Ai earthquake damage prediction system, ai earthquake damage prediction method, and ai earthquake damage prediction program

Abstract

To provide new technique for predicting earthquake damages to a pipe line in the event of an assumed earthquake, by using a mathematical model.SOLUTION: An AI earthquake damage prediction system for predicting earthquake damages to an embedded installation comprising an existing pipe line, includes a storage part and a prediction part. The storage part stores pipe line attribute information and a mathematical model for conducting earthquake damage prediction or parameters of the mathematical model, pipe line attribute information related to a pipe line, and earthquake information including earthquake indices of an assumed earthquake. The mathematical model has conducted learning by utilizing teacher data based on pipe line attribute information, event information including earthquake effect information related to effects of an earthquake on an event, and an earthquake history related to earthquakes having occurred in the past. The prediction part uses the mathematical model to conduct damage prediction of a pipe line when an earthquake occurs at a specific earthquake index, on the basis of the pipe line attribute information and the earthquake information, and outputs the prediction result.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an AI earthquake damage prediction system, an AI earthquake damage prediction method, and an AI earthquake damage prediction program. [Background technology]

[0002] In recent years, as the threat of natural disasters has become more serious, there has been a push to make buried pipelines, such as water pipes, sewer pipes, gas pipes, and fiber optics, earthquake-proof. However, it is difficult to implement earthquake-proofing measures for all pipelines from a cost perspective. Therefore, if it is possible to identify pipelines that are likely to leak or burst in an actual earthquake, it will be possible to make the pipelines earthquake-proof efficiently and effectively. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7288229

[0004] Here, the invention described in Patent Document 1 is an invention for accurately estimating the vulnerability of pipelines even in situations where earthquake indicators are not available, and it is not possible to easily predict damage based on information about earthquakes, such as the seismic intensity of earthquakes that have already occurred or are expected to occur. Summary of the Invention [Problem to be solved by the invention]

[0005] The present invention aims to provide a new technology for predicting earthquake damage to pipelines in the event of a predicted earthquake using a mathematical model. [Means for solving the problem]

[0006] In order to solve the above problems, the present invention provides an AI earthquake damage prediction system for predicting earthquake damage to buried facilities including existing pipelines, comprising: The apparatus includes a storage unit and a prediction unit, The storage unit stores pipeline attribute information, a mathematical model for predicting earthquake damage or parameters of the mathematical model, pipeline attribute information related to the pipeline, and earthquake information including an earthquake index of an expected earthquake; The mathematical model is a model that has been trained using teacher data based on pipeline attribute information, event information including earthquake impact information regarding the impact of earthquakes on events, and earthquake history regarding earthquakes that have occurred in the past, The prediction unit uses the mathematical model to predict damage to a pipeline when an earthquake with a specific earthquake index occurs based on pipeline attribute information and the earthquake information, and outputs the prediction result.

[0007] With this configuration, it is possible to predict damage to pipelines in the event of an expected earthquake by inputting information about the earthquake, including earthquake indicators such as the expected seismic intensity. Even if event information is acquired about events such as leakage caused by influences other than earthquakes, such as corrosion deterioration and electrolytic corrosion leakage, it is possible to predict pipeline deterioration using a model trained from event information that takes into account the influence of earthquakes based on earthquake influence information.

[0008] In a preferred embodiment of the present invention, the event information is information regarding a pipeline on which an event investigation has been carried out and / or information regarding a pipeline on which an event has suddenly occurred.

[0009] With this configuration, it is possible to predict earthquake damage to pipelines using a model that has been trained using event information relating to event investigations such as water leakage investigations and unexpected events.

[0010] In a preferred form of the present invention, the mathematical model is a model that has been trained using training data generated based on event information regarding events that occurred due to the effects of an earthquake identified based on the earthquake impact information.

[0011] With this configuration, earthquake damage prediction can be performed using a model that has undergone highly accurate learning, without using event information regarding events that occurred due to causes other than earthquakes that are identified based on earthquake impact information.

[0012] In a preferred embodiment of the present invention, the AI ​​earthquake damage prediction system further includes a learning unit, The storage unit stores earthquake history regarding earthquakes that have occurred in the past, The data processing unit generates teacher data based on the earthquake history, the pipeline attribute information, and the event information, The learning unit is a model that has been trained using the generated teacher data.

[0013] With this configuration, the AI ​​earthquake damage prediction system can learn based on information about the pipeline, including pipeline attribute information and event information, and information about earthquakes, including earthquake history, and can improve the accuracy of the mathematical model.

[0014] In a preferred embodiment of the present invention, the AI ​​earthquake damage prediction system further includes a reception unit and a display processing unit, The storage unit stores map information including pipeline attribute information to which position information relating to a pipeline position is assigned and position information, The reception unit receives a selection of an earthquake index, The display processing unit displays a map showing the earthquake damage prediction result based on the pipeline position information, the map information, and the earthquake damage prediction result for a specified earthquake index.

[0015] With this configuration, when an earthquake index such as seismic intensity is specified, the results of earthquake damage prediction for the specified earthquake index can be easily confirmed on a map.

[0016] In a preferred embodiment of the present invention, the display processing unit displays a map in which pipelines are filled in based on the earthquake damage prediction result as the map showing the earthquake damage prediction result.

[0017] With this configuration, the pipelines displayed on the map can be filled in based on the results of the earthquake damage prediction, making it easy to visually confirm the results of the earthquake damage prediction.

[0018] In a preferred embodiment of the present invention, the AI ​​earthquake damage prediction system further includes a display processing unit and a reception unit. The storage unit stores pipeline attribute information to which pipeline position information is assigned and map information including a map of a location where the pipeline is laid, The display processing unit processes display of the pipeline on a map based on the map information and the position information of the pipeline, The reception unit receives a designation of a pipeline for which a damage forecast is to be displayed from among pipelines displayed on a map, The display processing unit processes and displays the damage prediction results for a specified pipeline.

