JP2016130435A - Method, program and system of determining sensing point in sewerage system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method and a system for determining proper sensing points in a sewerage system.SOLUTION: The method for determining proper sensing points in a sewerage system includes the steps of: calculating time series data of water level at plural points (manhole) in a sewerage system on the basis of various kinds of databases (S12); calculating a delay time τ at a specific point (lower most manhole) in the sewerage system with respect to other points of manholes in the time series data (S14, S16); calculating significance probability at each of the other points of manholes by means of testing of statistical hypothesis by using calculated delay time τ and the time series data at the other points of manholes (S18, S20); and determining optimum installation points (sensing point) of sensors on the basis of the calculated significance probability (S22 to S30).SELECTED DRAWING: Figure 6

Description

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

  Urban sewers are intended to discharge rainwater that flows into sewer pipes during rainfall into rivers and to treat sewage such as domestic wastewater. Pumps and sewage treatment plants are located downstream of the sewer network. is set up. Since these sewerage facilities are designed taking into account the rainfall and inflow, etc., there has been almost no overflow from sewerage pipes in the past.

  However, in recent years, inundation damage has frequently occurred in cities due to torrential rain that exceeds the planned rainfall, and it is necessary to take measures against inundation in advance. As one of the countermeasures against inundation, for example, sewage status data such as sewer pipe water level, flow rate, and flow velocity is measured in real time by a sensor to grasp the situation in the sewer pipe, and efficiency at the pump station and sewage treatment plant. It is known to perform a general drainage operation (see, for example, Patent Document 1).

JP 2010-203964 A JP 2006-18763 A

  Generally, when a large number of sensors are installed, the amount of measurement data becomes enormous and the installation cost becomes high. Therefore, it is desirable that the number of sensors installed be as small as possible. However, in order to accurately grasp the state of the sewer network, the number of sensors installed cannot be reduced without any darkness.

  Patent Document 2 and the like disclose a technique for predicting the amount of sewage flowing into a sewage treatment plant using a preset delay time, but this technique is for predicting the amount of sewage inflow. However, it is not for determining the location and number of sensors.

  In one aspect, an object of the present invention is to provide a sewer network sensing location determination method, program, and apparatus capable of determining an appropriate sensing location in a sewer network.

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

  Appropriate sensing points can be determined in the sewer network.

It is a figure which shows roughly the hardware constitutions of the sensing location determination apparatus which concerns on 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 manhole DB, FIG.3 (b) is a figure which shows the data structure of sewer line DB. It is a figure which shows the data structure of landform DB. It is a figure which shows the data structure of rainfall DB. It is a flowchart which shows the process of a sensing location determination apparatus. It is the schematic of a sewer network. FIG. 8A is a graph showing an example of the time series data of the water level of the human hole 3, and FIG. 8B is a graph showing an example of the time series data of the water level of the human hole 9. (C) is a graph showing an example of time-series data of the water level of the human hole 17. It is a graph which shows 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. It is a figure for demonstrating the process of step S18 of FIG. It is a figure (the 1) which shows a test result table. FIG. 12A and FIG. 12B 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 the figures for demonstrating a modification.

  Hereinafter, an embodiment of a sensing location determination device will be described in detail with reference to FIGS. The sensing location determination apparatus of the present embodiment has a human hole (appropriate for installing a sensor for detecting sewage status data such as water level, flow rate, flow velocity, etc., from a plurality of human holes (manholes) included in the sewer network. This is a device for determining an appropriate sensing location.

  FIG. 1 shows a hardware configuration of a sensing location determination device 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 CPU (Central Processing Unit) 90, a ROM (Read Only Memory) 92, a RAM (Random Access Memory) 94, a storage A hard disk drive (HDD (Hard Disk Drive)) 96, 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 or the like, and the input unit 95 includes a keyboard, a mouse, or 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 from the portable storage medium 91 by the portable storage medium drive 99 (sensing location determination). By executing (including a program), it functions as each unit shown in FIG.

  Specifically, when the CPU 90 executes the program, the CPU 90 functions as the input reception unit 12, the calculation unit 14 as the first to third calculation units, the determination unit 16, and the output unit 18 illustrated 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. FIG. 2 also shows a database group (including a sewer manhole DB 22, a sewer line DB 24, a terrain DB 26, and a rainfall 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. The input receiving unit 12 transmits the information (N) to the determination unit 16 when the user inputs the maximum number (maximum number of sensors) N of sensors installed in the sewer network. In addition, the input receiving unit 12 transmits an instruction to start the sensing location determination process to the calculation unit 14 when the input of the maximum number of sensors N is received.

