MODELING DEVICE

A modeling device models observation data and includes a database unit that stores a mathematical model based on environment data obtained from the observation data, a statistical model based on a parameter, and region information including each point at which the observation data is obtained; a data reading unit that acquires the observation data and the environment data; and a modeling processing unit that applies the mathematical model or the statistical model. The modeling processing unit calculates a model output by applying the acquired environment data to the mathematical model at each point included in the region information. When a difference between the calculated model output and the acquired observation data is determined to be equal to or greater than a preset threshold, the modeling processing unit calculates a parameter from the acquired observation data and applies the statistical model using the calculated parameter.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a modeling device.

2. Description of Related Art

As a background technology of the invention, there is the technology disclosed in JP-A-2004-86896. According to JP-A-2004-86896, “to provide an adaptive prediction model construction method or an adaptive prediction model construction system capable of efficiently constructing a model using time-series data with high precision,” “when an error of a prediction value output from a prediction model increases, that is, a feature of time-series data learned by the prediction model is different from a feature of time-series data at a prediction time point, a model is updated by relearning the prediction model. When the error increases more, that is, it is necessary to reexamine the prediction model on the whole, a model configuration is also changed to reexamine the prediction model on the whole. Therefore, the adaptive prediction model construction method or the adaptive prediction model construction system capable of efficiently constructing a prediction model with high prediction precision is provided.”

In modeling of a system in the real world, there are a method of using a mathematical model based on physical or mathematical principles and a method of using a statistical model by learning. In the method of using a mathematical model, there are the advantages that constraints such as absolute constraints which are necessarily obeyed can be expressed or a relational expression or the like established between parameters can be expressed. By defining such constraints difficult in learning, it is easy to model a system and predict a behavior of a model. Therefore, it is easy to handle the model.

In the method of using a statistical model, on the other hand, there are the advantages that previously unclear constraints or constraints difficult to input because of being very detailed can be input often. As disclosed in JP-A-2004-86896, there is the advantage that a prediction model which is a statistical model is easily adapted to a recent situation by learning.

In an actual system, there are both a portion in which a parameter or a behavior is determined to be explicit and a portion known to be implicit. A mathematical model is suitable for the former modeling and a statistical model is suitable for the latter modeling. However, in the technology disclosed in JP-A-2004-86896, handling a statistical model is described and the description of the handling of the mathematical model is not found.

SUMMARY OF INVENTION

Accordingly, the invention provides a technology for facilitating prediction of a behavior of a model using a mathematical model and a statistical model together.

To resolve the foregoing problems, for example, configurations described in the claims are adopted. The present specification includes a plurality of solutions to resolve the foregoing problems, for example, a modeling device for modeling observation data and includes a database unit that stores a mathematical model based on environment data obtained from the observation data, a statistical model based on a parameter, and region information including each point at which the observation data is obtained; a data reading unit that acquires the observation data and the environment data; and a modeling processing unit that applies the mathematical model or the statistical model. The modeling processing unit calculates a model output by applying the acquired environment data to the mathematical model at each point included in the region information. When a difference between the calculated model output and the acquired observation data is determined to be equal to or greater than a preset threshold, the modeling processing unit calculates a parameter from the acquired observation data and applies the statistical model using the calculated parameter.

According to the invention, it is possible to use a mathematical model and a statistical model together and it is possible to facilitate prediction of a behavior of a model.

DESCRIPTION OF EMBODIMENTS

Modes for carrying out the invention will be described with reference to the following drawings.

1. System Configuration

First, a configuration of a system will be described with reference toFIG. 1.FIG. 1is a diagram illustrating an example of a configuration of a system. InFIG. 1, a modeling device101is a device that constructs a model of a modeling target and performs a simulation using the constructed model, and includes a data reading unit102, a modeling processing unit103, a prediction processing unit104, a modeling correction processing unit105, and a database106. Here, the modeling target is, for example, observation data of a sensor.

