CN104636817A - Water leakage survey plan making device, system and water leakage survey plan making method - Google Patents

Water leakage survey plan making device, system and water leakage survey plan making method Download PDF

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CN104636817A
CN104636817A CN201410645901.6A CN201410645901A CN104636817A CN 104636817 A CN104636817 A CN 104636817A CN 201410645901 A CN201410645901 A CN 201410645901A CN 104636817 A CN104636817 A CN 104636817A
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water leakage
information
water
investigation
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足立进吾
高桥信补
小熊基朗
武本刚
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Hitachi Ltd
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Hitachi Ltd
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Abstract

The invention provides a water leakage survey plan making device, a system and a water leakage survey plan making method, wherein a water leakage survey plan of a better cost performance can be made out on the premise that whether the water leakage occurs or not cannot be determined. According to at least one selected from the water leakage information, the pipeline information and the survey/maintenance information, the predicted model information within the predicted region is generated. Based on the predicted model information, the predicted water leakage information within the region is generated. Based on the predicted water leakage information, the water leakage survey plan is made out. The water leakage survey plan comprises the application sequence of the water leakage plan. The predicted water leakage information is generated based on the predicted value of the expectation value and the nondeterminacy of the water leakage.

Description

Water leakage investigation plan making device and system and water leakage investigation plan making method
Technical Field
The present invention relates to a water leakage investigation plan making device, and more particularly to a water leakage investigation plan making device for making a plan with a relatively high cost performance.
Background
Claim 1 of patent document 1 describes "a water leakage survey target route extraction system that analyzes past water leakage repair data collected and accumulated in a process of a water distribution pipeline network for supplying clean water to an end consumer, the system comprising: a main DB for storing past water leakage repairing data and data of the number of embedded water supply pipes; a data extraction unit which extracts data such as the number of repairs per drawing number and the number of buried water supply pipes, the year of repair, and the year of laying, per construction number, from the master DB; a grid evaluation unit that calculates the number of repairs or the repair rate of each figure number for the entire distribution pipe network based on the data obtained from the data extraction unit, and displays the calculated values in numerical values or in an emphasized manner for each figure; a project number list extraction means for extracting a project number to which a drawing number having a high risk of water leakage obtained by the grid evaluation means belongs, and listing the project number, the number of repairs in the drawing, the number of all the repairs, the number of buried water supply pipes, and the repair rate; and a water leakage investigation priority evaluation unit that calculates a water leakage investigation priority based on the parameter set by the user from the extracted project number list.
Documents of the prior art
Patent document
Patent document 1: japanese patent laid-open publication No. 2011-59799
Disclosure of Invention
The water leakage investigation target route extraction system of patent document 1 can extract a region with a high risk of water leakage from repair information, but cannot allocate limited resources (personnel, etc.) to determine which region to perform the water leakage investigation next time. Further, the water leakage survey target route extraction system of patent document 1 cannot cope with uncertainty of the risk of water leakage.
Therefore, an object of the present invention is to provide a water leakage investigation plan creating device that can create a water leakage investigation plan with relatively high cost performance even when there is uncertainty about water leakage with limited resources.
In order to solve the above problem, for example, the structure described in the claims is adopted. The present application includes a plurality of means for solving the above-described problems, and an example of the present application is a water leakage investigation plan making device for making a water leakage investigation plan for a plurality of regions in which water piping networks are divided, the water leakage investigation plan making device including:
a measurement information collection unit that collects measurement information on a water flow rate from a measurement device including a flowmeter provided in the water piping network; a water usage storage unit that stores water usage information in the area; a water leakage amount estimation unit that estimates the amount of water leakage from the water in the area based on the measurement information and the water usage amount information; a pipeline information storage unit that stores pipeline information including information on extension of the water pipe network in the area; an investigation/repair information storage unit for storing investigation/repair information including a water leakage investigation and a pipeline repair execution time in the area; a prediction model learning unit that generates prediction model information for predicting a change in the amount of water leakage of the area based on at least one of the amount of water leakage information, the pipeline information, and the survey/repair information; a water leakage prediction unit that generates predicted water leakage information of the area based on the prediction model information; and an investigation plan making unit for making a water leakage investigation plan for determining an execution order of water leakage investigation in the plurality of areas based on the predicted water leakage amount information,
the water leakage prediction unit generates, as the predicted water leakage information, predicted values of both an expected value of water leakage and uncertainty of the expected value of water leakage, and the survey plan making unit makes the water leakage survey plan using a water leakage cost calculated from both the expected value of water leakage and the predicted value of uncertainty.
According to the present invention, a water leakage investigation plan making device that makes a plan with a high qualification rate can be provided.
Problems, structures, and effects other than those described above will be more clearly understood by the following description of the embodiments.
Drawings
Fig. 1 is a block diagram of a water leakage investigation planning apparatus in the present embodiment.
Fig. 2 is a hardware block diagram of the water leakage survey planning apparatus in the present embodiment.
Fig. 3 is a view showing a water leakage investigation plan setting device in which a water pipe network and a water distribution block are set as water leakage investigation plan setting targets.
Fig. 4 is a diagram showing meter pipe information of the water distribution pipe registered in the pipe information storage unit.
Fig. 5 is a diagram showing meter pipe information of the water supply pipe registered in the pipe information storage section.
Fig. 6 is a diagram showing the table water leakage survey information registered in the survey/repair history storage unit.
Fig. 7 is a diagram showing table pipe repair information registered in the investigation/repair history storage unit.
Fig. 8 is a diagram showing an example of actual transition of the amount of leakage water of the water distribution block.
Fig. 9 is a diagram showing a predicted transition of the amount of water leakage by the water leakage amount predicting unit.
Fig. 10 is a diagram showing table prediction model information registered in the prediction model storage unit.
Fig. 11 is a diagram showing the water leakage plan information registered in the water leakage plan storage unit.
Fig. 12 is a flowchart showing a process of the prediction model learning unit.
Fig. 13 is a flowchart showing the processing of the water leakage amount predicting unit.
Fig. 14 is a flowchart showing the processing of the survey planning unit.
Fig. 15 is a view showing a screen display by the screen display unit relating to the water leakage investigation plan.
Fig. 16 is a diagram showing a screen display performed by the screen display unit relating to the result of water leakage prediction.
Description of the symbols
100 water leakage survey plan making system
101 water leakage survey plan making device
111 prediction model learning unit
112 water leakage amount prediction unit
113 survey plan making unit
121 pipeline information storage unit
122 investigation/repair information storage section
125 cost information storage unit
131 prediction model storage unit
132 survey plan storage unit
174 screen display part
Detailed Description
Hereinafter, embodiments will be described with reference to the drawings. In addition, the same reference numerals are given to substantially the same portions, and the repetitive description will not be made.
Fig. 1 is a block diagram of a water leakage survey planning apparatus 101 according to the present embodiment.
The water leakage survey plan creating device 101 includes: the water leakage amount estimating unit 110, the prediction model learning unit 111, the water leakage amount predicting unit 112, the survey planning unit 113, the pipeline information storage unit 121, the survey/repair information storage unit 122, the measurement information storage unit 123, the water usage amount storage unit 124, the cost information storage unit 125, the prediction model storage unit 131, the survey plan storage unit 132, the survey terminal IF unit 171, the measuring device IF unit 172, the table lookup terminal IF unit 173, and the screen display unit 174.
