Method for optimizing arrangement of water-level gauge, method for predicting water level in manhole, and system for predicting water level in manhole

Abstract

To predict the water level of a manhole more effectively.SOLUTION: The method for optimizing the arrangement of a water-level gauge includes: a data acquisition step of acquiring water level data acquired by each water-level gauge of a plurality of manholes provided in a predetermined area; a classification step of performing clustering on each piece of water-level data and classifying the manholes into at least one cluster; and a determination step of determining at least one representative manhole according to each cluster from a plurality of manholes included in at least one cluster.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method for optimizing the arrangement of water level gauges, a method for predicting the water level in a manhole, and a system for predicting the water level in a manhole. [Background technology]

[0002] In recent years, there have been a series of incidents of water overflowing from manholes and causing flooding damage due to increased rainfall caused by torrential rains and thunderstorms. Therefore, in areas where flooding is expected, there is a need to measure the fluctuation (especially the rise) of the water level in manholes in real time and to predict flooding. For example, Patent Document 1 discloses a technology for predicting the water level and inflow at pumping stations using information such as the water level in a sewer main line, the water level at pumping stations, and the amount of rainfall. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2002-285634 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology described in Patent Document 1 is only a prediction of the inflow amount to a pumping station, and is premised on using information on the water level in the sewer pipe. Therefore, it is not possible to predict overflow onto the ground. Furthermore, in order to predict overflow onto the ground, it is necessary to monitor the water level in the manhole, which is the space connecting the sewer pipe to the ground. However, since there are many manholes, it is necessary to install many water level gauges to monitor the water level in all the manholes and predict the fluctuation of the water level, which is difficult to realize.

[0005] Therefore, the present invention has been made in consideration of the above problems, and an object of the present invention is to provide a new and improved method for optimizing the placement of water level gauges, a method for predicting the water level in a manhole, and a system for predicting the water level in a manhole, which are capable of more efficiently predicting the water level in a manhole. [Means for solving the problem]

[0006] In order to solve the above problems, according to one aspect of the present invention, there is provided a method for optimizing the placement of water level gauges, the method including a data acquisition step of acquiring each water level data acquired by each water level gauge provided in a plurality of manholes provided in a specified area, a classification step of classifying the plurality of manholes into one or more clusters by clustering each of the water level data, and a determination step of determining one or more representative manholes for each cluster from the plurality of manholes included in the one or more clusters.

[0007] In addition, in order to solve the above problem, according to another aspect of the present invention, there is provided a method for predicting water levels in a manhole, comprising: a past data acquisition step of acquiring past time-series water level data of a manhole located in a specified area and predicted rainfall data for the specified area; and an output step of inputting the acquired past time-series water level data and predicted rainfall data of the manhole into a specified engine, thereby outputting predicted information of the water level of the manhole at a future time point.

[0008] In addition, in order to solve the above problem, according to another aspect of the present invention, there is provided a manhole water level prediction system, comprising: a past data acquisition unit that acquires past time-series water level data of a manhole included in a target area and predicted rainfall data for the specified area, and an output unit that outputs predicted information of the water level of the manhole at a future point in time by inputting the acquired past time-series water level data and predicted rainfall data of the manhole into a specified engine. Effect of the Invention

[0009] As described above, according to the present invention, it is possible to more efficiently predict the water level in a manhole. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is an explanatory diagram for explaining an overview of an information processing system according to an embodiment of the present invention. [Diagram 2] 1 is a block diagram showing an example of a functional configuration of a server 10 according to a first embodiment of the present invention. [Diagram 3] FIG. 2 is a diagram showing an example of clustering processing of manholes 4 by a classification unit 102 according to the present embodiment. [Figure 4] FIG. 4 is a diagram showing an example of a result of a clustering process performed by a classification unit 102 according to the present embodiment. [Diagram 5] FIG. 4 is a diagram showing an example of a result of a clustering process performed by a classification unit 102 according to the present embodiment. [Figure 6] 10 is a flowchart showing an example of the flow of optimization processing for the arrangement of water level gauges by the server 10 according to the present embodiment. [Figure 7] FIG. 11 is a block diagram showing an example of a functional configuration of a server 10 according to a second embodiment of the present invention. [Figure 8] FIG. 2 is a diagram showing an overview of a process for calculating a predicted water level value by a learning engine 112 according to this embodiment. [Figure 9] 10 is a flowchart showing an example of a flow of a prediction process for a water level in a manhole by the server 10 according to the present embodiment. [Figure 10] 1 is a graph for explaining an example of the present embodiment. [Figure 11] FIG. 2 is an explanatory diagram for explaining an example of a hardware configuration of the server 10 according to the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functional configurations are designated by the same reference numerals, and duplicated explanations will be omitted.