[0019] With this configuration, it is possible to easily check the results of earthquake damage prediction for a pipeline by specifying the pipeline displayed on the map. Effect of the Invention

[0020] The present invention can provide a novel technique for predicting earthquake damage to pipelines when a predicted earthquake occurs using a mathematical model. [Brief description of the drawings]

[0021] [Figure 1] Block diagram showing the overall system configuration [Diagram 2] System hardware configuration diagram [Diagram 3] Example of data structure of pipeline attribute information, event information, and earthquake history [Figure 4] Example of data structure of training data [Diagram 5] Screen display example [Figure 6] Screen display example [Figure 7] Processing Flowchart DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] The present invention will be described in more detail below with reference to the accompanying drawings. Although the drawings show preferred embodiments, the present invention can be implemented in different forms and is not limited to the embodiments described in this specification. In this embodiment, the configurations and operations of an earthquake damage prediction device and a learning device are described, but similar effects can be achieved by a method executed by the devices, a computer program, etc. The computer program may be provided as a non-transitory computer-readable recording medium.

[0023] In the embodiments described below, the AI ​​earthquake damage prediction system predicts earthquake damage to pipelines that supply fluids, predicting damage to existing pipelines that supply liquids, such as water supply pipes; however, insofar as damage prediction is concerned, it may also predict earthquake damage to buried facilities and pipelines, such as gas pipes.

[0024] In addition, the AI ​​earthquake damage prediction system will be used by businesses such as local governments that manage pipelines to predict earthquake damage to the pipelines they manage. However, there is no limit to the scope of pipelines for which damage predictions are made, and earthquake damage predictions, as described below, may be made to existing pipelines laid throughout the country.

[0025] <Terminology> The definitions of terms and phrases used in this specification are as follows: Raw data refers to data regarding pipelines stored in a pipeline database (pipe DB) provided in a system for pipeline management such as GIS. Processed data is data generated by transforming raw data obtained from a pipeline database, etc., by adjusting the format, units, and number of digits, or by combining multiple data, and includes prediction data and training data. The prediction data is data input to the earthquake damage prediction device 1 for earthquake damage prediction, and is processed data generated based on pipeline attribute information and earthquake information, which is provisional data related to anticipated earthquakes. Teacher data is data used to learn a mathematical model that predicts earthquake damage, and is processed data that is generated based on pipeline attribute information, event information related to events that have occurred in the pipeline, and earthquake history, which is the history of earthquakes that have occurred in the location where the pipeline is installed.

[0026] For raw data, conversion processing such as replacement, addition, and deletion of information may be performed as necessary to make the raw data usable within the system. For example, the data that has undergone this conversion processing becomes processed data related to the pipeline. In this embodiment, the raw data is data acquired from a pipeline DB, which is a database related to pipelines managed by a business operator and includes pipeline attribute information and event information, and the prediction data that has been preprocessed for use in earthquake damage prediction and the teacher data that has been preprocessed for use in learning the mathematical model are processed data. If processing is not required, the raw data is used as the processed data. In this embodiment, the processed data is used as it is, and the teacher data is processed data on which various conversion processes have been performed on various information including pipeline attribute information and event information for use in learning the mathematical model. In addition, the prediction data is used as the processed data on which various conversion processes have been performed on various information including pipeline attribute information for earthquake damage prediction using the mathematical model. In addition, in a case where conversion is not required for information related to various pipelines including pipeline attribute information and event information, data obtained by combining pipeline attribute information and event information may be used as teacher data. In addition, in a case where conversion is not required for pipeline attribute information and event information, data obtained by combining pipeline attribute information and event information may be used as prediction data.

[0027] The raw data is data related to pipelines, and includes pipeline attribute information indicating the characteristics (attributes) of the pipeline and event information related to events that have occurred in the pipeline. In this embodiment, the pipeline attribute information related to the pipeline and event information related to events such as water leakage that have occurred in the pipeline are linked to the pipeline attribute information by a unique pipeline ID included in the pipeline attribute information. In this embodiment, the AI ​​earthquake damage prediction system 0 predicts earthquake damage to the pipeline using a mathematical model that has been trained based on earthquake information assigned based on the earthquake history related to earthquakes that have occurred in an area managed by a specific business operator, such as an area managed by a specific local government, and the pipeline attribute information related to the pipeline.

[0028] <System configuration> 1 is a block diagram showing a system configuration of an embodiment of the present invention. As shown in FIG. 1, the AI ​​earthquake damage prediction system 0 includes an earthquake damage prediction device 1 and a learning device 2.

[0029] The earthquake damage prediction device 1 is a device for predicting earthquake damage, which is a prediction of damage caused by an anticipated earthquake, and is a device that is installed at the base of a business operator that manages pipelines in a local government or the like.

[0030] The earthquake damage prediction device 1 is a device equipped with functions such as a GIS (Geographic Information System) for a business operator to manage information related to pipelines within its base. The earthquake damage prediction device 1 manages pipeline information including pipeline attribute information and event information for pipelines managed by the business operator in a pipeline database (pipe DB). It is assumed that the pipeline attribute information stored in the pipeline DB or the pipeline information including the pipeline attribute information and event information has one or more pieces of location information indicating the location where the pipeline and event occurred, such as an address, a code such as coordinates or EPSG code, hashed location information such as Geohash or Quadkey, mesh code such as standard area mesh (collectively referred to as location information), etc., are added.

[0031] The learning device 2 is a device that learns a mathematical model for earthquake damage prediction. In this embodiment, the learning device 2 learns the mathematical model based on information about pipelines, including pipeline attribute information and event information, which are managed in the earthquake damage prediction device 1, and earthquake history about earthquakes that have occurred in the past.

