JP2022047729A - Dam water level prediction support system - Google Patents

Abstract

To properly present region information to be a ground of predicting the state amount of a dam.SOLUTION: A state amount prediction support device 10 includes: an input unit 12 for receiving first time-series information Q1, Q2, Q3 as flow rate information of a river 102 in an upstream region 102a of a target dam 104 for storing water of the river 102; and a related-region extraction unit 20 for calculating first micro region information and first macro region information used to calculate a predicted value after a predetermined time has passed of a state amount H of the water level of the target dam 104 in consideration of a downstream delay time by using the first time-series information Q1, Q2, and Q3.SELECTED DRAWING: Figure 1

Description

The present invention relates to a state quantity prediction support device and a program.

As a background technique in the present technical field, the following abstract of Patent Document 1 describes "... upstream dam discharge start time storage means for calculating the upstream dam discharge start time indicating the time when the upstream dam started discharge, and the power generation. A power generation start time storage means for calculating the power generation start time indicating the time when the machine started power generation, a downstream dam water entry start time storage means for calculating the downstream dam water entry start time indicating the time when water entry to the downstream dam started, and a downstream dam water entry start time storage means. The flow-down delay time for calculating the flow-down delay time indicating the time when the discharge from the upstream dam is started and the water is started to enter the downstream dam from the upstream dam discharge start time, the power generation start time, and the downstream dam water entry start time. It is provided with a calculation means. "

Japanese Unexamined Patent Publication No. 2013-78179

However, the above-mentioned Patent Document 1 does not particularly describe that the area information that is the basis for predicting the state quantity of the dam, such as the water level of the dam, is appropriately presented. For this reason, it was difficult to operate the dam by utilizing the area information that is the basis for predicting the state quantity of the dam.
The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a state quantity prediction support device and a program capable of appropriately presenting area information that is a basis for predicting the state quantity of a dam.

In order to solve the above problems, the state quantity prediction support device of the present invention has an input unit that receives first time-series information, which is the flow rate information of the river in the upstream area of the target dam, which is a dam that stores water of the river. , The first micro region information and the first micro region information used when calculating the predicted value of the state quantity regarding the water level of the target dam after a predetermined time by using the first time series information and taking into account the flow delay time. It is characterized by including a related area extraction unit for calculating macro area information.

According to the present invention, it is possible to appropriately present the area information that is the basis for predicting the state quantity of the dam.

It is a block diagram of the state quantity prediction support apparatus by a suitable 1st Embodiment. It is a figure which shows the example of the observed flow rate, the correlation coefficient, and the degree of association. It is a figure which shows the example of the 1st micro region information and 1st macro region information. It is a figure which shows the example of the 1st related area display screen 70. It is a figure which shows the example of the 2nd related area display screen 130. It is a figure which shows the example of the parameter correction screen 200.

[First Embodiment]
<Structure of the first embodiment>
FIG. 1 is a block diagram of a state quantity prediction support device 10 (computer) according to a preferred first embodiment.
The state quantity prediction support device 10 predicts various state quantities such as the water level H of the target dam 104 to be predicted. The predicted value of the water level H is called the predicted water level Hh (predicted state quantity). Water flows into the target dam 104 from the upstream area 102a of the river 102, and the target dam 104 discharges water to the downstream area 102b of the river 102. Flow measuring devices 30 are provided at three observation points m1, m2, and m3 in the upstream area 102a. These flow rate measuring devices 30 measure the flow rate at each observation point and output the results as flow rate information Q1, Q2, Q3 (first time series information). The flow rate information Q1, Q2, and Q3 are time-series information that becomes a function of time t. The observation points m1, m2 and m3 are, for example, other dams located upstream of the target dam 104, but the observation points m1, m2 and m3 are not limited to the dam.

Further, a range including at least a part of the upstream area 102a and in which precipitation is monitored is referred to as a precipitation monitoring range 106. In the illustrated example, the precipitation monitoring range 106 is divided into "6 × 6 = 36" spatial mesh points k1 to k36. The meteorological information server 110 supplies the precipitation information rp1 to rp36 (second time-series information), which are the current precipitation amount and the future predicted precipitation amount at the spatial mesh points k1 to k36, to the state amount prediction support device 10. Here, the information indicating the precipitation information rp1 to rp36 and the measurement points of each precipitation is referred to as a precipitation map mrp.

Precipitation information rp1 to rp36 are also time-series information that is a function of time t. The terrain information server 112 supplies the terrain information C in the precipitation monitoring range 106 to the state quantity prediction support device 10. The display device 116 displays various image data supplied from the state quantity prediction support device 10. The input device 118 includes a mouse, a keyboard, and the like, and inputs various data and commands to the state quantity prediction support device 10 based on the user's operation.

