WO2021199241A1 - Dispositif d'analyse, procédé d'analyse et support de stockage - Google Patents

Dispositif d'analyse, procédé d'analyse et support de stockage Download PDF

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Publication number
WO2021199241A1
WO2021199241A1 PCT/JP2020/014746 JP2020014746W WO2021199241A1 WO 2021199241 A1 WO2021199241 A1 WO 2021199241A1 JP 2020014746 W JP2020014746 W JP 2020014746W WO 2021199241 A1 WO2021199241 A1 WO 2021199241A1
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WIPO (PCT)
Prior art keywords
displacement
facility
distribution
predicted
target
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PCT/JP2020/014746
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English (en)
Japanese (ja)
Inventor
孝和 石井
奈緒 赤川
寛道 平田
翔平 大野
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日本電気株式会社
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Priority to JP2022512966A priority Critical patent/JP7294529B2/ja
Priority to PCT/JP2020/014746 priority patent/WO2021199241A1/fr
Publication of WO2021199241A1 publication Critical patent/WO2021199241A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/04—Interpretation of pictures
    • G01C11/06—Interpretation of pictures by comparison of two or more pictures of the same area
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C7/00—Tracing profiles
    • G01C7/02—Tracing profiles of land surfaces
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques

Definitions

  • the present disclosure relates to a technique for analyzing the influence of displacement of the ground surface, and more particularly to a technique for analyzing a load on a facility based on the displacement of the ground surface height measured by SAR (Synthetic Aperture Radar).
  • SAR Synthetic Aperture Radar
  • Patent Document 1 describes a technique for visualizing the displacement of the ground surface in the region of interest obtained by the interference SAR image.
  • Patent Document 2 describes the types of repair work for structures or terrain related to the cause of displacement, which are estimated based on the results of interference analysis of multiple SAR image data with different acquisition times and sensor observation data. The SAR image analysis system to be estimated is described.
  • Patent Document 1 can visualize the displacement of the ground surface in the region of interest. However, it is not possible to grasp the phenomenon that occurs in the facility due to the change in the ground by the technique described in Patent Document 1.
  • the factor of displacement can be estimated, and the repair work related to the factor of displacement can be estimated. However, it is not possible to identify facilities where the phenomenon that occurs will exceed future standards.
  • the purpose of this disclosure is to provide an analyzer that can grasp facilities where the target phenomenon such as uneven displacement or stress abnormality may at least exceed the standard in the future.
  • the analyzer includes a predictive means for predicting the displacement of the ground surface height within the target range based on the transition of the displacement of the ground surface height within the target range, and the displacement. Based on the distribution of the first predicted displacement, which is the displacement predicted based on the transition of, the detection means for detecting the target phenomenon including the stress abnormality and the uneven displacement of the facilities within the target range, and the target phenomenon are detected. It is provided with an output means for outputting the information of the facility.
  • the analyzer is a detection means for detecting a target phenomenon including stress abnormality and uneven displacement of a facility within the target range based on the distribution of displacement at the height of the ground surface within the target range. And an output means for outputting the information of the facility in which the target phenomenon is detected.
  • the analysis method predicts the displacement of the ground surface height within the target range based on the transition of the ground surface height displacement within the target range, and obtains the displacement transition. Based on the distribution of the first predicted displacement, which is the displacement predicted based on the above, the target phenomenon including the stress abnormality and the uneven displacement of the facility within the target range is detected, and the information of the facility where the target phenomenon is detected is detected. Is output.
  • the analysis method detects a target phenomenon including stress abnormality and uneven displacement of a facility within the target range based on the distribution of displacement of the ground surface height within the target range, and the above-mentioned The information of the facility where the target phenomenon is detected is output.
  • the storage medium includes a prediction process for predicting the displacement of the ground surface height within the target range based on the transition of the displacement of the ground surface height within the target range, and the displacement. Based on the distribution of the first predicted displacement, which is the displacement predicted based on the transition of, the detection process for detecting the target phenomenon including the stress abnormality and the uneven displacement of the facilities within the target range, and the detection process for detecting the target phenomenon.
  • the output process for outputting the information of the facility and the program for causing the computer to execute are stored.
  • the storage medium is a detection process for detecting a target phenomenon including stress abnormality and uneven displacement of a facility within the target range based on the distribution of displacement of the ground surface height within the target range.
  • a program for causing the computer to execute the output process for outputting the information of the facility in which the target phenomenon is detected is stored.
  • One aspect of the present disclosure is also realized by the program stored in the above-mentioned storage medium.
  • This disclosure has the effect of being able to identify facilities where target phenomena such as uneven displacement and stress abnormalities may at least exceed the standards in the future.
  • FIG. 1 is a block diagram showing an example of the configuration of the analyzer according to the first and second reference examples of the present disclosure.
  • FIG. 2 is a flowchart showing an example of the operation of the analyzer 10 of the first reference example of the present disclosure.
  • FIG. 3 is a block diagram showing a configuration of an analysis system for modified examples of the first and second reference examples of the present disclosure.
  • FIG. 4 is a block diagram showing an example of a detailed configuration of a learning device, an analysis device, and a geospatial information storage device included in the analysis system of the modified examples of the first and second reference examples of the present disclosure.
  • FIG. 5 is a flowchart showing an example of the operation of the analyzer of the first reference example of the present disclosure.
  • FIG. 6 is a diagram showing an example of the configuration of the analyzer according to the first embodiment of the present disclosure.
  • FIG. 7 is a flowchart showing an example of an operation of learning the determination model of the analyzer of the first embodiment of the present disclosure.
  • FIG. 8 is a flowchart showing an operation of detecting a target phenomenon of the analyzer of the first embodiment of the present disclosure.
  • FIG. 9 is a flowchart showing the operation of the prediction process of the analyzer of the first embodiment of the present disclosure.
  • FIG. 10 is a block diagram showing an example of the configuration of the analyzer according to the second embodiment of the present disclosure.
  • FIG. 11 is a flowchart showing an example of the operation of the analyzer according to the second embodiment of the present disclosure.
  • FIG. 12 is a block diagram showing an example of the configuration of the analyzer according to the third embodiment of the present disclosure.
  • FIG. 13 is a flowchart showing an example of the operation of the analyzer according to the third embodiment of the present disclosure.
  • FIG. 14 is a block diagram showing an example of the configuration of the analyzer according to the fourth embodiment of the present disclosure.
  • FIG. 15 is a flowchart showing an example of the operation of the analyzer according to the fourth embodiment of the present disclosure.
  • FIG. 16 is a diagram showing an example of the hardware configuration of the computer according to the embodiment of the present disclosure.
  • FIG. 17 is a diagram showing an example of the type of embankment land.
  • FIG. 18 is a diagram showing an example of surface geology.
  • FIG. 19 is a diagram showing a riverbed.
  • FIG. 1 is a block diagram showing an example of the configuration of the analyzer 10 according to the first reference example of the present disclosure.
  • the analyzer 10 includes a first receiving unit 111, a first extracting unit 112, a learning unit 113, a second receiving unit 121, a second extracting unit 122, and a determination unit 123. It includes an output unit 124, a model storage unit 125, and a geospatial information storage unit 131.
  • the analyzer 10 may be realized as a combination of two or more devices that are communicably connected to each other.
  • a terminal device for the user to input data to the analysis device 10 may be communicably connected to the analysis device 10 via, for example, a communication network.
  • a communication network for example, a communication network.
  • First receiving unit 111 receives data representing the displacement of the height of the ground surface as learning data.
  • the user may use the terminal device described above to input data representing the displacement of the height of the ground surface to the first receiving unit 111.
  • the first receiving unit 111 receives data representing the displacement of the height of the ground surface from the terminal device.
  • the height displacement represents, for example, the transition of height at the same point (or a point considered to be the same) on the ground surface obtained by observations at multiple time points in the past. Height displacement is sometimes referred to as height variation.
  • the height is, for example, the height at a point on the ground surface obtained by observation using a radar mounted on a flying object such as an artificial satellite or an aircraft as a synthetic aperture radar (SAR). In the following description, such observations will be referred to as observations by Synthetic Aperture Radar (SAR).
  • SAR Synthetic Aperture Radar
  • the transition of the height may be represented by data that can specify, for example, a plurality of values representing the height obtained by observations at a plurality of past time points and the order in which the heights are observed.
  • the data representing the transition of the height may be, for example, data including a plurality of combinations of a value representing the height and data representing the time point at which the height is obtained by observation.
  • the unit of data representing the time point may be appropriately determined.
  • the data representing the time point may represent a date, or may represent a date and a time.
  • the unit of time may also be set as appropriate.
  • the displacement data may include information (eg, latitude and longitude information) representing the position of a point on the ground surface where the displacement of the height represented by the displacement data has been measured.
  • the information representing the position may be other information that can identify the position on the ground surface.
  • the information indicating the position of the point is referred to as the point information.
  • the above-mentioned displacement of the height of the ground surface may represent the displacement of the height at each of a plurality of points on the ground surface.
  • the data representing the displacement of the height of the ground surface is referred to as the ground surface displacement data.
  • the ground surface displacement data may be a combination of displacement data at a plurality of points.
  • the data representing the displacement of the height of the ground surface received by the first receiving unit 111 as learning data is referred to as learning displacement data.
  • the training displacement data is data (aging displacement map) that represents the time-series displacement of the ground surface of the region obtained by analyzing the observation data obtained by observing the same region many times at multiple times. Also written). The transition of displacement in time series is referred to as aged displacement.
  • the first receiving unit 111 sends the received learning displacement data to the first extracting unit 112.
  • the first extraction unit 112 receives the learning displacement data from the first receiving unit 111. For example, the first extraction unit 112 extracts the point information of the point where the transition of the height represented by the displacement data included in the learning displacement data is observed from the learning displacement data. The first extraction unit 112 extracts the value of the geospatial information at the point whose position is represented by the extracted point information from the geospatial information stored in the geospatial information storage unit 131 described later.
  • the geospatial information is, for example, information representing at least one of the state of the ground surface and the underground state of the ground surface.
  • the geospatial information may be at least one of the information obtained from a so-called geographic information system (Geographic Information System).
  • Geographic Information System Geographic Information System
  • the geospatial information may be data obtained by observation from an artificial satellite, an aircraft, or the like.
  • the geospatial information may be data obtained by a field survey.
  • the geospatial information may be information representing the result of analysis based on the data obtained by measurement or survey.
  • the geospatial information may be artificially determined information based on the data obtained by measurement or survey.
  • the geospatial information may be referred to as GIS (Geographic Information System) data.
  • the geospatial information may be acquired from the geospatial information system in advance and stored in the geospatial information storage unit 131.
  • a plurality of types of geospatial information may be stored in the geospatial information storage unit 131.
  • the geospatial information may be represented in a format in which the value of the geospatial information of the point specified by the point information (for example, latitude and longitude) can be specified. Specific examples of geospatial information will be described in detail later.
  • the first extraction unit 112 may extract the value of the predetermined type of geospatial information at the point specified by the point information.
  • the first extraction unit 112 may extract the values of all types of geospatial information stored in the geospatial information storage unit 131 at the points specified by the point information.
  • the first extraction unit 112 does not have to extract the value of the geospatial information.
  • the first extraction unit 112 sets the value of the geospatial information to a value indicating that the value does not exist (for example, 0, etc.). It may be set.
  • the first extraction unit is extracted in the learning displacement data (in other words, the aged displacement map) and the area where the learning displacement data represents the aged displacement (specifically, a plurality of points in the area).
  • the value of the geospatial information is sent to the learning unit 113.
  • Geospatial Information Storage 131 stores geospatial information.
  • the geospatial information is stored in the geospatial information storage unit 131 in a form capable of identifying the state of the ground surface at a designated point.
