JP6503745B2 - Method, program and apparatus for determining sensing location of sewer network - Google Patents

Description

  The present invention relates to a method, program and apparatus for determining a sensing location of a sewer network.

  The purpose of urban sewerage is to discharge rainwater that flows into the sewage pipeline into the river when it rains, and to treat sewage such as domestic drainage, and there are pumping stations and sewage treatment plants in the lower reaches of the sewer network. is set up. Since these sewerage systems are designed in consideration of the amount of rainfall, inflow, etc., in the past, there was almost no overflow from the sewer pipe.

  However, in recent years, since flood damage in cities has frequently occurred due to heavy rains and the like exceeding planned rainfall, it is necessary to take inundation measures in advance. As one of the measures against inundation, for example, measure the sewage condition data such as water level, flow rate and velocity of the sewer pipe with sensor in real time to grasp the situation in the sewer pipe, efficiency in the pump station and sewage treatment plant It is known to perform a typical drainage operation (see, for example, Patent Document 1 etc.).

Unexamined-Japanese-Patent No. 2010-203964 JP, 2006-18763, A

  Generally, it is desirable that the number of sensors be set as small as possible, since installing a large number of sensors results in an enormous amount of measurement data and high installation costs. However, in order to accurately grasp the situation of the sewer network, it is not possible to reduce the number of sensors installed indiscriminately.

  In addition, although the technique which estimates the amount of sewage which flows in into a sewage treatment plant using the delay time set beforehand is disclosed by patent document 2 grade | etc., This technique is for estimating the amount of sewage inflow. It is not for determining the installation location or number of sensors.

  In one aspect, the present invention aims to provide a method, program and apparatus for determining a sensing location in a sewer network that can determine an appropriate sensing location in a sewer network.

  In one aspect, the sensing location determination method of the sewer network calculates at least one time-series data of water level, flow rate, and flow velocity at a plurality of locations of the sewer network based on data on the sewer network and data on rainfall. The delay time with respect to each of the time-series data at each place other than the specific place in the specific place of the sewer network is calculated, and the calculated delay time and the time series at each place other than the specific place Using data, a computer executes a process of calculating the significance probability of each place other than the specific place by a statistical hypothesis test, and determining the sensing place where the sensor is to be arranged based on the calculated significance probability. It is the sensing location determination method of the sewer network.

  Appropriate sensing points can be determined in the sewer network.

It is a figure showing roughly the hardware constitutions of the sensing part determination device concerning one embodiment. It is a functional block diagram of a sensing location determination device. Fig.3 (a) is a figure which shows the data structure of sewer sewer hole DB, FIG.3 (b) is a figure which shows the data structure of sewer pipeline DB. It is a figure which shows the data structure of topography DB. It is a figure which shows the data structure of rainfall amount DB. It is a flowchart which shows a process of a sensing location determination apparatus. It is the schematic of a sewer network. 8 (a) is a graph showing an example of time series data of the water level of the human hole 3, and FIG. 8 (b) is a graph showing an example of time series data of the water level of the human hole 9, (C) is a graph which shows an example of the time series data of the water level of the person hole 17. FIG. It is a graph which shows the cross correlation function (correlation coefficient) of the person hole 3 and the person hole 17, and the cross correlation function (correlation coefficient) of the person hole 9 and the person hole 17. It is a figure for demonstrating the process of FIG.6 S18. It is a figure (the 1) which shows a test result table. 12 (a) and 12 (b) are diagrams (part 2) showing a test result table. It is a figure which shows the example of an output of the optimal installation location of a sensor. It is a figure for demonstrating the effect of this embodiment. Fig.15 (a)-FIG.15 (c) are figures for demonstrating a modification.

  Hereinafter, one embodiment of a sensing location determination device will be described in detail based on FIGS. 1 to 14. The sensing location determination device according to the present embodiment is a human hole suitable for installing a sensor that detects sewage condition data such as water level, flow rate, flow velocity, etc. from a plurality of human holes (manholes) included in the sewer network It is an apparatus for determining an appropriate sensing location.

