Deformation extraction device, topographic situation monitoring system, topographic situation monitoring method, and topographic situation monitoring program

Abstract

To provide a deformation extraction device, a topographic state monitoring system, a topographic state monitoring method, and a topographic state monitoring program that detect a change in topography.SOLUTION: A deformation extraction device 2 comprises: a communication unit 20 that acquires measurement data 221 of environmental information acquired by a measuring instrument 4 and point group data 222 of an object acquired by a distance measuring device 5; a storage unit 22 that stores the acquired measurement data 221 and point group data 222; and a processing unit 21 that, on the basis of the measurement data 221 and point group data 222, calculates the amount of change in volume in at least two pieces of point group data 222, of the point group data 222 of the object, and determines whether the calculated amount of change in volume exceeds a threshold.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a deformation extraction device that detects changes in terrain, a terrain condition monitoring system, a terrain condition monitoring method, and a terrain condition monitoring program. [Background technology]

[0002] In light of recent climate change and the frequent occurrence of natural disasters, it is becoming increasingly important to predict or detect disasters in each region in response to changes in weather conditions and topographical features. Disaster prediction makes it possible to provide evacuation guidance before a disaster occurs, identify hazards in each region, and formulate plans for repair work, which is expected to help reduce damage caused by disasters. Furthermore, by understanding the situation at the site of a disaster based on accurate disaster detection, it becomes possible to grasp the scale of the disaster and formulate repair plans that take into account the costs required for recovery work, allowing for appropriate disaster response.

[0003] For example, Patent Document 1 below discloses a technology that accurately predicts or detects landslides by detecting changes in the condition of the ground surface using multiple measuring means such as cameras and laser scanners and predicting the risk of landslides occurring at the measured locations. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-174013 Summary of the Invention [Problem to be solved by the invention]

[0005] Conventional technology calculates the amount of change in the ground surface shape in a target area based on measurement data acquired by multiple measurement tools, such as cameras and laser scanners. It is then possible to predict the risk level in the target area by comparing the calculated ground surface shape with a pre-set risk level. As a result, it is possible to predict and detect the occurrence of landslides based on the amount of change in the ground surface shape. However, while it is possible to predict and detect the risk level of landslides in a target area from the amount of change in the ground surface, it is not possible to take into account the amount of change in volume in the target area. As a result, it is difficult to, for example, detect areas of a target object that are small in volume and prone to erosion, or to estimate the amount of soil and sand to be removed during excavation work to level the terrain or restoration work at a disaster site.

[0006] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a deformation extraction device that detects changes in terrain, a terrain condition monitoring system, a terrain condition monitoring method, and a terrain condition monitoring program. [Means for solving the problem]

[0007] The deformation extraction device according to the present disclosure comprises a communication unit that acquires measurement data of environmental information acquired by a measuring instrument and point cloud data of an object acquired by a ranging device, a memory unit that stores the acquired measurement data and point cloud data, and a processing unit that calculates the amount of volume change in at least two of the point cloud data of the object based on the measurement data and point cloud data, and determines whether the calculated amount of volume change exceeds a threshold value.

[0008] The terrain condition monitoring system of the present disclosure comprises a deformation extraction device having a measuring instrument that acquires measurement data of environmental information, a ranging device that acquires point cloud data of an object, a communication unit that acquires measurement data and point cloud data of the amount of change in volume of the object, a memory unit that stores the acquired measurement data and point cloud data, and a processing unit that calculates the amount of change in volume in at least two of the point cloud data of the object based on the measurement data and point cloud data and determines whether the calculated amount of change in volume exceeds a threshold value.

[0009] The method for monitoring terrain conditions according to the present disclosure includes the steps of acquiring measurement data of environmental information obtained by a measuring instrument, acquiring point cloud data of an object obtained by a ranging device, storing the acquired measurement data and point cloud data, calculating the amount of volume change in at least two of the point cloud data of the object based on the measurement data and point cloud data, and determining whether the calculated amount of volume change exceeds a threshold value.

[0010] The terrain condition monitoring program of the present disclosure causes a computer to execute the steps of acquiring measurement data of environmental information acquired by a measuring instrument, acquiring point cloud data of an object acquired by a ranging device, storing the acquired measurement data and point cloud data, calculating the amount of volume change in at least two of the point cloud data of the object based on the measurement data and point cloud data, and determining whether the calculated amount of volume change exceeds a threshold value. [Effects of the Invention]