[0012] In addition, in this specification and drawings, the manholes will be distinguished as necessary, for example, a representative manhole described below will be referred to as a representative manhole 4A, and other manholes will be referred to as a manhole 4B. However, when there is no need to distinguish between the manholes, each manhole will be simply referred to as a manhole 4.

[0013] One embodiment of the present invention relates to an information processing system capable of efficiently predicting the water level of a manhole. Such a system can be used, for example, to predict the water level of a manhole and predict in advance flood damage caused by overflow of the manhole onto the ground. As such a sewerage rainwater management system, there is a conventional system that combines the weather conditions of the area to be monitored, a short-term rainfall prediction model, and a rainwater runoff analysis model in the monitored area. However, in such a system, the accuracy of the runoff analysis model is not guaranteed due to changes in topography, urban development, construction, etc. In addition, although such a system is only suitable for static analysis such as hazard maps, it is difficult to predict flooding in real time. In order to realize real-time monitoring, a technology for sequentially predicting the water level of a manhole is required.

[0014] However, conventional systems for measuring the water level in a sewer pipe in real time are, for example, devices called smart manholes, in which a water level gauge and a wireless transmission system are integrated in a manhole, and are very expensive. Therefore, it is not realistic to install such systems in all of the many manholes. In the method for optimizing the placement of water level gauges according to one embodiment of the present invention, a clustering method is used to optimize which manholes in a given area should have water level gauges placed in them. A cluster here refers to a cluster in which the temporal changes in water level in multiple manholes included in the cluster are similar. Therefore, the water level of other manholes in the same cluster can be estimated by simple calculation. This makes it possible to cover the expected flooded area with a minimum number of water level gauges (which are expensive because they have a wireless transmission function). In addition, the time-series water level data obtained from the water level gauges whose placement has been optimized by this method and the forecast information on rainfall in the short term ahead can be input into a pre-trained water level prediction engine to output the predicted water level of the manhole. This makes it possible to predict flooding in the expected flooded area with high accuracy using a limited number of water level gauges.

[0015] Fig. 1 is an explanatory diagram for explaining an overview of an information processing system according to this embodiment. The information processing system 1 according to this embodiment has a server 10, for example, as shown in Fig. 1. The information processing system 1 according to this embodiment can be used via, for example, another server 20 or an information terminal 30.

[0016] The server 10, another server 20, and an information terminal 30 are connected to each other via a network.

[0017] The server 10 according to this embodiment is a server for predicting the water level of each manhole 4 in a water level prediction target area 2 including a plurality of manholes 4 within the area. Specifically, the server 10 acquires past water level data and the like of some manholes 4 (representative manhole 4A in the example shown in FIG. 1) among the plurality of manholes 4 provided in a sewer pipe 3 in the water level prediction target area 2, inputs the acquired data to a learning engine, and outputs prediction information on the water level of the manhole 4 output from the learning engine. The server 10 outputs the prediction information to, for example, another server 20 or an information terminal 30. The type of the sewer pipe 3 is not particularly limited, and the sewer pipe 3 may be a storm water pipe, a sewage pipe, or a combined sewer pipe.

[0018] The server 10 may be hardware such as a computer having a hardware configuration described later. The hardware that realizes the server 10 and the manner in which the hardware is provided (for example, whether it is a stationary server or a processor of an edge device) are not particularly limited, and the server 10 may be realized by one or more computers or servers. The same applies to hardware that realizes the other servers 20. Furthermore, the hardware that realizes the information terminal 30 may be a computer or server, or may be realized by any terminal such as a smartphone, a mobile phone, a tablet, a notebook computer, or a game console.