  FIG. 1 shows the hardware configuration of a sensing location determination apparatus 10 according to the present embodiment. The sensing location determination device 10 is a terminal such as a PC (Personal Computer), and as shown in FIG. 1, a central processing unit (CPU) 90, a read only memory (ROM) 92, a random access memory (RAM) 94, and a storage. And a network interface 97, a display unit 93, an input unit 95, a portable storage medium drive 99, and the like. Each part of the sensing location determination device 10 is connected to the bus 98. The display unit 93 includes a liquid crystal display and the like, and the input unit 95 includes a keyboard, a mouse and the like. In the sensing location determination device 10, the CPU 90 stores a program (including a sensing location determination program) stored in the ROM 92 or the HDD 96, or a program read by the portable storage medium drive 99 from the portable storage medium 91 (sensing location determination Functions as each unit shown in FIG. 2 by executing the program.

  Specifically, when the CPU 90 executes a program, the CPU 90 functions as the input reception unit 12, the arithmetic unit 14 as the first to third calculation units, the determination unit 16, and the output unit 18 shown in FIG. 2. In the present embodiment, the function as the determination unit is realized including the determination unit 16 and the output unit 18. Note that FIG. 2 also shows a database group (including a sewer personal hole DB 22, a sewer pipeline DB 24, a topography DB 26, and a rainfall amount DB 28) stored in the HDD 96.

  The input reception unit 12 acquires information input by the user via the input unit 95, and transmits the acquired information to the calculation unit 14 and the determination unit 16. In addition, the input reception part 12 transmits the said information (N) to the determination part 16, when the user inputs the largest number (maximum number of sensors) N of the sensor installed in a sewer network. Further, at the stage of receiving the input of the maximum number of sensors N, the input reception unit 12 transmits an instruction to start the process of determining the sensing location to the calculation unit 14.

  The calculation unit 14 calculates significance probability of each location (sensor installation location candidate: each human hole in this embodiment) of the sewer network using various databases, and a test result table (FIG. 11 etc.) Create a reference). The details of the processing by the calculation unit 14 will be described later.

  The determination unit 16 determines whether a sensor installation location (sensing location) of an appropriate position and number has been determined based on the test result table created by the computation unit 14. As a result of the determination, when the sensing position of the appropriate position and number is determined, the determination unit 16 notifies the output unit 18 of that. On the other hand, when the sensing location of the appropriate position and number is not determined, the determination unit 16 notifies the calculation unit 14 to that effect. In addition, when receiving the notification from the determination unit 16, the calculation unit 14 reduces the sensor installation location candidates, and then calculates the significance probability of each of the sensor installation location candidates again to create a test result table.

  The output unit 18 outputs the information of the sensor installation location (sensing location) of the appropriate position and number via the display unit 93 and the like.

  Here, the data structure of the various databases (22, 24, 26, 28) shown in FIG. 2 will be described in detail based on FIGS.

  The data structure of sewer sewer hole DB22 is shown by FIG. 3 (a). The sewerage person hole DB 22 stores information related to manholes (manholes) included in the sewer network. Specifically, as shown in FIG. 3 (a), the sewer human-hole DB 22 is "human hole number", "latitude", "longitude", "human hole ground height (altitude)", "water collection area" , "Impermeable area rate", "equivalent roughness", and "slope slope" fields. The identification number assigned to the human hole is stored in the field of "human hole number", and the human hole is provided in the fields of "latitude", "longitude" and "human hole ground height (elevation)" Position and elevation information is stored. Moreover, the area of land taken by each human hole is stored in the "water collection area" field, and the impervious range (for example, the roof of a building) of the water collection area is stored in the "impermeable area ratio" field. The percentage occupied by paved roads etc. is stored. Also, in the fields of “equivalent roughness” and “slope slope”, values of the equivalent roughness and slope slope of the land each human hole receives are stored.