The water leakage survey planning system 100 includes: a water leakage survey plan preparation device 101, a survey terminal 181, a measurement device 182, and a look-up table terminal 183.
The configuration of the water leakage investigation plan setting device 101 in which a water pipe network, a water distribution block, and the like are targets for water leakage investigation plan setting will be described later with reference to fig. 3.
The water leakage amount estimation unit 110 estimates the amount of water leakage from each area in a predetermined cycle (for example, one day) by using the measurement information registered in the measurement information storage unit 123 and the usage amount information registered in the water usage amount storage unit 124 as inputs, and transmits the estimated amount of water leakage to the prediction model learning unit 111 as the amount of water leakage information.
The estimation processing of the amount of water leakage may use a known technique such as estimation based on the nighttime minimum flow rate or water balance calculation (estimation of the amount of water leakage by subtracting the amount of water usage from the cumulative value of the amount of water distribution, or other reasons). The leakage water amount information output from the leakage water amount estimation unit 110 is supplemented by the description of fig. 8.
The prediction model learning unit 111 learns a prediction model for calculating a prediction of both an expected value of future water leakage and uncertainty thereof for each region by using as input the water leakage amount information of the region such as the water distribution block received from the water leakage amount estimating unit 110, the pipe line information registered in the pipe line information storage unit 121, and the survey/repair information registered in the survey/repair information storage unit 122, and transmits the learned prediction model to the prediction model storage unit 131 as prediction model information. Details of the processing of the prediction model learning section 111 will be described later by the explanation of fig. 12.
The water leakage amount prediction unit 112 receives the prediction model information registered in the prediction model storage unit 131 as an input, calculates a prediction of both the expected value of the water leakage amount for each region and the uncertainty thereof, and transmits the result as predicted water leakage amount information to the survey planning unit 113 and the screen display unit 174. The details of the processing of the water leakage amount predicting section 112 will be described later by the explanation of fig. 13.
The survey plan making unit 113 receives the water leakage amount prediction information received from the water leakage amount prediction unit 112 and the cost information registered in the cost information storage unit 125 as input, performs a water leakage survey plan making calculation process using the water leakage cost calculated from the prediction of both the expected value of the water leakage amount and the uncertainty thereof as an evaluation index of the water leakage survey plan, and transmits the made water leakage survey plan to the survey plan storage unit 132.
Here, the water leakage survey plan is a plan for determining the order of execution of water leakage surveys in a plurality of regions. The water leakage check is a problem in which, for example, an operator checks whether or not water leakage occurs by a sound check of the water pipe. Details of the processing of the survey planning section 113 will be described later by the explanation of fig. 13.
The pipeline information storage unit 121 registers pipeline information including extension information of the water pipeline network, which is related to the water pipeline network to be planned by the water leakage investigation planning apparatus 101. Specifically, the pipe information of the water distribution pipe, which will be described later in the description of fig. 4, and the pipe information of the water supply pipe, which will be described later in the description of fig. 5, are registered.
The investigation/repair information storage unit 122 registers investigation/repair information including the execution timing of the water leakage investigation and the pipe repair in relation to the water piping network as the planning target of the water leakage investigation planning device 101. Specifically, water leakage investigation information to be described later in the description of fig. 6 and pipe repair information to be described later in the description of fig. 7 are registered.
The measurement information storage unit 123 registers sensor measurement information in the water network that is a plan setting target of the water leakage survey plan setting device 101. The sensor measurement data (measurement values of pressure or flow rate) of each measuring device is registered in time series for a period, for example, 1 minute period, sent by the measuring device 182.
The water usage storage unit 124 registers water usage information of a water user in a water network to be planned by the water leakage survey planning apparatus 101. The water user introduces water from the distribution pipe of the water network through the water supply pipe, and a water meter for collecting fees is provided at the introduction point. The water usage measured by the water meter is periodically read by a meter reader or an automatic meter reading system. The water usage storage unit 124 records the water usage information of each water user collected as described above, that is, the water usage information used by a certain water user for a certain period of time.
The cost information storage unit 125 registers cost information including costs required for water leakage detection and pipeline repair and loss due to water leakage per unit amount in a water network that is a plan setting target of the water leakage detection plan setting device 101.
Here, the cost of water leakage investigation and pipe repair refers to: the water leakage investigation is performed for each area of the water pipe network by a method such as a sound method, and the cost required for repairing the pipe is applied to the water leakage found by the investigation. Further, the loss due to a unit amount of water leakage means a cost due to, for example, water leakage of any one cubic meter.
The prediction model storage unit 131 registers the prediction model information output by the prediction model learning unit 111. Specifically, it will be described later in the description of fig. 11.
The survey plan storage unit 132 registers survey plan information output from the survey plan creation unit 113. Specifically, it will be described later in the description of fig. 11.
The survey terminal IF unit 171 transmits information received from a survey terminal 181, which will be described later, to the survey/repair information storage unit 122, and adds or updates the water leakage survey information and the pipe repair information registered in the survey/repair information storage unit 122.
The measurement device IF unit 172 transmits information received from the measurement device 182 described later to the measurement information storage unit 123, and adds new measurement information to the measurement information storage unit 123.
The lookup table terminal IF unit 173 transmits information received from a lookup table terminal 183 described later to the water usage storage unit 124, and adds or updates the water usage information registered in the water usage storage unit 124.
The screen display unit 174 presents the water leakage prediction information received from the water leakage prediction unit 112 and the water leakage investigation plan information registered in the investigation plan storage unit 132 to the operator of the water leakage investigation plan setting device 101 through an output device such as a display, for example. Specifically, the presentation is performed using a table or a graph in the form described with reference to fig. 15 and 16.
Each of the measuring devices 182 is connected to the water leakage survey planning device 101 via a communication network. The measurement device such as a flow meter or a pressure gauge installed in the water network to be managed transmits the measured sensor data to the measurement device IF unit 172 of the water leakage investigation plan creating device 101 via the communication network. A specific example of the measuring apparatus will be described later in the description of fig. 3.
Each of the survey terminal 181 and the survey terminal 183 (portable information terminal such as PDA) is connected to the water leakage survey planning apparatus 101 via a communication network.
When water leakage investigation and pipeline repair are performed, the investigation terminal 181 inputs information on water leakage investigation and pipeline repair by the operation of the user of the investigation terminal, and transmits the information to the investigation terminal IF unit 171 of the water leakage investigation plan setting device 101.
When the water meter for billing is to be checked, the meter reading terminal 183 receives the read value of the water meter as an input by the operation of the user of the meter reading terminal, and transmits the information to the meter reading terminal IF unit 173 of the water leakage survey planning apparatus 101.
Fig. 2 is a hardware block diagram of the water leakage investigation planning apparatus 101 in the present embodiment. In fig. 2, the water leakage survey plan creating apparatus 101 includes: a CPU201, a memory 202, a media input/output unit 203, a communication control unit 204, an input unit 205, a display unit 206, a peripheral IF unit 207, and a bus 210.
The CPU201 executes a program on the memory 202. The memory 202 temporarily stores programs, tables, and the like. The medium input/output unit 203 is an interface with an information registration medium including, for example, an SD card.
The communication control unit 204 is connected to the network 220 and performs a communication interface with an external device. The input unit 205 is a user interface including a keyboard and a mouse.
The display unit 206 is the display described in fig. 1. The peripheral device IF unit 207 is an interface with a device located nearby, including a printer.
The bus 210 interconnects the CPU201, the memory 202, the media input/output unit 203, the communication control unit 204, the input unit 205, the display unit 206, and the peripheral IF unit 207.
