System, device, method and computer program for predicting risk of inundation

Abstract

The present invention relates to a flood risk prediction system, device, method, and computer program. A flood risk prediction system according to one embodiment of the present invention comprises: a plurality of drones, each collecting image data through at least one camera module and collecting sensor data through at least one sensor module within a data collection area; a path control module that divides the data collection area into a plurality of regions and controls the flight path of each of the plurality of drones; and an analysis module that generates analysis information by calculating the probability of flooding occurrence for each zone within the data collection area based on the image data and sensor data collected from the plurality of drones.

Description

Technology Field

[0001] The present invention relates to a flood risk prediction system, device, method, and computer program, and more specifically, to a flood risk prediction system, device, method, and computer program capable of predicting flood risk in rivers and urban areas using multiple drones. Background Technology

[0002] Recently, as the frequency of torrential rains and localized downpours has increased due to factors such as global warming, flood damage in rivers and urban areas is rapidly escalating. Consequently, river overflows are submerging roads and causing traffic paralysis, resulting in significant material damage and inflicting fatal harm on property and human lives.

[0003] Meanwhile, existing flood prediction methods rely on fixed water level gauges and CCTVs, making it difficult to cover extensive areas and resulting in the disadvantage of low accuracy in real-time flood prediction.

[0004] This invention is a research project conducted with support from the 2025 Jeonbuk Flood Safety Industry Technology Commercialization Support Project. "Research Project Title: Development of an AI Safety Management Monitoring System for Flooded Areas Using Waterproof Drone and Station Technology Capable of Simultaneous Land, Sea, and Air Movement" The problem to be solved

[0005] One objective of the present invention is to provide a flood risk prediction system, device, method, and computer program capable of predicting flood risk in advance in real time using a plurality of drones.

[0006] Another objective of the present invention is to provide a flood risk prediction system, device, method, and computer program capable of efficiently controlling drone paths so that multiple drones can detect without path overlap.

[0007] The objectives of the present invention are not limited to those mentioned above, and other unmentioned objectives will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0008] A flood risk prediction system according to one embodiment of the present invention may include a plurality of drones, each collecting image data through at least one camera module and collecting sensor data through at least one sensor module within a data collection area; a path control module that divides the data collection area into a plurality of regions and controls the flight path of each of the plurality of drones; and an analysis module that generates analysis information by calculating the probability of flood occurrence for each zone within the data collection area based on the image data and sensor data collected from the plurality of drones.

[0009] In one embodiment, the at least one camera module included in each of the plurality of drones includes a stereo camera, and the analysis module can calculate depth information of the data collection area based on the image data collected through the stereo camera.

[0010] In one embodiment, the at least one sensor module included in each of the plurality of drones may include at least one of a water level gauge, a flow meter, and a weather sensor.

[0011] In one embodiment, the path control module monitors the communication status and charging status of each of the plurality of drones and can control the path of each of the plurality of drones based on the communication status and charging status.

[0012] In one embodiment, the path control module can control the path of each of the plurality of drones based on the analysis information generated from the analysis module.

[0013] In one embodiment, the analysis module can predict the rate of water level rise and analyze the flow velocity pattern based on the image data and the sensor data, and calculate the probability of flooding occurrence for each zone within the data collection area by reflecting the spatial information of the data collection area.

[0014] In one embodiment, the analysis module can extract a plurality of visual elements from the image data and predict the probability of occurrence of a hydraulic jump within the data collection area based on the plurality of visual elements.

[0015] In one embodiment, the flood risk prediction system further includes an integrated control module that synchronizes the image data and sensor data collected from the plurality of drones in temporal and spatial units, and then provides the synchronized image data and sensor data to the analysis module, and the analysis module can generate the analysis information based on the synchronized image data and sensor data.

[0016] In one embodiment, the integrated control module can generate a flood risk map based on the analysis information.

[0017] In one embodiment, the flood risk prediction system may further include a visualization module that visualizes and provides the analysis information to the user. Effects of the invention

[0018] In the case of a flood risk prediction system, device, method, and computer program according to embodiments of the present invention, flood risk can be predicted in advance by collecting and analyzing video data and sensor data in real time using multiple drones.