  • the geospatial information may be represented by, for example, a value representing a state for each mesh in which the ground surface is divided.
  • the first extraction unit 112 extracts the value of the geospatial information representing the state in the mesh including the position specified by the point information as the value of the geospatial information of the point specified by the point information. do.
  • the size and shape of the mesh may be determined for each type of geospatial information.
  • the geospatial information may be represented in other formats.
  • the geospatial information may be represented, for example, by a boundary line between regions having different states and a value representing a state within the region separated by the boundary line.
  • the first extraction unit 112 extracts a value representing a state in the area including the position specified by the point information as a value of the geospatial information of the point specified by the point information.
  • the format of the geospatial information may be defined for each type of geospatial information.
  • Specific geospatial information includes, for example, type of embankment site, average slope angle, average precipitation (for example, average annual precipitation), surface geology, steep slope designation, sediment disaster warning area designation, liquefaction risk, etc. It may be whether or not a rainwater infiltration basin is possible, easiness of shaking during an earthquake, lowland where drainage is difficult, land use in urban areas, natural terrain classification, artificial terrain classification, surface geology, riverbed, facility information (presence or absence of construction, etc.).
  • the type of embankment construction site may represent the method of embankment, which is determined by the shape of the ground surface on which the embankment was made.
  • the types of embankment construction sites are, for example, “valley-filled embankment”, which is an embankment in which valleys and swamps are filled with embankment, and "belly-type embankment”, which is an embankment made on slopes.
  • the type of embankment site may further represent the scale of the embankment.
  • the type of embankment satisfying the standard for example, embankment having an area of 3000 square meters or more
  • the type of embankment that does not meet the criteria may be "valley-filled embankment”.
  • the angle of the embankment that meets the standard is 20 degrees or more with respect to the horizontal plane, and the height of the embankment is 5.
  • the type of embankment that is greater than or equal to a meter may be a large-scale embankment.
  • the type of embankment that does not meet the criteria may be "belly-type embankment".
  • the value of the type of embankment construction site may be any one of different numerical values, which are appropriately assigned in advance to, for example, "valley-filled embankment” and "belly-filled embankment”.
  • FIG. 17 is a diagram showing an example of the type of embankment land.
  • the example shown in FIG. 17 shows the distribution of the embankment land on the ground surface for each type of embankment land.
  • the average inclination angle may be, for example, data of the average inclination angle of the ground surface calculated in mesh units.
  • the value of the average inclination angle may be the calculated average inclination angle of the ground surface.
  • the average precipitation may be, for example, data of the average precipitation on the ground surface calculated in mesh units.
  • the average precipitation value may be the calculated average precipitation on the ground surface.
  • the surface geology may be data representing the surface geology (in other words, the type of geology) on the surface of the earth.
  • the type of geology may be predetermined. Different numerical values may be assigned to each type of geology in advance.
  • the value of the surface geology may be any one of the numerical values appropriately assigned to the geology in advance.
  • the steep slope designation may be data indicating whether or not it is designated as a steep slope by, for example, a local government.
  • the value of the steep slope designation may be, for example, a numerical value indicating that the slope is designated as a steep slope, or a numerical value indicating that the slope is not designated as a steep slope. As these numerical values, numerical values different from each other may be appropriately determined in advance.
  • the sediment-related disaster warning area designation may indicate whether or not it has been designated as a sediment-related disaster warning area by, for example, a local government.
  • the value of the sediment-related disaster warning area designation may be, for example, a numerical value indicating that it is designated as a sediment-related disaster warning area, or a numerical value indicating that it is not designated as a sediment-related disaster warning area. As these numerical values, numerical values different from each other may be predetermined.
  • the liquefaction risk may be, for example, data indicating the degree of risk of land liquefaction.
  • the liquefaction risk value may be a numerical value indicating the degree of liquefaction risk of the land.
  • the value of the liquefaction risk may be any one of a plurality of different numerical values representing different degrees. A numerical value indicating the degree of risk may be appropriately determined in advance.
  • Whether or not a rainwater infiltration basin can be installed is information indicating whether or not an infiltration facility can be installed based on the "infiltration facility installation judgment map" that indicates the result of determining whether or not an infiltration facility can be installed based on, for example, topography, soil quality, and groundwater level. It's okay.
  • the value of whether or not the rainwater infiltration basin can be installed may be a numerical value indicating that the installation is possible or a numerical value indicating that the installation is not possible. As these numerical values, numerical values different from each other may be appropriately determined in advance.
  • the easiness of shaking at the time of an earthquake may be, for example, data indicating the degree of easiness of shaking of the ground surface when an earthquake occurs.
  • the value of easiness of shaking at the time of an earthquake may be a numerical value indicating the degree of easiness of shaking of the ground surface in the event of an earthquake.
  • the value of easiness of shaking at the time of an earthquake may be any one of a plurality of numerical values indicating the degree of easiness of shaking of the ground surface in the event of an earthquake.
  • a numerical value indicating the degree of easiness of shaking of the ground surface may be appropriately determined in advance.
  • the difficult-to-drain lowland may represent, for example, whether or not the land is a difficult-to-drain lowland estimated from the altitude of the land or the difference in altitude from the surroundings.
  • the value of the lowland where drainage is difficult may be a numerical value indicating that the lowland is difficult to drain, or a numerical value indicating that the lowland is not difficult to drain. These numerical values may be appropriately determined in advance.
  • Urban land use may be a type of land use in the area designated as a city.
  • the type of land use in urban land use may be read, for example, from satellite images.
  • a land use type selected from a plurality of predetermined types may be set for an area included in an urban area. Different numerical values may be appropriately assigned to each of the plurality of predetermined types.
  • the value of the land use type set for the area may be the numerical value assigned to the type.
  • the natural terrain classification may be, for example, a type of terrain in a place that is not a building built by humans.
  • a plurality of terrain types that can be set as natural terrain classification may be appropriately determined in advance.
  • the terrain type selected from a plurality of terrain types predetermined as the types that can be set as the natural terrain classification may be set.
  • Different numerical values may be assigned to each of the plurality of types.
  • the terrain value in the natural terrain classification set in the area may be a numerical value assigned to the terrain type set in the area.
  • the artificial terrain classification may be, for example, a type of terrain in a place where a human has modified the terrain or a place where a building is built by a human.
  • a plurality of terrain types that can be set as artificial terrain classification may be appropriately determined in advance.
  • the terrain type selected from a plurality of terrain types predetermined as the types that can be set as the artificial terrain classification may be set.
  • Different numerical values may be assigned to each of the plurality of types.
  • the terrain value in the artificial terrain classification set in the area may be a numerical value assigned to the type of terrain set in the area.
  • the surface geology may represent, for example, the type of soil on the surface of the earth.
  • a plurality of soil types may be appropriately determined in advance.
  • different numerical values which are appropriately determined in advance, may be assigned to each of the plurality of soil types.
  • the type of soil based on the results of the survey may be set for the area.
  • the surface geological value of the area may be a numerical value assigned to the type of soil set in the area.
  • FIG. 18 is a diagram showing an example of surface geology.
  • FIG. 18 depicts the geological distribution in the surface layer including the ground surface.
  • the riverbed may be information indicating whether or not the area is a riverbed.
  • a numerical value indicating that it is a riverbed in other words, a numerical value indicating a riverbed
  • another numerical value indicating that it is not a riverbed in other words, a numerical value indicating a non-riverbed
  • a numerical value representing the riverbed may be set in the area that is the riverbed.
  • Numerical values representing non-river beds may be set in areas that are not riverbeds.
  • the value of the riverbed of the area may be a numerical value indicating the riverbed or a numerical value representing a non-riverbed set in the area.
  • FIG. 19 is a diagram showing a riverbed. In FIG. 19, an area determined to be a riverbed and an area other than the riverbed are drawn.
  • Facility information represents information about the facility.
  • the facility information may represent any of various information about the facility, which is predetermined.
  • the facility information indicates whether or not it is under construction.
  • a numerical value indicating that construction is underway and another numerical value indicating that construction is not underway may be appropriately set in advance.
  • Facility information indicating that construction is underway may be set for the area under construction.
  • Facility information indicating that construction is not underway may be set for an area that is not under construction.
  • a numerical value indicating that the construction is underway may be set.
  • a numerical value indicating that it is not under construction may be set.
  • the learning unit 113 receives the learning displacement data and the extracted geospatial information value of the region where the learning displacement data represents the secular displacement from the first extraction unit 112.
  • the learning unit 113 performs learning using the received displacement data for learning and the value of the geospatial information. In this learning, the learning unit 113 determines a combination of geospatial information that contributes to the displacement of the height at the target point based on at least a part of the values of the geospatial information of the target point. , Learn the judgment model.
  • the judgment model of this reference example represents, for example, the parameters of a program that receives the value of the geospatial information and outputs the combination of the geospatial information that contributes to the displacement of the height according to the value of the received geospatial information. It's okay.
  • the determination model is, for example, when the value of the received geospatial information satisfies the condition for at least a part of the values of the geospatial information, the geography that contributes to the displacement of the height according to the condition. It may represent a parameter of a program that outputs a combination of spatial information.
  • the judgment model is a combination of a condition that the received geospatial information value is for at least a part of the geospatial information value and a geospatial information that contributes to the height displacement when the condition is satisfied. Represented by. It should be noted that a plurality of conditions may exist.
  • Each of the plurality of conditions may be a condition for at least a part of geospatial information that is not necessarily the same.
  • a processor and a computer including such a processor) that executes the above-mentioned program using the above-mentioned parameters is also referred to as a determiner below.
  • the learning unit 113 uses heterogeneous mixed learning as the learning algorithm.
  • the learning algorithm is another algorithm that can learn a judgment model that receives the value of the geospatial information and outputs the combination of the geospatial information that contributes to the displacement of the height according to the condition for the value of the geospatial information.
  • multivariate analysis may also be used for multiple regression analysis and the like.
  • Heterogeneous mixture learning is described, for example, in the following references.
  • Heterogeneous mixture learning refers to learning of a heterogeneous mixture prediction model that makes predictions by combining prediction models based on a combination of different explanatory variables.
  • the heterogeneous mixture prediction model is represented by, for example, a plurality of sets of a combination of conditional expressions and a prediction formula when all the conditional expressions included in the combination are satisfied.
  • Each conditional expression is, for example, a conditional expression for the value of any one explanatory variable.
  • the combination of conditional expressions includes one or more conditional expressions.
  • Each prediction formula is represented by a linear form of explanatory variables that are not necessarily the same.
  • the learning unit 113 performs heterogeneous mixed learning so as to predict, for example, the height displacement after a predetermined period, using, for example, the height displacement as the objective variable and the geospatial information as the explanatory variable.
  • the predetermined period may be appropriately set in advance.
  • the learning unit 113 can obtain a plurality of sets of a combination of conditional expressions and a prediction expression when all the conditional expressions included in the combination are satisfied.
  • Each of the conditional expressions represents a condition for a value of one geospatial information that is not necessarily the same.
  • the combination of conditional expressions includes one or more conditional expressions as described above. The combination of these conditional expressions is referred to as a case classification condition.
  • the prediction formula is a formula for predicting the displacement of the height.
  • Each of the prediction formulas is represented by the linear form of one or more explanatory variables.
  • Each of the explanatory variables represents any one geospatial information. It can be said that the geospatial information represented by the explanatory variables included in the prediction formula is the geospatial information that contributes to the displacement of the height.
  • the case classification condition is satisfied means that all the conditional expressions included in the case classification condition are satisfied.
  • the prediction formula for the case classification condition represents the prediction formula when the case classification condition is satisfied.