As is clear from a comparison between fig. 1 and fig. 2, the water leakage investigation plan making apparatus 101 of fig. 1 operates by the CPU201 executing a program.
Fig. 3 is a diagram showing a water pipe network and a water distribution block to which the water leakage survey planning apparatus 101 targets for water leakage survey planning. In fig. 3, a distribution basin 301 for supplying water to a water network and a water network drawn in solid lines are shown. Furthermore, the flow meters 310 to 313 and the pressure meters 321 to 324 are shown as measuring devices provided in a water piping network.
The water distribution block is a region in which the flow rate of all water distribution pipes, of the regions set as the divided water pipe network, into and out of which water flows into and out of the region is measured by the flow meter. In the example of fig. 3, the areas 331 and 332 are provided with the flow meter 311 and the flow meters 312 and 313 in the inflow pipe to become water distribution blocks. In addition, the water distribution block is also called DMA (discrete measured Area).
The water leakage survey planning unit 113 of the water leakage survey planning apparatus 101 plans a water leakage survey plan for determining in which order the water leakage survey is to be conducted in a plurality of regions, with respect to the water distribution block or a region into which the water distribution block is further divided. Hereinafter, the unit of execution of the water leakage investigation including the water distribution block is referred to as a region.
Here, the water leakage investigation refers to a business in which a person uses a device such as a sound bar, a sound device, or a related water leakage detector to detect water leakage from a water pipe. A specific example of the water leakage investigation plan will be described later in the description of fig. 11.
Fig. 4 is a diagram showing meter pipe information 400 of the water distribution pipe registered in the pipe information storage unit 121. The columns of table pipe information 400 are: water distribution pipe ID information 401, position information 402, extension information 403, caliber information 404, pipe type information 405, year of installation information 406, incidental information 407, and region information 408. In the table pipe information 400, all information is registered in a possible range with one water distribution pipe as a line for all water distribution pipes of a water network managed by the water leakage survey plan setting device 101. Fig. 4 shows only one water distribution pipe as an example.
An ID uniquely determining a specific water distribution pipe among all water distribution pipes is stored in the water distribution pipe ID information 401. The position Information 402 stores coordinate Information for specifying the position where the water distribution pipe is buried in cooperation with a GIS (Geographic Information System). The extension information 403 and the caliber information 404 store information on the extension and the caliber of the water distribution pipe.
The Pipe type information 405 stores information such as Pipe types such as DIP (graphite cast Iron Pipe) and CIP (cast Iron Pipe), and Pipe specifications (presence or absence of an anti-corrosion jacket). The number of years of laying of the water distribution pipe is registered in the number of years of laying information 406. Or the burying years are registered, and the number of laying years can be obtained from these by calculation. The incidental information 407 stores information such as the number and position of incidental objects such as a hydrant and an air valve, and the number of water supply pipes taken out. The region information 408 stores information on the region (water distribution block) to which the water distribution pipe belongs.
Fig. 5 is a diagram showing meter water supply pipe information 500 of the pipe line information of the water supply pipe registered in the pipe line information storage unit 121. The columns of the meter water supply pipe information 500 are: water supply pipe ID information 501, position information 502, connection destination water distribution pipe ID information 503, extension information 504, caliber information 505, pipe type information 506, year of installation information 507, and region information 508. In the meter water supply pipe information 500, all the information is registered in a possible range for all the water supply pipes of the water supply network managed by the water leakage survey plan setting device 101, with one water supply pipe being a line. Fig. 5 shows only one water supply pipe as an example.
The water supply pipe ID information 501 stores an ID uniquely determining a specific water supply pipe among all the water supply pipes. In the position information 502, coordinate information that specifies the position where the water supply pipe is buried is stored in cooperation with the GIS. The water distribution pipe ID connected to the water supply pipe is stored in the connection destination water distribution pipe ID information 503.
The extension information 504 and the caliber information 505 store information on the extension and the caliber of the water supply pipe. The pipe type information 506 stores pipe type information such as PE (polyethylene pipe) and LP (lead pipe). The number of years of installation of the water supply pipe is registered in the installation year information 507. Or the number of years of burying may be registered. The area information 508 stores information on the area (water distribution block) to which the water supply pipe belongs.
Fig. 6 is a diagram showing water leakage survey information 600 in the table of water leakage survey information registered in the survey/repair information storage unit 122. The columns of the table water leakage survey information 600 are: survey ID information 601, period information 602, and target area information 603. In the water leakage survey information 600, all the information is registered with one area as a target and with such a target as one line for the history of water leakage surveys conducted in all the areas managed by the water leakage survey plan setting device 101. Fig. 6 shows only two water leakage investigation histories as an example.
The survey ID information 601 stores an ID that uniquely determines a specific survey history among all survey histories. The period information 602 stores information of a period during which the survey was conducted. The target area information 603 stores the area ID for which the water leakage survey is performed by the survey.
Fig. 7 is a diagram showing table pipe repair information registered in the survey/repair information storage unit 122. The columns of the meter pipe repair information 700 are: repair ID information 701, category information 702, pipe ID information 703, location information 704, reason information 705, water leakage prevention amount information 706, date and time information 707, and survey ID information 708. In the meter pipe repair information 700, all information is registered for the pipe repair history performed for the water distribution pipe and the water supply pipe managed by the water leakage survey plan making device 101, with one pipe repair being performed in a row. In fig. 7, only one pipeline repair history is displayed as an example.
An ID that uniquely decides a specific repair history among all the repair histories is stored in the repair ID information 701. Information indicating whether the repair target is a water distribution pipe or a water supply pipe and ID information of the target pipe are stored in the category information 702 and the pipe ID information 703. The location information 704 stores the location information where the repair is performed in cooperation with the GIS. That is, information on which position of the target pipe the repair is performed is stored.
The cause information 705 stores information of the cause of the estimated repair water leakage. For example, information due to aging deterioration, corrosion, overweight, unknown, and the like is stored. The water leakage prevention information 706 stores the result information of the water leakage (flow rate) observed from the repair site as the repair time of the water leakage to be repaired. Date and time information on which the repair was performed is stored in the date and time information 707. The investigation ID information 708 stores water leakage investigation ID information for finding a water leakage to be repaired. If the water leakage to be detected is not a water leakage found by a water leakage survey, but a water leakage found by, for example, a notification of a water leakage on the ground, ID information capable of identifying the reason for the finding is stored.
Fig. 8 is a diagram showing an example of actual transition of the amount of leakage water of the water distribution block. In the graph of fig. 8, the horizontal axis represents time, and the vertical axis represents the amount of water leakage in a specific area (water distribution block). Generally, as shown by the leakage water amount 801, the leakage water amount in the area monotonously increases except for the implementation period of the leakage water prevention operation shown by the prevention operation period 811 and the prevention operation period 812. Here, the water leakage prevention work refers to water leakage investigation and repair of water leakage found by the water leakage investigation.
As described above, the leakage water amount estimation unit 110 estimates the past leakage water amounts of the respective areas as shown in fig. 8 based on the information registered in the respective storage units.
Fig. 9 is a diagram showing the predicted transition of the amount of leakage water by the leakage water amount prediction unit 112. The prediction of both the expected value of the water leakage amount and the uncertainty of the water leakage amount in the specific area (water distribution block) by the water leakage amount prediction unit 112 will be described with reference to fig. 9. In the graph of fig. 9, the horizontal axis represents the elapsed time after the last water leakage investigation and repair in the area, and the vertical axis represents the amount of water leakage in the area.