[0019] In the case of a flood risk prediction system, device, method, and computer program according to embodiments of the present invention, it is possible to support a preemptive response by providing predicted flood risk information to local governments or public institutions in real time.

[0020] In the case of a flood risk prediction system, device, method, and computer program according to embodiments of the present invention, the efficiency of data collection can be maximized by efficiently controlling drone paths so that multiple drones can detect without path overlap.

[0021] However, the effects of the present invention are not limited to those exemplified above, and various other effects are included within this specification and may be extended in various ways without departing from the spirit and scope of the invention. Brief explanation of the drawing

[0022] FIG. 1 is a block diagram showing an electronic device according to various embodiments of the present invention. FIG. 2 is a block diagram showing a flood risk prediction system according to various embodiments of the present invention. Figure 3 is a diagram illustrating an example of a drone and a path control module included in the flood risk prediction system of Figure 1. Figure 4 is a diagram illustrating an example of the operation of an integrated control module included in the flood risk prediction system of Figure 1. FIG. 5 is a flowchart illustrating a flood risk prediction method according to various embodiments of the present invention. Specific details for implementing the invention

[0023] In describing each drawing, similar reference numerals have been used for similar components. In the attached drawings, the dimensions of the structures are depicted enlarged compared to their actual size for the clarity of the invention. The area and thickness of each component shown in the drawings are illustrated for convenience of explanation and are not necessarily limited to the area and thickness of the components depicted in this specification.

[0024] Terms such as "first," "second," etc., may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. A singular expression includes a plural expression unless the context clearly indicates otherwise.

[0025] In this specification, terms such as “comprising,” “having,” and “consisting of” are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0026] Furthermore, when it is said that one part is "connected" or "linked" with another part, this includes not only cases where they are directly connected or linked, but also cases where they are connected or linked with another part in between.

[0027] The features of each of the various embodiments of the present invention may be combined or combined with one another, either partially or wholly, and may technically enable various interlocking and operation. Each embodiment may be implemented independently of one another or may be implemented together in an associated relationship.

[0028] Hereinafter, various embodiments of the present invention will be described in more detail with reference to the attached drawings. Identical components in the drawings are denoted by the same reference numerals, and redundant descriptions of identical components are omitted.

[0029] FIG. 1 is a block diagram showing an electronic device according to various embodiments of the present invention.

[0030] Referring to FIG. 1, an electronic device (100) according to embodiments of the present invention may include a processor (110), a communication module (120), an input module (130), a display module (140), and a memory (150).

[0031] According to an embodiment, a processor (110), a communication module (120), an input module (130), a display module (140), and a memory (150) included in an electronic device (100) may each be electrically and / or physically connected to each other. Meanwhile, although FIG. 1 is illustrated as having an electronic device (100) that includes a processor (110), a communication module (120), an input module (130), a display module (140), and a memory (150), this is merely illustrative and the embodiments of the present invention are not limited thereto. For example, the electronic device (100) may be implemented with some of the above-described components omitted or with additional components other than those described above (e.g., a power management module, a battery, etc.).

[0032] The processor (110) can control at least one other component (e.g., a hardware or software component) of the electronic device (100) connected to the processor (110) by executing software (e.g., a program (151)), and can perform various data processing or operations. In one embodiment, as at least part of the data processing or operations, the processor (110) can store commands or data received from other components (e.g., an input module (130) or a communication module (120)) in volatile memory, process the commands or data stored in volatile memory, and store the resulting data in non-volatile memory. For example, memory (150) may include volatile memory or non-volatile memory.

[0033] In one embodiment, the processor (110) may include a main processor (e.g., a central processing unit or an application processor) or an auxiliary processor that can operate independently or together with it (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device (100) includes a main processor and an auxiliary processor, the auxiliary processor may be configured to use less power than the main processor or to be specialized for a designated function. Depending on the embodiment, the auxiliary processor may be implemented separately from the main processor or as part thereof.