[0032] The terminal device 3 may be used for inputting and outputting information, such as transferring raw data such as pipeline information to the earthquake damage prediction device 1. As the terminal device 3, devices such as a personal computer, a smartphone, a tablet terminal, and a personal computer may be used. A plurality of terminal devices 3 may be used, or only one terminal device 3 may be used.

[0033] In this embodiment, data exchange between the earthquake damage prediction device 1, the learning device 2, and the terminal device 3 is performed using portable storage media such as USB memory and CD-ROM in consideration of the risk of the data being handled being leaked. On the other hand, some or all of these businesses may be configured to be able to communicate with the earthquake damage prediction device 1 and the terminal device 3 connected to the learning device 2 via any network such as a VPN (Virtual Private Network) on an IP (Internet Protocol) network (not shown), and data may be exchanged via the network.

[0034] <Hardware configuration> Fig. 2 is a hardware configuration diagram. As shown in Fig. 2(a), the information processing device 10 (earthquake damage prediction device 1, terminal device 3) has a processing unit 101, a storage unit 102, a communication unit 103, an input unit 104, and an output unit 105, and is used to cause each unit and each process to function. Also, as shown in Fig. 2(b), the learning device 2 has a processing unit 201, a storage unit 202, and a communication unit 203, and is used to cause each unit and each process to function.

[0035] One or more computer devices, such as a general-purpose server or a personal computer, can be used as the earthquake damage prediction device 1, the learning device 2, and the terminal device 3. The earthquake damage prediction device 1 and the learning device 2 have functional configurations described below, but part of the functional configurations of the earthquake damage prediction device 1 and the learning device 2 may be placed in another device configured to be able to communicate with the earthquake damage prediction device 1 and the learning device 2.

[0036] Processing unit 101 and processing unit 201 have a processor such as a CPU (Central Processing Unit) capable of executing an instruction set, and execute an OS (Operating System), as well as a learning program (in the case of the learning device 2), an AI earthquake damage prediction program (in the case of the earthquake damage prediction device 1), an earthquake damage prediction device utilization program, or a learning device utilization program (in the case of the terminal device 3).

[0037] The storage unit 102 and the storage unit 202 have a volatile memory such as a random access memory (RAM) capable of storing an instruction set, and a non-volatile recording medium such as a hard disk drive (HDD) or a solid state drive (SSD) capable of recording an OS, etc. The storage unit 102 of the earthquake damage prediction device 1 stores an earthquake damage prediction program including a learning program, and a learned mathematical model for earthquake damage prediction or parameters of the learned mathematical model. The storage unit 202 of the learning device 2 stores a learning program. The storage unit 102 of the terminal device 3 stores a learning device utilization program or an earthquake damage prediction device utilization program, etc. The storage unit 102 of the earthquake damage prediction device 1 and the storage unit 202 of the learning device 2 store various data necessary for earthquake damage prediction or learning of a mathematical model, including pipeline attribute information, event information, earthquake information, earthquake history, and map information.

[0038] The communication unit 103 and the communication unit 203 have an interface for connecting to a network, and execute communication control with the network to communicate with other information processing devices 10 and the learning device 2. Furthermore, when connection to a network is not desirable from the viewpoint of risk management or the like, the information processing device 10 and the learning device 2 may not be provided with the communication unit 103 and the communication unit 203.

[0039] The input unit 104 has an operation input device capable of input processing such as a touch panel or a keyboard, an audio input device capable of audio input such as a microphone, etc. The output unit 105 has a display device capable of display processing such as a display, and an audio output device such as a speaker.

[0040] <Data Structure> Hereinafter, examples of data configurations stored in the storage unit 102 of the earthquake damage prediction device 1 or the storage unit 202 of the learning device 2 in this embodiment will be shown with reference to Figs.

[0041] 3(a) shows an example of pipeline attribute information showing the attributes of a pipeline. The pipeline attribute information is information showing the characteristics (attributes) of a pipeline, and is raw data obtained from a database (pipe DB) of a business entity such as a local government that manages the pipeline, but a part or all of it may be data obtained from an external source such as an external database.

[0042] The pipeline attribute information shown in Figure 3(a) is information about the pipeline, and is data in which feature quantities related to the pipeline, including the installation year indicating the year in which the pipeline was installed, the pipe type which is information about the material of the pipeline such as ductile cast iron pipe (DIP) or ordinary cast iron pipe (CIP), the pipeline diameter, and whether or not a leak investigation has been conducted, indicating the presence or absence of event information such as whether a leak investigation has been conducted, are linked with a unique ID that identifies the pipeline.

[0043] Furthermore, although not shown in FIG. 3(a), in this embodiment, the pipeline attribute information includes environmental information related to the environment, such as the ground and climate, at the position where the pipeline is laid. The ground information included in the environmental information is information related to the ground at the position where the pipeline is laid, and includes the average S-wave velocity (AVS), the surface ground amplification factor, the type of topography, and the geology. The environmental information related to the environment at the location where the pipeline is laid includes information such as the average temperature, average precipitation, and distance from major roads at the laying position. In addition, other information, such as the installation location of the pipeline, may be included as the pipeline attribute information, so long as it is a feature related to deterioration of the pipeline.

[0044] FIG. 3(b) shows an example of event information. The event information is information about events such as water leakage and breakage that have occurred in a pipeline. In this embodiment, the event information includes information about events such as water leakage discovered by an event investigation such as a water leakage investigation conducted by a business operator managing the pipeline, and information about water leakage in a pipeline that has been discovered due to a sudden water leakage accident or the like. In this embodiment, the event information is information about events that occur in a water pipe, such as water leakage, but may also be information about other events such as breakage or bursting of a gas pipe, so long as it is information about events that occur in a pipeline. In addition, in this embodiment, the event investigation is an investigation of water pipes, such as a water leakage investigation conducted on a pipeline managed by a specific business operator, but may also be an investigation of other pipelines, such as a gas pipe. In addition, an event-investigated pipeline is a pipeline for which an event investigation such as a water leakage investigation has already been conducted, and it is known whether an event has occurred or not.

