KR20170101519A - Apparatus and method for disaster monitoring using unmanned aerial vehicle - Google Patents

Abstract

A disaster monitoring apparatus using an unmanned aerial vehicle and a method thereof are presented. According to the present invention, the disaster monitoring apparatus using an unmanned aerial vehicle comprises a communication part, an image data analysis part, a disaster situation prediction part and a disaster situation counteracting part. The communication part transmits a control signal to an unmanned aerial vehicle and receives collection information including at least one among image data, voice data, and sensing data from the unmanned aerial vehicle. The image data analysis part analyzes the image data by at least one among a mechanical learning based-analysis method, an analysis method through combination of a plurality of image data and an analysis method through relation with the sensing data. The disaster situation prediction part applies an analysis result of the image data and weather information received from the outside to a prediction model to generate a prediction result. The disaster situation counteracting part transmits disaster alarm information, which is generated based on a scenario corresponding to the analysis result and the prediction result, to an external integral alarm system or outputs the disaster alarm information.

Description

[0001] APPARATUS AND METHOD FOR DISASTER MONITORING USING UNMANNED AERIAL VEHICLE [0002]

TECHNICAL FIELD The present invention relates to a disaster monitoring technique using an unmanned airplane, and more particularly, to a technique for monitoring a surrounding area by using an unmanned airplane, and anticipating a disaster such as a forest fire, flood or landslide to cope with a disaster situation.

Various methods have been used to monitor disasters such as forest fires and tsunamis, or to monitor natural ecosystems. In the past, mainly fixed monitoring stations or monitoring towers were installed, and the surroundings were monitored using CCTV installed at the monitoring station or the monitoring tower.

If CCTV is used, CCTV is installed in a fixed tower, although CCTV is advantageous in that it can monitor the surroundings at a low price. In a place where the CCTV is not installed, it is inconvenient to grasp the situation by only the information by the report of the detector, and the accuracy of the situation grasping becomes poor.

Also, in case of repairing the CCTV, the person must go to the place where the CCTV is directly installed and repair the CCTV installed in the high place. Therefore, the monitoring method using the CCTV inherits the risk of falling. Therefore, CCTV is mainly used for monitoring in limited areas.

As the frequency of natural disasters increases due to factors such as environmental pollution and deepening of climate change, satellite information is used to analyze the possibility of various disasters and the damage situation.

However, since the time for a satellite to stay in a specific area is limited, surveillance using a satellite is mainly used only to detect an abnormal change. Therefore, surveillance for a specific area utilizes the surrounding area information obtained from CCTV or unmanned aircraft.

In particular, surveillance technology using unmanned aerial vehicles can be provided in real time at the scene of an accident where people can not approach in the event of a disaster. Therefore, it is attracting attention as a victim search and rescue equipment that can secure the golden time in the earliest time.

In addition, through cameras and sensors installed on unmanned airplanes, construction site supervision, building safety diagnosis, traffic violation control, hunting area monitoring, aging facility management, water pollution monitoring, illegal waste monitoring, illegal waste water emission monitoring, It can be used to prevent public damage.

Unmanned aerial vehicles are also used to investigate the types of earthquakes that occur near the facilities due to combined disasters such as heavy rains and earthquakes. Remote sensing, field physics exploration and monitoring surveillance techniques are used to extract areas where landslides (landslides, landslides, etc.) Unmanned airplanes are also used to manage hazardous areas in the soil through 3D visualization techniques.

In addition, it is recognized as an effective technology for building an integrated response system through monitoring illegal fishing in the ocean, monitoring border areas, detecting and tracking targets, and analyzing linkage with ICT technology and linking with weather information.

Therefore, it is necessary to develop technologies that can monitor and forecast disasters by utilizing the characteristics of UAVs and respond to disasters in case of disasters.

Korean Patent Publication No. 10-2003-0015547, February 25, 2003 (name: unmanned operation disaster alarm device)

An object of the present invention is to provide surveillance information of a place that is difficult for a person to approach, such as a mountain, a coast, etc., to be provided in real time.

It is also an object of the present invention to predict occurrence of disasters such as forest fires, floods, and landslides using surveillance information transmitted in real time, and to respond to disaster situations.