[0019] The other server 20 and the information terminal 30 are examples of information processing devices used to obtain and utilize, for example, prediction information regarding the water level of a manhole. The other server 20 and the information terminal 30 may be owned by, for example, an authority on sewerage in a national or local government, or may be used by general users. Such prediction information may be used, for example, to take measures to prevent flooding in the water level prediction target area 2 or to notify residents of the possibility of flooding.

[0020] As described later, the server 10 may have, as another function or in addition, a function for determining which manholes 4 among the manholes 4 included in the water level prediction target area 2 should have water level gauges installed at all times. When many manholes 4 are provided in the water level prediction target area 2, it may be difficult to install water level gauges in all of them due to cost and equipment constraints. On the other hand, if water level gauges are not installed in appropriate manholes 4, it may be difficult to predict the water level of the manholes 4. Therefore, the server 10 according to one embodiment can optimize which manholes 4 should have limited water level gauges installed among the manholes 4 provided in the water level prediction target area 2. This makes it possible to predict the water level of the manholes 4 with higher accuracy within the constraints on the installation of water level gauges.

[0021] In the following, in the first embodiment, an example of a method for optimizing the arrangement of water level gauges by the server 10 will be described. In the second embodiment, an example of a method for predicting the water level in a manhole by the server 10 will be described. These embodiments may be realized by the same server 10 or different servers.

[0022] Fig. 2 is a block diagram showing an example of a functional configuration of the server 10 according to the first embodiment of the present invention. As shown in Fig. 2, the server 10 according to this embodiment includes a data acquisition unit 101, a classification unit 102, a determination unit 103, and a water level / rainfall DB (database) 104.

[0023] The data acquisition unit 101 has a function of acquiring each water level data acquired by each water level gauge installed in a plurality of manholes provided in a predetermined area. For example, the data acquisition unit 101 may acquire water level data accumulated in the past from the water level / rainfall DB 104 or an external server. The water level data referred to here is time-series water level data. The time-series water level data is water level data acquired continuously or intermittently for several months, for example.

[0024] The water level data can be appropriately acquired from a water level gauge installed in a manhole 4 provided within the water level prediction target area 2. The type of water level gauge is not particularly limited in terms of the manner of installation, and it is sufficient if it can continuously or intermittently acquire the water level in the manhole.

[0025] The data acquiring unit 101 may also acquire data on the amount of precipitation related to the manhole. The data on the amount of precipitation related to the manhole may be, for example, data on the amount of precipitation in the past provided by an organization such as the Japan Meteorological Agency in the area in which the water level prediction target area 2 in which the manhole 4 is located belongs, or may be data on the amount of precipitation based on a water gauge installed in the water level prediction target area 2.

[0026] The classification unit 102 has a function of classifying a plurality of manholes into one or more clusters by performing clustering on each water level data. The classification unit 102 classifies a plurality of manholes 4 into one or more clusters, for example, using a method such as hierarchical clustering. Here, the classification unit 102 determines one or more representative manholes for each cluster based on the approximation by hierarchical clustering. The approximation means, for example, the approximation of the temporal change in the water level. In other words, if there are manholes whose time series changes in the water level are similar to a certain extent, the approximation is determined to be high in the clustering process. The threshold value can be appropriately determined in the clustering process. The clustering method is not particularly limited, but a method capable of evaluating the distance within or outside the cluster is preferable.

[0027] 3 is a diagram showing an example of clustering processing of manholes 4 by the classification unit 102 according to this embodiment. The classification unit 102 performs clustering processing using a technique such as structured clustering for each of the water level data of manholes 4 acquired by the data acquisition unit 101. As a result, for example, the water level prediction target area 2 can be classified into a plurality of clusters 5 (four clusters 5 in the example shown in FIG. 3).

[0028] 4 and 5 are diagrams showing an example of the result of the clustering process by the classification unit 102 according to the present embodiment. For example, when there are 15 manholes in the water level prediction target area 2, it can be seen from the results of FIG. 4 and FIG. 5 that the manholes No. 4, No. 5, and No. 13 form a cluster independent of the other manholes. In addition, as shown in FIG. 5, the other manholes can also be classified into a plurality of clusters. For example, the clusters other than the above three manholes can be classified into a cluster of the manhole No. 9 alone and Cluster A. Furthermore, in Cluster A, the cluster of the manhole No. 6 alone can be classified into Cluster A1 and Cluster A2. The granularity of the clusters can be set appropriately, and for example, as described later, the granularity of the clusters can be determined based on the upper limit of the number of water level gauges that can be installed.