[0045] Furthermore, because event information is information about events that occur in a pipeline, if multiple events occur in one pipeline, multiple pieces of event information are linked. By linking multiple pieces of event information to one pipeline in this way, information about all events that have occurred in each pipeline can be used for learning. Furthermore, the longer a business operator manages a pipeline, the more event information related to the number of event investigations and unexpected events will be accumulated. Therefore, the amount of event information linked to pipeline attribute information will also increase.

[0046] In this embodiment, the event information is registered by specifying a pipeline ID, and is linked to the pipeline attribute information. However, when the pipeline attribute information and the event information each include location information, the event information may be registered without specifying a pipeline ID. In this case, the event information may be registered in a manner linked to the pipeline attribute information of the pipeline existing near the location where the event occurred, based on the pipeline attribute information and the location information included in the event information. When information on a pipeline leak discovered due to a sudden water leakage accident is registered as event information, the event information may be registered without being linked to the pipeline attribute information. In addition, when the event information and the pipeline attribute information are registered without being linked, the data processing unit 12 of the earthquake damage prediction device 1 or the learning device 2 may perform a process of linking the pipeline attribute information and the event information based on the pipeline attribute information and the location information added to the event information.

[0047] The event information shown in FIG. 3(b) includes a water leakage data ID, which is a unique ID for identifying an event, an event occurrence year (water leakage year) related to the year in which the event occurred as timing information, and earthquake impact information related to the presence or absence of the impact of the earthquake. The timing information is information related to the timing of the event occurring in the pipeline. In the data shown in FIG. 3(b), data related to the year in which the event occurred is included as timing information, but the timing information may include the time until the event occurred, obtained from the year in which the pipeline was laid and the year in which the event occurred. In addition, if an event has occurred multiple times in the pipeline, the timing information may include the time from the previous event occurrence to the current event occurrence. In this embodiment, the event information and the timing information are managed in association with the same water leakage data ID. In addition, the event information may include an event type, such as corrosion deterioration, electrolytic corrosion water leakage, and vibration water leakage in the water pipe.

[0048] In addition, causes of leakage discovered by event investigations such as leakage investigations include causes other than earthquakes, such as leakage due to electrolytic corrosion, leakage due to vibration, and corrosion deterioration. The earthquake impact information is information for identifying event information related to an event that clearly occurred due to the influence of an earthquake, such as a leakage accident that occurred immediately after the occurrence of an earthquake or discovered by an event investigation such as a leakage investigation conducted after an earthquake, and is information related to the influence of an earthquake on the occurrence of an event. Therefore, even if an event such as leakage discovered by an event investigation has occurred in a pipeline, if the event has occurred without being affected by the earthquake, such as leakage due to electrolytic corrosion or corrosion deterioration, learning is performed as a pipeline without leakage due to an earthquake. In addition, in this embodiment, the event information including earthquake impact information is information registered by a water utility or the like that discovered the event, such as by conducting a leakage investigation or repairing the pipeline, but may also be information acquired by a sensor or the like arranged in the pipeline or its vicinity.

[0049] In this embodiment, the pipeline attribute information is registered in association with the pipeline attribute information, but if no event such as a water leak has occurred, the pipeline attribute information may not be associated with the pipeline. In this case, the data processing unit 12 performs a process of associating the event information indicating that there is no water leak with the pipeline attribute information.

[0050] FIG. 3(c) shows an example of an earthquake history showing the history of earthquakes that have occurred and have affected the pipeline. In this embodiment, the earthquake history is a history of earthquakes that have occurred in the past within the range managed by a business operator such as a specific local government (for example, a specific city, ward, town, or village), but may also be information on the history of earthquakes that have occurred nationwide and may include location information on the location of the earthquake. In this embodiment, the earthquake history is a history of earthquakes with a predetermined seismic intensity or higher, but may also be a history of earthquakes within a range of scales or magnitudes set based on other earthquake indicators. Since it is difficult to divide earthquake information or earthquake history for each business operator, in this embodiment, one earthquake information and earthquake history as shown in FIG. 3(c) are registered for each business operator. Therefore, all pipelines in each pipeline segment that is within the range managed by a specific business operator such as a local government refer to one earthquake history and earthquake information for that pipeline segment.

[0051] The earthquake history shown in Fig. 3(c) includes an earthquake ID, which is a unique ID indicating an earthquake, an occurrence year, and an earthquake index indicating the magnitude of the earthquake. In this embodiment, the earthquake index is seismic intensity, which is relatively easy to obtain, but one or more of seismic intensity, magnitude, peak ground acceleration, peak ground velocity, SI value (Spectrum Intensity), etc. may be used as the earthquake index. In addition, the earthquake index may be an earthquake index such as seismic intensity obtained from a database such as a public database.

[0052] In this embodiment, the earthquake damage prediction process is performed using earthquake information, which is data on provisional earthquakes that are expected to occur. The earthquake information may have a data structure including the year of occurrence and earthquake index, similar to the earthquake history shown in FIG. 3(c), but when the year for the damage prediction is fixed, such as when the earthquake damage prediction is performed in the year in which the prediction was performed, only earthquake indexes such as seismic intensity may be used. The earthquake damage prediction device 1 predicts earthquake damage to the pipeline using prediction data generated based on the earthquake information, which is provisional data on earthquakes that are expected to occur. The earthquake information used for the earthquake damage prediction may be data on provisional earthquakes input by the operator in the terminal device 3. In addition, the present invention preferably uses earthquake indexes such as seismic intensity and magnitude so that the user can easily recognize it intuitively, but earthquake indexes such as acceleration that can be converted into earthquake indexes that are familiar to users, such as seismic intensity and magnitude, may also be used.