  • the learning unit 113 determines the geospatial information represented by the explanatory variables included in the prediction formula for the case classification condition as the geospatial information that contributes to the displacement of the height. To generate.
  • the determination model outputs the information of the geospatial information determined as the geospatial information that contributes to the displacement of the height.
  • "generating a judgment model” refers to learning the judgment model and generating data representing the judgment model obtained by the learning.
  • the learning unit 113 stores the obtained determination model (in other words, data representing the obtained determination model) in the model storage unit 125.
  • Second receiving unit 121 receives information (for example, latitude and longitude information) that identifies the position of a point on the ground surface.
  • information for example, latitude and longitude information
  • the user may use the terminal device described above to input information for identifying the position of a point on the ground surface into the second receiving unit 121.
  • the second receiving unit 121 receives information for identifying the position of a point on the ground surface from the terminal device.
  • the point information received by the second receiving unit 121 is referred to as target point information.
  • a point whose position is specified by the target point information is referred to as a target point.
  • the target point information may represent the position of one target point. In that case, the target point information may include, for example, one combination of information representing latitude and information representing longitude.
  • the target point information may represent the positions of a plurality of target points. In that case, the target point information may include, for example, a plurality of combinations of information representing latitude and information representing longitude.
  • the target point information may represent, for example, the positions of a plurality of points (also referred to as grid points) that are regularly arranged in the area.
  • the target point information may include information for specifying the area and information for specifying the target point in the area.
  • the information for specifying the region is, for example, when the shape of the region is rectangular, for example, the latitude and longitude of one vertex and two vectors (first vector) representing the two sides of the rectangle starting from that vertex. It may include a vector and a second vector).
  • the information for identifying the target point in the region may be, for example, an interval in which the target point exists in the direction of the first vector and an interval in which the target point exists in the direction of the second vector.
  • the target point information is not limited to these examples.
  • each part described below may repeat the operation for one target point for the plurality of target points.
  • the second receiving unit 121 sends the received target point information to the second extracting unit 122.
  • Second extraction unit 122 receives the target point information from the second receiving unit 121.
  • the second extraction unit 122 extracts the value of the geospatial information at the target point specified by the received target point information from the geospatial information stored in the geospatial information storage unit 131.
  • the second extraction unit 122 may extract a predetermined value of the geospatial information from all the geospatial information stored in the geospatial information storage unit 131. In this case, for example, the geospatial information that has no relation to the condition for the value of the geospatial information and is confirmed in advance not to contribute to the displacement of the height may be excluded from the target of the value extraction. If there is geospatial information for which a value has not been set at the target point, the second extraction unit 122 may set the value of the geospatial information to a numerical value (for example, 0) indicating that the value does not exist. good.
  • a numerical value for example, 0
  • the second extraction unit 122 sends the received target point information and the extracted value of the geospatial information at the target point to the determination unit 123.
  • the determination unit 123 receives the value of the geospatial information at the target point from the second extraction unit 122.
  • the determination unit 123 may receive the target point information from the second extraction unit 122.
  • the determination unit 123 determines the combination of geospatial information that contributes to the height displacement at the target point according to the determination model stored in the model storage unit 125. Specifically, the determination unit 123 specifies, for example, the condition satisfied by the received value of the geospatial information at the target point among the plurality of conditions included in the determination model. The determination unit 123 determines that the combination of geospatial information that contributes to the height displacement when the specified condition is satisfied is the combination of the geospatial information that contributes to the height displacement at the target point. The combination of geospatial information that contributes to the displacement of the height at the target point can be regarded as the cause of the fluctuation in height. The number of types of geospatial information included in the combination of geospatial information may be one. The number of types of geospatial information included in the combination of geospatial information may be two or more.
  • the determination unit 123 sends the determined information of the combination of geospatial information that contributes to the displacement of the height at the target point to the output unit 124.
  • Output unit 124 receives from the determination unit 123 information on a combination of geospatial information that contributes to the displacement of the height at the target point.
  • the output unit 124 outputs the received information of the combination of geospatial information that contributes to the displacement of the height at the target point.
  • the output unit 124 may display, for example, a combination of geospatial information that contributes to the displacement of the height at the target point on a display or the like.
  • the output unit 124 may send a combination of geospatial information that contributes to the displacement of the height at the target point to another information processing device, the terminal device described above, or the like.
  • FIG. 2 is a flowchart showing an example of the operation of the analyzer 10 of this reference example.
  • the first receiving unit 111 receives the displacement and the position of the height at a plurality of points on the ground surface (step S101). Specifically, the first receiving unit 111 receives information on the displacement of heights at a plurality of points on the ground surface and point information representing the positions of the plurality of points. As mentioned above, the height displacement is, for example, the height displacement obtained by observation by SAR. The first receiving unit 111 sends the received displacements and positions of heights at a plurality of points on the ground surface to the first extraction unit 112 as learning displacement data.
  • the first extraction unit 112 extracts the value of the geospatial information at a plurality of points (step S102). That is, the first extraction unit 112 stores the value of the geospatial information at the position indicated by the point information of each of the plurality of points received by the first receiving unit 111 in the geospatial information storage unit 131. Extract from spatial information. The first extraction unit 112 sends out the displacement data for learning, the displacement of the height at a plurality of points on the ground surface, the point information of the plurality of points, and the value of the extracted geospatial information to the learning unit 113. do.
  • the learning unit 113 learns the determination model (step S103). Specifically, the learning unit 113 receives the displacement of the height at a plurality of points on the ground surface, the point information of the plurality of points, and the value of the extracted geospatial information from the first extraction unit 112. .. The learning unit 113 learns the above-mentioned determination model by using the displacement of the height and the value of the geospatial information at each of the plurality of points. The learning unit 113 stores the determination model obtained as a result of learning in the model storage unit 125.
  • the analyzer 10 may perform the above operations from step S101 to step S103 in advance. It is not necessary to perform the operation of step S104 following step S103.
  • step S104 the second receiving unit 121 receives the position (that is, the target point information).
  • the second receiving unit 121 sends the received target point information to the second extracting unit 122.
  • the second extraction unit 122 extracts the value of the geospatial information at the target point (step S105).
  • the second extraction unit 122 may extract the value of the geospatial information at the position specified by the received target point information.
  • the determination unit 123 determines the combination of geospatial information that contributes to the displacement of the height at the target point by the determination model (step S106).
  • the output unit 124 outputs the obtained combination of geospatial information (step S107).
  • the analyzer 10 may perform the operations from step S104 to step S107 for each of the plurality of target points, for example.
  • step S104 the analyzer 104 may collectively receive the displacements and positions of the heights of the plurality of target points. Then, the analyzer 104 may perform the operations of step S105 and step S106 for each of the plurality of target points.
  • step S107 the analyzer 104 may collectively output a combination of geospatial information of a plurality of target points.
  • This reference example has the effect of being able to determine the factors that cause fluctuations in the height of the ground surface.
  • the reason is that the learning unit 113 contributes to the displacement of the height at the position of the target point as a factor of the fluctuation of the height based on at least a part of the values of the geodata information at the position of the target point. This is because the determination model for determining the combination of is learned.
  • the determination model generated by the learning unit 113 of this modification outputs a value indicating the magnitude of the contribution of the geospatial information in addition to the information of the geospatial information that contributes to the displacement of the height.
  • the learning unit 113 uses, for example, the displacement of the height as the objective variable and the geospatial information as the explanatory variable to perform heterogeneous mixed learning, thereby performing the case classification condition and the prediction formula for the case classification condition. You can get multiple pairs of. Before learning, the learning unit 113 of this reference example converts the value of each geospatial information so that the range of the geospatial information is the same for each geospatial information (for example, 0 or more and 1 or less). ..
  • the case classification condition is a combination of conditional expressions.
  • the prediction formula for the case classification condition is a prediction formula when all the conditional expressions included in the case classification condition are satisfied.
  • the prediction formula is represented by the linear form of the explanatory variables.
  • Explanatory variables represent geospatial information.
  • the learning unit 113 considers the geospatial information represented by the explanatory variables included in the prediction formula as the geospatial information that contributes to the displacement of the height. Then, the learning unit 113 considers the coefficient of the explanatory variable representing the geospatial information as the magnitude of the contribution of the geospatial information in the prediction formula.
  • the learning unit 113 generates the following determination model.
  • the judgment model determines the geospatial information represented by the explanatory variables included in the prediction formula for the case classification condition as the geospatial information that contributes to the displacement of the height.
  • the judgment model also uses the coefficients of the explanatory variables included in the prediction formula for the case classification conditions when the case classification conditions are satisfied, and the magnitude of the contribution to the displacement of the height of the geospatial information represented by the explanatory variables. Judge as a sword.
  • the determination model outputs the information of the geospatial information determined as the geospatial information contributing to the displacement of the height and the information indicating the magnitude of the contribution of the geospatial information to the displacement of the height.
  • the determination unit 123 refers to a combination of geospatial information that contributes to the height displacement at the target point and the geospatial information included in the combination with respect to the height displacement according to the determination model stored in the model storage unit 125. Determine the magnitude of the contribution. Specifically, the determination unit 123 specifies, for example, a condition satisfied by the value of the received geospatial information among a plurality of conditions included in the determination model. The determination unit 123 determines that the combination of geospatial information that contributes to the height displacement when the specified condition is satisfied is the combination of the geospatial information that contributes to the height displacement at the target point.
  • the determination unit 123 determines that the magnitude of the contribution of the geospatial information to the height displacement, which contributes to the height displacement when the specified condition is satisfied, is the height of the geospatial information at the target point. It is determined that it is the magnitude of the contribution to the displacement.
  • the determination unit 123 sends to the output unit 124 information on the combination of geospatial information that contributes to the displacement of the height at the target point and information indicating the magnitude of the contribution of the geospatial information included in the combination.
  • Output unit 124 receives from the determination unit 123 information on the combination of geospatial information that contributes to the displacement of the height at the target point and information indicating the magnitude of the contribution of the geospatial information included in the combination.
  • the output unit 124 outputs the received information on the combination of geospatial information that contributes to the displacement of the height at the target point and the information indicating the magnitude of the contribution of the geospatial information included in the combination.
  • the output unit 124 may display, for example, a combination of geospatial information that contributes to the displacement of the height at the target point and the magnitude of the contribution on a display or the like.
  • the output unit 124 may send out the combination of geospatial information that contributes to the displacement of the height at the target point and the magnitude of the contribution to another information processing device, the terminal device described above, or the like.
  • FIG. 3 is a block diagram showing the configuration of the analysis system 1 of the modified example of the first reference example.
  • the analysis system 1 includes a learning device 11, an analysis device 21, a geospatial information storage device 31, and a terminal device 51.
  • the learning device 11, the analyzer 21, the geospatial information storage device 31, and the terminal device 51 are communicably connected to each other by the network 40, which is a communication network.
  • the analysis system 1 realizes the function of the analysis device 10 of the first reference example by the learning device 11, the analysis device 21, and the geospatial information storage device 31.
  • the terminal device 51 is the terminal device described above.
  • FIG. 4 is a block diagram showing an example of a detailed configuration of the learning device 11, the analysis device 21, and the geospatial information storage device 31 included in the analysis system 1 of this modified example.
  • the transfer of data between the components of the learning device 11, the analyzer 21, and the geospatial information storage device 31 realized by the network 40 of FIG. 3 is drawn by a line connecting the components. ing.
  • the learning device 11 includes a first receiving unit 111, a first extracting unit 112, a learning unit 113, a first reading unit 114, and a transmitting unit 115.
  • the first receiving unit 111, the first extraction unit 112, and the learning unit 113 are the same as the units of the first reference example having the same name and having the same reference numerals.