  The calculation unit 14 calculates the significance probability of each location (sensor installation location candidate: each human hole in the present embodiment) of the sewer network using various databases, and the test result table summarizing the significance probabilities (FIG. 11 and the like) Browse). Details of the processing by the calculation unit 14 will be described later.

  The determination unit 16 determines whether or not an appropriate position and number of sensor installation locations (sensing locations) have been determined based on the test result table created by the calculation unit 14. As a result of the determination, when an appropriate position and number of sensing locations are determined, the determination unit 16 notifies the output unit 18 to that effect. On the other hand, when an appropriate position and the number of sensing locations are not determined, the determination unit 16 notifies the calculation unit 14 to that effect. In addition, when the calculation part 14 receives notification from the determination part 16, after calculating a sensor installation location candidate, it calculates again the significance probability of each sensor installation location candidate, and produces a test result table.

  The output unit 18 outputs information on an appropriate position and number of sensor installation locations (sensing locations) 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 with reference to FIGS.

  FIG. 3A shows the data structure of the sewer manhole DB 22. In the sewer personal hole DB 22, information related to human holes (manholes) included in the sewer network is stored. Specifically, as shown in FIG. 3 (a), the sewer manhole DB 22 is “person number”, “latitude”, “longitude”, “manhole ground height (elevation)”, “water collection area”. , “Impermeable area ratio”, “equivalent roughness”, and “slope gradient” fields. An identification number assigned to the human hole is stored in the “human hole number” field, and a human hole is provided in the “latitude”, “longitude” and “human hole ground height (elevation)” fields. Information on position and altitude is stored. The field of “Catchment area” stores the area of land that each manhole serves, and the field of “Impermeable area ratio” stores the impervious area of the catchment area (for example, the roof of a building, Stores the percentage occupied by paved roads). In the “equivalent roughness” and “slope gradient” fields, the values of the equivalent roughness and slope gradient of the land each manhole is responsible for are stored.

  FIG. 3B shows the data structure of the sewer line DB 24. In the sewer line DB 24, information on the sewer line provided between the manholes is stored. Specifically, as shown in FIG. 3 (b), the sewer pipe line DB 24 includes “manhole number (upstream, downstream)”, “pipe line type”, “pipe length”, “pipe width”, Each field includes “pipe height”, “roughness coefficient”, and “pipe bottom height (elevation) (upstream, downstream)”. In the “manhole number (upstream, downstream)” field, an identification number (manhole number) of the upstream and downstream manholes of the sewer pipe is stored. In the “Pipeline type” field, the pipeline type (rectangular etc.) is stored. In the “Pipeline length” field, the length of the pipeline is stored. In the “Pipeline width” field, The width of the pipe line is stored, and the height of the pipe line is stored in the “pipe line height” field. The “roughness coefficient” field stores the roughness coefficient of the pipeline, and the “pipe bottom height (elevation) (upstream, downstream)” field indicates the altitude at which the bottom of the upstream side of the pipeline is located. The altitude at which the bottom portion on the downstream side of the pipeline is located is stored.

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

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

  The data structures of the various databases in FIGS. 3 to 5 are examples. That is, various changes can be made to the contents of the fields of various databases. Moreover, you may add another database as needed.

(Processing of sensing location determination apparatus 10)
Next, the processing of the sensing location determination device 10 will be described in detail along the flowchart of FIG. 6 with reference to other drawings as appropriate. In addition, in this embodiment, the case where a sensing location (optimum installation location of a sensor) is determined in a sewer network as shown in FIG. 7 is demonstrated as an example. 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 main line). Under such conditions, if sensors are installed in all human holes, there are a maximum of 17 sensor installation location candidates as shown in FIG. In this embodiment, the sensing location determination apparatus 10 shall determine an appropriate location below the maximum number of sensors determined by the user as a sensing location from these sensor installation location candidates.

  In the process of FIG. 6, first, in step S <b> 10, the input receiving unit 12 receives an input of the maximum number of sensors N. In this case, when the user inputs the maximum sensor number N through the input unit 95, the input reception unit 12 receives the maximum sensor number N and transmits it to the determination unit 16. Further, the input receiving unit 12 gives an instruction to start the processing to the calculation unit 14 when the input of the maximum number of sensors N is received. In the present embodiment, it is assumed that the user inputs “5” as the maximum sensor number N.