[0053] Fig. 4(a) shows an example of teacher data used for learning processing, which is generated based on the pipeline attribute information, event information, and earthquake history shown in Fig. 3. The data shown in Fig. 4(a) is data related to pipeline damage caused by earthquakes, and is data generated by pre-processing the pipeline attribute information, event information, and earthquake history by the data processing unit 12 described later.

[0054] The teacher data is data used for learning the mathematical model, and in this embodiment, includes the pipeline ID, the year of installation, the number of earthquakes experienced, the experienced seismic intensity, and the presence or absence of earthquake leakage. Although not shown in FIG. 4, the teacher data also includes ground information and environmental information included in the pipeline attribute information. The number of earthquakes experienced is the number of earthquakes that have occurred in the area where the pipeline is installed, which is identified based on the earthquake history. The experienced seismic intensity is the seismic intensity of earthquakes that have occurred up to the time of damage prediction in the area where the pipeline is installed, which is identified based on the earthquake history. In this embodiment, the experienced seismic intensity is the largest seismic intensity of earthquakes experienced by the pipeline, but may include the seismic intensity of all earthquakes experienced by the pipeline. The presence or absence of earthquake leakage is the presence or absence of an event that has occurred in the pipeline due to the influence of an earthquake, which is identified based on the earthquake influence information included in the event information.

[0055] Fig. 4(b) shows an example of prediction data, which is processed data generated based on pipeline attribute information and earthquake information. In this embodiment, the prediction data includes pipeline attribute information such as pipeline ID and installation year, and earthquake information such as the number of experienced earthquakes and earthquake index, and is data with a uniform format. In this embodiment, the number of experienced earthquakes included in the prediction data is the number of earthquakes that the pipeline experienced from installation to the time damage prediction is performed, which is obtained by the data processing unit 12 based on the earthquake history shown in Fig. 3(c) and the installation year included in the pipeline attribute information.

[0056] The map information is information related to a map of a pipeline, and includes image information used when displaying the map and position information related to a position on the map. In this embodiment, the position information is latitude and longitude, but may be coordinate information on the map, etc. In this embodiment, the map information is linked to the pipeline attribute information based on the position information.

[0057] 4(a) and 4(b), in this embodiment, the teacher data and the prediction data include data obtained by processing environmental information at the location where the pipeline is installed, which is included in the pipeline attribute information, and ground information highly related to damage caused by earthquakes. The teacher data and the prediction data include data related to the environment at the location where the pipeline is installed, such as the average temperature, minimum temperature, maximum temperature, average precipitation, traffic volume, building density, and distance from major roads at the installation location. The teacher data and the prediction data also include data related to the ground, such as average S-wave velocity (AVS), surface ground amplification factor, type and geology of the terrain, slope, and elevation.

[0058] <Functional configuration> As shown in Fig. 1, the earthquake damage prediction device 1 includes a reception unit 11, a data processing unit 12, a prediction unit 13, and a display processing unit 14. The learning device 2 includes an acquisition unit 21 and a learning unit 22. This is information processing by software (a program stored transiently or non-transiently in a storage unit or the like) specifically realized by hardware (a processing unit or the like).

[0059] <Functional configuration of Earthquake Damage Prediction Device 1> The reception unit 11 of the earthquake damage prediction device 1 performs processing to receive necessary information from various information processing devices including the terminal device 3. In this embodiment, the reception unit 11 receives earthquake information and earthquake history from the terminal device 3, and receives pipeline information including pipeline attribute information and event information from the pipeline DB. The reception unit 11 may also perform processing to receive pipeline attribute information and event information from the terminal device 3. The reception unit 11 may also perform processing to receive data related to pipelines provided via a portable storage medium such as a USB.

[0060] In addition, the reception unit 11 receives input from the user via the input unit 104, including specification of the pipeline for which earthquake damage prediction is to be performed and the earthquake index expected when performing earthquake damage prediction, and performs processing to store the input in the memory unit 102 of the earthquake damage prediction device 1.

[0061] The data processing unit 12 in the earthquake damage prediction device 1 generates prediction data based on the pipeline attribute information and earthquake information. The prediction data generated by the data processing unit 12 is processed data generated by converting the pipeline attribute information and earthquake information into an easy-to-handle form. In this embodiment, the prediction data is some or all of the attributes stored in the pipeline DB as raw data, and is data that includes pipeline attribute information including the pipeline ID and installation year, and earthquake information including seismic intensity. In addition, the prediction data may include any data related to the pipeline, and in this embodiment, it includes environmental information and ground information included in the pipeline attribute information.

[0062] In this embodiment, the data processing unit 12 generates prediction data based on the pipeline attribute information and earthquake information related to an expected earthquake. In this embodiment, the prediction data is data generated by combining part or all of the pipeline attribute information with part or all of the earthquake information, and is data in which the format, unit, number of digits, etc. have been converted. The data processing unit 12 may also perform conversion processing, such as calculating the time from the laying of the pipeline to the occurrence of an earthquake based on the year of laying and the year of the occurrence of the earthquake, to calculate a value suitable for the format of the prediction data from multiple pieces of information such as the pipeline attribute information, earthquake information, and earthquake history. The data processing unit 12 may also perform processing to calculate the number of earthquakes experienced by the pipeline before the prediction is made based on the year of laying of the pipeline, the earthquake history, and the earthquake information, and set the number of experienced earthquakes to be included in the prediction data.