  • the first reading unit 114 reads the geospatial information from the geospatial information storage unit 131 of the geospatial information storage device 31 via the input / output unit 132. Specifically, the first reading unit 114 transmits a request for geospatial information to the input / output unit 132 of the geospatial information storage device 31, and is read from the geospatial information storage unit 131 by the input / output unit 132. , The requested geospatial information may be received from the input / output unit 132.
  • the request for geospatial information may include point information that identifies the point (eg, latitude and longitude information).
  • the requested geospatial information refers to the value of the geospatial information at the point specified by the point information.
  • the transmission unit 115 transmits the determination model (in other words, the parameter of the determination device) learned by the learning unit 113 to the analyzer 21.
  • the analyzer 21 includes a second receiving unit 121, a second extracting unit 122, a determination unit 123, an output unit 124, a model storage unit 125, a second reading unit 126, and a receiving unit 127.
  • the second receiving unit 121, the second extraction unit 122, the determination unit 123, the output unit 124, and the model storage unit 125 are the same as the unit having the same name given the same code in the first reference example, respectively. be.
  • the second reading unit 126 reads the geospatial information from the geospatial information storage unit 131 of the geospatial information storage device 31 via the input / output unit 132. Specifically, the second reading unit 126 transmits a request for geospatial information to the input / output unit 132 of the geospatial information storage device 31, and is read from the geospatial information storage unit 131 by the input / output unit 132. , The requested geospatial information may be received from the input / output unit 132.
  • the request for geospatial information may include point information that identifies the point (eg, latitude and longitude information).
  • the request for geospatial information generated and transmitted by the second reading unit 126 may include type information that specifies the type of geospatial information.
  • a plurality of types of type information may be specified.
  • the input / output unit 132 sets the values of all types of geospatial information specified by the type information at the points specified by the point information, as described later. , Is sent to the second reading unit 126.
  • the receiving unit 127 receives the determination model from the transmitting unit 115 of the learning device 11.
  • the receiving unit 127 stores the received determination model in the model storage unit 125.
  • the geospatial information storage device 31 includes a geospatial information storage unit 131 and an input / output unit 132.
  • the geospatial information storage unit 131 is the same as the geospatial information storage unit 131 of the first reference example.
  • the input / output unit 132 receives a request for geospatial information.
  • the source of the request for geospatial information is the first reading unit 114 or the second reading unit 126.
  • the request for geospatial information may include information that identifies the location.
  • the input / output unit 132 extracts the value of the geospatial information of the point specified by the information for specifying the point included in the request for the geospatial information from the geospatial information stored in the geospatial information storage unit 131. ..
  • the input / output unit 132 may extract the values of all types of geospatial information at the specified points.
  • the request for geospatial information may include information that identifies the type of geospatial information.
  • the input / output unit 132 may extract the values of all types of geospatial information included in the request for geospatial information, which are specified by the information that specifies the type of geospatial information.
  • the input / output unit 132 transmits the extracted geospatial information value to the source of the geospatial information request.
  • the operation of the analysis system 1 of this modification is the same as the operation of the analysis device 10 of the first reference example shown in FIG. 2, except for the following differences.
  • the above-mentioned difference is that, for example, the reading of the geospatial information is performed via the first reading unit 114 and the input / output unit 132, or via the second reading unit 126 and the input / output unit 132, and the determination model. Is a point where the delivery is performed via the transmission unit 115 and the reception unit 127.
  • FIG. 1 is a diagram showing a configuration of an analyzer 10 of a second reference example of the present disclosure.
  • the configuration of the analyzer 10 of this reference example is the same as the configuration of the analyzer 10 of the first reference example.
  • the components of the analyzer 10 of this reference example are the same as the components of the analyzer 10 of the first reference example, which are given the same name and reference numeral, except for the differences described below.
  • the learning unit 113 of this reference example learns a determination model different from the determination model learned by the learning unit 113 of the first reference example.
  • the learning unit 113 of this reference example is the same as the learning unit 113 of the first reference example.
  • the learning unit 113 of this reference example similarly to the learning unit 113 of the first reference example, the learning unit 113 of this reference example has the learning displacement data and the region in which the learning displacement data represents the secular displacement from the first extraction unit 112. Receives the extracted geospatial information values. Similar to the learning unit 113 of the first reference example, the learning unit 113 of this reference example stores the determination model obtained by learning in the model storage unit 125.
  • the learning unit 113 of this reference example also performs heterogeneous mixture learning, for example, using the height displacement as the objective variable and the geospatial information as the explanatory variable, for example, predicting the height displacement after a predetermined period.
  • the learning unit 113 can obtain a plurality of sets of a combination of conditional expressions and a prediction expression when all the conditional expressions included in the combination are satisfied.
  • each of the conditional expressions represents a condition for one geospatial information value that is not necessarily the same.
  • the combination of conditional expressions includes one or more conditional expressions as described above. The combination of these conditional expressions is referred to as a case classification condition.
  • the prediction formula is a formula for predicting the displacement of the height.
  • Each of the prediction formulas is represented by the linear form of one or more explanatory variables.
  • Each of the explanatory variables represents any one geospatial information. It can be said that the geospatial information represented by the explanatory variables included in the prediction formula is the geospatial information that contributes to the displacement of the height.
  • the case classification condition is satisfied means that all the conditional expressions included in the case classification condition are satisfied.
  • the prediction formula for the case classification condition represents the prediction formula when the case classification condition is satisfied.
  • the learning unit 113 of this reference example generates a determination model that predicts the displacement of the height by the prediction formula for the case classification condition when the case classification condition is satisfied.
  • the determination model outputs information on the displacement at the predicted height.
  • the determination unit 123 of this reference example receives the value of the geospatial information at the target point from the second extraction unit 122, similarly to the determination unit 123 of the first reference example.
  • the determination unit 123 may receive the target point information from the second extraction unit 122.
  • the determination unit 123 of this reference example predicts the displacement of the height at the target point according to the determination model stored in the model storage unit 125. Specifically, the determination unit 123 specifies, for example, the condition satisfied by the received value of the geospatial information at the target point among the plurality of conditions included in the determination model. The determination unit 123 predicts the displacement of the height by using the prediction formula when the specified condition is satisfied.
  • the determination unit 123 sends information indicating the displacement of the predicted height to the output unit 124.
  • Output unit 124 receives information representing the predicted height displacement from the determination unit 123.
  • the output unit 124 outputs the received information representing the displacement of the height.
  • the output destination of the output unit 124 is the same as the output unit of the output unit 124 of the first reference example.
  • FIG. 5 is a flowchart showing an example of the operation of the analyzer 10 of this reference example.
  • steps S101 and S102 shown in FIG. 5 are the same as the operations of steps S101 and S102 of the analyzer 10 of the first reference example shown in FIG.
  • step S203 the learning unit 113 of this reference example generates the above-mentioned determination model for predicting the displacement of the height.
  • the analyzer 10 of this reference example does not need to perform the operations after step S104 after step S101, step S102, and step S203.
  • steps S104 and S105 are the same as the operations of steps S104 and S105 of the analyzer 10 of the first reference example shown in FIG.
  • step S206 the determination unit 123 predicts the height displacement at the target point by the determination model.
  • step S207 the output unit 124 outputs the displacement at the predicted height.
  • This reference example has the effect of being able to predict fluctuations in the height of the ground surface.
  • the reason is that the learning unit 113 learns a determination model that predicts the height displacement at the position of the target point based on at least a part of the values of the geospatial information at the position of the target point.
  • the learning unit 113 may generate a plurality of determination models that individually predict the displacement of the height after each of the plurality of periods by performing heterogeneous mixed learning for each of the plurality of periods.
  • the length of each of the plurality of periods may be, for example, a multiple of a predetermined length of a predetermined period.
  • the length of each of the plurality of periods may be determined according to appropriately determined rules.
  • the length of each of the plurality of periods may be specified by the user, for example.
  • the learning unit 113 stores the generated plurality of determination models in the model storage unit 125.
  • Each determination model may be configured to predict height displacement and output information representing the predicted height displacement and information representing the period.
  • the determination unit 123 reads out a plurality of determination models stored in the model storage unit 125.
  • the determination unit 123 predicts the displacement of the height after different periods have elapsed, respectively, according to the plurality of determined determination models read out.
  • the determination unit 123 sends out the prediction of the displacement of the height after the lapse of different periods and each period to the output unit 124.
  • the output unit 124 outputs a prediction of height displacement after each of a plurality of periods has elapsed.
  • the output unit 124 may output information representing a plurality of periods and a prediction of height displacement after each period has elapsed.
  • Second modification of the second reference example is the same as the configuration of the analyzer 10 of the second reference example shown in FIG.
  • the learning unit 113 of this modification predicts the height displacement after a predetermined period elapses by the prediction formula for the case classification condition, and further contributes to the height displacement.
  • the judgment model analyzes the geospatial information that contributes to the height displacement, and as described above, the geospatial information represented by the explanatory variables included in the prediction formula that predicts the height displacement, the height displacement. It is done by analyzing it as geospatial information that contributes to.
  • the determination model outputs the information of the geospatial information analyzed as the geospatial information that contributes to the height displacement.
  • the determination unit 123 predicts the height displacement and analyzes the geospatial information that contributes to the height displacement by the determination model.
  • the determination unit 123 sends information representing the predicted height displacement and information representing the geospatial information contributing to the height displacement to the output unit 124.
  • the output unit 124 outputs information representing the predicted height displacement and information representing the geospatial information that contributes to the height displacement.
  • the second modification of the second reference example can be configured like the first modification of the second reference example.
  • the learning unit 113 of the present modification may generate the same determination model as the determination model of the second modification of the second reference example for each of the plurality of different periods. Specifically, the learning unit 113 generates a plurality of determination models for predicting the displacement of the height after the lapse of different periods and determining the cause of the displacement.
  • the factor is either geospatial information.
  • the determination unit 123 predicts the displacement of the height after a plurality of different periods have elapsed at the target point and determines the factors contributing to the displacement of the height by the generated plurality of determination models.
  • the determination unit 123 provides the output unit 124 with information indicating a predicted height displacement and information indicating a factor contributing to the height displacement after a plurality of different periods have elapsed at the target point. Send out.
  • the output unit 124 outputs information representing the predicted displacement of the height and information representing the factors contributing to the displacement of the height.
  • This modification is an example in which the second modification of the second reference example is modified like the first modification of the first reference example.
  • the learning unit 113 of the present modification may generate a determination model for determining the magnitude of the contribution of the factor in addition to predicting the displacement of the height and determining the factor.
  • the determination unit 123 of this modification determines the cause of the height displacement and the magnitude of the contribution of the factor.
  • the determination unit 123 of this modification may send information indicating the displacement of the predicted height and information indicating the determined factor and the magnitude of the contribution of the factor to the output unit 124.
  • the output unit 124 may output information representing the displacement of the predicted height and information representing the determined factor and the magnitude of the contribution of the factor.
  • This modification is an example in which the third modification of the second reference example is modified like the first modification of the first reference example.
  • the learning unit 113 of this modified example predicts the displacement of the height after a different period elapses and determines the factor of the displacement of the height, and in addition, the contribution of the factor is large. Generate multiple judgment models to judge the height.
  • the determination unit 123 of the present modification uses a plurality of determination models to predict the displacement of the height after different periods have elapsed, and determine the factors of the displacement of the height and the magnitude of the contribution of the factors.
  • the determination unit 123 may send information indicating the displacement of the predicted height and information indicating the determined factor and the magnitude of the contribution of the factor to the output unit 124.
  • the output unit 124 outputs information representing the displacement of the predicted height and information representing the determined factor and the magnitude of the contribution of the factor.
  • ⁇ 6th variant of the 2nd reference example The function of the analyzer 10 of the second reference example and the first to fifth modified examples of the second reference example is a combination of a plurality of devices as in the second modified example of the first reference example. Can be achieved by.