[0011] According to the present disclosure, by understanding the change in volume of a target area, it is possible to detect areas where the terrain is prone to erosion, and therefore repair work can be carried out in areas prone to erosion before disasters such as heavy rain or flooding occur. Furthermore, by understanding the volume, it is possible to estimate the amount of earth and sand that will be removed during excavation work to level the terrain or during recovery work at a disaster site, making it possible to more appropriately prevent damage from disasters or respond to disasters when they occur. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a configuration diagram showing a terrain condition monitoring system according to a first embodiment. [Figure 2] 5 is a diagram showing an example of each measurement data according to the first embodiment. FIG. [Figure 3] 1 is a diagram illustrating an example of the hardware configuration of a deformation extraction device according to the first embodiment. FIG. [Figure 4] 10 is a flowchart of a deformation extraction process according to the first embodiment. [Figure 5] 4 is a conceptual diagram showing the calculation of the volume change amount performed by the deformation extraction device according to the first embodiment. FIG. [Figure 6] 4 is a conceptual diagram of the amount of volume change calculated by the deformation extraction device according to the first embodiment. FIG. [Figure 7] 3 is a diagram showing an example of a display screen displayed by the display device according to the first embodiment. FIG. [Figure 8] FIG. 10 is a configuration diagram of a terrain condition monitoring system according to a second embodiment. [Figure 9] 10 is a flowchart of a deformation extraction process according to the second embodiment. [Figure 10] FIG. 10 is a configuration diagram of a terrain condition monitoring system according to a third embodiment. [Figure 11] FIG. 11 is a functional block diagram of a prediction unit in a deformation extraction device according to a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] The following describes in detail the deformation extraction device, terrain condition monitoring system, terrain condition monitoring method, and terrain condition monitoring program according to the embodiments of the present invention with reference to the drawings. Note that the present invention is not limited to these embodiments. Furthermore, the same reference numerals are used to designate the same components and members in each embodiment, and their description will be omitted.

[0014] Embodiment 1 <Configuration of terrain condition monitoring system 100> FIG. 1 is a diagram illustrating an example of the configuration of a terrain condition monitoring system 100 according to a first embodiment. The terrain condition monitoring system 100 monitors the condition of terrain, landmarks, and the like (hereinafter referred to as "objects") whose shape has changed due to factors such as deterioration or disasters, and issues an alarm when the degree of change in shape exceeds a threshold. The terrain condition monitoring system 100 can be applied to any object whose shape changes due to some kind of influence, and can be used, for example, to detect sediment or sand flow in rivers, damage to levees along rivers, and collapse of slopes formed by excavation or embankment during road construction. The system can also be applied to the maintenance and management of roads, coasts, erosion control, construction, and the like. This disclosure describes an example in which the terrain condition monitoring system 100 is applied to a river management company that manages rivers. In particular, the disclosure will be described using as an example the work of removing sediment accumulated in rivers to prevent riverbed rise or blockage of river channels and prevent flooding or flood damage.

[0015] As shown in Fig. 1, the topographical condition monitoring system 100 according to the first embodiment includes a management device 1 and a deformation extraction device 2. The management device 1 and the deformation extraction device 2 are communicably connected to each other via a network 3. The network 3 is, for example, a wide area network (WAN) such as the Internet or a local area network (LAN).

[0016] The management device 1 is a computer installed in, for example, the office of a management business operator, and is operated and managed by an administrator. The administrator is, for example, a management officer at the Ministry of Land, Infrastructure, Transport and Tourism or a local government. The office is a management office that manages rivers and the like, such as a government office of the country or a local government, a dam management office, or a river management office. The management device 1 is not limited to being installed in an office, and may also be installed on a server outside the office or on the cloud.

[0017] The deformation extraction device 2 is, for example, a computer installed in the administrator's office, and supports users of the topographical condition monitoring system 100 in monitoring the condition of rivers. River condition monitoring includes, for example, detecting and removing sediment or sand flow in the river. The deformation extraction device 2 is not limited to being installed in an office, but may also be installed on a server outside the office or on the cloud.

[0018] A user of the terrain condition monitoring system 100 is, for example, but not limited to, a manager of the Ministry of Land, Infrastructure, Transport and Tourism or a local government that manages the conditions of the rivers described above. Hereinafter, a user of the terrain condition monitoring system 100 may be simply referred to as a "user."

[0019] The management device 1 includes a communication unit 10, a processing unit 11, an input unit 12, and an output unit 13. The management device 1 also has an externally connected display device 14 and input device 15. The deformation extraction device 2 also includes a communication unit 20, a processing unit 21, and a storage unit 22.

[0020] The communication units 10, 20 are connected to the network 3 and are interfaces for transmitting and receiving information to and from each other via the network 3, for example, LAN terminals. The communication units 10, 20 are not limited to LAN terminals that are wired network terminals, but may also perform wireless communication using, for example, a wireless LAN system. Furthermore, the connection between the management device 1 and the deformation extraction device 2 is not limited to only the network 3, but may also be connected via a wired connection that allows direct communication and allows the transmission and reception of information.

[0021] The input unit 12 in the management device 1 is an input interface for inputting user instructions and data from outside the management device 1, and is, for example, a USB terminal. The input unit 12 is connected to the input device 15 by a USB cable, and the processing unit 11 accepts user instructions from the input device 15 via the input unit 12. Here, the input device 15 may be any device that accepts some kind of input operation from the user, and may be, for example, a keyboard, a mouse, or a touch panel. The input device 15 and the input unit 12 may be connected wirelessly or via a wired connection.

[0022] The processing unit 11 in the management device 1 is a processor such as a CPU, and is connected to the communication unit 10, the input unit 12, and the output unit 13. When a user operates the input unit 12, the processing unit 11 processes the input information and sends it to the communication unit 10. The communication unit 10 transmits the information received from the processing unit 11 to the deformation extraction device 2.