It is also an object of the present invention to make it possible to track a location where a disaster occurred using video data and audio data.

According to another aspect of the present invention, there is provided an apparatus for monitoring a disaster using an unmanned airplane, the apparatus comprising: a control unit for transmitting a control signal to an unmanned airplane or receiving collected information including at least one of image data, An analysis unit for analyzing the image data using at least one of an analysis method based on a machine learning based analysis, a combined analysis of a plurality of image data, and an analysis of association with the sensing data, And a disaster alert information generation unit for generating disaster alert information based on the analysis result and the corresponding scenario corresponding to the prediction result, to an external integrated alarm system Disaster to transmit the disaster alert information And a situation counterpart.

According to another aspect of the present invention, there is provided a disaster monitoring method performed by a disaster monitoring apparatus using an unmanned aerial vehicle, comprising: receiving acquisition information including at least one of image data, voice data, and sensing data from an unmanned air vehicle; Analyzing the image data using at least one of a learning-based analysis, a combined analysis of a plurality of image data, and a correlation analysis with the sensing data, analyzing the analysis result of the image data and the weather information received from the outside Model, generating a prediction result, transmitting the disaster alert information generated based on the analysis result and the corresponding scenario corresponding to the prediction result to an external integrated alarm system, or outputting the disaster alert information .

According to the present invention, it is possible to receive surveillance information of a place that is hard to access by people, such as a mountain or a coast, in real time.

Further, according to the present invention, it is possible to predict the occurrence of disasters such as forest fires, floods, and landslides using the monitoring information transmitted in real time, and to respond to a disaster situation.

Further, according to the present invention, it is possible to track a location where a disaster occurred by using image data and voice data.

FIG. 1 is a schematic view of an environment to which a disaster monitoring apparatus using an unmanned aerial vehicle according to an embodiment of the present invention is applied.
2 is a block diagram illustrating a configuration of a disaster monitoring apparatus using an unmanned aerial vehicle according to an embodiment of the present invention.
3 is a block diagram illustrating a configuration of an image data analysis unit according to an embodiment of the present invention.
4 is a flowchart illustrating a disaster monitoring method using an unmanned aerial vehicle according to an embodiment of the present invention.
FIG. 5 is a diagram for explaining a process of fusing multi-sensor image data in step S420 of FIG.
6 is a block diagram illustrating a computer system in accordance with an embodiment of the present invention.

The present invention will now be described in detail with reference to the accompanying drawings. Hereinafter, a repeated description, a known function that may obscure the gist of the present invention, and a detailed description of the configuration will be omitted. Embodiments of the present invention are provided to more fully describe the present invention to those skilled in the art. Accordingly, the shapes and sizes of the elements in the drawings and the like can be exaggerated for clarity.

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 is a schematic view of an environment to which a disaster monitoring apparatus using an unmanned aerial vehicle according to an embodiment of the present invention is applied.

As shown in FIG. 1, a disaster monitoring system using an unmanned aircraft includes a unmanned airplane 100, a disaster monitoring apparatus 200 using an unmanned airplane, and an integrated alarm system 300.

First, the UAV 100 captures and monitors an image while moving over a wide area, and generates surveillance information. Then, the unmanned airplane 100 transmits the generated monitoring information to the disaster monitoring apparatus 200 using the unmanned airplane in real time. At this time, the UAV 100 may fly to correspond to the established surveillance operation plan, or may fly in accordance with command signals and control signals received from the disaster monitoring apparatus 200 using the UAV.

And the UAV 100 may include a flight control module and an operation control module. At this time, the flight control module receives the command signal and the control signal from the disaster monitoring device 200 using the unmanned airplane, and can transmit the flight information to the disaster monitoring device 200 using the unmanned airplane. The flight control module may serve as an unmanned airplane controller responsible for the overall operation of the UAV 100, such as landing and landing, navigation, and communication of the UAV 100.

In addition, the flight control module can perform a sensor interlocking function for interlocking the data acquisition equipment (payloads) mounted on the UAV 100 and a flight control function for controlling the flight of the UAV 100.

