph of the buildings arranged around the construction section.
4 is a first conceptual diagram of an IoT-based building condition monitoring system.
5 is a second conceptual diagram of an IoT-based building condition monitoring system.
6 is a diagram for explaining a concept in which a wide area area is divided into a plurality of unit areas.
7 is a diagram for explaining the concept of a plurality of buildings included in a unit area.
8 is a configuration diagram of the monitoring system of the present invention.
9 is a functional block diagram of the database server of FIG. 8 .
10 is a functional block diagram of the monitoring device of FIG. 8 .
11 is a flow chart of the diagnosis process in the normal mode of the individual diagnosis unit.
12 is a flowchart of a diagnosis process of an individual diagnosis unit in an individual event diagnosis mode.
13 is a flowchart of a diagnosis process of a global area diagnosis unit in a wide area event diagnosis mode.
14 is a flowchart of the diagnosis process of the local diagnosis unit in the local event diagnosis mode.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. Prior to this, the terms or words used in this specification and claims should not be construed as being limited to the usual or dictionary meaning, and the inventor appropriately uses the concept of the term in order to explain his/her invention in the best way. It should be interpreted as meaning and concept consistent with the technical idea of the present invention based on the principle that it can be defined. Therefore, the embodiments described in this specification and the configurations shown in the drawings are only the most preferred embodiments of the present invention and do not represent all of the technical ideas of the present invention, so various alternatives can be made at the time of this application. It should be understood that there may be equivalents and variations.

Hereinafter, with reference to FIGS. 3 to 5, an IoT-based building condition monitoring system (hereinafter, referred to as "monitoring device") to which an IoT-based building condition monitoring device according to a preferred embodiment of the present invention is applied (hereinafter, referred to as the "monitoring system"). In the following, in order to clarify the gist of the present invention, descriptions of conventionally known matters are omitted or simplified.

First, referring to FIG. 3 , a situation in which a building is adjacent to a surrounding construction section may occur. The construction section may have various types of construction, such as building construction on the ground and underground subway construction. As such, buildings adjacent to construction sites must be monitored in real time in terms of cracks and collapse. In addition, monitoring data related to buildings adjacent to construction sites can be useful evidence for compensation for damages in case of building cracks and damage due to construction, and can prevent unexpected human damage due to construction in advance. The present invention provides real-time building reliability in both a special situation where a building is adjacent to a construction site, a disaster situation that can cause building cracks and collapses over a wide area such as an earthquake, and a general situation where there is no special external force or vibration applied to the building. We want to provide a monitoring system that can monitor conditions in real time using various parameters and provide useful information to supervisory authorities, citizens and building owners.

Referring to FIG. 4 , in the monitoring system of the present invention, a data collection device (gateway) may collect sensor data from a sensor attached to a building and store it in a database. In addition, the monitoring device (measurement collection server) may provide a manager with information reflecting real-time building conditions using sensor data stored in the database. The database may include a measurement DB and a management standard DB. The measurement DB may store installation locations, sensor history information, types of sensed values, etc. as sensor attribute information, and may store sensed values. Sensing values may be matched for each building/sensor and stored in time series.

The management standard DB can store alarm analysis data, statistical analysis data, and risk analysis data. The alarm analysis data may be alarm-related data (eg, alarm history for each building). Statistical analysis data may be data obtained by statistically processing sensing values stored in a measurement DB. For example, the statistical analysis data may be information on the distribution of building slopes by region and trend information on changes in building slopes by building. The risk analysis data may be risk information for each building determined according to risk assessment criteria. The risk analysis can be determined through an algorithm that increases the risk level when the cracks of the building are large or the slope of the building is large by reflecting the state of cracks and the slope of the building. At this time, the structure of the building and the state of the ground may be applied as factors for risk analysis.