  The data structure of sewer pipe line DB24 is shown by FIG.3 (b). The sewer pipeline DB 24 stores information on the sewer pipeline provided between the human hole and the human hole. Specifically, as shown in FIG. 3 (b), the sewer pipeline DB 24 has “person hole number (upstream, downstream)”, “pipe type”, “pipe length”, “pipe width”, It has each field of "pipe height", "roughness coefficient", "pipe bottom height (altitude) (upstream, downstream)". In the field of “person hole number (upstream, downstream)”, identification numbers (person hole numbers) of the person holes on the upstream side and the downstream side of the sewer pipe are stored. The "pipeline type" field stores the type (pipeline etc.) of the pipeline, the "pipeline length" field stores the pipe length, and the "pipeline width" field , The width of the conduit is stored, and the height of the conduit is stored in the "pipe height" field. The roughness coefficient of the pipe is stored in the field of "roughness coefficient", and in the field of "pipe bottom height (elevation) (upstream, downstream)", the elevation at which the bottom on the upstream side of the pipe is located , And the elevation at which the bottom on the downstream side of the conduit is located.

  A data structure of the terrain DB 26 is shown in FIG. The terrain DB 26 is a database that manages information of each of a plurality of areas obtained by dividing the area where the sewer network is provided in a mesh. Specifically, as shown in FIG. 4, the terrain DB 26 has fields of “mesh number”, “ground height (altitude)”, “roughness coefficient”, and “building occupancy rate”. An identification number assigned to each area is stored in the field of "mesh number". The field of "ground height (altitude)" stores the elevation of each area. The roughness coefficient and the building occupancy of each area are stored in the fields of "roughness coefficient" and "building occupancy".

  The data structure of the rainfall amount DB 28 is shown in FIG. The rainfall amount DB 28 is a database that stores specific changes in rainfall amount in the past. For example, as illustrated in FIG. 5, the rainfall amount DB 28 stores changes in the rainfall amount of the areas A to C at a certain date and time (19:00 to 22:00 of 2014/12/24). The areas A to C are areas including a plurality of areas divided in the mesh shape of FIG. 4.

  In addition, the data structure of the various databases of FIGS. 3-5 is an example. That is, the contents of the fields of the various databases can be variously changed. In addition, other databases may be added as needed.

(Process of sensing location determination device 10)
Next, the processing of the sensing location determination apparatus 10 will be described in detail with reference to the other drawings along the flowchart of FIG. 6 as appropriate. In the present embodiment, as an example, the case of determining the sensing location (the optimal installation location of the sensor) in the sewer network as shown in FIG. 7 will be described. In FIG. 7, when the sewer pipe is branched, it is assumed that the sensor is placed only on the thick pipe (that is, the trunk line). Under such conditions, if sensors are temporarily installed in all the human holes, there are a maximum of 17 sensor installation location candidates as shown by numbering in FIG. In the present embodiment, the sensing location determination device 10 determines an appropriate location equal to or less than the maximum number of sensors determined by the user from among these sensor installation location candidates as the sensing location.

  In the process of FIG. 6, first, in step S10, the input receiving unit 12 receives an input of the maximum number of sensors N. In this case, when the user inputs the maximum number of sensors N via the input unit 95, the input reception unit 12 receives the maximum number of sensors N and transmits it to the determination unit 16. Further, at the stage of receiving the input of the maximum number of sensors N, the input reception unit 12 instructs the calculation unit 14 to start processing. In the present embodiment, it is assumed that the user inputs “5” as the maximum number of sensors N.

  Next, in step S12, the calculation unit 14 calculates sewage data based on the information on the sewer pipe, the topography, and the rainfall amount. Specifically, based on each data of the sewer human-hole DB 22, the sewer pipeline DB 24, the topography DB 26, and the rainfall amount DB 28, the calculation unit 14 calculates each part (each human hole) of the sewer network by a known calculation method. Calculate time series data such as water level, flow rate, and flow velocity. In the present embodiment, as an example, it is assumed that the calculation unit 14 calculates time series data of the water level [m]. The time-series data of the water level in the human hole 3, the human hole 9, and the human hole 17 of FIG. 7 are shown in FIGS. 8A to 8C as an example.

