Damage prediction device, damage prediction method, and program

Abstract

To provide a damage prediction system capable of accurately predicting damage to power lines, etc. due to strong winds.SOLUTION: The damage prediction system includes: means of receiving a designation of target areas for impact assessment of strong winds; means of identifying an area where structures and trees existing in the target area may be scattered or collapse due to the strong wind; means of calculating the laying direction of linear members existing in the target area; and means of creating human-caused dangerous spot map information of the target area in which the laying direction and the range are superimposed on the map information of the target area.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a damage prediction device, a damage prediction method, and a program for predicting damage caused by strong winds. [Background technology]

[0002] In typhoon disasters, in addition to damage to power lines caused by the strong winds themselves, there is also the possibility of power outages caused by fallen trees and flying debris. More specifically, power outages caused by typhoons are likely to occur due to the following factors: (1) Local winds (increased wind speed due to terrain) (natural phenomenon); (2) Fallen trees and landslides in forest areas causing power poles to break, or damage to vacant houses or farm / forestry sheds, or contact with power lines due to flying debris, or power poles breaking (secondary damage: human-caused).

[0003] As a related technique, Patent Document 1 discloses a disaster occurrence prediction system that acquires typhoon information including the typhoon's predicted path and maximum wind speed, calculates the distance between the typhoon's center and a reference point, calculates the maximum wind speed at the reference point based on the calculated distance and the typhoon's maximum wind speed, and predicts the number of disasters that will occur based on the maximum wind speed at the reference point. Patent Document 2 discloses a power distribution line disaster prediction system that simulates the growth of trees near power distribution lines based on map data including power distribution line data and a tree growth database that stores tree growth data, and predicts disasters caused by grown trees coming into contact with the power distribution lines. However, Patent Documents 1 and 2 do not take into account the effects of terrain-related acceleration or damage caused by flying debris or fallen trees. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-256183 [Patent Document 2] Japanese Patent Application Laid-Open No. 2006-268784 Summary of the Invention [Problem to be solved by the invention]

[0005] There is a need for a method to assess damage to power lines and other structures caused by strong winds by assessing not only the damage caused by the strong winds themselves, but also the impact of factors such as fallen trees and flying debris.

[0006] The present disclosure provides a damage prediction device, a damage prediction method, and a program that can solve the above-mentioned problems. [Means for solving the problem]

[0007] The damage prediction device disclosed herein includes a means for accepting the designation of a target area for assessment of the impact of strong winds, a means for identifying the range in which structures in the target area will be blown away or collapsed due to the impact of the strong winds, a means for calculating the laying direction of linear members in the target area, and a means for creating human-caused danger location map information for the target area by superimposing the laying direction and the range on map information for the target area. The linear member is a power transmission and communication network, the structure is flying debris, and the means for identifying the range uses the weight, resistance coefficient, and lift coefficient of the flying debris to estimate the range into which the flying debris will fly and the ease with which the flying debris will fly.

[0008] The damage prediction method disclosed herein is a damage prediction method executed by a computer, and includes the steps of accepting the designation of a target area for assessment of the impact of strong winds, identifying the range in which structures present in the target area will be blown away or collapsed due to the impact of the strong winds, calculating the laying direction of linear members present in the target area, and creating human-caused hazard location map information for the target area by superimposing the laying direction and the range on map information for the target area. The linear member is a power transmission and communication network, and the structure is flying debris. In the step of identifying the range, the weight, resistance coefficient, and lift coefficient of the flying debris are used to estimate the range into which the flying debris will fly and the ease with which the flying debris will fly.

[0009] The program disclosed herein includes the steps of: receiving designation of a target area for assessment of the impact of strong winds; identifying the range of structures in the target area that will be blown away or collapsed by the impact of the strong winds; calculating the laying direction of linear members in the target area; and creating human-caused hazard location map information for the target area by superimposing the laying direction and the range on map information for the target area. The linear member is a power transmission and communication network, the structure is a flying object, and the step of specifying the range includes a process of estimating the range into which the flying object will fly and the likelihood of the flying object flying using a weight, a resistance coefficient, and a lift coefficient of the flying object. Execute the following. [Effects of the Invention]

[0010] According to the above-described damage prediction device, damage prediction method, and program, damage to electric wires and the like caused by strong winds can be predicted with high accuracy. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram illustrating an example of a damage prediction device according to each embodiment. [Figure 2] 10 is a flowchart illustrating an example of a damage prediction process according to the first embodiment. [Figure 3] 10 is a flowchart illustrating an example of extreme value statistical processing of meteorological observation data. [Figure 4] 10 is a flowchart illustrating an example of a typhoon simulation and extreme value statistical processing. [Figure 5] 1 is a flowchart illustrating an example of a procedure for performing a wind tunnel test. [Figure 6] 10 is a flowchart illustrating an example of a computational fluid analysis process. [Figure 7] 10 is a flowchart showing an example of a process for deriving a multiple regression equation based on topographical factors and evaluating a strong wind based on the multiple regression equation. [Figure 8] FIG. 10 is a diagram showing an example of a map of locations at risk of strong winds. [Figure 9] 10 is a flowchart showing an example of a process for extracting a dangerous scattering object and a process for estimating a degree of danger. [Figure 10] 10 is a flowchart illustrating an example of a process for evaluating the overhead line direction of a power transmission line communication network. [Figure 11] FIG. 10 is a diagram showing an example of a flying object danger location map. [Figure 12] FIG. 10 is a diagram showing an example of a damage risk map. [Figure 13] An example of a method for dealing with a typhoon using a damage risk map will be described. [Figure 14] 10 is a flowchart illustrating an example of a damage prediction process according to the second embodiment. [Figure 15]10 is a flowchart illustrating an example of a tree extraction process and a risk estimation process. [Figure 16] FIG. 10 is a diagram showing an example of a map of areas at risk of fallen trees. [Figure 17] FIG. 11 is a diagram showing an example of a damage risk map according to the second embodiment. [Figure 18] 11 is a flowchart illustrating an example of a damage prediction process according to the third embodiment. [Figure 19] FIG. 11 is a diagram showing an example of a damage risk map according to the third embodiment. [Figure 20] FIG. 2 is a diagram illustrating an example of a hardware configuration of a damage prediction device according to each embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] First Embodiment Hereinafter, a damage prediction device according to a first embodiment of the present disclosure will be described with reference to FIGS. (composition) FIG. 1 is a block diagram showing an example of a damage prediction device according to each embodiment. The damage prediction device 10 predicts damage caused by strong winds such as typhoons. The damage prediction device 10 predicts damage by taking into account the speed increase due to terrain of the strong winds that cause damage, and further predicts damage caused by secondary disasters such as flying debris and fallen trees, which are other factors (defined as human causes in this specification) in strong wind damage. For example, the damage prediction device 10 displays, as a damage prediction, the degree of speed increase due to the influence of terrain in the target prediction area, the range where flying debris will arrive, the range where the impact of fallen trees will occur, etc. The damage prediction device 10 has an input receiving unit 11, a memory unit 12, and a control unit 13.

[0013] The input receiving unit 11 receives input of various data used for typhoon damage prediction. For example, the input receiving unit 11 receives information specifying a target area for damage prediction, map information of the area, three-dimensional topographical information, meteorological observation data for the target area and its vicinity, typhoon data (typhoon best track data), at least one of meteorological observation data and topographical factor data, information on the power transmission and communication network such as power lines, telephone lines, communication lines, and trolley wires (overhead wires) in the target area (for example, satellite images of the power transmission and communication network taken from a satellite in the sky, images taken by a so-called drone, visually confirmed location information of the locations where the power transmission and communication network is laid, etc.), The input receiving unit 11 acquires information about structures that may be blown away by strong winds (for example, satellite images of signs, roofs, roof tiles, etc. taken from a satellite in the sky, images of signs, etc. taken by a so-called drone, and information about the type, size, and location of signs, etc. confirmed visually), information about structures that may collapse due to strong winds (for example, satellite images of trees, steel towers, etc. taken from a satellite in the sky, images of trees, etc. taken by a so-called drone, and information about the type, thickness, height, and location of trees, etc. confirmed visually), and information about the results of wind tunnel experiments. The input receiving unit 11 writes and stores this acquired information in the storage unit 12. The input receiving unit 11 also accepts instructions and operations from the operator, generates information according to the operations, and outputs the information to the control unit 13.

[0014] The memory unit 12 stores the data acquired by the input receiving unit 11, as well as a flying object database that stores the type, size, weight, lift coefficient, and drag coefficient of structures that may become flying objects, and a fallen object database that stores the type, thickness, height, age, and load applied when the object collapses (or the wind speed when the object collapses) of trees and other objects that may collapse (other than trees, for example, towers and pillars) that are likely to collapse, and associates these with each other.