Referring to FIG. 5 , various sensors may be installed at various locations in a building to monitor a building state. At this time, sensors such as a structure inclinometer, a crack measuring system, a displacement meter, and an accelerometer may be installed. Each of the plurality of sensors may generate a sensing value for a sensing factor in charge and transmit the sensing value to the data collection device. Sensors and data collection devices can communicate using wireless communication standards such as LoRa, Wifi, and Zigbee. In other words, sensors and data collection devices can form an IoT (Internet of Things) network with a wireless communication standard. The data collection device may be installed per building or per geographical area. The data collection device may integrate data collected from a plurality of sensors and provide the data to the monitoring device. In this case, the data collection device may have an edge computing function for efficiency of system operation.

The monitoring device may collect state information of the building at regular intervals determined on a daily basis. In addition, the monitoring device can promptly notify the status from time to time and send SNS text messages and notifications to the building manager when a building collapse or crack is displaced beyond a management value.

Sensors installed at each location of the building may be attached to the outer wall of the building. In this case, the sensor may be configured as a non-powered system, and a battery or a solar system may be used as a power source. In the case of an acceleration sensor, the data collection device may send an event signal only when the acceleration sensor itself recognizes the management value of the acceleration sensor. In addition, the monitoring system may be able to measure the vibration and collapse of a building by converging precision GPS and sensor data.

The sensor may transmit sensing values corresponding to the tilt deflection angle, inclination, and acceleration according to the change of the target structure (building) to the data collection device through wireless communication.

The data collection device may transmit information collected by various sensors to a database server through wireless communication. Wireless communication standards (4G, 5G, ...) may be applied to communication between the data collection device and the data collection server.

The measurement collection server (monitoring device) can use the information stored in the database to perform operations such as measurement data analysis, warning and notification.

Hereinafter, specific functions and actions of the monitoring system having the above concept will be described with reference to FIGS. 6 to 14 .

First, the monitoring device may monitor conditions of a plurality of buildings distributed over a wide area. 6 illustrates a wide area (A). Here, the wide area may be a city, county, district, or dong. The wide area A may be divided into a plurality of unit areas z1, z2, ..., zn.

Referring to FIG. 7 , at least one building to be monitored (B1, B2, ..., Bn) may exist in each unit area (zn).

Referring to FIG. 8 , the monitoring system may include a building to be monitored (B), a gateway 10, a database server 20, and a monitoring device 30.

As before, the buildings to be monitored (B) may be distributed throughout the wide area (A). In addition, the wide area area (A) is divided into a plurality of unit areas (z1, z2, ..., zn, hereinafter collectively referred to as 'z'), and at least one monitoring target building (B) is provided in each of the plurality of unit areas. may exist.

The sensor unit 1 may be installed in each of the plurality of buildings B to be monitored.

The sensor unit 1 may sense state information of a building related to cracks or collapse. The sensor unit 1 may include at least one of a structure inclinometer, a crack measuring system, a displacement meter, and an accelerometer. A plurality of sensors belonging to the sensor unit 1 may be installed at different locations or partially at the same location.

The gateway 10 may collect sensing values from sensor units 1 installed in a plurality of buildings. The gateway 10 and the sensor unit 1 may communicate through the first communication network 2 . Here, wireless communication standards such as LoRa, Wifi, and Zigbee may be applied to the first communication network 2 .

The gateway 10 may collect sensing values corresponding to building state information from the sensor unit 1 installed in the monitoring target building B belonging to at least one unit area z. Here, the sensed value corresponding to the building state information may be a sensed value of at least one of a structure inclinometer, a crack measuring system, a displacement meter, and an accelerometer.

The database server 20 may communicate with the gateway 10 through the second communication network 3 . At this time, the second communication network 3 may support wireless communication standards (4G, 5G, ...).

The database server 20 and the monitoring device 30 may communicate through an internet network (not shown).