At this time, for the flight control, sensors such as an acceleration sensor, a gyro sensor, an altitude sensor, a magnetic sensor and a GPS may be connected to the UAV 100, and the flight control module performs connection and control of these various sensors. The flight control module may be an unmanned airplane controller responsible for overall operation related to flight information transmission, take-off and landing, navigation and communication of the UAV 100.

The operation control module of the UAV 100 controls the data acquisition equipment (payload) such as a camera and a sensor mounted on the UAV 100, controls the captured image memory, controls the USB port, Power can be controlled, and operation of a communication modem or the like can be controlled.

Next, a disaster monitoring apparatus 200 using an unmanned airplane receives collection information including at least one of image data, voice data, and sensing data from the UAV 100, and detects occurrence of a disaster using the received collection information Disaster prevention, and response to disaster situations. In addition, the disaster monitoring apparatus 200 using the UAV can control the attitude and altitude of the UAV 100, and can control the automatic vertical takeoff and landing and autonomous travel.

The uplink from the disaster monitoring apparatus 200 using the unmanned airplane to the UAV 100 transmits a command signal and a control signal. The downlink from the UAV 100 to the disaster monitoring apparatus 200 using an unmanned airplane transmits collection information including at least one of flight information, image data, voice data, and sensing data.

At this time, the disaster monitoring apparatus 200 using the unmanned airplane and the UAV 100 can transmit the collection information of the disaster situation using the Wi-Fi / LTE RF communication, and the communication method is not limited thereto. The disaster monitoring apparatus 200 using the unmanned airplane may be located within an area of the operation range area where the unmanned airplane 100 can be controlled for controlling the unmanned airplane 100, 200 can receive real-time moving images for control, monitoring, disaster prevention, and response of the UAV 100 from the UAV 100.

The disaster monitoring apparatus 200 using the unmanned airplane processes the image data, the sound data, and the sensing data, which are collected information received from the UAV 100, and uses the processed result to perform disaster prevention and response to a disaster situation Can be performed.

The disaster monitoring apparatus 200 using an unmanned aerial vehicle can analyze image data using at least one analysis method among a machine learning based analysis, a combination analysis of a plurality of image data, and a linkage analysis with sensing data. Also, the image data can be analyzed using the image data corresponding to the voice data, and the occurrence of a disaster can be determined using the analysis result of the image data.

In addition, the disaster monitoring apparatus 200 using the unmanned airplane controls the flight of the unmanned airplane 100 when it is determined that a disaster has occurred. And applies the collected information received from the UAV 100 to the disaster prediction model to generate a prediction result.

Also, the disaster monitoring apparatus 200 using the unmanned airplane can generate disaster alert information based on the analysis result and the corresponding scenario corresponding to the predicted result, and transmit the generated disaster alert information to the external integrated alarm system. The disaster monitoring apparatus 200 using the unmanned airplane may output the generated disaster alarm information through various methods such as a speaker or a display.

Hereinafter, the configuration and function of the UAV 100 will be described in more detail.

The UAV 100 includes a flight control module and an operation control module, and the flight control module performs a sensor interlock function and a flight control function.

The sensor interlocking function of the flight control module manages the data acquisition equipment mounted on the UAV 100 and performs the sensor interlocking. The sensor interlocking function of the flight control module may include a device for data collection for performing disaster monitoring, disaster prevention and response missions, and a sensor plug-in / out module for mounting the devices. In addition, the sensor interlocking function of the UAV 100 may include an image / sensing storage module for storing captured images and sensing data.

In this case, the data acquisition equipment mounted on the UAV 100 may be a visible light camera, an infrared camera, an ultrasound sensor, a light detection and ranging (LIDAR), a SAR (synthetic aperture radar) Or the like. And data collection equipment for collecting collection information including at least one of image data such as a camera and the like, voice data and sensing data can be loaded through the gimbals.

The visible light camera mounted on the UAV 100 is used for flame, smoke detection, immersion detection, and surface change detection which can be detected in the visible light band, and captures and stores still images (still cuts) and moving images. The acquired still images and moving images can be wirelessly transmitted to the disaster monitoring apparatus 200 using the unmanned airplane. And infrared (thermal) cameras can detect flames and heat sources and detect surface temperatures through temperature measurements.