The state quantity prediction support device 10 is equipped with hardware as a general computer such as a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), and an SSD (Solid State Drive). The SSD stores an OS (Operating System), an application program, various data, and the like. The OS and application programs are expanded in RAM and executed by the CPU. In FIG. 1, the inside of the state quantity prediction support device 10 shows a function realized by an application program or the like as a block.

That is, the state quantity prediction support device 10 includes an input unit 12 (input means), a degree of association analysis unit 13, a state quantity prediction unit 14 (state quantity prediction means), a prediction result display unit 15, and an area display unit 16. A command receiving unit 18 and a related area extraction unit 20 (related area extraction means) are provided. Further, the related region extraction unit 20 includes a macro region extraction unit 22 and a micro region extraction unit 24.

The input unit 12 receives the above-mentioned flow rate information Q1, Q2, Q3 from the observation points m1, m2, m3, receives precipitation information rp1 to rp36 from the meteorological information server 110, and receives terrain information C from the terrain information server 112. Receive. Then, the input unit 12 stores the received flow rate information Q1, Q2, Q3 and precipitation information rp1 to rp36 in the storage unit as time-series information for each predetermined cycle (for example, 1 hour). The precipitation information rp1 to rp36 include the amount of snowfall converted into the amount of precipitation by a predetermined method.

The relevance degree analysis unit 13 obtains the relevance degree f (first relevance degree) which is the relevance degree between the water level H of the target dam 104 and the flow rate information Q1, Q2, Q3. Further, the association degree analysis unit 13 obtains the association degree g (second association degree) which is the association degree between the water level H of the target dam 104 and the precipitation information rp1 to rp36. In other words, the relevance analysis unit 13 obtains the relevance degree g based on the degree of influence of the precipitation information rp1 to rp36 on the water level H of the target dam 104. Here, the details of the degree of relevance f and g will be described later.

The state quantity prediction unit 14 calculates a state quantity prediction value such as a predicted water level Hh based on the degree of association f, g and the like. The prediction result display unit 15 causes the display device 116 to display a state quantity predicted value such as the predicted water level Hh. The related region extraction unit 20 includes a macro region extraction unit 22 and a micro region extraction unit 24.

The macro area extraction unit 22 calculates the first and second macro area information (details will be described later). Further, the micro region extraction unit 24 calculates the first and second micro region information (details will be described later). The area display unit 16 causes the display device 116 to display the first and second macro area information and the first and second micro area information. The command receiving unit 18 receives various data and commands from the input device 118.

<Prediction of state quantity based on flow rate information>
It is considered that the flow rate information Q1, Q2, Q3 and / or the precipitation information rp1 to rp36 are mainly related to predicting the state amount such as the water level H of the target dam 104 after T time from the present. However, since the state quantity such as the water level H may fluctuate due to other factors, other factors may be added to the prediction of the state quantity.
First, with reference to FIG. 2, the state quantity prediction using the flow rate information Q1, Q2, and Q3 of the river 102 will be described.

FIG. 2 is a diagram showing an example of an observed flow rate Xq (t), a correlation coefficient PX0, PX1, PX2 (degree of influence), and a degree of association f0, f1, f2.
The observed flow rate Xq (t) is the flow rate of water flowing into the target dam 104 at time t. Here, any of the observation points m1, m2, and m3 shown in FIG. 1 is referred to as an observation point mx, and the flow rate information at the observation point mx is referred to as Qx. The correlation coefficient PX0 shown in FIG. 2 is a correlation coefficient between the above-mentioned observed flow rate Xq (t) and the flow rate information Qx (t) at the time t of the observation point mx. Further, the correlation coefficient PX1 is a correlation coefficient between the observed flow rate Xq (t) and the flow rate information Qx (t-1) at the time t-1 of the observation point mx (one hour before the time t). Further, the correlation coefficient PX2 is a correlation coefficient between the observed flow rate Xq (t) and the flow rate information Qx (t-2) at the time t-2 of the observation point mx (two hours before the time t).

The correlation coefficients PX0, PX1 and PX2 actually change continuously according to the magnitude of the observed flow rate Xq (t). However, in the example shown in FIG. 2, the correlation coefficients PX0, PX1 and PX2 are defined for the flow rate ranges of 100 to 300 [ton / sec] and 301 to 500 [ton / sec], respectively. Further, for the correlation coefficients PX0, PX1 and PX2 in each flow rate range, "1" is assigned to the maximum correlation coefficient and "0" is assigned to the other correlation coefficients, and the degree of association f0, f1. , F2 (first degree of association).

In the example shown in FIG. 2, since the correlation coefficient PX2 = 0.90 is the maximum value for the flow rate range of 100 to 300 [ton / sec], the degree of association f0, f1, f2 is [0, 0,1]. Further, since the correlation coefficient PX1 = 0.95 is the maximum value for the flow rate range of 301 to 500 [ton / sec], the degree of association f0, f1, f2 becomes [0,1,0]. .. However, if none of the correlation coefficients PX0, PX1, and PX2 are significant, the degree of association f0, f1, and f2 may be "0". Hereinafter, the degree of relevance f0, f1, f2, etc. may be collectively referred to as the degree of relevance f.

