WO2022091245A1 - Deterioration risk estimation method and system - Google Patents

Deterioration risk estimation method and system Download PDF

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Publication number
WO2022091245A1
WO2022091245A1 PCT/JP2020/040403 JP2020040403W WO2022091245A1 WO 2022091245 A1 WO2022091245 A1 WO 2022091245A1 JP 2020040403 W JP2020040403 W JP 2020040403W WO 2022091245 A1 WO2022091245 A1 WO 2022091245A1
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deterioration risk
deterioration
external environment
environment data
information
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PCT/JP2020/040403
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French (fr)
Japanese (ja)
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真悟 峯田
翔太 大木
守 水沼
宗一 岡
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日本電信電話株式会社
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Priority to PCT/JP2020/040403 priority Critical patent/WO2022091245A1/en
Priority to JP2022558666A priority patent/JPWO2022091245A1/ja
Publication of WO2022091245A1 publication Critical patent/WO2022091245A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N17/00—Investigating resistance of materials to the weather, to corrosion, or to light

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  • the present invention relates to a deterioration risk estimation method and a system for estimating the deterioration risk of equipment.
  • infrastructure equipment There are many types of infrastructure equipment that support our lives, and the number is enormous. In addition, infrastructure equipment is exposed to various environments not only in urban areas but also in mountainous areas and coastal areas, hot spring areas and cold areas, and even in the sea and underground, and the form and speed of deterioration vary. .. In order to maintain infrastructure equipment with these characteristics, it is necessary to grasp the current state of deterioration through visual inspections and other inspections.
  • Non-Patent Document 1 Non-Patent Document 2
  • Non-Patent Document 2 since these facilities are buried in the ground, it is difficult to visually inspect the deterioration state. Therefore, it is difficult to perform appropriate maintenance according to the deteriorated state.
  • Non-Patent Document 3 Non-Patent Document 3
  • the underground environment changes greatly depending on the ground environment (weather).
  • the water content in the ground changes due to rainfall, and the change in water content over time also differs depending on whether the ground surface is exposed or asphalt pavement.
  • the underground environment changes depending on the amount of solar radiation, temperature / humidity, air volume, vegetation, etc. For this reason, it is not easy to estimate the risk of deterioration of underground equipment with accuracy and reliability that match the actual situation.
  • the present invention has been made to solve the above problems, and an object of the present invention is to make it possible to estimate the deterioration risk of buried equipment with accuracy and reliability suitable for the actual situation. ..
  • the deterioration risk estimation method is an algorithm showing the relationship between the first step of acquiring the external environment data of the target site, the acquired external environment data, and the deterioration risk of the target equipment buried in the target site.
  • the second step of constructing, the third step of newly acquiring the external environment data of the target site, and the fourth step of estimating the deterioration risk of the equipment by the algorithm using the newly acquired external environment data are provided.
  • External environmental data includes at least one of the above-ground environmental data obtained by observation from the ground: rainfall, temperature, humidity, amount of solar radiation, soil information, vegetation information, surface pavement information, distance from water area, altitude, and slope. include.
  • the deterioration risk estimation system includes a construction circuit for constructing an algorithm showing the relationship between the acquired external environment data of the target site and the deterioration risk of the target equipment buried in the target site, and the target site. It is equipped with an acquisition device that acquires the external environment data of the device, an estimation circuit that estimates the deterioration risk of equipment by an algorithm using the external environment data acquired by the acquisition device, and an output device that outputs the deterioration risk estimated by the estimation circuit.
  • External environmental data is at least one of the above-ground environmental data obtained by observation from the ground: rainfall, temperature, humidity, solar radiation, soil information, vegetation information, surface pavement information, distance from water area, altitude, slope. including.
  • the deterioration risk is estimated using an algorithm showing the relationship between the external environmental data of the target site and the deterioration risk of the target equipment buried in the target site, it is buried.
  • the risk of deterioration of the equipment can be estimated with the accuracy and reliability that match the actual situation.
  • FIG. 1 is a configuration diagram showing a configuration of a deterioration risk estimation device according to an embodiment of the present invention.
  • FIG. 2 is a flowchart illustrating a deterioration risk estimation method according to an embodiment of the present invention.
  • FIG. 3 is an explanatory diagram showing an example of acquisition of external environment data.
  • FIG. 4 is an explanatory diagram showing an example of the estimated risk output.
  • This deterioration risk estimation system includes a construction circuit 101, an acquisition device 102, an estimation circuit 103, an output device 104, an input device 105, and a storage device 106. Further, this deterioration risk estimation system includes an image processing device 107.
  • the construction circuit 101 constructs an algorithm showing the relationship between the acquired external environmental data of the target site and the deterioration risk of the target equipment buried in the target site.
  • the construction circuit 101 constructs the algorithm.
  • the algorithm can be constructed by the construction circuit 101 by inputting the external environment data of the target area prepared in advance by using the input device 105.
  • the constructed algorithm is stored in, for example, the storage device 106.
  • the storage device 106 is, for example, an external storage device composed of a fixed disk device, an SSD (Solid State Drive), or the like.
  • External environmental data includes at least one of the above-ground environmental data obtained by observation from the ground: rainfall, temperature, humidity, solar radiation, soil information, vegetation information, surface pavement information, distance from water area, altitude, and slope. It includes.
  • the acquisition device 102 acquires the external environment data of the target area.
  • the acquisition device 102 can be configured, for example, from a sensor or a measuring device that measures external environment data. Further, by connecting these to a personal computer, the acquisition device 102 can be obtained. Further, the acquisition device 102 can be obtained by connecting a personal computer to a device having an image or moving image shooting function by communication.
  • the external storage data acquired by the acquisition device 102 is stored, for example, by the storage device 106.
  • the acquisition device 102 can be configured from a surveying system (Mobile Mapping System; MMS) using a mobile measurement vehicle, a drone, or the like.