  Next, in step S12, the calculation unit 14 calculates sewage data based on information on sewer pipes, topography, and rainfall. Specifically, the calculation unit 14 calculates each location (each human hole) of the sewer network by a known calculation method based on each data of the sewer manhole DB22, the sewer pipe DB24, the topography DB26, and the rainfall DB28. Calculate time-series data such as water level, flow rate, and flow velocity. In the present embodiment, as an example, the calculation unit 14 calculates time-series data of the water level [m]. 8A to 8C show, as an example, time series data of water levels in the human hole 3, the human hole 9, and the human hole 17 in FIG.

  Next, in step S14, the computing unit 14 calculates a cross-correlation function R (τ) between each human hole and the most downstream human hole (here, human hole 17) using the calculated time series data. Note that τ means the time difference between changes in the time series data at each location (human hole) and the time series data at the most downstream location (human hole 17). FIG. 9 shows a cross-correlation function (correlation coefficient) between the human hole 3 and the human hole 17 and a cross-correlation function (correlation coefficient) between the human hole 9 and the human hole 17. 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 are time-shifted.

Next, in step S16, the calculation unit 14 calculates the maximum value of the cross-correlation function. That is, the calculation unit 14 obtains the smallest time difference τ in which the correlation coefficient is positive and the second-order derivative (d ² R (τ) / dτ ² ) of the time difference cross-correlation function is 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 diagram of FIG. 10, the calculation unit 14 time-shifts the time series data of the human hole 3 to the right by the delay time T3 (see the broken line graph). Further, as shown in the middle diagram of FIG. 10, the calculation unit 14 time-shifts the time series data of the human hole 9 to the right by the delay time T9 (see the broken line graph). It should be noted that time shift is similarly performed for other time series data of human holes.

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

  Next, in step S22, the computing unit 14 creates a test result table for sensor installation location candidates (human holes). FIG. 11 shows an example of the test result table. 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 summarized.

  Next, in step S24, the determination unit 16 determines whether or not all significant probabilities are significant. In this case, for example, the determination unit 16 determines that all significant probabilities are significant based on whether or not all P-values are equal to or less than a predetermined threshold value (for example, 0.05). Determine whether or not. If the determination 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 calculating part 14 performs the process which subtracts a sensor installation location candidate. In this case, the calculation unit 14 excludes the human hole having the largest P-value exceeding the threshold value in step S28 (in FIG. 11, the human hole 2 surrounded by the thick broken line frame) from the sensor installation location candidates, and then performs step S20. Return to.

  Returning to step S20, the calculation unit 14 calculates again the significance probability of the remaining sensor installation location candidates (here, human hole 1, human holes 3 to 16). In the next step S22, the calculation unit 14 again creates a test result table for the remaining sensor installation location candidates (human holes). In this case, as shown in FIG. 12A, a test result table for the human hole 1 and the human holes 3 to 16 is created. Next, in step S24, the determination unit 16 determines again whether or not all 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 affirmative, that is, if all significant probabilities are significant, the determination unit 16 proceeds to step S26. If transfering it to step S26, the determination part 16 will judge whether a sensor installation location candidate is N or less. If the determination is negative, the process proceeds to step S28, and the human 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 processes and determinations in steps S20 to S28 are repeatedly executed until the determination in step S26 is affirmed. An example of the test result table at the stage where the determination in step S26 is affirmed is shown in FIG. 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 N (= 5) or less.

  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 uses the human holes (human holes 6, 9, 11, 13, 16) included in the test plan table of FIG. Output via. In this case, the output unit 18 may output an optimum installation location of the sensor (see a black circle in FIG. 13) using a diagram showing a sewer network as shown in FIG.

  As described above in detail, according to this embodiment, the calculation unit 14 is based on various databases (22, 24, 26, 28), and time-series data of water levels at a plurality of locations (human holes) in the sewer network. (S12), and calculating the delay time τ of the time series data in the specific part of the sewer network (the most downstream human hole 17) with respect to each of the time series data in the other human holes (S14, S16). Using the delay time τ and the time series data in each of the other foramen, the significance probability for each of the other foramen is calculated by a statistical hypothesis test (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 this 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 depending on the number of installed sensors in the present embodiment and a comparative example (an example in which a statistical hypothesis test is performed without considering delay time). The standard error means an error when the water level of the most downstream human hole 17 is predicted using the installed sensor. Comparing the present embodiment with the comparative example, it can be confirmed that the model accuracy is generally improved (standard error is smaller) in the present embodiment than in the comparative example. In the comparative example, the standard error increases rapidly when the number of sensors is reduced, but it can be seen that the standard error gradually increases in this embodiment. From these, it can be seen that in this 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 this embodiment, as shown in FIG. 12A and FIG. 13, even if the sensing location is reduced to the number N (= 5) or less of the maximum number of sensors input by the user, the water level of the most downstream human hole is accurately obtained. Can be estimated. Therefore, in this 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 status of the sewer network, and to reduce the cost of installing the sensors It becomes.