The data reading unit102is a processing unit that performs a process of receiving data provided by an external data provider109or an internal data provider108as an input. The modeling processing unit103is a processing unit that partitions a modeling target region and performs mathematical modeling and statistical modeling. The prediction processing unit104is a processing unit that performs a prediction process. The modeling correction processing unit105is a modeling correction processing unit that corrects modeling while performing a prediction process.

The database106is a processing unit that retains the data and provides the data to each processing unit. As the data managed by the database106, there are external data110, internal data111, statistical model information112, mathematical model information113, and region partition information114. A network107is a communication medium that connects the modeling device101to devices such as the internal data provider108and the external data provider109and is local area network (LAN) or the Internet.

The internal data provider108is a device that provides internal data online. Here, internal data is data for which data serving as a parameter can be observed much in addition to modeling target data. In general, the sensor is installed independently. When much sensor data or the like can be acquired in addition to sensor data which is the modeling target data, the data becomes internal data.

The external data provider109is a device that provides external data online. Here, external data is data for which only data serving as parameter can be acquired in addition to the modeling target data. In general, external data is data of other companies, and parameter data may not be opened and acquired except for the modeling target data in some cases.

The external data110is information which is acquired from the external data provider109and is managed as one piece of data managed by the database106. The internal data111is data which is acquired from the internal data provider108and is managed as one piece of data managed by the database106.

The statistical model information112is information such as mathematical expression that expresses statistical model and is managed as one piece of data managed by the database106. The mathematical model information113is information such as mathematical expression expressing mathematical model and is managed as one piece of data managed by the database106. The region partition information114is information regarding partition of the modeling target region and is managed as one piece of data managed by the database106.

In the embodiment, further, an example will be described assuming that the modeling device101models a production quantity of oil, the internal data provider108provides information regarding a well or a production quantity, and the external data provider109provides only a production quantity and a position of the well.

The embodiment is not limited to only the production quantity of oil, but a production quantity of iron or the like may be used. When a production quantity of iron is modeled, it may be assumed that the internal data provider108provides temperature or the like of melt iron and the external data provider109provides environment information such as temperature.

Each unit of the modeling device101may be configured with dedicated hardware or may be configured by causing a central processing unit (CPU) to execute a program equivalent to each unit. A part of the modeling device101may be configured with dedicated hardware and other parts may be configured by causing the CPU to execute a program. Hereinafter, an example in which the modeling device101is configured by causing the CPU to execute a program equivalent to each unit will be described.

FIG. 2is a diagram illustrating an example of a hardware configuration of the modeling device101and a diagram illustrating a configuration of a general personal computer (PC). The internal data provider108and the external data provider109may also be configured similarly. InFIG. 2, a CPU201is a central processing unit and is a device that executes a program recorded in a memory202or transmitted from a hard disk208to the memory202in advance.

The program may be introduced by a storage medium which can be accessed by the modeling device101and can be detachably mounted, as necessary. In this case, a device that reads the storage medium is connected to an interface203. As the storage medium and the device reading the storage medium, an optical disc (a CD, a DVD, a Blu-ray disc: registered trademark) and a drive therefor, a flash memory and a read and writer therefor are generally known and may be used.

The program may be introduced to the modeling device101via a communication medium (a communication line or a carrier wave on a communication line) and may be stored in the hard disk208, as necessary, by a network interface204. The memory202temporarily records a program or data.

The interface203is an interface that enables each unit to communicate with each other in the modeling device101and each unit in the modeling device101is connected. The network interface204is a device that communicates with a PC or the like, other than the modeling device101. In the embodiment, the network interface204is connected to the network107.

A keyboard205is a device that is operated by an operator of the modeling device101to input an instruction or data to the modeling device101. A display206is a device that displays a processing result or the like on a screen. A mouse207is a device that designates a position on a screen of the display206and delivers any action to the CPU201by allowing the operator to move a pointer displayed on the screen of the display206and press a button at any location.

The display206and the mouse207or the like may be replaced with a touch panel. In this case, the pointer is not necessary. The hard disk208is a device that stores a program and data and includes, for example, a magnetic disk or a nonvolatile memory. In this case, the program and the data stored in the hard disk208is normally retained even when the hard disk208is turned off and subsequently turned on.