[0023] The output unit 13 is an output interface for outputting data acquired by the processing unit 11 from the deformation extraction device 2, calculation results, etc., to outside the management device 1, and examples of such an interface include an HDMI terminal (HDMI is a registered trademark), a DVI terminal, and a D-Sub terminal. The output unit 13 is connected to the display device 14 via various cables, and transmits various pieces of information acquired from the processing unit 11 to the display device 14.

[0024] The display device 14 is a device for displaying information obtained from the deformation extraction device 2, and examples thereof include an LCD display, a plasma display, an organic EL display, a smartphone, and a portable tablet device. Furthermore, since multiple users monitor the river conditions in the management office, the display device 14 may be a large multi-screen system combining multiple displays to improve visibility. Furthermore, while the display device 14 is provided in the management device 1 in FIG. 1, it may also be provided in the deformation extraction device 2. When the display device 14 is provided in the deformation extraction device 2, it may be configured to function as an HMI (Human Machine Interface), allowing a user to operate the deformation extraction device 2 by directly touching the display device 14, for example.

[0025] The processing unit 21 of the deformation extraction device 2 is a processor such as a CPU, and is connected to the communication unit 20 and the storage unit 22. The processing unit 21 executes a topographical condition monitoring program installed in the storage unit 22, controls the operation of the communication unit 20 and the storage unit 22, and performs calculation processing, etc. The functions executed by the processing unit 21 include a threshold determination unit 211, a deformation extraction unit 212, and a screen generation unit 213, and details of each function will be described later.

[0026] The processing unit 21 of this embodiment, as a terrain condition monitoring program, executes the following steps: acquiring measurement data of environmental information acquired by a measuring instrument, acquiring point cloud data of an object acquired by a distance measuring device, storing the acquired measurement data and point cloud data, calculating volume changes in at least two of the point cloud data of the object based on the measurement data and point cloud data, and determining whether the calculated volume changes exceed a threshold. Details of the measurement data, point cloud data, and volume changes will be described later.

[0027] The storage unit 22 is a storage medium that stores the terrain condition monitoring program executed by the processing unit 21 and various data, and examples thereof include a hard disk drive (HDD), a solid state drive (SDD), a USB flash memory, an SD card, etc. In this embodiment, the storage unit 22 is provided inside the deformation extraction device 2, but it may also be provided externally.

[0028] The storage unit 22 stores measurement data 221 acquired from the measuring equipment 4, point cloud data 222 acquired from the distance measuring device 5, and deformation result data 223. Details of the measurement data 221, point cloud data 222, and deformation result data 223 will be described later.

[0029] The measuring device 4 may be, for example, an anemometer that measures wind speed, a water level gauge that measures the water level of a river at each observation site, or a seismometer that measures the magnitude and depth of an earthquake epicenter. Examples of anemometers include cup-type anemometers and ultrasonic anemometers, which are devices capable of measuring wind speed and volume. Examples of water level gauges that can be used include float-type water level gauges, air bubble-type water level gauges, reed switch-type water level gauges, hydraulic water level gauges, and ultrasonic water level gauges. However, water level measurement data may also be acquired using a river camera that can monitor the current state of the river in real time in conjunction with the water level gauge. In this case, the river camera may be, for example, a fixed CCTV (closed-circuit TV) camera. Images acquired by the river camera may be either still images or videos. Still images may be in formats such as JPEG, PNG, and GIF, while videos may be in formats such as AVI, MOV, MPEG-2, MPEG-4, and Motion-JPEG. Examples of seismometers include high-sensitivity seismometers, broadband seismometers, and strong motion seismometers. The measuring equipment 4 is not limited to these, and may be any equipment capable of acquiring measurement data 221 of environmental information such as meteorology or geology. The timing at which the measuring equipment 4 acquires the measurement data 221 and the timing at which the deformation extraction device 2 acquires the measurement data 221 from the measuring equipment 4 may be at preset intervals or at any timing.

[0030] FIG. 2 shows an example of measurement data 221 acquired by a measuring device 4. As shown in FIG. 2, different measurement data 221 is acquired for each measuring device 4. The measurement data (anemometer) includes the observation site where the anemometer is installed, the latitude and longitude of the observation site, the date and time the measurement data was acquired, and the wind speed and wind direction, all of which are associated with the identification number of each installed anemometer. The measurement data (water level meter) includes the river name observed by the water level meter, the observation site where the water level meter is installed, the date and time the measurement data was acquired, the water level, which is the measurement data, and the water level line to which the measured water level corresponds, all of which are associated with the identification number of each installed water level meter. The measurement data (seismometer) includes the epicenter name indicating the location of the earthquake observed by the seismometer, the latitude and longitude of the observation site, the date and time the measurement data was acquired, the depth of the epicenter, which is the measurement data, and the magnitude (M), which is an index indicating the amount of energy generated by an earthquake, all of which are associated with the identification number of each installed seismometer. Furthermore, the measurement data 221 acquired by the measuring device 4 described above is an example, and is not limited to the above.