  • FIG. 6 is a diagram showing an example of the configuration of the analyzer 12 of the present embodiment.
  • the analyzer 12 includes a first receiving unit 111, a first extracting unit 112, a learning unit 113, a second receiving unit 121, a second extracting unit 122, and a determination unit 123.
  • a model storage unit 125 and a geospatial information storage unit 131 are provided.
  • the analyzer 12 further includes a prediction unit 141, a detection unit 142, an output unit 143, a target information reception unit 144, and an observation result storage unit 145.
  • the first receiving unit 111, the first extracting unit 112, the learning unit 113, the second receiving unit 121, the second extracting unit 122, the determination unit 123, the model storage unit 125, and the geospatial information storage unit 131 of the present embodiment are , Each function in the same way as the part to which the same name and the same code are given. The differences between the present embodiment and the second reference example will be described below.
  • the first receiving unit 111 receives the observation data representing the result of the observation of the height of the ground surface, and stores the received observation data in the observation result storage unit 145.
  • the observation data is, for example, displacement data obtained by observation by SAR performed at a plurality of past time points.
  • Observation data may be obtained, for example, at a plurality of points (that is, observation points) at equal intervals. The intervals between the observation points may differ depending on the direction. For example, the north-south spacing of the observation points may differ from the east-west spacing of those observation points.
  • the observation data may be different from the learning data.
  • Observation data is obtained, for example, by analyzing a plurality of SAR images in a time series created based on the results of radar observations in the X band (wavelength of about 3 cm (centimeters)) or C band (wavelength of about 6 cm). It shows the transition of the displacement of the height of the ground surface.
  • Time-series interference SAR analysis includes, for example, a PS (Persistent Scatterers) method and an SBAS (Small Basseline Subset) method.
  • PS method Persistent Scatterers
  • SBAS Stel Basseline Subset
  • the displacement data which is the observation data, identifies the information that identifies the position of the observation point, the information that identifies the time when the observation was performed, and the information that represents the displacement of the height obtained by the observation. can.
  • the observation result storage unit 145 stores the above-mentioned observation data stored by the first receiving unit 111.
  • the geospatial information storage unit 131 may store the same geospatial information as the geospatial information storage unit 131 of the second reference example.
  • the geospatial information storage unit 131 of the present embodiment stores facility information as a type of geospatial information.
  • the facility is, for example, a buried facility. Buried facilities are buried underground, for example, water pipes, pipeline networks such as gas, buried power generation related facilities, power transmission facilities, distribution facilities, gas storage facilities, oil storage facilities, water pipes, etc. It is a facility.
  • the geospatial information storage unit 131 may store the information of the range in which these facilities are buried as the range of the facilities as the geospatial information.
  • the information on the range in which the buried facility is buried may be, for example, the range of the image of the buried facility when the buried facility is projected vertically onto the ground surface.
  • the value of the geospatial information of the facility may be, for example, the identification information (for example, the identification number) assigned to each facility.
  • the value of the geospatial information of the facility may represent, for example, the type of each facility.
  • Facilities may include non-buried facilities such as oil storage facilities and gas storage facilities installed on the surface of the earth. In that case, the geospatial information storage unit 131 may store information on the range of those facilities as geospatial information.
  • the target information receiving unit 144 receives information representing an area from, for example, the terminal device 51.
  • the area represented by the information received by the target information reception unit 144 is referred to as a target area.
  • the target area is, for example, an area including the above-mentioned facility.
  • the size of the region may be, for example, 4 km (kilometer) square, 10 km square, or the like. The size of the area may be determined as appropriate.
  • the shape of the region does not have to be square. The shape of the region may be appropriately determined.
  • the target information reception unit 144 sends information representing the received target area to the prediction unit 141.
  • the prediction unit 141 receives information representing the target information from the target information reception unit 144.
  • the prediction unit 141 extracts the observation data representing the observation result in the target region from the observation data stored in the observation result storage unit 145.
  • the prediction unit 141 may read the observation data at the observation point included in the target area from the observation result storage unit 145.
  • the observation data is the transition of the displacement of the height of the ground surface.
  • the prediction unit 141 predicts the future displacement of the ground surface height at each observation point based on the received observation data. Specifically, the prediction unit 141 approximates the relationship between the elapsed time and the height displacement by using the observation data representing the transition of the height displacement of the ground surface as described above for each of the observation points. You may derive a prediction formula that represents the above. In the present embodiment, the prediction formula represents an approximate straight line that approximately represents the relationship between the elapsed time and the displacement of the height.
  • the method for calculating the approximate straight line may be a method selected from various existing methods such as the least squares method.
  • the approximate expression does not necessarily have to represent a straight line.
  • the approximate expression may be a second-order or higher-order expression.
  • the approximate expression may be non-linear.
  • the prediction unit 141 predicts the displacement of the ground surface height in the target area based on the transition of the ground surface height displacement in the target area (also referred to as the target range).
  • the prediction unit 141 sends the result of prediction of the height displacement (in other words, information representing the predicted height displacement) to the detection unit 142.
  • the prediction unit 141 may send a prediction formula to the detection unit 142 as information representing the displacement of the predicted height.
  • the detection unit 142 may receive the prediction formula and predict the displacement of the height by the received prediction formula. For example, when predicting the displacement of the height at one or more predetermined time points, the prediction unit 141 calculates the displacement of the height at one or more predetermined time points by using the prediction formula, and the calculated height is calculated.
  • the displacement may be sent to the detection unit 142 as information representing the displacement at the predicted height.
  • the calculated height displacement is sent to the detection unit 142 as information representing the predicted height displacement.
  • the displacement of the height predicted by the prediction formula is also referred to as the first predicted displacement.
  • the prediction unit 141 sends, in addition to the information indicating the displacement of the predicted height, the information indicating the target range and the information indicating the positions of the observation points included in the target range to the detection unit 142. good.
  • the information representing the positions of the observation points included in the target range may be the information representing the target range.
  • the prediction unit 141 may send information representing the positions of the observation points included in the target range to the detection unit 142 as information representing the target range.
  • the detection unit 142 receives from the prediction unit 141 information indicating the displacement of the predicted height, information indicating the target range, and information indicating the position of the observation point.
  • the detection unit 142 receives the range of the facility within the target range from the second extraction unit 122.
  • the detection unit 142 detects the target phenomenon within the target range based on the displacement of the height predicted by using the prediction formula.
  • Uneven displacement represents a non-uniform displacement of height in the same facility.
  • Uneven displacement means, for example, that the subsidence or ascent rates are not the same in one facility area.
  • the phenomenon that a facility sinks while tilting is an example of uneven displacement.
  • stress anomalies indicate, for example, that the facility is stressed at the boundary between two regions with different speeds of displacement (ie, sinking or ascending). Stress anomalies occur, for example, when there are two or more areas within a facility where the rate of displacement within the area is uniform but sinks at different rates.
  • the detection unit 142 predicts using a prediction formula as information representing the predicted height displacement, for example, the height displacement (height displacement) at one or more time points in the future. You may receive (also referred to as the predicted value).
  • the predicted value of the height displacement predicted using the prediction formula is expressed as the first predicted displacement as described above.
  • the detection unit 142 may receive a prediction formula as information representing the displacement of the predicted height. If the detector 142 is configured to receive a prediction formula as information representing a displacement of the predicted height, the detection unit 142 may use the received prediction formula to, for example, the height at one or more time points in the future.
  • the predicted value of the displacement of the height may be calculated. When the predicted value of the height displacement at two or more time points is calculated, the detection unit 142 may detect the target phenomenon at each of the two or more time points.
  • the detection unit 142 generates a distribution of the height displacement (that is, the first predicted displacement) predicted by using the prediction formula in the target range.
  • the detection unit 142 may calculate the slope in the distribution of the first predicted displacement.
  • the slope may be, for example, the magnitude of the slope.
  • the magnitude of the gradient is, for example, when the distribution of displacement is represented by a curved surface in a three-dimensional space of the position on the ground surface (two-dimensional) and the magnitude of displacement (one-dimensional), the inclination of the curved surface is maximized. Represents the magnitude of tilt in the direction.
  • the method for calculating the slope may be a method appropriately selected from various existing methods.
  • the detection unit 142 may calculate the magnitude of the spatial change in the slope in the first predicted displacement distribution.
  • the magnitude of the spatial change in inclination is also simply referred to as the spatial change in inclination and the change in inclination.
  • the change in inclination does not mean the magnitude of the change in inclination at the same point with the passage of time, but the magnitude of the change in inclination at the same time point with the change in spatial position.
  • the detection unit 142 reads out the information of the facilities existing in the target range from the geospatial information storage unit 131 via the second extraction unit 122. Specifically, the detection unit 142 may send, for example, information representing the target range to the second extraction unit 122. The detection unit 142 receives information representing the area of the facility in the target range from the second extraction unit 122.
  • the detection unit 142 may detect a portion of the first predicted displacement distribution in which the inclination is larger than a predetermined reference (for example, a predetermined inclination threshold value). The detection unit 142 determines whether or not the detected portion is included in the area of the facility. The detection unit 142 may detect the unequal displacement in the facility when there is a portion in the area of the facility where the slope of the distribution of the first predicted displacement is larger than a predetermined reference.
  • a predetermined reference for example, a predetermined inclination threshold value
  • the unequal displacement detected based on the height displacement predicted using the prediction formula may be referred to as the first unequal displacement.
  • a portion where the slope of the distribution of the first predicted displacement is larger than a predetermined reference is referred to as a first non-uniform displacement portion.
  • the detection unit 142 has a first non-uniform displacement portion included in a region (denoted as a first influence region) including a region of the facility and a range in which the distance from the region of the facility is closer than the reference (denoted as the first reference). If present, unequal displacement may be detected at the facility.
  • the first criterion may be, for example, an appropriately determined distance.
  • the detection unit 142 may detect a portion of the first predicted displacement distribution in which the change in slope is larger than a predetermined reference (for example, a predetermined change threshold value). The detection unit 142 determines whether or not the detected portion is included in the area of the facility. The detection unit 142 may detect a stress abnormality in the facility when there is a portion in the area of the facility where the change in inclination is larger than a predetermined reference.
  • a predetermined reference for example, a predetermined change threshold value
  • the stress abnormality detected based on the height displacement predicted by using the prediction formula may be referred to as the first stress abnormality.
  • the first stress abnormality On the ground surface, a portion where the magnitude of the inclination of the distribution of the first predicted displacement is larger than a predetermined reference is referred to as a first stress abnormal portion.
  • the first stress abnormality portion included in the area (denoted as the first influence area) including the area of the facility and the range in which the distance from the area of the facility is closer than the reference (denoted as the first reference) is included. If present, stress abnormalities may be detected at the facility.
  • the first criterion may be, for example, an appropriately determined distance. In the description of this embodiment, the first criterion for detecting stress abnormalities is the same as the first criterion for detecting unequal displacement, but may be different.
  • the detection unit 142 is predicted by the determination unit 123 (specifically, the determination unit 123 predicts by the above-mentioned determination model), for example, each of the above-mentioned predetermined one or more time points included in the target range.
  • the height displacement at the observation point may be received from the determination unit 123.
  • the displacement of the height predicted by this determination model is also referred to as the second predicted displacement.
  • the detection unit 142 may detect the target phenomenon based on the second predicted displacement, specifically, based on the distribution of the second predicted displacement.
  • the unequal displacement detected based on the second predicted displacement may be referred to as the second unequal displacement.
  • the stress abnormality detected based on the second predicted displacement may be referred to as the second stress abnormality.
  • the detection unit 142 generates a distribution of the height displacement (that is, the second predicted displacement) predicted by the determination model in the target range.