In addition, the UAV 100 is equipped with an ultra-spectral sensor capable of acquiring the spectral characteristics of the surface water to detect smoke and heat sources, detect immersion sites, and confirm the moisture and vegetation coverage of the soil. The unmanned airplane 100 may be equipped with a lidar and the distance may be measured using a laser of lidar. Accordingly, the UAV 100 can sense smoke and topography changes, and can detect surface roughness and the like.

When the unmanned air vehicle 100 includes the SAR, the unmanned airplane can detect an object or measure a distance using a frequency band such as microwave or radio wave. Through this, it is possible to detect plants, detect terrain changes and surface roughness, and the like.

In addition, the UAV 100 may be equipped with an environmental sensor for collecting temperature, humidity, wind direction and wind speed. At this time, the types of sensors mounted on the UAV 100 are not limited thereto. In addition, the UAV 100 can track location information of emergency data related to an emergency such as explosion sound or gunshot detected through a microphone (microphone), and the UAV 100 can move to the location of emergency data , It is possible to monitor the situation of the position or to track the target by combining the video data and the audio data.

The sensor plug-in / out module provides the data acquisition equipment and camera control module mounted on the UAV 100 in a form that can be plugged in / out to suit the situation in case of disaster monitoring, disaster prevention and disaster. The sensor plug-in / out module transmits the collected information to the image / sensing storage module.

Next, the flight control function of the flight control module can control the flight of the UAV 100 using an acceleration sensor, a gyro sensor, an altitude sensor, a magnetic sensor, and a GPS sensor of the UAV 100. And the flight control function may include a receiver control receiver for steering the UAV 100 and a self-operating communication module for automatic operation.

2 is a block diagram illustrating a configuration of a disaster monitoring apparatus using an unmanned aerial vehicle according to an embodiment of the present invention.

2, the disaster monitoring apparatus 200 using an unmanned aerial vehicle includes a communication unit 210, an image data analysis unit 220, a disaster situation predicting unit 230, and a disaster situation counterpart 240 .

First, the communication unit 210 transmits a control signal to an unmanned airplane or receives collection information including at least one of image data, voice data, and sensing data from an unmanned airplane.

The communication unit 210 includes at least one of image data, voice data, and sensing data from an unmanned airplane flying in accordance with the established surveillance operation plan or flying in accordance with the control signal of the disaster monitoring apparatus 200 using the unmanned airplane And collects the collected information.

At this time, the communication unit 210 can receive image data, audio data, and sensing data including at least one of a still image and a moving image from an unmanned aerial vehicle in real time.

The image data analysis unit 220 may analyze the image data using at least one of analysis method based on machine learning based analysis, combined analysis of a plurality of image data, and analysis linked with sensing data.

The image data analyzing unit 220 may perform a preprocessing process of removing noise of the image data received from the UAV and performing correction. At this time, the image data analyzing unit 220 can correct the image data according to the characteristics of the equipment for data collection mounted on the UAV.

At this time, the image data analyzer 220 may perform image data analysis after detecting image feature points and ROI (Region of Interest) from the image data. In addition, the image data analyzing unit 220 can use the analysis result of the image data to determine whether or not a disaster occurs.

Then, the image data analysis unit 220 can use the analysis result of the collected information to set a disaster-vulnerable area that can be utilized in a disaster situation and prepare a hazard analysis map. The image data analyzing unit 220 may focus on a place corresponding to the disaster vulnerable zone set or a place corresponding to the danger analysis map to determine whether a disaster has occurred.

Next, the disaster state predicting unit 230 applies the analysis result of the image data and the weather information received from the outside to the prediction model to generate the prediction result. The disaster state predicting unit 240 can perform disaster prevention measures such as disaster movement path estimation such as forest fire, measurement of rainfall distribution, water monitoring of a vulnerable ground for rainfall, and minute change detection of a terrain.

Finally, the disaster state counter 240 generates the disaster alarm information based on the analysis result and the corresponding scenario corresponding to the prediction result. Then, the disaster state counter 240 transmits the generated disaster alarm information to the external integrated alarm system or outputs the disaster alarm.

At this time, the disaster situation counterpart 240 presents the corresponding scenario according to the disaster situation, visualizes and displays the disaster alert information, or transmits the disaster alert information in real time to the integrated alarm system to monitor the disaster area, You can warn about danger.