The hard disk208may store an operating system (OS) in advance. In this way, for example, a program can be designated using a file name. Here, the OS is basic software of a computer and an OS which is generally widely known may be used.

The stored program may include programs equivalent to the data reading unit102, the modeling processing unit103, the prediction processing unit104, the modeling correction processing unit105, and the database106. The program equivalent to the database106may be a database program which is generally widely used.

2. Data Structure

Hereinafter, a data structure will be described with reference toFIGS. 3 to 7. First, an example of the data structure of the external data110according to the embodiment will be described with reference toFIG. 3. An external data ID301illustrated inFIG. 3is a number of uniquely specifying external data, an oil well position302is a coordinate position in an index of an oil well, and a production quantity (p)303is a production quantity of oil.

For example, the external data ID301which is “1” indicates that the oil well position302is “(100, 100)” and the production quantity (p)303is “1234.” The external data110is intended data which is rarely acquired in addition to a modeling target value in the system. In the example ofFIG. 3, a value of the production quantity (p)303indicates an observation value by a sensor, but is a modeling target value which should be obtained by prediction, and indicates a situation in which only information of the oil well position302can be used in the modeling of the value.

Next, an example of a data structure of the internal data11according to the embodiment will be described with reference toFIG. 4. An internal data ID401illustrated inFIG. 4is a number for uniquely specifying internal data, an oil well position402is a coordinate position in the index of an oil well, an organic matter content (TOC)403is a content of organic material in strata, a well length (WL)404is the length of a well, and a production quantity (p)405is a production quantity of oil.

For example, the internal data ID401which is 1 indicates that the oil well position402is (300, 300), the organic matter content (TOC)403is 345, the well length (WL)404is 2000, and the production quantity (p)405is 3456.

The internal data111is intended data for which a parameter of the modeling target value can be acquired in addition to the modeling target value. In the example ofFIG. 4, the production quantity (p)405indicates an observation value by a sensor, but is a modeling target value which should be obtained by prediction, and indicates that the organic matter content (TOC)403or the well length WL404can be used in the modeling of the value in addition to the oil well position402. The organic matter content (TOC)403and the well length WL404can be said to be information regarding an environment in which the production quantity (p)405can be obtained.

Next, an example of a data structure of the statistical model information112according to the embodiment will be described with reference toFIG. 5. A statistical model information ID501illustrated inFIG. 5is a number for uniquely specifying statistical model information, a statistical model502is an expression of the statistical model, and a statistical model parameter503is a parameter in the statistical model.

For example, the statistical model information ID501which is S1 indicates that the statistical model502is p(x, y)=ax+by and the statistical model parameter503is (a, b)=(6.1, 6.2). Here, p indicates a production quantity and corresponds to the production quantity (p)303and the production quantity (p)405, and x and y indicate a coordinate position in the index of an oil well and correspond to the oil well positions302and402.

For the statistical model information, a statistical model parameter is determined by machine learning. For example, the statistical model information ID501which is S1 indicates that the statistical model is expressed as a linear sum of x and y. In particular, values of a and b are 6.1 and 6.2 as a result of the machine learning and the statistical model parameter.

As the statistical model, additionally, for example, the statistical model information ID501which is S3 indicates that p(x, y)=exp(ax+by+c) and is expressed as an exponential function. In addition, a logarithmic function, a trigonometric function, a quadratic or more polynomial, or the like may be used.

Next, an example of a data structure of the mathematical model information113according to the embodiment will be described with reference toFIG. 6. A mathematical model information ID601illustrated inFIG. 6is a number for uniquely specifying mathematical model information, a mathematical model602is an expression of the mathematical model, and a target region603is a region which is a mathematical model target.

The target region603is indicated by a polygon with multiple vertexes and is assumed to turn a target region clockwise in the embodiment. Also, the first vertex and the final vertex are the same and one vertex (x, y) is a coordinate position in an index. Further, although not illustrated, a region in which there is a hole may be designated. In this case, a plurality of polygons may be defined, the second polygon may be inside the first polygon, and the turning order may be counterclockwise. In this way, a region inside the first polygon and outside the second polygon may be designated.