[0034] The processor (110) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence model may be generated through machine learning. Machine learning refers to learning that optimizes parameters with given data using a model composed of multiple parameters. Such learning may be performed, for example, on the electronic device (100) itself where the artificial intelligence is performed, or through a separate server. An artificial intelligence learning paradigm refers to various learning methods and approaches, and may include, for example, supervised learning, unsupervised learning, semisupervised learning, reinforcement learning, self-supervised learning, or transfer learning. An artificial intelligence learning algorithm may include, for example, linear regression, logistic regression, decision trees, random forest, support vector machines (SVM), or artificial neural networks, but is not limited to the examples described above.An artificial neural network may be a Transformer, a deep neural network (DNN), a convolutional neural network (CNN), a deep convolutional neural network (DCNN), a recurrent neural network (RNN), a generative adversarial network (GAN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), deep Q-networks, a feedforward algorithm, or a combination of two or more of the above, but is not limited to the examples described above. An artificial intelligence model may include multiple artificial neural network layers. In addition to a hardware structure, an artificial intelligence model may additionally or substantially include a software structure.

[0035] The communication module (120) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (100) and an external electronic device (e.g., an electronic device or server other than the electronic device (100)), and the performance of communication through the established communication channel. The communication module (120) may include one or more communication processors that operate independently of the processor (110) and support direct communication (e.g., wired communication) or wireless communication. In one embodiment, the communication module (120) may include a wireless communication module (e.g., a cellular communication module, a short-range wireless communication module, or a GNSS (global navigation satellite system) communication module) or a wired communication module (e.g., a LAN (local area network) communication module, or a power line communication module). The corresponding communication module among these communication modules is Bluetooth TM It can communicate with external electronic devices through local area communication networks such as Wi-Fi (wireless fidelity) direct or IrDA (infrared data association), legacy cellular networks, 5G networks, next-generation communication networks, the Internet, or computer networks (e.g., LAN or WAN). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips).

[0036] The input module (130) may receive commands or data to be used for a component of the electronic device (100) (e.g., processor (110)) from outside the electronic device (100) (e.g., user). The input module (130) may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen), but is not limited thereto.

[0037] The display module (140) can visually provide information to an external (e.g., user) of the electronic device (100). The display module (140) may include, for example, a display (e.g., a display panel), a holographic device, or a projector, and may further include a control circuit for controlling said device. Additionally, the display module (140) may further include a touch screen to recognize touch input from the user.

[0038] In one embodiment, the display module (140) can display a graphic user interface (GUI) for receiving user input (e.g., touch input, etc.).

[0039] The memory (150) can store various data used by at least one component of the electronic device (100) (e.g., processor (110) or input module (130)). The data may include, for example, input image data or output data for software (e.g., program (151)) and related instructions.

[0040] In one embodiment, memory (150) may store a learned model (e.g., a model learned through a processor (110) or a separate server). For example, at least some of the programs (151) stored in memory (150) may be learned models. Accordingly, the processor (110) may use the learned models by executing the programs (151).

[0041] The program (151) may be stored as software in memory (150) and may include, for example, an operating system, middleware, or an application.

[0042] FIG. 2 is a block diagram showing a flood risk prediction system according to various embodiments of the present invention.

[0043] Figure 3 is a diagram illustrating an example of a drone and a path control module included in the flood risk prediction system of Figure 1.

[0044] Figure 4 is a diagram illustrating an example of the operation of an integrated control module included in the flood risk prediction system of Figure 1.

[0045] Referring to FIG. 2, a flood risk prediction system (200) according to embodiments of the present invention can collect video data and sensor data in real time using a plurality of drones (210) and analyze them to predict flood risk in advance.

[0046] In addition, the flood risk prediction system (200) according to the embodiments of the present invention can support a local government or public institution in taking preemptive action by providing the predicted flood risk information to an external entity, for example, a local government or public institution (e.g., a disaster response agency) in real time.

[0047] In one embodiment, with reference to FIG. 1 and FIG. 2, the flood risk prediction system (200) of FIG. 2 may be the electronic device (100) described with reference to FIG. 1 and / or may be implemented as the electronic device (100).

[0048] For example, the operation of each component included in the flood risk prediction system (200) illustrated in FIG. 2 can be performed by the processor (110) illustrated in FIG. 1. As an example, the flood risk prediction system (200) includes a processor (110) (e.g., CPU), and the processor (110) of the flood risk prediction system (200) can perform an operation for flood risk prediction.