The prediction of the transition of the water leakage output from the water leakage prediction unit 112 relates to three time series, i.e., an expected value 901 of the predicted water leakage, a low value 903 of the predicted water leakage, and a high value 902 of the predicted water leakage, which correspond to the elapsed time after the last water leakage check and repair. The high value 902 of the predicted water leakage amount and the low value 903 of the predicted water leakage amount are values for representing uncertainty of the expected value 901 of the predicted water leakage amount. For example, the high value and the low value may be set to an upper limit value and a lower limit value of a 95% confidence region calculated from a degree of correspondence between past water leakage amount transitions of the prediction model.
In general, the occurrence of water leaks is associated with a number of uncertainties and is therefore difficult to handle as a critical event. Therefore, the prediction error by only the expected value of the amount of leakage water is large, and the uncertainty of the prediction cannot be considered to be different for each area.
The water leakage investigation plan creating device 101 outputs the high value and the low value as uncertainty of the expected value in addition to the expected value, thereby making it possible to predict more useful water leakage when creating an effective water leakage investigation plan. As a method of expressing uncertainty of the expected value, for example, a deviation from the expected value may be used in addition to the high value and the low value.
Fig. 10 is a diagram showing table prediction model information 1000 registered in the prediction model storage unit 131. The columns of the table prediction model information 1000 are: prediction model ID information 1001, target area information 1002, model formula information 1003, explanatory variable information 1004, coefficient information 1005, and category information 1006.
The table prediction model information 1000 is registered in a row of one prediction model for all the prediction models output from the prediction model learning unit 111. In fig. 10, two prediction models are shown as an example. In addition, the table prediction model information 1000 includes: at least one prediction model can be applied to one area for all areas to be planned by the water leakage survey planning apparatus 101.
The prediction model ID information 1001 stores an ID that uniquely determines a prediction model. The target area information 1002 stores an area ID to which a prediction model can be applied. Information that can determine the applicable region is stored for a model applicable to a plurality of regions.
The model equation information 1003 stores an equation number used for prediction by the prediction model. The explanatory variable information 1004 stores explanatory variable information of a region used when the model is applied to each region of the prediction model. The prediction model learning unit 111 and the water leakage amount prediction unit 112 acquire specific values of the area explanatory variables from the pipeline information storage unit 121 and the investigation/repair information storage unit 122 as necessary. The explanatory variables will be described later.
Specific values of coefficients of prediction equations indicated by model equation information 1003 are stored in coefficient information 1005. The category information 1006 stores information indicating which of the general prediction model and the region-by-region prediction model the prediction model corresponds to.
The prediction model learning unit 111 outputs the prediction model information described in fig. 10. As described in the category information 1006, the prediction model output by the prediction model learning unit 111 is divided into a region-by-region prediction model that can be applied to only a specific region and a general prediction model that can be applied to a plurality of regions by substituting values in the region.
For example, the prediction model having a prediction model ID of E4a332 shown in fig. 10 is a region-by-region prediction model. As a specific example of the prediction formula of the region-by-region prediction model, the water leakage survey plan creating device 101 uses the following prediction formula, for example. Hereinafter, the formulae (1a) to (1c) are collectively referred to as formula (1).
L(t)＝L0+k×t ···(1a)
Lh(t)＝L(t)+Dh0+mh×t ···(1b)
L1(t)＝L(t)-D10-m1×t ···(1c)
In this case, the amount of the solvent to be used,
l (t): expected value [ m ] of predicted water leakage3/h]
L0: same initial value (after water leak repair) [ m3/h]
Lh (t): high value [ m ] for predicting water leakage3/h]
Dh 0: same initial value (after water leak repair) [ m3/h]
L1 (t): low value [ m ] for predicting water leakage3/h]
D10: same initial value (after water leak repair) [ m3/h]
k, mh, m 1; positive coefficient
t: elapsed time [ day ]
For example, the prediction model with the prediction model ID of E3AGE shown in fig. 10 is a general prediction model. As a specific example of the prediction formula of the general prediction model, the water leakage investigation plan creating apparatus 101 uses the following prediction formula, for example. Hereinafter, the formulae (2a) to (2c) are collectively referred to as formula (2).
L(t，x)＝L0(x)+k(x)×t ···(2a)
Lh(t，x)＝L(t，x)+Dh0+mh×t ···(2b)
L1(t，x)＝L(t，x)-D10-m1×t ···(2c)
[ EQUATION 1 ]
L0(x)＝exp(α0+Σsαs×xs) ···(2d)
[ equation 2 ]
k(x)＝exp(β0+Σsβs×xs) ···(2e)
In this case, the amount of the solvent to be used,
l (t, x): expected value [ m ] of predicted water leakage3/h]
L0 (x): same initial value (after water leak repair) [ m3/h]
Lh (t, x): high value [ m ] for predicting water leakage3/h]
Dh 0: same initial value (after water leak repair) [ m3/h]
L1(t, x): low value [ m ] for predicting water leakage3/h]
D10: same initial value (after water leak repair) [ m3/h]
α0、β0、αs、βs: coefficient of performance
k (x), mh, m 1; positive coefficient
s: index of explanatory variable (index)
xs: explanatory variable of index s
x: all the explanatory variables
t: elapsed time [ day ]
For s, indices of all explanatory variables are obtained.
As the explanatory variables of the general prediction model to be processed by the water leakage investigation planning device 101, any index calculated from information stored in the pipeline information storage unit 121, the investigation/repair information storage unit 122, etc., such as the number of water leakage repairs in the past in the area and the number of water leakage repairs in the same number of water leakage repairs, the number of water leakage repairs found by notification (notification of the number of water leakage repairs), the number of water supply pipes in the area, the number of years of laying of the oldest pipes in the area, and the number/total extension of pipes of 30 years or more, for example, can be used.
Even if the same prediction equation (2) is used, if different explanatory variable groups are used, the water leakage investigation planning apparatus 101 handles these as other prediction modes. The general-purpose prediction model registered in the prediction model storage unit 131 includes not only the model output by the prediction model learning unit 111 but also a general-purpose prediction model arbitrarily added by the user of the water leakage investigation plan making apparatus 101.
For example, a general prediction model learned in a region that is not a target of planning of the water leakage survey planning apparatus 101 may be stored in the prediction model storage unit 131, and the water leakage amount prediction unit 112 may use the general prediction model in predicting the water leakage amount in the region that is the target of planning of the water leakage survey planning apparatus 101. The model relating to the expected value may be fixed to any additional general prediction model, and the prediction model learning unit 111 may learn the coefficient relating to uncertainty, that is, only Dh0, D10, mh, and m1 in the example of the prediction formula (2).
Fig. 11 is a diagram showing the water leakage survey plan information 1100 registered in the survey plan storage unit 132. As described above, the water leakage investigation plan determines the execution order of water leakage investigation in a plurality of regions. For example, in addition to the execution order, the period during which the water leakage survey is executed in each area during the planning target period may be determined. However, water leakage investigation may not be performed in some regions. The water leakage investigation plan information 1100 indicates a period for determining to perform water leakage investigation in each area, and indicates in which period of the planned periods 1102 to 1104 the water leakage investigation is performed for each area indicated by the area ID information 1101.