For example, in which the mathematical model information ID601is M1 indicates that the mathematical model602is p(x, y, TOC, WL)=(2500−(x−350)*(x−350)−(y−350)*(y−350)*TOC*WL/1000000 and the target region603is (300, 300)−(300, 400)−(400, 400)−(400, 300)−(300, 300).

Here, p indicates a production quantity and corresponds to the production quantity (p)303and the production quantity (p)405, x and y indicate a coordinate position in the index of an oil well and correspond to the oil well positions302and402, TOC indicates a content of organic material and corresponds to the organic matter content (TOC)403, and WL indicates a length of a well and corresponds to the well length (WL)404.

The mathematical model information113is a model which is set in advance by the operator or the like and, for example, an oil production system obeys based on a predetermined principle. The mathematical model602is set in the target region603so that the target region603is in each mathematical model information ID601. However, there may be a region overlapping the target region603and plurality of mathematical model602may be set in the region.

In the example illustrated inFIG. 6, the parameters TOC and WL indicated in the internal data111are used as the mathematical model602. But this is because information indicating that the internal data111can be acquired in the target region603can be obtained at the time of defining the mathematical model602and is set as definition for use. The mathematical model602may be defined in the target region603in which only the external data110can acquire. In this case, the mathematical model602is a mathematical model in which the parameters of the external data110are used.

Next, a data structure of the region partition information114according to the embodiment will be described with reference toFIG. 7. A region partition information ID701illustrated inFIG. 7is a number for uniquely specifying a region partition information, a target region702is a region to be targeted, and an application model ID703is the statistical model information ID501or the mathematical model information ID601to be applied.

For example, the region partition information ID701which is 1 indicates that the target region702is (100, 100)−(100, 200)−(200, 200)−(200, 100)−(100, 100) and the application model ID703is S1 of the statistical model information ID501. The region partition information ID701and the target region702may be set in advance by the operator or the like. Further,FIG. 7illustrates an example in which the application model ID703is set in each target region702, but plurality of application model ID703may be set in one target region702.

The region partition information114indicates which model may be applied to the target region702which is each of the partitioned regions. A certain coordinate position may be included in a plurality of regions, that is, may be included in plurality of target region702. In this case, an output of the model at the coordinate position is a sum of outputs of models indicated by plurality of application model ID703corresponding to plurality of target region702including the coordinate position.

3. Process Flow and Screen

Up to here, the data structures have been described. Hereinafter, process flows and screens will be described. The description of the process flows include description of a modeling process flow for allocating a model to a region with reference toFIGS. 8, 9A, and 9B, description of a prediction process flow for predicting a new position with reference toFIGS. 10 and 11, and description of a modeling correction process flow for correcting the allocated model with reference toFIGS. 12A and 12B.

First, an example of the modeling process flow of the system according to the embodiment will be described with reference toFIG. 8. Step801is a process in which the CPU201of the modeling device101executes a program of the data reading unit102and reads the external data110and the internal data111from the external data provider109and the internal data provider108via the network107.

Here, the reading of the external data110from the external data provider109may be performed via Internet with, for example, a general protocol such as File Transfer Protocol (FTP) or Hyper Text Transfer Protocol (HTTP). The reading of the internal data111from the internal data provider108may be performed using FTP or HTTP or may be performed using general file sharing or a database. Further, the internal data provider108and the modeling device101may be connected via the interface203. Thus, the external data110and the internal data111are read to the modeling device101and the CPU201stores the external data110and the internal data111in the database106.

Step802is a process in which the CPU201of the modeling device101reads the mathematical model information113. Information of the mathematical model information113is generated in advance by manpower or the like in accordance with property of a modeling target and the information is read and stored in the database106. The information may be read by registering data from a keyboard, a file, or a network using function of general database software.

Step803is a process in which the CPU201of the modeling device101executes a program of the modeling processing unit103, performs modeling, and stores an execution result in the region partition information114and the statistical model information112. This process will be described with reference toFIGS. 9A and 9B. Step804is a process in which the CPU201of the modeling device101displays the result on the display206.