  • the detection unit 142 may calculate the slope in the distribution of the second predicted displacement.
  • the detection unit 142 may calculate the change in slope in the second predicted displacement distribution.
  • the detection unit 142 may detect a portion of the second predicted displacement distribution whose slope is larger than a predetermined reference (for example, a predetermined slope threshold value). The detection unit 142 determines whether or not the detected portion is included in the area of the facility. In the following description, a portion where the slope in the distribution of the second predicted displacement is larger than a predetermined reference is referred to as a second non-uniform displacement portion. When the second non-uniform displacement portion included in the area of the facility exists, the detection unit 142 may detect the non-uniform displacement in the facility.
  • a predetermined reference for example, a predetermined slope threshold value
  • the detection unit 142 has a second non-uniform displacement portion included in a region (denoted as a second influence region) including a region of the facility and a range in which the distance from the region of the facility is closer than the reference (denoted as the second reference). If present, unequal displacement may be detected at the facility.
  • the second criterion may be, for example, an appropriately determined distance.
  • the detection unit 142 may detect a portion of the second predicted displacement distribution in which the change in slope is larger than a predetermined reference (for example, a predetermined change threshold value). The detection unit 142 determines whether or not the detected portion is included in the area of the facility.
  • a predetermined reference for example, a predetermined change threshold value.
  • the detection unit 142 determines whether or not the detected portion is included in the area of the facility.
  • the portion where the change in slope in the distribution of the second predicted displacement is larger than the predetermined reference is referred to as the second stress abnormal portion.
  • the detection unit 142 may detect the stress abnormality in the facility.
  • the detection unit 142 has a second stress abnormality portion included in a region (denoted as a second influence region) including a region of the facility and a range in which the distance from the region of the facility is closer than the reference (denoted as the second reference). If present, stress abnormalities may be detected at the facility.
  • the second criterion may be, for example, an appropriately determined distance. In the description of this embodiment, the second criterion for detecting stress abnormalities is the same as the second criterion for detecting unequal displacement, but may be different.
  • the detection unit 142 may detect the target phenomenon based on the first predicted displacement and the second predicted displacement, specifically, based on the difference between the first predicted displacement and the second predicted displacement.
  • the target phenomenon detected based on the difference between the first predicted displacement and the second predicted displacement may be referred to as the third target phenomenon.
  • the detection unit 142 may calculate the difference between the first predicted displacement and the second predicted displacement for each observation point. Then, the detection unit 142 may derive the distribution of the difference between the first predicted displacement and the second predicted displacement in the target range. The detection unit 142 may calculate the slope in the distribution of the difference between the first predicted displacement and the second predicted displacement.
  • the detection unit 142 may detect a portion of the distribution of the third predicted displacement whose slope is larger than a predetermined reference (for example, a predetermined slope threshold value).
  • a predetermined reference for example, a predetermined slope threshold value.
  • the stress abnormality detected based on the slope of the difference between the first predicted displacement and the second predicted displacement and a predetermined slope reference may be referred to as a third stress abnormality.
  • the portion where the slope in the distribution of the third predicted displacement is larger than the predetermined reference is referred to as the third stress abnormal portion.
  • the detection unit 142 may determine whether or not the third stress abnormal portion is included in the area of the facility. When the third stress abnormality portion included in the area of the facility is present, the detection unit 142 may detect the stress abnormality in the facility.
  • the detection unit 142 has a third stress abnormality portion included in a region (denoted as a third influence region) including a region of the facility and a range in which the distance from the region of the facility is closer than the reference (denoted as the third criterion). If present, stress abnormalities may be detected at the facility.
  • the third criterion may be, for example, an appropriately determined distance.
  • the first criterion, the second criterion, and the third criterion may all be different. At least two of the first criterion, the second criterion, and the third criterion may be the same.
  • the slope thresholds may all be the same.
  • the slope threshold may differ depending on the type of distribution in which the slope threshold is used as the reference for the slope.
  • the change thresholds may all be the same.
  • the change threshold may differ depending on the type of distribution in which the change threshold is used as a reference for the change in slope.
  • the detection unit 142 may detect a portion of the distribution of the third predicted displacement in which the change in slope is larger than a predetermined reference (for example, a predetermined change threshold value). The detection unit 142 determines whether or not the detected portion is included in the area of the facility.
  • a predetermined reference for example, a predetermined change threshold value.
  • the detection unit 142 determines whether or not the detected portion is included in the area of the facility.
  • the stress abnormality detected based on the change in the slope of the difference between the first predicted displacement and the second predicted displacement may be referred to as the fourth stress abnormality.
  • the portion where the change in slope in the distribution of the third predicted displacement is larger than the predetermined reference is referred to as the fourth stress abnormal portion.
  • the detection unit 142 may detect the stress abnormality in the facility.
  • the detection unit 142 may detect all of the first non-uniform displacement, the second non-uniform displacement, the first stress abnormality, the second stress abnormality, the third stress abnormality, and the fourth stress abnormality as target phenomena.
  • the detection unit 142 targets one or two predetermined ones or two of the first non-uniform displacement, the second non-uniform displacement, the first stress abnormality, the second stress abnormality, the third stress abnormality, and the fourth stress abnormality. It may be detected as a phenomenon.
  • the detection unit 142 sends information on the facility where the target phenomenon is detected to the output unit 143.
  • the facility information may be information that represents the location of the area of the facility.
  • the detection unit 142 may further send information on the area where the target phenomenon is detected (for example, information indicating the location of the detected target phenomenon) to the output unit 143.
  • the detection unit 142 may send information indicating the type of the detected target phenomenon to the output unit 143.
  • the type of the target phenomenon may be the above-mentioned first non-uniform displacement, second non-uniform displacement, first stress abnormality, second stress abnormality, third stress abnormality, and fourth stress abnormality.
  • the type of the target phenomenon may be uneven displacement and stress abnormality.
  • the detection unit 142 may send at least one of the first target distribution, the second target distribution, and the distribution of the difference between the first target displacement and the second target displacement to the output unit 143.
  • Second extraction unit 122 receives information representing the target range.
  • the second extraction unit 122 extracts the value of the geospatial information about the facility in the target range represented by the received information from the geospatial information stored in the geospatial information storage unit 131.
  • the second extraction unit 122 specifies the range of facilities in the target range based on the value of the extracted geospatial information.
  • the second extraction unit 122 may specify the range of the facility according to the type of facility.
  • the second extraction unit 122 may specify the range of the facility for each individual facility.
  • the range of the facility may be represented by information indicating whether or not the facility is buried at the observation point for each of the above-mentioned observation points.
  • the information representing the target range may be information representing the positions of the observation points included in the target range.
  • the scope of the facility may be represented by other information that represents the perimeter of the scope of the facility.
  • the second extraction unit 122 sends information on the facilities existing in the target range to the detection unit 142.
  • the second extraction unit 122 may further extract the value of the geospatial information at the observation point included in the target range from the geospatial information stored in the geospatial information storage unit 131. Then, the second extraction unit 122 sends out the information representing the position of each observation point included in the target range and the extracted value of the geospatial information at the observation point included in the target range to the determination unit 123. ..
  • the determination unit 123 receives information representing the position of each observation point included in the target range and the value of the geospatial information at the observation point included in the target range from the second extraction unit 122.
  • the determination unit 123 uses the above-mentioned determination model, which predicts the displacement of the future height based on at least a part of the values of a plurality of types of geospatial information generated in advance by learning, to set the value of the geospatial information. Based on this, the height displacement at each observation point is predicted.
  • the determination model includes a condition for each value of at least a part of the geospatial information, and a prediction formula for predicting the displacement of the future height when all of the conditions are satisfied. Represented by.
  • the prediction formula is represented by the linear sum of the variables that represent each geospatial information.
  • a plurality of determination models for predicting height displacement at one or more of the above-mentioned predetermined time points may be prepared.
  • the determination unit 123 may use those determination models to predict the height at one or more of the above-mentioned predetermined time points.
  • the determination unit 123 may send the displacement of the predicted height to the detection unit 142.
  • the output unit 143 may receive information on the facility in which the target phenomenon is detected from the detection unit 142.
  • the output unit 143 may receive information on the area where the target phenomenon is detected from the detection unit 142.
  • the output unit 143 may receive information indicating the type of the detected target phenomenon from the detection unit 142.
  • the output unit 143 may receive at least one of the first target distribution, the second target distribution, and the distribution of the difference between the first target displacement and the second target displacement from the detection unit 142.
  • the output unit 143 outputs the above-mentioned received information.
  • the output unit 143 may generate a screen representing the above-mentioned received information, and display the generated screen on the display of the analyzer 12.
  • the output unit 143 may send the received information to the terminal device 51, for example, in the form of the screen described above.
  • the output unit 143 has, for example, on a map of the target area, at least one of the first target distribution, the second target distribution, and the distribution of the difference between the first target displacement and the second target displacement for each observation point. You may generate a colored screen that represents the value.
  • the output unit 143 may superimpose, for example, a figure showing the range of the facility where the target phenomenon is detected on the screen.
  • the output unit 143 may superimpose a figure indicating the position of the region where the target phenomenon is detected on the screen.
  • the output unit 143 may further superimpose information indicating the type of the detected target phenomenon on the screen.
  • FIG. 7 is a flowchart showing an example of an operation of learning the determination model of the analyzer 12 of the present embodiment.
  • step S101, step S102, and step S203 are the operations of step S101, step S102, and step S203 of the analyzer 10 of the second reference example shown in FIG. 5, respectively. It is the same as the operation.
  • FIG. 8 is a flowchart showing the operation of detecting the target phenomenon of the analyzer 12 of the present embodiment.
  • the target information receiving unit 144 receives the information for specifying the target range (step S301).
  • the prediction unit 141 reads out the observation result within the target range from the observation result storage unit 145 (step S302).
  • the observation result read out is, for example, data representing the transition of the displacement of the height of the ground surface at the observation point included in the target range.
  • the prediction unit 141 estimates the above-mentioned first predicted displacement at each of the observation points based on the transition of the height displacement at each of the observation points in the target range (step S303).
  • the second extraction unit 122 extracts information on facilities within the target range (for example, buried facilities) (step S304).
  • the determination unit 123 performs the prediction process (step S305).
  • the prediction process obtains the above-mentioned second predicted displacement at each of the observation points within the target range.
  • the detection unit 142 calculates the difference between the first predicted displacement and the second predicted displacement at each of the observation points within the target range (step S306). For example, the detection unit 142 detects the target phenomenon based on the distribution of the first predicted displacement, the distribution of the second predicted displacement, and the distribution of the difference between the first predicted displacement and the second predicted displacement. (Step S307).
  • the detection unit 142 identifies the target phenomenon detected in the range of the facility (step S308).
  • the range of the facility may be the range in which the facility exists.
  • the range of the facility may include a range in which the facility exists and a range within a predetermined distance from the range in which the facility exists.
  • the output unit 143 outputs the information of the facility in which the target phenomenon is detected (step S309).
  • FIG. 9 is a flowchart showing the operation of the prediction process of the analyzer 12 of the present embodiment.
  • step S314, step S315, and step S316 are the same as the operations of step S104, step S105, and step S206 of the analyzer 10 of the second reference example shown in FIG. 5, respectively.
  • step S317 the determination unit 123 sends out a second predicted displacement, which is a displacement of the predicted height.
  • the second predicted displacement at two or more time points is predicted by a plurality of determination models that make predictions at those time points.
  • This embodiment has the effect of being able to grasp facilities where the target phenomenon such as uneven displacement or stress abnormality may at least exceed the standard in the future.
  • the reason is that the detection unit 142 detects the target phenomenon based on the distribution of the ground surface height displacement predicted based on the transition of the ground surface height displacement within the target range. ..