[0015] The control unit 13 controls the process of estimating damage to the power transmission and communication network in a target area. For example, the control unit 13 estimates strong winds approaching the target area and calculates whether or not the wind speed will increase, taking into account the topography of the target area. The control unit 13 extracts signs and other objects in the target area that may become flying debris and trees and other objects that may fall, and evaluates the risk they pose to the power transmission and communication network. The risk refers to the risk of flying debris coming into contact with and damaging power lines and utility poles, and the risk of fallen trees coming into contact with and damaging power lines and other objects. The control unit 13 calculates the extent of damage to the power transmission network and calculates the degree of risk that indicates the likelihood of these risks occurring. The control unit 13 includes a trigger estimation unit 14, a human factor estimation unit 15, a map creation unit 16, and an output unit 17.

[0016] The trigger estimation unit 14 estimates the strong winds that cause damage. For example, the trigger estimation unit 14 estimates the maximum wind speed for each wind direction and estimates the maximum wind speed for each area within the target area based on the topography of the target area. Each area refers to, for example, each region created by dividing the target area into several hundred meters. The trigger estimation unit 14 includes a meteorological data strong wind estimation unit 141, a typhoon data strong wind estimation unit 142, a topographical speed increase analysis unit 143, and a topographical factor strong wind estimation unit 144.

[0017] The weather data strong wind estimation unit 141 performs extreme value statistical analysis based on weather observation data for the target area to estimate the maximum level of strong wind that may occur in the target area (for example, strong winds that occur once every several decades, the same applies below). Weather observation data refers to observation data such as wind speed and wind direction obtained from meteorological offices of the Japan Meteorological Agency or private observation equipment. For example, the weather data strong wind estimation unit 141 selects the maximum wind speed for each wind direction recorded in past weather observation data and estimates the possibility of strong winds of this magnitude arriving. Furthermore, for example, if typhoons do not often strike the target area, the weather data strong wind estimation unit 141 may select the maximum wind speed for each wind direction recorded in past weather observation data, extrapolate this wind speed, and estimate that a typhoon with a strength that is a predetermined multiple of the strongest winds ever recorded will arrive.

[0018] The typhoon data strong wind estimation unit 142 performs a typhoon simulation based on data on typhoons that have arrived in the target area, estimating the maximum level of strong winds that may occur in the target area. The typhoon simulation involves using past typhoon data (typhoon best track data) published by the Japan Meteorological Agency to organize typhoon parameters (central pressure drop, maximum gyroscopic wind speed radius, traveling speed, traveling direction, and closest approach distance) at the time of closest approach to the target area and the annual frequency of typhoons, applying each to a probability distribution, and conducting a Monte Carlo simulation to generate a virtual typhoon with typhoon parameters that follow the probability distribution. The typhoon data strong wind estimation unit 142 uses this typhoon simulation to generate virtual typhoons for, for example, 10,000 years, estimate the maximum wind speed of typhoons in the target area over the 10,000 years, and evaluate the strong winds using extreme value statistical analysis.

[0019] In addition, in areas where typhoons do not often strike or where nearby meteorological observation data is available for a long period of time (for example, about 10 years), analysis is performed using the meteorological data strong wind estimation unit 141, and in areas where typhoons strike frequently, analysis is performed using the typhoon data strong wind estimation unit 142, and strong winds can be estimated using either one of them.

[0020] The terrain acceleration analysis unit 143 performs computational fluid dynamics analysis using a terrain model that reproduces the terrain of the target area to analyze how the maximum level of strong wind estimated by the weather data strong wind estimation unit 141 or the typhoon data strong wind estimation unit 142 increases or decreases due to the influence of the terrain, and evaluates the wind speed in each area of ​​the target area while taking into account the influence of the terrain. Furthermore, when evaluating the wind speed while taking into account the influence of the terrain, a wind tunnel test using a terrain model of the target area may be performed instead of computational fluid dynamics analysis using a terrain analysis model. When performing the wind tunnel test, the input receiving unit 11 acquires the result data of the wind tunnel test. The result data of the wind tunnel test includes, for example, the wind speed used in the wind tunnel test and the wind speed measured at each position on the terrain model. The terrain acceleration analysis unit 143 estimates the wind speed in each area of ​​the target area based on the result data of the wind tunnel test and the maximum level of strong wind estimated by the weather data strong wind estimation unit 141 or the typhoon data strong wind estimation unit 142. For example, if the wind tunnel test result data shows that the wind speed measured in area A within the target region is 1.1 times the wind speed given in the wind tunnel test, then it is analyzed that a strong wind 1.1 times the maximum wind speed will arrive in area A. The terrain acceleration analysis unit 143 records the wind speed in each area in the memory unit 12.

[0021] In areas with complex topography where computational fluid analysis is difficult, analysis using wind tunnel experiments can be performed, and if it is necessary to carry out a wide-area evaluation while keeping costs down, analysis using a terrain analysis model can be performed, and strong winds can be estimated using either one.

[0022] The terrain factor strong wind estimation unit 144 estimates strong winds taking into account the influence of terrain acceleration in the target area using a multiple regression equation that shows the relationship between terrain factors and wind speed, derived from past meteorological observation data and the results of computational fluid dynamics analysis. The terrain factors are parameters that can represent the terrain around the evaluation point, such as undulation (maximum elevation difference within a circle with a radius of several tens of kilometers), land area (the proportion of land within a circle with a radius of several tens of kilometers), sea area (the proportion of sea within a circle with a radius of several tens of kilometers), openness (the total number of directions with no obstacles within a 360° perimeter, assuming that an obstacle area is an area 200 meters higher than the evaluation point), obstacle distance (shortest distance to the obstacle area), and coastal distance (shortest distance to the sea) are typical examples. The topographical factor strong wind estimation unit 144 creates a multiple regression equation that can estimate strong winds taking into account the influence of topography from the topographical factors by multiple regression analysis using these topographical factors as explanatory variables and wind speeds obtained from observation data (wind speeds) at multiple points and the results of computational fluid dynamics analysis at multiple points as objective variables, and stores the created multiple regression equation in the storage unit 12. Then, the topographical factor strong wind estimation unit 144 uses this multiple regression equation to estimate strong winds taking into account the influence of the topography of the target area.

[0023] The human cause estimation unit 15 estimates secondary damage caused by strong winds. For example, the human cause estimation unit 15 estimates damage caused by flying objects and damage caused by fallen trees when the strong wind estimated by the inciting cause estimation unit 14 occurs. The human cause estimation unit 15 has a flying object extraction unit 151, a flying object influence calculation unit 152, a fallen tree extraction unit 153, a fallen tree influence calculation unit 154, an overhead line direction calculation unit 155, and an overhead line damage evaluation unit 156.

[0024] The flying object extraction unit 151 extracts structures that may become flying objects, such as signs and roofs, from various structures present in the target area. For example, the flying object extraction unit 151 analyzes a visible light image of the target area photographed from a satellite and extracts candidates for flying objects, such as signs, from the image. The flying object extraction unit 151 estimates the size and shape of the extracted signs and records these in the storage unit 12. Alternatively, the flying object extraction unit 151 extracts roofs, signs, etc. that may be flying objects, by image analysis from images obtained by, for example, photographing with a drone. Furthermore, when a sign or the like is visually confirmed, the size, shape, location information, etc. of the confirmed sign are acquired by the input receiving unit 11, and this information is recorded in the storage unit 12. In addition, if sufficient satellite data is available or if visual inspection or confirmation by drones, etc. is not possible (for example, if the area is wide or if it is not possible for safety reasons), extraction can be performed using images taken from satellites, and if visual inspection or confirmation by drones, etc. is possible, extraction can be performed using information obtained by visual inspection or drones, etc., and either one can be used to extract hazardous materials that may be flying.

[0025] The flying object influence calculation unit 152 calculates the scattering risk, scattering distance, and scattering range of the structure extracted by the flying object extraction unit 151. For example, the flying object influence calculation unit 152 refers to a flying object database for roofs, signs, etc. that are likely to be scattered and extracted from the image, reads the weight, drag coefficient, and lift coefficient from the size, shape, etc., of the scattered object, and calculates the scattering distance, scattering range, and scattering risk of the scattered object. If the flying object database does not contain data corresponding to the size and shape of the scattered object extracted from the image, the flying object influence calculation unit 152 reads the weight, drag coefficient, and lift coefficient of the scattered object that is most similar in type, size, and shape, and estimates the weight, drag coefficient, and lift coefficient of the extracted scattered object by linear interpolation or the like. The scattering risk is a value indicating the wind speed at which the scattered object will be lifted from its fixed state and scattered. For example, if a wind of "X1" km / s blows and debris is lifted up, and it is assumed that a wind of "X2" km / s will blow, the risk of debris being lifted up is the ratio of the square of the wind speed, X2 2 / X1 2The flying object influence calculation unit 152 calculates the scattering distance, scattering range, and scattering risk level based on the maximum wind speed at the location where the flying object is placed estimated by the inducement estimation unit 14, and the weight, wind speed, drag coefficient, and lift coefficient read from the flying object database. Note that the calculation method for calculating the wind speed required to blow flying object from the weight, drag coefficient, and lift coefficient of the flying object, and the calculation method for calculating the scattering distance and scattering range from the weight, wind speed, drag coefficient, and lift coefficient of the flying object are well known, so their explanation will be omitted.