An example of a model input, estimation, and correction screen will be described with reference toFIG. 13. A screen1301illustrated inFIG. 13is an example of a screen displayed when the modeling is performed and constituted by including a mathematical model information reading button1302, a statistical model estimation button1303, a model display button1304, an application range correction button1305, a mathematical modeling window1306, and a statistical modeling window1307.

The mathematical model information reading button1302is a button used to instruct to read mathematical model information, the statistical model estimation button1303is a button used to instruct to estimate a statistical model, the model display button1304is a button used to instruct to display a model, and the application range correction button1305is a button used to instruct to correct an application range. Step802may be performed in response to an instruction of the mathematical model information reading button1302or step803may be performed in response to an instruction of the statistical model estimation button1303.

The mathematical modeling window1306is a modeling window on which a modeling situation by a mathematical model is displayed, and an observation value1308and a mathematical modeling estimation value1309are displayed for each target region or a target point. Here, the observation value1308is an actually observed value. The mathematical modeling estimation value1309is a result estimated by the mathematical model. The application model ID703may be displayed at each target point or one or two or more target regions displayed on the mathematical modeling window1306and may be displayed in response to an instruction of the model display button1304.

The statistical modeling window1307is a window on which a modeling situation by a statistical model is displayed. A residual1310, a statistical model estimation value1311, and a model application range1312are displayed for each target region or target point. Here, the residual1310is a difference between the mathematical model estimation value1309and the actual observation value1308and the statistical model estimation value1311is an estimation value by a statistical model estimated from the residual by the mathematical model. The model application range1312is an arrow indicating an application model of a model. The arrow is expanded or contracted to correct the application range of the model and the application range may be decided in response to an instruction of the application range correction button1305. The application model ID703may be displayed on the statistical modeling window1307.

On the screen1301, an observation value, an estimation value of each model, a difference between the observation value and the estimation value, and an application range of the model can be displayed so that it is easy to understand the application range.

Next, the modeling process of step803illustrated inFIG. 8will be described with reference toFIGS. 9A and 9B. Step901illustrated inFIG. 9Ais a process in which the CPU201of the modeling device101extracts points in a target point collection one by one to set the point as a target point and determines there is an unprocessed point. Here, the target point collection is a collection of points which are modeling targets and are, for example, points included in a sum collection of the target region702of the region partition information114. One point may be a point that represents one region of the target region702.

When there is an unprocessed point as a determination result of step901, the process proceeds to step902. After the process is performed on each point, that is, when there is no unprocessed point, the process proceeds to step908illustrated inFIG. 9B. Step902is a process in which the CPU201of the modeling device101sets “0” in the model output at the target point extracted in step901to perform initialization. Here, the model output is a variable that retains a value which is an output of the modeling process flow. Thereafter, addition is repeated using the model output as the variable and the value of the variable is finally set as an output result at the target point.

Step903is a process in which the CPU201of the modeling device101extracts the mathematical model602included in the mathematical model information113one by one, sets the mathematical model602as a target mathematical model, and determines whether there is an unprocessed mathematical model602. When there is unprocessed mathematical model602as a determination result of step903, the process proceeds to step904. After the process is performed on each mathematical model602, that is, when there is no unprocessed mathematical model602, the process proceeds to step906.

Step904is a process in which the CPU201of the modeling device101determines whether the target is included in the target region603of the target mathematical model. When it is determined that the target point is included in the target region603as a determination result of step904, the process proceeds to step905. When it is determined that the target point is not included in the target region603, the process returns to step903and another target mathematical model is processed.

Step905is a process in which the CPU201of the modeling device101calculates an output of the target mathematical model at the target point and adds the output to the variable of the model output at the target point. In this calculation, the information of the external data110and the internal data111read in step801is applied and an expression of the mathematical model602serving as target mathematical model is used. Instead of performing step801, the external data110and the internal data111, in which the coordinate position of the target point matches the oil well positions302and402, may be read in step905.