For example, fig. 11 shows three water leakage surveys including a survey cycle 1111, a survey cycle 1112, and a survey cycle 1113 planned for a three-year planned period in the region 331, and two water leakage surveys including a survey cycle 1121 and a survey cycle 1122 planned for the region 332. The water leakage survey plan information 1100 stores the survey plan information described above for all regions of the planning target.
Fig. 12 is a flowchart showing the processing of the prediction model learning unit 111. Fig. 12 shows an operation flow until the prediction model learning unit 111 extracts information on each registered region, learns the information to generate a prediction model, and transmits the prediction model to the prediction model storage unit 131. In the start step 1200, the prediction model learning unit 111 starts processing.
In the input information receiving step 1201, the prediction model learning unit 111 receives the pipeline information registered in the pipeline information storage unit 121 and the survey/repair information registered in the survey/repair information storage unit 122, and further receives the past water leakage amount information of each area from the water leakage amount estimating unit 110.
In the water distribution region extraction step 1202, the prediction model learning unit 111 extracts one water distribution region (area) as a learning target of the region-by-region prediction model. However, the prediction model learning unit 111 extracts only the region where the past transition of the amount of leakage water is estimated by the leakage water amount estimation unit 110.
In the region-by-region prediction model learning step 1203, the prediction model learning unit 111 learns a region-by-region prediction model for the extracted region.
For example, in order to learn a prediction model by region using the prediction equation (1) as a prediction equation, the prediction model learning unit 111 calculates an initial value L0 and a coefficient k for reproducing actual data of the water leakage amount 801 between the prevention operation period 811 and the prevention operation period 812 of fig. 8 received from the water leakage amount estimation unit 110. For this calculation, a known technique such as a least square method can be used.
As a method for determining the initial values Dh0, D10 and the coefficients mh, m1, the prediction model learning unit 111 can determine the minimum initial values Dh0, D10, the coefficients mh, m1 in a range where the actual data is fixed to the high value lh (t) and the low value L1(t), using the prediction formula (1a) obtained as described above, for example.
At the determination step 1204, the prediction model learning unit 111 determines whether or not the region-by-region prediction model is learned for all regions that can be learned. When there are areas that have not been learned, return is made to the extraction step 1202 for water distribution areas. When learning is finished for all regions that can be learned, the extraction step 1205 of the generic prediction model is advanced.
In the general prediction model extraction step 1205, the prediction model learning unit 111 extracts one general prediction model as a learning target.
In the region-by-region prediction model extraction step 1206, the prediction model learning unit 111 extracts all regions in which the explanatory variable groups used by the general prediction model that can be used to calculate the learning target are present, and extracts the region-by-region prediction model for the region from these regions.
The pipeline information storage unit 121 and the survey/repair information storage unit 122 do not always have to register all information of all pipes in the characteristics of the water pipeline used for decades when buried underground. Therefore, information necessary for calculating a specific explanatory variable is not registered for each region, and the specific explanatory variable may not be calculated. Therefore, in the region-by-region prediction model extraction step 1206, the prediction model learning unit 111 extracts only the region in which all the used explanatory variable groups can be calculated and the region-by-region prediction model.
In the general prediction model learning step 1207, the prediction model learning unit 111 learns the extracted general prediction model. In learning to determine the coefficients of the general prediction model, learning is performed based on the extracted coefficients of the region-by-region prediction model. Hereinafter, a case where equation (2) is used for the prediction equation of the general prediction model and a case where equation (1) is used for the prediction equations of the region-by-region prediction models will be described.
The index (index) of the extracted region and region-by-region prediction model is p, the coefficients of prediction formula (1) of each region-by-region prediction model are L0p, kp, Dh0p, D10p, mhp, m1p, and the predicted values are lp (t), lhp (t), and L1p (t). Note that xsp represents the value of the explanatory variable in the index s in the region p, and xp represents the explanatory variables in the region p collectively.
At this time, the prediction model learning unit 111 calculates α 0, β 0, α s, and β s so that the coefficients L0p and kp of the region-by-region prediction models are constantly reproduced by the expressions (2d) and (2 e). For this calculation, a known technique such as a least square method can be used. The prediction model learning unit 111 uses the prediction formula (2a) obtained as described above, and then determines the minimum coefficients Dh0, D10, mh, and m1 so as to satisfy the following two formulae for all regions p.
Lh(t，xp)≥Lhp(t)
L1(t，xp)≤L1p(t)
In determination step 1208, the prediction model learning unit 111 determines whether or not all the common prediction models have been learned. When there is a generic prediction model that has not been learned, return is made to the extraction step 1205 in which the generic prediction model is extracted. When learning is completed for all the generic prediction models, the routine proceeds to a transmission step 1209 of output information. In the output information transmission step 1209, the prediction model learning unit 111 transmits the learned prediction model information to the prediction model storage unit 131. In the end step 1210, the prediction model learning unit 111 ends the process.
Fig. 13 is a flowchart showing the processing of the leakage water amount predicting unit 112. Fig. 13 shows an operation flow until the water leakage prediction unit 112 predicts future water leakage from the prediction model, selects the predicted water leakage information based on the prediction model having the smallest difference between the high value and the low value, and transmits the information to the survey planning unit 113 and the screen display unit 174. At step start 1300, the water leakage amount prediction unit 112 starts processing.
In the input information receiving step 1301, the water leakage amount prediction unit 112 receives the prediction model information created by the prediction model storage unit 131 according to the flow of fig. 12. The water leakage amount prediction unit 112 receives information necessary for calculating the explanatory variables of each area from the pipeline information storage unit 121, the survey/repair information storage unit 122, and the like as needed.
In the water distribution area extraction step 1302, the water leakage amount prediction unit 112 extracts a water distribution area (area) that is a target of water leakage amount prediction. In the prediction model extraction step 1303, the water leakage prediction unit 112 extracts all prediction models applicable to the extracted region from the received prediction model information.
In the prediction step 1304 based on the general prediction model, the water leakage prediction unit 112 predicts the water leakage of the extracted area using each of the general prediction models in the extracted prediction models. Here, the prediction of the water leakage using the general prediction model by the water leakage prediction unit 112 is a process of calculating a value of an explanatory variable necessary for the extracted area, and applying the calculated value of the explanatory variable to the general prediction model to calculate an expected value, a high-order value, and a low-order value of the predicted water leakage shown in fig. 9.
At the determination step 1305, the water leakage amount prediction unit 112 determines whether or not the prediction model for each area applicable to the extracted area is included in the extracted prediction models. If included, the process proceeds to a prediction step 1306 based on a prediction model by region, and if not, the process proceeds to a prediction result selection step 1307.
In the prediction step 1306 based on the region-by-region prediction model, the water leakage amount prediction unit 112 predicts the amount of water leakage of the region extracted using the region-by-region prediction model. Here, the prediction of the amount of water leakage refers to a process of calculating the expected value, the high value, and the low value of the predicted amount of water leakage shown in fig. 9, as in the case of the general prediction model.
In the prediction result selection step 1307, the water leakage prediction unit 112 compares the prediction results of the water leakage based on the extracted prediction models, selects the prediction model having the smallest difference between the high value and the low value, and sets the predicted water leakage information based on the selected prediction model as the predicted water leakage information for the area.
In determination step 1308, the water leakage predicting unit 112 determines whether or not to perform the water leakage predicting process for all the areas. When there is a region for which prediction processing has not been performed, return is made to the water distribution region extraction step 1302. When the prediction processing for all regions is finished, the process proceeds to step 1309 of transmitting output information.