  • MMS Mobile Mapping System
  • the image processing device 107 extracts external environment data from the image including the landscape of the target area acquired in this way.
  • the external storage data extracted by the image processing device 107 is stored, for example, by the storage device 106.
  • the estimation circuit 103 estimates the risk of deterioration of the equipment by the algorithm constructed by the construction circuit 101 using the external environment data acquired by the acquisition device 102.
  • the estimation circuit 103 can be configured from, for example, a personal computer provided with an execution environment for machine learning and deep learning algorithms and performing estimation calculations by the above algorithms.
  • the output device 104 outputs the deterioration risk estimated by the estimation circuit 103.
  • the output device 104 can be, for example, a monitor of a personal computer.
  • the output device 104 has a function of displaying the deterioration risk estimated by the estimation circuit 103 on an image or a moving image including a landscape of the target area.
  • the output device 104 can be a mobile terminal device such as a smartphone or a smart glass. Using these devices, it is possible to output the estimated deterioration risk on the screen when shooting the landscape of the target area.
  • the above-mentioned display can be obtained by providing a function of transmitting the deterioration risk estimated from the estimation circuit 103 to the above-mentioned device by communication.
  • the acquisition device 102 acquires the external environment data of the target area.
  • the underground equipment for which the deterioration risk is to be estimated, or the external environment data of the target area where the underground equipment is installed is acquired.
  • the external environmental data may include at least one of rainfall, temperature, humidity, solar radiation, soil information, vegetation information, surface pavement information, distance from water area, altitude, and slope, and other data.
  • underground environment data such as soil species information, depth information, and groundwater information can be considered.
  • rainfall data when using rainfall data as external environmental data, it is possible to use the average rainfall amount for one year or the average rainfall interval in the year when the equipment in the target area is installed or in the same year as when used in other target areas. can.
  • the rainfall data when used as the external environmental data, it is possible to use the data in which the rainfall amount and the rainfall interval in one year are used as the frequency distribution.
  • the temperature, humidity, amount of solar radiation, etc. can be used as external environment data, and the average value and frequency distribution for one year can be used as external environment data.
  • Data on rainfall, temperature, humidity, and amount of solar radiation can be obtained from, for example, public data of the Japan Meteorological Agency.
  • Soil information includes information on the color of soil that can be confirmed from the ground and the presence or absence of rocks. From the color of the soil, the approximate type of soil can be determined, and the color of the land contains useful information on the presence or absence of corrosiveness.
  • the form of the soil color information is not particularly limited, but can be expressed by RGB, CMYK, LAB, LRV, HEX value, or the like.
  • rocks are useful information related to the drainage of soil and suggesting the presence or absence of scratches formed when laying equipment.
  • vegetation information there is information such as the presence or absence of vegetation. Vegetation information is an important factor related to the oxygen concentration in the ground, drainage status, and the amount of solar radiation.
  • the surface pavement information includes information such as whether the surface of the place where the equipment is located is covered with concrete, asphalt, or the like. For example, even if there is rainfall, the change in water content in the underground environment becomes small, and the volatilization of water from the ground surface becomes difficult, so it is more stable over time than soil with bare soil.
  • the surface pavement information includes what percentage of the surface of the ground where the equipment was laid, whether the pavement covers it, and whether there is an exposed part. Even in a place where asphalt is paved, if there is a slightly exposed part of the soil, rainfall will permeate from this part and water will volatilize, so this information is also useful for estimation.
  • the distance from the water area is an effective factor in estimating the risk of deterioration.
  • the water content is likely to be maintained high. Elevation and slope are effective factors for estimating the deterioration of underground equipment because they affect how water flows when water infiltrates due to rainfall.
  • External environmental data can be extracted from images or videos that include the landscape of the target area.
  • soil information, vegetation information, and surface pavement information can be extracted from images such as still images and moving images including landscapes of the target area.
  • the information obtained by a person visually acquiring the information from the image can be used as external image data.
  • external environment data can be extracted from the image by object detection using a machine learning algorithm.
  • the distance from the water area, altitude, and inclination can be extracted from other reference objects shown in the image.
  • information visually acquired by a person from an image or information extracted by object detection using a machine learning algorithm can be used as external environment data.
  • an image can be acquired by an acquisition device 102 composed of an MMS, a drone, or the like, and information extracted by the image processing device 107 from the acquired image can be used as external environment data.
  • FIG. 3 it is possible to acquire external environment data by an object detection algorithm from an image 303 obtained by photographing a target site 302 in which an underground facility 301 whose deterioration risk is to be estimated is buried. can.
  • the target site 302 there is soil on the ground surface 321 and grass 322 is breeding, and from image 303, the presence of grass and the color of the soil (RGB value) can be acquired as external environmental data.
  • a state in which rocks, pavements, etc. are not detected in the image processing of the target site 302 can be acquired as external environment data.
  • the construction circuit 101 constructs an algorithm showing the relationship between the acquired external environment data and the deterioration risk of the target equipment buried in the target site.
  • This algorithm can be constructed from the relationship between the external environmental data at each of the plurality of target points and the degree of deterioration of the equipment installed at each of the plurality of target points.
  • the degree of deterioration corresponding to the external environment data used for constructing the algorithm is known in advance.
  • the above-mentioned algorithm can be constructed by fitting a mathematical model determined by a person so that external environment data is input and the degree of deterioration of the corresponding equipment is output.
  • the estimation circuit 103 can acquire the external environment data of the target area. This acquisition can be carried out at the time when the risk of deterioration of the target equipment is to be estimated.
  • the estimation circuit 103 uses the newly acquired external environment data, and the risk of deterioration of the target equipment installed at the target site is performed by the algorithm constructed by the construction circuit 101. To estimate.
  • the answer output by inputting the acquired external environment data into the above algorithm can be used as an estimated value of deterioration risk.
  • Deterioration risk can be categorized in stages, for example A, B, C, D.