  • FIG. 10 is a block diagram showing an example of the configuration of the analyzer 13 of the present embodiment.
  • the analyzer 13 includes a target information receiving unit 144, an observation result storage unit 145, a calculation unit 151, a detection unit 142, a second extraction unit 122, and a geospatial information storage unit 131. , And the output unit 143.
  • the differences between the analyzer 13 of the present embodiment and the analyzer 12 of the first embodiment will be mainly described.
  • Target Information Reception Department 144 receives the information of the target range in the same manner as the target information reception unit 144 of the first embodiment.
  • the target information reception unit 144 sends the information of the target range to the calculation unit 151.
  • the calculation unit 151 receives the information of the target range from the target information reception unit 144.
  • the calculation unit 151 reads out the observation data at the observation point included in the target range indicated by the received information from the observation result storage unit 145.
  • the calculation unit 151 determines the difference between the displacement of the ground surface height at the time before the predetermined period and the displacement of the newest ground surface height at each of the observation points included in the target range (in other words, before the predetermined period). Displacement of the height of the ground surface from) is calculated.
  • the calculation unit 151 sends information on the target range and the displacement of the ground surface height from before a predetermined period to the detection unit 142.
  • the geospatial information storage unit 131 of the present embodiment stores at least information such as a range of a facility such as the above-mentioned buried facility as a value of geospatial information.
  • Second extraction unit 122 receives, for example, information on the target range from the detection unit 142, and extracts information on facilities included in the target range from the geospatial information stored in the geospatial information storage unit 131.
  • the second extraction unit 122 sends the extracted facility information to the detection unit 142.
  • the detection unit 142 receives the information of the target range and the displacement of the height of the ground surface from the predetermined period before from the calculation unit 151.
  • the detection unit 142 receives information on facilities within the target range from the geospatial information storage unit 131 via the second extraction unit 122. Specifically, the detection unit 142 sends the information of the target range to the second extraction unit 122, and receives the information of the facilities within the target range detected by the second extraction unit 122 from the second extraction unit 122.
  • the detection unit 142 detects the target phenomenon in the target range based on the distribution of the displacement of the ground surface height from before a predetermined period. As described above, the target phenomenon is at least one of the uneven displacement and the stress abnormality.
  • the detection unit 142 may detect a region in which the slope of the displacement distribution of the ground surface height from before a predetermined period is larger than the predetermined reference (for example, the slope threshold value) as a non-uniform displacement region. Then, when the non-uniform displacement region is detected in the range of the facility, the detection unit 142 may detect the non-uniform displacement of the facility. When the non-uniform displacement region is detected in the range including the range of the facility and the range within a predetermined distance from the range of the facility, the detection unit 142 may detect the non-uniform displacement in the facility.
  • the predetermined reference for example, the slope threshold value
  • the magnitude of the change in the inclination of the displacement distribution of the ground surface height from before the predetermined period (hereinafter, simply referred to as the change in the inclination) is based on the predetermined reference (for example, the change threshold value).
  • the predetermined reference for example, the change threshold value.
  • a large region may be detected as an abnormal stress region.
  • the detection unit 142 may detect the stress abnormality of the facility.
  • the detection unit 142 may detect the stress abnormality in the facility.
  • the detection unit 142 sends the distribution of the displacement of the height of the ground surface within the target range, the information of the detected target phenomenon, and the information of the facility where the target phenomenon is detected to the output unit 143.
  • Output unit 143 receives from the detection unit 142 the distribution of the displacement of the height of the ground surface within the target range, the information of the detected target phenomenon, and the information of the facility where the target phenomenon is detected.
  • the output unit 143 outputs information on the facility in which the target phenomenon is detected.
  • the format of the information output by the output unit 143 may be the same as the format of the information output by the output unit 143 of the first embodiment.
  • FIG. 11 is a flowchart showing an example of the operation of the analyzer 13 of the present embodiment.
  • the operations of steps S301 and S302 of the analyzer 13 are the same as the operations of steps S301 and S302 of the analyzer 13 of the first embodiment shown in FIG.
  • step S403 From the observed data read out in step S403, the calculation unit 151 calculates the displacement of the ground surface height from before a predetermined period at each of the observation points within the target range (step S403).
  • step S304 of the analyzer 13 is the same as the operation of step S304 of the analyzer 13 of the first embodiment shown in FIG.
  • step S407 the detection unit 142 detects the target phenomenon based on the distribution of the displacement of the ground surface height from before a predetermined period (step S407).
  • the operations of steps S308 and S309 of the analyzer 13 are the same as the operations of steps S308 and S309 of the analyzer 13 of the first embodiment shown in FIG.
  • This embodiment has the effect of being able to identify facilities whose target phenomena such as uneven displacement and stress abnormalities exceed the standard.
  • the reason is that the detection unit 142 detects the target phenomenon based on the distribution of the displacement of the height of the ground surface within the target range.
  • FIG. 12 is a diagram showing an example of the configuration of the analyzer 14 of the present embodiment.
  • the analyzer 14 includes a prediction unit 141, a detection unit 142, and an output unit 143.
  • the prediction unit 141 predicts the displacement of the ground surface height within the target range based on the transition of the ground surface height displacement within the target range.
  • the detection unit 142 detects a target phenomenon including stress abnormality and non-uniform displacement of facilities within the target range based on the distribution of the first predicted displacement, which is the displacement predicted based on the transition of the displacement.
  • the output unit 143 outputs the information of the facility in which the target phenomenon is detected.
  • the prediction unit 141, the detection unit 142, and the output unit 143 of the present embodiment function in the same manner as the prediction unit 141, the detection unit 142, and the output unit 143 of the first embodiment, respectively.
  • FIG. 13 is a flowchart showing an example of the operation of the analyzer 14 of the present embodiment.
  • the prediction unit 141 predicts the displacement of the ground surface height within the target range based on the transition of the ground surface displacement within the target range (step S501).
  • the detection unit 142 detects the target phenomenon in the facility in the target range based on the predicted displacement distribution (step S502).
  • the output unit 143 outputs information on the facility in which the target phenomenon is detected (step S503).
  • FIG. 14 is a diagram showing an example of the configuration of the analyzer 15 of the present embodiment.
  • the analyzer 15 includes a detection unit 142 and an output unit 143.
  • the detection unit 142 detects the target phenomenon including the stress abnormality and the uneven displacement of the facility within the target range based on the distribution of the displacement of the ground surface height within the target range.
  • the output unit 143 outputs the information of the facility in which the target phenomenon is detected.
  • the detection unit 142 and the output unit 143 of the present embodiment function in the same manner as the detection unit 142 and the output unit 143 of the second embodiment, respectively.
  • FIG. 15 is a flowchart showing an example of the operation of the analyzer 15 of the present embodiment.
  • the detection unit 142 detects the target phenomenon in the facility in the target range based on the distribution of the displacement of the height of the ground surface within the target range (step S602).
  • the output unit 143 outputs information on the facility in which the target phenomenon is detected (step S603).
  • Each of the analyzer 10, the learning device 11, the analyzer 12, the analyzer 13, the analyzer 14, the analyzer 15, and the analyzer 21 according to the embodiment of the present disclosure is loaded with the program read from the storage medium. It can be realized by a computer that includes a memory and a processor that executes the program. Each of the analyzer 10, the learning device 11, the analyzer 12, the analyzer 13, the analyzer 14, the analyzer 15, and the analyzer 21 according to the embodiment of the present disclosure can be realized by dedicated hardware. Each of the analyzer 10, the learning device 11, the analyzer 12, the analyzer 13, the analyzer 14, the analyzer 15, and the analyzer 21 according to the embodiment of the present disclosure is based on the combination of the above-mentioned computer and dedicated hardware. It can also be realized.
  • FIG. 16 shows a computer 1000 capable of realizing each of the analyzer 10, the learning device 11, the analyzer 12, the analyzer 13, the analyzer 14, the analyzer 15, and the analyzer 21 according to the embodiment of the present disclosure. It is a figure which shows an example of a hardware configuration.
  • the computer 1000 includes a processor 1001, a memory 1002, a storage device 1003, and an I / O (Input / Output) interface 1004.
  • the computer 1000 can access the storage medium 1005.
  • the memory 1002 and the storage device 1003 are storage devices such as a RAM (Random Access Memory) and a hard disk, for example.
  • the storage medium 1005 is, for example, a storage device such as a RAM or a hard disk, a ROM (Read Only Memory), or a portable storage medium.
  • the storage device 1003 may be a storage medium 1005.
  • the processor 1001 can read and write data and programs to the memory 1002 and the storage device 1003.
  • Processor 1001 can access other devices, for example, via the I / O interface 1004.
  • Processor 1001 can access the storage medium 1005.
  • the computer 1000 is operated as any one of the analyzer 10, the learning device 11, the analyzer 12, the analyzer 13, the analyzer 14, the analyzer 15, and the analyzer 21 according to the embodiment of the present disclosure.
  • the program is stored.
  • the processor 1001 uses the computer 1000 stored in the storage medium 1005 as the analyzer 10, the learning device 11, the analyzer 12, the analyzer 13, the analyzer 14, the analyzer 15, and the analyzer according to the embodiment of the present disclosure.
  • the program to be operated as any of 21 is loaded into the memory 1002. Then, when the processor 1001 executes the program loaded in the memory 1002, the computer 1000 uses the analyzer 10, the learning device 11, the analyzer 12, the analyzer 13, and the analyzer 14 according to the embodiment of the present disclosure. It operates as an analyzer 15 or an analyzer 21.
  • the first receiving unit 111, the first extracting unit 112, the learning unit 113, the first reading unit 114, and the transmitting unit 115 are, for example, by a processor 1001 that executes a program loaded into the memory 1002 from the storage medium 1005 that stores the program. It can be realized.
  • the second receiving unit 121, the second extracting unit 122, the determining unit 123, the output unit 124, the second reading unit 126, and the receiving unit 127 execute, for example, a program loaded into the memory 1002 from the storage medium 1005 that stores the program. It can be realized by the processor 1001.
  • the input / output unit 132 can be realized by, for example, a processor 1001 that executes a program loaded into the memory 1002 from the storage medium 1005 that stores the program.
  • the prediction unit 141, the detection unit 142, the output unit 143, the target information reception unit 144, and the calculation unit 151 can be realized by, for example, a processor 1001 that executes a program loaded into the memory 1002 from the storage medium 1005 that stores the program. can. Further, the model storage unit 125, the geospatial information storage unit 131, and the observation result storage unit 145 can be realized by the memory 1002 included in the computer 1000 and the storage device 1003 such as a hard disk device.
  • a part or all of the first receiving unit 111, the first extraction unit 112, the learning unit 113, the first reading unit 114, and the transmitting unit 115 can be realized by a dedicated circuit that realizes the functions of each unit.
  • a part or all of the geospatial information storage unit 131 and the input / output unit 132 can also be realized by a dedicated circuit that realizes the functions of each unit.
  • a part or all of the prediction unit 141, the detection unit 142, the output unit 143, the target information reception unit 144, the observation result storage unit 145, and the calculation unit 151 can be realized by a dedicated circuit that realizes the functions of each unit.
  • (Appendix 2) Learned to predict future height displacements based on at least some values of multiple types of geospatial information that represent at least one of the surface conditions and the underground conditions of the surface.
  • a determination means for predicting the displacement of the ground surface within the target range based on the value of the geospatial information within the target range is provided by the determination model.
  • the detection means detects the target phenomenon based on the distribution of the first predicted displacement and the value of the second predicted displacement, which is the displacement predicted by the determination model based on the value of the geospatial information.
  • the analyzer according to Appendix 1.