Referring to FIG. 9 , the database server 20 may include a measurement DB 21 and a management standard DB 22 . The measurement DB 21 may collect, store, and manage sensing values from the sensor unit 1 installed in the building B to be monitored through the gateway 10 . The measurement DB 21 may collect sensed values from the sensor unit 1 through the gateway 10 at a predetermined normal collection period. The general collection period may be the same for all sensed values, and may be different according to the properties of the sensed values. The measurement DB may store installation locations, sensor history information, types of sensed values, etc. as sensor attribute information, and may store sensed values. Here, the installation location may be geographical coordinate information. The sensed value may include an analog measurement value. Here, the sensed value type may be a slope, a crack, a displacement, or an acceleration. The sensed value type serves to classify sensed values of a plurality of sensors belonging to the sensor unit 1 . Sensing values may be matched for each building/sensor and stored in time series.

The management standard DB 22 may store alarm analysis data, statistical analysis data, and risk analysis data. The alarm analysis data may be alarm-related data (eg, alarm history for each building). Statistical analysis data may be data obtained by statistically processing sensing values stored in a measurement DB. For example, the statistical analysis data may be information on the distribution of building slopes by region and trend information on changes in building slopes by building. The risk analysis data may be risk information for each building determined according to risk assessment criteria. The risk analysis can be determined through an algorithm that increases the risk level when the cracks of the building are large or the slope of the building is large by reflecting the state of cracks and the slope of the building. At this time, the structure of the building and the state of the ground may be applied as factors for risk analysis. Statistical analysis data may be generated by the statistical diagnosis unit 32 to be described later statistically processing the sensed values stored in the measurement DB 21 . The risk analysis data may be generated by the risk analysis unit 33 to be described later statistically processing the sensed values stored in the measurement DB 21 . Data related to the alarm may be a building-specific alarm, notification, and alarm history of the alarm unit 37 described later.

Referring to FIG. 10 , the monitoring device 30 may perform various analyzes, guidance, and warnings using the sensing values stored in the measurement DB 21 .

The monitoring device 30 includes an individual diagnosis unit 31, a statistical diagnosis unit 32, a risk analysis unit 33, a wide area diagnosis unit 34, a local diagnosis unit 35, an interface unit 36, an alarm unit ( 37) and a communication unit 38.

The individual diagnosis unit 31 may perform individual diagnosis for each building B to be monitored. The individual diagnosis unit 31 may determine whether or not the sensing value of each sensor unit 1 of the building B to be monitored indicates an abnormal symptom. In addition, when it is determined by the individual diagnosis unit 31 that an abnormal symptom occurs in any one of the plurality of monitored buildings B, the monitoring device 30 operates in a wide-area event diagnosis mode, a local event diagnosis mode, and an individual event diagnosis mode. Detailed diagnosis can be performed in any one of the diagnosis modes.

Specifically, referring to FIG. 11 , the individual diagnosis unit 31 generates sensing values for each of the monitoring target building B and each of the plurality of sensing values of the sensor unit 1 of each monitoring target building B. It can be evaluated (S10).

Then, the individual diagnosis unit 31 may determine whether at least one sensing value to be evaluated exceeds a preset abnormal symptom threshold for the sensing value to be evaluated (S20).

In S20, if it is determined that at least one sensing value to be evaluated exceeds a preset abnormal symptom threshold for the sensing value to be evaluated, it may be determined that an abnormal symptom will occur in the building to be monitored from which the sensed value is collected.

In contrast, if it is determined in S20 that all of the sensing values to be evaluated are less than a preset abnormal symptom threshold for the sensing values to be evaluated, it can be determined that no abnormal symptom has occurred in the building to be monitored from which the sensing values are collected.