[0031] Returning to FIG. 1, the distance measuring device 5 will be described. The distance measuring device 5 irradiates a laser onto the object to be measured and acquires the shape of the object as point cloud data (3D point cloud data) using the reflected light. In addition to coordinate information (x, y, z), the point cloud data may also include color information (R, G, B), reflection intensity, reflectance, angle information, and the like, for each point constituting the object. The measurement method used by the distance measuring device 5 may be a known method such as a time-of-flight method or a phase-shift method, and may be selected according to the object. The point cloud data measured by the distance measuring device 5 is stored in a memory unit (not shown) installed in the distance measuring device 5 and transmitted to the deformation extraction device 2 via wired or wireless communication.

[0032] The ranging device 5 may be, for example, a Field Viewer (registered trademark), a Field LiDAR (registered trademark), a UAV (Unmanned Air Vehicle), or an aerial LP. The ranging device 5 according to the present disclosure is preferably equipped with a function capable of acquiring image data, such as photographs or videos, of an object when acquiring point cloud data of the object. The ranging device 5 may be permanently installed to perform fixed-point measurements or may be portable to perform portable measurements. In the case of a ranging device 5 permanently installed to perform fixed-point measurements, the imaging settings (e.g., angle of view, imaging direction, scale, brightness, contrast, resolution, etc.) may be configured in advance according to the surrounding environment. The imaging settings may be configured automatically by a control unit (not shown) of the ranging device 5 in response to an instruction from the deformation extraction device 2. In the present disclosure, the timing for acquiring image data and point cloud data is when the measurement data acquired by the measuring instrument 4 exceeds a predetermined threshold, but this is not limited thereto and may be at predetermined intervals or at any timing. The image data acquired by the distance measuring device 5 is included in the point cloud data and transmitted to the deformation extraction device 2. Furthermore, the acquisition of image data is not limited to the above-described configuration, and a configuration in which a separate camera is provided to acquire image data may also be used.

[0033] <Example of hardware configuration of deformation extraction device 2> FIG. 3 shows an example of the hardware configuration of the deformation extraction device 2. As shown in FIG. 3, the deformation extraction device 2 includes a processor 100a, a memory 101a, and an interface circuit 102a. The processor 100a, memory 101a, and interface circuit 102a are communicatively connected to each other via a system bus 103a, allowing them to send and receive information. The communication unit 20 in FIG. 1 is configured to be connectable to the interface circuit 102a and can receive information from the management device 1. In the example configuration of FIG. 3, the processing unit 21 is implemented by the processor 100a, and the storage unit 22 is implemented by the memory 101a. The processor 100a of the deformation extraction device 2 executes the functions of the processing unit 21 by reading and executing a topographical condition monitoring program stored in the memory 101a. The contents of the storage unit 22 may be stored in the same memory 101a or in a separate memory.

[0034] The processor 100a is an example of a processing circuit and includes one or more of a CPU (Central Processing Unit), a DSP (Digital Signal Processor), and a system LSI (Large Scale Integration). The memory 101a includes one or more of non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Enable Program Read Only Memory), magnetic disk, flexible memory, optical disk, compact disk, and DVD (Digital Versatile Disc). The deformation extraction device 2 may include integrated circuits such as an ASIC (Application Specific Integrated Circuit) and an FPGA (Field Programmable Gate Array).

[0035] In the terrain condition monitoring system 100, the deformation extraction device 2 may be configured to have the functions of the management device 1. Furthermore, although the hardware configuration has been described in the first embodiment, other embodiments of the present disclosure may also employ a similar hardware configuration.

[0036] <Deformation extraction process by the deformation extraction device 2> The deformation extraction process in the deformation extraction device 2 will be described with reference to FIG.

[0037] FIG. 4 shows a flowchart of the deformation extraction process performed by the deformation extraction device 2 of the topographical condition monitoring system 100. In step S101, the deformation extraction device 2 acquires measurement data 221 from the measuring device 4. The deformation extraction device 2 stores the acquired measurement data 221 in the memory unit 22. Next, in step S102, the threshold determination unit 211 of the deformation extraction device 2 determines whether the measurement data 221 acquired from the measuring device 4 exceeds a predetermined threshold value for the measurement data 221. If the acquired measurement data 221 does not exceed the predetermined threshold value in step S102, the deformation extraction device 2 continues acquiring the measurement data 221 in step S101. If the acquired measurement data 221 exceeds the predetermined threshold value, the processing proceeds to step S103. The threshold value for the measurement data 221 set in the threshold determination unit 211 can be changed by the user.

[0038] In step S103, the deformation extraction device 2 acquires point cloud data 222 indicating the topographical condition of the target object from the distance measuring device 5. The point cloud data 222 acquired at this time may include image data captured by the distance measuring device 5. Furthermore, the point cloud data 222 acquired at this time may be acquired after automatically updating the settings of the distance measuring device 5 in accordance with the information of the measurement data 221 acquired in step S101.

[0039] In step S104, the deformation extraction unit 212 of the deformation extraction device 2 calculates a volume change amount (Q) indicating a change in the topographical condition of the object based on the point cloud data 222 acquired in step S103 and the past point cloud data 222 stored in the memory unit 22.