  • Appendix 3 The analyzer according to Appendix 2, wherein the detecting means detects the target phenomenon based on the distribution of the difference between the first predicted displacement and the second predicted displacement.
  • the detection means is an inclination in the distribution of the difference between the first predicted displacement and the second predicted displacement in the third influence region including the region of the facility and the region where the distance from the region is closer than the third reference.
  • the analyzer according to Appendix 3 for detecting the stress abnormality in the facility when there is a portion that satisfies the predetermined criteria.
  • the detection means has a portion where the slope of the distribution of the second predicted displacement satisfies a predetermined reference in the second influence region including the region of the facility and the range where the distance from the region is closer than the second reference.
  • the analyzer according to Appendix 5 that detects the uneven displacement in the facility.
  • the detection means determines the magnitude of the spatial change in the slope of the distribution of the second predicted displacement in the second influence region including the region of the facility and the range where the distance from the region is closer than the second reference.
  • the analyzer according to Appendix 7, which detects the stress abnormality in the facility when there is a portion that meets the criteria.
  • the determination model is represented by a condition for each value of at least a part of the geospatial information and a prediction formula for predicting the displacement of the future height when all of the conditions are satisfied.
  • the analyzer according to any one of Appendix 2 to 8, wherein the prediction formula is represented by a linear sum of variables representing the geospatial information.
  • Appendix 10 The analyzer according to any one of Appendix 1 to 9, wherein the detecting means detects the uneven displacement in a facility within the target range based on the slope of the distribution of the first predicted displacement.
  • the detection means has a portion where the slope of the distribution of the first predicted displacement satisfies a predetermined reference in the first influence region including the region of the facility and the range where the distance from the region is closer than the first reference.
  • the analyzer according to Appendix 10 for detecting the uneven displacement in the facility is not limited.
  • Appendix 12 The analyzer according to any one of Appendix 1 to 11, wherein the detection means detects the stress abnormality in a facility within the target range based on a spatial change in the inclination of the distribution of the first predicted displacement.
  • the detection means determines the magnitude of the spatial change in the slope of the distribution of the first predicted displacement in the first influence region including the region of the facility and the range where the distance from the region is closer than the first reference.
  • the analyzer according to Appendix 12, which detects the stress abnormality in the facility when there is a portion that meets the criteria.
  • Appendix 15 The analyzer according to Appendix 14, wherein the detecting means detects the uneven displacement based on the slope of the distribution.
  • Appendix 16 The analyzer according to Appendix 14 or 15, wherein the detection means detects the stress abnormality based on a spatial change in the slope of the distribution.
  • (Appendix 18) Learned to predict future height displacements based on at least some values of multiple types of geospatial information that represent at least one of the surface conditions and the underground conditions of the surface. Based on the value of the geospatial information in the target range, the displacement of the ground surface in the target range is predicted by the determined determination model. Addendum 17 for detecting the target phenomenon based on the distribution of the first predicted displacement and the value of the second predicted displacement, which is the displacement predicted by the determination model based on the value of the geospatial information. Analysis method.
  • Appendix 19 The analysis method according to Appendix 18 for detecting the target phenomenon based on the distribution of the difference between the first predicted displacement and the second predicted displacement.
  • Appendix 21 The analysis method according to any one of Appendix 18 to 20, which detects the uneven displacement in a facility within the target range based on the slope of the distribution of the second predicted displacement.
  • Appendix 23 The analysis method according to any one of Appendix 18 to 22, which detects the stress abnormality in the facility within the target range based on the spatial change of the slope of the distribution of the second predicted displacement.
  • the determination model is represented by a condition for each value of at least a part of the geospatial information and a prediction formula for predicting the displacement of the future height when all of the conditions are satisfied.
  • the analysis method according to any one of Appendix 18 to 24, wherein the prediction formula is represented by a linear sum of variables representing the geospatial information.
  • Appendix 26 The analysis method according to any one of Appendix 17 to 25, which detects the uneven displacement in a facility within the target range based on the slope of the distribution of the first predicted displacement.
  • Appendix 27 In the first influence region including the region of the facility and the range where the distance from the region is closer than the first reference, when there is a portion where the slope of the distribution of the first predicted displacement satisfies the predetermined criterion, the facility , The analysis method according to Appendix 26, which detects the uneven displacement.
  • Appendix 28 The analysis method according to any one of Appendix 17 to 27, which detects the stress abnormality in the facility within the target range based on the spatial change of the slope of the distribution of the first predicted displacement.
  • Appendix 29 In the first influence region including the region of the facility and the range where the distance from the region is closer than the first reference, the portion where the magnitude of the spatial change of the slope of the distribution of the first predicted displacement satisfies the predetermined criterion.
  • the analytical method according to Appendix 28 which detects the stress abnormality in the facility, if present.
  • Appendix 31 The analysis method according to Appendix 30, which detects the uneven displacement based on the slope of the distribution.
  • Appendix 32 The analysis method according to Appendix 30 or 31, which detects the stress abnormality based on the spatial change in the slope of the distribution.
  • (Appendix 33) Prediction processing that predicts the displacement of the ground surface height within the target range based on the transition of the ground surface height displacement within the target range, and Based on the distribution of the first predicted displacement, which is the displacement predicted based on the transition of the displacement, the detection process for detecting the target phenomenon including the stress abnormality and the uneven displacement of the facilities within the target range, and the detection process.
  • Output processing that outputs information on the facility where the target phenomenon was detected, and A storage medium that stores a program that causes a computer to execute a program.
  • the program predicts future height displacements based on at least some values of multiple types of geospatial information that represent at least one of the surface conditions and the underground conditions of the surface.
  • the computer is further made to execute a determination process for predicting the displacement of the ground surface within the target range based on the value of the geospatial information within the target range.
  • the detection process detects the target phenomenon based on the distribution of the first predicted displacement and the value of the second predicted displacement, which is the displacement predicted by the determination model based on the value of the geospatial information.
  • the storage medium according to Appendix 33.
  • Appendix 35 The storage medium according to Appendix 34, wherein the detection process detects the target phenomenon based on the distribution of the difference between the first predicted displacement and the second predicted displacement.
  • Appendix 36 In the detection process, the slope in the distribution of the difference between the first predicted displacement and the second predicted displacement in the third influence region including the region of the facility and the region where the distance from the region is closer than the third reference. 35.
  • the storage medium according to Appendix 35 which detects the stress abnormality in the facility when there is a portion that satisfies a predetermined criterion.
  • Appendix 39 The storage medium according to any one of Appendix 34 to 38, wherein the detection process detects the stress abnormality in a facility within the target range based on a spatial change in the inclination of the distribution of the second predicted displacement.
  • Appendix 40 In the detection process, the magnitude of the spatial change in the slope of the distribution of the second predicted displacement is determined in the second influence region including the region of the facility and the range where the distance from the region is closer than the second reference.
  • the storage medium according to Appendix 39 which detects the stress abnormality in the facility when there is a portion that meets the criteria.
  • the determination model is represented by a condition for each value of at least a part of the geospatial information and a prediction formula for predicting the displacement of the future height when all of the conditions are satisfied.
  • the storage medium according to any one of Appendix 34 to 40, wherein the prediction formula is represented by a linear sum of variables representing the geospatial information.
  • the magnitude of the spatial change in the slope of the distribution of the first predicted displacement is determined in the first influence region including the region of the facility and the range where the distance from the region is closer than the first reference.
  • the storage medium according to Appendix 35 which detects the stress abnormality in the facility when there is a portion that meets the criteria.
  • Appendix 47 The storage medium according to Appendix 46, wherein the detection process detects the uneven displacement based on the slope of the distribution.
  • Appendix 48 The storage medium according to Appendix 46 or 47, wherein the detection process detects the stress abnormality based on a spatial change in the slope of the distribution.
  • Analysis system 10 Analysis device 11 Learning device 12 Analysis device 13 Analysis device 14 Analysis device 15 Analysis device 21 Analysis device 31 Geospatial information storage device 40 Network 51 Terminal device 111 1st receiving unit 112 1st extraction unit 113 Learning unit 114th 1 Read unit 115 Transmit unit 121 Second receiver 122 Second extract unit 123 Judgment unit 124 Output unit 125 Model storage unit 126 Second read unit 127 Receive unit 131 Geospatial information storage unit 132 Input / output unit 141 Prediction unit 142 Detection unit 143 Output unit 144 Target information reception unit 145 Observation result storage unit 151 Calculation unit 1000 Computer 1001 Processor 1002 Memory 1003 Storage device 1004 I / O interface 1005 Storage medium

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  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Electromagnetism (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Testing Or Calibration Of Command Recording Devices (AREA)

Abstract

La présente invention concerne un dispositif d'analyse avec lequel il est possible de déterminer des installations où il existe une possibilité de phénomènes cibles, tels qu'un déplacement irrégulier et des anomalies de contrainte, au moins dépassant une norme dans le futur. Un dispositif d'analyse 14 selon un aspect de la présente invention comprend : une unité de prédiction 141 qui prédit un déplacement de la hauteur de la surface du sol dans une région cible sur la base de la transition du déplacement de la hauteur de la surface du sol à l'intérieur d'une région cible ; une unité de détection 142 qui, sur la base de la distribution d'un premier déplacement prédit, qui est le déplacement prédit sur la base d'une transition de déplacement, détecte des phénomènes cibles comprenant des anomalies de contrainte et un déplacement irrégulier dans des installations dans la région cible ; et une unité de sortie 143 qui délivre des informations concernant les installations où les phénomènes cibles ont été détectés.
PCT/JP2020/014746 2020-03-31 2020-03-31 Dispositif d'analyse, procédé d'analyse et support de stockage WO2021199241A1 (fr)

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WO2023162245A1 (fr) * 2022-02-28 2023-08-31 日本電気株式会社 Système de détermination d'impact, procédé de détermination d'impact et support d'enregistrement
WO2023162244A1 (fr) * 2022-02-28 2023-08-31 日本電気株式会社 Système de détermination d'effet, procédé de détermination d'effet et support d'enregistrement

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JPH0921636A (ja) * 1995-07-10 1997-01-21 Shimizu Corp 地盤・建造物挙動監視装置
JP2006194822A (ja) * 2005-01-17 2006-07-27 Oyo Corp 加速度センサを用いる地盤等の変位モニタリング方法
JP2017090397A (ja) * 2015-11-17 2017-05-25 中国電力株式会社 測定装置
JP2017215248A (ja) * 2016-06-01 2017-12-07 国立研究開発法人宇宙航空研究開発機構 変状度判定方法及び変状度判定システム
JP2020016923A (ja) * 2018-07-23 2020-01-30 株式会社パスコ 家屋異動推定装置及びプログラム

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Publication number Priority date Publication date Assignee Title
JPH0921636A (ja) * 1995-07-10 1997-01-21 Shimizu Corp 地盤・建造物挙動監視装置
JP2006194822A (ja) * 2005-01-17 2006-07-27 Oyo Corp 加速度センサを用いる地盤等の変位モニタリング方法
JP2017090397A (ja) * 2015-11-17 2017-05-25 中国電力株式会社 測定装置
JP2017215248A (ja) * 2016-06-01 2017-12-07 国立研究開発法人宇宙航空研究開発機構 変状度判定方法及び変状度判定システム
JP2020016923A (ja) * 2018-07-23 2020-01-30 株式会社パスコ 家屋異動推定装置及びプログラム

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023162245A1 (fr) * 2022-02-28 2023-08-31 日本電気株式会社 Système de détermination d'impact, procédé de détermination d'impact et support d'enregistrement
WO2023162244A1 (fr) * 2022-02-28 2023-08-31 日本電気株式会社 Système de détermination d'effet, procédé de détermination d'effet et support d'enregistrement

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