[0029] The determination unit 103 has a function of determining one or more representative manholes for each cluster from among a plurality of manholes included in the one or more clusters. For example, as shown in FIG. 3, the determination unit 103 determines one or more representative manholes 4A for each cluster 5 based on the result of clustering by the classification unit 102.

[0030] For example, as shown in Figures 4 and 5, manholes 4, 5, and 13 may be determined as representative manholes 4A in which water level gauges should be installed. Since manholes 4, 5, and 13 are in a cluster independent of other manholes, it is difficult to estimate the water levels of these manholes from the results of other manholes. Therefore, these manholes are determined as representative manholes 4A to optimize the placement of water level gauges.

[0031] Furthermore, when an upper limit on the number of water level gauges to be placed is set, the determination unit 103 may determine the representative manhole based on the clustering results. As shown in Fig. 5, when the upper limit on the number of water level gauges to be placed is five, for example, the determination unit 103 may determine, as representative manholes, manhole number 9, which is the most isolated from other clusters when viewed from the top of the structure tree, and manhole number 8, which is the most inclusive of adjacent manholes when viewed from the bottom of the structure tree. This makes it possible to realize the most effective placement that reflects the clustering results.

[0032] Similarly, if the upper limit of the number of water level gauges to be placed is seven, in addition to the above five, manhole number 1 in Cluster A1 and manhole number 6 in Cluster A2 may be determined as representative manholes. Since manhole number 1 and manhole number 6 are the most isolated from other manholes, it is preferable to select them in order to further improve accuracy.

[0033] Next, the optimization process of the arrangement of water level gauges by the server 10 according to this embodiment will be described. FIG. 6 is a flow chart showing an example of the flow of the optimization process of the arrangement of water level gauges by the server 10 according to the embodiment. First, in step S101, the data acquisition unit 101 of the server 10 acquires past water level data of a plurality of manholes, etc. Then, in step S103, the classification unit 102 of the server 10 performs a clustering process on the water level data, etc., and classifies the plurality of manholes into one or more clusters. Then, in step S105, the determination unit 103 of the server 10 determines a representative manhole for each of the clustered clusters.

[0034] The first embodiment of the present invention has been described above. According to this embodiment, even if water level gauges are not installed in all manholes provided in the water level prediction target area 2, by installing a water level gauge in at least one manhole among the manholes included in each cluster as a representative manhole, it is possible to obtain sufficient data for the manhole water level prediction described below. This makes it possible to effectively monitor and predict the water level even when there are constraints on costs, installation, etc. In addition, by using precipitation (also called rainfall) data in the clustering process, it is possible to associate changes in water level with precipitation, making it possible to perform optimization that reflects the effects of precipitation.

[0035] Next, a second embodiment of the present invention will be described. Fig. 7 is a block diagram showing an example of a functional configuration of a server 10 according to the second embodiment of the present invention. As shown in Fig. 7, the server 10 according to this embodiment has a data acquisition unit 111, a learning engine 112, an output unit 113, and a water level / rainfall DB (database) 114.

[0036] The data acquisition unit 111 has a function of acquiring past time-series water level data of a manhole provided in a predetermined area (water level prediction target area 2) and predicted rainfall data related to the predetermined area. For example, the data acquisition unit 111 may acquire past time-series water level data of a manhole and rainfall data related to the manhole as data necessary for training the learning engine 112. The rainfall data related to the manhole may be the same as the rainfall data described in the first embodiment. In addition, the data acquisition unit 111 may acquire past time-series water level data of a manhole adjacent to the manhole, as described later. The predicted rainfall data related to the predetermined area may be data acquired from the Japan Meteorological Agency, for example, or may be data appropriately predicted by a rain gauge, etc., appropriately installed in the predetermined area. The time-series water level data and rainfall data may be data acquired every few minutes, for example. These data may be stored in the water level / rainfall DB 114, or may be acquired from an external source as appropriate.

[0037] The learning engine 112 is an engine having a learning model that uses the acquired current water level data of the one manhole, the water levels of adjacent manholes, and future predicted rainfall data as input values ​​and calculates a predicted value of future water level data as an output value. Below, the learning model of the learning engine 112 will be explained separately for training and prediction use.