  • the deterioration risk can be a quantitative numerical value. If the target equipment can be regarded as the same and the estimation algorithm has already been constructed for the target site, the risk can be estimated using the already constructed algorithm.
  • the estimated risk is output.
  • the estimated risk is output by displaying it on a display device so that the user can see it.
  • the deterioration risk can be displayed together with the image 303 obtained by photographing the target area 302 in which the underground equipment 301 for which the deterioration risk is to be estimated is buried.
  • the deterioration risk is estimated by using an algorithm showing the relationship between the external environmental data of the target site and the deterioration risk of the target equipment buried in the target site. It will be possible to estimate the deterioration risk of the equipment being installed with the accuracy and reliability that match the actual situation.
  • 101 construction circuit, 102 ... acquisition device, 103 ... estimation circuit, 104 ... output device, 105 ... input device, 106 ... storage device, 107 ... image processing device.

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Abstract

In the present invention, a construction circuit (101) constructs an algorithm representing the relationship between external environment data that has been acquired for a given location and the deterioration risk of a given facility buried in the given location. An acquisition device (102) acquires external environment data for a given location. An estimation circuit (103) uses the external environment data acquired by the acquisition device (102) to estimate the deterioration risk of a facility with the algorithm constructed by the construction circuit (101). An output device (104) outputs the deterioration risk estimated by the estimation circuit (103).

Description

劣化リスク推定方法およびシステムDeterioration risk estimation method and system
　本発明は、設備の劣化リスクを推定する劣化リスク推定方法およびシステムに関する。 The present invention relates to a deterioration risk estimation method and a system for estimating the deterioration risk of equipment.
　我々の生活を支えるインフラ設備は種類も多く、数も膨大である。また、インフラ設備は、市街地だけでなく、山岳地や海岸付近、温泉地や寒冷地、さらに海中や地中に至るまで多様な環境に晒されており、劣化の形態や進行速度も様々である。こうした特徴を持つインフラ設備の保全には、目視などの点検による劣化の現状把握が必要である。 There are many types of infrastructure equipment that support our lives, and the number is enormous. In addition, infrastructure equipment is exposed to various environments not only in urban areas but also in mountainous areas and coastal areas, hot spring areas and cold areas, and even in the sea and underground, and the form and speed of deterioration vary. .. In order to maintain infrastructure equipment with these characteristics, it is necessary to grasp the current state of deterioration through visual inspections and other inspections.
　インフラ設備として、例えば、鋼管柱、支持アンカ、鋼配管などに代表される金属製の地中設備は、土壌に接するために腐食しやすく、外部環境に応じて異なる速さで劣化が進行する（非特許文献１，非特許文献２) 。しかしながら、これら設備は、地中に埋設されているがゆえに劣化状態を目視点検で確認することは難しい。このため、劣化状態に応じて適切にメンテナンスを行うことが困難となっている。 As infrastructure equipment, for example, metal underground equipment such as steel pipe columns, support anchors, and steel pipes are prone to corrosion due to contact with soil, and deterioration progresses at a different rate depending on the external environment (. Non-Patent Document 1, Non-Patent Document 2). However, since these facilities are buried in the ground, it is difficult to visually inspect the deterioration state. Therefore, it is difficult to perform appropriate maintenance according to the deteriorated state.
　直接目視点検できない地中設備の保全方法として、リスク値に基づく管理が考えられる。この場合、地中設備の劣化は、設備の外部環境に応じた速さで生じることを利用し、外部環境のパラメータから劣化リスクの大きさを推定し、これに応じて保全方法を決定する。 As a maintenance method for underground equipment that cannot be visually inspected directly, management based on risk values can be considered. In this case, the deterioration of the underground equipment occurs at a speed corresponding to the external environment of the equipment, the magnitude of the deterioration risk is estimated from the parameters of the external environment, and the maintenance method is determined accordingly.
　例えば、海岸付近に設置された設備は、海から飛来する塩の影響で腐食しやすいことが知られている。従って単純には、海岸からの距離に関係する値を外部環境のパラメータとしてリスク値を計算するモデルを構築すれば、これを使って例えば海岸付近は陸地の環境よりも劣化リスクが大きいと推定できる。従って、このリスク推定結果に基づくことで、例えば点検や更改周期は陸地よりも海岸付近の設備を短く設定する、といった保全方法の採択が可能となる。 For example, it is known that equipment installed near the coast is easily corroded by the influence of salt flying from the sea. Therefore, simply by constructing a model that calculates the risk value using the value related to the distance from the coast as a parameter of the external environment, it can be estimated that, for example, the vicinity of the coast has a greater risk of deterioration than the land environment. .. Therefore, based on this risk estimation result, it is possible to adopt a conservation method such as setting the equipment near the coast shorter than the land for the inspection and renewal cycle.
　しかしながら、上述したような単純な方法で推定されたリスク値では、推定精度が低く信頼性も乏しい。さらに、地中設備のリスク推定を困難にする要因として、土中の腐食劣化メカニズムが複雑であることが挙げられる。どの外部環境パラメータから劣化リスクを推定するべきかが明らかにされておらず、既存の方法で土中の劣化の度合や進行速度を定量的に推定することは難しい（非特許文献３）。 However, with the risk value estimated by the simple method as described above, the estimation accuracy is low and the reliability is poor. Furthermore, a factor that makes it difficult to estimate the risk of underground equipment is that the mechanism of corrosion deterioration in soil is complicated. It has not been clarified from which external environmental parameter the deterioration risk should be estimated, and it is difficult to quantitatively estimate the degree of deterioration and the progress rate in the soil by the existing method (Non-Patent Document 3).