[0026] The fallen tree extraction unit 153 extracts trees that may fall from among trees and the like present in a target area. For example, the fallen tree extraction unit 153 analyzes a visible light image of the target area taken from a satellite and extracts trees and the like from the image. The fallen tree extraction unit 153 estimates the height, thickness, and surrounding conditions of the extracted trees and records these in the storage unit 12. Alternatively, the fallen tree extraction unit 153 extracts trees that may fall from images obtained by, for example, taking photos with a drone, by image analysis. Furthermore, when a tree that may fall is visually confirmed, the type, height, thickness, surrounding conditions, location information, etc. of the confirmed tree are acquired by the input receiving unit 11, and this information is recorded in the storage unit 12. In addition, if sufficient satellite data is available or if visual inspection or confirmation by drones, etc. is not possible (for example, if the area is wide or if it is not possible for safety reasons), extraction can be performed using images taken from satellites, and if visual inspection or confirmation by drones, etc. is possible, extraction can be performed using information obtained by visual inspection or drones, etc., and either one can be used to extract hazardous materials that may be flying.

[0027] The fallen tree influence calculation unit 154 calculates the degree of risk of a tree falling and the extent of the tree falling for the tree or the like extracted by the fallen tree extraction unit 153. For example, for a tree or the like extracted from an image that may fall, the fallen tree influence calculation unit 154 references a fallen object database to read information on the type, height, thickness, surrounding conditions, etc. of the tree to determine the amount of load required to cause the tree to fall, and calculates the extent of the tree falling risk and the degree of risk of a tree falling. If the data for the extracted tree is not registered in the fallen object database, the fallen tree influence calculation unit 154 reads data on the tree most similar in type and height and estimates the load required to cause the extracted tree to fall using linear interpolation or the like. Alternatively, if the fallen object database records the type of tree and the wind speed at the time of tree fall in association with each other, the fallen tree influence calculation unit 154 reads the wind speed at the time of tree fall from the fallen object database. The degree of risk of a tree falling is a value indicating the wind speed required to cause a tree to fall. For example, if a tree falls when the wind blows at "X1" km / s, and the wind is expected to blow at "X2" km / s, the risk of the tree falling is the ratio of the square of the wind speed, X2 2 / X1 2 The tree falling effect calculation unit 154 calculates the range of fallen trees and the risk of tree falling based on the maximum wind speed at the location of the tree estimated by the trigger estimation unit 14 and the load read from the fallen object database. Note that the calculation method for calculating the wind speed required to fall a tree based on the load at the time of tree falling is well known, so a description thereof will be omitted. The range of fallen trees can be calculated, for example, as a circle centered on the location information of the tree and with a radius equal to the height of the tree.

[0028] The overhead line direction calculation unit 155 calculates the overhead line direction (the direction in which the power lines are laid) and overhead line positions of power lines and the like present in the target area. For example, the overhead line direction calculation unit 155 analyzes a visible light image of the target area taken from a satellite and extracts power lines and the like from the image. The overhead line direction calculation unit 155 records the overhead line direction and overhead line positions of the extracted power lines and the like in the storage unit 12. Alternatively, the overhead line direction calculation unit 155 extracts the overhead line direction and overhead line positions of power lines and the like by image analysis from an image obtained by, for example, taking a picture with a drone. Furthermore, when the overhead line direction and overhead line positions of power lines and the like are visually confirmed, the confirmed information is acquired by the input acceptance unit 11, and this information is recorded in the storage unit 12. In addition, if sufficient satellite data is available or if visual inspection or confirmation by drones, etc. is not possible (for example, if the area is wide or if it is not possible for safety reasons), extraction can be performed using images taken from satellites, and if visual inspection or confirmation by drones, etc. is possible, extraction can be performed using information obtained by visual inspection or drones, etc., and either one can be used to extract the direction of the overhead wires, etc.

[0029] The overhead line damage assessment unit 156 assesses the risk of damage for each wind direction by assessing the overhead line direction of the power transmission and communication network based on the maximum wind speed for each wind direction and area estimated by the inciting factor estimation unit 14. For example, the memory unit 12 stores the wind speed and wind direction (the relationship between the overhead line direction and the wind direction) at which damage to the power transmission and communication network occurs, in association with the type and thickness of the power transmission line or communication line, and the overhead line damage assessment unit 156 estimates locations in the power transmission and communication network where damage may occur based on this information. Alternatively, the overhead line damage assessment unit 156 may calculate the force acting on the power transmission lines, utility poles, etc. from the maximum wind speed for each wind direction and area, determine whether damage will occur, and estimate locations where damage may occur.

[0030] The map creation unit 16 creates map information showing predicted damage caused by strong winds. The map creation unit 16 has a strong wind danger location map creation unit 161, a flying object danger location map creation unit 162, a fallen tree danger location map creation unit 163, and an overhead line danger level map creation unit 164. The strong wind danger location map creating unit 161 creates map information (strong wind danger location map) showing wind speeds for each area by wind direction. The flying object danger area map creating unit 162 creates map information (flying object danger area map) that indicates the area where flying objects may arrive. The fallen tree danger area map creating unit 163 creates map information (fallen tree danger area map) showing the range of influence of fallen trees. The overhead line danger map creating unit 164 creates an overhead line danger map in which the strong wind danger map is superimposed on at least one of the flying object danger map and the fallen tree danger map.

[0031] The output unit 17 outputs various information to a display device or an electronic file. For example, the output unit 17 outputs various maps created by the map creation unit 16 to a display device.

[0032] (operation) Next, the operation of the damage prediction device 10 according to the first embodiment will be described with reference to Figures 2 to 13. In the first embodiment, a damage prediction process will be described assuming a target area with many private houses and few forests. FIG. 2 is a flowchart showing an example of a damage prediction process according to the first embodiment. It is assumed that map information of the target area for prediction, three-dimensional topographical information, for example, information on topographical factors every few hundred meters, are registered in advance in the memory unit 12. First, the user sets the target area for prediction in the damage prediction device 10 based on the typhoon's path prediction, etc. (Step S101). For example, if the target area is a rectangular area, the setting of the target area may be performed by specifying the latitude and longitude of the vertices of the area. The input receiving unit 11 acquires setting information of the set target area and records the information in the memory unit 12. Next, the user instructs the damage prediction device 10 to execute damage prediction processing. The input receiving unit 11 receives this instruction and instructs the control unit 13 to execute damage prediction processing. The control unit 13 first executes the trigger estimation processing using the trigger estimation unit 14. Depending on the target area, the trigger estimation unit 14 performs at least one of extreme value statistical processing of meteorological observation data (step S102), typhoon simulation and extreme value statistical processing (step S103), and strong wind estimation taking into account the influence of topographical acceleration using a multiple regression equation of topographical factors (step S108). For example, if meteorological observation data from the past 10 years of the target area is available, the trigger estimation unit 14 performs extreme value statistical processing of meteorological observation data and strong wind estimation taking into account the influence of topographical acceleration using a multiple regression equation of topographical factors. If typhoon data for the target area is available, the trigger estimation unit 14 performs typhoon simulation and extreme value statistical processing and strong wind estimation taking into account the influence of topographical acceleration using a multiple regression equation of topographical factors. The trigger estimation unit 14 may perform all three processes.

[0033] For example, the trigger estimation unit 14 uses the weather data strong wind estimation unit 141 to perform extreme value statistical processing of the weather observation data (step S102). The flow of extreme value statistical processing of weather observation data is shown in FIG. 3. The input reception unit 11 acquires weather observation data for the target area over the past 10 years (step S1) and records the acquired weather observation data in the storage unit 12. Next, the weather data strong wind estimation unit 141 performs extreme value statistical analysis (step S2). For example, the weather data strong wind estimation unit 141 selects the largest wind among the strong winds included in the weather observation data and analyzes that if two strong winds of the same magnitude (maximum magnitude) have occurred in the past 10 years, the probability that the largest wind will occur in a year is 2 / 10. The weather data strong wind estimation unit 141 may extrapolate to estimate strong winds with wind speeds exceeding those in past weather observation data. The weather data strong wind estimation unit 141 calculates the largest wind speed and its occurrence probability for each wind direction based on past weather observation data and records the analysis results in the storage unit 12. The weather data strong wind estimation unit 141 estimates strong winds that will occur in the target area based on the results of extreme value statistical analysis (steps S3 and S104). The weather data strong wind estimation unit 141 calculates the maximum wind speeds by wind direction that will occur in the target area and the probability of their occurrence.