[0049] As another example, the flood risk prediction system (200) illustrated in FIG. 2 may be at least one processor included in the processor (110) illustrated in FIG. 1 and / or may be performed by said at least one processor. For example, the flood risk prediction system (200) may be at least one auxiliary processor (e.g., GPU) included in the processor (110) and / or may be performed by said at least one auxiliary processor.

[0050] Additionally, according to an embodiment, the flood risk prediction system (200) may include at least one of the components of the electronic device (100) described with reference to FIG. 1, for example, a communication module (e.g., communication module (120) of FIG. 1), an input module (e.g., input module (130) of FIG. 1), a display module (e.g., display module (140) of FIG. 1), and a memory (e.g., memory (150) of FIG. 1).

[0051] Hereinafter, the operation of the flood risk prediction system (200) according to embodiments of the present invention will be described in more detail with reference to FIG. 2.

[0052] Referring to FIG. 2, in one embodiment, the flood risk prediction system (200) may include at least one drone (210), a path control module (220), an analysis module (230), an integrated control module (240), and a visualization module (250).

[0053] A drone (210), which is an unmanned aircraft, is an aircraft that is flown by being remotely controlled from the ground without a pilot being directly on board. It can be an unmanned flying device that can be lifted into the air by the rotation of a propeller and can be controlled and flown by radio wave guidance.

[0054] According to an embodiment, the flood risk prediction system (200) may include multiple drones (210), that is, a plurality of drones (210). For example, each of the plurality of drones (210) has its path controlled independently and can fly over various areas where flood prediction is required, e.g., rivers and urban areas. Meanwhile, the areas where flood prediction is required (e.g., rivers and urban areas, etc.) may be referred to as 'data collection areas'.

[0055] Each of the plurality of drones (210) can collect image data and sensor data in a data collection area. To this end, each of the plurality of drones (210) may include at least one camera module for collecting image data and at least one sensor module for collecting sensor data.

[0056] In an embodiment, each of the plurality of drones (210) can assign spatiotemporal coordinate tags to the collected image data and / or sensor data using GPS information in order to reflect spatial information regarding the image data and / or sensor data.

[0057] At least one camera module included in each of the plurality of drones (210) can collect various images of the data collection area as image data, for example, images of the water surface of a river, images of structures around river embankments and sluice gates, images of roads, images of drainage ditches, images of low-lying areas, etc., but is not limited thereto.

[0058] In one embodiment, at least one camera module for collecting image data included in each of the plurality of drones (210) may include a stereo camera. The stereo camera utilizes a stereo vision method using at least two cameras, and stereo vision can record a scene from two different perspectives and use the difference to represent the location, relationship, and structure within the scene, and depth information can be measured from the difference in pixel positions between the two images in the camera images.

[0059] On the other hand, single-image-based camera modules have limitations in that they can only collect qualitative information sufficient to read the scale of a fixed water level staff. Additionally, the method of installing a fixed water level staff requires the installation of a structure, which imposes restrictions on installation locations and operating environments.

[0060] In contrast, in the case of the flood risk prediction system (200) according to the embodiments of the present invention, the camera module included in each of the plurality of drones (210) includes a stereo camera, thereby enabling real-time quantitative measurement of depth information between the water surface of a data collection area, for example, a river area, and a reference plane without a separate reference point. Furthermore, in the case of the flood risk prediction system (200) according to the embodiments of the present invention, depth information can be measured using a drone (210) equipped with a stereo camera (camera module) without installing a separate structure compared to a single image-based camera module.

[0061] According to an embodiment, depth information based on image data collected by a plurality of drones (210) can be generated by an analysis module (230) and / or an integrated control module (240) described later.

[0062] In addition, in one embodiment, at least one sensor module for collecting sensor data included in each of the plurality of drones (210) may include at least one of a water level gauge, a flow meter, and a weather sensor. For example, the flow meter may be a sensor for measuring the flow velocity in the data collection area, and the water level gauge may be a sensor for measuring the water level within the data collection area. In addition, the weather sensor included in each of the plurality of drones (210) may include, but is not limited to, a LiDAR sensor, an RGB sensor, an IR sensor, a radar, a thermal imaging camera, etc.