  Further, in the present embodiment, when calculating the delay time τ, 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 the largest. Is calculated as a delay time. Thereby, an appropriate value can be calculated as the delay time τ.

  In addition, although the calculating part 14 demonstrated the case where the calculation 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. 15A to 15C, data similar to the water level time-series data (see FIGS. 8A to 8C) is obtained. And the determination part 16 should just determine the optimal installation location of a sensor by the process similar to the said embodiment. Moreover, the calculating part 14 is good also as calculating the time series data of the flow velocity in step S12. In this case, the calculation unit 14 obtains a value obtained by dividing the distance between the human holes by the flow rate of the sewage between the human holes (the time during which the sewage flows between the human holes), and integrates the value to the most downstream human hole. Thus, the delay time may be calculated. In the above embodiment, a plurality of time series data is calculated from the water level, the flow rate, and the flow velocity, and the processing shown in FIG. 6 is executed using each time series data, and the optimum sensor installation determined from each time series data is performed. The final optimal installation location of the sensor may be determined from the location.

  In the above embodiment, the determination unit 16 has described the case where the significance of each human hole is determined using the P-value in step S24. However, the present invention is not limited to this, and a t value may be used. . Further, not only the P-value and the t value, but also an F value and standard error used in the hypothesis test may be used. In FIG. 6, the user inputs the maximum number of sensors N, and the sensing location determination device 10 has been described to determine that the optimal installation location of the sensor is N or less. However, the present invention is not limited to this. Absent. For example, when all the variables are significant in the test result table (when step S24 is affirmed), the human hole 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 device 10 may execute the processing of FIG. 6 in accordance with an input from a terminal (client) connected to the network, and transmit the processing result to the terminal (client).

  The above processing functions can be realized by a computer. In that case, a program describing the processing contents of the functions that the processing apparatus should have is provided. By executing the program on a computer, the above processing functions are realized on the computer. The program describing the processing contents can be recorded on a computer-readable recording medium (except for a carrier wave).

  When the program is distributed, for example, it is sold in the form of a portable recording medium such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory) on which the program is recorded. It is also possible to store the program in a storage device of a server computer and transfer the program from the server computer to another computer via a network.

  The computer that executes 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 own 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 according to the program. Further, each time the program is transferred from the server computer, the computer can sequentially execute processing according to the received program.