[0034] Furthermore, for example, the trigger estimation unit 14 uses the typhoon data strong wind estimation unit 142 to perform typhoon simulation and extreme value statistical processing (step S103). The flow of typhoon simulation and extreme value statistical processing is shown in Figure 4. The input reception unit 11 acquires typhoon data from the past several decades in the target area (step S11) and records the acquired typhoon data in the storage unit 12. The typhoon data includes parameters such as the central pressure drop, maximum gyrosymmetric wind speed radius, traveling speed, traveling direction, and closest approach distance. The typhoon data strong wind estimation unit 142 creates a probability distribution of the typhoon parameters (step S12). The typhoon data strong wind estimation unit 142 calculates the probability distribution of the values ​​of each typhoon parameter and calculates the annual occurrence frequency of typhoons. Next, the typhoon data strong wind estimation unit 142 executes a Monte Carlo simulation (step S13). For example, the typhoon data strong wind estimation unit 142 randomly determines the values ​​of typhoon parameters using the Monte Carlo method based on the probability distribution of each typhoon parameter created in step S12, and sets the determined values ​​in the simulator to simulate typhoons that occur in one year. The typhoon data strong wind estimation unit 142 executes the Monte Carlo simulation, for example, 10,000 times to calculate the annual maximum wind speed for 10,000 years (step S14) and records the calculated value in the storage unit 12. Next, the typhoon data strong wind estimation unit 142 executes extreme value statistical analysis based on the annual maximum wind speed for 10,000 years (step S15). For example, the typhoon data strong wind estimation unit 142 selects the strongest wind among the annual maximum wind speeds for 10,000 years and calculates the probability of occurrence of a strong wind of the same magnitude in 10,000 years. The typhoon data strong wind estimation unit 142 calculates the strongest wind speed and occurrence probability for each wind direction based on the simulation results for 10,000 years and records the analysis results in the storage unit 12. The typhoon data strong wind estimation unit 142 estimates strong winds that will occur in the target area based on the results of the extreme value statistical analysis (steps S16 and S104). The typhoon data strong wind estimation unit 142 calculates the maximum wind speeds by wind direction that will occur in the target area and the probability of their occurrence.

[0035] Once the estimation of strong winds is completed based on meteorological observation data or typhoon data (step S104), a wind tunnel test using a terrain model (step S105) or a computational fluid analysis using a terrain model (step S106) is performed to evaluate the effect of the terrain of the target area on increasing wind speed (step S107). Known methods can be used for wind tunnel testing and computational fluid analysis. For example, if it is desired to perform a wide-area evaluation at low cost, computational fluid analysis using a terrain model can be performed, and if the terrain is so complex that computational fluid analysis is difficult, wind tunnel testing can be performed. Furthermore, if the terrain is not significantly affected, both processes can be omitted.

[0036] FIG. 5 shows an example of the procedure for conducting a wind tunnel test. First, a terrain model of the target area is created (step S21). Next, a wind tunnel test is conducted using the terrain model. Wind speed is measured in each area of ​​the terrain model (step S22). The wind tunnel test is conducted for each wind direction. Next, the wind speed is evaluated taking into account the influence of terrain acceleration (steps S23 and S107). For example, based on the wind speed at each location measured in the wind tunnel test, the user calculates the degree of acceleration that will occur at each location in the target area (e.g., how many times the wind speed will increase, how much (m / s) the speed will increase, etc.). The user inputs information indicating the degree of acceleration that will occur for each wind direction and location to the damage prediction device 10. The input receiving unit 11 acquires the information indicating the degree of acceleration for each wind direction and location and records it in the memory unit 12.

[0037] FIG. 6 shows the flow of the computational fluid analysis process. First, a three-dimensional terrain model that reproduces the terrain of the target area is created (step S21a). The terrain model is registered in the storage unit 12. Next, the terrain speed increase analysis unit 143 performs computational fluid analysis (step S22a). Using the terrain model, the terrain speed increase analysis unit 143 simulates wind flow for each wind direction and calculates the degree of speed increase for each area in the target area. Next, the terrain speed increase analysis unit 143 evaluates the wind speed taking into account the influence of terrain speed increase (steps S23a, S107). When the terrain speed increase analysis unit 143 performs computational fluid analysis, it records information indicating the degree of speed increase for each area in the storage unit 12.

[0038] Furthermore, the trigger estimation unit 14 may use the terrain factor strong wind estimation unit 144 to estimate strong winds taking into account the influence of terrain acceleration using a multiple regression equation of terrain factors (step S108). For example, the user inputs information on terrain factors such as the degree of relief, land or sea, openness, obstacle distance, and coastal distance at each location in the target area (locations where wind speed can be acquired) and the maximum wind speed for each wind direction at that location to the damage prediction device 10. Meteorological observation data may be used for the maximum wind speed for each wind direction at each location, or the wind speed calculated by adding the influence of terrain acceleration calculated in steps S105 to S107 to the strong wind estimated in steps S102 to S104 may be used. The input receiving unit 11 acquires this information and records it in the storage unit 12. The terrain factor strong wind estimation unit 144 derives the relationship between wind speed and the degree of terrain, land, sea, openness, obstacle distance, and coastal distance at multiple locations within the target area using multiple regression analysis. The unit 144 then derives multiple regression equations showing the relationship between terrain factors and wind speed for each wind direction. The derived multiple regression equations are then stored in the storage unit 12. By substituting the terrain factors at any location within the target area into the multiple regression equation, the maximum wind speed at that location can be estimated. Figure 7 shows an example of the process for deriving a multiple regression equation based on terrain factors and evaluating strong winds at an evaluation location. Depending on the target area, either wind speed observation data or computational fluid dynamics analysis results are used. For example, if wind speed observation data is available at multiple locations within the target area, the input receiving unit 11 acquires the wind speed observation data at the multiple locations (step S31). Next, the terrain factor strong wind estimation unit 144 acquires the terrain factors at the multiple locations where the observation data was obtained from the storage unit 12 (step S32). Alternatively, if observation data is not available, the terrain factor strong wind estimation unit 144 acquires wind speed data obtained by computational fluid dynamics analysis (step S33). Next, the terrain factor strong wind estimation unit 144 acquires the terrain factors of the locations where the wind speed data was obtained from the storage unit 12 (step S34). Next, the terrain factor strong wind estimation unit 144 performs multiple regression analysis using the terrain factors at multiple locations as explanatory variables and the wind speeds at multiple locations as response variables (step S35), and derives a multiple regression equation (step S36).Next, the terrain factor strong wind estimation unit 144 reads and acquires the terrain factors of a predetermined evaluation point from the storage unit 12 (step S37). Next, the terrain factor strong wind estimation unit 144 calculates the strong wind taking into account the terrain factors at the evaluation point (step S38). The terrain factor strong wind estimation unit 144 substitutes the terrain factors at the evaluation point into a multiple regression equation to calculate the wind speed of the strong wind at the evaluation point.

[0039] Next, the trigger estimation unit 14 uses the results of steps S107 and S108 to estimate strong winds for each area of ​​the target region, taking into account the influence of terrain-induced wind speed acceleration (step S109). For example, the trigger estimation unit 14 divides the target region into mesh-like areas, for example, in units of tens to hundreds of meters, and for each divided area, estimates the maximum wind speed by wind direction, reflecting the influence of terrain-induced wind speed acceleration, using the results of wind tunnel testing, computational fluid dynamics analysis, or multiple regression equation (step S109). For example, if the results of wind tunnel testing or computational fluid dynamics indicate that a wind speed increase of +5 (m / s) will occur in a certain "area A," the trigger estimation unit 14 estimates the wind speed in area A to be the value obtained by adding +5 (m / s) to the wind speed in "area A" estimated in step S104. Furthermore, for example, for "area B" for which the results of wind tunnel testing or computational fluid analysis have not been obtained, the triggering factor estimation unit 14 estimates the maximum wind speed in area B by substituting the topographical factors of "area B" into the multiple regression equation calculated in step S108. The triggering factor estimation unit 14 estimates the maximum wind speed for each area by wind direction as a strong wind taking into account the influence of topographical acceleration, and records the estimated maximum wind speed in the memory unit 12.