The observed flow rate Xq (t) shown in FIG. 2 is the observed flow rate at the current time t, but the flow rate in the future can be predicted in the same manner. It can be predicted based on the degree of relevance f. When predicting the flow rate or the like that will occur in the future for a certain time (T), this T is called the prediction target time. For example, regarding the value of the prediction target time T, "0" represents "current time", "1" represents "1 hour later", and "2" represents "2 hours later". Then, the set of the degree of relevance f at a specific prediction target time T is called "first micro-region information" with respect to the prediction target time T. The predicted value of the observed flow rate Xq (t) generated based on the flow rate information Q1, Q2, Q3 is called the predicted flow rate Xqh (t). The state quantity prediction unit 14 (see FIG. 1) calculates the predicted flow rate Xqh (t) by the following equation (1).

In the equation (1), t is a time variable, i is a time difference (unit: h) with respect to the current time, and is hereinafter referred to as “reference time difference”. Although m is the number of observation points (“3” in the example of FIG. 1), it is the number of observation points (1, 2, 3) in the parentheses of the equation (1). n is the number of reference time differences i required for the calculation. Further, X _q is a variable that determines the degree of relevance, but in the equation (1), it represents the flow rate flowing into the target dam 104. “Q _m (ti)” is the flow rate of the observation point m at the time (ti), and “fm _{, i} (ti)” is the flow rate Q _m (t) at the time (ti). It becomes the degree of relevance corresponding to ti). Further, b _m is a constant for each observation point m.
The above-mentioned equation (1) is also used for each of the first micro-region information corresponding to a plurality of prediction target times T (for example, after 1 hour: T = 1, after 2 hours: T = 2, etc.). Can be calculated by

FIG. 3 is a diagram showing an example of the first micro region information 52, 53, 54 and the first macro region information 60.
In FIG. 3, the reference time difference i represents the current or past time difference used to predict the water level H. For example, regarding the reference time difference i, "0" represents "current time", "1" represents "1 hour ago", and "2" represents "2 hours ago".

In the first micro region information 52, the prediction target time T is “0”, and the reference time difference i = 0, 1, 2 and the observation points m1, m2, m3 correspond to each of the nine degree of association f. Is a set of. Similarly, in the first micro-region information 53, the prediction target time T is “1”, and the reference time difference i = 0,1,2 and the observation points m1, m2, m3 correspond to nine pieces. It is a set of the degree of relevance f. Similarly, in the first micro-region information 54, the prediction target time T is “2”, and the reference time difference i = 0, 1, 2 and the observation points m1, m2, m3 correspond to the nine cases. It is a set of the degree of relevance f.

In FIG. 3, the total value group 58 is shown for reference, and is the result of summing the degree of relevance f for each observation point m1, m2, m3 in each of the first micro region information 52, 53, 54. be. The first macro region information 60 is a value obtained by further summing the values of the total value group 58 for each observation point m1, m2, m3. In the illustrated example, the degree of relevance f of each observation point m1, m2, m3 in the first macro area information 60 is 0, 1, 2, respectively.

FIG. 4 is a diagram showing an example of the first related area display screen 70.
The first related area display screen 70 is a screen displayed on the display device 116 by the area display unit 16 (see FIG. 1), and the first micro area display units 72, 73, 74 and the first macro area. The display unit 80 and the like are included. The first micro-region display unit 72 displays the first micro-region information 52 (see FIG. 3) and the like at the prediction target time T = 0. Therefore, the first micro region display unit 72 includes icons vm1, vm2, vm3, vm4, a flow rate display image vc, and a correlation display image vd.

Here, the icon vm4 corresponds to the target dam 104 (see FIG. 1), and the icons vm1, vm2, vm3 correspond to the observation points m1, m2, m3. Then, among the icons vm1, vm2, vm3, the display mode of the icon having the highest degree of relevance f in the corresponding prediction target time T (here, T = 0) is displayed in a mode different from the display modes of the other icons. In FIG. 3, the total value group 58 at the prediction target time T = 0 was 0,0,1 with respect to the observation points m1, m2, and m3. Therefore, in the first micro region display unit 72 of FIG. 4, the icons vm1 and vm2 are made white, and the icon vm3 is provided with a mesh.

Further, the flow rate display image vc displays the change on the time series of the flow rate information (flow rate information Q3 in the above example) at the observation points m1, m2, and m3 having the highest degree of relevance f. Further, the correlation display image vd has three regions divided in the vertical direction. These three regions are the observed flow rate Xq (t) at the predicted target time T = 0 and the flow rate information Q3 (t), Q3 (t-1), Q3 (at the observation point m3 in the above example). Represents the correlation between t-2) and. That is, these three regions correspond to the correlation coefficients PX0, PX1, and PX2 (see FIG. 2), respectively. As shown in FIG. 2, since the correlation coefficients PX0, PX1, and PX2 are continuous quantities, in the example of the correlation display image vd shown in FIG. 4, there is a phase relationship depending on the display mode such as dots, hatches, and meshes. The numerical range of numbers is displayed.