If it is determined that an abnormal symptom has occurred in S20, the individual diagnosis unit 31 can determine whether a wide-area event has occurred (S30). In operation S30 , the individual diagnosis unit 31 may determine that a wide area event has occurred if the vibration frequencies of the plurality of monitored buildings distributed in the wide area are within the standard deviation range. At this time, it is possible to determine whether a wide-area event has occurred using the vibration frequency of only the representative monitoring target building of each of a plurality of unit areas distributed in the wide-area area. Here, the representative monitoring target building may be a building with the highest risk in the corresponding unit area. As will be described later, the risk level of each building to be monitored may be analyzed by the risk analysis unit 33 . In addition, the vibration frequency may be calculated using a value sensed by the accelerometer for a predetermined time. And, the wide-area event may be a disaster event such as an earthquake that causes oscillations over a wide-area area. Unlike this, the individual diagnosis unit 310 may determine that a wide area event has occurred if the settlement of a plurality of monitored buildings distributed in a wide area is within a standard deviation range. At this time, it is possible to determine whether a wide area event has occurred using the amount of settlement of only the representative monitoring target building in each of a plurality of unit areas distributed in the wide area area. Here, the representative monitoring target building may be a building with the highest risk in the corresponding unit area. As will be described later, the risk level of each building to be monitored may be analyzed by the risk analysis unit 33 . And, the settlement may be calculated using the sensing value of the accelerometer. Also, the wide-area event may be a disaster event such as an earthquake that causes land subsidence over a wide-area area.

If it is determined that a wide-area event has occurred in S30, the monitoring device 30 may enter a wide-area event diagnosis mode (S40). Details of the wide area event diagnosis mode will be described later.

If it is determined that no wide-area event has occurred in S30, the individual diagnosis unit 31 can determine whether a local event has occurred (S50).

In S50, the individual diagnosis unit 31 determines that a local event occurs when a plurality of target buildings for monitoring in which an abnormal symptom is detected are located within a predetermined standard radius (eg, 200 m), and a plurality of target buildings for monitoring in which an abnormal symptom is generated are located within a preset reference radius (eg, 200 m). can be judged to be Here, the local event may be an event that affects a local area compared to a wide area, such as ground subsidence due to a sinkhole, ground collapse or vibration due to a construction site.

If it is determined that a local event has occurred in S50, the monitoring device 30 may enter a local event diagnosis mode (S60).

In contrast, when it is determined that no local event has occurred in S50 , the individual diagnosis unit 31 may enter an individual event diagnosis mode.

Referring to FIG. 12 , in the individual event diagnosis mode, the individual diagnosis unit 31 may apply a prediction algorithm to a building in which an individual event occurs (S81). At this time, the individual diagnosis unit 31 may generate a change trend curve of the sensed value for each sensed value attribute using past sensed values. By extending the change trend curve to a future time point, a sensed value at a future time point may be predicted for each sensed value attribute.

Then, the individual diagnosis unit 31 may evaluate the predicted sensing value through the change trend curve generated in S81 for a preset period (S82). In S82, the individual diagnosis unit 31 may determine whether an error average between a predicted value of the sensed value and an actual measured value at the same point in time during a preset simple evaluation period is within a preset standard error range. And, if it is determined that the average error between the predicted value and the measured value of the sensed value at the same time during the preset simple evaluation period is within the preset standard error range, the individual diagnosis unit 31 uses the sensed value before the end of the simple evaluation period. A point in time when the sensed value exceeds the risk reference value may be calculated. In addition, an alarm for a time point exceeding the calculated risk reference value is sent through the alarm unit 37 to a monitoring target building in which the error average between the predicted value and the measured value at the same time point during the preset simple evaluation period is within the preset standard error range. can be done by the manager of In this way, based on reliable predicted values, it is possible to provide reliable alarms for buildings in which abnormal signs have occurred. The risk reference value may be set for each sensor attribute. The risk reference value is a value that recognizes the need for precise diagnosis or maintenance of a building and may be set by a designer. The risk reference value may be higher than the abnormal symptom threshold described in S20 of FIG. 11 .

Referring to FIG. 12 , in the wide-area event diagnosis mode, the wide-area diagnosis unit 34 may collect sensed values in a wide-area area at a predetermined emergency collection period (S41). Here, the emergency collection period may be shorter than the preset normal collection period. This is to respond quickly through fast sensing value collection since disasters such as wide-area events can occur in an instant and cause quite serious damage.

And, if the average of the magnitudes of vibrations found in the wide area identified through the sensing values collected through S41 exceeds the preset vibration reference value or the average of the magnitudes of the settlements found in the wide area exceeds the preset standard settlement amount, the wide area diagnosis unit ( 34) may provide an alarm through the alarm unit 37 to managers of buildings to be monitored throughout the wide area (S42).