[0040] Here, the volume change (Q) calculated by the deformation extraction device 2 will be explained using FIG. 5. FIG. 5 is a conceptual diagram illustrating the calculation method of the volume change (Q) performed by the deformation extraction unit 212 of the deformation extraction device 2 in step S104 of FIG. 4. As shown in FIG. 5, of the point cloud data 222 acquired by the deformation extraction device 2 in step S103, the data acquired at "time n" will be referred to as "point cloud data A," and the data acquired at "time n-1," which is a time earlier than point cloud data A, will be referred to as "point cloud data B." Furthermore, the point cloud data 222 acquired by the deformation extraction device 2 can be used to determine the volume of an object whose topographical condition is to be confirmed. As shown in FIG. 5, the volume change (Q), which is the comparison result between "point cloud data A" and "point cloud data B," indicates that the volume has increased compared to the time when "point cloud data B" was acquired. This indicates that a topographical change that increased the volume of the object occurred due to some influence after "point cloud data B," which was before the acquisition of the measurement data 221. In the present disclosure, it can be inferred that sediment or sand flow may have occurred in a river managed by a river operator, which is a user.

[0041] Returning to Figure 4, we will explain the processing that occurs after the deformation extraction device 2 calculates the amount of volume change in step S104. After calculating the amount of volume change in step S104, the deformation extraction device 2 generates a deformation extraction result indicating the calculation result of the amount of volume change by the deformation extraction unit 212 of the deformation extraction device 2 in step S105.

[0042] The deformation extraction results generated by the deformation extraction unit 212 of the deformation extraction device 2 are explained below with reference to FIG. 6. FIG. 6 shows an example of the deformation extraction results generated by the deformation extraction unit 212 of the deformation extraction device 2 in step S105 of FIG. 4. In FIG. 6, assume that flooding occurred in the target river before the acquisition of "point cloud data A." As shown in FIG. 6, in step S105, the deformation extraction device 2 displays the increase and decrease in volume change in the target area of ​​the river based on the volume change (Q) obtained by comparing "point cloud data A" with "point cloud data B" acquired by the distance measurement device 5. In the "point cloud data (deformation extraction results)" in FIG. 6, areas where sediment has increased and areas where sediment has decreased are marked in different ways based on the deformation extraction results obtained by comparing "point cloud data A" with "point cloud data B." Furthermore, as shown in FIG. 6, a cross-sectional view can also be generated as the deformation extraction result of the river at "point Y." When the cross-sectional view is examined, it can be seen that the amount of sediment is increasing because the amount of change has increased in the sedimentation area on the left bank. "Point X," "Point Y," and "Point Z" are, for example, river distance markers indicating the distance from the river mouth, but are not limited to these and the user may select any location. The deformation extraction results generated by the deformation extraction unit 212 of the deformation extraction device 2 are stored in the memory unit 22 as deformation result data 223.

[0043] Returning to Figure 4, the processing after the deformation extraction device 2 generates the deformation extraction result in step S105 will be described. In step S106, the threshold determination unit 211 of the deformation extraction device 2 determines whether the calculated volume change (Q) exceeds a preset threshold. If the calculated volume change (Q) does not exceed the preset threshold in step S106, the deformation extraction device 2 ends the flow, but if the calculated volume change (Q) exceeds the preset threshold, the processing proceeds to step S107. Furthermore, the threshold value for the volume change (Q) set in the threshold determination unit 211 can be changed at will by the user.

[0044] If the threshold determination unit 211 of the deformation extraction device 2 determines in step S106 that the volume change amount (Q) has exceeded the preset threshold, it issues an alarm to the user in step S107. By checking the alarm from the deformation extraction device 2, the user can understand that there has been a change or abnormality in the object under management. The alarm may be issued to the display device 14 connected to the user's management device 1. The alarm may also be issued from an audio device such as a speaker installed near the display device 14. The alarm is not limited to these, as long as it can be perceived by the user with the five senses.

[0045] <Display screen on the display device 14> The display screen 6 displayed on the display device 14 connected to the management device 1 will be described with reference to FIG.

[0046] 7 shows an example of the display screen 6 displayed by the display device 14 connected to the management device 1. The display screen 6 includes a point cloud data display area 61 for displaying point cloud data 222 of objects managed by the terrain condition monitoring system 100, and a still image display area 62 for displaying image data acquired from the distance measuring device 5.

[0047] The functions of the display screen 6 shown in FIG. 7 will be described. The point cloud data display area 61 displays the point cloud data 222 acquired by the distance measuring device 5 or the deformation result data 223 indicating the deformation extraction results. The user can freely check the displayed point cloud data 222 or deformation result data 223 by moving the point cloud data display area 61 up, down, left, right, and zooming in and out through operation of the input device 15 connected to the management device 1. The content displayed in the still image display area 62 can be changed by the user selecting image data of the target object through operation of the input device 15. In other words, by arbitrarily changing the image data in the still image display area 62, the point cloud data 222 or deformation result data 223 that can be displayed in the point cloud data display area 61 can be easily changed.