In FIG. 4, the first micro-region display units 73 and 74 display the first micro-region information 53, 54 (see FIG. 3) and the like at the prediction target time T = 1 and 2. Therefore, the first micro region display units 73 and 74, like the first micro region display unit 72, include icons vm1 to vm4, a flow rate display image vc, and a correlation display image vd, respectively. .. In FIG. 3, the total value group 58 at the prediction target time T = 1 was 0,0,1 with respect to the observation points m1, m2, and m3. Therefore, also in the first micro region display unit 73 of FIG. 4, the icons vm1 and vm2 are made white, and the icon vm3 is provided with a mesh.

Since the flow rate display image vc in the first micro region display unit 73 represents the flow rate information Q3 at the observation point m3, it has the same shape as the flow rate display image vc in the first micro region display unit 72. However, the correlation display image vd in the first micro region display unit 72 represents the correlation coefficients PX0, PX1 and PX2 with respect to the prediction target time T = 0, whereas the correlation display in the first micro region display unit 73. The image vd represents the correlation coefficients PX0, PX1, and PX2 at the prediction target time T = 1. Therefore, the display mode of the correlation display image vd in the first micro region display units 72 and 73 is slightly different.

Next, in FIG. 3, the total value group 58 at the prediction target time T = 2 was 0,1,0 with respect to the observation points m1, m2, and m3. Therefore, in the first micro region display unit 74 of FIG. 4, the icons vm1 and vm3 are made white, and the icon vm2 is provided with a mesh. Further, since the flow rate display image vc of the first micro region display unit 74 represents the flow rate information Q2 at the observation point m2, the shape is different from the flow rate display image vc of the first micro region display units 72 and 73.

Further, in FIG. 4, the first macro area display unit 80 includes the icons vm1 to vm4. Of these, the icons vm1, vm2, vm3 correspond to the first macro area information 60 (see FIG. 3), and the display mode corresponding to the value of the first macro area information 60 (however, mesh>. Hatch> white nuki) is given. In the above-mentioned example, the correlation coefficients PX0, PX1 and PX2 are applied to express the magnitude of the influence, but other indexes such as the importance may be applied.

As shown in FIG. 4, when the first micro region display unit 72, 73, 74 and the first macro region display unit 80 are displayed on the display device 116, among the plurality of observation points m1, m2, m3, At a glance, the user can grasp which flow rate is the basis for the predicted value of the state quantity such as the predicted water level Hh. As a result, the user can operate the target dam 104 by utilizing the information of the observation point that is the basis for predicting the state quantity of the target dam 104.

<Prediction of state quantity based on precipitation information>
Next, the state quantity prediction support using the precipitation information rp1 to rp36 in the precipitation monitoring range 106 of the target dam 104 will be described. As described above, the relevance analysis unit 13 obtains the relevance degree g, which is the relevance degree between the water level H of the target dam 104 and the precipitation information rp1 to rp36. Specifically, the relevance degree analysis unit 13 is related as the second micro region information at a specific prediction target time T (for example, the current time at T = 0), similarly to the flow rate information Q1, Q2, and Q3 described above. Calculate the degree g. The second micro-regional information is based on the magnitude of the degree of relevance g of the second micro-regional information that affects the state quantity of the target dam 104 for each spatial mesh point k1 to k36 (see FIG. 1). handle.

The predicted value of the observed flow rate Xr (t) generated based on the precipitation information rp1 to rp36 is called the predicted flow rate Xhr (t). The state quantity prediction unit 14 calculates the predicted flow rate Xhr (t) based on the following equation (2). Since the precipitation information rp1 to rp36 include the forecast value of the future time, the precipitation amount after T time can be obtained by using the future forecast value. In this case, as shown in the following equation (2), the calculation may be executed starting from "i = -T".

Similar to the above equation (1), in the equation (2), t is a time variable and i is a reference time difference (unit: h). However, in the equation (2), n is the number of positive reference time differences i among the reference time differences i required for the calculation. Further, X _r is a variable that determines the degree of relevance, but in the equation (2), it is a variable that is set according to the topographical information C, the intensity of rain, and the like. Further, although k is the number of spatial mesh points (“36” in the example of FIG. 1), it is the number of spatial mesh points (1 to 36) in the parentheses of the equation (2). "R _k (t-i)" is the amount of precipitation at the spatial mesh point k at the time (ti), and "g _{k, i} (ti)" is the amount of precipitation at the time (ti). The degree of relevance corresponding to R _k (ti). Further, b _k is a constant for each spatial mesh point k.