[0063] According to an embodiment, each of the plurality of drones (210) may include a sealed housing design structure. For example, each of the plurality of drones (210) includes a power supply unit (e.g., battery, etc.) for supplying power required for the overall configuration, a flight controller (FC), a motor, an electronic speed control (ESC), a GPS positioning module, etc. Since such components require waterproofing and dustproofing, each of the plurality of drones (210) may include a sealed housing design structure capable of waterproofing and dustproofing.

[0064] Each of the plurality of drones (210) can be configured to communicate with a path control module (220). For example, each of the plurality of drones (210) can receive a path control signal from the path control module (220) through a communication module (e.g., the communication module (120) of FIG. 1) and have its path controlled.

[0065] Additionally, each of the multiple drones (210) can communicate with the path control module (220), so that the communication status, charging status, etc. of the drones (210) can be monitored by the path control module (220). Accordingly, if the communication status and / or charging status of at least one of the multiple drones (210) is determined to be in a defective state, such as falling below a threshold, the path control module (220) can transmit a return command instruction to the drone (210).

[0066] According to an embodiment, a plurality of drones (210) may include drones (210) with different operating modes. For example, a plurality of drones (210) may include at least one fixed-wing drone and at least one rotary-blade drone. In this case, at least one fixed-wing drone among the plurality of drones (210) may fly over a long-distance section and / or a wide-area section where wide-area observation is required within the data collection area to collect image data and / or sensor data, and at least one rotary-blade drone among the plurality of drones (210) may fly at a low altitude over a section where precise observation is required, such as an area where a sluice gate, bridge, etc. are located within the data collection area to collect image data and / or sensor data. However, this is merely illustrative and the embodiments of the present invention are not limited thereto. For example, a plurality of drones (210) may include a plurality of drones (210) with the same operating mode (e.g., fixed-wing drone, rotary-wing drone, tilt-rotor drone, hybrid drone, etc.).

[0067] The path control module (220) can control the flight path of each of the multiple drones (210). For example, the path control module (220) can control the flight paths of the multiple drones (210) so that the multiple drones (210) can fly over a data collection area without overlapping flight paths and collect data (e.g., image data and / or sensor data). Accordingly, the efficiency of data collection can be maximized.

[0068] To explain more specifically, referring to FIG. 3, each of the multiple drones (210a, 210b, 210c, 210d, 210e) can fly to collect data in the data collection area (AA).

[0069] For example, the path control module (220) can divide the data collection area (AA) into multiple areas (A1, A2, A3, A4, A5) and control multiple drones (210a, 210b, 210c, 210d, 210e) to fly within the data collection area (AA) in different areas (A1, A2, A3, A4, A5). In other words, the areas (A1, A2, A3, A4, A5) where the multiple drones (210a, 210b, 210c, 210d, 210e) fly may correspond to different areas.

[0070] Meanwhile, although the above description is based on the assumption that the path control module (220) assigns one drone (210a, 210b, 210c, 210d, 210e) to one area (A1, A2, A3, A4, A5), this is merely illustrative and the embodiments of the present invention are not limited thereto. For example, the path control module (220) may also control at least two drones (210a, 210b, 210c, 210d, 210e) to fly by assigning them to one area (A1, A2, A3, A4, A5).

[0071] Additionally, in FIG. 3, the flood risk prediction system (200) is described based on including five drones (210a, 210b, 210c, 210d, 210e), but this is merely an example for convenience of explanation and the number of multiple drones (210) is not limited thereto.

[0072] According to an embodiment, the path control module (220) may assign a priority to each of the plurality of areas (A1, A2, A3, A4, A5) included in the data collection area (AA) based on the importance of data collection (e.g., flood vulnerability, population density, history of past damage, etc.) and control the paths of the plurality of drones (210) based on the priority. For example, the path control module (220) may control the number of drones (210), flight time, flight speed, flight altitude, and / or shooting cycle assigned to the corresponding area by reflecting the priority of each of the plurality of areas (A1, A2, A3, A4, A5) included in the data collection area (AA), but is not limited thereto.