In the output information transmission step 1309, the water leakage amount prediction unit 112 transmits the calculated water leakage amount prediction information to the survey planning unit 113 and the screen display unit 174. In the end step 1310, the water leakage amount prediction unit 112 ends the process.
When there are a plurality of prediction models that can be applied to a specific area, the water leakage prediction unit 112 can output predicted water leakage information based on the prediction model having the smallest difference between the high value and the low value in the prediction step 1306 based on the area-by-area prediction model, thereby outputting predicted water leakage smaller than the uncertainty.
Further, when updating of the pipe is scheduled during the exclusive period of the water leakage survey plan, the water leakage amount prediction unit 112 may perform processing in consideration of the information. For example, when updating of the pipeline affects the calculation result of the explanatory variable of the area, it is possible to output the prediction by the general prediction model using the calculation result of the explanatory variable using the updated information of the pipeline for the updated period.
Fig. 14 is a flowchart showing the processing of the survey planning unit 113. Fig. 14 shows an operation flow until the survey plan making unit 113 transmits water leakage survey plan information obtained in consideration of costs related to water leakage survey and loss due to water leakage to the survey plan storage unit 132. At the start step 1400, the survey plan making unit 113 starts the processing.
In the input information reception step 1401, the survey plan making unit 113 receives the leakage water amount prediction information of each area created by the leakage water amount prediction unit 112 according to the flow of fig. 13, and also receives the cost information from the cost information storage unit 125.
In the optimization problem configuration step 1402, the survey plan making unit 113 configures a mathematical optimization problem for making a water leakage survey plan. First, as a constraint condition of the mathematical optimization problem constituted by the survey planning unit 113, the total number of areas where water leakage surveys are simultaneously conducted is equal to or less than the determined number of water leakage surveys regardless of the period to be planned. Secondly, a period for performing the water leakage investigation in one area is continuously secured as a period required for the water leakage investigation in all areas of the area. The former takes into account labor costs involved in water leakage investigation, and the latter takes into account work efficiency.
Further, the objective function for minimizing the mathematical optimization problem configured by the survey planning unit 113 can be set to, for example, an objective function of a sum of evaluation values considering the cost of water leakage survey and uncertainty of water leakage amount prediction of the water leakage cost. A specific example of the mathematical optimization problem constituted by the survey plan modulating unit 113 will be described below. As the index, an index a indicating a region and an index t of each month in a planning period (for example, three years) are used.
As a specific example when the period during which the water leakage investigation is performed in each area during the planning target period is determined as the water leakage investigation plan, the main determination variable may be a binary variable y _ { a, t } which takes a value of 1 only for the month t at which the water leakage investigation in the starting area a is started and 0 in addition.
When defining a binary variable z _ { a, t } which takes a value of 1 for only month t for which water leakage is investigated in area a and otherwise 0, the following constraint condition can be applied as the relationship between y _ { a, t } and z _ { a, t }.
[ equation 3 ]
<math> <mrow> <msub> <mi>z</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>=</mo> <msubsup> <mi>&Sigma;</mi> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>l</mi> <mi>a</mi> </msub> </msubsup> <msub> <mi>y</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> <mo>-</mo> <mi>n</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </math>
Wherein,
1_ a: number of months (positive integer) required for water leakage survey of area a
By this restriction formula, it is continuously ensured that the period for performing the water leakage test in one area is as long as the period required for completing the water leakage test in all the areas of the area.
The restriction conditions for limiting the total number of regions where water leakage investigation is simultaneously conducted can be described by the following formulas.
[ EQUATION 4 ]
<math> <mrow> <msub> <mi>&Sigma;</mi> <mi>a</mi> </msub> <msub> <mi>z</mi> <mrow> <mi>a</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> <mo>&le;</mo> <mi>T</mi> </mrow> </math>
Wherein,
t: water leakage investigation group number (positive integer)
The survey plan creating unit 113 may set the objective function f that is minimized by the mathematical optimization problem as: for example, the total cost is a sum of the water leakage cost and the investigation cost, using the predicted value CS of the investigation cost and the predicted value CW of the water leakage cost as evaluation indexes.
f＝CS+CW
Here, the survey plan making unit 113 calculates the water leakage survey fee C _ a for the area a registered in the cost information storage unit 125 as a predicted value of the survey cost as follows, for example.
[ EQUATION 5 ]
CS=ΣaCaΣtya，t ···(3)
On the other hand, the survey plan making unit 113 calculates the predicted value CW of the water leakage cost from the prediction of both the expected value of the water leakage amount and the uncertainty thereof.
For example, a parameter representing uncertainty between a high value and a low value is set for calculation of the predicted water leakage amount in each region, and the relationship between the parameter and the water leakage cost is evaluated. And calculating the maximum water leakage cost of the parameter when the parameter takes any value in a preset uncertainty set.
Specifically, it is calculated as
[ equation 6 ]
CW＝w max||≤1ΣaΣt[(1-a)Lha(t；{ya，τ}τ)+(1+a)Lla(t；{ya，τ}τ）]/2 ···（4)
In this case, the amount of the solvent to be used,
w: marginal cost per unit amount of water leakage registered in cost information storage 125
Lh _ a (t; { y _ { a, τ } } high value of predicted water leakage for region a at month t when decision variable y _ { a, t } is determined
L1_ a (t; { y _ { a, τ } } lower value of predicted water leakage for region a at month t when determining variable y _ { a, t }, is determined
Here, _ a is a parameter representing uncertainty of the area a, and the uncertainty set of the parameter is: a set in which the norm (norm) of vectors arranged with the parameter _ a for all regions as elements is 1 or less is used.
Further, specific calculation methods of Lh _ a and L1_ a are as follows: the water leakage amount is determined by the high value and the low value of the water leakage amount determined by the elapsed time after the water leakage investigation and repair received from the water leakage amount predicting unit 112, the last period of the water leakage investigation and repair before the water leakage investigation planning period registered in the water leakage amount predicting unit 112, and the execution time of the water leakage investigation and repair in the water leakage investigation planning period determined by the determination variable.
In the optimization problem solving step 1403, the survey plan making unit 113 performs the process of solving the optimization problem configured in the optimization problem configuring step 1402, and converts the obtained optimal solution into water leakage survey plan information. Known techniques such as heuristics (metahearistics) such as genetic algorithms and branch and bound methods can be applied to the solving process.
In the output information reception step 1404, the survey plan making unit 113 transmits the calculated water leakage survey plan information to the survey plan storage unit 132. In the end step 1405, the survey plan making unit 113 ends the process.
And supplementing the predicted value CW of the water leakage cost. In general, a water leakage investigation plan obtained by applying only a water leakage cost obtained by predicting an expected value of a water leakage amount to an objective function of a mathematical optimization problem is used, and when an actual water leakage amount is separated from the predicted water leakage amount, the cost (objective function value of the mathematical optimization problem) is significantly worse than an evaluation at a planning time.
The water leakage cost using only the expected value is an objective function obtained by using, for example, a CWA determined by the following equation
[ EQUATION 7 ]
CWA＝w ΣaΣtLa(t；{ya，τ}τ) ···(5)
In this case, the amount of the solvent to be used,
l _ a (t; { y _ { a, τ } } expected value of predicted water leakage amount of area a of month t at which variable { y _ { a, t } is decided is determined
Since the prediction of the amount of water leakage must include uncertainty, the cost when applying the water leakage investigation plan using the expected value is almost always larger than the objective function value of the optimal solution of the mathematical optimization problem. Therefore, in the above-described predicted value CW, a consideration method of reliable (robust) optimization is applied to calculate the cost based on the occurrence of a typical error in the predicted leakage amount. By making a water leakage investigation plan using such an evaluation value, it can be expected that the cost will be reduced when the water leakage investigation plan is actually applied, as compared with a case where an evaluation value obtained by applying only an expected value of the predicted water leakage amount is used.