  Next, in step S14, the calculation unit 14 calculates the cross-correlation function R (τ) of each human hole and the most downstream human hole (here, the human hole 17) using the calculated time-series data. In addition, (tau) means the time difference of a change of the time-series data of each location (human hole) and the time-series data of the most downstream location (human hole 17). In FIG. 9, the cross correlation function (correlation coefficient) of the human hole 3 and the human hole 17 and the cross correlation function (correlation coefficient) of the human hole 9 and the human hole 17 are shown. More specifically, FIG. 9 shows a change in correlation with the time-series data of the human hole 17 when the time-series data of the human hole 3 and the human hole 9 is time-shifted.

Next, in step S16, the calculation unit 14 calculates the local maximum value of the cross correlation function. That is, the calculation unit 14 obtains the smallest time difference τ in which the correlation coefficient becomes positive and the second derivative (d ² R (τ) / dτ ² ) of the cross-correlation function of the time difference becomes zero. The obtained time difference τ means the delay time of the most downstream human hole 17 with respect to other human holes. In FIG. 9, the delay time of the human hole 17 with respect to the human hole 3 is shown as T3, and the delay time of the human hole 17 with respect to the human hole 9 is shown as T9.

  Next, in step S18, the calculation unit 14 shifts each time series data by the maximum value (delay time τ). In this case, as shown in the upper part of FIG. 10, the calculation unit 14 shifts the time series data of the human hole 3 to the right by the delay time T3 (see the broken line graph). In addition, as shown in the middle part of FIG. 10, the calculation unit 14 shifts the time series data of the human hole 9 to the right by the delay time T9 (see the broken line graph). The time shift is similarly performed for time series data of other human holes.

  Next, in step S20, the calculation unit 14 calculates a significant probability. In this case, the operation unit 14 performs a statistical hypothesis test on the time series data of each human hole time-shifted by the delay time to calculate a significance probability.

  Next, in step S22, the calculation unit 14 creates a test result table of sensor installation location candidates (human holes). An example of the test result table is shown in FIG. In the test result table of FIG. 11, t values and P (Probability) -values of sensor installation location candidates (here, human holes 1 to 16) are collected.

  Next, in step S24, the determination unit 16 determines whether all the significant probabilities are significant. In this case, for example, the determination unit 16 determines that all significant probabilities are significant based on whether all P-values are equal to or less than a predetermined threshold (for example, 0.05). Determine if it is or not. If the determination here is negative, that is, if there is a sensor installation location candidate whose P-value exceeds the threshold, the process proceeds to step S28. In step S28, the calculation unit 14 performs a process of subtracting the sensor installation location candidate. In this case, the operation unit 14 excludes the human hole having the largest P-value exceeding the threshold value (the human hole 2 surrounded by a thick broken line frame in FIG. 11) in step S28 from the sensor installation location candidate. Return to

  If it returns to step S20, the calculating part 14 will calculate again the significant probability of the remaining sensor installation location candidate (here human hole 1, human holes 3-16). Then, in the next step S22, the computing unit 14 creates again the verification result table of the remaining sensor installation location candidates (human holes). In this case, as shown in FIG. 12 (a), a verification result table regarding the human hole 1 and the human holes 3 to 16 is created. Next, in step S24, the determination unit 16 again determines whether all the significant probabilities are significant. If the determination in step S24 is negative, the process proceeds to step S28. In the case of FIG. 12A in step S28, the calculation unit 14 excludes the human hole 10 surrounded by the thick broken line frame, and returns to step S20.

  If the determination in step S24 is affirmed, that is, if all the significant probabilities are significant, the determination unit 16 proceeds to step S26. If it transfers to step S26, the determination part 16 will determine whether a sensor installation location candidate is N or less. If the determination here is denied, the process proceeds to step S28, and the person hole having the highest significance probability (P-value) is excluded from the sensor installation location candidates included in the test result table, and the process proceeds to step S20. Return.

  Thereafter, the process / determination in steps S20 to S28 is repeatedly performed until the determination in step S26 is affirmed. In addition, the example of the test result table | surface in the step in which the judgment of step S26 is affirmed is shown by FIG.12 (b). In FIG. 12B, all significant probabilities (P-values) are less than the threshold (0.05), and the number of sensor installation location candidates (human holes) is less than or equal to N (= 5).