　さらに、地中環境は、地上の環境（天候）に大きく依存して変化する。例えば、降雨によって地中の含水率は変化するうえ、地表面が土むき出しなのか、アスファルト舗装なのかによっても含水率の経時変化は異なる。また、日射量、温・湿度、風量、植生などでも地中環境は変化する。このため、地中設備の劣化リスクについて実態に合った精度と信頼性で推定することは容易ではない。 Furthermore, the underground environment changes greatly depending on the ground environment (weather). For example, the water content in the ground changes due to rainfall, and the change in water content over time also differs depending on whether the ground surface is exposed or asphalt pavement. In addition, the underground environment changes depending on the amount of solar radiation, temperature / humidity, air volume, vegetation, etc. For this reason, it is not easy to estimate the risk of deterioration of underground equipment with accuracy and reliability that match the actual situation.
　上述したように、従来は、地中に埋設されている設備の劣化リスクについて実態に合った精度と信頼性で推定することができないという問題があった。 As mentioned above, conventionally, there was a problem that the deterioration risk of equipment buried in the ground could not be estimated with accuracy and reliability suitable for the actual situation.
　本発明は、以上のような問題点を解消するためになされたものであり、埋設されている設備の劣化リスクについて、実態に合った精度と信頼性で推定できるようにすることを目的とする。 The present invention has been made to solve the above problems, and an object of the present invention is to make it possible to estimate the deterioration risk of buried equipment with accuracy and reliability suitable for the actual situation. ..
　本発明に係る劣化リスク推定方法は、対象地の外部環境データを取得する第１ステップと、取得した外部環境データと、対象地に埋設された対象となる設備の劣化リスクとの関係を示すアルゴリズムを構築する第２ステップと、新たに対象地の外部環境データを取得する第３ステップと、新たに取得した外部環境データを用いてアルゴリズムにより設備の劣化リスクを推定する第４ステップとを備え、外部環境データは、地上からの観測により得られる地上環境データである降雨、気温、湿度、日射量、土壌情報、植生情報、地表舗装情報、水域からの距離、標高、傾斜のうち少なくとも１つを含む。 The deterioration risk estimation method according to the present invention is an algorithm showing the relationship between the first step of acquiring the external environment data of the target site, the acquired external environment data, and the deterioration risk of the target equipment buried in the target site. The second step of constructing, the third step of newly acquiring the external environment data of the target site, and the fourth step of estimating the deterioration risk of the equipment by the algorithm using the newly acquired external environment data are provided. External environmental data includes at least one of the above-ground environmental data obtained by observation from the ground: rainfall, temperature, humidity, amount of solar radiation, soil information, vegetation information, surface pavement information, distance from water area, altitude, and slope. include.
　また、本発明に係る劣化リスク推定システムは、取得した対象地の外部環境データと、対象地に埋設された対象となる設備の劣化リスクとの関係を示すアルゴリズムを構築する構築回路と、対象地の外部環境データを取得する取得装置と、取得装置が取得した外部環境データを用いてアルゴリズムにより設備の劣化リスクを推定する推定回路と、推定回路が推定した劣化リスクを出力する出力装置とを備え、外部環境データは、地上からの観測により得られる地上環境データである降雨、気温、湿度、日射量、土壌情報、植生情報、地表舗装情報、水域からの距離、標高、傾斜のうち少なくとも１つを含む。 Further, the deterioration risk estimation system according to the present invention includes a construction circuit for constructing an algorithm showing the relationship between the acquired external environment data of the target site and the deterioration risk of the target equipment buried in the target site, and the target site. It is equipped with an acquisition device that acquires the external environment data of the device, an estimation circuit that estimates the deterioration risk of equipment by an algorithm using the external environment data acquired by the acquisition device, and an output device that outputs the deterioration risk estimated by the estimation circuit. , External environmental data is at least one of the above-ground environmental data obtained by observation from the ground: rainfall, temperature, humidity, solar radiation, soil information, vegetation information, surface pavement information, distance from water area, altitude, slope. including.
　以上説明したように、本発明によれば、対象地の外部環境データと対象地に埋設された対象となる設備の劣化リスクとの関係を示すアルゴリズムを用いて劣化リスクを推定するので、埋設されている設備の劣化リスクについて、実態に合った精度と信頼性で推定できる。 As described above, according to the present invention, since the deterioration risk is estimated using an algorithm showing the relationship between the external environmental data of the target site and the deterioration risk of the target equipment buried in the target site, it is buried. The risk of deterioration of the equipment can be estimated with the accuracy and reliability that match the actual situation.
図１は、本発明の実施の形態に係る劣化リスク推定装置の構成を示す構成図である。FIG. 1 is a configuration diagram showing a configuration of a deterioration risk estimation device according to an embodiment of the present invention. 図２は、本発明の実施の形態に係る劣化リスク推定方法を説明するフローチャートである。FIG. 2 is a flowchart illustrating a deterioration risk estimation method according to an embodiment of the present invention. 図３は、外部環境データの取得の一例を示す説明図である。FIG. 3 is an explanatory diagram showing an example of acquisition of external environment data. 図４は、推定したリスクの出力の一例を示す説明図である。FIG. 4 is an explanatory diagram showing an example of the estimated risk output.
　以下、本発明の実施の形態に係る劣化リスク推定システムについて図１を参照して説明する。この劣化リスク推定システムは、構築回路１０１、取得装置１０２、推定回路１０３、出力装置１０４、入力装置１０５、および記憶装置１０６を備える。また、この劣化リスク推定システムは、画像処理装置１０７を備える。 Hereinafter, the deterioration risk estimation system according to the embodiment of the present invention will be described with reference to FIG. This deterioration risk estimation system includes a construction circuit 101, an acquisition device 102, an estimation circuit 103, an output device 104, an input device 105, and a storage device 106. Further, this deterioration risk estimation system includes an image processing device 107.