[0038] The data acquired during training of the learning model of the learning engine 112 may include not only past time-series water level data and past precipitation data, but also, for example, past time-series water level data of a manhole adjacent to a certain manhole. A manhole adjacent to a certain manhole may be, for example, a manhole provided at least on the upstream side or downstream side of the certain manhole in the sewer pipe 3. These data may be, for example, summarized into a variable such as a vector as a composite explanatory variable.

[0039] Then, the past water level data of the actual manhole, the past water level data of the adjacent manhole, the past accumulated precipitation data, and the resulting water level of the manhole from that time to a future time are used as teacher data, and a learning model can be generated by training using, for example, nonlinear multiple regression analysis such as support vector regression or machine learning such as a neural network. Note that such a learning model may be appropriately updated even when prediction is used. For example, such training may utilize water level data and precipitation data acquired by the water level gauge of each manhole in optimizing the arrangement of the water level gauge in the above embodiment.

[0040] A learning engine (Trained Water Level Prediction Engine) based on a learning model trained on these data is used to calculate a predicted value of the water level in the manhole r minutes (r is any positive number) from the present time. FIG. 8 is a diagram showing an overview of the process of calculating a predicted value of the water level by the learning engine 112 according to this embodiment. As shown in FIG. 8, the learning engine 112 uses the past water level data l of the manhole to be predicted. P,tand the historical water level data of nearby manholes Pref,t and the predicted precipitation data after r minutes x t+r Using this, the predicted value of the manhole water level after r minutes, l P,t,r The learning engine 112 may or may not use past precipitation data as an input value. The predicted value of the water level of the manhole may be calculated, for example, every few minutes. The past water level data of the neighboring manholes may be obtained directly by a water level gauge, or may be estimated based on the water level gauge of the representative manhole determined using the technology of the first embodiment and the correlation between the water levels of the neighboring manholes and the representative manhole obtained in the process of the clustering process. Even in the latter case, since it reflects time-series data, the accuracy of the prediction can be improved.

[0041] Furthermore, the learning engine 112 may use past water level data of a manhole to calculate a predicted value of the water level of another manhole different from the first manhole. In this case, for example, the other manhole may be a manhole adjacent to the first manhole. By using the above-mentioned correlation and the predicted value of the water level of a representative manhole where a water level gauge is installed, it is possible to predict the water level of an adjacent manhole where a water level gauge is not installed.

[0042] The output unit 113 has a function of outputting predicted information of the water level of a manhole at a future time point calculated by the learning engine 112. That is, the output unit 113 can output the predicted value of the water level of the manhole described above and information based on the predicted value (e.g., the fact that the water level in the manhole has reached a dangerous water level, the magnitude of the change in the water level, etc.). This output predicted information can be transmitted to the server 20, the information terminal 30, etc., for example.

[0043] Next, the prediction process of the manhole water level by the server 10 according to the present embodiment will be described. FIG. 9 is a flowchart showing an example of the flow of the prediction process of the manhole water level by the server 10 according to the present embodiment. First, in step S111, the data acquisition unit 111 of the server 10 acquires past water level data and predicted rainfall data of multiple manholes. Next, in step S113, the learning engine 112 of the server 10 inputs the acquired data and calculates predicted water level data of the manhole in the future. Then, in step S115, the output unit 113 of the server 10 outputs prediction information of the manhole water level based on the calculated predicted water level data.

[0044] The second embodiment of the present invention has been described above. According to this embodiment, when predicting the water level of a manhole provided in the water level prediction target area 2, it is possible to predict the water level of the manhole included in the water level prediction target area 2 by only using a learning model using the water level data of the manhole and the precipitation data without using the topographical information of the water level prediction target area 2 or the model information on the sewer pipe. Even if not all the manholes included in the water level prediction target area 2 are provided with a water level gauge, it is possible to predict the water level of not only the representative manhole but also the adjacent manholes if there is water level data of the representative manhole identified using the technology described in the first embodiment, for example.