And, the wide-area diagnosis unit 34 may perform a wide-area event impact evaluation (S43). In this case, immediately after the end of the wide-area event, the wide-area diagnosis unit 34 may calculate virtual sensed values for all sensed value attributes for all buildings to be monitored, assuming that the wide-area event occurs identically. The virtual sensing value may be calculated by Equation 1 below.

[Equation 1]

Virtual sensing value = Sensing value at the end of the wide-area event immediately before the end + (Change in the sensed value due to the occurrence of the wide-area event immediately before the end) * Accumulation coefficient

The accumulation coefficient of Equation 1 may be a number exceeding 1. This is to reflect the effect of building fatigue due to accumulated events. The start time of the wide-area event may be the time when an abnormal sign of any one of the buildings to be monitored is detected among the detected wide-area areas in the same manner as in S20 of FIG. 11 . The end point of the wide-area event is the condition under which the wide-area event can be considered to have occurred in S30 of FIG. It may be a point in time when the settlement of a plurality of buildings to be monitored distributed in (within the standard deviation range) is offset.

In addition, the wide-area diagnosis unit 34 may calculate the disaster risk for each building to be monitored according to Equation 2 below using the virtual sensing value.

[Equation 2]

Disaster risk = (assumed sensing value / risk reference value) *100

The wide-area diagnosis unit 34 may provide, through the warning unit 37, a warning about a standard disaster risk level or higher to a manager of a building to be monitored whose disaster risk level is higher than the standard disaster risk level (eg, 80%).

Referring to FIG. 14 , upon entering the local event diagnosis mode, the local diagnosis unit 35 may determine a local event occurrence area (S61). In this case, an area connecting a plurality of target buildings to be monitored with abnormal signs located within a predetermined reference radius to the building to be monitored located at the outermost area may be determined as a local event occurrence area.

Also, the local diagnosis unit 35 may analyze the sensed value of the local event occurrence region. In this case, the local diagnosis unit 35 may generate a nonlinear function for each of the sensed values collected in the local event occurrence region during the event evaluation period. Then, the point of time when the sensed value reaches the potential risk reference value may be calculated through the nonlinear function. Here, the potential risk reference value may be a lower value than the above-described risk reference value. Factors such as vibration and ground subsidence occurring at construction sites can increase the rate of collapse and cracking of buildings due to continuous stimulation. Therefore, it may be desirable to determine the risk state of the building through a potential risk reference value lower than the risk reference value. When the temporary risk reference value for each building to be monitored is reached, a notification may be provided to the outside through the alarm unit 37 .

And, the local diagnosis unit 35 may evaluate the local event influence (S63). The local diagnosis unit 35 may extract the size of the fluctuation range of the sensed value during the event evaluation period with respect to the monitored building located within the local event occurrence area. Also, the local diagnosis unit 35 may extract a predetermined number of buildings to be monitored based on the size of the fluctuation range of the sensed value. Then, the local diagnosis unit 35 may specify a central region of an area where the higher-order preset number of buildings to be monitored are located by using the coordinates of the higher-order preset number of target-to-monitor buildings. In addition, the local diagnosis unit 35 may provide a notification of the central area to a building manager to be monitored located in a local event occurrence area through the alarm unit 37 . Here, the central region is a central region of a region in which a local event occurs, and may be an region where a cause (eg, a construction site) of the local event exists.

The statistical diagnosis unit 32 may statistically process the sensed values stored in the measurement DB to generate information such as gradient distribution information for each unit area and change trend of the slope of a building to be monitored, and provide the information to the management reference DB 22 .

The risk analysis unit 33 may generate risk information for each building determined according to the risk determination criteria and provide the information to the management standard DB 22 . At this time, the risk analysis may be determined through an algorithm that increases the risk level when the cracks of the building are large or the slope of the building is large by reflecting the state of cracks and the slope of the building. At this time, the structure of the building and the state of the ground may be applied as factors for risk analysis.