[0040] Next, the map creation unit 16 uses the strong wind danger location map creation unit 161 to create a strong wind danger location map (step S110) and record the created strong wind danger location map in the storage unit 12. The strong wind danger location map creation unit 161, for example, ranks the level of strong winds into several stages according to wind speed, assigns a predetermined color to each rank, and creates map information (strong wind danger location map) of the target area in which each area is color-coded according to the strong wind rank. For example, the strong wind danger location map creation unit 161 ranks the wind speed according to wind speed, such as crimson, red, orange, yellow, yellow-green, light blue, and blue, in descending order, and assigns a color corresponding to the rank according to the maximum wind speed of each area. An example of the strong wind danger location map M1 is shown in FIG. 8. The strong wind danger location map M1 shown in FIG. 8 is a diagram showing the degree of wind speed increase in a certain wind direction. In the strong wind danger location map M1, each area is colored according to wind speed, taking into account the speed increase due to terrain. By referring to the strong wind danger location map M1, it is possible to grasp areas where strong winds are likely to occur. The strong wind danger location map creation unit 161 creates a strong wind danger location map for each wind direction (for example, for each of the 16 directions).

[0041] Furthermore, the control unit 13 executes a human factor estimation process using the human factor estimation unit 15. The human factor estimation unit 15 executes at least one of a process for extracting hazardous materials scattered by satellite image analysis (step S111) and a process for extracting hazardous materials scattered by visual inspection, drone photography, etc. (step S112) depending on the target area. For example, if information on the status of signs and the like existing in the target area can be obtained by visual inspection or drone photography, the process for extracting hazardous materials scattered by visual inspection, drone photography, etc. (step S112) is executed, and if such information cannot be obtained, the process for extracting hazardous materials scattered by satellite image analysis (step S111) is executed. Alternatively, both processes may be executed.

[0042] For example, the human factor estimation unit 15 uses the flying object extraction unit 151 to perform a flying object extraction process by satellite image analysis (step S111) and evaluate the flying object risk, flying distance, etc. (step S113). Figure 9 shows an example of the flying object extraction process and the risk estimation process. A user inputs a visualized image of a target area taken from a satellite into the damage prediction device 10. The input reception unit 11 acquires the image and records it in the storage unit 12 (step S41). The flying object extraction unit 151 reads the image from the storage unit 12 and performs image analysis (step S42) to extract signs and roofs contained in the image (step S43). For example, the flying object extraction unit 151 has previously learned the features of flying objects such as signs and roofs, and extracts a portion of the image that matches the learned features as flying objects. Next, the flying object influence calculation unit 152 estimates the weight, drag coefficient, and lift coefficient of the flying object (step S44). For example, the flying object extraction unit 151 identifies the type (e.g., a roof or a sign) and size of the flying object from the extracted image. Next, the flying object influence calculation unit 152 references the flying object database in the storage unit 12 based on the type and size of the identified flying object and acquires the weight, drag coefficient, and lift coefficient corresponding to the type and size of the identified flying object from the flying object database. Next, the flying object influence calculation unit 152 evaluates the scattering distance, scattering range, and scattering risk of the flying object based on the estimated weight, drag coefficient, and lift coefficient of the flying object and the wind speed in the area where the flying object is located (the wind speed of the strong wind estimated in step S109) (steps S45 and S113). For example, the flying object influence calculation unit 152 calculates the wind speed at which the flying object will be blown away based on the size, weight, drag coefficient, and lift coefficient of the identified flying object, and compares it with the maximum wind speed in the area to calculate the risk. In addition, the flying object impact calculation unit 152 may calculate the flight distance of the flying object when the wind blows at the maximum wind speed in the area based on the size, weight, drag coefficient, and lift coefficient of the identified flying object, and may determine the flight range as a circle with a radius equal to this flight distance.

[0043] Even when scattering hazardous materials are extracted by visual inspection, drone photography, etc., extraction of scattering hazardous materials and evaluation of scattering hazard, etc., can be performed in the same manner as in the flowchart of Fig. 9. For example, when drone photography is performed, the processing of steps S41 to S45 is performed based on the captured image. When visual inspection is performed, for example, the user inputs the type and size estimate of the scattering material visually confirmed to the damage prediction device 10, and the input receiving unit 11 acquires this information and records it in the memory unit 12. The scattering material influence calculation unit 152 reads and acquires the type and size of the scattering material from the memory unit 12, and executes the processing of steps S44 to S45.

[0044] Next, depending on the target area, the overhead line direction calculation unit 155 executes at least one of an overhead line direction evaluation process of the power transmission communication network by satellite image analysis (step S114) and an overhead line direction evaluation process of the power transmission communication network by visual inspection, drone photography, etc. (step S115). For example, if information confirming the power transmission line communication network existing in the target area can be obtained by visual inspection or drone photography, the overhead line direction evaluation process of the power transmission communication network by visual inspection, drone photography, etc. is executed, and if such information cannot be obtained, the overhead line direction calculation unit 155 executes an overhead line direction evaluation process of the power transmission communication network by satellite image analysis. Alternatively, the overhead line direction calculation unit 155 may execute both processes.

[0045] For example, the overhead line direction calculation unit 155 performs overhead line direction evaluation processing of the power transmission and communication network by satellite image analysis (step S114). FIG. 10 shows an example of the overhead line direction evaluation processing of the power transmission and communication network. A user inputs a visualized image of a target area photographed from a satellite to the damage prediction device 10. The input reception unit 11 acquires the image and records it in the storage unit 12 (step S51). The overhead line direction calculation unit 155 reads the image from the storage unit 12 and performs image analysis (step S52) to extract the power transmission and communication network contained in the image. For example, the scattered object extraction unit 151 has previously learned the features of power transmission lines, telephone lines, and communication lines, and extracts a portion of the image that matches the learned features as the power transmission and communication network. Next, the overhead line direction calculation unit 155 connects the extracted portions to calculate the overhead line direction of the power transmission and communication network (step S53). The overhead line direction calculation unit 155 records the overhead line positions and directions of the power transmission and communication network in the storage unit 12. For example, when a power transmission line is laid between point A and point B in the image, the overhead line direction calculation unit 155 converts the position information of point A and point B into actual position information and records the converted information in the storage unit 12.

[0046] Even when overhead line direction evaluation processing is performed using drone photography or the like, the overhead line direction can be evaluated in the same manner as in the flowchart of Fig. 10. For example, when drone photography is performed, the processing of steps S51 to S53 is performed based on the captured image. When visual confirmation is performed, for example, the user inputs position information (actual position information) of the visually confirmed start point, end point, and relay point of the power transmission line communication network into the damage prediction device 10, and the input receiving unit 11 records this information in the storage unit 12.

[0047] Next, the overhead line damage assessment unit 156 assesses the predicted damage range of the power transmission communication network (step S116). For example, information defining the relationship between the wind speed and the overhead line direction and the wind direction when damage occurs to the power transmission communication network is registered in advance in the storage unit 12, and the overhead line damage assessment unit 156 predicts the range of damage to the power transmission communication network based on the wind direction and wind speed for each area estimated in step S109 and the overhead line direction of the power transmission communication network. The overhead line damage assessment unit 156 records the predicted range of damage to the power transmission communication network in the storage unit 12.

[0048] Next, the human cause estimation unit 15 integrates the scattering risk, scattering range, and predicted damage range of the power transmission and communication network (step S117). The human cause estimation unit 15 evaluates the scattering risk and scattering range evaluated in step S114 in combination with the predicted damage range of the power transmission and communication network evaluated in step S116. For example, the human cause estimation unit 15 calculates the range included in the scattering range of the scattered objects in the power transmission and communication network based on the overhead line position and overhead line direction of the power transmission and communication network.

[0049] Next, the map creation unit 16 uses the flying object danger location map creation unit 162 to create a flying object danger map (step S118) and stores the created flying object danger map in the storage unit 12. The flying object danger location map creation unit 162 creates an image (flying object danger location map) by, for example, superimposing lines indicating the position and direction of overhead power lines of the power transmission and communication network on map information of the target area and superimposing the range of flying object scattering. An example of the flying object danger location map M2 is shown in FIG. 11. In the flying object danger location map M2 shown in FIG. 11, L1 to L6 represent the position and direction of overhead power lines of the power transmission and communication network, and E1 to E3 represent the range of flying object scattering. By referring to the flying object danger location map M2, it is possible to grasp the range of impact of flying object in the power transmission and communication network. Although not shown in the drawings, the flying object danger location map creation unit 162 may display the predicted damage range evaluated in step S116 on a flying object danger level map. Furthermore, the flying object danger location map creation unit 162 may display the flying object danger level on the flying object danger location map.

[0050] Next, the overhead line danger map creation unit 164 creates an overhead line danger map of the power transmission line communication network (step S119). The overhead line danger map creation unit 164 reads and acquires the strong wind danger location map and the flying object danger map from the storage unit 12, superimposes them to create an overhead line danger map, and stores the map in the storage unit 12. The output unit 17 outputs the overhead line danger map to the display device (step S120). FIG. 12 shows an example of a damage danger map M3. By referring to the damage danger map M3, it is possible to evaluate the scattering range of flying objects in the power transmission communication network, taking into account the influence of speed increase due to the terrain. For example, it is possible to estimate the scattering direction of flying objects according to the wind direction and identify areas that are likely to be particularly affected by flying objects. It is also possible to identify areas that are particularly susceptible to flying objects by taking into account the influence of speed increase.