The above-mentioned equation (2) is also used for each second micro-region information corresponding to a plurality of prediction target times T (for example, after 1 hour: T = 1, after 2 hours: T = 2, etc.). Therefore, the predicted flow rate Xhr (t) can be calculated. However, at each mesh point, it is preferable to process and display the degree of relevance g _{k, i} (X _r ) from n hours before to T hours after. This processing can be applied to statistical processing such as maximum value and average value, but is not limited to these.

FIG. 5 is a diagram showing an example of the second related area display screen 130.
The second related area display screen 130 is a screen displayed on the display device 116 by the area display unit 16 (see FIG. 1), and is a screen displayed on the display device 116, and is a second micro area display unit 132, 133, 134 (second micro area information). ) And the second macro area display unit 140 (second macro area information). The second micro-region display units 132, 133, and 134 display the second micro-region information at the prediction target times T = 0, T = 1, and T = 2, respectively.

The second micro region display units 132, 133, and 134 each have 6 × 6 = 36 mesh points (unsigned), and these mesh points are spatial mesh points k1 to the precipitation monitoring range 106. It corresponds to each of k36 (see FIG. 1). Further, the target dam point pr indicates a mesh point where the target dam 104 (see FIG. 1) is located. In the illustrated example, the mesh points are marked with "white blanks", "dots" or "hatch". "White nuki" indicates that there is no significant degree of association g, "dot" indicates that there is a significant degree of association g, and "hatch" indicates that there is a significant and relatively large degree of association g. Show that.

Further, the second macro area display unit 140 also has 6 × 6 = 36 mesh points (unsigned), and these mesh points are spatial mesh points k1 to k36 (FIG. 6) in the precipitation monitoring range 106. 1) is supported respectively. The second macro area display unit 140 includes an area display image 142 which is a thick line surrounding some mesh points. The region shown by the region display image 142 is a mesh point included in a region in which a significant degree of association g exists in any of the second micro region information. In other words, the region indicated by the region display image 142 is the region where the "dot" or "hatch" is shown in the second micro region display units 132, 133, 134.

As shown in FIG. 5, when the second micro-region display unit 132, 133, 134 and the region display image 142 are displayed on the display device 116, any of a plurality of spatial mesh points k1 to k36 (see FIG. 1). At first glance, the user can grasp whether the amount of precipitation in the water is the basis for the predicted value of the state quantity such as the predicted water level Hh. As a result, the user can operate the target dam 104 by utilizing the information of the spatial mesh point that is the basis for predicting the state quantity of the target dam 104.

<Modification of parameters>
FIG. 6 is a diagram showing an example of the parameter correction screen 200.
The parameter correction screen 200 is a screen displayed on the display device 116 by the prediction result display unit 15 and the area display unit 16 (see FIG. 1), and is the prediction target time setting unit 210, the correlation coefficient setting unit 220, and the observation. It includes a point display unit 230, a related data display unit 240, and a prediction result display unit 260. The prediction target time setting unit 210 sets the prediction target time T based on the user's operation in the input device 118 (see FIG. 1).

The correlation coefficient setting unit 220 includes an observation point setting unit 221, a reference time difference display unit 222, a default correlation coefficient display unit 224, and a correlation coefficient correction unit 226. The observation point setting unit 221 selects one of a plurality of observation points (for example, m1, m2, m3 shown in FIG. 1) based on the operation of the user. The reference time difference display unit 222 displays the reference time difference i. In the illustrated example, the reference time difference i of "0", "1", and "2" is displayed as the reference time difference i. However, the latter two are marked with a minus sign "-" to clarify that they indicate past timing.

The default correlation coefficient display unit 224 displays the default correlation coefficient of each reference time difference i corresponding to the set prediction target time T and the observation point. The default correlation coefficient is a default value of the correlation coefficient (see FIG. 2), and is a value obtained by calculation based on, for example, past measurement results. The correlation coefficient correction unit 226 inputs a correlation coefficient correction value based on the user's operation. The sum of the default correlation coefficient and the modified value of the correlation coefficient is called the applied correlation coefficient (not shown). In the illustrated example, the default correlation coefficient and the correlation coefficient correction value are displayed in "%" units.

The observation point display unit 230 includes an upstream area image 232 showing the upstream area 102a (see FIG. 1) of the river 102, and icons vm1, vm2, vm3, vm4. The meanings of the icons vm1 to vm4 are the same as those of the first macro area display unit 80 on the first related area display screen 70 (see FIG. 4). Further, the meanings of the display modes (mesh, hatch, etc.) of the icons vm1, vm2, vm3 are the same as those of the first macro area display unit 80.

The related data display unit 240 includes a flow rate estimated value image 242, 244, a correlation display image 246, and a legend display unit 248. The flow rate estimation value images 242 and 244 represent the estimated value of the flow rate (Q3 in the above example) at the observation point (m3 in the illustrated example) selected by the observation point setting unit 221.