Step906is a process in which the CPU201of the modeling device101determines whether a difference between the observation value and the value of the model output at the target point is equal to or less than a threshold. When it is determined that the difference is equal to or less than the threshold as a determination result of step906, the process proceeds to step907. When it is determined that the difference is not equal to or less than the threshold, the process returns to step901and another target point is processed.

Here, the threshold is a value used to determine whether the modeling may be performed as a mathematical model, and is set in advance by the operator or the like. The observation value may be the production quantity (p)303of the external data110or the production quantity (p)405of the internal data111read in step801or may be read in step906instead of performing step801.

Step907is a process in which the CPU201of the modeling device101marks the target point as a mathematical model region. As the process, a matrix that takes a value of 0 or 1 for determining whether each point is a mathematical model or not may be written as an initial value 0 for all the elements in the memory202and a value of a matrix element corresponding to the target point may be set to 1. The mathematical model information ID601of the target mathematical model may be set in the application model ID703.

Step908illustrated inFIG. 9Bis a process in which the CPU201of the modeling device101determines whether there is a point at which the difference between the value of the model output and the observation value is equal to or greater than the threshold. When it is determined that there is the point at which is equal to or greater than the threshold as a determination result of step908, coordinate positions of the model output and the observation value of which is determined to be equal to or greater than the threshold are set as the target points and the process proceeds to step909. When it is determined that there is no point at which is equal to or greater than the threshold, the process proceeds to step913. Here, the threshold is a value used to determine whether modeling may be performed as a statistical model and is set in advance by the operator or the like.

Step909is a process in which the CPU201of the modeling device101extracts the statistical model502included in the statistical model information112one by one, sets the statistical model502as target statistical model, and determines whether there is an unprocessed statistical model502. When there is the unprocessed statistical model502as a determination result of step909, the process proceeds to step910. After the process is performed on each statistical model502, that is, when there is no unprocessed statistical model502, the process returns to step908and another target point is processed.

Step910is a process in which the CPU201of the modeling device101calculates the statistical model parameter503by applying the difference between the value of the model output and the observation value to the statistical model502. In the application to the statistical model502, a least-squares method or the like may be used. There is a general statistical software that has a function of estimating a parameter from data and a model (a model such as the statistical model502in which a parameter is not determined), and the statistical software may be used.

Therefore, plurality of target point may be extracted in step908. For example, plurality of target point at which the difference is determined to be equal to or greater than the threshold and at which a distance between the coordinate positions is equal to or less than a preset value may be extracted, or plurality of target point at which is determined to be equal to or greater than the threshold and which do not include coordinate position of a point at which is less than the threshold between the coordinate positions may be extracted.

In addition, (a, b) of the statistical model parameter503may be obtained by the least-squares method or the like by setting the coordinate positions of the plurality of target points may be set as “(x, y).” Which statistical model is applied among plurality of statistical model502in the statistical model information112may be set in advance to be limited.

Step911is a process in which the CPU201of the modeling device101stores the calculated statistical model parameter in the statistical model parameter503of the statistical model information112. In practice, this is a process of adding a new record in which the statistical model parameter503is determined to the statistical model information112using the database106.

Step912is a process in which the CPU201of the modeling device101calculates an output of the target statistical model at the target point and adds the output to the variable of the model output at the target point. The calculation of the output of the target statistical model is calculation performed applying the coordinate position and the statistical model parameter503of the target point to an expression of the statistical model502serving as the target statistical model.

Step913is a process in which the CPU201of the modeling device101stores which model should be applied to each point included in the target point collection in the region partition information114. The application of all the models contributing to the variable of the model output is registered in the region partition information114. The statistical model information ID501serving as the target statistical model in step912may be stored in the application model ID703.

After step913, the region partition information114may be corrected by an operator. Thus, an example of a screen for a manual input used for the correction will be described with reference toFIG. 14. A correction window (region partition information display)1401illustrated inFIG. 14is a window on which the region partition information is displayed for correction input. A region partition information display window1402is a window on which the region partition information is displayed with a different format from the correction window1401.

A peculiar observation value1403is an observation value that is actually observed and does not obey a model that is obeyed by peripheral observation values. A pointer1404is a marker for allowing the operator to select an observation value by moving in conjunction with movement of a mouse207of the operator, a region addition button1405is a button used to define a new region from the selected observation value, and a region deletion button1406is a button used to delete selected region partition information.