[0045] In the above embodiment, the water level of the manhole is predicted using the predicted rainfall data, but the present technology is not limited to such an example. For example, the learning model of the learning engine 112 can be configured without using the predicted rainfall data, and the learning engine 112 can calculate the predicted value of the water level of the manhole at a future time point using the past water level data of the manhole. This makes it possible to predict the water level when it is not necessary to consider the amount of rainfall. In addition, in the above embodiment, the water level of the manhole is predicted using the water level gauge arranged optimally by the technology according to the first embodiment, but the present technology is not limited to such an example. For example, even if all manholes are provided with water level gauges, the technology according to the present embodiment can be applied.

[0046] In addition, the predicted precipitation data used for predicting the water level may be data obtained from an organization such as the Japan Meteorological Agency, but may also be data of predicted precipitation that has been processed as in the past. Generally, predicted precipitation data obtained from the Japan Meteorological Agency and other organizations that provide weather data is provided at intervals (for example, every 30 minutes), so it may be difficult to predict for a time shorter than the interval. Therefore, for example, as the predicted precipitation data, precipitation data measured after the time corresponding to the predicted precipitation data may be substituted as the predicted precipitation data. For example, in the case where there is predicted precipitation data provided at 15:00 for 20 minutes later (i.e., 15:20), if the precipitation is actually measured at 15:20 and data is obtained, the precipitation data measured at 15:20 may be used as the predicted precipitation data for 15:20 predicted at the stage of 15:00. This enables more accurate prediction of the water level. Furthermore, the data on the predicted precipitation amount actually input to the learning engine 112 may be a net value, or may be data with a positive or negative margin added to the predicted precipitation amount. For example, if the predicted precipitation amount is 30 mm, 45 mm, which is 50% more, and 15 mm, which is 50% less, may be used as input values. This makes it possible to provide water level prediction information that takes volatility into account.

[0047] FIG. 10 is a graph for explaining an example of this embodiment. The solid line graph in FIG. 10 indicates the actual water level, the dashed line indicates the predicted water level of the manhole obtained by inputting data of the predicted rainfall data that is 50% higher than the above-mentioned data into the learning engine 112, and the dashed line indicates the predicted water level of the manhole obtained by inputting data of the predicted rainfall data that is 50% lower than the above-mentioned data into the learning engine 112. As shown in this graph, the predicted rainfall data, the water level data of the manhole, and the water level data of the manhole adjacent to the manhole are acquired at 5-minute intervals, and the predicted water level and the actual measured water level are output at 5-minute intervals. In addition, as described above, the predicted rainfall data is appropriately replaced with the actual rainfall data as appropriate. As shown in the graph in FIG. 10, it can be seen that there is no large deviation between the predicted water level and the actual measured water level, and they are almost the same. In this way, by using the past water level data and predicted rainfall data of the manhole and the above-mentioned learning engine, it is possible to predict the water level of the manhole with high accuracy.

[0048] The information processing described above is realized by cooperation between software and the hardware of the server 10 described below. The hardware configuration described below is also applicable to the server 20 and the information terminal 30.

[0049] 11 is an explanatory diagram for explaining an example of a hardware configuration of the server 10 according to the present embodiment. For example, as shown in FIG 10, the server 10 includes a control unit 11, a memory 12, a storage 13, a transmission / reception unit 14, an input / output unit 15, etc., which are electrically connected to each other via a bus 16.

[0050] The control unit 11 is a computing device that controls the overall operation of the server 10, controls the transmission and reception of data between each element, and performs information processing necessary for application execution and authentication processing. For example, the control unit 11 is a CPU (Central Processing Unit) and / or GPU (Graphics Processing Unit), and executes programs for this system stored in the storage 13 and deployed in the memory 12 to perform various information processing as described above.

[0051] The memory 12 includes a main memory configured with a volatile storage device such as a DRAM (Dynamic Random Access Memory), and an auxiliary memory configured with a non-volatile storage device such as a flash memory, an HDD (Hard Disc Drive), etc. The memory 12 is used as a work area for the control unit 11, and also stores a BIOS (Basic Input / Output System) executed when the server 10 is started, various setting information, and the like.

[0052] The storage 13 stores various programs such as application programs. A database that stores data used in each process may be constructed in the storage 13.

[0053] The transceiver 14 connects the server 10 to a network and / or a blockchain network. The transceiver 14 may include a short-range communication interface such as Bluetooth (registered trademark) and Bluetooth Low Energy (BLE).