However, the flow rate estimated value image 244 shown by the broken line is an estimation result based on the default correlation coefficient shown in the default correlation coefficient display unit 224. Further, the flow rate estimation value image 242 shown by the solid line is an estimation result based on the applied correlation coefficient (the sum of the default correlation coefficient and the correlation coefficient correction value). The correlation display image 246 represents a numerical range of the applied correlation coefficient depending on the display mode such as “white blank”, “dot”, and “hatch”. The legend display unit 248 displays a legend of the display mode in the correlation display image 246.

The prediction result display unit 260 includes a water level estimated value image 262,264 showing a time-series change in the predicted water level Hh (see FIG. 1). However, the water level estimated value image 264 shown by the broken line is an estimation result based on the default correlation coefficient shown in the default correlation coefficient display unit 224. Further, the water level estimated value image 262 shown by the solid line is an estimation result based on the applied correlation coefficient (the sum of the default correlation coefficient and the correlation coefficient correction value).

In FIG. 6, when the user operates the prediction target time setting unit 210, the observation point setting unit 221 or the correlation coefficient correction unit 226, the prediction result display unit 15 and the area display unit 16 (see FIG. 1) are changed. The state of each part in FIG. 6 is changed according to the parameter.

<Effect of embodiment>
According to the preferred embodiment as described above, the first time series information (Q1, Q2, Q3) which is the flow rate information of the river 102 in the upstream area 102a of the target dam 104 which is a dam for storing the water of the river 102. Predicted value after a predetermined time of the state quantity (H) regarding the water level of the target dam 104 by using the input unit 12 for receiving The state quantity prediction unit 14 for calculating the state quantity predicted value (Hh), and the first micro region information 52, 53, 54 and the first macro region information used for calculating the state quantity predicted value (Hh). A related area extraction unit 20 for calculating 60 is provided. Thereby, the area information that is the basis for predicting the state quantity (H) of the target dam 104 can be appropriately presented.

Here, the related region extraction unit 20 includes a micro region extraction unit 24 and a macro region extraction unit 22, and the micro region extraction unit 24 has different observation points m1, m2, along the upstream region 102a of the river 102. The first time-series information (Q1, Q2, Q3) acquired from the flow rate measuring device 30 arranged in m3 and the first degree of association (f) corresponding to the first time-series information (Q1, Q2, Q3). , F0, f1, f2), and the first micro region information 52, 53, 54 is calculated for each observation point m1, m2, m3, and the macro region extraction unit 22 performs each first micro region. It is more preferable to have a function of calculating the first macro area information 60 by combining the area information 52, 53, 54.
As a result, detailed information for each observation point m1, m2, m3 can be acquired based on the individual first micro region information 52, 53, 54, and the whole can be obtained based on the first macro region information 60. You can get a bird's-eye view of information.

Further, in the micro region extraction unit 24, the degree of influence (PX0, PX0,) on the state quantity (H) of the target dam 104 by the first time series information (Q1, Q2, Q3) at each observation point m1, m2, m3. It is more preferable to have a function of calculating the first degree of association (f, f0, f1, f2) based on the size of PX1, PX2). As a result, the first degree of relevance based on the magnitude of the degree of influence (PX0, PX1, PX2) of the first time series information (Q1, Q2, Q3) on the state quantity (H) of the target dam 104. (F, f0, f1, f2) can be calculated.

Further, the input unit 12 has a function of acquiring the topographical information C in the precipitation monitoring range 106 including at least a part of the upstream region 102a and the precipitation map mrp, and the related region extraction unit 20 further Second micro-region information used when calculating the state quantity predicted value (Hh) after a predetermined time using the second time-series information (rp1 to rp36) indicating the time-series precipitation based on the precipitation map mrp. It is more preferable to have a function of calculating (132, 133, 134) and the second macro region information (140). As a result, the state quantity predicted value (Hh) is obtained using not only the first time-series information (Q1, Q2, Q3) regarding the flow rate but also the second time-series information (rp1 to rp36) indicating the time-series precipitation. Can be calculated.

Further, the related area extraction unit 20 has a second time-series information (rp1 to rp36) for each spatial mesh point (k1 to k36) created based on the topographical information C, and a second time-series information (rp1 to rp36). ), And the micro region extraction unit 24 that calculates the second micro region information (132, 133, 134) for each spatial mesh point (k1 to k36) based on the second degree of association (g). , It is more preferable to include a macro region extraction unit 22 for calculating the second macro region information (140) by combining the second micro region information (132, 133, 134).
As a result, detailed information for each spatial mesh point (k1 to k36) can be acquired based on the individual second micro region information (132, 133, 134), and the second macro region information (140). You can get a bird's-eye view of the information based on.