3 is a block diagram illustrating a configuration of an image data analysis unit according to an embodiment of the present invention.

3, the image data analyzing unit 220 of the disaster monitoring apparatus 200 using an unmanned aerial vehicle is a module for processing and analyzing image data. The image data analyzing unit 220 includes an image preprocessing module 221, an image correction module 223 An image analysis module 225, and an image storage module 227.

First, the image preprocessing module 221 can perform image preprocessing for removing noise of the received image data and correcting the noise.

The image preprocessing module 221 may perform correction such as geometric correction, low-illuminance image correction, noise reduction, and bad image correction on the image characteristics photographed using the image equipment mounted on the UAV.

The low-illuminance image and the noise image correction can be performed using techniques such as Adaptive Gamma Correction and Short Time Fourier Transform, and the bad weather image correction can be performed using techniques such as MSE (Mean Squared Error) of Atmospheric Light, But is not limited thereto.

The image correction module 223 can correct the image data according to the characteristics of the data acquisition equipment mounted on the UAV.

The image correction module 223 can correct the geometric distortion range caused by the distortion of the sensor due to the altitude, attitude, velocity change, global curvature, and atmospheric reflection.

Further, the image correction module 223 can correct the reduction effect of the brightness value due to the atmospheric effect. At this time, the image correction module 223 may perform geometric correction to measure and correct the geometric accuracy based on the GCP (ground control point) to compare the error between the image coordinates of the image pixel and the actual geographical coordinates. The image correction module 223 may perform the radiation correction, which is an image processing technique for adjusting the contrast and brightness of the image to correct the sensor sensitivity change.

Next, the image analysis module 225 detects the image feature points and the ROI (Region of Interest), and analyzes the image data using at least one of analysis based on machine learning based analysis, combined analysis of a plurality of image data, Can be analyzed.

The image analysis module 225 detects image feature points and ROIs necessary for disaster prediction, monitoring, and disaster prevention. At this time, the image analysis module 225 can detect image feature points and ROI by applying Convolution, Oriented FAST and Rotated BRIEF algorithm to the preprocessed image data.

The image analysis module 225 may perform a machine learning based analysis or analyze a plurality of image data in order to determine whether a disaster occurs or not. At this time, the image analysis module 225 can perform image data analysis based on the corrected image image, the detected image feature point, and the ROI using an artificial neural network or the like. Then, the image analysis module 225 can accurately analyze the image data by superimposing the analysis results of the respective heterogeneous image data between the layers.

In addition, the image analysis module 225 may analyze at least two pieces of collected information among image data, voice data, and sensing data to determine whether a disaster occurs or not. At this time, the image analysis module 225 can perform analysis by linking the sensing data received from the satellite image, the CCTV image, and the ground sensor.

Finally, the image storage module 227 stores the analysis result of the image data.

At this time, the image data analyzing unit 220 may divide the images into a plurality of computers and a plurality of processor cores for real-time processing. The image data analysis unit 220 realizes real-time analysis using accelerators such as a GPU (Graphics Processing Unit) and an MIC (Many Integrated Core), and can efficiently use computer resources with optimal load distribution.

Hereinafter, a disaster monitoring method performed by the disaster monitoring apparatus using an unmanned aerial vehicle according to an embodiment of the present invention will be described in detail with reference to FIGS. 4 and 5. FIG.

4 is a flowchart illustrating a disaster monitoring method using an unmanned aerial vehicle according to an embodiment of the present invention.

First, the disaster monitoring apparatus 200 using the unmanned airplane acquires collection information including at least one of image data, voice data, and sensing data from the unmanned airplane (S410).

The disaster monitoring apparatus 200 using an unmanned airplane collects collected information including at least one of image data, sound data, and sensing data, which are image information captured from an unmanned airplane. Accordingly, the disaster monitoring apparatus 200 using the UAV can acquire real-time images of areas where manpower can not be accessed, or obtain meteorological resources including temperature, wind direction, and wind velocity.

Next, the disaster monitoring apparatus 200 using the unmanned airplane analyzes the collected image data (S420).