　構築回路１０１は、取得した対象地の外部環境データと、対象地に埋設された対象となる設備の劣化リスクとの関係を示すアルゴリズムを構築する。入力装置１０５を用い、後述する取得装置１０２で取得した対象地の外部環境データを入力すると、構築回路１０１がアルゴリズムを構築する。また、例えば、予め用意されている対象地の外部環境データを、入力装置１０５を用いて入力することで、構築回路１０１によりアルゴリズムを構築することができる。 The construction circuit 101 constructs an algorithm showing the relationship between the acquired external environmental data of the target site and the deterioration risk of the target equipment buried in the target site. When the external environment data of the target area acquired by the acquisition device 102 described later is input using the input device 105, the construction circuit 101 constructs the algorithm. Further, for example, the algorithm can be constructed by the construction circuit 101 by inputting the external environment data of the target area prepared in advance by using the input device 105.
　構築したアルゴリズムは、例えば、記憶装置１０６が記憶する。記憶装置１０６は、例えば、固定ディスク装置やＳＳＤ（Solid State Drive）などから構成された外部記憶装置である。外部環境データは、地上からの観測により得られる地上環境データである降雨、気温、湿度、日射量、土壌情報、植生情報、地表舗装情報、水域からの距離、標高、傾斜のうち少なくとも１つを含むものである。 The constructed algorithm is stored in, for example, the storage device 106. The storage device 106 is, for example, an external storage device composed of a fixed disk device, an SSD (Solid State Drive), or the like. External environmental data includes at least one of the above-ground environmental data obtained by observation from the ground: rainfall, temperature, humidity, solar radiation, soil information, vegetation information, surface pavement information, distance from water area, altitude, and slope. It includes.
　取得装置１０２は、対象地の外部環境データを取得する。取得装置１０２は、例えば、外部環境データを計測するセンサもしくは測定装置から構成することができる。また、これらをパーソナルコンピュータに接続することで取得装置１０２とすることができる。また、画像もしくは動画の撮影機能を有する装置とパーソナルコンピュータを通信で接続することで、取得装置１０２とすることができる。取得装置１０２が取得した外部記憶データは、例えば、記憶装置１０６が記憶する。 The acquisition device 102 acquires the external environment data of the target area. The acquisition device 102 can be configured, for example, from a sensor or a measuring device that measures external environment data. Further, by connecting these to a personal computer, the acquisition device 102 can be obtained. Further, the acquisition device 102 can be obtained by connecting a personal computer to a device having an image or moving image shooting function by communication. The external storage data acquired by the acquisition device 102 is stored, for example, by the storage device 106.
　また、移動計測車両による測量システム（モービルマッピングシステム、Mobile Mapping System；ＭＭＳ）やドローンなどから取得装置１０２を構成することができる。画像処理装置１０７は、このようにして取得した対象地の景観を含む画像から、外部環境データを抽出する。画像処理装置１０７が抽出した外部記憶データは、例えば、記憶装置１０６が記憶する。 In addition, the acquisition device 102 can be configured from a surveying system (Mobile Mapping System; MMS) using a mobile measurement vehicle, a drone, or the like. The image processing device 107 extracts external environment data from the image including the landscape of the target area acquired in this way. The external storage data extracted by the image processing device 107 is stored, for example, by the storage device 106.
　推定回路１０３は、取得装置１０２が取得した外部環境データを用いて、構築回路１０１が構築したアルゴリズムにより設備の劣化リスクを推定する。推定回路１０３は、例えば、機械学習や深層学習アルゴリズムの実行環境を備え、上記アルゴリズムによる推定計算を実施するパーソナルコンピュータから構成することができる。 The estimation circuit 103 estimates the risk of deterioration of the equipment by the algorithm constructed by the construction circuit 101 using the external environment data acquired by the acquisition device 102. The estimation circuit 103 can be configured from, for example, a personal computer provided with an execution environment for machine learning and deep learning algorithms and performing estimation calculations by the above algorithms.
　出力装置１０４は、推定回路１０３が推定した劣化リスクを出力する。出力装置１０４は、例えば、パーソナルコンピュータのモニタとすることができる。出力装置１０４は、推定回路１０３が推定した劣化リスクを、対象地の景観を含む画像もしくは動画上に表示する機能を有する。例えば、出力装置１０４は、スマートフォンなどの携帯端末装置や、スマートグラスとすることができる。これらの機器を用いて、対象地の景観を撮影しているときに、推定した劣化リスクを画面上に出力することができる。この場合、推定回路１０３から推定された劣化リスクを通信によって上記機器に送信する機能を備えることで、上述した表示ができる。 The output device 104 outputs the deterioration risk estimated by the estimation circuit 103. The output device 104 can be, for example, a monitor of a personal computer. The output device 104 has a function of displaying the deterioration risk estimated by the estimation circuit 103 on an image or a moving image including a landscape of the target area. For example, the output device 104 can be a mobile terminal device such as a smartphone or a smart glass. Using these devices, it is possible to output the estimated deterioration risk on the screen when shooting the landscape of the target area. In this case, the above-mentioned display can be obtained by providing a function of transmitting the deterioration risk estimated from the estimation circuit 103 to the above-mentioned device by communication.
　次に、本発明の実施の形態に係る劣化リスク推定方法について図２のフローチャートを参照して説明する。 Next, the deterioration risk estimation method according to the embodiment of the present invention will be described with reference to the flowchart of FIG.
　まず、第１ステップＳ１０１で、取得装置１０２が、対象地の外部環境データを取得する。第１ステップＳ１０１では、劣化リスクを推定したい地中設備、もしくはこの地中設備が設置された環境である対象地の外部環境データを取得する。このとき、外部環境データは、降雨、気温、湿度、日射量、土壌情報、植生情報、地表舗装情報、水域からの距離、標高、傾斜のうち少なくとも１つを含んでいればよく、この他の外部環境データとしては、例えば土壌種情報や深さ情報、地下水の情報などの地中環境データも考えられる。 First, in the first step S101, the acquisition device 102 acquires the external environment data of the target area. In the first step S101, the underground equipment for which the deterioration risk is to be estimated, or the external environment data of the target area where the underground equipment is installed is acquired. At this time, the external environmental data may include at least one of rainfall, temperature, humidity, solar radiation, soil information, vegetation information, surface pavement information, distance from water area, altitude, and slope, and other data. As the external environmental data, for example, underground environment data such as soil species information, depth information, and groundwater information can be considered.