[0073] In an embodiment, the path control module (220) monitors the communication status and charging status of each of the plurality of drones (210) and, based thereon, can control the path of the plurality of drones (210). For example, the path control module (220) can control a drone (210) with a relatively low communication status and / or charging status to fly over a data collection area (AA) adjacent to the central control center of the flood risk prediction system (200). As another example, the path control module (220) can issue a return command instruction to the drone (210) and control another adjacent drone (210) to fly in that area to collect data if the communication status and / or charging status is below a threshold.

[0074] In one embodiment, the path control module (220) can control the paths of a plurality of drones (210) based on analysis information generated by the operation of the analysis module (230) described below. In other words, the path control module (220) can control the paths of a plurality of drones (210) by utilizing the analysis information generated by the operation of the analysis module (230) as feedback information.

[0075] For example, if a zone where the risk of flooding has rapidly increased is detected by the operation of the analysis module (230) in the data collection area (AA), the path control module (220) may reduce the data collection cycle (or increase the frequency of data collection) of at least one drone (210) flying in the area overlapping with the zone and / or increase the number of drones (210) assigned to the area overlapping with the zone.

[0076] Referring again to FIG. 2, the analysis module (230) can perform a flood prediction operation based on data collected from a plurality of drones (210), for example, image data and sensor data. For example, the analysis module (230) can generate analysis information by calculating the probability of flooding occurring by zone within a data collection area (AA) based on image data and sensor data collected from a plurality of drones (210). As an example, the analysis module (230) can generate analysis information by predicting the rate of water level rise and analyzing flow velocity patterns based on image data and sensor data, reflecting spatial information (e.g., terrain, roads, etc.) of the data collection area (AA), and synthesizing this to calculate the probability of flooding occurring by zone within the data collection area (AA).

[0077] According to an embodiment, data collected from a plurality of drones (210) (e.g., image data and / or sensor data) is provided to an integrated control module (240) described below, and the data collected by the integrated control module (240) may be provided to an analysis module (230) after being preprocessed. For example, the integrated control module (240) may synchronize the data collected from the plurality of drones (210) in temporal and spatial units and then provide the synchronized data to the analysis module (230).

[0078] In one embodiment, the analysis module (230) may perform a flood prediction operation using at least one artificial intelligence model. For example, at least one artificial intelligence model may be supervised learning to calculate the probability of flood occurrence through learning data including past flood damage history, rainfall data, etc., and the analysis module (230) may generate analysis information by calculating the probability of flood occurrence by zone within a data collection area (AA) based on image data and sensor data collected from a plurality of drones (210) using at least one artificial intelligence model learned in this way. According to the embodiment, the at least one artificial intelligence model may include a Random Forest model, but is not limited thereto, and various artificial intelligence models and / or algorithms may be applied.

[0079] In an embodiment, the analysis module (230) can calculate depth information based on image data provided from a plurality of drones (210). For example, as described above, if at least one camera module included in each of the plurality of drones (210) includes a stereo camera, the analysis module (230) can calculate the distance to the water surface and the height difference between the water surface and the reference plane based on the image data collected from the plurality of drones (210) and the parallax information of two images, and calculate the depth information between the water surface and the reference plane of the data collection area (AA), for example, a river area. In this case, the analysis module (230) can calculate the probability of flooding more precisely by reflecting the calculated depth information.

[0080] In an additional embodiment, the analysis module (230) may calculate risk level and inspection priority information for each zone within the data collection area (AA) based on analysis information, namely, the calculated probability of flooding by zone. Such risk level and inspection priority information may be provided in real time to a local government or public institution (e.g., a disaster response agency), and accordingly, the local government or public institution may be able to prioritize and preemptively respond to areas with a high probability of flooding based on the risk level and inspection priority information.

[0081] In one embodiment, the analysis module (230) can detect a hydraulic jump in a river within a data collection area (AA) based on image data and sensor data collected from a plurality of drones (210).

[0082] Specifically, a water jump refers to a phenomenon of rapid change in flow velocity occurring near rivers or sluice structures. It can be a hydraulic disaster that causes casualties due to strong eddies, pressure differences, and drop in elevation, making it crucial to identify the location where such water jumps are detected. However, such water jumps are difficult to detect using only water level or flow velocity sensors, and they present a problem of difficulty in recognition due to the simultaneous presence of stagnant water surfaces and turbulent sections.