Further, the micro region extraction unit 24 has a second time series information (rp1 to rp36) at each spatial mesh point (k1 to k36) based on the degree of influence on the state quantity (H) of the target dam 104. It is more preferable to calculate the degree of association (g) of 2. Thereby, the second degree of association (g) can be calculated based on the magnitude of the influence of the second time series information (rp1 to rp36) on the state quantity (H) of the target dam 104.

Further, the function of displaying the prediction target time T related to the state quantity predicted value (Hh) of the target dam 104 on the display device 116, the first micro area information 52, 53, 54, and the first macro area information 60. , The second micro region information (132, 133, 134), the second macro region information (140), and a function of displaying one or more of the information on the display device 116. It is more preferable to further include the area display unit 16. As a result, the first micro region information 52, 53, 54, the first macro region information 60, the second micro region information (132, 133, 134), and / or the second macro region information (140) are obtained. It can be grasped visually.

Further, the first micro region information 52, 53, 54 for each observation point m1, m2, m3, and / or the second micro region information (132, 133, 134) for each spatial mesh point (k1 to k36). ) Is further provided, and the area display unit 16 further includes a command receiving unit 18 for receiving the correction command of), and the area display unit 16 responds to the correction command with the first micro region information 52, 53, 54 and / or the second micro region information (132, 133). , 134) is recalculated and redisplayed on the display device 116, which is more preferable. As a result, the user can visually grasp the correction result while appropriately correcting the first micro-region information 52, 53, 54 and / or the second micro-region information (132, 133, 134). ..

Further, the area further includes a command receiving unit 18 for receiving the first time-series information (Q1, Q2, Q3), the second time-series information (rp1 to rp36), and / or the display command information regarding the prediction target time T. The display unit 16 displays the first time-series information (Q1, Q2, Q3), the second time-series information (rp1 to rp36), and / or the prediction target time T on the display device 116 based on the display command information. It is more preferable to have a function to display. As a result, the user visually corrects the correction result while appropriately correcting the first micro region information 52, 53, 54, the second micro region information (132, 133, 134), and / or the prediction target time T. Can be grasped.

[Modification example]
The present invention is not limited to the above-described embodiment, and various modifications are possible. The above-described embodiments are exemplified for the purpose of explaining the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the configurations described. Further, another configuration may be added to the configuration of the above embodiment, and a part of the configuration may be replaced with another configuration. In addition, the control lines and information lines shown in the figure show what is considered necessary for explanation, and do not necessarily show all the control lines and information lines necessary for the product. In practice, it can be considered that almost all configurations are interconnected. Possible modifications to the above embodiment are, for example, as follows.

(1) Since the hardware of the state quantity prediction support device 10 in the above embodiment can be realized by a general computer, a program or the like for executing the above-mentioned various processes is stored in a storage medium or distributed via a transmission line. You may.

(2) Each of the above-mentioned processes has been described as a software-like process using a program in the above embodiment, but a part or all of them may be an ASIC (Application Specific Integrated Circuit) or an FPGA (Field). It may be replaced with hardware-like processing using Programmable Gate Array) or the like.

10 State quantity prediction support device (computer)
12 Input unit (input means)
14 State quantity prediction unit (state quantity prediction means)
16 Area display unit 18 Command receiving unit 20 Related area extraction unit (related area extraction means)
22 Macro area extraction unit 24 Micro area extraction unit 30 Flow measuring device 52, 53, 54 First micro area information 60 First macro area information 102 River 102a Upstream area 104 Purpose dam 106 Precipitation monitoring range 116 Display device 132, 133,134 Second micro area display unit (second micro area information)
140 Second macro area display section (second macro area information)
C Topographic information H Water level (state quantity)
T Prediction target time g Degree of relevance (second degree of relevance)
Hh predicted water level (predicted state quantity)
mrp Precipitation map k1 to k36 Spatial mesh points Q1, Q2, Q3 Flow information (first time series information)
m1, m2, m3 Observation points rp1 to rp36 Precipitation information (second time series information)
f, f0, f1, f2 degree of relevance (first degree of relevance)
PX0, PX1, PX2 Correlation coefficient (degree of influence)

The present invention relates to a dam water level prediction support system .

However, the above-mentioned Patent Document 1 does not particularly describe that the area information that is the basis for predicting the state quantity of the dam, such as the water level of the dam, is appropriately presented. For this reason, it was difficult to operate the dam by utilizing the area information that is the basis for predicting the state quantity of the dam.
The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a dam water level prediction support system capable of appropriately presenting area information that is a basis for predicting the state quantity of a dam.

In order to solve the above problems, the dam water level prediction support system of the present invention is a dam that stores water in a river. Using the input unit that receives the first time-series information indicating the measured flow rate information and the first time-series information, the predicted flow rate of the observed flow rate flowing into the target dam is predicted, taking into account the flow delay time. The relationship of calculating the first micro-regional information, which is the information obtained by reducing the first micro-regional information, which is composed of the information on the degree of relevance used when calculating the predicted value after the lapse of time. It is characterized by including a region extraction unit.