The disaster monitoring apparatus 200 using the unmanned airplane can perform the preprocessing of the collected information collected from the unmanned airplane. In particular, the disaster monitoring apparatus 200 using an unmanned aerial vehicle performs pre-processing of image data, detects image feature points and ROIs, and performs at least one of machine learning based analysis, joint analysis of a plurality of image data, It is possible to analyze image data by one analysis method.

In addition, the disaster monitoring apparatus 200 using an unmanned aerial vehicle may combine heterogeneous image data and combine multi-view image data to perform image data analysis for image analysis and accurate reading. At this time, the disaster monitoring apparatus 200 using an unmanned airplane may perform a coupling analysis with the collected image, and may fuse the combined analysis result with the image based on the pixel.

FIG. 5 is a diagram for explaining a process of fusing multi-sensor image data in step S420 of FIG.

As shown in FIG. 5, the disaster monitoring apparatus 200 using an unmanned aerial vehicle can extract spatial features from visible light images, and re-adjust the SAR based image. The disaster monitoring apparatus 200 using an unmanned aerial vehicle may perform a black-and-white image fusion and a color image fusion of a visible light image and a SAR image.

The disaster monitoring apparatus 200 using the unmanned airplane performs image preprocessing by removing noise of the image data received from the unmanned airplane and correcting the noise. At this time, the disaster monitoring apparatus 200 using the unmanned airplane can correct the image data according to characteristics of the data acquisition equipment mounted on the unmanned airplane.

In this manner, the disaster monitoring apparatus 200 using an unmanned airplane combines disparate image data or combines multi-view image data for accurate analysis and reading of images, thereby monitoring and detecting the occurrence of a disaster such as a landslide And can estimate the route of the disaster such as forest fires.

Next, the disaster monitoring apparatus 200 using the unmanned airplane determines whether a disaster corresponding to the analysis result of the image data is a disaster type that can detect a precursor of a disaster (S430).

For example, in the case of a disaster type such as a landslide that can detect a precursor of a disaster, the disaster monitoring apparatus 200 using an unmanned airplane performs step S440 described later.

On the other hand, if it is difficult to detect a precursor of a disaster such as a fire or a flood, or in case of a disaster already occurring, the disaster monitoring apparatus 200 using the unmanned airplane performs step S450 described later.

That is, if the type of the disaster corresponding to the analysis result of the image data is a disaster capable of detecting a precursor of the disaster, the disaster monitoring apparatus 200 using the unmanned airplane generates the prediction result using the analysis result (S440) .

The disaster monitoring apparatus 200 using an unmanned aerial vehicle collects meteorological resources including at least one of temperature, wind direction and wind speed. The disaster monitoring apparatus 200 using the unmanned airplane can predict the disaster situation using the analysis result of the image data and the collected weather resources.

At this time, the disaster monitoring apparatus 200 using the UAV can extract a series of scenario selection-related characteristic data according to a disaster situation from the internal database using the XML format analysis result, in order to derive a prediction result. And generates a prediction result that allows selection of a corresponding scenario corresponding to the extracted characteristic data.

At this time, the collection of meteorological resources can be received from the organization providing the meteorological information, and the meteorological resources collected in order to predict the possible disasters and respond to the disasters can be stored in the internal database and used. Here, the internal database may store information such as the analysis result and weather resource.

On the other hand, if the type of the disaster corresponding to the analysis result of the image data is difficult to detect a precursor of a disaster, or if the disaster occurred already, the disaster monitoring apparatus 200 using the unmanned airplane omits step S440, To generate the disaster alert information (S450).

In addition, the disaster monitoring apparatus 200 using the unmanned aerial vehicle generates the prediction result by performing step S440, and then generates the disaster alert information through step S450.

The disaster monitoring apparatus 200 using an unmanned aerial vehicle selects an appropriate disaster response scenario based on the analysis result and the prediction result. For convenience of explanation, it has been described that the disaster monitoring apparatus 200 using the unmanned aerial vehicle selects a corresponding scenario, but the present invention is not limited to this, and a corresponding scenario may be selected from the outside.

Finally, the disaster monitoring apparatus 200 using the unmanned airplane transmits the disaster alert information to the integrated alarm system or outputs the disaster alert information (S460).