[0083] Accordingly, the analysis module (230) of the flood risk prediction system (200) according to the embodiments of the present invention extracts a plurality of visual elements from image data collected from camera modules included in a plurality of drones (210), such as a turbulence generation area, a section of rapid change in flow velocity, a change in reflectance and color of the water surface, and a reverse diffusion of waves, such as the degree of splashing, a vortex pattern, and a foam pattern on the water surface, and can predict the probability of a water level phenomenon occurring within a data collection area (AA) through the plurality of visual elements. For example, the analysis module (230) can merge the weights of the plurality of visual elements and predict the probability of a water level phenomenon occurring based on the sum of the merged weights, and if the sum of the merged weights exceeds a preset threshold value, the area can be classified as a water level phenomenon risk zone.

[0084] According to the embodiment, since the pattern of water surge occurrence differs depending on the river structure (e.g., weir, spillway, culvert, bridge, etc.), the analysis module (230) may set different weights for each of the multiple visual elements depending on the type of river structure, but is not limited thereto.

[0085] Next, referring again to FIG. 2, the integrated control module (240) can manage each module within the flood risk prediction system (200) and can manage data collected from multiple drones (210) and / or analysis information generated from the analysis module (230). For example, as described above, data collected from multiple drones (210) (e.g., image data and / or sensor data) is provided to the integrated control module (240), and the data collected by the integrated control module (240) can be provided to the analysis module (230) after being preprocessed.

[0086] In one embodiment, with reference to FIG. 4, the integrated control module (240) can generate a flood risk map (HTM) based on analysis information generated from the analysis module (230), for example, information on the probability of flooding calculated for each zone within the data collection area (AA). For example, the integrated control module (240) can generate a flood risk map (HTM) in the form of a heatmap, which is a map-shaped layer based on the probability of flooding calculated for each zone within the data collection area (AA), with colors distinguished by flooding probability grades (L1, L2, L3).

[0087] Referring again to FIG. 2, the visualization module (250) can display and provide to the user analysis information, for example, a map (e.g., a GIS-based map) showing the risk of flooding and / or the risk of flooding phenomena, based on the probability of flooding occurrence predicted by the analysis module (230), and step-by-step warning issuance information according to the flood risk level. For example, the flood risk prediction system (200) includes a separate display module (e.g., the display module (140) of FIG. 1), and the visualization module (250) can display various information through the display module and provide to the user.

[0088] According to an embodiment, the visualization module (250) may provide the user with a flood risk map (HTM) generated by the operation of the aforementioned integrated control module (240), but is not limited thereto.

[0089] As described with reference to FIGS. 2 to 4, the flood risk prediction system (200) according to embodiments of the present invention can collect video data and sensor data in real time using a plurality of drones (210) and analyze them to predict flood risk in advance. In addition, the flood risk prediction system (200) according to embodiments of the present invention can support a local government or public institution in taking preemptive action by providing the predicted flood risk information to an external entity, for example, a local government or public institution (e.g., a disaster response agency) in real time.

[0090] FIG. 5 is a flowchart illustrating a flood risk prediction method according to various embodiments of the present invention.

[0091] Referring to FIGS. 1 to 5, the flood risk prediction method of FIG. 5 can be performed on the flood risk prediction system (200) (or electronic device (100)) described with reference to FIGS. 1 to 4. Accordingly, redundant descriptions will not be repeated.

[0092] First, the flood risk prediction method of FIG. 5 may include a method of dividing a data collection area and assigning a drone's path (S610). For example, the method of dividing a data collection area and assigning a drone's path (S610) may be substantially the same or similar to the operation of a path control module (220) that divides an area within a data collection area (AA) and controls the paths of a plurality of drones (210) as described with reference to FIG. 2 to 4.

[0093] Next, the flood risk prediction method of FIG. 5 may include a step of collecting data (S620). For example, the step of collecting data (S620) may be substantially the same or similar to the operation of collecting data (e.g., image data and / or sensor data) within a data collection area (AA) by a plurality of drones (210) described with reference to FIG. 2 to 4.