[0054] The input / output unit 15 includes information input devices such as a keyboard, a microphone, and a mouse, and output devices such as a display and a speaker.

[0055] A bus 16 is commonly connected to all of the above elements, and transmits, for example, address signals, data signals and various control signals.

[0056] The information processing performed by an external device such as the information processing device 10 may be on-premise or cloud-based. For example, the external device in cloud-based form may provide the above-mentioned functions and processes in the form of SaaS (Software as a Service) or cloud computing.

[0057] In the above embodiment, the information processing device 10 performs various storage and control, but multiple external devices may be used instead of the information processing device 10. That is, various information and programs may be distributed and stored in multiple external devices using block chain technology or the like.

[0058] The above embodiment is not limited to the information processing system 1, and may be an information processing method or an information processing program. The information processing method includes each step of the information processing system 1. The information processing program causes at least one computer to execute each step of the information processing system 1.

[0059] Finally, although various embodiments according to the present disclosure have been described, these are presented as examples and are not intended to limit the scope of the invention. The novel embodiment can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. The embodiments and their modifications are included within the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims.

[0060] The following embodiments are also included in the scope of the present invention. (Item 1) A method for optimizing the arrangement of water level gauges, comprising the steps of: A data acquisition step of acquiring water level data acquired by each of water level gauges provided in a plurality of manholes provided in a predetermined area; A classification step of classifying the plurality of manholes into one or more clusters by performing clustering on each of the water level data; A determination step of determining one or more representative manholes for each cluster from a plurality of manholes included in the one or more clusters; A method for optimizing the placement of water level gauges, including: (Item 2) A method for optimizing the arrangement of the water level gauge according to item 1, In the determination step, the one or more representative manholes are determined for each cluster based on the result of the clustering. How to optimize water level gauge placement. (Item 3) A method for optimizing the arrangement of water level gauges according to item 2, The clustering used in the classification step is hierarchical clustering; In the determining step, the one or more representative manholes are determined for each cluster based on the proximity obtained by the hierarchical clustering. How to optimize water level gauge placement. (Item 4) A method for optimizing the arrangement of water level gauges according to item 2 or 3, comprising: In the determination step, when an upper limit on the number of the water level gauges to be arranged is set, the representative manhole is determined based on a result of the clustering. How to optimize water level gauge placement. (Item 5) A method for optimizing the arrangement of a water level gauge according to any one of items 1 to 4, In the data acquisition step, data on the amount of precipitation relating to the manhole is further acquired, In the classification step, clustering is performed using the precipitation data and each of the water level data. How to optimize water level gauge placement. (Item 6) A method for optimizing the arrangement of a water level gauge according to any one of items 1 to 5, The water level data is time-series water level data. How to optimize water level gauge placement. (Item 7) A method for predicting a water level in a manhole, comprising the steps of: A past data acquisition step of acquiring past time-series water level data of a manhole provided in a predetermined area and predicted rainfall data for the predetermined area; an output step of inputting the acquired past time-series water level data of the one manhole and the predicted rainfall data into a predetermined engine to output predicted information of the water level of the one manhole at a future time point; A method for predicting water levels in a manhole, including: (Item 8) Item 7: A method for predicting a water level in a manhole according to the present invention, In the past data acquisition step, past time-series water level data of another manhole adjacent to the one manhole is further acquired, In the output step, past time-series water level data of the other manholes is input to the predetermined engine. How to predict manhole water levels. (Item 9) Item 7 or 8, a method for predicting a water level in a manhole according to item 7 or 8, The past time-series water level data includes estimated water level data estimated based on water level data acquired by a water level gauge installed in a manhole different from the one manhole. How to predict manhole water levels. (Item 10) A method for predicting a water level in a manhole according to any one of items 7 to 9, The predetermined engine is an engine based on a learning model obtained by learning using at least past time-series water level data of the one manhole and past rainfall data related to the target area. How to predict manhole water levels. (Item 11) Item 11. A method for predicting a water level in a manhole according to item 10, The predetermined engine further learns using past time-series water level data of other manholes adjacent to the one manhole, How to predict manhole water levels. (Item 12) A method for predicting a water level in a manhole according to any one of items 7 to 11, In the output step, prediction information of water levels of other manholes included in the target area at a future time point is output together with or instead of the one manhole. How to predict manhole water levels. (Item 13) A manhole water level prediction system, comprising: A past data acquisition unit that acquires past time-series water level data of one manhole included in a target area and predicted rainfall data for the specified area; An output unit that outputs prediction information of a water level of the first manhole at a future time point by inputting the acquired past time-series water level data of the first manhole and the predicted rainfall data into a predetermined engine; A manhole water level prediction system, including: [Explanation of symbols]