  When the determination in step S26 is affirmed and the process proceeds to step S30, the output unit 18 determines and outputs the optimum installation location of the sensor. In this case, the output unit 18 sets the display unit 93 or the like as the optimum installation location of the sensor, with the human holes (human holes 6, 9, 11, 13, 16) included in the verification plan table of FIG. Output through. In this case, the output unit 18 may output the optimum installation location (see black circle in FIG. 13) of the sensor using a diagram showing a sewer network as shown in FIG.

  As described above in detail, according to the present embodiment, the operation unit 14 is based on various databases (22, 24, 26, 28), and time series data of water level in a plurality of locations (human holes) in the sewer network Were calculated (S12), and the delay time τ with respect to each of the time-series data in the other human holes of the time-series data in the specific location of the sewer network (the most downstream man hole 17) was calculated (S14, S16) Using the delay time τ and time series data in each of the other human holes, the statistical probability test is performed to calculate the significance probability of each of the other human holes (S18, S20). And the determination part 16 and the output part 18 determine the optimal installation location (sensing location) of a sensor based on the calculated significant probability (S22-S30). Thus, in the present embodiment, an appropriate sensing location can be determined by determining the sensing location in consideration of the delay time. FIG. 14 shows the transition of the standard error according to the number of installed sensors in the present embodiment and the comparative example (example in which the statistical hypothesis test is performed without considering the delay time). In addition, a standard error means the difference | error at the time of estimating the water level of the most downstream manhole 17 using the installed sensor. Comparing the present embodiment with the comparative example, it can be confirmed that the model accuracy is generally improved (the standard error is smaller) in the present embodiment than in the comparative example. Further, in the comparative example, the standard error rapidly increases when the number of sensors is reduced, but it is understood that the standard error gradually increases in the present embodiment. From these, it is understood that in the present embodiment, the model is stable, and even if the number of sensors is reduced, the influence on the model accuracy is small. For this reason, in the present embodiment, as shown in FIG. 12A and FIG. 13, even if the number of sensing points is reduced to the number N (= 5) or less of the maximum number of sensors input by the user, It is possible to estimate Therefore, in the present embodiment, by reducing the number of sensors as much as possible, it is possible to reduce the amount of data that needs to be processed when predicting the situation of the sewer network, and to reduce the sensor installation cost. It becomes.

  Further, in the present embodiment, when the delay time τ is calculated, when the time-series data in the human hole 17 other than the most downstream is time-shifted, the correlation with the time-series data in the most downstream human hole 17 is largest. Shift time is calculated as the delay time. Thus, an appropriate value can be calculated as the delay time τ.

  In addition, although the said embodiment demonstrated the case where the calculating part 14 calculated the time series data of the water level in the sensor installation location candidate of a sewer network in step S12, it is not restricted to this. For example, the calculation unit 14 may calculate time series data of the flow rate. In this case, as shown in FIGS. 15 (a) to 15 (c), data similar to time series data of water level (see FIGS. 8 (a) to 8 (c)) can be obtained. The determination unit 16 may determine the optimum installation location of the sensor by the same process as the above embodiment. In addition, the calculation unit 14 may calculate time series data of the flow velocity in step S12. In this case, the calculation unit 14 obtains a value (time for sewage to flow between the human holes) obtained by dividing each distance between the human holes by the flow velocity of the sewage between the human holes, and integrates the value to the most downstream human hole. Therefore, the delay time may be calculated. In the above embodiment, a plurality of time series data of water level, flow rate, and flow velocity are calculated, and the processing of FIG. 6 is executed using each time series data, and the optimum installation of the sensor determined from each time series data From the location, the final installation location of the sensor may be determined.

  In the above embodiment, in step S24, the determination unit 16 has described the case of determining the significance of each human hole using the P-value. However, the present invention is not limited to this, and a t value may be used. . Moreover, you may use not only P value and t value but F value, standard error, etc. which are used by a hypothesis test. Further, in FIG. 6, the case has been described where the user inputs the maximum number of sensors N, and the sensing location determination device 10 determines that the optimum installation location of the sensor is N or less. Absent. For example, in the test result table, when all variables become significant (when step S24 is affirmed), the human holes remaining in the test result table may be determined as the optimum installation location of the sensor.