[0048] According to this embodiment, as described above, the deformation extraction device 2 calculates the volume change in at least two of the point cloud data of the object based on the measurement data of environmental information acquired by the measuring equipment and the point cloud data of the object acquired by the ranging device. The deformation extraction device 2 can also determine the scale of the volume change in the object by determining whether the calculated volume change exceeds a threshold. Based on the volume change extraction results calculated by the deformation extraction device 2, users can appropriately perform repair work before a disaster occurs or disaster response in the event of a disaster. For example, by identifying areas likely to be affected in the event of a disaster before a disaster occurs, users can consider the costs and time required for repair work, thereby preventing risks economically and systematically. Similarly, when a disaster occurs, appropriate consideration of the equipment and time required for repair work depending on the scale of the disaster enables safe and economical disaster response. Furthermore, users can refer to a screen displaying the volume change extraction results created by the deformation extraction device 2 to respond to disasters taking into account the actual situation of the object.

[0049] Embodiment 2 <Configuration of terrain condition monitoring system 100> The configuration of the terrain condition monitoring system 100 in the second embodiment will be described with reference to Fig. 8. Fig. 8 shows a configuration diagram of the terrain condition monitoring system 100. In Fig. 8, components with the same reference numerals as in Fig. 1 indicate components that are the same as or equivalent to those in the first embodiment. Here, while the communication in the network 3 in the first embodiment was between the management device 1, the deformation extraction device 2, the measuring instrument 4, and the distance measuring device 5, in the second embodiment, an external server 7 is also connected so as to be able to communicate with the network 3.

[0050] The external server 7 is, for example, a website from which observation data and forecast data published by the Japan Meteorological Agency can be obtained. The information published by the Japan Meteorological Agency is, for example, disaster information 71, which is information for protecting oneself from disasters, or weather information 72, which is statistical data related to weather. The disaster information 71 and weather information 72 are, for example, disaster prevention weather information and weather statistical information published by the Japan Meteorological Agency, but are not limited to these.

[0051] The deformation extraction device 2 of the second embodiment periodically acquires disaster information 71 and weather information 72 from the external server 7 via the network 3. The deformation extraction device 2 stores external data 224, which is at least one of the disaster information 71 and weather information 72 acquired from the external server 7, in the storage unit 22. The threshold determination unit 211 of the deformation extraction device 2 can set a threshold for the external data 224 in advance. Therefore, the deformation extraction device 2 determines whether the acquired external data 224 exceeds the preset threshold.

[0052] <Deformation extraction process by the deformation extraction device 2> The deformation extraction process in the deformation extraction device 2 will be described with reference to FIG.

[0053] Figure 9 shows a flowchart of the deformation extraction process performed by the deformation extraction device 2. In Figure 9, steps SN (N is a natural number) equivalent to those in embodiment 1 are numbered the same, and a description will be given of the characteristic parts in embodiment 2, with a description of equivalent steps being omitted. In step S201, the deformation extraction device 2 acquires external data 224 from the external server 7 and stores it in the memory unit 22.

[0054] Next, in step S102, the threshold determination unit 211 of the deformation extraction device 2 determines whether or not the external data 224 acquired from the external server 7 exceeds a preset threshold value for the external data 224. If the acquired external data 224 does not exceed the preset threshold value in step S202, the deformation extraction device 2 continues acquiring the external data 224 in step S201, and if the acquired external data 224 exceeds the preset threshold value, the process proceeds to step S103. Furthermore, the threshold value for the external data 221 set in the threshold determination unit 211 can be changed at the user's discretion.

[0055] According to this embodiment, the deformation extraction device 2 can determine whether or not it is necessary to acquire the point cloud data 222 by the distance measuring device 5 based on the external data 224 acquired from the external server 7. This allows the user to quickly prepare a repair plan before a disaster occurs or to respond to a disaster immediately before the disaster occurs, when an environmental change or the like, not limited to the measuring device 4, is detected.

[0056] Embodiment 3 <Configuration of terrain condition monitoring system 100> The configuration of the terrain condition monitoring system 100 in the fourth embodiment will be described with reference to Fig. 10. Fig. 10 shows a configuration diagram of the terrain condition monitoring system 100. In Fig. 10, components with the same reference numerals as those in Fig. 1 indicate components that are the same as or equivalent to those in the first embodiment. Here, the deformation extraction device 2 in the third embodiment has a prediction unit 214 as a function of the processing unit 21, and a trained model 225 in the memory unit 22.

[0057] <Deformation extraction process by the deformation extraction device 2> The prediction unit 214 of the deformation extraction device 2 will now be described in detail using FIG. 11. FIG. 11 is a diagram illustrating the functional blocks of the deformation extraction device 2. The prediction unit 214 includes a learning unit 2141 and an inference unit 2142. The learning unit 2141 learns the measurement data 221 acquired from the measuring device 4 and the deformation result data 223 of the object when the measurement data 221 was acquired, for example, through so-called supervised learning according to a neural network model. Here, supervised learning is a technique in which pairs of input and result labels are provided to the learning unit 2141 to learn the features of the training data and infer a result from the input. The learning unit 2141 acquires training data created based on a combination of the measurement data 221 and the deformation result data 223 stored in the memory unit 22, and generates a trained model 225 by learning a threshold value for the measurement data 221 through supervised learning according to the acquired training data. At this time, the trained model 225 generated by the training unit 2141 is stored in the storage unit 22.