  The above-described embodiment is an example of a preferred embodiment of the present 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) Based on the data on the sewer network and the data on the rainfall, at least one time series data of the water level, the flow rate and the flow velocity in the plurality of locations of the sewer network is calculated.
Calculate the delay time for each of the time-series data in a location other than the specific location of the time-series data in a specific location of the sewer network,
Using the calculated delay time and the time-series data in each location other than the specific location, calculate the significance probability of each location other than the specific location by statistical hypothesis test,
Determining a sensing location to place the sensor based on the calculated significance;
A method for determining a sensing location of a sewer network, wherein the processing is performed by a computer.
(Additional remark 2) In the said process to determine, the said sensing location is determined based on the conditions which the calculated said significant probability should satisfy | fill, and the preset maximum number of sensing locations. The sensing location determination method of the sewer network of 1.
(Additional remark 3) In the process which calculates the said delay time, when the time series data in parts other than the said specific part are time-shifted, the shift time when the correlation with the said time series data in the said specific part becomes the largest is used. The sensing location determination method of the sewer network according to appendix 1 or 2, characterized in that it is calculated as a delay time.
(Additional remark 4) In the process which calculates the said time series data, when calculating the time series data of the flow velocity in the several location of the said sewer network,
In the process of 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 locations. Sensing location determination method.
(Appendix 5) Based on the data on the sewer network and the data on the rainfall, calculate at least one time-series data of the water level, flow rate, and flow velocity at a plurality of locations of the sewer network,
Calculate the delay time for each of the time-series data in a location other than the specific location of the time-series data in a specific location of the sewer network,
Using the calculated delay time and the time-series data in each location other than the specific location, calculate the significance probability of each location other than the specific location by statistical hypothesis test,
Determining a sensing location to place the sensor based on the calculated significance;
A sewer network sensing location determination program that causes a computer to execute processing.
(Additional remark 6) In the said process to determine, the said sensing location is determined based on the conditions which the calculated said significant probability should satisfy | fill, and the preset maximum number of sensing locations, It is characterized by the above-mentioned. 5. Sewer network sensing location determination program according to 5.
(Additional remark 7) In the process which calculates the said delay time, when the time series data in parts other than the said specific part are time-shifted, the shift time when the correlation with the said time series data in the said specific part becomes the largest is used. The sewer network sensing location determination program according to appendix 5 or 6, characterized in that it is calculated as a delay time.
(Additional remark 8) In the process which calculates the said time series data, when calculating the time series data of the flow velocity in the several location of the said sewer network,
In the process of 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 locations. Sensing location determination program.
(Additional remark 9) The 1st calculation part which calculates at least 1 time series data of the water level in the several location of the said sewer network, flow volume, and the flow velocity based on the data regarding a sewer network, and the data regarding rainfall,
A second calculation unit that calculates a delay time for each of the time series data in a place other than the specific part of the time series data in the specific part of the sewer network;
Using the calculated delay time and the time series data in each of the locations other than the specific location, a third calculation unit for calculating the significance probability of each location other than the specific location by a statistical hypothesis test;
A sewer network sensing location determination apparatus, comprising: a determination unit that determines a sensing location where a sensor is arranged based on the calculated significance probability.
(Additional remark 10) The said determination part determines the said sensing location based on the conditions which the calculated said significant probability should satisfy | fill, and the preset maximum number of sensing locations, The additional remark 9 characterized by the above-mentioned. Sensing location determination device for sewer network described in 1.
(Additional remark 11) When the said 2nd calculation part carries out time shift of the said time series data in places other than the said specific location, the shift time when the correlation with the said time series data in the said specific location becomes the largest is a delay time. The sewer network sensing location determination device according to appendix 9 or 10, characterized in that it is calculated as:
(Supplementary Note 12) When the first calculation unit calculates time-series data of flow velocities at a plurality of locations of the sewer network,
The second calculation unit calculates a delay time based on the time-series data of the flow velocity and the distance between the plurality of locations, and the sensing location of the sewer network according to appendix 9 or 10, Decision device.

10 Sensing Location Determination Device 14 Calculation Unit (First to Third Calculation Units)
16 Judgment part (part of decision part)
18 Output part (part of the decision part)
90 computers

Claims (6)

Based on the data on the sewer network and the data on the rainfall, calculate at least one time-series data of the water level, the flow rate and the flow velocity at a plurality of locations of the sewer network,
Calculate the delay time for each of the time-series data in a location other than the specific location of the time-series data in a specific location of the sewer network,
Using the calculated delay time and the time-series data in each location other than the specific location, calculate the significance probability of each location other than the specific location by statistical hypothesis test,
Determining a sensing location to place the sensor based on the calculated significance;
A method for determining a sensing location of a sewer network, wherein the processing is performed by a computer.   The said determination process WHEREIN: The said sensing location is determined based on the conditions which the calculated said significant probability should satisfy | fill, and the preset maximum number of sensing locations, The said sensing location is characterized by the above-mentioned. To determine the sensing location of the sewer network.   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 that maximizes the correlation with the time-series data at the specific location is calculated as the delay time. The sensing location determination method of the sewer network of Claim 1 or 2 characterized by the above-mentioned. In the process of calculating the time series data, when calculating the time series data of the 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 the time-series data of the flow velocity and the distance between the plurality of locations. Sensing location determination method. Based on the data on the sewer network and the data on the rainfall, calculate at least one time-series data of the water level, the flow rate and the flow velocity at a plurality of locations of the sewer network,
Calculate the delay time for each of the time-series data in a location other than the specific location of the time-series data in a specific location of the sewer network,
Using the calculated delay time and the time-series data in each location other than the specific location, calculate the significance probability of each location other than the specific location by statistical hypothesis test,
Determining a sensing location to place the sensor based on the calculated significance;
A sewer network sensing location determination program that causes a computer to execute processing. A first calculation unit that calculates at least one time-series data of a water level, a flow rate, and a flow velocity at a plurality of locations of the sewer network based on data on the sewer network and rainfall data;
A second calculation unit that calculates a delay time for each of the time series data in a place other than the specific part of the time series data in the specific part of the sewer network;
Using the calculated delay time and the time series data in each of the locations other than the specific location, a third calculation unit for calculating the significance probability of each location other than the specific location by a statistical hypothesis test;
A sewer network sensing location determination apparatus, comprising: a determination unit that determines a sensing location where a sensor is arranged based on the calculated significance probability.

Images