The disaster monitoring apparatus 200 using the unmanned airplane generates disaster alert information based on the corresponding scenario and outputs the generated disaster alert information or transmits the generated disaster alert information to an external integrated alarm system to cope with a disaster situation.

At this time, the disaster monitoring apparatus 200 using the unmanned airplane can present at least one of the analysis result, the prediction result, and the disaster alert information in order to provide the disaster information to the administrator or the user.

Various methods such as image, image, text, sound and the like can be applied as a method of expression, and a disaster monitoring apparatus 200 using an unmanned airplane can display disaster alert information, provide a disaster response scenario, It can visualize the status of disasters and the prediction result of disasters on a web basis, provide disaster situation notification, response manual according to disaster situation, and scenario corresponding to disaster type.

In addition, the disaster monitoring apparatus 200 using an unmanned aerial vehicle can communicate various data related to a disaster such as an analysis result, a prediction result, and a corresponding scenario by interlocking with an external integrated alarm system. At this time, the disaster monitoring apparatus 200 using the unmanned airplane can generate a message to be transmitted to the integrated alarm system by extracting the association element with the protocol of the integrated alarm system (for example, CAP (Common Alerting Protocol)) message, Can be stored in the database of the disaster monitoring apparatus 200 using the unmanned aerial vehicle.

As described above, the disaster monitoring apparatus 200 using an unmanned aerial vehicle according to an embodiment of the present invention can monitor a disaster area in real time and warn of a risk of a disaster. It can provide information such as fire suppression information, flood map, and flood status tracked in real time, and can provide detailed map of damage area caused by disaster.

In addition, the disaster monitoring apparatus 200 using an unmanned aerial vehicle according to an embodiment of the present invention can provide an alert for an additional damage expected area, thereby supporting a disaster response activity. In addition, it can be used for restoration work such as calculation of damage area due to disaster, measurement of damage status, and establishment of measures for restoring the ground.

6 is a block diagram illustrating a computer system in accordance with an embodiment of the present invention.

Referring to FIG. 6, embodiments of the present invention may be implemented in a computer system 600, such as a computer readable recording medium. 6, the computer system 600 includes one or more processors 610, a memory 630, a user input device 640, a user output device 650, and a storage 630, which communicate with one another via a bus 620. [ 660 < / RTI > In addition, the computer system 600 may further include a network interface 670 connected to the network 680. The processor 610 may be a central processing unit or a semiconductor device that executes the processing instructions stored in the memory 630 or the storage 660. [ Memory 630 and storage 660 may be various types of volatile or non-volatile storage media. For example, the memory may include a ROM 631 or a RAM 632. [

Thus, embodiments of the invention may be embodied in a computer-implemented method or in a non-volatile computer readable medium having recorded thereon instructions executable by the computer. When computer readable instructions are executed by a processor, the instructions readable by the computer are capable of performing the method according to at least one aspect of the present invention.

As described above, the apparatus and method for monitoring a disaster using an unmanned aerial vehicle according to the present invention are not limited to the configurations and methods of the embodiments described above, All or some of the embodiments may be selectively combined.

100: Unmanned aircraft
200: Disaster monitoring system using unmanned aircraft
210: communication unit 220: video data analysis unit
230: Disaster situation predicting unit 221: Image preprocessing module
223: Image correction module 235: Image analysis module
237: image storage module 240: disaster situation counterpart
300: Integrated alarm system 600: Computer system
610: Processor 620: Bus
630: Memory 631: ROM
632: RAM 640: user input device
650: User output device 660: Storage
670: Network interface 680: Network

Claims (1)

A communication unit for transmitting a control signal to an unmanned air vehicle or receiving collection information including at least one of image data, voice data and sensing data from the unmanned air vehicle,
An image data analyzing unit for analyzing the image data by at least one of analyzing methods based on a machine learning based analysis, a combined analysis of a plurality of image data, and an analysis of association with the sensing data,
A disaster state prediction unit for applying the analysis result of the image data and the weather information received from outside to the prediction model to generate a prediction result,
A disaster alert counter for outputting the disaster alert information generated based on the analysis result and the corresponding scenario corresponding to the prediction result to an external integrated alarm system,
A disaster monitoring device using an unmanned aircraft.

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