[0051] Next, with reference to FIG. 13 , an example of typhoon response work using a damage risk map will be described. Before the typhoon approaches (step S61: before approach), the administrator of the power transmission and communication network prioritizes preventive measures based on the damage risk map (step S62). For example, the administrator may assign the highest priority to an area where strong winds are predicted and debris is present in the damage risk map, and then assign the next highest priority to an area where strong winds are predicted and within the range of debris to be scattered. Thus, preventive measures such as removing debris from high-risk areas are prioritized (step S63). After the typhoon has passed (step S61: after passage), the administrator prioritizes inspections and countermeasures (such as repairs) based on the damage risk map (step S64). Then, inspections and countermeasures are performed in the highest-risk areas according to this priority (step S65). This allows for early inspection and restoration of areas with the greatest damage priority, with the minimum necessary personnel. In other words, it will be possible to reduce the number of personnel required for widespread inspections, which previously made it difficult to identify areas with severe damage, and this will also lead to earlier recovery.

[0052] As described above, the damage prediction device 10 according to the first embodiment targets an area with many residential buildings and few forests. It estimates strong winds, taking into account the effect of wind speed acceleration due to topography, evaluates the risk and range of scattering of flying debris such as signs, roofs, and roof tiles, and evaluates the predicted damage range of the power transmission and communication network. By combining these, it creates a damage risk map for the power transmission and communication network that takes into account damage caused not only by strong winds but also by flying debris. This allows the administrator of the power transmission and communication network to take preventive measures to prevent damage to the power transmission and communication network, such as removing flying debris, according to the priority of risks indicated by the damage risk map before the typhoon approaches, thereby reducing power outage damage and other damage. Furthermore, after the typhoon passes, the damage risk map can be used to narrow down inspection locations and prioritize countermeasures, enabling early restoration and reducing inspection personnel and costs.

[0053] Furthermore, when estimating the triggering factors, methods such as extreme value statistical analysis of meteorological observation data, typhoon simulation and extreme value statistical analysis, wind tunnel testing using terrain models, computational fluid analysis using terrain models, and strong wind estimation methods that take into account the influence of topographical acceleration using a multiple regression equation for topographical factors can be used depending on the evaluation location, thereby improving the accuracy of estimating strong winds that take into account the influence of topographical acceleration. For example, extreme value statistical analysis of meteorological observation data can be used if the location is close to a meteorological observation station or a location where observation data is available, typhoon simulation and extreme value statistical analysis can be used in locations where typhoons frequently strike, and wind tunnel testing and computational fluid analysis can be used in locations located on complex terrain that is likely to be greatly affected by the terrain. By using appropriate methods depending on the geographical characteristics of the target area, the accuracy of estimating strong winds can be improved.

[0054] In addition, when estimating human causes, hazardous materials can be extracted by analyzing satellite images, visual inspection, drone photography, etc., and the accuracy of estimating the affected area can be improved by evaluating the degree of risk of scattering, scattering distance, and scattering range of these materials.

[0055] Furthermore, by incorporating the direction of the overhead power lines of the power transmission and communication network into the evaluation, it is possible to estimate the wind direction that will cause the greatest damage, thereby improving the accuracy of estimating the affected area.

[0056] Second Embodiment The second embodiment will be described below with reference to FIGS. In the second embodiment, a damage prediction process will be described assuming a target area with few houses and many forests. The configuration of the damage prediction device 10 is the same as that described in the first embodiment.

[0057] (operation) FIG. 14 is a flowchart showing an example of damage prediction processing according to the second embodiment. Steps S101 to S110 are the same as those in the first embodiment, and therefore their explanations are omitted. The control unit 13 performs a human-caused risk estimation process in addition to a trigger risk estimation process. In the second embodiment, the human-caused risk estimation unit 15 estimates the impact of fallen trees on the power transmission line network. Depending on the target area, the human-caused risk estimation unit 15 performs at least one of a process for extracting trees at risk of falling using satellite image analysis (step S121) and a process for extracting trees at risk of falling using visual inspection, drone photography, or the like (step S122). For example, if information on the status of trees in the target area can be obtained using visual inspection or drone photography, the human-caused risk estimation unit 15 performs the process for extracting trees at risk of falling using visual inspection, drone photography, or the like (step S122). If such information is not available, the human-caused risk estimation unit 15 performs the process for extracting trees at risk of falling using satellite image analysis (step S121). Alternatively, the human-caused risk estimation unit 15 may perform both processes.

[0058] For example, the human factor estimation unit 15 uses the fallen tree extraction unit 153 to perform a process for extracting trees at risk of falling by satellite image analysis (step S121) and evaluate the risk of falling trees and the extent of tree fall (step S123). Figure 15 shows an example of the process for extracting trees at risk of falling and the process for estimating risk of falling trees. A user inputs a visualized image of a target area captured by a satellite into the damage prediction device 10. The input receiving unit 11 acquires the image and records it in the storage unit 12 (step S71). The fallen tree extraction unit 153 reads the image from the storage unit 12 and performs image analysis (step S72) to extract trees that may be fallen (step S73). For example, the fallen tree extraction unit 153 has previously learned the features of trees that may be fallen, as well as towers and pillars that may be fallen, and extracts parts of the image that match the learned features as trees that may be fallen. Next, the fallen tree extraction unit 153 identifies the type, height, thickness, etc. of the tree from the extracted image. Next, the fallen tree effect calculation unit 154 refers to the fallen object database in the storage unit 12 based on, for example, the type, height, and thickness of the identified tree, and obtains from the fallen object database the load required to fall the tree according to the type, height, and thickness of the identified tree. Next, the fallen tree effect calculation unit 154 evaluates the tree's falling risk and the extent of the tree's fall based on the load that would cause the tree to fall and the wind speed in the area where the tree is located (the wind speed of the strong wind estimated in step S109) (steps S74 and S123). For example, the fallen tree effect calculation unit 154 calculates the wind speed at the time the tree falls based on the identified tree's height and thickness, and compares this with the maximum wind speed in the area to calculate the tree's risk of falling. Alternatively, the fallen tree effect calculation unit 154 may determine the extent of the fallen tree as a circle centered on the tree's location and with a radius equal to the tree's height. Also, taking into consideration the surrounding conditions, if a tree that may fall is surrounded by large trees, the range of impact of the fallen tree will be reduced by the large trees, so the range of the fallen tree may be limited to the range of the large trees.

[0059] Even when scattering hazardous materials are extracted by visual inspection, drone photography, etc., it is possible to extract trees at risk of falling and evaluate the risk of falling trees in the same manner as in the flowchart of Figure 15. For example, when drone photography is performed, the processing of steps S71 to S74 is performed based on the captured image. When a tree is visually inspected, for example, the user inputs estimated values ​​of the type, size, and height of the tree visually inspected into the damage prediction device 10, and the input receiving unit 11 acquires this information and records it in the memory unit 12. The fallen tree influence calculation unit 154 reads and acquires the type, height, and thickness of the tree from the memory unit 12, and executes the processing of step S74.

[0060] Next, the overhead line direction calculation unit 155 executes at least one of the following processes depending on the target area: an overhead line direction evaluation process of the power transmission and communication network by satellite image analysis (step S114), and an overhead line direction evaluation process of the power transmission and communication network by visual inspection, drone photography, etc. (step S115). Then, the overhead line damage evaluation unit 156 evaluates the predicted damage range of the power transmission and communication network (step S116). These processes are the same as those in the first embodiment, so their explanation will be omitted.

[0061] Next, the human cause estimation unit 15 integrates the risk of fallen trees, the range of fallen trees, and the predicted damage range of the power transmission and communication network (step S124). The human cause estimation unit 15 evaluates the risk of fallen trees and the range of fallen trees evaluated in step S123 in combination with the predicted damage range of the power transmission and communication network evaluated in step S116. For example, the human cause estimation unit 15 calculates the range of the power transmission and communication network that is included in the range of fallen trees based on the overhead line positions and overhead line directions of the power transmission and communication network.

[0062] Next, the map creation unit 16 uses the fallen tree danger area map creation unit 163 to create a fallen tree danger area map (step S125), and stores the created fallen tree danger area map in the storage unit 12. The fallen tree danger area map creation unit 163 creates an image (fallen tree danger area map) by, for example, superimposing lines indicating the position and direction of the overhead wires of the power transmission and communication network on map information of the target area, and further superimposing the range of fallen trees. An example of the fallen tree danger area map M4 is shown in FIG. 16. In the flying object danger area map M4 shown in FIG. 16, L1 to L6 represent the position and direction of the overhead wires of the power transmission and communication network, and W1 to W3 represent the range of fallen trees. By referring to the fallen tree danger area map M4, it is possible to understand the range of impact of fallen trees in the power transmission and communication network. Although not shown in the drawings, the fallen tree danger area map creating unit 163 may display the predicted damage range evaluated in step S116 on the fallen tree danger area map. Furthermore, the fallen tree danger area map creating unit 163 may display the degree of risk of falling trees on the fallen tree danger area map.