[0063] The data processing unit 12 performs preprocessing of various information used for earthquake damage prediction, converting raw data into easy-to-handle processed data by converting the format, number of digits, units, etc. in the AI ​​earthquake damage prediction system 0. In this embodiment, the data processing unit 12 is provided in both the earthquake damage prediction device 1 and the learning device 2, but may be provided in either the earthquake damage prediction device 1 or the learning device 2.

[0064] In addition, it is assumed that information related to pipelines, such as pipeline attribute information and event information, may have different formats for each business operator, and some information may be missing. The data processing unit 12 may perform processing such as unifying the data format of the pipeline information, including the pipeline attribute information and event information, so that it is easy to use in the AI ​​earthquake damage prediction system, or complementing the data.

[0065] The prediction unit 13 performs earthquake damage prediction using a mathematical model based on the prediction data generated by the data processing unit 12. In this embodiment, the prediction unit 13 outputs an earthquake damage occurrence rate, which is the probability that an event such as a water leak will occur in the pipeline due to the influence of an earthquake, as a pipeline earthquake damage prediction based on the prediction data generated based on the pipeline attribute information and earthquake information related to an assumed earthquake. The prediction unit 13 also performs pipeline earthquake damage prediction based on earthquake indices related to earthquakes occurring in the pipeline, but may perform multiple earthquake damage predictions for one pipeline based on multiple assumed earthquake indices such as seismic intensity 5 and seismic intensity 6.

[0066] The prediction unit 13 performs a simulation of damage to the pipeline in the event of an earthquake. In this embodiment, the prediction unit 13 predicts earthquake damage to the pipeline based on prediction data generated from earthquake information, which is dummy data related to an earthquake of an expected seismic intensity, and pipeline attribute information related to the pipeline, and stores the obtained prediction result in the memory unit 102 of the earthquake damage prediction device 1 in association with the pipeline attribute information. In this embodiment, the prediction unit 13 calculates the probability of damage to the pipeline in an earthquake of a specific seismic intensity as a prediction result of earthquake damage, but the degree of damage (damage) to the pipeline due to the earthquake may also be used. In addition, the prediction unit 13 may predict damage due to an earthquake that has already occurred based on earthquake information registered based on information related to the earthquake that has occurred, such as an earthquake alert, for pipelines that have experienced an earthquake but have not yet been identified as having leaks.

[0067] In this embodiment, the prediction unit 13 predicts earthquake damage at a specific seismic intensity. In this embodiment, the earthquake index used by the prediction unit 13 is seismic intensity, which is easy to collect data on, but earthquake damage to pipelines may be predicted using other earthquake indexes, such as earthquake acceleration.

[0068] The following describes the mathematical model used to predict earthquake damage to a pipeline in the prediction unit 13. The mathematical model used in the prediction unit 13 is a trained model that predicts earthquake damage to a pipeline based on prediction data generated based on pipeline attribute information about the pipeline and event information about damage caused by an earthquake that has occurred to the pipeline.

[0069] In this embodiment, the mathematical model is a model that uses a gradient boosting technique, but any mathematical model formed by a machine learning technique may be used, such as gradient descent, boosting, decision tree, neural network, logistic regression, k-nearest neighbor method, or a technique that combines multiple techniques. The prediction unit 13 may predict earthquake damage to a pipeline by using a model that combines multiple techniques or multiple models that are each formed by a different technique.

[0070] The display processing unit 14 performs display processing of various necessary information including the prediction results by the prediction unit 13. In this embodiment, various screens that are display processed by the display processing unit 14 are displayed on the earthquake damage prediction device 1, but may be displayed on other devices such as the terminal device 3. Below, examples of screens that are display processed by the display processing unit 14 are shown using the drawings.

[0071] FIG. 5 shows an example of a pipeline information display screen that displays information on a pipeline selected by a user. The pipeline information display screen is a screen that is displayed by pressing and selecting a pipeline displayed on a map, and displays information on the pipeline including pipeline attribute information and the results of earthquake damage prediction. When a pipeline on the map is pressed, a pipeline associated with the pipeline is selected based on the position information at the position, and the reception unit 11 receives the selection of the pipeline on the map. In this embodiment, the pipeline information display screen displays, as information on the selected pipeline, pipeline attribute information including the pipeline category, installation year, pipe type, and diameter, and the results of earthquake damage prediction including the probability of leakage of the pipeline at each seismic intensity. The pipeline information display screen as shown in FIG. 5 may be displayed simultaneously with a map related to the pipeline, or may be displayed on a different screen.

[0072] FIG. 6 also shows a prediction result map display screen, which is a map that displays the results of earthquake damage prediction at a specific seismic intensity. The prediction result map display screen is a screen that displays the results of earthquake damage prediction, and shows the occurrence probability of an event due to the influence of an earthquake, which is the earthquake damage prediction result when an earthquake with a predetermined earthquake index (in the example shown in FIG. 6, seismic intensity 5+) occurs in each pipeline. In the example shown in FIG. 6, the pipelines on the map are colored with different chromatic colors for each occurrence probability. In this embodiment, the pipelines displayed on the map are colored with different chromatic colors based on the occurrence probability of each event in the pipeline, but as long as the method shows the occurrence probability of each event in the pipeline, the display may be performed in a configuration different from the example shown in FIG. 6, such as filling the pipelines with different shades of color or different patterns based on the occurrence probability of the event. In addition, the pipeline information display screen shown in FIG. 5 may be displayed by pressing and selecting a pipeline displayed on the prediction result map display screen.

[0073] Furthermore, in this embodiment, the earthquake damage prediction device 1 includes a classification unit. In this embodiment, the classification unit performs classification based on the estimated value, which is the provisional earthquake damage prediction result obtained by the prediction unit 13, and the pipeline attribute information, so that pipelines having similar pipeline attribute information and estimated values ​​are classified into the same classification.