　上述した外部環境データは、劣化リスクを推定したい地中設備の劣化に関与する重要な因子であることが分かっており、これら情報を用いることで、高い信頼性で実態にあったリスク推定を行うことが可能となる。 The above-mentioned external environmental data is known to be an important factor involved in the deterioration of underground equipment for which deterioration risk is to be estimated, and by using this information, risk estimation that is highly reliable and realistic is performed. Is possible.
　例えば、外部環境データとして降雨のデータを用いる場合は、対象地の設備が設置された年もしくは他の対象地で用いる場合と同じ年の１年間の平均降雨量や、平均降雨間隔を用いることができる。また、外部環境データとして降雨のデータを用いる場合、１年間の降雨量や降雨間隔を頻度分布にしたデータを用いることができる。気温や湿度、日射量なども同様に、外部環境データとして用いることができ、これらの１年間の平均値や頻度分布を外部環境データとすることができる。降雨、気温、湿度、日射量のデータは、例えば気象庁の公開データ等から取得可能である。 For example, when using rainfall data as external environmental data, it is possible to use the average rainfall amount for one year or the average rainfall interval in the year when the equipment in the target area is installed or in the same year as when used in other target areas. can. Further, when the rainfall data is used as the external environmental data, it is possible to use the data in which the rainfall amount and the rainfall interval in one year are used as the frequency distribution. Similarly, the temperature, humidity, amount of solar radiation, etc. can be used as external environment data, and the average value and frequency distribution for one year can be used as external environment data. Data on rainfall, temperature, humidity, and amount of solar radiation can be obtained from, for example, public data of the Japan Meteorological Agency.
　土壌情報は、地上から確認できる土の色や岩石類の有無の情報がある。土の色からは、土のおよその種類が判別でき、土地の色には、腐食性の有無に有効な情報が含まれている。土の色情報の形態は特に制限しないが、ＲＧＢやＣＭＹＫ、ＬＡＢ、ＬＲＶ、ＨＥＸ値などで表現することができる。また、岩石類は、土壌の水はけに関係し、また設備を敷設する際にできる傷の有無などを示唆する有効な情報である。 Soil information includes information on the color of soil that can be confirmed from the ground and the presence or absence of rocks. From the color of the soil, the approximate type of soil can be determined, and the color of the land contains useful information on the presence or absence of corrosiveness. The form of the soil color information is not particularly limited, but can be expressed by RGB, CMYK, LAB, LRV, HEX value, or the like. In addition, rocks are useful information related to the drainage of soil and suggesting the presence or absence of scratches formed when laying equipment.
　また植生情報としては、草木の有無などの情報がある。植生情報は、地中の酸素濃度や水はけ状況、さらに日射量に関わる重要な因子である。地表舗装情報には、設備が存在する場所の地表をコンクリートやアスファルト等で覆われているかなどの情報がある。例えば、降雨があった場合でも地中環境の含水率変化は小さくなり、地表面からの水の揮発もおきづらくなるため、土壌むき出しの土壌と比べて経時的に安定している。 Also, as vegetation information, there is information such as the presence or absence of vegetation. Vegetation information is an important factor related to the oxygen concentration in the ground, drainage status, and the amount of solar radiation. The surface pavement information includes information such as whether the surface of the place where the equipment is located is covered with concrete, asphalt, or the like. For example, even if there is rainfall, the change in water content in the underground environment becomes small, and the volatilization of water from the ground surface becomes difficult, so it is more stable over time than soil with bare soil.
　また、地表舗装情報には、設備が敷設された場所の地表の何％を舗装が覆っているか、土むき出し部分があるかどうかなどがある。アスファルト舗装されている場所でも、わずかに土壌むき出し部分があると、この箇所から降雨が浸透し水の揮発も生じる環境となるため、これらの情報も推定に有効な情報である。 In addition, the surface pavement information includes what percentage of the surface of the ground where the equipment was laid, whether the pavement covers it, and whether there is an exposed part. Even in a place where asphalt is paved, if there is a slightly exposed part of the soil, rainfall will permeate from this part and water will volatilize, so this information is also useful for estimation.
　水域からの距離は、劣化リスクの推定に有効な因子である。例えば水田や河川、海から近い土地では含水率が高く保持される可能性が高い。標高および傾斜は、降雨によって水が浸透した際に水分がどう流れていくかに影響するため、地中設備の劣化推定に有効因子である。 The distance from the water area is an effective factor in estimating the risk of deterioration. For example, in paddy fields, rivers, and land near the sea, the water content is likely to be maintained high. Elevation and slope are effective factors for estimating the deterioration of underground equipment because they affect how water flows when water infiltrates due to rainfall.
　外部環境データは、対象地の景観を含む画像もしくは動画から抽出することができる。例えば、土壌情報、植生情報や地表舗装情報は、対象地の景観を含む静止画像や動画像などの画像から抽出することが可能である。また、人が画像から目視で情報を取得することで得られた情報を、外部画像データとすることができる。また、機械学習アルゴリズムを用いた物体検出によって、画像から外部環境データを抽出することができる。 External environmental data can be extracted from images or videos that include the landscape of the target area. For example, soil information, vegetation information, and surface pavement information can be extracted from images such as still images and moving images including landscapes of the target area. Further, the information obtained by a person visually acquiring the information from the image can be used as external image data. In addition, external environment data can be extracted from the image by object detection using a machine learning algorithm.
　また、例えば水域からの距離や標高、傾斜も、画像に写る他の基準物から抽出することが可能である。これについても、人が画像から目視で取得した情報や、機械学習アルゴリズムを用いた物体検出によって抽出した情報を、外部環境データとすることができる。前述したように、ＭＭＳやドローンなどから構成した取得装置１０２により画像を取得し、取得した画像より、画像処理装置１０７が抽出した情報を、外部環境データとすることができる。 Also, for example, the distance from the water area, altitude, and inclination can be extracted from other reference objects shown in the image. Also for this, information visually acquired by a person from an image or information extracted by object detection using a machine learning algorithm can be used as external environment data. As described above, an image can be acquired by an acquisition device 102 composed of an MMS, a drone, or the like, and information extracted by the image processing device 107 from the acquired image can be used as external environment data.