[0063] Next, the overhead line danger map creation unit 164 creates an overhead line danger map of the power transmission line communication network (step S119'). The overhead line danger map creation unit 164 reads and acquires the strong wind danger location map and the fallen tree danger location map from the storage unit 12, superimposes them to create an overhead line danger map, and records the map in the storage unit 12. The output unit 17 outputs the overhead line danger map to the display device (step S120). FIG. 17 shows an example of the damage danger map M5. By referring to the damage danger map M5, it is possible to evaluate the range of fallen trees in the power transmission communication network while taking into account the influence of acceleration due to the terrain. For example, it is possible to estimate the direction of tree fall according to the wind direction and identify areas that are likely to be particularly affected by fallen trees. It is also possible to identify areas that are particularly susceptible to tree fall by taking into account the influence of acceleration.

[0064] As described above, according to the second embodiment, for an area with few residential buildings and a large forest, strong winds can be estimated taking into account the effect of wind speed acceleration due to topography, the risk of fallen trees and the extent of fallen trees can be assessed, and the predicted damage area of ​​the power transmission and communication network can be assessed. These assessments can then be combined to create a damage risk map for the power transmission and communication network that takes into account damage caused not only by strong winds but also by fallen trees. This allows the administrator of the power transmission and communication network to take preventive measures to prevent damage to the power transmission and communication network before a typhoon approaches, such as prioritizing tree removal from high-risk areas according to the priority of risk indicated in the damage risk map, thereby reducing power outage damage and other damage. Furthermore, after the typhoon passes, the administrator can narrow down inspection locations and prioritize countermeasures based on the damage risk map, enabling early restoration and reducing the number of inspection personnel and costs.

[0065] Furthermore, when estimating human causes, it is possible to extract nearby trees at risk of falling by analyzing satellite images or by visual inspection or drone photography, etc., and by evaluating the risk of these trees falling and the extent of the fallen trees, it is possible to improve the accuracy of estimating the damage area. Furthermore, with regard to estimating the contributing factors, it is possible to obtain the same effect as the first prevailing form.

[0066] Third Embodiment The third embodiment will be described below with reference to FIGS. In the third embodiment, a configuration will be described in which the first embodiment and the second embodiment are combined. The configuration of the damage prediction device 10 is the same as that described in the first embodiment.

[0067] (operation) FIG. 18 is a flowchart showing an example of damage prediction processing according to the second embodiment. Each process has been explained in the first and second embodiments, so it is not illustrated. First, a target area is set (step S101). The trigger factor estimation unit 14 performs the processes of steps S102 to S109. The map creation unit 16 creates a strong wind danger location map (step S110). Next, the human factor estimation unit 15 performs the processes of steps S111 to S117, and the map creation unit 16 creates a flying object danger location map (step S118). Furthermore, the human factor estimation unit 15 performs the processes of steps S121 to S123, steps S114 to S116, and step S124, and the map creation unit 16 creates a fallen tree danger location map (step S125). Next, the overhead line danger map creation unit 164 creates an overhead line danger map (step S119''). The overhead line danger map creation unit 164 reads and acquires the strong wind danger area map, the flying object danger area map, and the fallen tree danger area map from the memory unit 12, and creates an overhead line danger map by superimposing these maps. The map is then recorded in the memory unit 12, and the output unit 17 outputs the overhead line danger map to the display device (step S120). Fig. 19 shows an example of a damage danger map M6. By referring to the damage danger map M6, it is possible to evaluate the range of flying object scattering and the range of fallen trees in the power transmission and communication network, taking into account the effect of increased speed due to terrain.

[0068] According to the third embodiment, it is possible to estimate strong winds taking into account the effect of wind speed acceleration due to terrain, evaluate the risk and range of flying debris, the risk and range of fallen trees, and the predicted range of damage to the power transmission and communication network, and combine these to create a damage risk map for the power transmission and communication network that takes into account damage caused not only by strong winds but also by flying debris and fallen trees. This allows the administrator of the power transmission and communication network to effectively prevent power outages by taking damage prevention measures such as removing flying debris and fallen trees in accordance with the priority of risk before the typhoon approaches, and to narrow down inspection locations and measures based on the priority after the typhoon has passed, which leads to early restoration and reduces the number of inspection personnel and costs.

[0069] In the first to third embodiments, at least one of a strong wind danger area map, a flying object danger area map, and a fallen tree danger area map is created, but it is also possible to perform only the processing of steps S101 to S110 to create only the strong wind danger area map. Also, it is also possible to create either a flying object danger area map or a fallen tree danger area map, or both, depending on the target area, without creating a strong wind danger area map.

[0070] FIG. 20 is a diagram illustrating an example of a hardware configuration of a damage prediction device according to each embodiment. The computer 900 includes a CPU 901 , a main memory device 902 , an auxiliary memory device 903 , an input / output interface 904 , and a communication interface 905 . The damage prediction device 10 described above is implemented in a computer 900. Each of the above-described functions is stored in the auxiliary storage device 903 in the form of a program. The CPU 901 reads the program from the auxiliary storage device 903, loads it into the main storage device 902, and executes the above-described processing in accordance with the program. The CPU 901 also allocates a storage area in the main storage device 902 in accordance with the program. The CPU 901 also allocates a storage area in the auxiliary storage device 903 for storing data being processed in accordance with the program.

[0071] A program for implementing all or part of the functions of the damage prediction device 10 may be recorded on a computer-readable recording medium, and the program may be loaded into a computer system and executed to perform processing by each functional unit. The term "computer system" as used herein includes hardware such as an OS and peripheral devices. If a WWW system is used, the term "computer system" also includes the homepage provision environment (or display environment). The term "computer-readable recording medium" refers to portable media such as CDs, DVDs, and USBs, as well as storage devices such as hard disks built into the computer system. If the program is distributed to the computer 900 via a communication line, the computer 900 may load the program into the main storage device 902 and execute the above-described processing. The program may be for implementing part of the above-described functions, or may be capable of implementing the above-described functions in combination with a program already stored in the computer system.

[0072] As described above, several embodiments according to the present disclosure have been described, but all of these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as defined in the claims, as well as in the scope and spirit of the invention.

[0073] <Additional Notes> The damage prediction device, the damage prediction method, and the program described in each embodiment can be understood, for example, as follows.

[0074] (1) The damage prediction device 10 according to the first aspect has a means for receiving the designation of a target area for the impact assessment of strong winds (input receiving unit 11), a means for identifying the range in which structures (roofs, signs, roof tiles, trees, etc.) present in the target area will be scattered or collapsed due to the impact of the strong winds (flying object impact calculation unit 152, fallen tree impact calculation unit 154), a means for calculating the laying direction (overhead line direction) of linear components (power transmission and communication network) present in the target area (overhead line direction calculation unit 155), and a means for creating human-caused risk map information for the target area (flying object hazard area map, fallen tree hazard area map) by superimposing the laying direction and the range on map information for the target area (flying object hazard area map creation unit 162, fallen tree hazard area map creation unit 163). This makes it possible to grasp the extent of the impact of debris and fallen trees caused by strong winds on the power line communication network.

[0075] (2) The damage prediction device 10 according to the second aspect is the damage prediction device 10 of (1), wherein the structure is flying debris, and the means for identifying the range uses the weight, resistance coefficient, and lift coefficient of the flying debris to estimate the range into which the flying debris will scatter and the ease with which the flying debris will scatter. This makes it possible to estimate the extent of impact of flying debris such as signs, roofs, and roof tiles.

[0076] (3) The damage prediction device 10 according to the third aspect is the damage prediction device 10 of (1) to (2), wherein the structure is a tree, tower, or pillar, and the means for specifying the range estimates the range of collapse and the likelihood of collapse of the structure using the thickness and height of the structure. This makes it possible to estimate the extent of the impact of fallen trees, etc.

[0077] (4) The damage prediction device 10 according to the fourth aspect is the damage prediction device 10 of (3), wherein the means for specifying the range estimates the range by assuming that the structures are the flying debris when the target area is an area with many houses and few forests, and estimates the range by assuming that the structures are the trees, towers, or pillars when the target area is an area with few houses and many forests. The extent of impact of flying debris or fallen trees can be estimated according to the environment of the target area.