The observed flow rate Xq (t) shown in FIG. 2 is the observed flow rate at the current time t, but the flow rate in the future can be predicted in the same manner. It can be predicted based on the degree of relevance f. When predicting the flow rate or the like that will occur in the future for a certain time (T), this T is called the prediction target time. For example, regarding the value of the prediction target time T, "0" represents "current time", "1" represents "1 hour later", and "2" represents "2 hours later". Then, the set of the degree of relevance f at a specific prediction target time T is called "first micro-region information" with respect to the prediction target time T. The predicted value of the observed flow rate Xq (t) generated based on the flow rate information Q1, Q2, Q3 is called the predicted flow rate Xqh _T (t) . The state quantity prediction unit 14 (see FIG. 1) calculates the predicted flow rate Xqh _T (t) by the following equation (1).

The predicted value of the observed flow rate Xr (t) generated based on the precipitation information rp1 to rp36 is called the predicted flow rate Xhr _T (t) . The state quantity prediction unit 14 calculates the predicted flow rate Xhr _T (t) based on the following equation (2). Since the precipitation information rp1 to rp36 include the forecast value of the future time, the precipitation amount after T time can be obtained by using the future forecast value. In this case, as shown in the following equation (2), the calculation may be executed starting from "i = -T".

Claims (11)

An input unit that receives the first time-series information, which is the flow rate information of the river in the upstream area of the target dam, which is a dam that stores the water of the river.
Using the first time-series information, the first micro region information and the first macro used when calculating the predicted value of the state quantity regarding the water level of the target dam after a predetermined time in consideration of the flow delay time. A state quantity prediction support device characterized by having a related area extraction unit for calculating area information. The related region extraction unit includes a micro region extraction unit and a macro region extraction unit.
The micro area extraction unit
Based on the first time-series information acquired from the flow measuring devices arranged at different observation points along the upstream area of the river and the first degree of association corresponding to the first time-series information. Then, the first micro region information is calculated for each observation point.
The state quantity prediction support device according to claim 1, wherein the macro region extraction unit has a function of combining the respective first micro region information to calculate the first macro region information. The micro-region extraction unit has a function of calculating the first degree of association based on the magnitude of the influence of the first time-series information at each observation point on the state quantity of the target dam. The state quantity prediction support device according to claim 2, wherein the device has. The input unit has a function of acquiring topographical information and a precipitation map in a precipitation monitoring range including at least a part of the upstream area.
The related region extraction unit further uses the second time-series information indicating the time-series precipitation based on the precipitation map to use the second micro-region to calculate the predicted value after the predetermined time. The state quantity prediction support device according to claim 1, further comprising a function of calculating information and second macro area information. The related area extraction unit
The second time-series information for each spatial mesh point created based on the terrain information and the second degree of association corresponding to the second time-series information are described for each spatial mesh point. A micro-region extraction unit that calculates the second micro-region information,
The state quantity prediction support device according to claim 4, further comprising a macro region extraction unit that calculates the second macro region information by combining the second micro region information. The micro area extraction unit
The feature is that the second time-series information at each of the spatial mesh points has a function of calculating the second degree of relevance based on the magnitude of the influence of the target dam on the state quantity. The state quantity prediction support device according to claim 5. Using the first micro-region information, the first macro-region information, and / or the second micro-region information and the second macro-region information, the state quantity with respect to the water level of the target dam is predicted after a predetermined time. The state quantity prediction support device according to claim 1, further comprising a state quantity prediction unit for calculating a state quantity prediction value which is a value. A function to display the predicted target time related to the predicted value of the target dam on the display device, and
The display of any one or more of the first micro region information, the first macro region information, the second micro region information, and the second macro region information. The state quantity prediction support device according to claim 5 or 6, further comprising an area display unit having a function of displaying on the device. Further comprising a command receiver for receiving correction commands for the first micro-regional information for each observation point and / or for the second micro-regional information for each of the spatial mesh points.
8. The area display unit is characterized in that, in response to the correction command, the first micro area information and / or the second micro area information is recalculated and redisplayed on the display device. The state quantity prediction support device described in. Further provided with a command receiving unit for receiving the first time-series information, the second time-series information, and / or the display command information regarding the predicted target time.
The area display unit is characterized by having a function of displaying the first time-series information, the second time-series information, and / or the predicted target time on the display device based on the display command information. The state quantity prediction support device according to claim 8. An input means for receiving the first time-series information, which is the flow rate information of the river in the upstream area of the target dam, which is a dam that stores the water of the river using a computer.
Using the first time-series information, the first micro region information and the first macro used when calculating the predicted value of the state quantity regarding the water level of the target dam after a predetermined time in consideration of the flow delay time. Related area extraction means for calculating area information,
A program to function as.

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