[0074] When the earthquake damage prediction device 1 includes a classification unit, the prediction unit 13 performs earthquake damage prediction using the pipeline attribute information and a mathematical model, and obtains an estimated value that is a provisional prediction result. The estimated value is a provisional prediction result based on the pipeline attribute information, and in this embodiment, it is the probability of earthquake damage occurrence, but it may be the time from earthquake damage to the pipeline being damaged, etc. The classification unit performs classification based on the estimated value obtained by the prediction unit 13 and the pipeline attribute information. The prediction unit 13 determines a final prediction result based on the estimated value and the classification result by the classification unit. In this embodiment, the prediction unit 13 determines a final earthquake damage prediction result by weighting the estimated value of the pipeline for which earthquake damage prediction is performed and the estimated value of the pipeline classified in the same classification as the pipeline, based on the classification result.

[0075] <Functional configuration of learning device 2> The following describes the functional configuration of the learning device 2 shown in Fig. 1. The learning device 2 includes an acquisition unit 21, a data processing unit 12, and a learning unit 22.

[0076] The acquisition unit 21 performs a process of acquiring various information necessary for earthquake damage prediction, including pipeline attribute information and event information. The acquisition unit 21 also acquires various information related to pipelines, including pipeline attribute information and event information managed in the earthquake damage prediction device 1, but if it is undesirable for the earthquake damage prediction device 1 and the learning device 2 to be connected to a network from a security standpoint, the acquisition unit 21 may also perform a process of acquiring data transferred via a portable storage medium such as a USB. If the acquired data is not suitable for use as is, the acquisition unit 21 may also perform a process of adjusting the format of the acquired data as preprocessing.

[0077] In addition, the acquisition unit 21 may perform processing to acquire information necessary for earthquake damage prediction directly from external systems such as a weather information providing system or a map information providing system that is communicatively connected via a network, etc., using various methods such as using an API.

[0078] The acquisition unit 21 converts data acquired from an external system, such as an external system, into a format that can be used by the system to be used in learning the mathematical model, and stores the converted data in the storage unit 102. The conversion process in the acquisition unit 21 is a process of converting data acquired from an external system into a predetermined number of digits or units, or converting the data into a format that can be handled by the earthquake damage prediction device 1 or the learning device 2. The acquisition unit 21 collects external data by transmitting location information to an API, such as a web API (Application Programming Interface) provided by the external system, and performs a predetermined conversion. At that time, the acquisition unit 21 performs a process of linking the acquired data to pipeline attribute information or event information, but may also specify a pipeline to be linked based on location information linked to the pipeline attribute information or event information, and perform the linking process. The reception unit 11 of the earthquake damage prediction device 1 may perform the same process as the acquisition unit 21, such as a process of acquiring external data.

[0079] The data processing unit 12 in the learning device 2 generates teacher data used for learning a mathematical model based on the pipeline attribute information, event information, and earthquake history.

[0080] In this embodiment, the data processing unit 12 generates the teacher data shown in Fig. 4 based on the pipeline attribute information, event information, and earthquake history shown in Fig. 3. The data processing unit 12 generates the teacher data based on raw data including the acquired pipeline attribute information and event information, and the earthquake history. The data processing unit 12 generates the teacher data by changing the format, unit, number of digits, etc. of the raw data, but it may also perform processing to obtain data with units and number of digits suitable for learning from multiple data, such as calculating the number of earthquakes experienced by the pipeline until earthquake damage prediction based on the year of installation and the year of occurrence of earthquakes included in the earthquake history, and using this as the number of experienced earthquakes included in the teacher data.

[0081] In this embodiment, the data processing unit 12 acquires pipeline attribute information and event information related to pipelines in an area managed by a specific business operator (such as a specific city), and generates teacher data based on the earthquake history that has occurred in the area managed by the specific business operator. Furthermore, when the data processing unit 12 determines that an earthquake has influenced the occurrence of an event based on the earthquake impact information, it identifies the earthquake that influenced the occurrence of the event based on the timing information (the year in which the event occurred) and the year in which the earthquake occurred included in the earthquake history, and uses this to generate teacher data.

[0082] The learning unit 22 learns a mathematical model for earthquake damage prediction using a combination of pipeline attribute information, event information, and earthquake history as teacher data. In this embodiment, the learning unit 22 performs learning using teacher data having the data configuration shown in Fig. 4. The learning unit 22 uses processed data preprocessed by the data processing unit 12 as teacher data to learn the mathematical model.

[0083] In this embodiment, the learning unit 22 performs learning using teacher data generated based on pipeline attribute information and event information on pipelines managed by the operator, and earthquake history on earthquakes that occurred within the operator's management range. However, the learning unit 22 may perform learning using teacher data generated based on pipeline attribute information, event information, and earthquake history of each operator or nationwide earthquake history of multiple operators. In this embodiment, the learning unit 22 of the learning device 2 installed at the administrator's base performs learning based on processed data acquired from the base of each operator, but may perform learning using processed data generated by processing raw data acquired from the base of each operator. In addition, the learning unit 22 may generate a national model that is a mathematical model that is trained based on teacher data generated based on data on pipelines nationwide. In addition, the learning unit 22 may further perform learning of the national model again based on teacher data generated based on pipeline attribute information, event information, and earthquake history managed by each operator, and generate a base model that is a mathematical model suitable for each operator. In addition, the learning unit 22 may perform re-learning on the national model that has been trained once, using data acquired from the previous learning to the current learning. The method of generating a nationwide model is not limited to the above, and the learning unit 22 may perform a process of integrating multiple mathematical models obtained by combining parameters of the mathematical models, etc., to generate a nationwide model.

[0084] <Processing flow> An example of the process flow for earthquake damage prediction and learning of a mathematical model will be shown below with reference to the drawings. The process flow explained with reference to FIG. 7 is only an example, and the process may be performed in a different order from the process described below.