[0061] 10 Server 20 Servers 30 Information terminal 101 Data Acquisition Section 102 Classification Department 103 Decision Section 111 Data Acquisition Section 112 Learning Engine 113 Output section

Claims

1. A method for optimizing the arrangement of water level gauges, comprising the steps of: A data acquisition step of acquiring water level data acquired by each of water level gauges provided in a plurality of manholes provided in a predetermined area; A classification step of classifying the plurality of manholes into one or more clusters by performing clustering on each of the water level data; A determination step of determining one or more representative manholes for each cluster from a plurality of manholes included in the one or more clusters; A method for optimizing the placement of water level gauges, including:

2. 2. The method for optimizing the arrangement of water level indicators according to claim 1, comprising the steps of: In the determination step, the one or more representative manholes are determined for each cluster based on the result of the clustering. How to optimize water level gauge placement.

3. 3. The method for optimizing the arrangement of water level indicators according to claim 2, comprising the steps of: The clustering used in the classification step is hierarchical clustering; In the determining step, the one or more representative manholes are determined for each cluster based on the proximity obtained by the hierarchical clustering. How to optimize water level gauge placement.

4. A method for optimizing the arrangement of water level gauges according to claim 2 or 3, comprising the steps of: In the determination step, when an upper limit on the number of the water level gauges to be arranged is set, the representative manhole is determined based on a result of the clustering. How to optimize water level gauge placement.

5. 2. The method for optimizing the arrangement of water level indicators according to claim 1, comprising the steps of: In the data acquisition step, data on the amount of precipitation relating to the manhole is further acquired, In the classification step, clustering is performed using the precipitation data and each of the water level data. How to optimize water level gauge placement.

6. 2. The method for optimizing the arrangement of water level indicators according to claim 1, comprising the steps of: The water level data is time-series water level data. How to optimize water level gauge placement.

7. A method for predicting a water level in a manhole, comprising the steps of: A past data acquisition step of acquiring past time-series water level data of a manhole provided in a predetermined area and predicted rainfall data for the predetermined area; an output step of inputting the acquired past time-series water level data and predicted rainfall data of the one manhole into a predetermined engine to output predicted information of the water level of the one manhole at a future time point; A method for predicting water levels in a manhole, including:

8. A method for predicting a water level in a manhole according to claim 7, In the past data acquisition step, past time-series water level data of another manhole adjacent to the one manhole is further acquired, In the output step, past time-series water level data of the other manholes is input to the predetermined engine. How to predict manhole water levels.

9. A method for predicting a water level in a manhole according to claim 7 or 8, comprising: The past time-series water level data includes estimated water level data estimated based on water level data acquired by a water level gauge installed in a manhole different from the one manhole. How to predict manhole water levels.

10. A method for predicting a water level in a manhole according to claim 7 or 8, comprising: The predetermined engine is an engine based on a learning model obtained by learning using at least the past time-series water level data of the one manhole and the past rainfall data related to the target area. How to predict manhole water levels.

11. A method for predicting a water level in a manhole according to claim 10, The predetermined engine further learns using past time-series water level data of other manholes adjacent to the one manhole, How to predict manhole water levels.

12. A method for predicting a water level in a manhole according to claim 7 or 8, comprising: In the output step, prediction information of water levels of other manholes included in the target area at a future time point is output together with or instead of the one manhole. How to predict manhole water levels.

13. A manhole water level prediction system, comprising: A past data acquisition unit that acquires past time-series water level data of one manhole included in a target area and predicted rainfall data for the specified area; An output unit that outputs prediction information of the water level of the first manhole at a future time point by inputting the acquired past time series water level data of the first manhole and the predicted rainfall data into a predetermined engine; A manhole water level prediction system, including:

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