[0094] Next, the flood risk prediction method of FIG. 5 may include a step of synchronizing data (S630). For example, the step of synchronizing data (S630) may be substantially the same or similar to the operation of the integrated control module (240) described with reference to FIG. 2 to FIG. 4, which synchronizes and processes data collected from a plurality of drones (210) in temporal and spatial units.

[0095] Next, the flood risk prediction method of FIG. 5 may include a step of generating analysis information (S640) by analyzing data. For example, the step of generating analysis information (S640) by analyzing data may be substantially the same or similar to the operation of the analysis module (230) described with reference to FIG. 2 to 4.

[0096] Additionally, in an embodiment, the flood risk prediction method of FIG. 5 may further include the step of visually providing information about flood risk to a user based on analysis information in a manner substantially identical or similar to the operation of the flood risk prediction system (200) described with reference to FIG. 2 to 4, and / or the step of providing the analysis information to a local government or public institution in real time.

[0097] The embodiments according to the present invention described above may be implemented in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, etc., either individually or in combination. The program instructions recorded on the computer-readable recording medium may be those specifically designed and configured for the present invention, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media may include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions may include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware device may be configured to operate as one or more software modules to perform processing according to the present invention, and vice versa.

[0098] Meanwhile, at least some of the methods or processes performed by the methods described above (e.g., the methods of FIG. 5) may be implemented by hardware, software, firmware, middleware, microcode, a hardware description language, or a combination of two or more of the above. Where at least some of the methods or processes described above are implemented by software, firmware, middleware, or microcode, program code or code segments for performing the necessary tasks may be stored on a machine or computer-readable medium, such as a storage medium, thereby enabling a processor to perform the necessary tasks.

[0099] Although the present invention has been described above with reference to embodiments thereof, those skilled in the art will understand that various modifications and changes can be made to the invention without departing from the spirit and scope of the invention as described in the claims. Explanation of the symbols

[0100] 100: Electronic device 110: Processor 120: Communication module 130: Input Module 140: Display Module 150: Memory 151: Program 200: Flood Risk Prediction System 210: Drone 220: Path control module 230: Analysis Module 240: Integrated Control Module 250: Visualization Module

Claims

Claim 1 A plurality of drones, each collecting image data through at least one camera module and sensor data through at least one sensor module within a data collection area; a path control module that divides the data collection area into a plurality of regions and controls the flight path of each of the plurality of drones; A flood risk prediction system comprising: an analysis module that generates analysis information by calculating the probability of flooding occurrence for each zone within the data collection area based on the image data and sensor data collected from the plurality of drones; wherein at least one camera module included in each of the plurality of drones includes a stereo camera; wherein the analysis module calculates depth information of the data collection area based on the difference in pixel positions of two images based on the image data collected through the stereo camera; wherein the path control module controls the path of each of the plurality of drones based on the analysis information generated from the analysis module; wherein the analysis module predicts the rate of rise in water level and analyzes the flow velocity pattern based on the image data and sensor data, and calculates the probability of flooding occurrence for each zone within the data collection area by reflecting the spatial information of the data collection area; wherein the analysis module extracts a plurality of visual elements including the degree of splashing, vortex patterns, and foam patterns of the water surface from the image data, and predicts the probability of a hydraulic jump occurring within the data collection area based on the plurality of visual elements. Claim 2 delete Claim 3 A flood risk prediction system according to claim 1, wherein the at least one sensor module included in each of the plurality of drones comprises at least one of a water level gauge, a flow meter, and a weather sensor. Claim 4 In claim 1, the path control module monitors the communication status and charging status of each of the plurality of drones and controls the path of each of the plurality of drones based on the communication status and charging status, a flood risk prediction system. Claim 5 delete Claim 6 delete Claim 7 delete Claim 8 A flood risk prediction system according to claim 1, further comprising an integrated control module that synchronizes the image data and sensor data collected from the plurality of drones in temporal and spatial units, and then provides the synchronized image data and sensor data to the analysis module, wherein the analysis module generates the analysis information based on the synchronized image data and sensor data. Claim 9 In claim 8, the integrated control module is a flood risk prediction system that generates a flood risk map based on the analysis information. Claim 10 A flood risk prediction system according to claim 1, further comprising a visualization module that visualizes and provides the analysis information to a user.

Images