[0058] The inference unit 2142 uses the measurement data 221 newly acquired from the measuring device 4 as data for inference. The inference unit 2142 infers a threshold value of the measurement data 221 obtained using the trained model 225. That is, by inputting the newly acquired measurement data 221 into the trained model 225, it is possible to output a threshold value of the measurement data 221.

[0059] The threshold value of the measurement data 221 inferred by the inference unit 2142 is output to the threshold determination unit 211. The threshold determination unit 211 determines whether or not it is necessary to acquire point cloud data 222 by the distance measuring device 5, based on the threshold value of the measurement data 221 inferred by the inference unit 2142. If the threshold determination unit 211 determines that it is necessary to acquire the point cloud data 222, the deformation extraction unit 212 calculates the amount of change in volume of the object based on the point cloud data 222 acquired by the threshold determination unit 211, and extracts a deformation extraction result. At this time, the extracted deformation extraction result is stored in the memory unit 22 as deformation result data 223, and the deformation result data 223, together with the measurement data 221, is input to the learning unit 2141 as learning data.

[0060] In this embodiment, the threshold value of the measurement data 221 is inferred using the learned model 225 learned by the prediction unit 214 of the deformation extraction device 2, but the learned model may be acquired from outside the deformation extraction device 2 and the measurement data 221 may be output based on the learned model. The deformation extraction device 2 may further infer the threshold value of the deformation result data 223, automatically update the threshold value of the deformation extraction result based on the measurement data 221 and the deformation result data 223, and issue an alert to the user.

[0061] In this embodiment, a case has been described in which supervised learning is applied to the learning algorithm used by the learning unit 2141, but the present invention is not limited to this. As for the learning algorithm, semi-supervised learning and the like can also be applied in addition to supervised learning.

[0062] It is also possible to add or remove data from the target that the learning unit 2141 collects learning data for during the process. Furthermore, the learning unit 2141 that has learned the threshold value of the measurement data 221 for a certain target may be applied to another deformation extraction device 2, and the threshold value of the measurement data 221 for the other deformation extraction device 2 may be re-learned and updated.

[0063] The learning algorithm used in the learning unit 2141 can be deep learning, which learns to extract the features themselves, which are the measurement data 221 and the deformation result data 223, or machine learning can be performed according to other known methods, such as genetic programming, functional logic programming, support vector machines, etc.

[0064] The learning unit 2141 and the inference unit 2142 are used to learn the threshold value of the measurement data 221, but may be, for example, devices separate from the deformation extraction device 2 and connected to the deformation extraction device 2 via a network. The learning unit 2141 and the inference unit 2142 may also exist on a cloud server separate from the deformation extraction device 2.

[0065] According to this embodiment, the deformation extraction device 2 can infer a threshold value for the measurement data 221 based on newly acquired measurement data 221 in addition to the measurement data 221 and deformation result data 223 stored in the storage unit 22. Therefore, the deformation extraction device 2 can acquire point cloud data 222 that predicts future risks and calculate the amount of volume change by referencing damage caused by disasters that have occurred from the past to the present. By referencing the deformation extraction results calculated based on the data stored by the deformation extraction device 2, users can predict objects where abnormalities are likely to occur and take measures to repair those objects. Furthermore, in the event of a disaster, automatic prediction and analysis of the topographical conditions of threatened objects can be used to prevent secondary damage, even in the midst of urgent disaster response efforts.

[0066] In this embodiment, the management device 1 and the deformation extraction device 2 are connected to the network 3 to exchange information, but this is not limiting. For example, the management device 1 may be realized as a portable device by a mobile terminal in which the functions of the deformation extraction device 2 are incorporated.

[0067] The terrain condition monitoring system 100 shown in each embodiment is an example, and each embodiment can be combined, modified, or omitted as appropriate. In addition, combining it with other known technologies is also within the scope of the technical ideas shown in the embodiments.

[0068] Various aspects of the present disclosure are summarized below as appendices.

[0069] (Appendix 1) a communication unit that acquires measurement data of environmental information acquired by a measuring instrument and point cloud data of an object acquired by a distance measuring device; a storage unit that stores the acquired measurement data and the point cloud data; a processing unit that calculates volumetric changes in at least two of the point cloud data of the object based on the measurement data and the point cloud data, and determines whether the calculated volumetric changes exceed a threshold; A deformation extraction device comprising: (Appendix 2) The processing unit When the measurement data acquired by the communication unit exceeds a threshold, the point cloud data is acquired from the distance measuring device, and the amount of change in volume is calculated based on the acquired point cloud data. 2. The deformation extraction device according to claim 1, (Appendix 3) The processing unit When the measurement data acquired by the communication unit exceeds a threshold, the setting of the distance measuring device is automatically updated based on the measurement data that exceeds the threshold, and then the point cloud data is acquired. 3. The deformation extraction device according to claim 2, (Appendix 4) The processing unit Based on the acquired measurement data, a process is performed to automatically update the automatically updated settings of the distance measuring device back to the settings before the automatic update. 4. The deformation extraction device according to claim 3, (Appendix 5) The processing unit If the calculated volume change amount exceeds a threshold, an alarm is issued. 5. A deformation extraction device according to any one of claims 1 to 4. (Appendix 6) The processing unit A screen is generated that shows the calculated volume change amount as a result of the deformation extraction. 6. A deformation extraction device according to any one of claims 1 to 5. (Appendix 7) The communication unit Acquire external data, which is at least one of disaster information and weather information, from an external server; The processing unit When the external data acquired by the communication unit exceeds a threshold, the point cloud data is acquired from the distance measuring device, and the amount of change in volume is calculated based on the acquired point cloud data. 7. A deformation extraction device according to any one of claims 1 to 6. (Appendix 8) The processing unit A trained model is generated by training the deformation result data created based on the measurement data and the volume change amount, and a threshold value for the measurement data or a threshold value for the volume change amount is inferred using the trained model. 8. A deformation extraction device according to any one of claims 1 to 7. (Appendix 9) The processing unit Inferring a threshold value for the measurement data based on the trained model and inference data, which is the newly acquired measurement data. 9. The deformation extraction device according to claim 8, [Explanation of symbols]