[0078] (5) The damage prediction device 10 according to the fifth aspect is the damage prediction device 10 of (1) to (4), and further includes a means (triggering factor estimation unit 14) for calculating the wind speed for each area based on the topographical information of the target area, taking into account the increase in speed due to the topography of the target area, a means (strong wind hazard map creation unit 161) for creating strong wind hazard map information for the target area by superimposing the wind speed for each area on map information of the target area, and a means (overhead line hazard map creation unit 164) for creating damage hazard map information for the linear member by superimposing the human-caused hazard map information and the strong wind hazard map information. This makes it possible to further grasp the effect of speed increase due to the influence of terrain.

[0079] (6) The damage prediction device 10 according to the sixth aspect is the damage prediction device 10 of (5), wherein the means for calculating the wind speed by area performs extreme value statistical analysis based on meteorological observation data or extreme value statistical analysis by typhoon simulation, and calculates the wind speed taking into account the topography of the target area by wind tunnel experiments based on a topographical model that simulates the topography of the target area or numerical fluid analysis based on three-dimensional data that reproduces the topography of the target area. This allows the wind speed to be calculated taking into account the increase in speed due to the influence of the terrain.

[0080] (7) The damage prediction device 10 according to the seventh aspect is the damage prediction device 10 of (5) to (6), and the means for calculating strong winds by area calculates wind speed taking into account the topography of the target area based on a multiple regression equation obtained by multiple regression analysis of the relationship between the topographical factors of the target area and wind speed, and the topographical factors of the evaluation target position of the target area. This makes it possible to calculate wind speed taking into account topographical factors, that is, wind speed taking into account the increase in speed due to the influence of topography.

[0081] (8) The damage prediction device 10 according to the eighth aspect is a damage prediction device 10 according to any one of (1) to (7), and includes a means for accepting the designation of a target area for the assessment of the impact of strong winds (input accepting unit 11), a means for calculating the wind speed for each area based on the topographical information of the target area, taking into account the increase in speed due to the topography of the target area (triggering factor estimating unit 14), and a means for creating map information of strong wind danger areas for the target area by superimposing the wind speed for each area on the map information of the target area (strong wind danger area map creating unit 161). This makes it possible to grasp wind speed taking into account the effects of terrain.

[0082] (9) A damage prediction method according to a ninth aspect is a damage prediction method executed by a computer, and includes the steps of accepting the designation of a target area for assessment of the impact of strong winds, identifying the range in which structures in the target area will be blown away or collapsed due to the impact of the strong winds, calculating the laying direction of linear members in the target area, and creating human-caused risk map information for the target area by superimposing the laying direction and the range on map information for the target area.

[0083] (10) The program according to the tenth aspect causes a computer 900 to execute the steps of accepting the designation of a target area for assessment of the impact of strong winds, identifying the range in which structures in the target area will be blown away or collapsed due to the impact of the strong winds, calculating the laying direction of linear members in the target area, and creating human-caused risk map information for the target area by superimposing the laying direction and the range on map information for the target area. [Explanation of symbols]

[0084] 10 Damage prediction device, 11 Input reception unit, 12 Memory unit, 13 Control unit, 14 Cause estimation unit, 141 Weather data strong wind estimation unit, 142 Typhoon data strong wind estimation unit, 143 Terrain speed increase analysis unit, 144 Terrain factor strong wind estimation unit, 15 Human factor estimation unit, 151 Flying object extraction unit, 152 Flying object impact calculation unit, 153 Fallen tree extraction unit, 154 Fallen tree impact calculation unit, 155 Overhead line direction calculation unit, 156 Overhead line damage evaluation unit, 16 Map creation unit, 161 Strong wind danger area map creation unit, 162 Flying object danger area map creation unit, 163: Fallen tree danger location map creation unit, 164: Overhead line danger map creation unit, 17: Output unit, 900: Computer, 901: CPU, 902: Main storage device, 903: Auxiliary storage device, 904: Input / output interface, 905: Communication interface

Claims

1. a means for accepting designation of areas for high wind impact assessment; a means for identifying an area in which structures present in the target area will be blown away or destroyed by the strong wind; a means for calculating the laying direction of linear members present in the target area; a means for creating map information of human-caused danger points in the target area by superimposing the laying direction and the range on map information of the target area; and The linear member is a power transmission and communication network, the structure is a flying object, and the means for specifying the range estimates the range into which the flying object will fly and the likelihood of the flying object flying using the weight, resistance coefficient, and lift coefficient of the flying object; Damage prediction device.

2. the structure is a tree, tower, or pillar, and the means for specifying the range estimates the range of collapse and the likelihood of collapse of the structure using the thickness and height of the structure; The damage prediction device according to claim 1 .

3. The means for specifying the range is If the target area is an area with many residential houses and few forests, the range is estimated assuming that the structures are the flying debris; If the target area is an area with few houses and many forests, the range is estimated assuming that the structure is the tree, the tower, or the pillar. The damage prediction device according to claim 2 .

4. a means for calculating a wind speed taking into consideration an increase in speed due to the topography of the target area based on topographical information of the target area; means for creating map information of strong wind danger areas of the target area by superimposing the wind speed on map information of the target area; means for creating damage risk map information for the linear members by superimposing the human-caused danger location map information and the strong wind danger location map information; The damage prediction device according to any one of claims 1 to 3, further comprising:

5. The means for calculating the wind speed performs extreme value statistical analysis based on meteorological observation data or extreme value statistical analysis using a typhoon simulation, and calculates the wind speed taking into account the topography of the target area by wind tunnel testing based on a topographical model that simulates the topography of the target area or by computational fluid dynamics analysis based on three-dimensional data that reproduces the topography of the target area. The damage prediction device according to claim 4.

6. The means for calculating the strong wind calculates the wind speed taking into consideration the topography of the target area based on a multiple regression equation obtained by multiple regression analysis of the relationship between the topographical factors of the target area and the wind speed, and the topographical factors of the evaluation target position of the target area. The damage prediction device according to claim 4 or 5.

7. a means for accepting designation of areas for high wind impact assessment; a means for identifying an area in which structures present in the target area will be blown away or destroyed by the strong wind; a means for calculating the laying direction of linear members present in the target area; a means for creating map information of human-caused danger points in the target area by superimposing the laying direction and the range on map information of the target area; and The linear member is a power transmission and communication network, The means for specifying the range is If the target area is an area with many residential houses and few forests, the range is estimated assuming that the structure is airborne debris; If the target area is an area with few houses and many forests, the range is estimated assuming that the structure is a tree, tower, or pillar. Damage prediction device.

8. A damage prediction method executed by a computer, comprising: receiving designation of an area subject to a strong wind impact assessment; A step of identifying an area where structures present in the target area will be blown away or collapsed due to the influence of the strong wind; calculating a laying direction of linear members present in the target area; creating map information of human-caused danger points in the target area by superimposing the laying direction and the range on map information of the target area; and The linear member is a power transmission and communication network, the structure is a flying object, and in the step of specifying the range, a range into which the flying object will fly and the likelihood of the flying object flying are estimated using a weight, a resistance coefficient, and a lift coefficient of the flying object; Damage prediction method.

9. A damage prediction method executed by a computer, comprising: receiving designation of an area subject to a strong wind impact assessment; A step of identifying an area where structures present in the target area will be blown away or collapsed due to the influence of the strong wind; calculating a laying direction of linear members present in the target area; creating map information of human-caused danger points in the target area by superimposing the laying direction and the range on map information of the target area; and The linear member is a power transmission and communication network, In the step of specifying the range, If the target area is an area with many residential houses and few forests, the range is estimated assuming that the structure is airborne debris; If the target area is an area with few houses and many forests, the range is estimated assuming that the structure is a tree, tower, or pillar. Damage prediction method.

10. On the computer, receiving designation of an area subject to a strong wind impact assessment; A step of identifying an area where structures present in the target area will be blown away or collapsed due to the influence of the strong wind; calculating a laying direction of linear members present in the target area; creating map information of human-caused danger points in the target area by superimposing the laying direction and the range on map information of the target area; and The linear member is a power transmission and communication network, the structure is a flying object, and the step of specifying the range includes a process of estimating the range into which the flying object will fly and the likelihood of the flying object flying, using a weight, a resistance coefficient, and a lift coefficient of the flying object; A program that executes the following.

11. On the computer, receiving designation of an area subject to a strong wind impact assessment; A step of identifying an area where structures present in the target area will be blown away or collapsed due to the influence of the strong wind; calculating a laying direction of linear members present in the target area; creating map information of human-caused danger points in the target area by superimposing the laying direction and the range on map information of the target area; and The linear member is a power transmission and communication network, In the step of specifying the range, If the target area is an area with many residential houses and few forests, the range is estimated assuming that the structure is airborne debris; If the target area is an area with few houses and many forests, estimating the range by assuming that the structure is a tree, tower, or pillar; A program that executes the following.

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