　例えば、図３に示すように、劣化リスクを推定したい地中設備３０１が埋設されている対象地３０２を撮影することで得られた画像３０３から、物体検出アルゴリズムによって外部環境データを取得することができる。対象地３０２には、地表３２１に土があり、また、草３２２が繁殖しており、画像３０３より、草があることや、土の色（ＲＧＢ値）が外部環境データとして取得できる。また、画像３０３より、対象地３０２の画像処理では、岩石や舗装などが検出されない状態も、外部環境データとして取得できる。 For example, as shown in FIG. 3, it is possible to acquire external environment data by an object detection algorithm from an image 303 obtained by photographing a target site 302 in which an underground facility 301 whose deterioration risk is to be estimated is buried. can. In the target site 302, there is soil on the ground surface 321 and grass 322 is breeding, and from image 303, the presence of grass and the color of the soil (RGB value) can be acquired as external environmental data. Further, from the image 303, a state in which rocks, pavements, etc. are not detected in the image processing of the target site 302 can be acquired as external environment data.
　次に、第２ステップＳ１０２で、構築回路１０１が、取得した外部環境データと、対象地に埋設された対象となる設備の劣化リスクとの関係を示すアルゴリズムを構築する。このアルゴリズムは、複数の対象地点の各々における外部環境データと、複数の対象地点の各々に設置されている設備の劣化度合との関係性から構築することができる。アルゴリズムの構築に用いる外部環境データに対応する劣化度合は、予め判明しているものである。 Next, in the second step S102, the construction circuit 101 constructs an algorithm showing the relationship between the acquired external environment data and the deterioration risk of the target equipment buried in the target site. This algorithm can be constructed from the relationship between the external environmental data at each of the plurality of target points and the degree of deterioration of the equipment installed at each of the plurality of target points. The degree of deterioration corresponding to the external environment data used for constructing the algorithm is known in advance.
　例えば、人が決定した数理モデルに、外部環境データを入力とし、対応する設備の劣化度合とを出力とするようにフィッティングすることで、上述したアルゴリズムを構築することができる。また、外部環境データに対応する回答が判明している劣化度合となるように、深層学習などの機械学習に学習させることで、これらの関係を示すアルゴリズムを構築することができる。 For example, the above-mentioned algorithm can be constructed by fitting a mathematical model determined by a person so that external environment data is input and the degree of deterioration of the corresponding equipment is output. In addition, it is possible to construct an algorithm showing these relationships by training machine learning such as deep learning so that the degree of deterioration for which the answer corresponding to the external environment data is known is known.
　次に、第３ステップＳ１０３で、新たに対象地の外部環境データを取得する。前述したように、推定回路１０３により、対象地の外部環境データを取得することができる。この取得は、対象とする設備の劣化リスクを推定したい時点で実施することができる。 Next, in the third step S103, the external environment data of the target site is newly acquired. As described above, the estimation circuit 103 can acquire the external environment data of the target area. This acquisition can be carried out at the time when the risk of deterioration of the target equipment is to be estimated.
　次に、第４ステップＳ１０４で、推定回路１０３が、新たに取得した外部環境データを用いて、構築回路１０１が構築してあるアルゴリズムにより、対象地に設置されている対象となる設備の劣化リスクを推定する。上記アルゴリズムに、取得した外部環境データを入力することで出力された解答を、劣化リスクの推定値とすることができる。劣化リスクは、例えばＡ，Ｂ，Ｃ，Ｄのように段階的に分類することができる。また、劣化リスクは、定量的な数値とすることができる。なお、対象とする設備が同一とみなせ、かつ、推定アルゴリズムが既に構築されている対象地の場合、既に構築されているアルゴリズムを用いてリスクを推定することができる。 Next, in the fourth step S104, the estimation circuit 103 uses the newly acquired external environment data, and the risk of deterioration of the target equipment installed at the target site is performed by the algorithm constructed by the construction circuit 101. To estimate. The answer output by inputting the acquired external environment data into the above algorithm can be used as an estimated value of deterioration risk. Deterioration risk can be categorized in stages, for example A, B, C, D. In addition, the deterioration risk can be a quantitative numerical value. If the target equipment can be regarded as the same and the estimation algorithm has already been constructed for the target site, the risk can be estimated using the already constructed algorithm.
　上述したように、リスクを推定した後、推定したリスクを出力する。例えば、表示装置などに利用者に視認可能に表示することで、推定したリスクを出力する。例えば、図４に示すように、劣化リスクを推定したい地中設備３０１が埋設されている対象地３０２を撮影することで得られた画像３０３とともに、劣化リスクを表示することができる。 As mentioned above, after estimating the risk, the estimated risk is output. For example, the estimated risk is output by displaying it on a display device so that the user can see it. For example, as shown in FIG. 4, the deterioration risk can be displayed together with the image 303 obtained by photographing the target area 302 in which the underground equipment 301 for which the deterioration risk is to be estimated is buried.
　以上に説明したように、本発明によれば、対象地の外部環境データと対象地に埋設された対象となる設備の劣化リスクとの関係を示すアルゴリズムを用いて劣化リスクを推定するので、埋設されている設備の劣化リスクについて、実態に合った精度と信頼性で推定できるようになる。 As described above, according to the present invention, the deterioration risk is estimated by using an algorithm showing the relationship between the external environmental data of the target site and the deterioration risk of the target equipment buried in the target site. It will be possible to estimate the deterioration risk of the equipment being installed with the accuracy and reliability that match the actual situation.