[0070] 1 Management device, 14 Display device, 2 Deformation extraction device, 20 Communication unit, 21 Processing unit, 22 Memory unit, 221 Measurement data, 222 Point cloud data, 223 Deformation result data, 224 External data, 225 Trained model, 4 Measuring equipment, 5 Distance measuring device, 7 External server, 71 Disaster information, 72 Weather information, 100 Terrain condition monitoring system

Claims

1. a communication unit that acquires measurement data of environmental information acquired by a measuring instrument and point cloud data of an object acquired by a distance measuring device; a storage unit that stores the acquired measurement data and the point cloud data; a processing unit that calculates volume changes in at least two of the point cloud data of the object based on the measurement data and the point cloud data, and determines whether the calculated volume changes exceed a threshold value; A deformation extraction device comprising:

2. The processing unit When the measurement data acquired by the communication unit exceeds a threshold, the point cloud data is acquired from the distance measuring device, and the amount of change in volume is calculated based on the acquired point cloud data. The deformation extraction device according to claim 1 .

3. The processing unit When the measurement data acquired by the communication unit exceeds a threshold, the setting of the distance measuring device is automatically updated based on the measurement data that exceeds the threshold, and then the point cloud data is acquired.

3. The deformation extraction device according to claim 1 or 2.

4. The processing unit Based on the acquired measurement data, a process is performed to automatically update the automatically updated settings of the distance measuring device back to the settings before the automatic update. The deformation extraction device according to claim 3 .

5. The processing unit If the calculated volume change amount exceeds a threshold, an alarm is issued.

3. The deformation extraction device according to claim 1 or 2.

6. The processing unit A screen is generated that shows the calculated volume change amount as a result of the deformation extraction.

3. The deformation extraction device according to claim 1 or 2.

7. The communication unit Acquire external data, which is at least one of disaster information and weather information, from an external server; The processing unit When the external data acquired by the communication unit exceeds a threshold, the point cloud data is acquired from the distance measuring device, and the amount of change in volume is calculated based on the acquired point cloud data.

3. The deformation extraction device according to claim 1 or 2.

8. The processing unit A trained model is generated by training the deformation result data created based on the measurement data and the volume change amount, and a threshold value for the measurement data or a threshold value for the volume change amount is inferred using the trained model.

3. The deformation extraction device according to claim 1 or 2.

9. The processing unit Inferring a threshold value for the measurement data based on the trained model and inference data, which is the newly acquired measurement data. The deformation extraction device according to claim 8 .

10. a measuring device for acquiring measurement data of environmental information; a distance measuring device for acquiring point cloud data of an object; The amount of change in volume of the object A deformation extraction device having a communication unit that acquires the measurement data and the point cloud data, a storage unit that stores the acquired measurement data and the point cloud data, and a processing unit that calculates a volume change amount in at least two of the point cloud data of the object based on the measurement data and the point cloud data, and determines whether the calculated volume change amount exceeds a threshold value; A terrain condition monitoring system comprising:

11. further comprising a management device that manages the object; The management device a display device that displays a screen showing the volume change calculated by the deformation extraction device as a deformation extraction result; 11. The terrain condition monitoring system according to claim 10.

12. The distance measuring device is acquiring image data of the object from which the point cloud data was obtained; The deformation extraction device acquires the image data.

12. The terrain condition monitoring system according to claim 10 or 11.

13. A step of acquiring measurement data of environmental information acquired by a measurement device; A step of acquiring point cloud data of an object acquired by a distance measuring device; storing the acquired measurement data and point cloud data; calculating a volume change amount in at least two of the point cloud data of the object based on the measurement data and the point cloud data; determining whether the calculated volume change amount exceeds a threshold value; A method for monitoring terrain conditions comprising:

14. To the computer A step of acquiring measurement data of environmental information acquired by a measurement device; A step of acquiring point cloud data of an object acquired by a distance measuring device; storing the acquired measurement data and point cloud data; calculating a volume change amount in at least two of the point cloud data of the object based on the measurement data and the point cloud data; determining whether the calculated volume change amount exceeds a threshold value; Terrain condition monitoring program that executes.

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