The predicted value CW of the water leakage cost is not limited to the above. For example, the cost of water leakage may be calculated by giving greater weight to the cost of an area with greater uncertainty. Further, the structure of the mathematical optimization problem is not limited to the above. For example, a restriction condition or the like for setting an upper limit of the investigation cost is set, and a predicted value of the water leakage cost can be used as an objective function of the minimization. The water leakage cost of equation (4) may also be calculated using the water leakage cost of equation (5) as an objective function of the mathematical optimization problem.
Fig. 15 is a diagram showing a screen display of the screen display unit 174 based on the water leakage investigation plan. The water leakage investigation plan display screen 1501 displayed on the display or the like by the screen display unit 174 includes: a water distribution block display 1502, a cost display 1503, and a water leakage survey schedule display 1504.
The screen display unit 174 displays, on the water distribution block display 1502, a water pipe network, a water distribution block, an area, and the like on a map for the area to be planned by the water leakage investigation planning apparatus 101 in cooperation with the GIS. In this example, a water distribution block 331 and a water distribution block 332 are shown.
The screen display unit 174 displays the cost evaluation result of the water leakage investigation plan in the cost display 1503. The row of the survey cost 1541 shows the cost of the water leakage survey calculated by the equation (3), for example. The sum of the water leakage rates calculated by equation (4), for example, is shown in the line of the loss rate 1542. The total of the survey cost and the loss cost is displayed on the line of the total cost 1543.
A cost evaluation value in consideration of uncertainty of prediction is shown in a column of a reliable cost (robust cost) 1532. For example, an evaluation value based on the evaluation formula (4) is displayed in the evaluation of the loss cost. On the other hand, the column of the average cost 1533 shows the cost evaluation result of the water leakage investigation plan when the predicted average (expected value) is used. For example, the evaluation value based on the evaluation formula (5) is displayed in the evaluation of the loss cost, instead of the evaluation value based on the evaluation formula (4).
The screen display unit 174 displays a water leakage investigation schedule table on the water leakage investigation schedule display 1504 in the same manner as in fig. 11.
Fig. 16 is a diagram showing a screen display by the screen display unit 174 based on the result of the water leakage prediction. The water leakage prediction display screen 1601 displayed on the display or the like by the screen display unit 174 includes: a water distribution block display 1502, a water leakage prediction table display 1603, and a water leakage prediction icon display 1604.
The screen display unit 174 displays the prediction transition of the water leakage amount based on the water leakage amount prediction unit 112 on the water leakage prediction table display 1603. The area ID of the prediction target is displayed in a column of the area information 1631. The column of the average predicted value 1632 shows the expected value of the predicted leakage amount. The high value of the predicted leakage amount is shown in the column of the high predicted value 1633. The lower value of the predicted leakage amount is shown in the column of the lower prediction value 1633. When the user of the water leakage investigation planning apparatus 101 performs an operation of changing the time of the display target, the screen display unit 174 converts the time of the display target into a specified current (predicted time) or future time.
In the water leakage prediction icon display 1604, the screen display unit 174 displays the transition of the prediction of the amount of water leakage by the water leakage prediction unit 112 by an icon, as in fig. 9. The vertical line 1650 indicates the time (current) at which the water leakage survey plan is created, the left side of the vertical line 1650 indicates the past water leakage, and the right side of the vertical line 1650 indicates the future water leakage prediction result. The user of the water leakage investigation planning apparatus 101 performs an operation of changing the area to be displayed, and the screen display unit 174 changes the display so as to indicate the result of the water leakage prediction in the specified area.
The region where the water leakage investigation planning apparatus 101 predicts the amount of water leakage or performs investigation planning is not necessarily all the regions of the water pipe network to which the water leakage investigation planning apparatus is applied. For example, predictions may be made for only all regions, while survey planning may be made for only some regions.
As described above, the water leakage investigation plan creating device 101 can create a water leakage investigation plan with relatively high cost performance even when there is uncertainty about water leakage with limited resources.
The above-described embodiments are directed to the establishment of an investigation plan regarding water leakage in a water pipe, but the present invention can also be applied to the establishment of a maintenance plan for equipment other than a water pipe, such as the establishment of an investigation plan regarding gas leakage.
The present invention is not limited to the above-described embodiments, and various modifications are also included. For example, the above-described embodiments are described in detail for the sake of understanding, but are not limited to having all the configurations described.
Note that, some or all of the above-described structures, functions, processing units, and the like may be realized by hardware such as integrated circuit design, for example. The respective structures and functions described above may be realized by software that is analyzed by a processor and executes a program for realizing the respective functions. Information such as programs, tables, and files for realizing the respective functions may be stored in a storage device such as a memory, a hard disk, an SSD (Solid State Drive), or a storage medium such as an IC card, an SD card, or a DVD.
In addition, although the control lines or the information lines are shown as being considered necessary for the description, not all the control lines or the information lines need to be shown on the product. In practice, it is also conceivable to connect almost all the components to one another.

Claims (10)

1. A water leakage investigation plan making device for making a water leakage investigation plan for a plurality of regions in which a water grid is divided, the water leakage investigation plan making device comprising:
a measurement information collection unit that collects measurement information on a water flow rate from a measurement device including a flowmeter provided in the water piping network;
a water usage storage unit that stores water usage information in the area;
a water leakage amount estimation unit that estimates the amount of water leakage from the water in the area based on the measurement information and the water usage amount information;
a pipeline information storage unit that stores pipeline information including information on extension of the water pipe network in the area;
an investigation/repair information storage unit for storing investigation/repair information including a water leakage investigation and a pipeline repair execution time in the area;
a prediction model learning unit that generates prediction model information for predicting a change in the amount of water leakage of the area based on at least one of the amount of water leakage information, the pipeline information, and the survey/repair information;
a water leakage prediction unit that generates predicted water leakage information of the area based on the prediction model information; and
an investigation plan making unit for making a water leakage investigation plan for determining an execution order of water leakage investigation in the plurality of areas based on the predicted water leakage amount information,
the water leakage amount prediction unit generates, as the predicted water leakage amount information, a predicted value of both the expected value of the water leakage amount and the uncertainty of the expected value of the water leakage amount,
the survey plan making unit makes the water leakage survey plan using a water leakage cost calculated from predicted values of both the expected value and uncertainty of the amount of water leakage.
2. The water leakage investigation planning apparatus according to claim 1,
the water leakage prediction unit generates prediction values of both a high value and a low value of the water leakage as uncertainty prediction values of the expected value of the water leakage,
the prediction model learning unit generates a prediction formula for predicting a high value and a low value of the amount of water leakage and a coefficient of the prediction formula as the prediction model information.
3. The water leakage investigation planning apparatus according to claim 2,
when there are a plurality of the prediction models applicable to one area, the water leakage prediction unit selects and generates the predicted water leakage amount information in which the difference between the high value of the water leakage amount and the low value of the water leakage amount is the smallest in the corresponding prediction model.