The operator can determine, for example, that the peculiar observation value1403does not obey peripheral observation values, and change an application model or separate to another region while viewing the screen illustrated in FIG.14. For example, the CPU201may control the change in the application model such that the peculiar observation value1403designated with the point1404is detected, a display density of the region partition information ID 2 and the application model S1 corresponding to the peculiar observation value1403on the region partition information display window1402is changed, the application model S1 corresponding to the peculiar observation value1403is changed to another application model based on information regarding the application model input from the keyboard205in this display state.

The CPU201may control partition of the region such that a designation of the region addition button1405is detected and the region of the region partition information ID 2 is partitioned in the state in which the display density of the region partition information ID 2 and the application model S1 corresponding to the peculiar observation value1403on the region partition information display window1402is changed. The CPU201may control deletion of the region such that a designation of the region deletion button1406is detected and the region of the region partition information ID 2 is deleted in the state in which the display density of the region partition information ID 2 and the application model S1 corresponding to the peculiar observation value1403on the region partition information display window1402is changed.

An example of another screen for a manual input used for the correction will be described with reference toFIG. 15. Each of a correction window1501, a region partition information display window1502, a pointer1504, a region addition button1505, and a region deletion button1506are the same as each of the correction window1401, the region partition information display window1402, the pointer1404, the region addition button1405, and the region deletion button1406described with reference toFIG. 14. Thus, the description thereof will not be repeated.

An observation value1503indicates that a ground surface is expressed 2-dimensionally and is partitioned into small regions, and an observation value of each small region is expressed with a display density.FIG. 15illustrates an example of the square small region, but the invention is not limited to the square and a polygon may be used. A region of the target region702may be expressed by a plurality of small regions.

For example, when four small regions pointed by the pointer1504on the observation value1503have a display density indicating a peculiar observation value, a designation of one of the four small regions may be detected with the point1504and the process described with reference toFIG. 14may be performed by the CPU201. When a designation of the region addition button1505is detected, a direction (x, y) in which the region is partitioned may be input and the region may be partitioned in the input direction.

In this way, an operation based on a further real state can be performed by enabling display and input 2-dimensionally.

The modeling process has been described above. Hereinafter, a prediction process will be described. The prediction process is a process of predicting a predetermined point using a model after the modeling process.FIG. 10is a diagram illustrating an example of a prediction process flow of the system according to the embodiment.

Step1001is a process in which the CPU201of the modeling device101reads the external data110and the internal data111from the external data provider109and the internal data provider108via the network107. This process is the same process as step801in the modeling process. Step1002is a process in which the CPU201of the modeling device101reads the mathematical model information113. This process is also the same process as step802in the modeling process.

Step1003is a process in which the CPU201of the modeling device101executes a program of the prediction processing unit104, identifies a region including a prediction calculation target point using the region partition information114, and performs prediction calculation using the statistical model information112or the mathematical model information113to be applied to the identified region. This process will be described below with reference toFIG. 11.

Step1004is a process in which the CPU201of the modeling device101displays a result on the display206. The screen is the same as that in the example ofFIG. 13and a prediction result is displayed using the mathematical model estimation value1309and the residual1310, and the statistical model estimation value1311.

Next, step1003illustrated inFIG. 10will be described with reference toFIG. 11. Step1101illustrated inFIG. 11is a process in which the CPU201of the modeling device101extracts points in the target point collection one by one, sets the points as target points, and determines whether there is an unprocessed point. Here, the target point collection is a collection of points which are prediction targets and may be set by the operator or the like.

When there is an unprocessed point as a determination result of step1101, the process proceeds to step1102. After the process is performed on each point, that is, when there is no unprocessed point, the process ends. Step1102is a process in which the CPU201of the modeling device101sets 0 in the model output at the target point extracted in step1101. Here, the model output is the same variable as that of step902.