  In the above embodiment, the sensing location determination device 10 may be a server connected to a network such as the Internet. In this case, the sensing location determination apparatus 10 may execute the process of FIG. 6 according to an input from a terminal (client) connected to the network, and may transmit the processing result to the terminal (client).

  The above processing functions can be realized by a computer. In that case, a program is provided which describes the processing content of the function that the processing device should have. The above processing functions are realized on the computer by executing the program on the computer. The program in which the processing content is described can be recorded on a computer readable recording medium (except for the carrier wave).

  In the case of distributing the program, for example, the program is sold in the form of a portable recording medium such as a DVD (Digital Versatile Disc), a CD-ROM (Compact Disc Read Only Memory) or the like in which the program is recorded. Alternatively, the program may be stored in the storage device of the server computer, and the program may be transferred from the server computer to another computer via a network.

  The computer executing the program stores, for example, the program recorded on the portable recording medium or the program transferred from the server computer in its own storage device. Then, the computer reads the program from its storage device and executes processing according to the program. The computer can also read the program directly from the portable recording medium and execute processing in accordance with the program. The computer can also execute processing in accordance with the received program each time the program is transferred from the server computer.

  The embodiments described above are examples of preferred implementations of the invention. However, the present invention is not limited to this, and various modifications can be made without departing from the scope of the present invention.

In addition, the following additional remarks are disclosed regarding description of the above embodiment.
(Supplementary Note 1) At least one time-series data of water level, flow rate, and flow velocity at a plurality of locations of the sewer network is calculated based on the data on the sewer network and the data on the rainfall,
Calculating a delay time of each of the time-series data at a specific location of the sewer network with respect to each of the time-series data at a location other than the specific location,
Using the calculated delay time and the time-series data at each location other than the specific location, the statistical probability test is performed to calculate the significance probability of each location other than the specific location,
Determining a sensing location where a sensor is to be placed based on the calculated significance probability
A method for determining a sensing location of a sewer network, wherein a computer executes the processing.
(Supplementary Note 2) In the process of determining, the sensing location is determined based on the condition to be satisfied by the calculated significant probability and the maximum number of sensing locations set in advance. The method of determining the sensing location of the sewer network described in 1.
(Supplementary Note 3) In the process of calculating the delay time, when the time-series data at a location other than the specific location is time-shifted, a shift time at which the correlation with the time-series data at the specific location is maximized It calculates as a delay time, The sensing location determination method of the sewer network as described in Additional remark 1 or 2 characterized by the above-mentioned.
(Supplementary Note 4) In the process of calculating the time-series data, in the case of calculating time-series data of the flow velocity at a plurality of locations of the sewer network,
In the processing for calculating the delay time, the delay time is calculated based on the time series data of the flow velocity and the distance between the plurality of places. How to determine sensing location.
(Supplementary Note 5) At least one time-series data of water level, flow rate, and flow velocity at a plurality of locations of the sewer network is calculated based on the data on the sewer network and the data on the rainfall,
Calculating a delay time of each of the time-series data at a specific location of the sewer network with respect to each of the time-series data at a location other than the specific location,
Using the calculated delay time and the time-series data at each location other than the specific location, the statistical probability test is performed to calculate the significance probability of each location other than the specific location,
Determining a sensing location where a sensor is to be placed based on the calculated significance probability
The sensing location determination program of the sewer network characterized by making a computer perform a process.
(Supplementary Note 6) In the process of determining, the sensing location is determined based on the condition to be satisfied by the calculated significant probability and the maximum number of sensing locations set in advance. The sensing location determination program for the sewerage network described in 5.
(Supplementary Note 7) In the process of calculating the delay time, the shift time at which the correlation with the time-series data at the specific location is maximized when the time-series data at the location other than the specific location is time-shifted It calculates as a delay time, The sensing location determination program of the sewer network as described in Additional remark 5 or 6 characterized by the above-mentioned.
(Supplementary Note 8) In the process of calculating the time-series data, in the case of calculating time-series data of the flow velocity at a plurality of locations of the sewer network,
In the processing for calculating the delay time, the delay time is calculated based on time series data of the flow velocity and a distance between the plurality of places. Sensing location determination program.
(Supplementary Note 9) A first calculation unit that calculates at least one time-series data of water level, flow rate, and flow velocity at a plurality of locations of the sewer network based on data on the sewer network and data on rainfall.
A second calculator configured to calculate a delay time of each of the time-series data at a specific location of the sewer network with respect to each of the time-series data at a location other than the specific location;
A third calculator configured to calculate a significance probability of each place other than the specific place by a statistical hypothesis test using the calculated delay time and the time-series data in each place other than the specific place;
A determination unit of a sewer network, comprising: a determination unit configured to determine a sensing location at which a sensor is arranged based on the calculated significance probability.
(Supplementary Note 10) The determination part determines the sensing location based on a condition that the calculated significance probability should satisfy and the maximum number of sensing locations set in advance. The sensing location determination device of the sewer network as described in.
(Supplementary Note 11) When the second calculation unit time-shifts the time-series data at a place other than the specific place, a shift time at which the correlation with the time-series data at the specific place becomes largest is a delay time It calculates as, The sensing location determination apparatus of the sewer network as described in Additional remark 9 or 10 characterized by the above-mentioned.
(Supplementary Note 12) When the first calculation unit calculates time-series data of flow velocity at a plurality of locations of the sewerage network,
The sensing location of the sewer network according to Supplementary Note 9 or 10, wherein the second calculation unit calculates a delay time based on time series data of the flow velocity and a distance between the plurality of locations. Decision device.