The interface unit 36 may provide a user interface. The interface unit 26 may provide an interface for various information generated by the monitoring device and an interface for manipulation of the monitoring device.

The alarm unit 37 can promptly notify the state of the building to be monitored immediately from time to time and send SNS text messages and notifications to the building manager. The alarm unit 37 generates an alarm for the time when the risk reference value generated by the individual diagnosis unit 31 is exceeded, an alarm for a standard disaster risk level or higher generated by the wide-area diagnosis unit 34, and a local diagnosis unit 35. A history of alarms for the central area of the region where local events to be ordered and the timing of reaching a potential risk reference value for each monitoring target generated by the local diagnosis unit 35 may be stored in the management standard DB 22 .

The communication unit 38 may provide communication between the monitoring device 30 and the database server 20 and communication between the monitoring device 30 and a building manager terminal (not shown).

A: wide area
z1, z2, ..., zn: unit area
B1, B2, ..., Bn: buildings to be monitored
1: sensor part
2: 1st communication network
3: 2nd communication network
10: Gateway
20: database server
21: measurement DB
22: Management standard DB
30: monitoring device
31: individual diagnosis unit
32: statistical diagnosis unit
33: risk analysis unit
34: wide area diagnosis unit
35: local diagnosis unit
36: interface unit
37: alarm department
38: Ministry of Communications

Claims (7)

A database server 20 for collecting, storing, and managing sensing values from the sensor unit 1 installed in the building to be monitored (B); and
Includes a monitoring device 30 for analyzing and alarming the sensed value using the sensed value stored in the database server 20,
When the monitoring device 30 determines that an anomaly occurs in any one of the plurality of monitored buildings B, the monitoring device 30 operates in a wide-area event diagnosis mode, a local event diagnosis mode, and an individual event diagnosis mode. Perform detailed diagnosis in any one of the diagnosis modes,
The monitoring device 30 determines that a wide-area event has occurred when vibration frequencies of a plurality of monitored buildings distributed in a wide-area area are within a standard deviation range,
When it is determined that a wide-area event has occurred, the monitoring device 30 enters a wide-area event diagnosis mode;
The monitoring device 30 determines that a local event has occurred when a plurality of monitored buildings in which abnormal symptoms are detected and a plurality of monitored buildings in which an abnormal symptom has occurred are located within a preset reference radius,
If it is determined that a local event has occurred, the monitoring device 30 enters a local event diagnosis mode,
In local event diagnosis mode,
The monitoring device 30 determines, as a local event occurrence area, an area connecting a target building located at the outermost part of a plurality of target buildings located within a preset reference radius where abnormal signs have occurred, as a local event occurrence area;
The monitoring device 30 generates a nonlinear function for each of the sensed values collected in the local event occurrence area during the event evaluation period, and calculates a point in time when the sensed value reaches a potential risk reference value through the nonlinear function, An alarm is provided to the outside about when the potential risk standard value for each building to be monitored is reached,
The monitoring device 30 evaluates the local event impact,
When evaluating the impact of a local event, the monitoring device 30 extracts the magnitude of the fluctuation range of the sensed value during the event evaluation period with respect to the monitored building located within the local event occurrence area, and determines the magnitude of the fluctuation range of the sensed value Based on the size, a pre-determined number of buildings to be monitored is extracted, and the center area of the area where the top pre-set number of buildings to be monitored is located is specified using the coordinates of the buildings to be monitored of the pre-set number of top ranks, and the center area is located. An IoT-based building condition monitoring system, characterized in that for providing notification of the area to the building manager to be monitored located in the area where the local event occurs.
According to claim 1,
The database server 20 stores building slope distribution information by region and change trend information of building slope by building IoT-based building condition monitoring system.
According to claim 1,
The database server 20 is an IoT-based building condition monitoring system, characterized in that for storing the risk level information for each building determined according to the risk level determination criteria.
According to claim 1,
The database server 20 is an IoT-based building condition monitoring system, characterized in that for storing data related to the alarm.
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