Step1103is a process in which the CPU201of the modeling device101extracts regions included in the region partition information114one by one, sets the region as target region, and determines whether there is an unprocessed region. When there is the unprocessed region as a determination result of step1103, the process proceeds to step1104. After the process is performed on each region, that is, when there is no unprocessed region, the process proceeds to step1107.

Step1104is a process in which the CPU201of the modeling device101determines whether the target is included in the target region of the mathematical model. When it is determined that the target point is included in the target region as a determination result of step1104, the process proceeds to step1105. When it is determined that the target point is not included in the target region, the process returns to step1103and another target region is processed.

Step1105is a process in which the CPU201of the modeling device101acquires a model of the application model ID703in which the target region extracted in step1103is the target region702, and the model to be applied is specified through this acquisition. Statistical model information ID501of the statistical model information112and the mathematical model information ID601of the mathematical model information113are retrieved based on the specified model, that is, the acquired model ID, and information regarding a matching statistical model or mathematical model is acquired from the statistical model information112or the mathematical model information113.

Step1106is a process in which the CPU201of the modeling device101calculates an output of the model at the target point and adds the output to the variable of the model output at the target point. The model used for the calculation here is the model specified in step1105and is calculated by applying the information of the external data110and the internal data111read in step1001and the coordinate position of the target point to the information acquired in step1105.

When information necessary in the model is insufficient at the target point, the insufficient information may be estimated from information around the target point. In this case, the information around the target point is not used without changed, but may be apportioned, for example, by a distance between the target point and a point at which there is information.

Step1107is a process in which the CPU201of the modeling device101sets the value of the model output as the prediction calculation result at the target point.

The prediction calculation has been described above. Finally, modeling correction will be described. By continuing to learn for actual administration after the modeling, it is possible to make prediction with higher precision.FIGS. 12A and 12Bare diagrams illustrating an example of a modeling correction process flow according to the embodiment. Step1201illustrated inFIG. 12Ais a process in which the CPU201of the modeling device101executes a program of the modeling correction processing unit105and is the same process as step901, including the target point collection.

Step1202to step1206are the same processes as step1102to step1106. However, since the process equivalent to step1107is not performed in the modeling correction process, the process returns to step1201when there is no unprocessed region as a determination result of step1203.

Step1207illustrated inFIG. 12Bis a process in which the CPU201of the modeling device101determines whether there is a point at which the difference between the value of the model output and the observation value is equal to or greater than the threshold. When it is determined that there is the point at which the difference between the value of the model output and the observation value is equal to or greater than the threshold as a determination result of step1207, modeling correction is necessary, and thus coordinate positions of the model output and the observation value of which is determined to be equal to or greater than the threshold are set as the target points and the process proceeds to step1208. When it is determined that there is no point at which the difference between the value of the model output and the observation value is equal to or greater than the threshold, it is not necessary to correct the model and the process proceeds to step1212. Here, the threshold is set in advance by the operator or the like.

Step1208to step1212are the same processes as step909to step913. Step1213is a process in which the CPU201of the modeling device101sets the value of the corrected and newly generated model output in the prediction calculation result at the points included in the target point collection.

As described above, it is possible to perform the modeling in which the statistical model and the mathematical model are used together. The model can be adopted for each region. Therefore, even when there is a peculiar observation value, modeling appropriate for a region of the peculiar observation value can be performed separately from the other regions, and thus the peculiar observation value can be utilized.

Further, it is possible to predict a value of a new coordinate position using the modeling result. It is possible to correct the modeling in accordance with addition of a new model or a change of the observation value over time.

The invention is not limited to the foregoing embodiments and various modification examples are included. For example, the foregoing embodiments have been described in detail to easily describe the invention and all of the described configurations may not necessarily be included.

Some or all of the foregoing configurations, functions, processing units, and processing mechanisms may be realized in hardware by, for example, designing with integrated circuits or the like. The foregoing configurations, functions, and the like may be realized in software by causing a processor to analyze and execute a program realizing the functions. Information such as a program, a table, and a file realizing each function may be stored in a recording device such as a memory, a hard disk, or a solid state drive (SSD) or a recording medium such as an IC card, an SD card, or a DVD.