[0085] <Earthquake damage prediction processing flow> Fig. 7(a) shows a flow of processing for earthquake damage prediction. When the reception unit 11 receives earthquake information on an expected earthquake (S101), the data processing unit 12 generates prediction data based on the acquired earthquake information and pipeline attribute information (S102). The prediction unit 13 uses the prediction data generated by the data processing unit 12 and a mathematical model to perform earthquake damage prediction (S103).

[0086] <Learning process flow> The data processing unit 12 generates training data, which is data for learning, based on necessary information including pipeline attribute information, event information, and earthquake history (S201). In this embodiment, the data processing unit 12 generates training data shown in Fig. 4 based on the pipeline attribute information, event information, and earthquake history shown in Fig. 3.

[0087] In the above embodiment, an example in which the earthquake damage prediction device 1 and the learning device 2 are installed at the base of each business establishment has been described, but the learning device 2 may be installed only at the base of the manager of the AI ​​earthquake damage prediction system 0, and the mathematical model trained at the manager's base may be distributed to each business operator. In this case, the learning device 2 installed at the manager's base may obtain necessary data including pipeline information and earthquake history from the business operator's base and train the mathematical model.

[0088] In this embodiment, the AI ​​earthquake damage prediction system 0 displays the predicted earthquake damage prediction results for the pipeline on the screen, but it may also output them as files in various formats, such as CSV (comma separated values) or Excel. [Explanation of symbols]

[0089] 0 AI earthquake damage prediction system 1 Earthquake damage prediction device 2 Learning device 3 Terminal Equipment

Claims

1. An AI earthquake damage prediction system for predicting earthquake damage to buried facilities including existing pipelines, The apparatus includes a storage unit and a prediction unit, The storage unit stores pipeline attribute information, a mathematical model for predicting earthquake damage or parameters of the mathematical model, pipeline attribute information related to the pipeline, and earthquake information including an earthquake index of an expected earthquake; The mathematical model is a model that has been trained using teacher data based on pipeline attribute information, event information including earthquake impact information regarding the impact of earthquakes on events, and earthquake history regarding earthquakes that have occurred in the past, The prediction unit uses the mathematical model to predict damage to the pipeline when an earthquake with a specific earthquake index occurs based on the pipeline attribute information and the earthquake information, and outputs a prediction result. AI earthquake damage prediction system.

2. The event information is information about a pipeline that has undergone an event investigation and / or a pipeline where an event has suddenly occurred. The AI ​​earthquake damage prediction system according to claim 1.

3. The mathematical model is a model that is trained using teacher data generated based on event information regarding events that have occurred due to the effects of the earthquake identified based on the earthquake effect information. The AI ​​earthquake damage prediction system according to claim 1.

4. The AI ​​earthquake damage prediction system further includes a learning unit, The storage unit stores earthquake history regarding earthquakes that have occurred in the past, The data processing unit generates teacher data based on the earthquake history, the pipeline attribute information, and the event information, The learning unit is a model that has been learned using the generated teacher data. The AI ​​earthquake damage prediction system according to claim 1.

5. The AI ​​earthquake damage prediction system further includes a reception unit and a display processing unit, The storage unit stores map information including pipeline attribute information to which position information relating to a pipeline position is assigned and position information, The reception unit receives a selection of an earthquake index, The display processing unit displays a map showing the earthquake damage prediction result based on the pipeline position information, the map information, and the earthquake damage prediction result for the specified earthquake index. The AI ​​earthquake damage prediction system according to claim 1.

6. The display processing unit displays a map in which pipelines are filled in based on the earthquake damage prediction result as a map showing the earthquake damage prediction result. The AI ​​earthquake damage prediction system according to claim 5.

7. The AI ​​earthquake damage prediction system further includes a display processing unit and a reception unit, The storage unit stores pipeline attribute information to which pipeline position information is assigned and map information including a map of a location where the pipeline is laid, The display processing unit processes display of the pipeline on a map based on the map information and the position information of the pipeline, The reception unit receives a designation of a pipeline for which a damage forecast is to be displayed from among pipelines displayed on a map, The display processing unit displays the damage prediction result of the specified pipeline. The AI ​​earthquake damage prediction system according to claim 1.

8. An AI earthquake damage prediction method for predicting earthquake damage to buried facilities including existing pipelines, comprising: A prediction process is provided in which one or more computers store pipeline attribute information and a mathematical model for predicting earthquake damage or parameters of the mathematical model, pipeline attribute information related to the pipeline, and earthquake information including an earthquake index of an expected earthquake in a storage unit, and the computer uses the mathematical model to predict damage to the pipeline when an earthquake with a specific earthquake index occurs based on the pipeline attribute information and the earthquake information, and outputs the prediction result; The mathematical model is an AI earthquake damage prediction method that is a model that has been trained using pipeline attribute information, event information including earthquake impact information regarding the impact of earthquakes on events, and teacher data based on earthquake history regarding earthquakes that have occurred in the past.

9. An AI earthquake damage prediction program for predicting earthquake damage to buried facilities including existing pipelines, A computer that stores pipeline attribute information and a mathematical model for predicting earthquake damage or parameters of the mathematical model in a storage unit, pipeline attribute information related to the pipeline, and earthquake information including an earthquake index of an expected earthquake in a storage unit is made to function as a prediction unit, The mathematical model is a model that has been trained using teacher data based on pipeline attribute information, event information including earthquake impact information regarding the impact of earthquakes on events, and earthquake history regarding earthquakes that have occurred in the past, The prediction unit uses the mathematical model to predict damage to the pipeline when an earthquake with a specific earthquake index occurs based on the pipeline attribute information and the earthquake information, and outputs a prediction result. AI earthquake damage prediction program.

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