　なお、本発明は以上に説明した実施の形態に限定されるものではなく、本発明の技術的思想内で、当分野において通常の知識を有する者により、多くの変形および組み合わせが実施可能であることは明白である。 It should be noted that the present invention is not limited to the embodiments described above, and many modifications and combinations can be carried out by a person having ordinary knowledge in the art within the technical idea of the present invention. That is clear.
　１０１…構築回路、１０２…取得装置、１０３…推定回路、１０４…出力装置、１０５…入力装置、１０６…記憶装置、１０７…画像処理装置。 101 ... construction circuit, 102 ... acquisition device, 103 ... estimation circuit, 104 ... output device, 105 ... input device, 106 ... storage device, 107 ... image processing device.

Claims (5)

  1. 　対象地の外部環境データを取得する第１ステップと、
    　取得した外部環境データと、前記対象地に埋設された対象となる設備の劣化リスクとの関係を示すアルゴリズムを構築する第２ステップと、
    　新たに前記対象地の外部環境データを取得する第３ステップと、
    　新たに取得した外部環境データを用いて前記アルゴリズムにより前記設備の劣化リスクを推定する第４ステップと
    　を備え、
    　外部環境データは、地上からの観測により得られる地上環境データである降雨、気温、湿度、日射量、土壌情報、植生情報、地表舗装情報、水域からの距離、標高、傾斜のうち少なくとも１つを含む
    　ことを特徴とする劣化リスク推定方法。     The first step to acquire the external environment data of the target site,
    The second step of constructing an algorithm showing the relationship between the acquired external environmental data and the deterioration risk of the target equipment buried in the target site,
    The third step of newly acquiring the external environment data of the target area,
    It is equipped with a fourth step of estimating the deterioration risk of the equipment by the algorithm using the newly acquired external environment data.
    External environmental data includes at least one of the above-ground environmental data obtained by observation from the ground: rainfall, temperature, humidity, solar radiation, soil information, vegetation information, surface pavement information, distance from water area, altitude, and slope. Deterioration risk estimation method characterized by inclusion.
  2. 　請求項１記載の劣化リスク推定方法において、
    　外部環境データは、前記対象地の景観を含む画像から抽出されたものであることを特徴とする劣化リスク推定方法。     In the deterioration risk estimation method according to claim 1,
    A deterioration risk estimation method characterized in that the external environment data is extracted from an image including the landscape of the target area.
  3. 　取得した対象地の外部環境データと、前記対象地に埋設された対象となる設備の劣化リスクとの関係を示すアルゴリズムを構築する構築回路と、
    　前記対象地の外部環境データを取得する取得装置と、
    　前記取得装置が取得した外部環境データを用いて前記アルゴリズムにより前記設備の劣化リスクを推定する推定回路と、
    　前記推定回路が推定した劣化リスクを出力する出力装置と
    　を備え、
    　外部環境データは、地上からの観測により得られる地上環境データである降雨、気温、湿度、日射量、土壌情報、植生情報、地表舗装情報、水域からの距離、標高、傾斜のうち少なくとも１つを含む
    　ことを特徴とする劣化リスク推定システム。     A construction circuit that builds an algorithm that shows the relationship between the acquired external environmental data of the target site and the deterioration risk of the target equipment buried in the target site.
    An acquisition device that acquires external environment data of the target area,
    An estimation circuit that estimates the deterioration risk of the equipment by the algorithm using the external environment data acquired by the acquisition device, and an estimation circuit.
    It is equipped with an output device that outputs the deterioration risk estimated by the estimation circuit.
    External environmental data includes at least one of the above-ground environmental data obtained by observation from the ground: rainfall, temperature, humidity, solar radiation, soil information, vegetation information, surface pavement information, distance from water area, altitude, and slope. Deterioration risk estimation system characterized by inclusion.
  4. 　請求項３記載の劣化リスク推定システムにおいて、
    　前記対象地の景観を含む画像から外部環境データを抽出する画像処理装置をさらに備えることを特徴とする劣化リスク推定システム。     In the deterioration risk estimation system according to claim 3,
    A deterioration risk estimation system, further comprising an image processing device that extracts external environmental data from an image including a landscape of the target area.
  5. 　請求項３または４記載の劣化リスク推定システムにおいて、
    　前記出力装置は、前記対象地の景観を含む画像とともに前記推定回路が推定した劣化リスクを表示する
    　ことを特徴とする劣化リスク推定システム。     In the deterioration risk estimation system according to claim 3 or 4.
    The output device is a deterioration risk estimation system characterized by displaying a deterioration risk estimated by the estimation circuit together with an image including a landscape of the target area.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003262580A (en) * 2002-03-08 2003-09-19 Nippon Telegraph & Telephone East Corp Method for diagnosing corrosion of object embedded underground, corrosion diagnosing program, recording medium recording corrosion diagnosing program, and corrosion diagnosing apparatus
JP2017003558A (en) * 2015-06-10 2017-01-05 日本電信電話株式会社 Estimation method
US20170030850A1 (en) * 2014-04-15 2017-02-02 Homero Castaneda-Lopez Methods for evaluation and estimation of external corrosion damage on buried pipelines
JP2020143999A (en) * 2019-03-06 2020-09-10 西日本電信電話株式会社 Steel pipe column deterioration prediction system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003262580A (en) * 2002-03-08 2003-09-19 Nippon Telegraph & Telephone East Corp Method for diagnosing corrosion of object embedded underground, corrosion diagnosing program, recording medium recording corrosion diagnosing program, and corrosion diagnosing apparatus
US20170030850A1 (en) * 2014-04-15 2017-02-02 Homero Castaneda-Lopez Methods for evaluation and estimation of external corrosion damage on buried pipelines
JP2017003558A (en) * 2015-06-10 2017-01-05 日本電信電話株式会社 Estimation method
JP2020143999A (en) * 2019-03-06 2020-09-10 西日本電信電話株式会社 Steel pipe column deterioration prediction system

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