4. The water leakage investigation planning apparatus according to claim 3,
in the prediction model learning section,
each prediction model of the generated prediction model information is one of a region-by-region prediction model that is applicable to a specific region and a general prediction model that is applicable to a plurality of regions by substituting explanatory variables of the regions calculated from the pipe network information and the survey/repair information,
the coefficients of the general prediction model are learned and determined based on the coefficients of the region-by-region prediction model determined in advance.
5. The water leakage investigation planning apparatus according to claim 4,
the survey plan making unit determines a period for conducting water leakage survey in each area among the planned periods as a water leakage survey plan,
the survey plan making unit makes a water leakage survey plan satisfying a first constraint condition that the total number of areas to which water leakage surveys are simultaneously conducted during each period of a planning target period is equal to or less than a determined number of water leakage survey groups, and a second constraint condition that a period required for completing water leakage surveys in all areas of one area is continuously ensured during a period in which water leakage surveys are conducted in the area.
6. The water leakage investigation planning apparatus according to claim 5,
the survey plan making unit makes the water leakage survey plan such that: the total cost consisting of the sum of the water leakage cost and the investigation cost is minimal.
7. The water leakage investigation planning apparatus according to claim 6,
when the water leakage cost is evaluated as the evaluation index, the survey plan making unit sets a parameter indicating uncertainty between a high value and a low value in the predicted water leakage amount for each area, and evaluates the relationship between the parameter and the water leakage cost.
8. The water leakage investigation planning apparatus according to claim 7,
the prediction model learning unit uses at least one of the number of water leak repairs in the area, the number of water leak repairs notified in the area, the number of water supply pipes in the area, the number of years of laying the oldest pipe in the area, the number of aged pipes, and the total extension of aged pipes as the explanatory variable used for learning the general prediction model.
9. A water leakage investigation planning system is characterized by comprising:
the water leakage survey planning apparatus according to claim 1;
a measuring device including a flow meter that transmits measurement information to the water leakage survey planning device; and
and an investigation terminal which transmits investigation/repair information including a location of the water leakage found through the water leakage investigation to the water leakage investigation planning apparatus.
10. A method for planning a water leakage investigation plan for a plurality of areas in which water piping networks are divided, the method comprising:
a collecting step of collecting measurement information related to water flow;
a storage step of storing water usage information in the region;
estimating the amount of water leakage of the water in the area based on the measurement information and the water usage information;
a pipeline information accumulation step of accumulating pipeline information including information on extension of the water pipe network in the area;
an investigation/repair information accumulation step of accumulating investigation/repair information including a water leakage investigation and a pipeline repair execution time in the area;
a prediction model information generation step of generating prediction model information for predicting transition of water leakage of the area based on at least one of the water leakage amount information, the pipeline information, and the survey/repair information;
a predicted water leakage information generation step of generating predicted water leakage information of the area based on the prediction model information; and
a step of making a water leakage investigation plan for determining an execution order of water leakage investigation in the plurality of areas based on the predicted water leakage amount information,
the predicted water leakage amount information generating step generates, as the predicted water leakage amount information, a predicted value of both the expected value of the water leakage amount and the uncertainty of the expected value of the water leakage amount,
in the step of formulating, the water leakage survey plan is formulated using a water leakage cost calculated from predicted values of both the expected value and uncertainty of the water leakage amount.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105716803A (en) * 2016-01-29 2016-06-29 深圳市捷先数码科技股份有限公司 Integrated analysis device for leakage monitoring of water supply pipe network and method of analysis device
CN108235773A (en) * 2016-10-11 2018-06-29 富士地中信息股份有限公司 Water pipe more new management system
CN108350688A (en) * 2015-09-25 2018-07-31 株式会社东芝 Leakage diagnosis device, leakage diagnosis method and computer program
CN109791637A (en) * 2016-08-02 2019-05-21 申舒斯美国有限公司 The method and apparatus of System design based on model for water distribution system

Families Citing this family (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6442374B2 (en) * 2015-07-10 2018-12-19 株式会社日立製作所 Water leakage countermeasure support device
WO2018043417A1 (en) * 2016-08-30 2018-03-08 コニカミノルタ株式会社 Piping evaluation device, piping evaluation method, and piping evaluation program
WO2018164102A1 (en) 2017-03-10 2018-09-13 日本電気株式会社 Diagnosis cost output device, diagnosis cost output method, and computer-readable recording medium
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EP3776444A4 (en) * 2018-03-28 2022-01-19 Fracta Predicting pipe failure
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CN114135794B (en) * 2021-11-22 2023-11-24 杭州数梦工场科技有限公司 Method and device for detecting leakage of water network
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009020024A (en) * 2007-07-12 2009-01-29 Nippon Water Solution:Kk Water leak state monitoring device and method
JP2009192329A (en) * 2008-02-13 2009-08-27 Toshiba Corp Water leakage diagnostic device and water leakage diagnostic method for water distribution pipe line
JP2011059799A (en) * 2009-09-07 2011-03-24 Toshiba Corp Water leakage investigation target route extraction system
CN102679166A (en) * 2012-05-21 2012-09-19 宁波钢铁有限公司 Method for quickly checking leakage and determining leakage section and leakage amount of buried water supply pipe network
CN103292966A (en) * 2012-02-29 2013-09-11 株式会社日立制作所 Leak detection device

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS5468274A (en) * 1977-11-11 1979-06-01 Hitachi Ltd Tap water consumption estimating apparatus at minimum flow rate time
JPS56162029A (en) * 1980-05-19 1981-12-12 Hitachi Ltd Water leakage detecting method
JP5329871B2 (en) * 2008-08-25 2013-10-30 株式会社東芝 Leakage node estimation device
US8930150B2 (en) * 2012-02-01 2015-01-06 International Business Machines Corporation Leak detection in a fluid distribution network

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009020024A (en) * 2007-07-12 2009-01-29 Nippon Water Solution:Kk Water leak state monitoring device and method
JP2009192329A (en) * 2008-02-13 2009-08-27 Toshiba Corp Water leakage diagnostic device and water leakage diagnostic method for water distribution pipe line
JP2011059799A (en) * 2009-09-07 2011-03-24 Toshiba Corp Water leakage investigation target route extraction system
CN103292966A (en) * 2012-02-29 2013-09-11 株式会社日立制作所 Leak detection device
CN102679166A (en) * 2012-05-21 2012-09-19 宁波钢铁有限公司 Method for quickly checking leakage and determining leakage section and leakage amount of buried water supply pipe network

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108350688A (en) * 2015-09-25 2018-07-31 株式会社东芝 Leakage diagnosis device, leakage diagnosis method and computer program
CN108350688B (en) * 2015-09-25 2020-10-27 株式会社东芝 Water leakage diagnosis device, water leakage diagnosis method, and computer program
CN105716803A (en) * 2016-01-29 2016-06-29 深圳市捷先数码科技股份有限公司 Integrated analysis device for leakage monitoring of water supply pipe network and method of analysis device
CN105716803B (en) * 2016-01-29 2018-04-10 深圳市捷先数码科技股份有限公司 A kind of comprehensive analysis device and its method of the monitoring of water supply network leakage loss
CN109791637A (en) * 2016-08-02 2019-05-21 申舒斯美国有限公司 The method and apparatus of System design based on model for water distribution system
CN109791637B (en) * 2016-08-02 2023-10-13 申舒斯美国有限公司 Method and apparatus for model-based control of water distribution systems
CN108235773A (en) * 2016-10-11 2018-06-29 富士地中信息股份有限公司 Water pipe more new management system

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