10 Sensing location determination device 14 Arithmetic unit (first to third calculation units)
16 Judgment part (part of decision part)
18 Output unit (part of the decision unit)
90 computer

Claims (6)

Calculating at least one time series data of water level, flow rate and flow velocity at a plurality of locations of the sewer network based on data on the sewer network and data on rainfall amount,
Calculating a delay time of each of the time-series data at a specific location of the sewer network with respect to each of the time-series data at a location other than the specific location,
Using the calculated delay time and the time-series data at each location other than the specific location, the statistical probability test is performed to calculate the significance probability of each location other than the specific location,
Determining a sensing location where a sensor is to be placed based on the calculated significance probability
A method for determining a sensing location of a sewer network, wherein a computer executes the processing. In the process of the determination, the condition should the significance probability satisfies the maximum number of preset sensing portion, based on the sewer network of claim 1, wherein determining the sensing portion, characterized in that How to determine the location of   In the process of calculating the delay time, when the time-series data at a location other than the specific location is time-shifted, the shift time at which the correlation with the time-series data at the specific location is maximized is calculated as the delay time The sensing location determination method of the sewer network according to claim 1 or 2, characterized in that. In the process of calculating the time-series data, in the case of calculating time-series data of flow velocity at a plurality of locations of the sewer network,
The sewer network according to claim 1 or 2, wherein in the process of calculating the delay time, the delay time is calculated based on time series data of the flow velocity and a distance between the plurality of places. How to determine the location of Calculating at least one time series data of water level, flow rate and flow velocity at a plurality of locations of the sewer network based on data on the sewer network and data on rainfall amount,
Calculating a delay time of each of the time-series data at a specific location of the sewer network with respect to each of the time-series data at a location other than the specific location,
Using the calculated delay time and the time-series data at each location other than the specific location, the statistical probability test is performed to calculate the significance probability of each location other than the specific location,
Determining a sensing location where a sensor is to be placed based on the calculated significance probability
The sensing location determination program of the sewer network characterized by making a computer perform a process. A first calculation unit that calculates at least one time-series data of water level, flow rate, and velocity at a plurality of locations of the sewer network based on data on the sewer network and data on rainfall amount;
A second calculator configured to calculate a delay time of each of the time-series data at a specific location of the sewer network with respect to each of the time-series data at a location other than the specific location;
A third calculator configured to calculate a significance probability of each place other than the specific place by a statistical hypothesis test using the calculated delay time and the time-series data in each place other than the specific place;
A determination unit of a sewer network, comprising: a determination unit configured to determine a sensing location at which a sensor is arranged based on the calculated significance probability.

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